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Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments. Recent advances in computational approaches and challenges in their application to streamlining drug discovery are discussed.

The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications. The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream.

RPL is a directing convention for remote organizations with low force utilization and large defenseless to parcel misfortune. It is a proactive convention dependent on distance vectors and works on IEEE 802.15.4. RPL can uphold a wide assortment of connection layers, including IoT, which is a technology that taking hold of research and industries with a fast tramp. It is a collection of actuators and sensors that collect data which can be processed to produce actual information. Important parameters allied to human body and physical environment data such that humidity, temperature, pressure, pollution etc. have immense significance for computerization, failure recognition, well timed, and appropriate cure. In this manner, IoT network offered ascend to keen urban communities, home mechanization, savvy wellbeing, present day travel strategic and some more. There is a distance vector normalization Routing Protocol for Low force and Lossy organization (RPL) for IoT sending, which relies upon different course improvement Objective functions (OF). These capacities rely upon different networks in the vein of energy like Received Sign Strength Indicator (RSSI) and Expected Transmission tally (ETX) for course streamlining. Course enhancement is influenced by issue of burden adjusting. In this paper, an inclusive survey of existing load balancing schemes, matrices, Objective Functions and different RPL based Routing protocols with reference to load imbalance is represented and highlighted when load balancing merged with the RPL, how it had a great impact.

Abstract Escherichia coli is the most researched microbial organism in the world. Its varied impact on human health, consisting of commensalism, gastrointestinal disease, or extraintestinal pathologies, has generated a separation of the species into at least eleven pathotypes (also known as pathovars). These are broadly split into two groups, intestinal pathogenic E. coli (InPEC) and extraintestinal pathogenic E. coli (ExPEC). However, components of E. coli’s infinite open accessory genome are horizontally transferred with substantial frequency, creating pathogenic hybrid strains that defy a clear pathotype designation. Here, we take a birds-eye view of the E. coli species, characterizing it from historical, clinical, and genetic perspectives. We examine the wide spectrum of human disease caused by E. coli, the genome content of the bacterium, and its propensity to acquire, exchange, and maintain antibiotic resistance genes and virulence traits. Our portrayal of the species also discusses elements that have shaped its overall population structure and summarizes the current state of vaccine development targeted at the most frequent E. coli pathovars. In our conclusions, we advocate streamlining efforts for clinical reporting of ExPEC, and emphasize the pathogenic potential that exists throughout the entire species. A schematic characterization of the disease manifestations, genomic flexibility, population dynamics, and vaccine targets of Escherichia coli, a multi-faceted bacterium with pathogenic potential throughout the entire species.

Close proximities between organelles have been described for decades. However, only recently a specific field dealing with organelle communication at membrane contact sites has gained wide acceptance, attracting scientists from multiple areas of cell biology. The diversity of approaches warrants a unified vocabulary for the field. Such definitions would facilitate laying the foundations of this field, streamlining communication and resolving semantic controversies. This opinion, written by a panel of experts in the field, aims to provide this burgeoning area with guidelines for the experimental definition and analysis of contact sites. It also includes suggestions on how to operationally and tractably measure and analyze them with the hope of ultimately facilitating knowledge production and dissemination within and outside the field of contact-site research. Given the recent growing interest in interorganelle membrane contact sites, the field will benefit from clear rules to define and study them. In this Perspective, a panel of experts aims to provide this growing field with guidelines for experimental definition and analysis.

When faced with unfamiliar reaction space, synthetic chemists typically apply the reported conditions (reagents, catalyst, solvent and additives) of a successful reaction to a desired, closely related reaction using a new substrate type. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Consequently, an important goal in synthetic chemistry is the ability to transfer chemical observations quantitatively from one reaction to another. Here we present a holistic, data-driven workflow for deriving statistical models of one set of reactions that can be used to predict out-of-sample reactions. As a validating case study, we combined published enantioselectivity datasets that employ 1,1′-bi-2-naphthol (BINOL)-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines and developed statistical models. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. This technique creates opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development. A workflow for deriving statistical models of one set of reactions that can be used to predict related reactions is presented, facilitating catalyst and enantioselective reaction development.

We improve the best known sum-product estimates over the reals. We prove that \\[\\max(|A+A|,|A+A|)\\geq |A|^{\\frac{4}{3} + \\frac{2}{1167} - o(1)}\\,,\\] for a finite $A\\subset \\mathbb {R}$ , following a streamlining of the arguments of Solymosi, Konyagin and Shkredov. We include several new observations to our techniques. Furthermore, \\[|AA+AA|\\geq |A|^{\\frac{127}{80} - o(1)}\\,.\\] Besides, for a convex set A we show that \\[|A+A|\\geq |A|^{\\frac{30}{19}-o(1)}\\,.\\] This paper is largely self-contained.

This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI’s Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple’s Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process.

In this paper, we consider an M/M/1/N queue with working breakdown and two types of server vacations. Sensitivity analysis of the model is performed to aissue with the point of streamlining service rates by limiting the normal expense per unit time using the direct search method.

Many applications of quantum simulation require one to prepare and then characterize quantum states by efficiently estimatingk-body reduced density matrices (k-RDMs), from which observables of interest may be obtained. For instance, the fermionic 2-RDM contains the energy, charge density, and energy gradients of an electronic system, while the qubit 2-RDM contains the spatial correlation functions of magnetic systems. Naive estimation of such RDMs requires repeated state preparations for each matrix element, which makes for prohibitively large computation times. However, commuting matrix elements may be measured simultaneously, allowing for a significant cost reduction. In this work, we design schemes for such a parallelization with near-optimal complexity in the system sizeN. We first describe a scheme to sample all elements of a qubitk-RDM using onlyO(3klogk−1N)unique measurement circuits, an exponential improvement over prior art. We then describe a scheme for sampling all elements of the fermionic 2-RDM using onlyO(N2)unique measurement circuits, each of which requires only a localO(N)-depth measurement circuit. We prove a lower bound ofΩ(ε−2Nk)on the number of state preparations, Clifford circuits, and measurement in the computational basis required to estimate all elements of a fermionick-RDM, making our scheme for sampling the fermionic 2-RDM asymptotically optimal. We finally construct circuits to sample the expectation value of a linear combination ofωanticommuting two-body fermionic operators with onlyO(ω)gates on a linear array. These circuits allows for sampling any linear combination of fermionic 2-RDM elements inO(N4/ω)time, with a significantly lower measurement circuit complexity than prior art. Our results improve the viability of near-term quantum simulation of molecules and strongly correlated material systems.