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Flexible heaters with personal thermal management capabilities have great potential in thermal therapy applications due to their excellent flexibility, low power consumption, and portability. However, manufacturing wearable heating devices that are breathable, wear resistant and conformal for long-term use is still challenging. To address these issues, we designed a leather heater using breathable, biocompatible, and tailorable leather as the substrate through a simple in-situ polymerization polypyrrole strategy. This heater exhibits excellent heating and mechanical properties (reaching 64°C at a voltage of 5 V with efficient Joule heat generation of 2286 W/m2and uniform temperature distribution, and functioning properly after 1000 cycles of bendability tests). In addition, this heater displays better wear resistance and water vapor permeability rate (38.04 g/(m2h)). The cuttable and sewable of leather gives the strategy ability to be flexibly designed to mold the heater to the specific requirements of different body parts, providing a new approach to wearable thermal therapy.

The continuous advancement in the field of flexible and wearable electronics has led to increased research interest in safe, low-cost, and flexible zinc-ion batteries, particularly with a focus on flexible electrolytes. In this study, we present a leather gel electrolyte (LGE) that offers robust mechanical properties and an excellent electrochemical performance. LGE exhibits an ionic conductivity of 1.36 × 10–2S cm–1and achieves a capacity of 303.7 mAh g–1in flexible zinc–manganese dioxide batteries. Even after 1000 cycles, the capacity retention remains above 90%, demonstrating outstanding performance in protecting the zinc anode. Furthermore, such a flexible battery shows good resistance to damage due to the strong mechanical strength originating from leather. Notably, LGE utilizes green and sustainable leather as a raw material, making it a promising option for sustainable flexible devices.

The multiattribute scene understanding (MASU) tasks currently lie in capturing multiple attribute features and learning the complex correlations between different attributes. Traditional methods primarily focus on exploring multiscale local insights and employ direct approaches to merge global semantic data into the image models, thereby neglecting the full spectrum of global semantic features across different receptive fields. Furthermore, encapsulating a wide range of spatial details through deeper networks inevitably leads to a drastic increase in computational complexity. To address these challenges, we propose a novel end-to-end network named adjacent-atrous mechanism for expanding global receptive fields (AMEGRF-Net). Specifically, we introduce an efficient local-global feature learning paradigm that innovatively expands the model’s receptive field to enhance scene understanding without incurring additional computational overhead. A local feature sensing (LFS) module is proposed to enhance the distinctiveness between different categories within the feature space while refining the spatial feature learning capability and interchannel synergy. We present an innovative adjacent-atrous mechanism, adjacent-atrous global context modeling module (AGCM), to combine a broader global receptive field with a complex relationship capturing mechanism, achieving deep modeling of the intricate relationships between attributes and labels. Through extensive comparative experiments on three challenging public datasets, the superior performance of AEGRF-Net in handling high-resolution remote sensing images for MASU has been clearly demonstrated.

Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel Simulated Annealing (pSA) methods have been proposed, where the increase of searching threads can boost the speed of convergence. Although satisfactory solutions can be obtained by these methods, there is no rigorous mathematical analyses on their effectiveness. Thus, this article introduces a probabilistic model, on which a theorem about the effectiveness of multiple initial states parallel SA (MISPSA) has been proven. The theorem also demonstrates that the increasing parallelism in pSA algorithm with the reducing of search depth in each thread could obtain almost the same probability of finding the global optimal solution. We validated our theorem on AutoDock Vina, a widely used molecular docking tool with high accuracy and docking speed. AutoDock Vina uses a pSA strategy to find optimal molecular conformations. Under the premise that the total searching workload (i.e., thread number * iteration depth of each thread) remains unchanged, the docking accuracy from an aggressively parallelized SA searching method is almost the same or even better than those from the default exhaustiveness (parallelism degree) configuration of AutoDock Vina. Taking complex ‘1hnn’ as an example,with the increase (125x) in the number of initial states (from 8 to 1000) and the decrease in the search depth for each thread (from 15540 to 124, or 1/125 of the original search depth), the mean energy is −7.80 and −7.94, while the mean RMSD is 3.4 and 3.14, respectively. The result also implies that a considerable speedup (in this case 125x in theory) can be obtained by a highly parallelized SA algorithm implementation.

Future environmental perception (EP) in the Industrial Internet of Things (IIoT) is expected to interconnect massive sensing objects, merge various intelligent technologies, and efficiently control devices to adapt to a complex environment. It will optimize overall production value, including improving product quality, promoting multisensor collaboration, and decreasing energy consumption and costs. Current EP perceives the industrial environmental information through industrial sensors that are controlled by traditional programmable logic controllers (PLCs), but the sensors are not capable of collaboration and intercommunication. Some sensors are difficult to deploy in the terminal and can easily be corrupted in a harsh environment. In addition, the current EP lacks intelligent analysis of massive sensing data. Therefore, it is necessary to use the latest emerging techniques including knowledge graph (KG), artificial intelligence (AI) algorithms, and cloud control, to create environmental intelligent perception (EIP) in the industry. In this article, an overview of challenges for EP in a complex industrial environment is provided, potential schemes for designing EIP architectures are given, and potential practical application scenarios are discussed. Finally, a case study analysis of a multicrane visual sorting system is presented in the experiment, in which two mechanical arms operate cooperatively with the help of cloud PLCs and visual recognition algorithm.

