ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functional regions at an allosteric site. In addition to the known effects of mutations around functional sites, mutations at allosteric sites have been associated with protein structure, dynamics, and energy communication. As a result, identifying driver mutations at allosteric sites will be beneficial for deciphering the mechanisms of cancer and developing allosteric drugs. In this study, we provided a platform called DeepAlloDriver to predict driver mutations using a deep learning method that exhibited >93% accuracy and precision. Using this server, we found that a missense mutation in RRAS2 (Gln72 to Leu) might serve as an allosteric driver of tumorigenesis, revealing the mechanism of the mutation in knock-in mice and cancer patients. Overall, DeepAlloDriver would facilitate the elucidation of the mechanisms underlying cancer progression and help prioritize cancer therapeutic targets. The web server is freely available at: https://mdl.shsmu.edu.cn/DeepAlloDriver.

AlloReverse: multiscale understanding among hierarchical allosteric regulations.

Increasing data in allostery are requiring analysis of coupling relationships among different allosteric sites on a single protein. Here, based on our previous efforts on reversed allosteric communication theory, we have developed AlloReverse, a web server for multiscale analysis of multiple allosteric regulations. AlloReverse integrates protein dynamics and machine learning to discover allosteric residues, allosteric sites and regulation pathways. Especially, AlloReverse could reveal hierarchical relationships between different pathways and couplings among allosteric sites, offering a whole map of allostery. The web server shows a good performance in re-emerging known allostery. Moreover, we applied AlloReverse to explore global allostery on CDC42 and SIRT3. AlloReverse predicted novel allosteric sites and allosteric residues in both systems, and the functionality of sites was validated experimentally. It also suggests a possible scheme for combined therapy or bivalent drugs on SIRT3. Taken together, AlloReverse is a novel workflow providing a complete regulation map and is believed to aid target identification, drug design and understanding of biological mechanisms. AlloReverse is freely available to all users at https://mdl.shsmu.edu.cn/AlloReverse/ or http://www.allostery.net/AlloReverse/.

Autoencoder assisted subcarrier optimization for nonlinear frequency division multiplexing.

Nonlinear frequency division multiplexing (NFDM) is a novel optical communication technique that can achieve nonlinear free transmission. However, current design of NFDM is analogous to orthogonal frequency division multiplexing (OFDM), where sinc function is utilized as subcarriers, which may not be optimal for nonlinear spectrums. In this paper, we propose an auto-encoder (AE) assisted subcarrier optimization scheme for dual-polarized (DP) NFDM systems. Numerical verifications show that our scheme can improve the Q-factor by 1.54 dB and 0.62 dB compared to sinc subcarrier and linear minimum mean square error (LMMSE) equalization, respectively, in a 960 km transmission scenario. We also analyze the characteristics of the optimized subcarriers and discuss how they enhance the performance. Furthermore, we demonstrate the robustness of the optimized subcarriers to different modulation formats, transmission distances and bandwidth. Our work provides a new idea in subcarrier design for NFDM.

Improvement for a full-spectrum modulated nonlinear frequency division multiplexing transmission system.

Nonlinear frequency division multiplexing (NFDM), as a possible technique to overcome the limit imposed by Kerr nonlinearity in conventional coherent optical communication systems, has attracted widespread attention in the communication community in recent years. In order to fully utilize the available degrees of freedom in the nonlinear spectrum, this paper focuses on the full-spectrum (FS) modulated NFDM system. First, we maximize the data rate of discrete spectrum (DS) by optimizing the distribution of eigenvalues in DS part of FS. Then through introducing the probabilistic shaping (PS) into the FS system, and combined with linear minimum mean square (LMMSE) estimators, a 1120 km transmission with BER below the hard decision forward error correction (HD-FEC) threshold at 112 Gbps is achieved, where 128 subcarriers with PS-64QAM are used in the continuous spectrum (CS) and 13 eigenvalues with 64QAM are adopted in the discrete spectrum (DS). The achievable data rate is about 12% higher than that of pure CS modulation. Our work achieves the current FS NFDM system with the largest number of multiplexed eigenvalues, and provides a way to improve the performance of FS systems.

Nonlinear frequency domain PMD modeling and equalization for nonlinear frequency division multiplexing transmission.

Polarization mode dispersion (PMD) is one of the fundamental properties of a standard single-mode fiber. It affects the propagating signals and degrades the performance of high-speed optical fiber communication systems. PMD also gives an effect on the nonlinear spectra or scattering data in nonlinear frequency division multiplexing (NFDM) systems. However, PMD is usually described in the linear frequency domain, and there are few investigations about the influence of PMD in the nonlinear frequency domain (NFD). An NFD-PMD model is needed to understand the impact of PMD in the NFD. In this work, using a linear approximation method, we first propose an NFD-PMD model and verify its effectiveness. With the guide of the NFD-PMD model, a blind NFD-PMD equalization scheme is designed. The simulation results indicate that the proposed NFD-PMD equalization scheme has better performance than the training sequence method based on linear frequency domain equalization.

