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[Display omitted] Developing low-power FETs holds significant importance in advancing logic circuits, especially as the feature size of MOSFETs approaches sub-10 nanometers. However, this has been restricted by the thermionic limitation of SS , which is limited to 60 mV per decade at room temperature. Herein, we proposed a strategy that utilizes 2D semiconductors with an isolated-band feature as channels to realize sub-thermionic SS in MOSFETs. Through high-throughput calculations, we established a guiding principle that combines the atomic structure and orbital interaction to identify their sub-thermionic transport potential. This guides us to screen 192 candidates from the 2D material database comprising 1608 systems. Additionally, the physical relationship between the sub-thermionic transport performances and electronic structures is further revealed, which enables us to predict 15 systems with promising device performances for low-power applications with supply voltage below 0.5 V. This work opens a new way for the low-power electronics based on 2D materials and would inspire extensive interests in the experimental exploration of intrinsic steep-slope MOSFETs. [ABSTRACT FROM AUTHOR]

Motivation Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. Results In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs. [ABSTRACT FROM AUTHOR]

Motivation 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. Results In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus , respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model. Availability and implementation The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG. [ABSTRACT FROM AUTHOR]

RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV. [ABSTRACT FROM AUTHOR]

Although deep convolutional neural networks have achieved remarkable success in removing synthetic fog, it is essential to be able to process images taken in complex foggy conditions, such as dense or non-homogeneous fog, in the real world. However, the haze distribution in the real world is complex, and downsampling can lead to color distortion or loss of detail in the output results as the resolution of a feature map or image resolution decreases. Moreover, the over-stacking of convolutional blocks might increase the model complexity. In addition to the challenges of obtaining sufficient training data, overfitting can also arise in deep learning techniques for foggy image processing, which can limit the generalization abilities of the model, posing challenges for its practical applications in real-world scenarios. Considering these issues, this paper proposes a Transformer-based wavelet network (WaveletFormerNet) for real-world foggy image recovery. We embed the discrete wavelet transform into the Vision Transformer by proposing the WaveletFormer and IWaveletFormer blocks, aiming to alleviate texture detail loss and color distortion in the image due to downsampling. We introduce parallel convolution in the Transformer block, which allows for the capture of multi-frequency information in a lightweight mechanism. Such a structure reduces computational expenses and improves the effectiveness of the network. Additionally, we have implemented a feature aggregation module (FAM) to maintain image resolution and enhance the feature extraction capacity of our model, further contributing to its impressive performance in real-world foggy image recovery tasks. Through extensive experiments on real-world fog datasets, we have demonstrated that our WaveletFormerNet achieves superior performance compared to state-of-the-art methods, as shown through quantitative and qualitative evaluations of minor model complexity. Additionally, our satisfactory results on real-world dust removal and application tests showcase the superior generalization ability and improved performance of WaveletFormerNet in computer vision-related applications compared to existing state-of-the-art methods, further confirming our proposed approach's effectiveness and robustness. Our code is available at https://github.com/shengli666666/WaveletFormerNet. [Display omitted] • Combining Vision Transformer and wavelet transform alleviates image detail loss. • Parallel convolution captures frequency information in a lightweight mechanism. • The feature aggregation module enhances the model feature extraction capability. • Experimental results demonstrate WaveletFormerNet's superior dehazing performance. [ABSTRACT FROM AUTHOR]

For agricultural tractors fixed ballast cannot provide high-efficiency traction under time-varying resistance in the field. Combining the characteristics of electric tractors with high-weight battery packs, this paper develops an intelligent ballast control system including a battery position adjustment (BPA) mechanism and an active ballasting control method. A tractor traction performance prediction model was developed to predict traction performance parameters and load-transfer in real time. The movement of the battery pack enables load-transfer based on the BPA. To ensure the lowest sliding rate of optimal tractive efficiency, an active ballasting control method based on a particle swarm optimisation algorithm was proposed that enabled control of the optimal battery position. The results of MATLAB/Simulink simulation indicate that the traction resistance after grading and pre-treatment not only reduced the frequency of change in the resistance signal but also retain the original trend. The results of a hardware in the loop test showed that the average tractive efficiency in the mode of active ballasting mode higher than that of the no ballasting mode (increased by 6.6%) and fixed ballasting mode (increased by 4.7%). The mean wheel slip in active ballasting mode was lower than that of the no ballasting mode (decreased by 10.3%) and the fixed ballasting mode (decreased by 4.9%). In addition, the front axle dynamic load distribution ratio in active ballasting mode was always greater than 0.2, which ensured the operational stability of the tractor. This study provides theoretical support and technical reference for optimising the traction performance of electric tractors. • An intelligent ballast control system with active load-transfer is developed. • The concept of batteries instead of ballasts. • Battery position adjustment mechanism (BPA) is designed. • Active ballasting control method based on traction performance prediction model. • The maximum average tractive efficiency is increased by 6.6%. [ABSTRACT FROM AUTHOR]

Conventional silicon-based transistor downscaling is approaching its physical limits, and thus additional novel solutions are urgently desired to address this issue. Herein, we show that 2-D antimonene with heavy effective mass and high density of state (DOS) via strain engineering presents reliable transistor performance with the channel length (${L}_{\text {ch}}$) shrinking below 5 nm. As the biaxial tensile strain increases to 7%, the band switching gives rise to a heavy hole effective mass of $12.6{m}_{{0}}$ and a Van Hoff singularity-like DOS. This unique electronic structure can effectively suppress the tunneling current, resulting in steep subthreshold swings (SSs) and ideal ON-current (${I}_{ \mathrm{ON}}$). Especially, as ${L}_{\text {ch}}$ downscales to 2.2 nm, the OFF-current can be easily reduced to 0.1 $\mu \text{A}/\mu \text{m}$ with SS of 120 mV/dec (310 mV/dec for pristine antimonene) and ${I}_{ \mathrm{ON}}$ exceeds 900 $\mu \text{A}/\mu \text{m}$ , fulfilling the requirements for high-performance applications. Our results provide new insights on extending the scaling limit in energy-efficient gate-controlled devices. [ABSTRACT FROM AUTHOR]