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit–cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

Molecular docking (MD) is one of the core steps in the expensive and time-consuming process of drug design, which is basically an optimization problem based on scoring functions. AutoDock series MD software is widely accepted by academia and industry, among which AutoDock Vina (Vina) is the latest and most popular version due to its accuracy and relatively high speed. However, contrast to its prior version, i.e., AutoDock4, hardware acceleration approaches of Vina are rarely reported. In this article, we propose Vina-field-programmable gate array (FPGA), a hardware-accelerated Vina implementation with FPGA that exploits the low-level parallelism. First, the fixed-point quantization is analyzed and realized to accelerate the MD algorithm with a better energy efficiency in hardware. To boost the performance of the module-level computation, multiple in- module hardware pipelines have been designed and implemented. Besides, a strategy for fast accessing to block RAM (BRAM) is implemented by utilizing the layout of data, which brings four times memory access speed to the intermolecular and intramolecular energy computing modules. Under the same 140 ligand–receptor benchmarks, Vina-FPGA performs up to $6.9\times $ (average $3.7\times$ ) faster than a state-of-the-art CPU does while consuming only 2.5% energy with similar docking accuracies. Compared to the GPU-accelerated implementation or Vina-GPU, the average energy consumption of Vina-FPGA is merely 45%.

In transition metal dichalcogenides (TMDs), Ising superconductivity with an antisymmetric spin texture on the Fermi surface has attracted wide interest due to the exotic pairing and topological properties. However, it is not clear whether the Qvalley with a giant spin splitting is involved in the superconductivity of heavily doped semiconducting 2H-TMDs. Here by taking advantage of a high-quality monolayer WS2on hexagonal boron nitride flakes, we report an ionic-gating induced superconducting dome with a record high critical temperature of ∼6 K, accompanied by an emergent nonlinear Hall effect. The nonlinearity indicates the development of an additional high-mobility channel, which (corroborated by first principle calculations) can be ascribed to the population of Qvalleys. Thus, multivalley population at Kand Qis suggested to be a prerequisite for developing superconductivity. The involvement of Qvalleys also provides insights to the spin textured Fermi surface of Ising superconductivity in the large family of transition metal dichalcogenides.

Controlling the morphology of the metal–organic framework (MOF) for nanosheets is beneficial for understanding their crystal growth kinetics and useful for extending these MOF nanosheets to advanced applications, in particular for gas separation and device integration. However, synthesizing MOF nanosheets with uniform thickness or desirable size still remains challenging. Herein, we provide a crystal dissolution–growth strategy for fabricating dispersible porphyrin MOF nanosheets with lateral dimensions and nanometer thickness. A morphological transition (bulk crystals–nanosheets–bulk crystals) in Zn-TCPP was observed when controlling the crystal growth kinetics by adjusting the reaction parameters (temperature and acidity). These findings encouraged the synthesis of other types of nanosheets (Cu-TCPP, Zn-TCPP (Pd), and Cu-BDC nanosheets). Zn-TCPP (Pd) nanosheets were applied in field-effect transistors and exhibited photoresponse characteristics. This work demonstrates a new strategy for obtaining MOF nanosheets and casts a new light upon fabricating two-dimensional inorganic–organic hybrid materials with controlled thickness.

The B2–B19 one-stage and B2–B19–B19′ two-stage phase transformations have been observed in Ti51Pd30Ni19alloy under different heat treatment conditions. Differential scanning calorimetry, X-ray diffractometry and transmission electron microscopy were used to ascertain the phase transformations. The B2–B19 transformation can be obtained by solution treatment followed by furnace cooling, while the B2–B19–B19′ transformation can be realized by solution treatment with subsequently quenching. Annealing treatment at lower temperature does not change the nature of the phase transformations. It is found that elastic softening occurs in the B2–B19 one-stage phase transformation, while a discontinuous modulus variation in the B2–B19–B19′ two-stage transformation.

The oxidation behavior of TiNi, TiNiPd and the Ce-containing TiNiPd shape memory alloys are investigated using thermogravimetric analysis, X-ray analysis, scanning electron microscopy and electron probe microanalysis. It has been found that the oxidation scale of the alloys exhibits different sub-layers. Generally, the outmost layer is mainly composed of rutile TiO2. The TiNiO3phase exists for all the alloys, and the Ti4Pd2O phase for TiNiPd alloys as well. These oxides act as important media during the ion exchanging process during the oxidation and cause different oxidation resistance. The addition of Ce in the TiNiPd alloy effectively impedes the Ti ions outward diffusion, causes a wide distribution of Ni and Pd over the scale depth, and obviously improves the oxidation resistance.

