Recent bcoe items

Examining iron‐related off‐target binding effects of 18F‐AV1451 PET in the cortex of Aβ+ individuals

Thu, 11 Jun 2026 00:00:00 +0000

The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (¹⁸F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in ¹⁸F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aβ+) individuals, 20 amyloid beta negative (Aβ-) with MCI and 31 Aβ- control participants. After controlling for iron, increased ¹⁸F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aβ+ participants compared to Aβ- MCI and control participants. Within the Aβ+ group, significant correlations were seen between ¹⁸F-AV1451...

Locus Coeruleus Engagement Drives Network Connectivity Dynamics In Humans And Rats

Thu, 11 Jun 2026 00:00:00 +0000

Locus Coeruleus Engagement Drives Network Connectivity Dynamics In Humans And Rats

Associations between iron and mean kurtosis in iron-rich grey matter nuclei in aging.

Thu, 11 Jun 2026 00:00:00 +0000

OBJECTIVE: Elevated kurtosis values have been observed in subcortical grey matter structures of patients with neurodegenerative diseases. Here, we examined relationships between iron measures and kurtosis in iron-rich subcortical grey matter structures.Please check and confirm the affiliation 4 for the author "Xiaoping P. Hu".Affiliation 4 for Xiaoping P. Hu was incorrect since he is not associated with that department. We have removed this affiliation. Thanks! MATERIALS AND METHODS: Multi-shell diffusion and multi-echo gradient echo acquisitions were used to derive mean kurtosis and iron measures (R₂* and magnetic susceptibility), respectively, in subcortical grey matter nuclei and white matter tracts in a discovery cohort (110 healthy older and 63 younger adults) and replication cohort (72 healthy older adults).Please confirm if the author names are presented accurately and in the correct sequence (Ilana J. Bennett and Xiaoping P. Hu). Also, kindly confirm the...

Ultrasensitive, low-input detection of avocado sunblotch viroid via RPA-CRISPR and nanopore-array single-bead fluorescence readout.

Mon, 25 May 2026 00:00:00 +0000

Rapid and sensitive detection of plant pathogens, such as the Avocado Sunblotch Viroid (ASBVd), is essential for early disease management and agricultural biosecurity. Yet, most current diagnostic methods not only require relatively large sample inputs but also often lack the ultrasensitivity required for reliable detection with scarce or minimally collected plant material. Here, we report a novel low-input but ultrasensitive diagnostic platform that integrates isothermal recombinase polymerase amplification (RPA), CRISPR-Cas12a detection, and a solid-state nanopore array for the detection of ASBVd. The system leverages CRISPR-Cas12a collateral cleavage activity to generate single-bead fluorescent signals, which are captured by a nanopore array through pressure-driven blockage. Our platform achieves a detection limit down to 1.68 copies/μL while using only 40 nL of bead-fluorophore mixture per readout, which is over 100-fold less than conventional assays based on fluorescent readout...

ThIEF: Finding Genome-wide Trajectories of Epigenetics Marks

Thu, 21 May 2026 00:00:00 +0000

We address the problem of comparing multiple genome-wide maps representing nucleosome positions or specific histone marks. These maps can originate from the comparative analysis of ChIP-Seq/MNase-Seq/FAIRE-Seq data for different cell types/tissues or multiple time points. The input to the problem is a set of maps, each of which is a list of genomics locations for nucleosomes or histone marks. The output is an alignment of nucleosomes/histone marks across time points (that we call trajectories), allowing small movements and gaps in some of the maps. We present a tool called ThIEF (TrackIng of Epigenetic Features) that can efficiently compute these trajectories. ThIEF comes into two "flavors": ThIEF:Iterative finds the trajectories progressively using bipartite matching, while ThIEF:LP solves a k-partite matching problem on a hyper graph using linear programming. ThIEF:LP is guaranteed to find the optimal solution, but it is slower than ThIEF:Iterative. We demonstrate the utility...

OMGS: Optical Map-Based Genome Scaffolding

Thu, 21 May 2026 00:00:00 +0000

Due to the current limitations of sequencing technologies, de novo genome assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. While scaffolding is computationally easier than sequence assembly, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs and inaccuracies in the linkage information. Genome scaffolding tools either use paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical or genetic) as linkage information. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others). However, the most commonly used scaffolding tools have a serious limitation: they can only deal with one optical map at a time, forcing users to alternate or iterate over multiple maps. In this paper, we introduce a...

Selfish: discovery of differential chromatin interactions via a self-similarity measure

Thu, 21 May 2026 00:00:00 +0000

MOTIVATION: High-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging. RESULTS: We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel...

Bisulfite‐Conversion‐Based Methods for DNA Methylation Sequencing Data Analysis

Thu, 21 May 2026 00:00:00 +0000

Various techniques are available to profile DNA methylation, either genome‐wide or targeted at specific regions, such as CpG islands and gene promoters. In general, these techniques are divided into two categories: enrichment based and bisulfite conversion based methods. DNA methylation has been associated with gene expression, imprinting, transposon silencing, X‐chromosome inactivation, embryonic development, and cancer. Bisulfite‐conversion‐based methods involve the following three steps: chemical conversion of non‐methylated cytosines to uracils by treating DNA with sodium bisulfite; PCR amplification, and sequencing size‐selected DNA fragments. Sequencing errors, adapter contamination, end‐repair, and single‐nucleotide polymorphisms (SNPs) may affect erroneous methylation calls. Two common major performance measurements of an aligner for BS‐reads are mapping accuracy and methylation call accuracy. Both local alignment and alignment with indels are needed in order to map the...

Efficient and Accurate Detection of Topologically Associating Domains from Contact Maps

Thu, 21 May 2026 00:00:00 +0000

Continuous improvements to high-throughput conformation capture (Hi-C) are revealing richer information about the spatial organization of the chromatin and its role in cellular functions. Several studies have confirmed the existence of structural features of the genome 3D organization that are stable across cell types and conserved across species, called topological associating domains (TADs). The detection of TADs has become a critical step in the analysis of Hi-C data, e.g., to identify enhancer-promoter associations. Here we present East, a novel TAD identification algorithm based on fast 2D convolution of Haar-like features, that is as accurate as the state-of-the-art method based on the directionality index, but 75-80× faster. East is available in the public domain at https://github.com/ucrbioinfo/EAST.

RAmbler: de novo genome assembly of complex repetitive regions

Thu, 21 May 2026 00:00:00 +0000

Complex repetitive regions (also known as segmental duplications) in eukaryotic genomes often contain essential functional and regulatory information. Despite remarkable algorithmic progress in genome assembly in the last twenty years, modern de novo assemblers still struggle to accurately reconstruct these highly repetitive regions. When sequenced reads will be long enough to span all repetitive regions, the problem will be solved trivially. However, even the third generation of sequencing technologies on the market cannot yet produce reads that are sufficiently long (and accurate) to span every repetitive region in large eukaryotic genomes. In this work, we introduce a novel algorithm called RAmbler to resolve complex repetitive regions based on high-quality long reads (i.e., PacBio HiFi). We first identify potentially repetitive regions by mapping the HiFi reads to the draft genome assembly. Regions with sequencing coverage much higher then the average indicate a collapsed...

Higher Classification Accuracy of Short Metagenomic Reads by Discriminative Spaced k-mers

Thu, 21 May 2026 00:00:00 +0000

The growing number of metagenomic studies in medicine and environmental sciences is creating new computational demands in the analysis of these very large datasets. We have recently proposed a time-efficient algorithm called Clark that can accurately classify metagenomic sequences against a set of reference genomes. The competitive advantage of Clark depends on the use of discriminative contiguousk-mers. In default mode, Clark’s speed is currently unmatched and its precision is comparable to the state-of-the-art, however, its sensitivity still does not match the level of the most sensitive (but slowest) metagenomic classifier. In this paper, we introduce an algorithmic improvement that allows Clark’s classification sensitivity to match the best metagenomic classifier, without a significant loss of speed or precision compared to the original version. Finally, on real metagenomes, Clark can assign with high accuracy a much higher proportion of short reads than its closest competitor....