CdS thin films are commonly used as buffer layer in Cu2ZnSnSe4 (CZTSe) solar cells inherited from CuInGaSe2 (CIGS) solar cells. However, in addition to the toxicity problems with Cd in conventional CdS buffer layers, the unfavorable band alignment at the CZTSe/CdS heterojunction confines the further improvement of CZTSe solar cells. ZnSnO (ZTO) is studied in this work as promising buffer layers for CZTSe solar cells. The stoichiometric composition and thickness of the ZTO layer are first optimized by sputtering. Subsequently, the improvement of the performance is demonstrated by sputtering ZTO in Ar gas mixed with H2. The effect of H2 on sputtering ZTO is investigated. The presence of O vacancies in ZTO buffer layer and its effect on carrier transport and device performance are presented. Through optimization, comparable VOC and higher fill factor (FF) are obtained for the CZTSe/ZTO solar cells compared with CZTSe/CdS reference. [ABSTRACT FROM AUTHOR]

In the present study, a functionalized carboxymethyl cellulose-based adsorbent (PEI–PAM–CMC) was prepared via linking polyethyleneimine and polyacrylamide with carboxymethyl cellulose by epichlorohydrin. It possessed a sponge-like structure with a large number of micron-sized pores, which facilitated the mass transfer of Cr(VI). Under neutral conditions, Cr(VI) can be efficiently removed by the protonated PEI–PAM–CMC. The residual levels in the effluent were below 0.5 and 0.05 mg L−1 for 10 and 1 mg L−1 of Cr(VI), respectively. The adsorption data fitted well with the pseudo-second order kinetic model and Langmuir isotherm. Common ions including Cl−, H2PO4−, NO3−, K+, Na+, Ca2+, Mg2+ and Cr3+ had neglectable influence for Cr(VI) adsorption on the PEI–PAM–CMC except SO42−. Meanwhile, PEI–PAM–CMC showed good regeneration and reusability with a 7.5% decrease in the removal rate after ten adsorption/desorption cycles. XPS analysis and DFT calculation revealed that Cr(VI) uptake by the PEI–PAM–CMC was involved in electrostatic attraction and chemical binding. Electrostatics and induction were the main weak interaction between Cr(VI) and the adsorbent, and the major chemical adsorption sites were mainly distributed in the nitrogen groups. This research not only developed an efficient material for Cr(VI) removal but also provided a promising insight into an adsorption mechanism study. [ABSTRACT FROM AUTHOR]

DNA-binding proteins play a vital role in cellular processes. It is an extremely urgent to develop a high-throughput method for efficiently identifying DNA-binding proteins. According to the current research situation, some methods in machine learning and deep learning show excellent computational speed and accuracy, which are worthy of application. In this work, a novel predictor was proposed to predict DNA binding proteins called UMAP-DBP. Firstly, the feature extraction of primary protein sequence was realized based on physicochemical distance transformation, Profile-based auto-cross covariance and General series correlation pseudo amino acid composition. Secondly, uniform manifold approximation and projection (UMAP) and feature importance score methods were used for feature selection; there is a progressive relationship between them. Finally, the Adaboost operation engine with jackknife test were adopted for predicting DNA-binding proteins. For the jackknife test on the BP1075 and BP594, we obtained an overall accuracy of 82.97% and 82.14%, Cohen's kappa (CK) of 0.66 and 0.64, respectively. The results illustrate that a feasible method has been developed for predicting DNA-binding proteins by UMAP and Adaboost. This is the first study in which UMAP has been successfully applied to identify DNA-binding proteins. All the datasets and codes are accessible at https://github.com/Wang-Jinyue/UMAP-DBP. [ABSTRACT FROM AUTHOR]

• Seventeen TaAOX genes were identified from Chinese Spring genome. • Tandem and segmental duplication events were found during the TaAOX genes' evolution. • Most TaAOX genes might play a vital role in the response to various stresses in wheat. • Regulating some TaAOX s' expression may improve wheat adaptability to various stresses. Alternative oxidase (AOX) is an important terminal oxidase involved in the alternative oxidation pathway in plants, which is closely related to various biotic and abiotic stress responses. However, a comprehensive research on AOX gene family of wheat is still lacking. In this study, the members of wheat AOX (TaAOX) family were identified, and their molecular characteristics and gene expression patterns were systematically investigated. Seventeen TaAOX genes were identified from Chinese Spring (CS) genome, which were mapped on 7 chromosomes and mainly clustered on the long arm's distal end of the second homologous groups. Phylogenetic analysis showed that TaAOX genes were classified into four subgroups (Ia, Ib, Ic, and Id), and the Ia subgroup possessed the most members. Tandem duplication and segmental duplication events were found during the evolution of TaAOX genes and they were affected by purifying selection demonstrated by Ka/Ks analysis. The exon numbers of this family gene varied greatly from 1 to 9. Except for Ta3BSAOX14 , all the proteins encoded by the other 16 TaAOX genes contained the amino acid residues of the key active sites in the AOX domain (cd01053). The expression patterns of TaAOX genes in various tissues and under abiotic and biotic stresses were analyzed using public transcriptome data, furthermore, qRT-PCR analysis was performed for some selected TaAOX genes, and the results suggested that most members of this gene family play an important role in response to different stresses in common wheat. Our results provide basic information and valuable reference for further exploring the gene function of TaAOX family by using gene editing, RNAi, VIGS, and other technologies. [ABSTRACT FROM AUTHOR]