Lithium-sulfur battery (Li–S) is considered as one of the new-generation rechargeable batteries with high performance because of its extremely high theoretical capacity, energy density, environmental harmony and low cost. However, low electrical and ionic conductivity of sulfur, safety concerns and parasitic reaction generated by the dissolved polysulfide species in electrolyte hinder the commercialization of Li–S battery. Herein, we report a polyhedral porous structure comprising of carbon coating metal selenide nanoparticles (CoSe2/C), which could not only host sulfur for Li-S battery owing to its porous and conductive structure, but also mitigate the shuttle phenomenon by polysulfides adsorption and catalytic acceleration of redox kinetics. As a result, a performance enhanced CoSe2/C-S electrode for Li-S battery is achieved.

The exploration of transitional metal–organic frameworks (MOFs) is important because of their unique properties and promising applications. Hence, finding a suitable strategy to design transitional MOFs with different states has become a key issue. Herein, we develop a modulator-induced strategy for fabricating transitional MOFs with carboxylic ligands by building esterification reaction. The exposed metal sites, mesoporous systems, morphologies, crystallinities, and components of transitional MOFs can be finely controlled when different modulators are employed. Notably, the Pt/solid-transitional MOF catalyst with more mesopores enhances conversion in the hydrogenation reaction of n-hexene, and the flower-like-transitional MOF catalyst with more Lewis acid sites exhibits better performance in the cycloaddition reaction. Therefore, the modulator-induced strategy may provide significant inspiration for preparing various transitional MOFs by building suitable chemical reactions.

G protein-coupled receptors (GPCRs) are one of the most important drug targets, accounting for ∼34% of drugs on the market. For drug discovery, accurate modeling and explanation of bioactivities of ligands is critical for the screening and optimization of hit compounds. Homologous GPCRs are more likely to interact with chemically similar ligands, and they tend to share common binding modes with ligand molecules. The inclusion of homologous GPCRs in learning bioactivities of ligands potentially enhances the accuracy and interpretability of models due to utilizing increased training sample size and the existence of common ligand substructures that control bioactivities. Accurate modeling and interpretation of bioactivities of ligands by combining homologous GPCRs can be formulated as multitask learning with joint feature learning problem and naturally matched with the group lasso learning algorithm. Thus, we proposed a multitask regression learning with group lasso (MTR-GL) implemented by l2,1-norm regularization to model bioactivities of ligand molecules and then tested the algorithm on a series of thirty-five representative GPCRs datasets that cover nine subfamilies of human GPCRs. The results show that MTR-GL is overall superior to single-task learning methods and classic multitask learning with joint feature learning methods. Moreover, MTR-GL achieves better performance than state-of-the-art deep multitask learning based methods of predicting ligand bioactivities on most datasets (31/35), where MTR-GL obtained an average improvement of 38% on correlation coefficient (r2) and 29% on root-mean-square error over the DeepNeuralNet-QSAR predictors.

The development of advanced anode materials is crucial to enhance the performance of sodium-ion batteries (SIBs). In this study, SnSe2nanoparticles chemically embedded in a carbon shell (SnSe2@C) were fabricated from Sn–organic frameworks and evaluated as an anode material for SIBs. The structural characterization demonstrated that there existed C–Sn chemical bonds between the SnSe2nanoparticles and carbon shell, which could strongly anchor SnSe2nanoparticles to the carbon shell. Such a structure can not only facilitate charge transfer but also ensure the structural stability of the SnSe2@C electrode. In addition, the carbon shell also helped in the dispersion of SnSe2nanoparticles, thus offering more redox-active sites for Na+storage. The as-prepared SnSe2@C nanocomposite could deliver good cycling stability and a superior rate capability of 324 mA h g–1at 2 A g–1for SIBs.

Flexible zinc-ion batteries (FZIBs) have been acknowledged as a potential cornerstone for the future development of flexible energy storage, yet conventional FZIBs still encounter challenges, particularly concerning performance failure at low temperatures. To address these challenges, a novel anti-freezing leather gel electrolyte (AFLGE-30) is designed, incorporating ethanol as a hydrogen bonding acceptor. The AFLGE-30 demonstrates exceptional frost resistance while maintaining favorable flexibility even at −30°C; accordingly, the battery can achieve a high specific capacity of about 70 mAh/g. Cu//Zn battery exhibits remarkable stability at room temperature, retaining ∼96% efficiency after 120 plating/stripping cycles at 1 mA/cm2. Concurrently, the Zn//Zn symmetric batteries demonstrate a lifespan of 4100 h at room temperature, which is attributed to the enhancement of Zn2+deposition kinetics, restraining the formation of zinc dendrites. Furthermore, FZIBs exhibit minimal capacity loss even after bending, impacting, or burning. This work provides a promising strategy for designing low-temperature-resistant FZIBs.

A series of inhibitors of mTOR kinase based on a quaternary-substituted dihydrofuropyrimidine was designed and synthesized. The most potent compounds in this series inhibited mTOR kinase with Ki< 1.0 nM and were highly (>100×) selective for mTOR over the closely related PI3 kinases. Compounds in this series showed inhibition of the pathway and antiproliferative activity in cell-based assays. Furthermore, these compounds had excellent mouse PK, and showed a robust PK−PD relationship in a mouse model of cancer.