Comparative structural analysis of protein complexes with SPICE.

Thu, 21 May 2026 00:00:00 +0000

Computational tools for studying the structure of protein complexes are essential for providing mechanistic insights into protein-protein interactions and therapeutic drug design. Here, we present SPICE (Structural Protein Interaction Complex Evaluator), a web-based platform that allows structural biologists to perform rapid, modular analyses of protein complexes directly from Protein Data Bank (PDB) structures. SPICE allows users to define and execute analysis workflows via an intuitive web interface, reducing analysis times from minutes to seconds. The platform offers a broad range of analytical capabilities, including (i) detection of hydrogen bonds, salt bridges, and disulfide bonds; (ii) protein-protein interface mapping; and (iii) computation of solvent accessibility, van der Waals energetics, and other key geometric descriptors. SPICE further provides interactive 3D visualization and supports comparative analyses across multiple complexes, enabling the study of mutational...

mClass: Cancer Type Classification with Somatic Point Mutation Data

Thu, 21 May 2026 00:00:00 +0000

Cancer is a complex disease associated with abnormal DNA mutations. Not all tumors are cancerous and not all cancers are the same. Correct cancer type diagnosis can indicate the most effective drug therapy and increase survival rate. At the molecular level, it has been shown that cancer type classification can be carried out from the analysis of somatic point mutation. However, the high dimensionality and sparsity of genomic mutation data, coupled with its small sample size has been a hindrance in accurate classification of cancer. We address these problems by introducing a novel classification method called mClass that accounts for the sparsity of the data. mClass is a feature selection method that ranks genes based on their similarity across samples and employs their normalized mutual information to determine the set of genes that provide optimal classification accuracy. Experimental results on TCGA datasets show that mClass significantly improves testing accuracy compared to...

The regenerative role of neural crest stem cells in physical stimuli-enhanced peripheral nerve repair

Thu, 9 Apr 2026 00:00:00 +0000

Neural crest stem cells (NCSCs), capable of differentiating into neurons and Schwann cells, are essential for peripheral nerve regeneration. This study investigates the role of endogenous NCSC-like cells in mechano-electrical stimulation (MES)-enhanced peripheral nerve repair. In a critical-sized nerve injury model, MES leads to complete nerve reconnection, accompanied by a significant increase in NCSC-like cells at the injury sites. In vitro, MES promotes the simultaneous differentiation of NCSC-like cells into neurons and Schwann cells, with elevated neuregulin 1 (NRG1) expression, a key factor in Schwann cell development. Mechanistically, MES activates BMP/Smad signaling, driving neuronal differentiation and subsequent NRG1 secretion, which in turn promotes Schwann cell maturation through the ErBB/NFAT pathway. These findings demonstrate that MES enhances peripheral nerve regeneration by activating and directing stem cell differentiation, supporting a novel therapeutic approach...

Innovation in environmental engineering for community-engaged research

Thu, 9 Apr 2026 00:00:00 +0000

Innovation in environmental engineering for community-engaged research

Connectome-based predictive modelling predicts frailty levels in older adults

Thu, 26 Mar 2026 00:00:00 +0000

Frailty is characterized by a persistent and progressive decline in physiological reserves, leading to increased vulnerability to stressors and a heightened risk of adverse health outcomes, both physically and mentally. Despite frailty's prevalence in older adults, there is limited research on its neural substrates, especially using task-based brain functional connectivity. In this study, we used connectome-based predictive modelling (CPM) to find a linear relationship between task-based connectomes - taken from tasks that involved similar handgrip manipulations - and a separate measure of frailty: the maximum grip strength in older adults. We observed that the task-based connectomes were able to explain individual differences in grip strength, with the Subcortical and Cerebellum network, particularly the caudate nucleus, functional connectivity being the strongest predictor. These findings demonstrate that task-based functional connectomes can serve as personalized markers that...

Towards optimal selection of ultra-deep sequencing reads for de novo genome assembly

Thu, 12 Mar 2026 00:00:00 +0000

When sequencing a new genome, it is common practice to expect that 30-50× sequencing depth will be sufficient for a complete and highly contiguous assembly. With the rapid decrease in the cost of sequencing DNA, on small genomes it is not uncommon to have excessive sequencing data, sometimes exceeding 1000× sequencing depth (which we call ultra-deep). Because ultra-deep sequencing data significantly degrades the quality of the final assembly (for reasons not entirely clear to us), one faces the problem of how to select a subsample of the data for optimal assembly. The optimal read selection problem for genome assembly is largely unexplored. Here we first show that this problem is related to the minimum tiling path (MTP) problem which is known to be NP-hard. Then, we propose a heuristic (called AWinK) based on single-copy k-mer to select a subset of ultra-deep sequencing reads that maximizes the genomic coverage. Our experiments on both synthetic and real ultra-deep sequencing...

Prediction of DNA Methylation With Long-Range State-Space Models

Thu, 12 Mar 2026 00:00:00 +0000

The prediction of DNA methylation from the primary DNA sequence allows one to impute the methylation status of cytosines with insufficient sequencing coverage. Various deep learning models have been proposed in the literature, including transformer-based models and convolutional neural networks. In this study, we investigate the performance of long-range state-space models based on the Hyena architecture on the task of DNA methylation prediction on six plant species. First, we train the HyenaDNA framework to obtain a genome-wide foundation model for each species. Then, we fine-tune these foundation models using the sequence data surrounding the methylated or unmethylated cytosines. Extensive experimental results show that our model predicts DNA methylation with higher accuracy than state-of-the-art methods in the literature.

Babesia hegotelforum sp. nov., a zoonotic Babesia species previously referred to as Babesia sp. MO1.

Thu, 12 Mar 2026 00:00:00 +0000

A zoonotic Babesia species previously referred to as Babesia sp. MO1 is formally described and named here as Babesia hegotelforum sp. nov. This taxon is distinct from Babesia divergens based on genome-wide sequence divergence, phylogenetic placement, host associations, and clinical presentation. The parasite infects erythrocytes of humans, and eastern cottontail rabbits (Sylvilagus floridanus), and is transmitted by Ixodes dentatus. The holotype consists of a Giemsa-stained thin blood smear and cryopreserved infected erythrocytes from the cloned isolate BML-Bh-B12 at ≤10 passages in continuous in vitro culture. Paratype material includes five additional clones (BML-Bh-H1, BML-Bh-F12, BML-Bh-H6, BML-Bh-A3, and BML-Bh-F1) derived from BEI Resources strain NR-50441, along with the original mixed isolate NR-50441. This species description meets the requirements of the International Code of Zoological...

Near-field electrospinning of polymer/phage whispering gallery mode microfiber resonators for label-free biosensing

Fri, 27 Feb 2026 00:00:00 +0000

Whispering gallery mode (WGM) resonators have attracted attention as optical biosensors due to their capacity for rapid label-free detection. Separately, the M13 bacteriophage has been investigated as an alternative biorecognition element. In this work, a novel polymer WGM biosensor functionalized with filamentous virus biocapture agents was fabricated using near-field electrospinning (NFES) in a single step. Polyvinyl alcohol (PVA) was mixed with streptavidin-binding M13 bacteriophage and rhodamine 6 G in aqueous solution for blend electrospinning of active cavity microfiber-based resonators. Confocal fluorescence microscopy revealed alignment of M13 bioreceptors along the fiber axis, while x-ray photoelectron spectroscopy (XPS) and streptavidin-conjugated nanoparticle binding studies indicated that bioactive M13 bacteriophage were on the fiber surface. Dye-doped, crosslinked PVA/M13 fibers supported moderate quality (Q) factor first order WGM resonances within their cross-sections...