Searching for active and low‐cost electrocatalysts for the hydrogen evolution reaction (HER) is crucial to develop sustainable energy, yet it remains a significant challenge. Based on the density functional theory calculations, a new kind of double atom catalysts (HDACs) with hybrid metal and nonmetal center embedded in g‐CN is reported for the HER. It is demonstrated that the introduction of the nonmetal atoms (B, C, Si, P, and S) near the metal sites enables unique charge communication between them, which offers diatomic center a very different catalytic activity than the single atom counterparts. Out of 130 HDACs, Pd‐B, Ti‐C, Ir‐C, Cr‐Si, Mn‐Si, Co‐Si, Rh‐Si, Au‐Si, Ir‐P, Fe‐S, and Ni‐S pairs are identified as the high‐performance electrocatalysts with nearly ideal adsorption strength for proton. Machine learning analysis allows to directly identify the key characteristics that affect the catalytic activity and establish a predictable framework for a fast screen of the unknown chemical space of HDACs. This work paves a new avenue for designing and developing potential HER catalysts. [ABSTRACT FROM AUTHOR]

While undergoing structural deformation, DNA experiences changes in the interactions between its internal base pairs, presenting challenges to conventional elastic methods. To address this, we propose the Discrete Critical State (DCS) model in this paper. This model combines surface discrete frame theory with gauge theory and Landau phase transition theory to investigate DNA's structural deformation, phase transitions, and chirality. Notably, the DCS model considers both the internal interactions within DNA and formulates an overall equation using unified physical and geometric parameters. By employing the discrete frame, we derive the evolution of physical quantities along the helical axis of DNA, including geodesic curvature, geodesic torsion, and others. Our findings indicate that B-DNA has a significantly lower free energy density compared to Z-DNA, which is in agreement with experimental observations. This research reveals that the direction of base pairs is primarily governed by the geodesic curve within the helical plane, aligning closely with the orientation of the base pairs. Moreover, the geodesic curve has a profound influence on the arrangement of base pairs at the microscopic level and effectively regulates the configuration and geometry of DNA through macroscopic-level free energy considerations. [ABSTRACT FROM AUTHOR]

Using electrochemical CO2 reduction reaction (CO2RR) to synthesize value-added hydrocarbons provides a useful solution for environmental issues and energy crisis. However, this process is impeded by the low activity and selectivity of electrocatalysts toward targeted products. Employing density functional theory computations, the graphdiyne and holey graphyne supported single-atom catalysts (SACs, M/GDY and M/HGY) are demonstrated to be the promising candidates for the CO2RR. By taking full elemental diversity of metal sites across the periodic table, 25 catalysts are found to effectively activate CO2 and inhibit competitive hydrogen evolution, and 8 of them show higher activity for CH4 production than Cu(211). Remarkably, changing supports are found to greatly affect limiting potentials and reaction pathways, even leading to an "inert-active" transition for some metal centers. The resulting SACs, including Mn/GDY, Co/HGY, Ru/GDY, and Os/GDY, can achieve high activity with low limiting potentials of " -0.22 to -0.58 V. Machine learning enables to identify the critical role of the polarized charge and magnetic moment of metal atoms in affecting the activity. The built machine learning model also shows an interpretable capability to predict the activity of the other types of SACs, offering a great promise to quick screening of high-performance SACs. [ABSTRACT FROM AUTHOR]

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA. [Display omitted] • A novel model named DapNet-HLA is proposed to predict non-classical HLA binding sites. • Fused word embedding methods are applied to extract features from the datasets. • SENet and Bahdanau attention mechanisms are integrated to optimize and improve the effectiveness of BiLSTM. • Adaptive dual-attention mechanism network based on deep learning is designed to predict non-classical HLA binding sites. [ABSTRACT FROM AUTHOR]

In this paper, we report an electron injection photomultiplication (PM) type inverted organic photodetector (OPD) based on [6,6]-phenyl-C61-butyric acid methyl ester (PC 61 BM), poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)] (PCPDTBT), poly(3-hexylthiophene) (P3HT) and PbS quantum dots (QDs). The effects of PbS QDs added to PCPDTBT: P3HT on the optical and electrical properties of OPDs were investigated. It is found that when the mass ratio of PbS QDs is 5 wt%, the responsivity (R) and external quantum efficiency (EQE) of the device reach 15.3 A/W and 365% at −2 V bias and 430 nm illumination, respectively. The results show that PbS QDs can act as hole traps in the active layer to introduce a large amount of electron injection from the anode circuit, thereby increasing the photocurrent and realizing the PM phenomenon. However, too much doping will cause the trap material to agglomerate and fail, losing the multiplier effect. It provides a theoretical basis and method for high response polyphase inverted PM type OPDs. • An electron injection photomultiplication type inverted organic photodetector was prepared. • PbS QDs can act as hole traps in the active layer to introduce a large amount of electron injection. • When the mass ratio of PbS QDs was 5 wt%, the device performance was the best. • Excessive doping of PbS QDs will result in the disappearance of multiplication effect. [ABSTRACT FROM AUTHOR]

• Combined control method of traction and ballast based on load transfer is proposed. • The ballast system achieves optimal axle load by adjusting the battery's position. • Field ploughing experiments under two control methods re conducted for verification. • The total motor energy consumption was reduced by 7.3% in ploughing. • The combined control method is helpful in improving the uniformity of tillage depth. Agricultural tractors are significant agricultural power machines that must be equipped with different implements according to tillage requirements and operate in a complex field environment. Aiming at the problem that the traditional traction control system could not balance the agronomic requirements of tillage and high-efficiency operation, this study proposed a combined control method of traction and ballast based on load transfer. This method was used for electric tractors with a battery position adjustment (BPA) structure. The tractor-implement system model was built to calculate the dynamic loads of front and rear axles accurately. Then, a multi-objective optimization function for traction performance was constructed, and the multi-objective particle swarm algorithm was employed to solve the optimal battery pack target position. The tractor controller real-time measured the draught force and battery position data. Based on the combined control strategy, the battery pack position and the tillage depth were adjusted to accomplish the cooperative control of the active ballast system and the electro-hydraulic hitch system. Finally, a field test platform for the electric tractor was developed. Ploughing tests under the traction control method and combined control method were performed. The test results showed that the tillage depth uniformity and traction performance under the combined control method were superior to the traction control method. In particular, compared with the traction control method, the tillage depth fluctuation range in the combined control method was reduced by 30.6%, the wheel slip was decreased by 15.1%, the traction efficiency was enhanced by 3.7% and the total motor energy loss was decreased by 4.9%. This study provides a technical reference for improving the economy of energy consumption and the uniformity of tillage depth of tractors in ploughing. [ABSTRACT FROM AUTHOR]