Inorganic metal sulfides have received extensive investigation as anode materials in lithium-ion batteries (LIBs). However, applications of crystalline organic hybrid metal sulfides as anode materials in LIBs are quite rare. In addition, combining the nanoparticles of crystalline organic hybrid metal sulfides with conductive materials is expected to enhance the electrochemical lithium storage performance. Nevertheless, due to the difficulty of harvesting the nanoparticles of crystalline organic hybrid metal sulfides, this approach has never been tried to date. Herein, nanoparticles of a crystalline organic hybrid cadmium antimony sulfide (1,4-DABH2)Cd2Sb2S6(DCAS) were prepared by a top-down method, including the procedures of solvothermal synthesis, ball milling, and ultrasonic pulverization. Thereafter, the nanoparticles of DCAS with sizes of ∼500 nm were intercalated into graphene oxide nanosheets through a freeze-drying treatment and a DCAS@GO composite was obtained. Compared with the reported Sb2S3- and CdS-based composites, the DCAS@GO composite exhibited superior electrochemical Li+ion storage performance, including a high capacity of 1075.6 mAh g–1at 100 mA g–1and exceptional rate tolerances (646.8 mAh g–1at 5000 mA g–1). In addition, DCAS@GO can provide a high capacity of 705.6 mAh g–1after 500 cycles at 1000 mA g–1. Our research offers a viable approach for preparing the nanoparticles of crystalline organic hybrid metal sulfides and proves that intercalating organic hybrid metal sulfide nanoparticles into GO nanosheets can efficiently boost the electrochemical Li+ion storage performance.

ABSTRACTBackgroundAccumulating studies demonstrated that resistance of colon cancer (CC) to 5-fluorouracil (5-FU) contributes to adverse prognosis. We investigated how Kruppel-like factor 4 (KLF4) affected 5-FU resistance and autophagy in CC cells.MethodsKLF4 expression and its downstream target gene RAB26 in CC tissues was analyzed by bioinformatics analysis, and the effect of abnormal KLF4 expression on prognoses of CC patients was predicted. Luciferase reporter assay detected the targeted relationship between KLF4 and RAB26. The viability and apoptosis of CC cells were analyzed by CCK-8 and flow cytometry. The formation of intracellular autophagosomes was detected by confocal laser scanning microscopy and immunofluorescence staining. The mRNA and protein levels were assayed by qRT-PCR and western blot. A xenograft animal model was constructed to verify the function of KLF4. Rescue assay was employed to verify whether KLF4/RAB26 could affect 5-FU resistance in CC cells through autophagy.ResultsKLF4 and RAB26 were lowly expressed in CC. KLF4 correlated with patients’ survival. KLF4 was down-regulated in 5-FU resistant CC cells. KLF4 overexpression suppressed the proliferation and 5-FU resistance of CC cells, and inhibited LC3 II/I expression and autophagosome formation. Autophagy activator Rapamycin or sh-RAB26 treatment reversed the impact of KLF4 overexpression on 5-FU resistance. In vivo assay verified that KLF4 inhibited 5-FU resistance in CC cells. Rescue experiments revealed that KLF4 targeted RAB26 to inhibit CC cell autophagy, resulting in decreasing the resistance to 5-FU.ConclusionKLF4 strengthened the sensitivity of CC cells to 5-FU by targeting RAB26 to restrain autophagy pathway.

Surface modification is an effective strategy to address the critical issues of Ni-rich LiNi1-x-yCoxMnyO2(NCM) cathode material, including fast energy loss and stability in lithium ion battery. However, both single/double layers coating strategies suffer many fatal drawbacks, such as functional unitary or poor components contacting. Herein, we present a dual-component in one coating layer strategy, which composed of LixTiO2nano-particles embedding in amorphous silica (LTSO) through a facile method. The coating material could not only be used as a physical protective layer to enhance the stability of NCM cathode, but also improve the ion diffusion kinetics with the contribution from the coating components. Thus, the battery performance was significantly enhanced. This dual-component coating strategy is novel, effective, simple and scalable, which could be extended and applied to other practical Ni-rich material modification designs.

Although there are plenty of merits for lithium–sulfur (Li–S) batteries, their undesired shuttle effect and insulated nature are hindering the practical applications. Here, a conductive metal–organic framework (MOF)-modified separator has been designed and fabricated through a facile filtration method to address the issues. Specifically, its intrinsic microporous structure, hydrophilic polar property, and conductive feature could make it easy to contact with and trap polysulfides and boost the kinetics of electrochemical reactions. Both the physical and chemical properties of the as-prepared separator are beneficial to alleviating the shuttle effect and enhancing the rate capability. Accordingly, the electrochemical performance of the battery with a MOF-modified separator was significantly improved.

Unexpected Sc/Ln codoped rare earth metal–organic frameworks (RE-MOFs) molecular alloys (MAs) based on an oxalic acid ligands were obtained. Calcination of the RE-MOFs MAs gave the corresponding codoped rare earth oxides a strong and nearly pure red color upconversion luminescence. It allows the resulting lanthanide ion (Ln3+) doped upconversion materials a wide range of applications from optical communications to disease diagnosis. Moreover, the pyrolysis RE-MOFs MAs precursor has demonstrated to be an effective preparation method for a uniform Ln-doped Sc2O3system.