M13 bacteriophage spheroids as scaffolds for directed synthesis of spiky gold nanostructures

Fri, 27 Feb 2026 00:00:00 +0000

The spherical form (s-form) of a genetically-modified gold-binding M13 bacteriophage was investigated as a scaffold for gold synthesis. Repeated mixing of the phage with chloroform caused a 15-fold contraction from a nearly one micron long filament to an approximately 60 nm diameter spheroid. The geometry of the viral template and the helicity of its major coat protein were monitored throughout the transformation process using electron microscopy and circular dichroism spectroscopy, respectively. The transformed virus, which retained both its gold-binding and mineralization properties, was used to assemble gold colloid clusters and synthesize gold nanostructures. Spheroid-templated gold synthesis products differed in morphology from filament-templated ones. Spike-like structures protruded from the spherical template while isotropic particles developed on the filamentous template. Using inductively coupled plasma-mass spectroscopy (ICP-MS), gold ion adsorption was found to be comparatively...

Bifunctional M13 Bacteriophage Nanospheroids for the Synthesis of Hybrid Noncentrosymmetric Nanoparticles

Thu, 26 Feb 2026 00:00:00 +0000

Noncentrosymmetric nanostructures are composed of two different materials displayed on spatially distinct surface regions. These nontrivial building blocks facilitate self-assembly of hierarchical nanostructures with increased complexity and support cooperative material behaviors. The arrangement of M13 bacteriophage structural proteins within its capsid creates a promising monodisperse and readily modifiable template for these low-symmetry nanomaterials. Here, a 9-mer ZnS-binding peptide was inserted into the p3 minor coat protein (capsid tip) of an M13 bacteriophage with Au-binding motif fused to its p8 major coat protein (capsid body). Multiple vortex/rest cycles with chloroform were used to convert this bifunctional, Au/ZnS (p8/p3)-binding phage from filament to spheroid. The shape transformation was studied with transmission electron microscopy, circular dichroism spectroscopy, and fluorescence spectroscopy. The effects of the p3 peptide fusion and conversion temperature...

Whispering gallery mode emission from dye-doped polymer fiber cross-sections fabricated by near-field electrospinning

Thu, 26 Feb 2026 00:00:00 +0000

Whispering gallery mode (WGM) resonators demonstrate great potential for photonic and sensing applications. Yet, these devices are often disadvantaged by costly materials or complex fabrication approaches, in addition to lack of manufacturing scalability. Near-field electrospinning (NFES), a recently emerged facile fiber fabrication method, offers a solution. Here, WGM resonances are reported in Rhodamine 6G-doped poly(vinyl) alcohol (PVA) microfibers via NFES. Diameters are tuned over a range of more than 10 μm by varying substrate stage speed. Fibers display uniform distribution of dye, smooth surfaces, and circular cross-sections, all critical for supporting WGMs. High quality (Q) resonances are confirmed within fiber cross-sections through polarization experiments, free-spectral range analysis, and Mie-theory-derived mode assignment. In addition to WGMs, groups of associated spiral or conical modes are observed due to taper-induced weak optical confinement along the fiber...

Gold-Decorated M13 I‑Forms and S‑Forms for Targeted Photothermal Lysis of Bacteria

Thu, 26 Feb 2026 00:00:00 +0000

With the emergence of multidrug-resistant bacteria, photothermal therapy has been proposed as an alternative to antibiotics for targeting and killing pathogens. In this study, two M13 bacteriophage polymorphs were studied as nanoscaffolds for plasmonic bactericidal agents. Receptor-binding proteins found on the pIII minor coat protein targeted Escherichia coli bacteria with F-pili (F⁺ strain), while a gold-binding peptide motif displayed on the pVIII major coat protein templated Au nanoparticles. Temperature-dependent exposure to a chloroform-water interface transformed the native filamentous phage into either rod-like or spheroid structures. The morphology, geometry, and size of the polymorphs, as well as the receptor-binding protein and host cell receptor interaction were studied using electron microscopy. Au/template structures were formed through incubation with Au colloid, and optical absorbance was measured. Despite the closely packed Au nanoparticle layer...

Electric Field Polarity Controls Distribution of Viral Bioreceptors within Near-Field Electrospun Biohybrid Microfiber Optical Biosensors

Thu, 26 Feb 2026 00:00:00 +0000

Microorganisms (e.g., bacteria, fungi, and viruses) add indispensable functionality to a range of electrospun polymer materials and devices. The optimal distribution of bioactive agents on either the interior or exterior of the fiber is application-specific. Current microbe surface immobilization strategies and core-confinement techniques continue to pose a number of challenges. Here, we explore a simple strategy, utilizing electrostatic forces, to control the migration and surface concentration of the M13 bacteriophage within near-field electrospun poly(vinyl alcohol) (PVA) microfibers. Both the surface charge of the electrospun virus and the applied electric field polarity altered microbe placement. When doped with Rhodamine 6G (R6G), the circular microfiber cross sections formed active whispering gallery mode (WGM) resonators. These relatively high-quality (Q) optical cavities enabled us to sensitively probe the virus content of their outer layer, while functioning as...

Fuel-driven filamentous phage nanomotors

Thu, 26 Feb 2026 00:00:00 +0000

Virus-based nanocarriers have shown great potential for noninvasive delivery of drugs, diagnostics, and imaging agents to hard-to-reach anatomical locations. Yet, they largely depend on diffusion for transport, often lacking the force to actively penetrate biological barriers, and navigation to guide therapeutic agents. In these studies, the M13 bacteriophage, a linearly shaped virus, was converted from passive nanocarrier to actively propelled, fuel-driven nanomotor. Using the distinctive low symmetry of its capsid, a single Pt nanoparticle was added to one end of the M13 virus to form a tadpole-like structure. The Pt/M13 head/tail nanomotors exhibited notably enhanced diffusion in the presence of hydrogen peroxide fuel, and significantly improved uptake by SVOK3 ovarian cancer cells in vitro. Given the successes of the M13 bacteriophage as a nanocarrier, the demonstration of this simple, but comparatively mobile M13-based nanomotor platform represents an important step...

Evaluation of Smoke Exposure Risk from January 2025 Los Angeles Wildfires Using Crowdsourced Data

Wed, 25 Feb 2026 00:00:00 +0000

Wildfire smoke is an increasingly significant contributor to air pollution in the western United States, posing serious health risks and complicating efforts to assess personal exposure, particularly indoors. The January 2025 Palisades and Eaton Fires in Los Angeles County caused elevated levels of PM2.5 in the downwind cities. This study leverages a high-resolution network of crowdsourced PurpleAir sensors to evaluate indoor and outdoor PM2.5 levels before, during, and after the wildfire smoke events. We matched indoor–outdoor sensors and analyzed disparities in smoke exposure across communities with different CalEnviroScreen (CES) vulnerability scores, ventilation types, and home values. Results indicate that outdoor PM2.5 increased substantially during smoke days, with the highest CES-burdened communities experiencing the greatest ambient concentrations. Indoor PM2.5 also increased across all neighborhoods but indoor/outdoor (I/O) ratios declined during the smoke period, indicating...

Investigating the correlation between force output, strains, and pressure for active skeletal muscle contractions.