Photon catalysis (PC) is an intriguing quantum optical operation wherein the same number of photons are added to and subsequently subtracted from the relevant optical system. It is an important tool that facilitates photon number manipulation. However, most existing PC methods are suitable only for photonic states residing within few-photon subspace. In addition, the probability of PC decreases exponentially with increase in the number n of the Fock state | n 〉. Thus, this study proposed a high probability PC scheme for optical quantum states with large Fock state population based on nonlinear beam splitters. The results indicate that the fidelity and the probability of PC are both substantially improved. Moreover, in case of detector inefficiency, the scheme of PC with non-linear beam splitter was more robust. Thus, the proposed method provides a useful tool for manipulating optical quantum states with large Fock number state population. [ABSTRACT FROM AUTHOR]

[Display omitted] • The prepared PEI-JE had the same 3D network structure as the JE. • PEI-JE had a maximum adsorption capacity of 268.93 mg/g at 25 ℃. • Cr(VI) removal rate almost kept unchanged after 10 adsorption/desorption cycles. • Cr(VI) removal was involved in electrostatic adsorption, redox and complexation. • The Cr(VI) adsorbed on the surface of PEI-JE was completely reduced to Cr(III). In the present study, a novel PEI-modified Juncus effuses (PEI-JE) adsorbent was prepared based on the natural 3D network structure of JE via a simple and environmental-friendly method with the help of the ethanol/aqueous solution. SEM, EDS, ATR-FTIR and XPS were performed to reveal the physical and chemical structure of the PEI-JE. The Cr(VI) removal by the PEI-JE was investigated by bath adsorption experiments. Results showed that the developed PEI-JE exhibited the same 3D network structure as the JE, which not only was easy to separate from aqueous solution but also can expose more active sites and facilitate mass transfer. The optimal pH for Cr(VI) adsorption on the PEI-JE was 3, and adsorption equilibrium time was related to initial Cr(VI) concentrations. The adsorption kinetic obeyed pseudo-second-order kinetic model, and the adsorption isotherm showed the best fit with Freundlich model. The calculated maximum adsorption capacity was 268.93 mg/g at 25℃. Coexisting ions including Na+, K+, Al3+, H 2 PO 4 −, Cl−, NO 3 – had no remarkable effect on Cr(VI) adsorption, but the presence of SO 4 2−, HCO 3 – and CO 3 2– caused the efficiency losses of 10.9%, 41% and 66%, respectively. After 10 cycles of adsorption/desorption, Cr(VI) removal rate on the PEI-JE almost kept unchanged, demonstrating that the PEI-JE possessed good reusability. Mechanism study suggested that Cr(VI) removal was involved in a three-step reaction, namely electrostatic adsorption, redox and complexation. Film diffusion was the rate-limiting step and chemical adsorption was the main mechanism. The Cr(VI) adsorbed on the PEI-JE surface was completely reduced to Cr(III), demonstrating that PEI-JE is useful for Cr (VI) removal and detoxification. [ABSTRACT FROM AUTHOR]

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replication errors and protecting host DNA against degradation by restrictive enzymes. Hence, the accurate identification for 4 mC sites is greatly significant for understanding biological functions and treating gene diseases. In this paper, a novel model is designed for identifying 4 mC sites. Firstly, we extract features from original sequences by multi-source feature representation methods, which are mono-nucleotide binary and k -mer frequency, dinucleotide binary and position-specific frequency, ring-function-hydrogen-chemical properties, dinucleotide-based DNA properties and trinucleotide-based DNA properties. Subsequently, gradient boosting decision tree is applied to select the optimal feature set and remove redundant information. Finally, support vector machine is employed to predict 4 mC or non-4mC sites. The accuracies of six datasets reach 0.851, 0.859, 0.801, 0.87, 0.859 and 0.901, respectively, which are superior to previous prediction methods. Therefore, the results show that our predictor is a feasible and effective tool for identifying 4 mC sites. Furthermore, an online web server is established at http://dnan4c.zhanglab.site. [Display omitted] • A predictor is designed to identify DNA N4-methylcytosine sites based on multi-source features and GBDT. • Extract features from original sequences by multi-source feature representation methods. • GBDT is applied to select the optimal feature set and remove redundant information. • An online web server is established at http://dnan4c.zhanglab.site. [ABSTRACT FROM AUTHOR]

Morphological changes of podocyte mitochondria are observed in patients with mitochondrial cytopathy and nephrotic syndrome. However, whether mitochondrial dynamics involved in podocyte in lupus nephritis (LN) is still not clear. This study aims to investigate the associations between mitochondrial morphology and podocyte lesions and laboratory and pathological features in LN. The foot process width (FPW) and mitochondrial morphology were observed through electron microscope. Then the associations between mitochondrial morphology and podocyte lesions and laboratory features were explored in various International Society of Nephrology/Renal Pathology Society class LN patients. Foot process effacement and excessive mitochondria fission in podocyte were observed and proteinuria was positively correlated with FPW. Mitochondria area, circumference, and aspect ratio were negatively correlated with BUN, and 24h-UTP positively correlated with Alb. At the same time, Alb was negatively correlated with form factor. FPW, form factor, surface density, and numerical density on area were positively correlated with 24h-UTP. Excessive mitochondrial fission is associated with podocyte damage and proteinuria, whereas the mechanism still needs to be explored. [ABSTRACT FROM AUTHOR]