Probabilistic shaping and neural network-based optimization for a nonlinear frequency division multiplexing system.

A joint scheme introducing probabilistic shaping (PS) at the transmitter and utilizing a neural network (NN) equalizer at the receiver is proposed to improve the performance of the b-modulated nonlinear frequency division multiplexing (NFDM) system. Through a numerical simulation, we demonstrate that PS plays a leading role for low launch power case, which improves the performance of the system effectively, while the NN equalizer's superiority appears in a high launch power region, whose main role is to weaken the correlation among subcarriers for improving system performance. The proposed scheme would enlighten the optimum modulation and detection schemes of the NFDM system.

Robust neural network receiver for multiple-eigenvalue modulated nonlinear frequency division multiplexing system.

Nonlinear frequency division multiplexing (NFDM) has been shown to be promising in overcoming the fiber Kerr nonlinearity limit. In multiple-eigenvalue modulated NFDM systems, the transmission capacity increases with the number of modulated eigenvalues. However, as the number of modulated eigenvalues increases, the complexities of the signal waveform and the nonlinear Fourier transform (NFT) algorithm for demodulation increase dramatically as well, while the accuracy drops significantly. Meanwhile, impairments such as amplifier spontaneous emission noise and phase noise in practical channels would perturb the eigenvalues and the corresponding nonlinear spectra during transmission. Coupled with an increase in the modulation format order, it is difficult for NFT algorithm-based receivers to recover information. To enable the use of multiple-eigenvalue modulated NFDM systems, we propose an innovative receiver based on regression neural networks (NNs), which can demodulate information correctly for both single- and dual-polarization NFDM systems. The results show that it has strong robustness and has a certain tolerance to the impairments of communication systems. In the contrast that the poor demodulation performance of the NFT and the Euclidean minimum distance (MD) receivers for multi-eigenvalue modulated NFDM systems, our proposed NN receiver can achieve low bit error rate with 2 GBaud 16QAM modulation over 1,000 km transmission in four-eigenvalue modulated single-polarization NFDM systems. The performance of three receivers (NFT, MD and NN) in a two-eigenvalue modulated NFDM system are also compared, the NN receiver shows the best performance and appears more suitable for higher-order modulation formats.

Nonlinear-frequency-packing nonlinear frequency division multiplexing transmission.

A nonlinear frequency division multiplexing (NFDM) transmission system, designed specifically for nonlinear fiber channel, has the potential to overcome the nonlinear Shannon capacity limit. However, the spectral efficiency (SE) of the current proven NFDM transmission systems is still lower than that of the analogous orthogonal frequency division multiplexing system. It is extremely necessary to explore effective modulation scheme for the aim of increasing the SE of NFDM system. In this study, we first propose the nonlinear-frequency-packing nonlinear frequency division multiplexing (NFP-NFDM) transmission system. In NFP-NFDM, the spacing of nonlinear subcarriers is squeezed and more nonlinear subcarriers can be packed, but the inter carrier interference (ICI) is introduced. The method of NFP in nonlinear Fourier domain is carefully designed to reduce the complexity of ICI cancellation. Through numerical simulation, we illustrate the feasibility of NFP-NFDM transmission, and higher SE in NFP-NFDM than that of NFDM system is also demonstrated. The upper bound of the normalized SE for NFP-NFDM is estimated, which is higher than that of current NFDM system. Besides, we find out that the NFP scheme may have the advantage of reducing the signal-noise interaction in fiber transmission scenario, which indicates there may be a better way to load the data into the nonlinear Fourier domain.

Asymmetric Difluoroboron Quinazolinone-Pyridine Dyes with Large Stokes Shift: High Emission Efficiencies Both in Solution and in the Solid State.

Boron difluoroquinazolinone-pyridine (BODIQPy) and substituted derivatives have been designed and synthesized. Strong emission with high fluorescence quantum yield both in solution (up to 0.99) and in solid state (up to 0.60) was achieved in these asymmetric BODIQPys. For 6-halogen substituted BODIQPys, the heavy atomic effect of bromine and iodine was suppressed in solution due to the strong electron-withdrawing ability of the BODIQPy core. As a result, 6-iodine substituted BODIQPy shows the unusual fluorescence quantum yields (>0.70) in dichloromethane and tetrahydrofuran. In the solid state, the asymmetric structure induced a unique dimeric structure, and efficient luminescence was also observed. The formation of halogen bonds between carbonyl oxygen and bromine or iodine regulated the stacking mode and enhanced intermolecular interaction, resulting in a decline in the fluorescence quantum yield. In addition, a large Stokes shift was also achieved in these asymmetric BODIQPys.