Sat, 21 Feb 2026 00:00:00 +0000

Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Traditionally, experimentalists have shown that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure (IMP) generated by the muscle belly. However, IMP proves difficult to measure in vivo, due to variations from sensor placement and invasiveness of the procedure. Numerical biomechanical simulations offer a tool to analyze muscle contractions, enabling new insights into the correlations among non-invasive experimentally measurable quantities, such as strains and the force output. In this work, we investigate the correlations between the muscle force output, the principal, shear and volumetric strains experienced by the muscle, as well as the pressure developed within the muscle belly as the tissue undergoes isometric contractions with varying activation profiles...

Modeling Microenvironmental Effects in Heterogeneous Catalysis

Thu, 12 Feb 2026 00:00:00 +0000

Heterogeneous catalysis involves a complex interplay of adsorption, charge transfer, and catalyst restructuring at solid–gas or solid–liquid interfaces. While first-principles methods such as KS-DFT and AIMD accurately describe chemisorbed species, they struggle to capture weakly bound or dynamic molecules subject to thermal fluctuations. Continuum models provide macroscopic insight into electrostatics and transport but often neglect the interfacial molecular structure, especially within the Stern layer. The challenge is even greater at gas–solid interfaces, where the gas phase is typically ignored, giving rise to a long-standing pressure gap between theory and experiment. This Perspective advocates a statistical-mechanical description of interfacial species using classical density functional theory (cDFT), in which physisorption and gas/liquid-phase inhomogeneity near catalytic surfaces are represented by molecular density distributions rather than fixed atomic configurations....

On Secret-Message Transmission by Echoing Encrypted Probes

Thu, 29 Jan 2026 00:00:00 +0000

A scheme for secure communications, called “Secret-message Transmission by Echoing Encrypted Probes (STEEP)”, is revisited. STEEP is a round-trip scheme with a probing phase from one user to another and an echoing phase in the reverse direction. STEEP is shown to be broadly applicable to yield a positive secrecy rate in bits per channel use even if the receive channels at eavesdropper (Eve) are stronger than those between legitimate users in both forward and reverse directions. This paper focuses on STEEP in the following settings: using Gaussian probing signal and Gaussian linear encryption over MIMO Gaussian channel (G-STEEP); using phase-shift-keying probing signal and a nonlinear encryption over SISO channel (P-STEEP); and a variation of G-STEEP for multiple access communication (M-STEEP). In each of the settings, Eve is assumed to have any given number of antennas, and STEEP is shown to yield a positive secrecy rate subject to a sufficiently large power in the echoing phase,...

Identifying indicators of consciousness in AI systems

Thu, 15 Jan 2026 00:00:00 +0000

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

Simultaneous hyperspectral imaging and pyrometry for multi-phase temperature profiling of energetic composite reactions

Thu, 15 Jan 2026 00:00:00 +0000

High-temperature, multi-phase reactions in energetic composites present significant challenges in understanding their chemical processes and underlying mechanisms. These systems often involve rapid, heterogeneous reactions which require temporally and spatially resolved diagnostic tools. To address this need, this work develops a dual-camera system that integrates hyperspectral emission spectroscopy and three-color pyrometry, enabling simultaneous, high-speed measurements of gas-phase and condensed-phase temperatures. One camera captures RGB video for three-color pyrometry, while the other captures emission spectra using a slit-array mask and diffraction grating system. The slit-array mask is designed to achieve spatial coverage, allowing for 2D gas-phase temperature profiling. Gas-phase temperatures are derived using emission spectra based on Boltzmann equation, utilizing the intensity ratios of potassium emission lines near 580 nm and 693 nm. Demonstration experiments were conducted...

Accounting for Area Sources in Air Pollution Models

Sat, 20 Dec 2025 00:00:00 +0000

Area sources are important components of comprehensive air pollution models. The literature describes several approaches to modeling dispersion from such sources, but there is little consensus on an approach that can be applied to arbitrarily shaped area sources and is numerically efficient at the same time. This paper brings together ideas from previous work to propose an approach that meets these requirements. It is based on representing an area source as a set of line sources perpendicular to the wind direction; the number of line sources is determined by the specified precision of the concentration computed at a receptor impacted by the area source. Although AERMOD and the OML model incorporate versions of this approach, the open literature lacks an adequate description. This paper fills this important gap and also provides examples of its application. We show that different shaped area sources with the same emissions and emission density yield significantly different downwind...

Recent Advances in Securing Networked Systems

Thu, 4 Dec 2025 00:00:00 +0000

Recent Advances in Securing Networked Systems

Fine-tuned protein language model identifies antigen-specific B cell receptors from immune repertoires

Thu, 4 Dec 2025 00:00:00 +0000

Abstract Scalable identification of antigen-specific antibodies from whole immune repertoire V(D)J sequences is a central challenge in biomedical engineering. We show that protein language models (PLMs) fine-tuned on antibody heavy-chain sequences can directly predict antigen specificity from unselected immune repertoires. We assessed our model, Antigen Specificity Predictor (ASPred), against SARS-CoV-2, influenza, and HIV-AIDS antigens, observing comparable predictive performance. In the whole immune repertoire V(D)J sequences of mice immunized with the SARS-CoV-2 spike protein’s receptor-binding domain (RBD), ASPred identified antibody sequences specific to RBD. Several candidate sequences were validated, including one as a heavy chain-only nanobody with 20.7 nM dissociation constant. Molecular dynamics simulations supported the predicted interactions at coarse-grained and atomic levels. Benchmarking against Barcode-Enabled Antigen Mapping (BEAM) of B cell receptor sequence...

SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing

Thu, 4 Dec 2025 00:00:00 +0000

MOTIVATION: Single-cell Hi-C (scHi-C) technologies have significantly advanced our understanding of the 3D genome organization. However, scHi-C data are often sparse and noisy, leading to substantial computational challenges in downstream analyses. RESULTS: In this study, we introduce SHICEDO, a novel deep-learning model specifically designed to enhance scHi-C contact matrices by imputing missing or sparsely captured chromatin contacts through a generative adversarial framework. SHICEDO leverages the unique structural characteristics of scHi-C matrices to derive customized features that enable effective data enhancement. Additionally, the model incorporates a channel-wise attention mechanism to mitigate the over-smoothing issue commonly associated with scHi-C enhancement methods. Through simulations and real-data applications, we demonstrate that SHICEDO outperforms the state-of-the-art methods, achieving superior quantitative and qualitative results. Moreover, SHICEDO enhances...

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Thu, 4 Dec 2025 00:00:00 +0000

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods...

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Thu, 4 Dec 2025 00:00:00 +0000

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods...

Heterogeneous catalysis: Optimal performance at a phase boundary?

Fri, 21 Nov 2025 00:00:00 +0000

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. We substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized...

Defect-Driven Redox Interplay on Anatase TiO2: Surface-Structure Dependent Activation for CO2 Hydrogenation Catalysis

Fri, 21 Nov 2025 00:00:00 +0000

Titanium dioxide (TiO₂) is one of the most extensively studied oxides as an active catalyst or catalyst support, particularly in energy and environmental applications, but the atomistic mechanisms governing its dynamic response to reactive environments and their correlation to reactivity remain largely elusive. Using in situ environmental transmission electron microscopy (ETEM), synchrotron X-ray diffraction (XRD), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), temperature-programmed reduction (TPR), reactivity measurements, and theoretical modeling, we reveal the dynamic interplay between oxygen loss and replenishment of anatase TiO₂ under varying reactive conditions. Under H₂ exposure, anatase TiO₂ undergoes surface reduction via lattice oxygen loss, forming Ti₃O₅. In contrast, CO₂ exposure induces oxygen replenishment, reversing stoichiometry. In mixed H₂/CO₂ environments,...