• A novel predictor called iPro-GAN to identify promoters and their strength. • Moran-based spatial auto-cross correlation considering physicochemical properties of dinucleotide is used to extract sequence features. • A generative adversarial network based on deep convolution is established to solve the binary classification problem in bioinformatics for the first time. Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it. In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type. On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength. These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN. [ABSTRACT FROM AUTHOR]

Therapeutic peptides, as active substances involved in a variety of cell functions in the organism, are essential participants to complete complex physiological activities of the body. Therefo r e, the prediction of therapeutic peptides is essential for researching on peptide-based therapies. The method of using biological experiments is considered to be time-consuming and labor-intensive. As a fast and accurate method, deep learning can process massive amounts of data on therapeutic peptides. In this research, we raise a deep learning model called Pep-CNN to accurately predict therapeutic peptides. Firstly, we represent the features of the peptide sequence based on the sequence position, the physicochemical property, and the evolutionary-derived feature and use the vectors to represent the sequence. After fusing the features, we use the improved classifier of Convolutional Neural Network (imCNN) to classify and predict eight kinds of peptides. The results show that, compared with other models, Pep-CNN can identify peptides more accurately, which is more conductive to the further research of therapeutic peptides by biomedical scientists. The codes and benchmark datasets are accessible at https://github.com/alivelxj/Pep-CNN. • A new model called Pep-CNN was proposed to predict therapeutic peptides. • The different methods are applied to extract features from the dataset. • An improved convolutional neural network is used to classify the model. [ABSTRACT FROM AUTHOR]

RNA 5-hydroxymethylcytosine (5hmC) modification is the basis of the translation of genetic information and the biological evolution. The study of its distribution in transcriptome is fundamentally crucial to reveal the biological significance of 5hmC. Biochemical experiments can use a variety of sequencing-based technologies to achieve high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Therefore, it is urgent to develop more effective and feasible computational methods. In this paper, a novel and powerful model called iR5hmcSC is designed for identifying 5hmC. Firstly, we extract the different features by K-mer, Pseudo Structure Status Composition and One-Hot encoding. Subsequently, the combination of chi-square test and logistic regression is utilized as the feature selection method to select the optimal feature sets. And then stacking learning, an ensemble learning method including random forest (RF), extra trees (EX), AdaBoost (Ada), gradient boosting decision tree (GBDT), and support vector machine (SVM), is used to recognize 5hmC and non-5hmC. Finally, 10-fold cross-validation test is performed to evaluate the model. The accuracy reaches 85.27% and 79.92% on benchmark dataset and independent dataset, respectively. The result is better than the state-of-the-art methods, which indicates that our model is a feasible tool to identify 5hmC. The datasets and source code are freely available at https://github.com/HongyanShi026/iR5hmcSC. [Display omitted] • A new model named iR5hmcSC was proposed to predict RNA 5hmC sites. • K-mer, Pseudo Structure Status Composition and One-Hot encoding are applied to extract features from the dataset. • A new method combining chi-square test and logistic regression is used to reduce the dimensions of data. • The stacking learning is adopted to classify the model. [ABSTRACT FROM AUTHOR]

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed. [ABSTRACT FROM AUTHOR]

Promoter is a region of DNA that determines the transcription of a particular gene. There are several σ factors in the RNA polymerase, which has the function of identifying the promoter and facilitating the binding of the RNA polymerase to the promoter. Owing to the importance of promoter in genome research, it is an urgent task to develop computational tool for effectively identifying promoters and their strength facing the avalanche of DNA sequences discovered in the post-genomic age. In this paper, we develop a model named iPromoter-ET using the k -mer nucleotide composition, binary encoding and dinucleotide property matrix-based distance transformation for features extraction, and extremely randomized trees (extra trees) for feature selection. Its 1st layer is used to identify whether a DNA sequence is of promoter or not, while its 2nd layer is to identify promoter samples as being strong or weak promoter. Support vector machine and the five cross-validation are used to perform identification and assess performance, respectively. The results indicate that our model remarkably outperforms the existing models in both the 1st and 2nd layers for accuracy and stability. We anticipate that our proposed model will become a very effective intelligent tool, or at the least, a complementary tool to the existing modes of identifying promoters and their strength. Moreover, the datasets and codes for iPromoter-ET are freely available at https://github.com/shengli0201/iPromoter-ET. [Display omitted] • A novel identifying model named iPromoter-ET is proposed based on extremely randomized trees for feature selection. • Our iPromoter-ET model achieves remarkably higher accuracy for identifying promoters and their strength. • The performance of iPromoter-ET model outperforms those of existing related models. [ABSTRACT FROM AUTHOR]

The molybdenum (Mo) back contact is one of the crucial factor that affects the performance of kesterite Cu 2 ZnSnS 4 (CZTS) thin film solar cells. Thick MoS 2 which is formed during sulfurization process of CZTS is known to be harmful for the device performance. MoO x on surface of Mo substrate with proper thickness can enhance the efficiency of CZTS solar cells through reducing the thickness of MoS 2 layer and tailoring the work function of back contact. However, the role of oxidation states of Mo in MoO x on the performance of solar cells is unclear. Herein the effect of MoO x with different oxidation states on CZTS absorber and intermediate MoS 2 layer were investigated. Our results shown that the efficiency of CZTS solar cells on Mo substrate with proper surface oxidation status (MoO 2.03) can be as high as 2.86 times of the one fabricated on higher oxidized Mo (MoO 2.21), although the thinnest MoS 2 layer was present in the latter case. This work discloses the origin of the oxidation states of Mo for the efficiency enhancement of kesterite structured solar cells. The results will be also helpful to improve the performance of compound semiconductor by oxidation states control strategy. [Display omitted] • MoO x layer with various oxidation states of Mo was constructed on the Mo substrate surface. • MoS 2 layer with low n-type doping concentration was produced from MoO 2.03 intermediate layer. • Band bending of CZTS at the back contact region was reduced through an n−-MoS 2 layer. • Electrical properties of MoS 2 intermediate layer is proven more crucial than its thickness effect on the efficiency of CZTS solar cells. • Long wavelength response increment obtained through optimized back contact band alignment. [ABSTRACT FROM AUTHOR]