Sodium-ion batteries suffer the disadvantages of poor rate performance and cycling stability due to its sluggish sodiation kinetics. A rational design strategy for both materials compositions and structures has been proposed to meet these challenges. Herein, a triple-component composite derived from metal–organic frameworks, comprising FeS2, nitrogen–sulfur co-doped porous carbon, and reduced graphene oxide (FeS2@NSC/G), has been successfully synthesized. With the capacities contributions from different sodium storage routes (diffusion-controlled processes and surface capacitive processes) at varies rate conditions, it is aiming to make full use of each component in the electrode composite and their unique porous structures. Expected electrode properties have been achieved and related electrochemical behaviors have also been investigated. The strategy would present a promising thought for composites design, which could enhance high-rate electrochemical energy storage performances.

Objective To explore stable and functional microRNA (miRNA)–disease relationships using a genome-wide expression profile pattern matching strategy.Methods We applied the ranked microarray pattern matching strategy Gene Set Enrichment Analysis to identify miRNA permutations with similar expression patterns to diseases. We also used quantitative reverse transcription PCR to validate the predicted expression levels of miRNAs in three diseases: inflammatory bowel disease (IBD), oesophageal cancer, and colorectal cancer.Results We found that hsa-miR-200 c was upregulated more than 40-fold in oesophageal cancer. The expression of miR-16 and miR-124 was not consistently upregulated in IBD or colorectal cancer.Conclusions Our results suggest that this expression profile matching strategy can be used to identify functional miRNA–disease relationships.

Carbon materials derived from biomass waste are considered as potential electrocatalysts for applications in zinc-air batteries (ZABs) due to their low cost and good catalytic activity. Here, we reported the preparation of gel-based catalysts through utilizing hydrolyzed waste leather powder cross-linked with metallic salt solutions. After calcination, iron-nickel alloy anchored in nitrogen-doped porous carbon catalysts (FeNi@NDC) was achieved. Compared with commercial Pt/C catalyst, FeNi@NDC-800 exhibited lower E1/2(0.77 V) and better durability. More importantly, the resulting FeNi@NDC-800-based alkaline ZABs achieved power density of 93.01 mW/cm2and open circuit voltage of 1.45 V, which the FeNi@NDC-800-based neutral ZAB displayed a charge/discharge cycle stability of 275 h. This work opens up the possibility of rational design and preparation of low-cost and high-performance electrocatalysts from recyclable leather waste.

Flexible transparent conductive electrodes (FTCEs) are essential components for numerous optoelectronic devices. In this work, we have fabricated the hierarchical metal grids (HMG) FTCEs by a facile and low-cost, near-field photolithography strategy. Compared to normal metal grids (MG), the HMG structure can provide distinctly increased conductivity of the electrode yet without obvious reduction of the optical transmittance. This HMG sample possesses excellent optoelectronic performance and high mechanical flexibility, making it a promising component for practical applications.

Ladder-type electron-donating units for D-A copolymers applied in polymer solar cells usually comprise multiple tetrahedral carbon bridges bonded with out-of-plane alkyl chains for desirable solubility for device processing. However, molecular packing of resultant copolymers in the solid state and charge transport within devices are also impeded in spite of with multiple fused aromatic backbones. To mitigate this issue, a structurally well-defined ladder-type electron-donating heteroheptacene, 12H-dithieno[2′,3′:4,5]thieno[3,2-b:2′,3′-h]fluorene (DTTF) with an extended conjugated backbone and a single tetrahedral carbon bridge attached with two bulky alkyl chains was designed and synthesized. The copolymerization of DTTFwith 4,7-bis(4-hexylthiophen-2-yl)benzo[c][1,2,5]thiadiazole (DTBT) afforded a soluble D-A copolymer (PDTTF-DTBT) with a medium optical band gap of 1.72 eV and low-lying HOMO level at −5.36 eV. PDTTF-DTBTunprecedentedly exhibits strong intermolecular stacking ability and presents preferential face-on orientation on both ZnO and PEDOT:PSS layers. The improved packing order and appropriate phase separation of both the copolymer and PC71BM in the bulk heterojunction blend on the ZnO layer over on the PEDOT:PSS layer lead to much improved power conversion efficiency of ∼8.2% in the inverted solar cell device, among the highest for reported ladder-type D-A copolymers. The research demonstrates that it is an effective method to incorporate a single tetrahedral carbon bridge to the molecular center of a ladder-type heteroacene with heavily extended π-conjugation to prepare D-A copolymers toward highly efficient PSCs.

Metal–organic framework (MOF)-based derivatives have attracted an increasing interest in various research fields. However, most of the reported papers mainly focus on pristine MOF-based derivatives, and research studies on functional MOF-based derivative composites are rare. Here, a simple strategy has been reported to design functional MOF-based derivative composites by the encapsulation of metal nanoparticle (MNP) in MOF matrixes (MNP@MOF) and the high-temperature calcination of MNP@MOF composites. The as-prepared MNP@metal oxide composites with a hierarchical pore structure exhibited excellent catalytic activity and high stability for the CO oxidation reaction.