Author Correction: A universal language for finding mass spectrometry data patterns

Fri, 21 Nov 2025 00:00:00 +0000

Correction to: Nature Methodshttps://doi.org/10.1038/s41592-025-02660-z, published online 12 May 2025. This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.

Koina: Democratizing machine learning for proteomics research

Wed, 19 Nov 2025 00:00:00 +0000

Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which the community adopts these models is slow. This is in part due to a lack of findability and accessibility of these models as well as the technical challenges involved in incorporating these models into data analysis pipelines and demonstrating their reusability for end-users. Here we show Koina, an open-source decentralized and online-accessible model repository to facilitate publication of ML models. Koina enables ML model usage via an easy-to-use online interface, facilitating the integration of ML models in data analysis pipelines. Using the widely used FragPipe computational platform as an example, we demonstrate how Koina can be integrated with existing proteomics software tools...

A drug repurposing approach reveals targetable epigenetic pathways in Plasmodium vivax hypnozoites

Thu, 23 Oct 2025 00:00:00 +0000

Radical cure of Plasmodium vivax malaria must include elimination of quiescent 'hypnozoite' forms in the liver; however, the only FDA-approved treatments are contraindicated in many vulnerable populations. To identify new drugs and drug targets for hypnozoites, we screened the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library and a collection of epigenetic inhibitors against P. vivax liver stages. From both libraries, we identified inhibitors targeting epigenetics pathways as selectively active against P. vivax and P. cynomolgi hypnozoites. These include DNA methyltransferase inhibitors as well as several inhibitors targeting histone post-translational modifications. Immunofluorescence staining of Plasmodium liver forms showed strong nuclear 5-methylcystosine signal, indicating liver stage parasite DNA is methylated. Using bisulfite sequencing, we mapped genomic DNA methylation in sporozoites, revealing DNA methylation signals...

Nanoscale detection of metastable states in porous and granular media

Mon, 13 Oct 2025 00:00:00 +0000

Microseismicity in subsurface geologic environments, such as sandstone gas reservoirs, is expected in the presence of liquid or gas injection. Although difficult to predict, the potential for microseismic events is important to field-scale projects, such as geologic storage of CO2, whereby the gas is injected into natural sandstone formations. We conjecture that a primary factor causing microseismicity is the existence of metastable states in a granular porous medium and provide experimental evidence for its validity. External perturbation triggers abrupt relaxation events which, with a certain probability, can grow into macroscopic microseismic events. Here, the triggering perturbation is produced by cooling to a cryogenic temperature. As the "sensor" for the abrupt relaxation events, we use thin Al films deposited on the sandstone surface. We show that as the temperature is varied, the films' resistance exhibits sharp jumps, which we attribute to mechanical restructuring or...

A review of microfluidic approaches for carbon capture and storage research

Mon, 13 Oct 2025 00:00:00 +0000

The reliance on fossil fuels for the increasing global energy demand necessitates the advancement of carbon capture and storage technologies to mitigate anthropogenic CO₂ emissions. However, conventional experimental platforms have reached their practical limitations, stagnating further advancements in optimizing carbon capture and storage (CCS) processes. Microfluidic technologies have emerged as promising tools for investigating and optimizing CCS processes at the microscale, offering precise control over gas-liquid interactions, reaction kinetics, and multiphase flow dynamics, which would be very challenging with conventional macroscale platforms. This review uniquely consolidates carbon capture and storage advancements, providing a structured approach that starts from fundamental mechanisms and systematically progresses to advanced microfluidic-assisted CCS strategies. Unlike prior reports, this review demonstrates how microfluidics complements conventional macroscale...

Tumor growth inhibition by mSTEAP peptide nanovaccine inducing augmented CD8+ T cell immune responses

Thu, 9 Oct 2025 00:00:00 +0000

Poly(lactic-co-glycolic) acid (PLGA) has been successfully used in drug delivery and biomaterial applications, but very little attention has been directed towards the potential in vivo effects of peptide-loaded PLGA nanoparticles (NPs), specifically the potency of intravenous (IV) STEAP peptide-loaded PLGA-NP (nanovaccine) dosing and whether STEAP-specific CD8+ T cells directly play a key role in tumor inhibition. To address these concerns, syngeneic prostate cancer mouse models were established and treated with either mSTEAP peptide emulsified in incomplete Freund’s adjuvant (IFA) via subcutaneous (SC) injection or mSTEAP peptide nanovaccine containing the same amount of peptide via IV or SC injection. Meanwhile, mice were treated with either CD8b mAb followed by nanovaccine treatment, free mSTEAP peptide, or empty PLGA-NPs. Immune responses in these mice were examined using cytotoxicity assays at 14 days after treatment. Tumor size and survival in various treatment groups...

Heavily polluted Tijuana River drives regional air quality crisis

Thu, 11 Sep 2025 00:00:00 +0000

Industrial chemicals and untreated sewage have polluted the Tijuana River for decades, recently causing >1300 consecutive days of California beach closures. In summer 2024, wastewater flows surged to millions of gallons per day despite no rain, enhancing water-to-air transfer of hydrogen sulfide (H₂S) and other toxic gases at a turbulent hotspot. High wastewater flows and low winds led to nighttime H₂S peaks, reaching 4500 parts per billion (ppb)-exceeding typical urban levels of <1 ppb. H₂S levels and community malodor reports were strongly correlated (correlation coefficient r = 0.92), validating long-dismissed community voices and highlighting an environmental injustice. This study demonstrates that poor water quality can substantially affect air quality-although rarely included in air quality models and health assessments-with far-reaching implications as polluted waterways increase globally.

Néel spin-orbit torque in antiferromagnetic quantum spin and anomalous Hall insulators

Fri, 29 Aug 2025 00:00:00 +0000

Interplay between topological electrons and magnetic ordering enables efficient electrical control of magnetism. We extend the Kane-Mele model to include the exchange coupling to a collinear antiferromagnetic (AFM) order, which allows the system to exhibit the quantum anomalous Hall and quantum spin Hall effects in the absence of a net magnetization. These topological phases support a staggered Edelstein effect through which an applied electric field can generate opposite non-equilibrium spins on the two AFM sublattices, realizing the Néel-type spin-orbit torque (NSOT). Contrary to known NSOTs in AFM metals driven by conduction currents, our NSOT arises from pure adiabatic currents devoid of Joule heating, while being a bulk effect not carried by the edge currents. By virtue of the NSOT, the electric field of a microwave can drive the AFM resonance with a remarkably high efficiency, outpacing the magnetic field-induced AFM resonance by orders of magnitude in terms of power absorption.

Kingdom-wide CRISPR guide design with ALLEGRO

Thu, 28 Aug 2025 00:00:00 +0000

Designing CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) single guide RNA (sgRNA) libraries targeting entire kingdoms of life will significantly advance genetic research in diverse and underexplored taxa. Current sgRNA design tools are often species-specific and fail to scale to large, phylogenetically diverse datasets, limiting their applicability to comparative genomics, evolutionary studies, and biotechnology. Here, we introduce ALLEGRO, a combinatorial optimization algorithm designed to compose minimal, yet highly effective sgRNA libraries targeting thousands of species at the same time. Leveraging integer linear programming, ALLEGRO identified compact sgRNA sets simultaneously targeting multiple genes of interest for over 2000 species across the fungal kingdom. We experimentally validated sgRNAs designed by ALLEGRO in Kluyveromyces marxianus, Komagataella phaffii, Yarrowia lipolytica, and Saccharomyces cerevisiae, confirming successful genome...

Air-powered logic circuits for error detection in pneumatic systems

Thu, 28 Aug 2025 00:00:00 +0000

Air-powered logic circuits for error detection in pneumatic systems

Rapid development and optimization of paper microfluidic designs using software automation

Thu, 28 Aug 2025 00:00:00 +0000

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used...

