Two parallel trends are occurring in drug discovery. The first is that we are moving away from a symptom-based disease classification system to a system based on molecules and molecular states. The second is that we are shifting from targeting a single molecule toward targeting multiple molecules, pathways, or networks. Network medicine is an approach to understanding disease and discovering therapeutics looking at many molecules and how they interrelate, and it may play a critical role in the adoption of both trends.

Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL's end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordinate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs' local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.

Aim Cake decorating involves several hand intensive steps with high grip force during the application of icing. The purpose of this laboratory study was to evaluate forearm muscle activity, discomfort, productivity, and usability of an alternative tool for cake decorating compared to decorating with the traditional piping bag. Methods Participants (n = 17) performed 2 h of cake decorating tasks using the two tools. Subjective hand and arm fatigue, usability, upper extremity posture, and muscle activity from three forearm muscles were assessed for each tool. Outcome measures were evaluated using the Wilcoxon Signed Rank test and the paired t-test. Results Less fatigue was reported in the dominant hand (p = 0.001), forearm (p = 0.003) and shoulder (p = 0.02) for the alternative tool when compared to the piping bag. Average median (APDF 50%) and peak (APDF 90%) muscle activity was significantly less for the alternative tool across all three forearm muscles. The alternative tool significantly reduced grip force, an important risk factor for distal upper extremity pain and disorders. Participants rated usability of the alternative tool superior for refill and comfort but the traditional method was rated better for accuracy, stability, positioning and control. Conclusions The alternative tool was associated with less dominant arm fatigue, muscle activity, and grip force when compared with the piping bag. However, the alternative tool did not receive the best overall usability rating due to problems with accuracy and overflow, especially with smaller decorating tips. Recommendations were made for addressing these problems with the alternative tool.

Motivation: Biological analysis has shifted from identifying genes and transcripts to mapping these genes and transcripts to biological functions. The ENCODE Project has generated hundreds of ChIP-Seq experiments spanning multiple transcription factors and

Drug repositioning has been based largely on genomic signatures of drugs and diseases. One challenge in these efforts lies in connecting the molecular signatures of drugs into clinical responses, including therapeutic and side effects, to the repurpose of drugs. We addressed this challenge by evaluating drug-drug relationships using a phenotypic and molecular-based approach that integrates therapeutic indications, side effects, and gene expression profiles induced by each drug. Using cosine similarity, relationships between 445 drugs were evaluated based on high-dimensional spaces consisting of phenotypic terms of drugs and genomic signatures, respectively. One hundred fifty-one of 445 drugs comprising 450 drug pairs displayed significant similarities in both phenotypic and genomic signatures (P value < 0.05). We also found that similar gene expressions of drugs do indeed yield similar clinical phenotypes. We generated similarity matrixes of drugs using the expression profiles they induce in a cell line and phenotypic effects.