The Bruton’s tyrosine kinase (Btk) inhibitor ibrutinib has shown impressive clinical efficacy in a range of B-cell malignancies. However, acquired resistance has emerged, and second generation therapies are now being sought. Ibrutinib is a covalent, irreversible inhibitor that modifies Cys481 in the ATP binding site of Btk and renders the enzyme inactive, thereby blocking B-cell receptor signal transduction. Not surprisingly, Cys481 is the most commonly mutated Btk residue in cases of acquired resistance to ibrutinib. Mutations at other sites, including Thr474, a gatekeeper residue, have also been detected. Herein, we describe noncovalent Btk inhibitors that differ from covalent inhibitors like ibrutinib in that they do not interact with Cys481, they potently inhibit the ibrutinib-resistant Btk C481S mutant in vitroand in cells, and they are exquisitely selective for Btk. Noncovalent inhibitors such as GNE-431 also show excellent potency against the C481R, T474I, and T474M mutants. X-ray crystallographic analysis of Btk provides insight into the unique mode of binding of these inhibitors that explains their high selectivity for Btk and their retained activity against mutant forms of Btk. This class of noncovalent Btk inhibitors may provide a treatment option to patients, especially those who have acquired resistance to ibrutinib by mutation of Cys481 or Thr474.

Herein we report the development of nanostructured poly(1,4-dihydro-11H-pyrazino[2′,3′:3,4]cyclopenta[1,2-b]quinoxalin-11-one) (PPCQ), a novel conjugated ladderlike oligomer with the presence of a rich amount of heteroatoms, as the anode material. Beyond its remarkable lithium storage of 972 mAh g–1after 120 cycles, the superior cycle life and stable capacity performance of 489 mAh g–1revealed by ultralong testing of 1000 cycles (with an average Coulombic efficiency 99.8%) at a high current density of 2.5 A g–1indicate its excellent electrochemical stability to be promisingly applied for high-performance lithium-ion batteries (LIBs).

Pipe‐jacking engineering requires monitoring of the spatial distribution of thixotropic slurry behind the pipe segments, but timely thickness measurements are difficult during construction. Ground‐penetrating radar was used here during sewage pipe jacking, to evaluate the distribution of the slurry by considering its dielectric contrast with the surrounding soil. A series of circular ground‐penetrating radar data was collected along the inner surfaces of the pipe segments using an antenna with a frequency of 900 MHz. To enable accurate interpretation of the ground‐pene‐trating radar images, the dielectric parameters of the slurry were first measured by time‐domain reflectometry. Statistical analyses revealed the slurry as a special medium with extremely high dielectric constant and conductivity. Numerical simulation indicated that the ground‐penetrating radar images can be considerably improved by applying Karhunen–Loeve transformation followed by f–kStolt migration, which facilitates identification of the reflections from the slurry–soil interface. Based on all the processed ground‐penetrating radar field data, the thickness and the distribution of the slurry at different pipe segments were calculated using the two‐way travel time and the radar signal propagation velocity in the slurry. The results show that ground‐penetrating radar is indeed effective for estimating the quality of slurry injected during pipe jacking.

There are many new and potential drug targets in G protein-coupled receptors (GPCRs) without sufficient ligand associations, and accurately predicting and interpreting ligand bioactivities is vital for screening and optimizing hit compounds targeting these GPCRs. To efficiently address the lack of labeled training samples, we proposed a multi-task regression learning with incoherent sparse and low-rank patterns (MTR-ISLR) to model ligand bioactivities and identify their key substructures associated with these GPCRs targets. That is, MTR-ISLR intends to enhance the performance and interpretability of models under a small size of available training data by introducing homologous GPCR tasks. Meanwhile, the low-rank constraint term encourages to catch the underlying relationship among homologous GPCR tasks for greater model generalization, and the entry-wise sparse regularization term ensures to recognize essential discriminative substructures from each task for explanative modeling. We examined MTR-ISLR on a set of 31 important human GPCRs datasets from 9 subfamilies, each with less than 400 ligand associations. The results show that MTR-ISLR reaches better performance when compared with traditional single-task learning, deep multi-task learning and multi-task learning with joint feature learning-based models on most cases, where MTR-ISLR obtains an average improvement of 7% in correlation coefficient (r2) and 12% in root mean square error (RMSE) against the runner-up predictors. The MTR-ISLR web server appends freely all source codes and data for academic usages.1)

Excessive glutamate signaling is thought to underlie neurodegeneration in multiple contexts, yet the pro-degenerative signaling pathways downstream of glutamate receptor activation are not well defined. We show that dual leucine zipper kinase (DLK) is essential for excitotoxicity-induced degeneration of neurons in vivo. In mature neurons, DLK is present in the synapse and interacts with multiple known postsynaptic density proteins including the scaffolding protein PSD-95. To examine DLK function in the adult, DLK-inducible knockout mice were generated through Tamoxifen-induced activation of Cre-ERT in mice containing a floxed DLK allele, which circumvents the neonatal lethality associated with germline deletion. DLK-inducible knockouts displayed a modest increase in basal synaptic transmission but had an attenuation of the JNK/c-Jun stress response pathway activation and significantly reduced neuronal degeneration after kainic acid–induced seizures. Together, these data demonstrate that DLK is a critical upstream regulator of JNK-mediated neurodegeneration downstream of glutamate receptor hyper-activation and represents an attractive target for the treatment of indications where excitotoxicity is a primary driver of neuronal loss.