Multi-Objective Design Automation for Microfluidic Capture Chips

Thu, 28 Aug 2025 00:00:00 +0000

Microfluidic capture chips are useful for preparing or analyzing a wide range of different chemical, biological, and medical samples. A typical microfluidic capture chip contains features that capture certain targets (i.e. molecules, particles, cells) as they flow through the chip. However, creating optimal capture chip designs is difficult because of the inherent relationship between capture efficiency and flow resistance: as more capture features are added to the chip, the capture efficiency increases, but the additional features slow the flow of fluid through the chip. This paper introduces the use of multi-objective optimization to generate capture chip designs that balance the trade-off between maximizing target capture efficiency and minimizing resistance to fluid flow. Design automation for this important class of microfluidic chips has not been attempted previously. Our approach automatically produces a Pareto front of non-dominated chip designs in a reasonable amount...

Controlling Biomedical Devices Using Pneumatic Logic

Thu, 28 Aug 2025 00:00:00 +0000

Many biomedical devices are powered and controlled by electrical components. These electronics add to the cost of a device (possibly making the device too expensive for use in resource-limited or point-of-care settings) and can also render the device unsuitable for use in some environments (for example, high-humidity areas such as incubators where condensation could cause electrical short circuits, ovens where electronic components may overheat, or explosive or flammable environments where electric sparks could cause serious accidents). In this work, we show that pneumatic logic can be used to power and control biomedical devices without the need for electricity or electric components. Originally developed for controlling microfluidic “lab-on-a-chip” devices, these circuits use microfluidic valves like transistors in air-powered logic “circuits.” We show that a modification to the basic valve design—adding additional air channels in parallel through the valve—creates a “high-flow”...

Optimization of deep learning–based denoising for arterial spin labeling: Effects of averaging and training strategies

Thu, 28 Aug 2025 00:00:00 +0000

PURPOSE: Systematic study of the effects of averaging and other relevant training strategies in deep learning (DL)-based denoising is required to optimize such processing pipelines for improving the quality of arterial spin labeling (ASL) images. METHODS: Different averaging strategies, including windowed and interleaved averaging methods, and different levels of averaging before and after convolutional neural network-based and transformer-based denoising were studied. The experiments were performed on 152 single-delay ASL scans from 152 subjects, including pulsed and pseudo-continuous ASL acquisitions. Four-fold cross-validation was implemented in all experiments. The effect of including calibration scans (M₀) was studied and compared across images of different levels of signal-to-noise ratio (SNR). The generalizability of DL denoising was examined in experiments using low-SNR ground truth in training. The results were assessed using image-quality metrics including...

FPGA-based Acceleration of Time Series Similarity Prediction: From Cloud to Edge

Thu, 28 Aug 2025 00:00:00 +0000

With the proliferation of low-cost sensors and the Internet of Things, the rate of producing data far exceeds the compute and storage capabilities of today’s infrastructure. Much of this data takes the form of time series, and in response, there has been increasing interest in the creation of time series archives in the past decade, along with the development and deployment of novel analysis methods to process the data. The general strategy has been to apply a plurality of similarity search mechanisms to various subsets and subsequences of time series data to identify repeated patterns and anomalies; however, the computational demands of these approaches renders them incompatible with today’s power-constrained embedded CPUs. To address this challenge, we present FA-LAMP, an FPGA-accelerated implementation of the Learned Approximate Matrix Profile (LAMP) algorithm, which predicts the correlation between streaming data sampled in real-time and a representative time series...

Predicting Antibody–Antigen Interactions with Structure-Aware LLMs: Insights from SARS-CoV‑2 Variants

Thu, 28 Aug 2025 00:00:00 +0000

Predicting antibody-antigen interactions is a critical step in developing new therapeutics to defend against viral infections. However, measuring the extent of these interactions in vitro is costly and time-consuming. With the increased availability of experimental data, predictive methods using machine learning, particularly large language models (LLMs), have emerged as a powerful alternative to wet lab experiments. Here we focus on antibodies targeting SARS-CoV-2 variants, given the abundance of data on this highly contagious virus and the impact of COVID-19 on human life. The objective of this work is to predict the binding and the neutralizing properties of SARS-CoV-2 antibodies. While there are many studies on predicting binding, to the best of our knowledge, we are the first to address the problem of predicting the neutralizing properties of SARS-CoV-2 antibodies. Here we propose a new classifier that combines LLMs with structural information. Extensive experimental...

Characterization of the critical lift-off of a single flat-plate microchip particle in straight rectangular microchannel flows

Thu, 14 Aug 2025 00:00:00 +0000

Characterization of the critical lift-off of a single flat-plate microchip particle in straight rectangular microchannel flows

Matrix Profile Index Approximation for Streaming Time Series

Thu, 14 Aug 2025 00:00:00 +0000

Discovery of motifs (repeated patterns) in time series is a key factor across numerous industries and scientific fields. These and related problems have effectively been solved for offline analysis of time series; however, these approaches are computationally intensive and do not lend themselves to streaming time series, where the sampling rate imposes real-time constraints on computation and there is strong desire to locate computation as close as possible to the sensor. One promising solution is to use low-cost machine learning models to provide approximate answers to these problems. For example, prior work has trained models to predict the similarity of the most recently sampled window of data points to a representative time series used for training. This work addresses a more challenging problem: to predict not only the "strength" of the match, but also the relative location in the representative time series where the match occurs. We evaluate our approach on two different...

BioScript

Thu, 14 Aug 2025 00:00:00 +0000

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry that executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contribution of the paper is the BioScript type system. The type system ensures that certain types of errors, specific to biochemistry, do not occur, such as the interaction of chemicals that may be unsafe. Results are obtained using a custom-built compiler that implements the BioScript language and type system.

Acoustic Side Channel Attack Against DNA Synthesis Machines: Poster Abstract

Thu, 14 Aug 2025 00:00:00 +0000

Synthetic DNA molecules play an essential role in genomics research and are a promising, high-capacity data storage medium. Currently, researchers use automated DNA synthesizers to custom-build sequences of oligonucleotides (short DNA strands) using the nucleobases: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T). Research laboratories invest large amounts of capital to engineer unique oligonucleotide sequences. In our work, we demonstrate the vulnerability of commonly used DNA synthesizers to acoustic side-channel attacks, where confidentiality can be breached to steal precious DNA sequences. We introduce a novel methodology to reverse engineer the acoustic noise generated by the DNA synthesizer and extract the type and order of the nucleobases delivered to the output. To the best of our knowledge, this is the first work which highlights the possibility of physical-to-cyber attacks in DNA synthesis technologies.

Compiling Functions onto Digital Microfluidics

Thu, 14 Aug 2025 00:00:00 +0000

Compiling Functions onto Digital Microfluidics

Specification, Integration, and Benchmarking of Continuous Flow Microfluidic Devices: Invited Paper

Thu, 14 Aug 2025 00:00:00 +0000

The lack of standardization in the specification and representation of microfluidic designs and their corresponding architectures is one of the largest hurdles faced by the developers of Microfluidic Design Automation (MDA) tools. In this paper, we introduce MINT, a Microfluidic Hardware Description Language (MHDL) for defining components and devices in a human readable manner, and ParchMint, an MDA interchange format and associated benchmark suite that can be used to compare the performance of different physical design algorithms. We further demonstrate how the introduction of MINT and ParchMint into the engineering workflow can bridge the gaps from the specification to the fabrication of microfluidic devices. While recent efforts to democratize microfluidics have been recognized by the community, there is an unfortunate lack of open source tools, design languages, and standards. Consequently, microfluidic designs shared on open platforms such as Metafluidics[15] leave conceptual...