Diabetic Neuropathic Pain and Circadian Rhythm: A Future Direction Worthy of Study.

More than half of people with diabetes experience neuropathic pain. Previous research has shown that diabetes patients' neuropathic pain exhibits a circadian cycle, which is characterized by increased pain sensitivity at night. Additional clinical research has revealed that the standard opioid drugs are ineffective at relieving pain and do not change the circadian rhythm. This article describes diabetic neuropathic pain and circadian rhythms separately, with a comprehensive focus on circadian rhythms. It is hoped that this characteristic of diabetic neuropathic pain can be utilized in the future to obtain more effective treatments for it.

Deep Learning-Based Automated Approach for Determination of Pig Carcass Traits.

Pig carcass traits are among the most economically significant characteristics and are crucial for genetic selection in breeding and enhancing the economic efficiency. Standardized and automated carcass phenotyping can greatly enhance the measurement efficiency and accuracy, thereby facilitating the selection and breeding of superior pig carcasses. In this study, we utilized phenotypic images and data from 3912 pigs to propose a deep learning-based approach for the automated determination of pig carcass phenotypic traits. Using the YOLOv8 algorithm, our carcass length determination model achieves an average accuracy of 99% on the test set. Additionally, our backfat segmentation model, YOLOV8n-seg, demonstrates robust segmentation performance, with a Mean IoU of 89.10. An analysis of the data distribution comparing manual and model-derived measurements revealed that differences in the carcass straight length are primarily concentrated between -2 cm and 4 cm, while differences in the carcass diagonal length are concentrated between -3 cm and 2 cm. To validate the method, we compared model measurements with manually obtained data, achieving coefficients of determination (R2) of 0.9164 for the carcass straight length, 0.9325 for the carcass diagonal length, and 0.7137 for the backfat thickness, indicating high reliability. Our findings provide valuable insights into automating carcass phenotype determination and grading in pig production.

Exploring the Maintaining Period and the Differentially Expressed Genes between the Yellow and Black Stripes of the Juvenile Stripe in the Offspring of Wild Boar and Duroc.

Coloration is a crucial trait that allows species to adapt and survive in different environments. Wild boars exhibit alternating black (dark) and yellow (light) longitudinal stripes on their back during their infancy (juvenile stripes), and as adults, they transform into uniform wild-type coat color. Aiming to record the procedure of juvenile stripes disappearing, piglets (WD) with juvenile stripes were produced by crossing a wild boar with Duroc sows, and photos of their coat color were taken from 20 d to 220 d. The pigments in the hairs from the black and yellow stripes were determined. Furthermore, the differentially expressed genes between the black and yellow stripes were investigated in 5 WD with the age of 30 d using whole-transcriptome sequencing to explore the genetic mechanism of the juvenile stripes. The juvenile stripes started to disappear at about 70 d, and stripes were not distinguished with the naked eye at about 160 d; that is, the juvenile stripe completely disappeared. A hotspot of a differentially expressing (DE) region was found on chromosome 13, containing/covering 2 of 13 DE genes and 8 of 10 DE lncRNAs in this region. A network among ZIC4, ssc-miR-532-3p, and ENSSSCG00000056225 might regulate the formation of juvenile stripes. Altogether, this study provides new insights into spatiotemporal coat color pattern.

Bio-inspired aFGF modification functionalized piezoelectric chitosan films for promoting scald wound healing.

The application of acidic fibroblast growth factor (aFGF) has shown great potential in the treatment of scald or burn wounds with high morbidity and mortality, especially in promoting the repair of deep partial-thickness wounds. However, its short half-life and instability in vivo do pose challenges for clinical application. Herein, two kinds of bio-inspired modified piezoelectric chitosan (CS) films, namely heparin-coated CS film (HCS) and polydopamine-coated CS film (DCS), are facially fabricated and adopted as controlled-release platforms for local delivery of aFGF. Polydopamine or heparin layers serve as a bridge grafting on chitosan films, facilitating the loading of aFGF and enabling controlled release of aFGF from the piezoelectric film through intermolecular interactions. Additionally, these layers enhance the hydrophilicity and antibacterial properties of the bare CS film due to their inherent biological activities. Furthermore, the polydopamine coating imparts photothermal activity to the CS film. The in vivo experiments ascertain that the synergetic effect of the controlled-released aFGF and low temperature photothermal therapy collectively accelerate scald wound healing outcomes within 14 days by facilitating granulation formation, collagen deposition, re-epithelialization and angiogenesis. This study opens up new possibilities for the development of multifunctional chitosan-based wound dressings and the creation of innovative drug delivery platforms.