In two‐dimensional (2D) ground‐penetrating radar (GPR) data, the reflections from detection targets in depth can be severely obscured by strong scattering generated from near‐surface non‐target structures. By using GPR as a geotechnical non‐destructive testing device, it is still a challenge to eliminate the strong scattering caused by near‐surface rebars in a tunnel lining and to image and asses the grouting condition behind the tunnel lining. This study proposes a method for the reconstruction of GPR images, termed Maxwell curl equation datuming. To eliminate the deleterious effect caused by near‐surface diffractive scattering, we have redefined the reference surface to an actual geologic/engineered interface by using Maxwell curl equation datuming methodology based on the Kirchhoff integral solution. The datuming procedure can redefine the reference surface to a deeper horizon on which GPR transmitters and receivers appear to be located. We conducted a comparison between the datuming and migration to synthetic examples and case studies are presented for a physical model and real GPR data for assessments of shield tunnel grouting in the Shanghai Metro line No.9. The results show that the datuming technique is able to eliminate the strong scattering related to near‐surface rebars in a tunnel lining and improves the quality of deeper images beneath the tunnel lining. The datuming images make it easy to identify the distribution of tunnel grouting.

Through a case study of Shenzhen City, China, this study focused on a quantitative method for analyzing the spatial processes involved in green infrastructure changes associated with rapid urbanization. Based on RS, GIS and SPSS statistics software, the approach includes selection of the square analysis units and representative landscape metrics, quantification of the change types of landscape metrics in all analysis units through two indices and hierarchical cluster analysis of the above analysis units with different landscape metric change types (i.e. spatial attributes). The analyses verify that there is a significant sequence of continuous changes in green infrastructure in Shenzhen. They are the perforation, the segmentation, the fragmentation, the evanescence and the filling-in processes, which have a good spatio-temporal correspondence with urbanization and reflect the synthetic influence of urban planning, government policies and landforms. Compared with other studies on quantifying the spatial pattern, this study provides an alternative probe into linking the spatial pattern to spatial processes and the corresponding ecological processes in the future. These spatio-temporal processes offer many opportunities for identifying, protecting and restoring key elements in an urban green infrastructure network for areas in the early stages of urbanization or for non-urbanized areas.

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

The magnetic properties, electrical resistivity and related microstructures of (Nd,Pr)14.5Dy1.2Fe\text{balAl0.8B6magnets modified by DyN addition have been investigated. The coercivity of the magnets increases almost linearly as a function of DyN content without a considerable reduction in remanence. Similarly, the resistivity in the transversal orientation increases from 1.6 to about 2.0 \mbox{$\mu$}$\Omega$ m. N dissolves in the grain boundary Nd-rich phase, leading to the formation of an N-containing amorphous phase. This amorphous phase is found to enhance the electrical resistivity. Dy tends to diffuse into the matrix phase, enriched on the matrix side of the interface between the grain boundary phase and the matrix. The significant improvement of the coercivity is ascribed to the increased anisotropy of R2Fe14B by partial substitution of Dy for Nd.

Abstract: Based on the synchronous joint gravity and magnetic inversion of single interface by Pilkington and the need of revealing Cenozoic and crystalline basement thickness in the new round of oil-gas exploration, we propose a joint gravity and magnetic inversion method for two-layer models by concentrating on the relationship between the change of thickness and position of the middle layer and anomaly and discuss the effects of the key parameters. Model tests and application to field data show the validity of this method.

By analyzing dinucleotide position-frequency data of yeast nucleosome-bound DNA sequences, dinucleotide periodicities of core DNA sequences were investigated. Within frequency domains, weakly bound dinucleotides (AA, AT, and the combinations AA-TT-TA and AA-TT-TA-AT) present doublet peaks in a periodicity range of 10–11bp, and strongly bound dinucleotides present a single peak. A time-frequency analysis, based on wavelet transformation, indicated that weakly bound dinucleotides of core DNA sequences were spaced smaller (∼10.3bp) at the two ends, with larger (∼11.1bp) spacing in the middle section. The finding was supported by DNA curvature and was prevalent in all core DNA sequences. Therefore, three approaches were developed to predict nucleosome positions. After analyzing a 2200-bp DNA sequence, results indicated that the predictions were feasible; areas near protein-DNA binding sites resulted in periodicity profiles with irregular signals. The effects of five dinucleotide patterns were evaluated, indicating that the AA-TT pattern exhibited better performance. A chromosome-scale prediction demonstrated that periodicity profiles perform better than previously described, with up to 59% accuracy. Based on predictions, nucleosome distributions near the beginning and end of open reading frames were analyzed. Results indicated that the majority of open reading frames’ start and end sites were occupied by nucleosomes.

Abstract: In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geo- physical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.