Time- and resource-constrained scheduling for digital microfluidic biochips

Thu, 14 Aug 2025 00:00:00 +0000

Time- and resource-constrained scheduling for digital microfluidic biochips

Feature Extraction Accelerator for Streaming Time Series

Thu, 14 Aug 2025 00:00:00 +0000

Feature Extraction Accelerator for Streaming Time Series

Enhanced neuromorphogenesis of neural stem cells via the optimization of physical stimulus-responsive signaling pathways

Tue, 5 Aug 2025 00:00:00 +0000

BackgroundNeural stem cells hold significant promise for developing in vitro nerve models due to their capacity to differentiate into diverse neural cell types. While traditional biochemical approaches often restrict differentiation to a single phenotype, limiting the ability to study critical neuron-glia interactions, physical stimuli have been explored due to their capacity to drive multi-phenotypic differentiation of neural stem cells. However, underlying molecular mechanisms mediating the physical stimulation-induced neural stem cell differentiation, with an emphasis on electrical stimulation and mechanical stimulation, remain inadequately explored, hindering the comprehensive optimization and application of physical stimulation for enhanced neuromorphogenesis.MethodsIn this study, we explored the signaling pathways driving mechano-electrical stimulation-induced multi-phenotypic differentiation of NSCs using a piezoelectric platform with signaling inhibitors. The signaling...

Controllable Doping for Tunable and Multimodal Emission in ZnS-Based Mechanoluminescent Nanocrystals

Tue, 5 Aug 2025 00:00:00 +0000

Scaling mechanoluminescent materials to the nanoscale enhances their potential for biomedical applications due to improved sensitivity, resolution, and biocompatibility. Here, we report a versatile strategy for synthesizing wavelength-tunable mechanoluminescent ZnS nanocrystals doped with Ag⁺, Cu²⁺, or Mn²⁺. The method involves coassembly of ZnS and metal sulfide nanocrystals within silica nanoreactors, followed by high-temperature calcination to induce solid-state doping and phase transformation. The resulting ZnS:Ag⁺, ZnS:Cu²⁺, and ZnS:Mn²⁺ nanocrystals exhibit focused ultrasound-induced mechanoluminescence at 480, 500, and 585 nm, respectively. Notably, ZnS:Ag⁺ also shows photoluminescence and afterglow upon UV excitation. The luminescence intensity is highly dependent on Ag⁺ concentration, with 0.15% yielding the optimal emission. These nanocrystals were further applied to stimulate neuronal...

TRFill: synergistic use of HiFi and Hi-C sequencing enables accurate assembly of tandem repeats for population-level analysis.

Sat, 2 Aug 2025 00:00:00 +0000

The highly repetitive content of eukaryotic genomes, including long tandem repeats, segmental duplications, and centromeres, makes haplotype-resolved genome assembly hard. Repeat sequences introduce gaps or mis-joins in the assemblies. We introduce TRFill, a novel algorithm that can close the gaps in a draft chromosome-level assembly using exclusively PacBio HiFi and Hi-C data. Experimental results on human centromeres and tomato subtelomeres show that TRFill can improve the completeness and correctness of about two-thirds of the tandem repeats. We also show that the improved completeness of subtelomeric tandem repeats in the tomato pangenome enables a population-level analysis of these complex repeats.

TRFill: synergistic use of HiFi and Hi-C sequencing enables accurate assembly of tandem repeats for population-level analysis

Thu, 31 Jul 2025 00:00:00 +0000

A universal language for finding mass spectrometry data patterns

Fri, 25 Jul 2025 00:00:00 +0000

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the...

Beyond Stress Granules: G3BP1 and G3BP2 Redundantly Suppress SARS-CoV-2 Infection

Fri, 25 Jul 2025 00:00:00 +0000

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed unprecedented challenges to public health and economic stability. Central to SARS-CoV-2 pathogenesis is its ability to evade the host immune response by hijacking host pathways via the interaction between viral and host proteins. We identified Ras-GTPase-activating protein SH3 domain-binding protein 1/2 (G3BP1/G3BP2) as a critical host factor that interacts with the viral nucleocapsid (N) protein, emerging from a comparative analysis of proteomic data from multiple studies. We revisited the underlying molecular mechanisms by confirming the residues required for the interaction between G3BP1/G3BP2 and SARS-CoV-2 N protein and showed that this interaction disrupts stress granule formation. Intriguingly, we observed that the ablation of both G3BP1 and G3BP2 enhanced SARS-CoV-2 replication. Our data collectively supports the notion that G3BP1 and G3BP2 play a critical role in modulating...

An evaluation methodology for machine learning-based tandem mass spectra similarity prediction

Fri, 18 Jul 2025 00:00:00 +0000

BackgroundUntargeted tandem mass spectrometry serves as a scalable solution for the organization of small molecules. One of the most prevalent techniques for analyzing the acquired tandem mass spectrometry data (MS/MS) - called molecular networking - organizes and visualizes putatively structurally related compounds. However, a key bottleneck of this approach is the comparison of MS/MS spectra used to identify nearby structural neighbors. Machine learning (ML) approaches have emerged as a promising technique to predict structural similarity from MS/MS that may surpass the current state-of-the-art algorithmic methods. However, the comparison between these different ML methods remains a challenge because there is a lack of standardization to benchmark, evaluate, and compare MS/MS similarity methods, and there are no methods that address data leakage between training and test data in order to analyze model generalizability.ResultIn this work, we present the creation of a new evaluation...

Control of brain network dynamics across diverse scales of space and time

Fri, 18 Jul 2025 00:00:00 +0000

The human brain is composed of distinct regions that are each associated with particular functions and distinct propensities for the control of neural dynamics. However, the relation between these functions and control profiles is poorly understood, as is the variation in this relation across diverse scales of space and time. Here we probe the relation between control and dynamics in brain networks constructed from diffusion tensor imaging data in a large community sample of young adults. Specifically, we probe the control properties of each brain region and investigate their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum. In addition, through analysis of regional modal controllability and partitioning of modes, we determine whether the associated dynamics are fast or slow, as well as whether they are alternating or monotone. We find that brain regions that facilitate the control of energetically easy transitions are associated with...

Combining Neural and Behavioral Measures Enhances Adaptive Training

Thu, 17 Jul 2025 00:00:00 +0000

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4-13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD...

Enabling pan-repository reanalysis for big data science of public metabolomics data

Tue, 15 Jul 2025 00:00:00 +0000

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift Diffusion Model

Thu, 3 Jul 2025 00:00:00 +0000

Computational cognitive models are powerful tools for enhancing the quantitative and theoretical rigor of cognitive neuroscience. It is thus imperative that model users-researchers who develop models, use existing models, or integrate model-based findings into their own research-understand how these tools work and what factors need to be considered when engaging with them. To this end, we developed a philosophical toolkit that addresses core questions about computational cognitive models in the brain and behavioral sciences. Drawing on recent advances in the philosophy of modeling, we highlight the central role of model users' reasoning goals in the application and interpretation of formal models. We demonstrate the utility of this perspective by first offering a philosophical introduction to the highly popular drift diffusion model (DDM) and then providing a novel conceptual analysis of a long-standing debate about decision thresholds in the DDM. Contrary to most existing work,...

A Case Study of Environmental Footprints for Generative AI Inference: Cloud versus Edge

Mon, 30 Jun 2025 00:00:00 +0000

The rapid growth of generative AI has placed significant strain on traditional data center infrastructures and existing power grids, leading to soaring energy demands and environmental burdens that may disproportionately affect the local communities. Shifting AI inference from the cloud to edge devices could potentially reduce the reliance on network connections, enhance user privacy, and alleviate the escalating pressure data centers impose on the local electricity grid. In this work, we present a case study examining the environmental footprint and energy consumption when deploying a generative AI model on cloud and edge platforms. To this end, we model and evaluate the water consumption and carbon emissions associated with AI inference across these deployment scenarios. Our empirical results demonstrate that, for several state-of-the-art generative AI models deployable on both cloud and edge devices, a reduced environmental footprint is observed for edge platform deployments....