• A new fluorescence method was developed for the MPD determination. • The proposed method had a wide linear range of 10–600 μg L−1. • A low detection limit of 4 μg L−1 was obtained. • The method was successfully applied for detecting MPD in real samples. This work presented a new fluorescence method for the determination of m-phenylenediamine (MPD) based on the reaction between MPD and acetylacetone. The formed compound Hacac-MPD exhibited strong blue fluorescence emission under 365 nm excitation. Its fluorescence intensity had an excellent correlation with the MPD concentration Under the optimal conditions, the developed method gave a linear range of 10–600 μg L−1 with a low detection limit of 4 μg L−1. Furthermore, the coexistence of common inorganic ions and other aromatic amines had hardly influence on the determination of MPD, demonstrating high selectivity. Additionally, the MPD in spiked water samples and hair dye samples was successfully detected by the developed method, and the recoveries were 97.5%-101.6% and 98.3%-98.9%, respectively. The established method exhibited a good application prospect for the determination of MPD in environment and daily necessities. [ABSTRACT FROM AUTHOR]

Waste classification is regarded as one of the most important strategies for waste management, and its success depends on the active participation of the public. Based on the theory of planned behavior (TPB) and attitude-behavior-condition (A-B-C) theory, this research investigated the key factors affecting residents' domestic waste classification intention (WCI) and waste classification behavior (WCB), and explored the moderating effects of three external factors including infrastructure (INF), government publicity (GP), and incentive measures (IM) on the intention-behavior relationship. The valid data were collected from 584 residents via conducting a face-to-face interview in Chengdu, a pilot city of waste classification in China, and analyzed by using the structural equation model (SEM). Results showed that residents' WCI and WCB had a significant discrepancy with the average values of 4.07 and 2.81, respectively. Attitude (AT), perceived behavior control (PBC) and classification knowledge (CK) were significantly related to residents' WCI, with AT (β = 0.65, P < 0.001) having the greatest direct impact. Meanwhile, CK and INF had a stronger impact on the WCB than WCI. GP indirectly affected WCB through the intermediary effect of CK. In addition, INF and GP had a positively moderating effect on residents' waste classification intention-behavior relationship and can facilitate the intention-behavior conversion. By contrast, IM failed to promote the conversion of residents' classification intention into behavior, and had no direct significant influence on residents' WCB. Finally, some relevant policy suggestions were put forward for the new pilot cities of waste classification in China. • Key factors affecting residents' WCI and WCB were investigated. • Moderating effects of external factors on intention-behavior relation was explored. • Attitude had the greatest direct influence on residents' WCI. • CK and Infrastructure had a stronger impact on WCB than WCI. • Infrastructure and GP can strengthen the intention-behavior conversion. [ABSTRACT FROM AUTHOR]

Simultaneously improving activity and stability is a crucial yet challenge in the development of metallic single‐atom‐based catalysts. In current work, a novel approach is introduced to address this issue by combining post‐adsorption and secondary pyrolysis techniques to create a synergistic catalytic system, in which the single atoms (SAs) Fe sites played in the NC matrix (Fe─NC) are coupled with high‐entropy atomic clusters (HEACs). Theoretical calculations reveal that the incorporation of HEACs lead to a rehybridization of the 3d orbital configuration of Fe‐N4, which helps to balance the adsorption/desorption energy of oxygenated intermediates. In situ spectroscopy further reveals that the rate‐limiting step of OH* desorption on HEAC/Fe─NC in oxygen reduction reaction (ORR) is more facile compared to atomic Fe─NC, implying a higher ORR activity. Moreover, the synergistic effect of diffusion activation barriers and configuration entropy contributes to the structural stability of HEAC/Fe─NC, resulting in remarkable durability. Consequently, this unique catalyst exhibits half‐wave potentials of 0.927 and 0.828 V in an aqueous solution of KOH (0.1 m) and HClO4 (0.1 m), respectively, along with excellent durability. The findings propose a novel strategy for modulating the electronic structure of metallic SAs catalysts and enhancing their stability through strong interactions between SAs and HEACs. [ABSTRACT FROM AUTHOR]

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Accurate channel state information is of great importance in high-speed wireless communication. In this work, the problem of channel training for massive multiple-input multiple-output (MIMO) systems is investigated and a closed-loop channel training scheme is devised upon the maximum a posteriori (MAP) criterion. More specifically, at each channel use, the channel estimation is performed at the receiver according to the MAP criterion, and the expectation-maximization (EM) algorithm is employed to cope with the MAP estimation problem. Based on the parameter estimates, the predicted mean square error (PMSE) of the channel estimate is minimized to determine a preferred sounding beam for the next channel use, of which the index is fed back to the transmitter via a limited-rate channel. The performance superiority of the proposed approach is evidenced by comparing with various channel training schemes. [ABSTRACT FROM AUTHOR]