Detection of Pig Movement and Aggression Using Deep Learning Approaches.

Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m2 space; they were bred in stable social groups and a video was set up to record the whole day's activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig's identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R2 of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes.

Unsupervised 3D Pose Transfer With Cross Consistency and Dual Reconstruction.

The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator G which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With G as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches.

Enhanced uranium photoreduction on Ti3C2Tx MXene by modulation of surface functional groups and deposition of plasmonic metal nanoparticles.

Photocatalytic reduction of soluble hexavalent uranium (U(VI)) is a novel and efficient avenue to enriching U(VI), where the free U(VI) is firstly bound on the surface of photocatalysts and then reduced to insoluble tetravalent uranium (U(IV)) by photoelectrons. Therefore, constructing the efficient U(VI) binding sites on photocatalysts is an efficient strategy to boost catalytic activity toward U(VI) photoreduction. Herein, we successfully constructed an efficient catalyst for U(VI) photoreduction by depositing Ag nanoparticles on Ti3C2Tx MXene with abundant U(VI) binding sites (Ag/Ti3C2Tx-O). Impressively, the U(VI) extracting mass over Ag/Ti3C2Tx-O under light reached up to 1257.6 mg/g in 120 min, which was almost 11 times as high as that without light. Further mechanistic studies indicated that the U(VI) binding sites on Ti3C2Tx MXene in Ag/Ti3C2Tx-O were beneficial to the reduction of U(VI) by significantly decreasing its reduction potential. More importantly, hot electrons generated by Ag nanoparticles were transferred into the binding sites to easily reduce the bound U(VI), resulting in the remarkable performance of Ag/Ti3C2Tx-O during U(VI) enrichment.

Rational design of a sensitivity-enhanced tracer for discovering efficient APC-Asef inhibitors.

The adenomatous polyposis coli (APC)-Rho guanine nucleotide exchange factor 4 (Asef) protein-protein interaction (PPI) is essential for colorectal cancer metastasis, making it a promising drug target. Herein, we obtain a sensitivity-enhanced tracer (tracer 7) with a high binding affinity (Kd = 0.078 µM) and wide signal dynamic range (span = 251 mp). By using tracer 7 in fluorescence-polarization assays for APC-Asef inhibitor screening, we discover a best-in-class inhibitor, MAI-516, with an IC50 of 0.041 ± 0.004 µM and a conjugated transcriptional transactivating sequence for generating cell-permeable MAIT-516. MAIT-516 inhibits CRC cell migration by specifically hindering the APC-Asef PPI. Furthermore, MAIT-516 exhibits no cytotoxic effects on normal intestinal epithelial cell and colorectal cancer cell growth. Overall, we develop a sensitivity-enhanced tracer for fluorescence polarization assays, which is used for the precise quantification of high-activity APC-Asef inhibitors, thereby providing insight into PPI drug development.

Microglial Pten safeguards postnatal integrity of the cortex and sociability.

Microglial abnormalities may contribute to neurodevelopmental disorders. PTEN is implicated as a susceptibility gene for autism spectrum disorders and its germline ablation in mice causes behavioral abnormalities. Here we find postnatal PTEN deletion in microglia causes deficits in sociability and novel object recognition test. Mutant mice harbor markedly more activated microglia that manifest enhanced phagocytosis. Interestingly, two-week postponement of microglia PTEN ablation leads to no social interaction defects, even though mutant microglia remain abnormal in adult animals. Disturbed neurodevelopment caused by early PTEN deletion in microglia is characterized by insufficient VGLUT1 protein in synaptosomes, likely a consequence of enhanced removal by microglia. In correlation, in vitro acute slice recordings demonstrate weakened synaptic inputs to layer 5 pyramidal neurons in the developing cortex. Therefore, microglial PTEN safeguards integrity of neural substrates underlying sociability in a developmentally determined manner.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells.

SIRT6 belongs to the conserved NAD+-dependent deacetylase superfamily and mediates multiple biological and pathological processes. Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics, which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases. Here, developing a reversed allosteric strategy AlloReverse, we identified a cryptic allosteric site, Pocket Z, which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD+. Based on Pocket Z, we discovered an SIRT6 allosteric inhibitor named JYQ-42. JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation, with an IC50 of 2.33 µmol/L. JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production. JYQ-42, to our knowledge, is the most potent and selective allosteric SIRT6 inhibitor. This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.