Portable dual-mode microfluidic sensor for rapid and sensitive detection of DPA on chip

Thu, 22 May 2025 00:00:00 +0000

In this work, we developed a dual-mode portable device that integrated a 3D-printed microfluidic chip for detection of dipicolinic acid (DPA) on chip. The system uses a ratiometric fluorescence nanoprobe formed by embedding carbon dots (CDs) into an Eu3⁺ metal–organic framework (Eu-MOF). Upon reaction with DPA in the microchannel, red fluorescence was enhanced and blue fluorescence suppressed, enabling sensitive ratiometric detection of DPA on chip with a detection limit (LOD) of 0.04 µM. Interestingly, the composite EuMOF/CDs/DPA also exhibits peroxidase-like activity, catalyzing the oxidation of TMB into a blue-colored product (oxTMB), which allows for colorimetric detection with an LOD of 10.14 µM. To improve usability and reduce environmental or instrumental variability, incorporating a microfluidic chip into a semi-portable device and utilizing a smartphone, making the system portable and miniaturized for easy operation. In the smartphone-assisted mode, the LODs were 0.33...

Interposer-Based ESD Protection: A Potential Solution for μ-Packaging Reliability of 3D Chips

Thu, 22 May 2025 00:00:00 +0000

The ending of Moore's Law calls for innovations in integrated circuit (IC) technologies and chip designs. Heterogeneous integration (HI) emerges as a pathway towards smart future chips for more Moore time and for beyond-Moore time, featuring systems-on-integrated-chiplets (SoICs) and advanced micro-packaging (μ-packaging). Reliability, particularly with regard to electrostatic charge (ESD) failure, is a major challenge for 3D SoIC chips in μ-packaging, which is an emerging design-for-reliability challenge for future chips. This perspective article articulates that interposer-based ESD protection will be an important potential solution for 3D SoIC chips in μ-packaging against the devastating ESD failure problem.

MSTmap Online: enhanced usability, visualization, and accessibility

Thu, 8 May 2025 00:00:00 +0000

Genetic linkage maps are an essential tool in population genetics and plant breeding research, yet user-friendly online tools for constructing and visualizing them remain scarce. MSTmap Online addresses this gap by providing a modern, accessible platform for generating high-quality genetic linkage maps from genotypic data. The web server quickly computes linkage groups using the MSTmap algorithm and generates detailed output files, including publication-ready PDF visualizations of linkage groups. The server supports bookmarking and asynchronous processing, allowing users to revisit their results at a later time. A companion Python library for MSTmap Online enables seamless integration into custom analysis pipelines. MSTmap Online is free and open to all users with no login requirement at https://mstmap.org. The companion Python library is available at https://pypi.org/project/mstmap/.

Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder–decoder networks

Tue, 15 Apr 2025 00:00:00 +0000

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (¹⁵O-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more accessible and does not involve ionizing radiation. This study presents a convolutional encoder-decoder network with attention mechanisms to predict the gold-standard ¹⁵O-water PET CBF from multi-contrast MRI scans, thus eliminating the need for radioactive tracers. The model was trained and validated using 5-fold cross-validation in a group of 126 subjects consisting of healthy controls and cerebrovascular disease patients, all of whom underwent simultaneous ¹⁵O-water...

Whole-brain causal discovery using fMRI

Fri, 11 Apr 2025 00:00:00 +0000

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested...

Modeling thermocatalytic systems for CO 2 hydrogenation to methanol

Fri, 11 Apr 2025 00:00:00 +0000

The hydrogenation of CO₂ to CH₃OH over Cu-based catalysts holds significant potential for advancing carbon sequestration and sustainable chemical processes. While numerous studies have focused on catalyst development, the environmental effects on underlying reaction mechanisms have yet to be fully understood. In this work, we develop a grand potential theory for a comprehensive analysis of CO₂ hydrogenation to CH₃OH over Cu (111) and Cu (211) surfaces. By integrating electronic and classical density functional calculations to bridge the "pressure gap", the theoretical results revealed that the HCOO* formation rate may vary by several orders of magnitude depending on reaction conditions. The grand potential theory enables us to elucidate the molecular mechanisms underlying the need for high H₂ pressure, the prevalence of saturated CO₂ adsorption, and the important roles of CO and H₂O in hydrogenation....

Evaluation of affine fiber kinematics in porcine tricuspid valve leaflets using polarized spatial frequency domain imaging and planar biaxial testing

Fri, 11 Apr 2025 00:00:00 +0000

Collagen fibers are the primary load-bearing microstructural constituent of bodily soft tissues, and, when subjected to external loading, the collagen fibers reorient, uncrimp, and elongate. Specific to the atrioventricular heart valve leaflets, the collagen fiber kinematics form the basis of many constitutive models; however, some researchers claim that modeling the affine fiber kinematics (AFK) are sufficient for accurately predicting the macroscopic tissue deformations, while others state that modeling the non-affine kinematics (i.e., fiber uncrimping together with elastic elongation) is required. Experimental verification of the AFK theory has been previously performed for the mitral valve leaflets in the left-side heart; however, this same evaluation has yet to be performed for the morphologically distinct tricuspid valve (TV) leaflets in the right-side heart. In this work, we, for the first time, evaluated the AFK theory for the TV leaflets using an integrated biaxial testing-polarized...

Mechanism of high-temperature superconductivity in compressed H2-molecular–type hydride

Mon, 7 Apr 2025 00:00:00 +0000

The discovery of compressed atomic-type hydrides offers a promising avenue toward achieving room-temperature superconductivity, but it necessitates extremely high pressures to completely dissociate hydrogen molecules to release free electrons. Here, we report a remarkable finding of compressed H₂-molecular-type hydride CaH₁₄ exhibiting an unusual transition temperature (T_c) of 204.0 kelvin. The peculiarity of its electronic structure lies in the pronounced emergence of near-free electrons, which manifest metallic bonding, but molecular hydrogen fragments persist. This finding indicates that the necessary condition for superconducting transition is forming the Fermi sea with Cooper pairs rather than the monatomic hydrogen. Notably, the formation mechanism of free electrons can be effectively explained by the finite-depth potential wells model. Intriguingly, this H₂-molecular-type hydride can downgrade the required pressure...

Oxidation of V(IV) by Birnessite: Kinetics and Surface Complexation

Sat, 5 Apr 2025 00:00:00 +0000

Vanadium is a redox-active metal that has been added to the EPA's Contaminant Candidate List with a notification level of 50 μg L^-1 due to mounting evidence that V^V exposure can lead to adverse health outcomes. Groundwater V concentration exceeds the notification level in many locations, yet geochemical controls on its mobility are poorly understood. Here, we examined the redox interaction between V^IV and birnessite (MnO₂), a well-characterized oxidant and a scavenger of many trace metals. In our findings, birnessite quickly oxidized sparingly soluble V^IV species such as häggite [V₂O₃(OH)₂] into highly mobile and toxic vanadate (H_nVO₄^(3-n)-) in continuously stirred batch reactors under neutral pH conditions. Synchrotron X-ray absorption spectroscopic (XAS) analysis of in situ and ex situ experiments showed that oxidation of V^IV occurs...

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements.

Fri, 4 Apr 2025 00:00:00 +0000

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity...

Investigation on submicron particle separation and deflection using tilted-angle standing surface acoustic wave microfluidics