The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ax standard defined a set of new specifications to improve spectrum efficiency, power efficiency, and reliability of future wireless local area networks (WLANs). Among these specifications, the uplink multi-user multiple-input, multiple-output (MU-MIMO) remedies the uplink shortcomings of existing WLANs and enables high-efficiency uplink transmissions. This paper considers the problem of adaptive modulation and coding (AMC) for uplink MU-single-input, multiple-output (MU-SIMO) in the upcoming WLANs. Our target is to select the appropriate modulation and coding scheme (MCS) for the existing users so as to maximize the throughput and at the same time guarantee that all users can satisfy a frame error constraint. We adopt a deep learning approach to tackle this problem. We propose to let the access point (AP) leverage the estimated channel state information (CSI) of all users along with the estimated noise standard deviation as the input features to a deep convolutional neural network (DCNN) that is trained to perform AMC. Simulation results reveal that the proposed DCNN for the uplink MU-SIMO AMC outperforms rival machine learning techniques such as fully-connected deep neural network (DNN), k-nearest neighbors (KNN), support vector machines (SVM), Gaussian Naive Bayes (GNB), and random forest (RF) in terms of the throughput and the frame error rate (FER). [ABSTRACT FROM AUTHOR]

Defect suppression via precursor additives is crucial to achieving high efficiency and stable perovskite quantum dot light‐emitting diodes (Pe‐QLEDs). Herein, a strategy using 4‐dodecylbenzenesulfonic acid (DBSA) is developed to simultaneously suppress halide interstitial and vacancy defects by tuning the crystallization process of perovskite quantum dots (Pe‐QDs) through coordinate bonding and chemical circumstance modulation, leading to faster radiative recombination and less carrier accumulation in the device. Meanwhile, the Pe‐QDs with defect suppression demonstrate higher ion migration activation energies. Thanks to the defect suppression strategy with the aid of the alkyl sulfonic group, the obtained Pe‐QDs exhibit an external quantum efficiency (EQE) of up to 20.4% and an operating lifetime of over 100 h in QLED, which is the leading performance of green Pe‐QLEDs. This work provides a strategy from a different aspect for the perovskite QLED field in improving the performance and stability of devices. [ABSTRACT FROM AUTHOR]

In recent years, heteroatom-doped carbon nanomaterials without metal possess great potential in oxygen evolution reactions (OER). However, the synthesis process is complex and the energy waste is large (i.e. calcination). Molecular electrocatalysts, as a new type of electrocatalyst for oxygen evolution, have attracted a wide amount of attention. In this work, taurine (TA) was selected as an OER electrocatalyst, and the catalytic activity of TA was studied via theoretical calculations and experiment. The DFT results show that the OH in the electrolyte tends to adsorb on C or N and takes away the H atom on the amino group to form uncoordinated N atoms in TA molecules. In addition, the TA molecule is fixed in a conductive hydrogel with a hydrophilic surface, and the conductive network on the hydrogel further accelerates charge and mass transfer in the OER process. Therefore, this work opens the way for further research on molecular electrocatalysts. [Display omitted] • the TA molecules provided positively charged atoms with high activity towards the OER demonstrated by DFT calculations. • the conductive PPy networks allowed fast charge transfer during the OER. • The TA-PPy hydrogel was directly grown on the carbon cloth without the usage of binders. [ABSTRACT FROM AUTHOR]

Two-dimensional carbon nitrides (CxNy) have gained significant attention in various fields including hydrogen energy development, environmental remediation, optoelectronic devices, and energy storage owing to their extensive surface area, abundant raw materials, high chemical stability, and distinctive physical and chemical characteristics. One effective approach to address the challenges of limited visible light utilization and elevated carrier recombination rates is to establish heterojunctions for CxNy-based single materials (e.g. C2N3, g-C3N4, C3N4, C4N3, C2N, and C3N). The carrier generation, migration, and recombination of heterojunctions with different band alignments have been analyzed starting from the application of CxNy with metal oxides, transition metal sulfides (selenides), conductive carbon, and Cx′Ny′ heterojunctions. Additionally, we have explored diverse strategies to enhance heterojunction performance from the perspective of carrier dynamics. In conclusion, we present some overarching observations and insights into the challenges and opportunities associated with the development of advanced CxNy-based heterojunctions. [ABSTRACT FROM AUTHOR]

Layered double hydroxide (LDH) is frequently used for phosphate removal in water, while a desirable LDH adsorbent should have sufficiently high adsorption capacity and selectivity. To enhance the removal performance and selectivity of phosphate by LDH, La-doped layered double hydroxide/biochar (La–MgAl–LDH/BC) was synthesized by using a co-precipitation method. Investigation results showed that lanthanum (La) metal loading can significantly enhance the phosphate adsorption performance of La–MgAl–LDH. Then, the introduction of La–MgAl–LDH into BC improved the adsorption capacity of phosphate by the adsorbent, which is 1.8 times that of pure La–MgAl–LDH. The optimum La to Al molar ratio was 0.2 : 0.8 in La–MgAl–LDH materials, and the La–MgAl–LDH to BC mass ratio was 9.3 : 10. Furthermore, both La–MgAl–LDH and La–MgAl–LDH/BC exhibited an enhanced ability to resist interference from co-existing anions (Cl−, SO42−, NO3− and CO32−) compared to LDH. For batch experiments, the kinetic results indicated that the adsorption process of phosphate by La–MgAl–LDH/BC follows a pseudo-second-order kinetics model, reaching equilibrium quickly within 1 h. Furthermore, film diffusion and intra-particle diffusion simultaneously took place. The maximum monolayer phosphate adsorption capacity of La–MgAl–LDH/BC could reach 249.3 mg PO43− g−1 (513.6 mg PO43− g−1, converting to an equivalent La–MgAl–LDH). After four cycles, La–MgAl–LDH/BC showed good reusability, and over 63.5% of phosphate uptake was retained with minimal metal leaching. Additionally, phosphate could be rapidly removed from actual water and simulated water at low concentrations. The electrostatic attraction, ligand exchange, ion exchange and Lewis acid action jointly facilitate the adsorption of phosphate. [ABSTRACT FROM AUTHOR]

