To overcome the drawbacks of conventional coreless transformers (CLTs), where smaller magnetizing inductance results in large reactive power circulating current and switches’ turn-off current, this paper proposes a variable magnetic permeability CLT for wide input voltage range DC-DC converters via anti-resonant winding. Compared with the resonant converter using conventional two-winding CLT, the proposed converter exhibits lower switching loss and conduction loss on the primary side. These advantages come from the fact that the magnetic permeability and magnetizing impedance of CLTs can be greatly increased by adding theLCanti-resonant winding. Besides, compared with the ferrite-core resonant converter, both have the same external characteristics, and crucially, the proposed converter has a lighter weight and smaller volume by multiplexing the leakage inductance into a resonant inductance, while ensuring ZVS and a wide input voltage range. A detailed analysis of the proposed converter, including the variable magnetic permeability, the voltage gain, and the wide voltage range modulation, is presented, and a design method is given. Finally, an experimental prototype with an input voltage range of 250-400V and 72V/1kW output is designed to verify the theoretical analysis.

The phase out of conventional synchronous generators (SGs) and the vigorous development of renewable energy sources (RESs) are indisputably leading to a significant reduction in the inertia of power grids, blowing a hole in frequency security and stability. To address this issue, various fast-acting resources such as battery energy systems (BESS) are being discussed worldwide. Accurate quantifying the relative effectiveness of these resources in arresting frequency decline to conventional methods is, therefore, of great significance to securely operate low-inertia power systems (LIPS). To do so, an analytical model of the equivalent frequency-containment performance ratio (EFCPR) is proposed for heterogeneous resources having different response characteristics. Furthermore, two Sigmoid-function-based approaches are also proposed to extend the EFCPR model to aggregated resources, and a more general scenario taking delivery time instant of instantaneous response BESS into account. Numerical results on the Texas test case and the Great Britain power grids with real operation data (from September 2023 to December 2024) collected from the National Energy System Operator (NESO) website validate the EFCPR model and penetrate many of the parameters' impacts.

The increasing penetration of distributed energy resources (DERs) has facilitated the development of Peer-to-Peer (P2P) trading mechanism. An efficient P2P trading market framework is essential to integrate various kinds of DERs into new power systems while ensuring network security constraints (NSCs). This paper proposes a carbon-aware P2P trading market to realize joint energy and reserve trading for prosumers while satisfying NSCs of the distribution network (DN) simultaneously. A geometric series acceleration (GSA) method accelerated algorithm based on the consensus alternating direction method of multipliers (C-ADMM) is proposed to solve the distributed P2P trading problem. A data-driven method based on the two-sided distributionally robust chance constraint (TS-DRCC) is adopted to tackle with the uncertainty problem associated with DERs. Numerical tests on the IEEE 15-Bus distribution system and IEEE 141-Bus distribution system verify the advantages and effectiveness of the proposed method.

Modern data storage systems are characterized by a distributed architecture as well as the presence of multiple storage tiers and caches. Both system developers and operators are challenged with the complexity of such systems as it is hard to evaluate how a configuration change will impact the workload or system performance and identify the best configuration to satisfy some performance objective. DITIS is a new simulator that models the end-to-end execution of file requests on distributed tiered storage systems that addresses the aforementioned challenges efficiently without any costly system redeployments. The demonstration will showcase the key functionalities and benefits offered by DITIS, including (i) analyzing workload traces to understand their characteristics and the behavior of the underlying storage system; (ii) running simulations with different configurations to evaluate their impact on performance; and (iii) running optimizations over custom search spaces to find the best configuration that satisfies a given objective.

Density is one of the major design factors of magnetic random access memory (MRAM). Very recently, a tri-level cell (TLC) structure was proposed to enhance the storage density of MRAM. In this work, we propose a new self-reference sensing scheme for the TLC MRAM cell based on its unique property called state ordering. Simulation results show that compared to conventional design, our proposed self-reference scheme achieves on average 61% saving on sensing delay while also demonstrating significantly enhanced resilience to device parametric variations.

As the integration density rockets up for contemporary VLSI circuits, power consumption limits the scalability of technology advancement of CMOS. Spin transfer torque-magnetic random access memory (STT-MRAM), as one of the emerging non-CMOS technologies, has the promising prospect of low standby power, fast access speed and compatibility with the CMOS fabrication process. However, with the technology node scaling down, typical 1 Transistor-1 Magnetic Tunnel Junction (1T-1MTJ) STT-RAM cell suffers from severe reliability challenges, especially for read operation under temperature fluctuation. In this paper, we quantitatively analyze the temperature effect on read reliability of STT-RAM cell and propose a novel body-biasing feedback readout circuit design to improve the read sensing margin under different temperatures. The experiments based on 40nm CMOS technology and MTJ compact model validate the effectiveness of the proposed method. The improved sensing margin also permits a smaller sensing current for reading such that higher read energy efficiency can be achieved.

Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. The basic idea of connectivity map is to measure the similarity between disease gene expression signatures and compound-induced gene expression signatures. We evaluate different gene expression profile similarity metrics by comparing their ability to predict a compound's chemical grouping using the Anatomical Therapeutic Chemical (ATC) drug classification system. The results show that our simple eXtreme sum (XSum) and eXtreme cosine (XCos) measures perform significantly better than the standard Kolmogorov-Smirnov (KS) statistic in term of area under the Receiver Operating Characteristic (ROC) curve (AUC) and partial AUC.

Identifying drug-drug interactions (DDIs) is a major challenge in drug development. Previous attempts have established formal approaches for pharmacokinetic (PK) DDIs, but there is not a feasible solution for pharmacodynamic (PD) DDIs because the endpoint is often a serious adverse event rather than a measurable change in drug concentration. Here, we developed a metric "S-score" that measures the strength of network connection between drug targets to predict PD DDIs. Utilizing known PD DDIs as golden standard positives (GSPs), we observed a significant correlation between S-score and the likelihood a PD DDI occurs. Our prediction was robust and surpassed existing methods as validated by two independent GSPs. Analysis of clinical side effect data suggested that the drugs having predicted DDIs have similar side effects. We further incorporated this clinical side effects evidence with S-score to increase the prediction specificity and sensitivity through a Bayesian probabilistic model. We have predicted 9,626 potential PD DDIs at the accuracy of 82% and the recall of 62%. Importantly, our algorithm provided opportunities for better understanding the potential molecular mechanisms or physiological effects underlying DDIs, as illustrated by the case studies.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), infects an estimated two billion people worldwide and is the leading cause of mortality due to infectious disease. The development of new anti-TB therapeutics is required, because of the emergence of multi-drug resistance strains as well as co-infection with other pathogens, especially HIV. Recently, the pharmaceutical company GlaxoSmithKline published the results of a high-throughput screen (HTS) of their two million compound library for anti-mycobacterial phenotypes. The screen revealed 776 compounds with significant activity against the M. tuberculosis H37Rv strain, including a subset of 177 prioritized compounds with high potency and low in vitro cytotoxicity. The next major challenge is the identification of the target proteins. Here, we use a computational approach that integrates historical bioassay data, chemical properties and structural comparisons of selected compounds to propose their potential targets in M. tuberculosis. We predicted 139 target--compound links, providing a necessary basis for further studies to characterize the mode of action of these compounds. The results from our analysis, including the predicted structural models, are available to the wider scientific community in the open source mode, to encourage further development of novel TB therapeutics.

Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.

The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs.

MOTIVATION: Serious adverse drug reaction (SADR) is an urgent, world-wide problem. In the absence of any well-organized gene-oriented SADR information pool, a database should be constructed. Since the importance of a gene to a particular SADR cannot simply be defined in terms of how frequently the two are cited together in the literature, an algorithm should be devised to sort genes according to their relevance to the SADR topics. RESULTS: The SADR-Gengle database, which is made up of gene-SADR relationships extracted from Pubmed, has been constructed, covering six major SADRs, namely cholestasis, deafness, muscle toxicity, QT prolongation, Stevens-Johnson syndrome and torsades de points. The CitationRank algorithm, which inherits the principle of the Google PageRank algorithm that a gene should be highly ranked when biologically related to other highly ranked genes, is devised. The algorithm performs robustly in recovering SADR-related genes in the presence of extraneous noise, and the use of the algorithm has been extended to sorting genes in our database. Users can browse genes in a Google-type system where genes are ordered according to their descending relevance to the SADR topic selected by the user. The database also provides users with visualized gene-gene knowledge chain networks, helping them to systematize their gene-oriented knowledge chain whilst navigating these networks.

Identifying genetic factors responsible for serious adverse drug reaction (SADR) is of critical importance to personalized medicine. However, genome-wide association studies are hampered due to the lack of case-control samples, and the selection of candidate genes is limited by the lack of understanding of the underlying mechanisms of SADRs. We hypothesize that drugs causing the same type of SADR might share a common mechanism by targeting unexpectedly the same SADR-mediating protein. Hence we propose an approach of identifying the common SADR-targets through constructing and mining an in silico chemical-protein interactome (CPI), a matrix of binding strengths among 162 drug molecules known to cause at least one type of SADR and 845 proteins. Drugs sharing the same SADR outcome were also found to possess similarities in their CPI profiles towards this 845 protein set. This methodology identified the candidate gene of sulfonamide-induced toxic epidermal necrolysis (TEN): all nine sulfonamides that cause TEN were found to bind strongly to MHC I (Cw*4), whereas none of the 17 control drugs that do not cause TEN were found to bind to it. Through an insight into the CPI, we found the Y116S substitution of MHC I (B*5703) enhances the unexpected binding of abacavir to its antigen presentation groove, which explains why B*5701, not B*5703, is the risk allele of abacavir-induced hypersensitivity. In conclusion, SADR targets and the patient-specific off-targets could be identified through a systematic investigation of the CPI, generating important hypotheses for prospective experimental validation of the candidate genes.