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Durvalumab is a programmed death-ligand 1 (PD-L1) inhibitor with clinical activity in advanced urothelial cancer (AUC) 1 . AUC is characterized by several recurrent targetable genomic alterations 2 – 5 . This study ( NCT02546661 , BISCAY) combined durvalumab with relevant targeted therapies in biomarker-selected chemotherapy-refractory AUC populations including: (1) fibroblast growth factor receptor (FGFR) inhibitors in tumors with FGFR DNA alterations (FGFRm); (2) pharmacological inhibitor of the enzyme poly-ADP ribose polymerase (PARP) in tumors with and without DNA homologous recombination repair deficiency (HRRm); and (3) TORC1/2 inhibitors in tumors with DNA alteration to the mTOR/PI3K pathway 3 – 5 .This trial adopted a new, biomarker-driven, multiarm adaptive design. Safety, efficacy and relevant biomarkers were evaluated. Overall, 391 patients were screened of whom 135 were allocated to one of six study arms. Response rates (RRs) ranged 9–36% across the study arms, which did not meet efficacy criteria for further development. Overall survival (OS) and progression-free survival (PFS) were similar in the combination arms and durvalumab monotherapy arm. Biomarker analysis showed a correlation between circulating plasma-based DNA (ctDNA) and tissue for FGFRm. Sequential circulating tumor DNA analysis showed that changes to FGFRm correlated with clinical outcome. Our data support the clinical activity of FGFR inhibition and durvalumab monotherapy but do not show increased activity for any of the combinations. These findings question the targeted/immune therapy approach in AUC. The adaptive, biomarker-driven BISCAY trial evaluating durvalumab with targeted agents in patients with metastatic urothelial carcinoma based on tumor genomic alterations finds no added clinical benefit over durvalumab monotherapy.

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.

Target of rapamycin (TOR) is an evolutionarily conserved protein kinase that controls multiple cellular processes upon various intracellular and extracellular stimuli. Since its first discovery, extensive studies have been conducted both in yeast and animal species including humans. Those studies have revealed that TOR forms two structurally and physiologically distinct protein complexes; TOR complex 1 (TORC1) is ubiquitous among eukaryotes including animals, yeast, protozoa, and plants, while TOR complex 2 (TORC2) is conserved in diverse eukaryotic species other than plants. The studies have also identified two crucial regulators of mammalian TORC1 (mTORC1), Ras homolog enriched in brain (RHEB) and RAG GTPases. Of these, RAG regulates TORC1 in yeast as well and is conserved among eukaryotes with the green algae and land plants as apparent exceptions. RHEB is present in various eukaryotes but sporadically missing in multiple taxa. RHEB, in the budding yeast Saccharomyces cerevisiae, appears to be extremely divergent with concomitant loss of its function as a TORC1 regulator. In this review, we summarize the evolutionarily conserved functions of the key regulatory subunits of TORC1 and TORC2, namely RAPTOR, RICTOR, and SIN1. We also delve into the evolutionary conservation of RHEB and RAG and discuss the conserved roles of these GTPases in regulating TORC1.

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image.

The mechanistic target of rapamycin complex 1 (mTORC1) controls cell growth and metabolism in response to nutrients, energy levels, and growth factors. It contains the atypical kinase mTOR and the RAPTOR subunit that binds to the Tor signalling sequence (TOS) motif of substrates and regulators. mTORC1 is activated by the small GTPase RHEB (Ras homologue enriched in brain) and inhibited by PRAS40. Here we present the 3.0 ångström cryo-electron microscopy structure of mTORC1 and the 3.4 ångström structure of activated RHEB–mTORC1. RHEB binds to mTOR distally from the kinase active site, yet causes a global conformational change that allosterically realigns active-site residues, accelerating catalysis. Cancer-associated hyperactivating mutations map to structural elements that maintain the inactive state, and we provide biochemical evidence that they mimic RHEB relieving auto-inhibition. We also present crystal structures of RAPTOR–TOS motif complexes that define the determinants of TOS recognition, of an mTOR FKBP12–rapamycin-binding (FRB) domain–substrate complex that establishes a second substrate-recruitment mechanism, and of a truncated mTOR–PRAS40 complex that reveals PRAS40 inhibits both substrate-recruitment sites. These findings help explain how mTORC1 selects its substrates, how its kinase activity is controlled, and how it is activated by cancer-associated mutations. The cryo-electron microscopy and crystal structures of several mTORC1 complexes, and accompanying biochemical analyses, shed light on how mTORC1 is regulated and how cancer mutations lead to its hyperactivation. mTORC1 structures shed light on function Mechanistic target of rapamycin complex 1 (mTORC1) is a protein complex that is important for regulating cell growth and homeostasis and is aberrantly regulated in many diseases such as cancer, diabetes and neurodegeneration. Here, Nikola Pavletich and colleagues use cryo-electron microscopy and crystallography to determine the structures of several mTORC1 complexes. The structures and accompanying biochemical analysis provide mechanistic insights into how mTORC1 is allosterically activated by the GTPase RHEB, how it is inhibited by PRAS40, and how it recognizes substrates via the TOS motif. The findings also shed light on how cancer mutations lead to hyperactivation of mTORC1.

The fifth-generation (5G) wireless communication has an urgent need for target tracking. Digital programmable metasurface (DPM) may offer an intelligent and efficient solution owing to its powerful and flexible controls of electromagnetic waves and advantages of lower cost, less complexity and smaller size than the traditional antenna array. Here, we report an intelligent metasurface system to perform target tracking and wireless communications, in which computer vision integrated with a convolutional neural network (CNN) is used to automatically detect the locations of moving targets, and the dual-polarized DPM integrated with a pre-trained artificial neural network (ANN) serves to realize the smart beam tracking and wireless communications. Three groups of experiments are conducted for demonstrating the intelligent system: detection and identification of moving targets, detection of radio-frequency signals, and real-time wireless communications. The proposed method sets the stage for an integrated implementation of target identification, radio environment tracking, and wireless communications. This strategy opens up an avenue for intelligent wireless networks and self-adaptive systems. The authors present an intelligent metasurface system that uses a target detection algorithm combined with a depth camera, to automatically detect the position of moving targets and achieve real-time wireless communications. The system can operate for multiple targets in limited ambient light, outdoor and other realistic environments.

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR , a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/ .

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts D rug– T arget i nteractions using G raph E mbedding, graph M ining, and S imilarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.