Neurodegenerative diseases, characterized by traumatic or progressive loss of neurons in the brain and spinal, present significant medical and economic challenges to society with fast growing patient populations and yet unavailability of permanent cure-giving therapies. Current drug-based treatments are highly limited in a sense that they merely alleviate symptoms, delay the onset or slow down the progression of the disease at best--even so, with low efficacy and wide variability in therapeutic outcome at the cost of many side effects. With prospect to induce permanent recovery in affected neural functions at molecular level, stem cell therapies based on neural progenitor cell (NPC) transplantation have been extensively performed in animal models of neurodegeneration. However, laminin (LN) substrates commonly used to culture these NPCs may contain animal- derived pathogens and are relatively expensive to produce. In this project, we developed optimal combinations of synthetic peptides that mimick functional sequences of extracellular matrix proteins, on which human embryonic stem cell (hESC) derived NPCs were expanded for 10 passages and differentiated into neurons after 10th passage followed by molecular profiling. Cells cultured on the optimized peptide substrates proliferated at a rate similar to those on the LN substrate, also expressing multipotent NPC markers such as NESTIN, SOX1, and SOX2 at a comparable degree to LN-based culture as verified by gene expression analysis, immunofluorescence staining and flow cytometry. Cells differentiated on the synthetic peptides expressed neuronal markers, MAP2 and B3T, at a similar level to those on LN. Therefore, our synthetic peptide combinations support the long-term expansion and neuronal differentiation of NPCs, providing cheaper and xenogenic contaminant-free alternatives for the LN substrate, and holding promise as a feasible substrate for the large-scale production of NPCs and neurons for cell- based therapies

Capturing correct changes both locally and globally is crucial to predicting the behavior of physical systems. However, due to the nature of qualitative simulation techniques, they cannot avoid losing some information which is useful for finding precise global behavior. This paper describes how global constraints are represented and m a nipulated in current simulation systems, using a model of an internal combustion engine. The basic idea of our approach is to automatically generate additional information for maintaining global constraints during simulation so that simulation techniques can filter global behaviors with the sufficient information. This is done by automatically introducing variables and controlling their values to guide correct transitions between the behaviors. W e express this idea within the framework of Qualitative Process (QP) theory. This technique has been implemented and integrated into an existing qualitative simulation program QPE.

Understanding the interaction between dynamics and geometry is crucial to capturing commonsense physics. This paper presents a qualitative analysis of the direction of fluid flow. This analysis is dependent on qualitative descriptions of the surface geometry of rigid bodies in contact with the fluid and a pressure change in fluid. The key problem in designing an intelligent system to reason about fluid motion is how to partition the fluid at an appropriate level of representation. The basic idea of our approach is to incrementally generate the qualitatively different parts of fluid. We do this by dynamically analyzing the intereiction of geometry and pressure disturbance. Using this technique, we can derive all possible fluid flows.

Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a within-one measure that reaches 93.0% accuracy.

Nrf1 (also referred to as NFE2L1) is a member of the CNC-bZIP family of transcription factors that are characterized by a highly conserved CNC-domain, and a basic-leucine zipper domain required for dimerization and DNA binding. Nrf1 is ubiquitously expressed across tissue and cell types as various isoforms, and is induced by stress signals from a broad spectrum of stimuli. Evidence indicates that Nrf1 plays an important role in regulating a range of cellular functions including oxidative stress response, differentiation, inflammatory response, metabolism, and maintaining proteostasis. Thus, Nrf1 has been implicated in the pathogenesis of various disease processes including cancer development, and degenerative and metabolic disorders. This review summarizes our current understanding of Nrf1 and the molecular mechanism underlying its regulation and action in different cellular functions.

Monocytes are peripheral leukocytes that function in innate immunity. Excessive triglyceride (TG) accumulation causes monocyte death and thus can compromise innate immunity. However, the mechanisms by which TG mediates monocyte death remain unclear to date. Thus, this study aimed to elucidate the mechanisms by which TG induces monocyte death. Results showed that TG induced monocyte death by activating caspase-3/7 and promoting poly (ADP-ribose) polymerase (PARP) cleavage. In addition, TG induced DNA damage and activated the ataxia telangiectasia mutated (ATM)/checkpoint kinase 2 and ATM-and Rad3-related (ATR)/checkpoint kinase 1 pathways, leading to the cell death. Furthermore, TG-induced DNA damage and monocyte death were mediated by caspase-2 and -8, and caspase-8 acted as an upstream molecule of caspase-2. Taken together, these results suggest that TG-induced monocyte death is mediated via the caspase-8/caspase-2/DNA damage/executioner caspase/PARP pathways. [BMB Reports 2023; 56(3): 166-171].

Tumor subtype and menopausal status are strong predictors of breast cancer (BC) prognosis. We aimed to find and validate subtype- or menopausal-status-specific changes in tumor DNA methylation (DNAm) associated with all-cause mortality or BC progression. Associations between site-specific tumor DNAm and BC prognosis were estimated among The Cancer Genome Atlas participants (n = 692) with Illumina Infinium HumanMethylation450 BeadChip array data. All-cause mortality and BC progression were modeled using Cox proportional hazards models stratified by tumor subtypes, adjusting for age, race, stage, menopausal status, tumor purity, and cell type proportion. Effect measure modification by subtype and menopausal status were evaluated by incorporating a product term with DNAm. Site-specific inference was used to identify subtype- or menopausal-status-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset (GSE72308; n = 180). We identified a total of fifteen unique CpG probes that were significantly associated ( P ≤ 1 × 10 - 7 with survival outcomes in subtype- or menopausal-status-specific manner. Seven probes were associated with overall survival (OS) or progression-free interval (PFI) for women with luminal A subtype, and four probes were associated with PFI for women with luminal B subtype. Five probes were associated with PFI for post-menopausal women. A majority of significant probes showed a lower risk of OS or BC progression with higher DNAm. We identified subtype- or menopausal-status-specific DMRs and functional pathways of which top associated pathways differed across subtypes or menopausal status. None of significant probes from site-specific analyses met genome-wide significant level in validation analyses while directions and magnitudes of coefficients showed consistent pattern. We have identified subtype- or menopausal-status-specific DNAm biomarkers, DMRs and functional pathways associated with all-cause mortality or BC progression, albeit with limited validation. Future studies with larger independent cohort of non-post-menopausal women with non-luminal A subtypes are warranted for identifying subtype- and menopausal-status-specific DNAm biomarkers for BC prognosis.