Ti51Pd30Ni19 alloy was prepared in a different heat treatment state for retaining different dynamic mechanical properties. It has been found that specimens annealed at 1273 K followed by furnace cooling or by quenching exhibit different phase transformation behaviour. Subsequent annealing at lower temperature (after rolling) does not change the transformation sequence. DMTA (dynamic mechanical thermal analysis) measurement results reveal that the variation of the elastic modulus is different for the two kinds of transformation, which show elasticity softening and elastic step, respectively. The elastic modulus of low-temperature martensite differs as well. It has been found that damping of the peaks occurs on both heating and cooling the alloy across the phase transformation temperatures. The damping properties, tan δ, of the new phase possess higher values than those of the parent phase.

The oxidation kinetics of the nitrided TiAl-based alloys have been investigated at 800-1000 °C in hot air. It is concluded that nitridation is detrimental to the oxidation resistance of TiAl-based alloys under the present conditions. The nitrided alloys exhibited increased oxidizing rate with the prolongation of nitridation time at 800 °C. However, alloys nitrided at 940 °C for 50 h displayed a sign of better oxidation resistance than the other nitrided alloys at more severe oxidizing conditions. The parabolic rate law was considered as the basis of the data processing and interpretation of the mass gain vs. time data. As a comparison with it, attempts were made to fit the data with the power law. The oxidation kinetic parameters k n , k p and n were measured and the trends were discussed.

Introduction:Coronary microcirculation dysfunction (CMD) is recognized as a pivotal pathophysiological change of coronary artery disease (CAD) with diabetes, however, there is little evidence for CMD medication treatment. Though enhanced external counterpulsation (EECP), a non-invasive therapy to increase shear stress of coronary flow, has been proved to release angina and restore endothelial capacities, whether EECP could attenuate CMD remains unknown.Hypothesis:EECP could attenuate CMD in patients of CAD with diabetes.Methods:This is a single center, prospective, randomized, single blind, control clinical trial (ChiCTR1800019060). Forty-eight patients [age: (57.7±8.5) yrs, 30 males] diagnosed with CAD and diabetes were enrolled during March 2019 and October 2020 in the Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China. The main exclusion criteria are as follows: type 1 diabetes or diabetes history≤3 years; ≥ 70 years old; acute coronary syndrome; chronic total occlusion lesions and contraindications for EECP. All accepted coronary angiography and index of microcirculation resistance (IMR) test with pressure wire, then were randomized to EECP (n=24) or control (n=24) group. All followed standard medication therapy as guidelines recommended, EECP group took 35 times of EECP (60 minutes per time, 5 times a week and 7 weeks for whole procedure) after IMR test. Another IMR test would be done at the end of 12-week follow-up, the occurrence of angina and the metabolic parameters, FMD, PWV, CPET and TTE would also be evaluated.Results:The baseline IMR displayed no significant difference in both groups [EECP: (26.56±3.70) vs. Control: (26.91±4.22), P=0.74], while IMR markedly decreased in the EECP group than control at the end of follow-up [(24.02±3.15) vs. (26.78±4.63), P=0.02]. After multivariate linear regression analysis, FMD and PWV were found to independently correlated with IMR change [FMD: (β=0.45, P=0.014); PWV: (β=-0.27, P=0.038)]. Apart from above, EECP reduced angina occurrence and improved exercise tolerance, early diastolic function of left ventricular.Conclusion:EECP attenuates CMD in CAD with diabetes through coronary microcirculation resistance decrease.

Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-khit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits’ precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various kvalues in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVSfor academic purposes, where virtual screening of hits from large compound databases is accessible.

ABSTRACTSome antibodies exhibit elevated viscosity at high concentrations, making them poorly suited for therapeutic applications requiring administration by injection such as subcutaneous or ocular delivery. Here we studied an anti-IL-13/IL-17 bispecific IgG4antibody, which has anomalously high viscosity compared to its parent monospecific antibodies. The viscosity of the bispecific IgG4in solution was decreased by only ~30% in the presence of NaCl, suggesting electrostatic interactions are insufficient to fully explain the drivers of viscosity. Intriguingly, addition of arginine-HCl reduced the viscosity of the bispecific IgG4by ~50% to its parent IgG level. These data suggest that beyond electrostatics, additional types of interactions such as cation-π and/or π-π may contribute to high viscosity more significantly than previously understood. Molecular dynamics simulations of antibody fragments in the mixed solution of free arginine and explicit water were conducted to identify hotspots involved in self-interactions. Exposed surface aromatic amino acids displayed an increased number of contacts with arginine. Mutagenesis of the majority of aromatic residues pinpointed by molecular dynamics simulations effectively decreased the solution’s viscosity when tested experimentally. This mutational method to reduce the viscosity of a bispecific antibody was extended to a monospecific anti-GCGR IgG1antibody with elevated viscosity. In all cases, point mutants were readily identified that both reduced viscosity and retained antigen-binding affinity. These studies demonstrate a new approach to mitigate high viscosity of some antibodies by mutagenesis of surface-exposed aromatic residues on complementarity-determining regions that may facilitate some clinical applications.