Alzheimer's disease and Type 2 diabetes are two epidemiologically linked diseases which are closely associated with the misfolding and aggregation of amyloid proteins amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP), respectively. The co-aggregation of the two amyloid proteins is regarded as the fundamental molecular mechanism underlying their pathological association. The green tea extract epigallocatechin-3-gallate (EGCG) has been extensively demonstrated to inhibit the amyloid aggregation of Aβ and hIAPP proteins. However, its potential role in amyloid co-aggregation has not been thoroughly investigated. In this study, we employed the enhanced-sampling replica exchange molecular dynamics simulation (REMD) method to investigate the effect of EGCG on the co-aggregation of Aβ and hIAPP. We found that EGCG molecules substantially diminish the β-sheet structures within the amyloid core regions of Aβ and hIAPP in their co-aggregates. Through hydrogen-bond, π–π and cation–π interactions targeting polar and aromatic residues of Aβ and hIAPP, EGCG effectively attenuates both inter-chain and intra-chain interactions within the co-aggregates. All these findings indicated that EGCG can effectively inhibit the co-aggregation of Aβ and hIAPP. Our study expands the potential applications of EGCG as an anti-amyloidosis agent and provides therapeutic options for the pathological association of amyloid misfolding disorders. [ABSTRACT FROM AUTHOR]

Tertiary lymphoid structures (TLSs) play a crucial role in determining prognosis and response to immunotherapy in several solid malignancies. Nevertheless, the effect of TLS-associated gene signature based on The Cancer Genome Atlas (TCGA) cohort in patients with breast cancer (BRCA) remains controversial. Based on TCGA-BRCA dataset (n = 866), 9-gene was identified to construct an TLS signature and further analyzed its prognostic value. Then, we explored the relationship of this TLS signature with molecular subtype, immune microenvironment, tumor mutational burden (TMB). High-TLS signature patients had a better overall survival (OS) than low-TLS signature patients, consistent with the results in the METABRIC cohort. Multivariate analysis revealed that TLS signature remained an independent prognostic indicator for OS. In addition, we established a nomogram with the integration of TLS signature and other independent variables to predict individual risk of death. The comprehensive results showed that patients with high TLS signature benefit from immunotherapy; the signature was also correlated with inhibition of cell proliferation pathways, low TP53 mutation rate, high infiltration of B cells, CD8 + T cells, CD4 + T cells, and M1 macrophages. Therefore, TLS signature is a promising biomarker to distinguish the prognosis and immune microenvironment in BRCA. [ABSTRACT FROM AUTHOR]

The integrity of cement–salt rock interfaces (CSI) in salt cavern gas storage wells may be damaged during circulating gas injection, production, and long-term gas pressure maintenance. In this study, permeability tests were conducted on cement–salt rock samples under cyclic loading confining pressure at different holding times. The cracks and micro-annuli at the interface were visualised using X-ray computed tomography. The bonding strength of the interface after the permeability test was measured in a triaxial environment. The results showed that with an increase in the number of cycles, the cumulative volumetric strain rate and permeability first decreased rapidly and then tended to stabilise, and the decreasing amplitude was positively correlated with the holding time. When the holding times were 10, 30, and 60 min, the permeability change ratios were 0.844, 0.762, and 0.638, respectively, after one cycle and 0.722, 0.444, and 0.420, respectively, after five cycles. The permeability of CSI is mainly influenced by the holding time. The mechanical properties of the CSI specimens with similar permeabilities showed differences at different holding times. Cyclic loading easily degrades the mechanical properties, which can be improved by extending the holding time. The CSI is the main channel for gas leakage, extending the holding time, and increasing the number of cycles can effectively improve the pore structure of CSI and enhance the cement sheath integrity. This study provides a reference for long-term safe operation of salt cavern gas storage wells. Highlights: Permeability tests and triaxial shear bond strength tests after permeability tests were performed on a cement–salt rock interface. The effects of cyclic loading and holding time on the permeability and mechanical properties of the cement–salt rock interface were investigated. Microcracks' distribution within the cement–salt rock interface under cyclic loads was characterised using computed tomography. [ABSTRACT FROM AUTHOR]

The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue–residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a growing body of supervised and unsupervised machine learning methods have been applied to this field, yielding fruitful results. Here, we propose a novel self-supervised model, called encoder-transformation layer-decoder (ETLD) architecture, capable of capturing protein sequence latent features directly from MSAs. Compared to the typical autoencoder model, ETLD introduces a transformation layer with the ability to learn inter-site couplings, which can be used to parse out the two-dimensional residue–residue contacts map after a simple mathematical derivation or an additional supervised neural network. ETLD retains the process of encoding and decoding sequences, and the predicted probabilities of amino acids at each site can be further used to construct the mutation landscapes for mutation effects prediction, outperforming advanced models such as GEMME, DeepSequence and EVmutation in general. Overall, ETLD is a highly interpretable unsupervised model with great potential for improvement and can be further combined with supervised methods for more extensive and accurate predictions. [ABSTRACT FROM AUTHOR]

Tertiary lymphoid structures (TLSs) play a crucial role in determining prognosis and response to immunotherapy in several solid malignancies. Nevertheless, the effect of TLS-associated gene signature based on The Cancer Genome Atlas (TCGA) cohort in patients with breast cancer (BRCA) remains controversial. Based on TCGA-BRCA dataset (n = 866), 9-gene was identified to construct an TLS signature and further analyzed its prognostic value. Then, we explored the relationship of this TLS signature with molecular subtype, immune microenvironment, tumor mutational burden (TMB). High-TLS signature patients had a better overall survival (OS) than low-TLS signature patients, consistent with the results in the METABRIC cohort. Multivariate analysis revealed that TLS signature remained an independent prognostic indicator for OS. In addition, we established a nomogram with the integration of TLS signature and other independent variables to predict individual risk of death. The comprehensive results showed that patients with high TLS signature benefit from immunotherapy; the signature was also correlated with inhibition of cell proliferation pathways, low TP53 mutation rate, high infiltration of B cells, CD8 + T cells, CD4 + T cells, and M1 macrophages. Therefore, TLS signature is a promising biomarker to distinguish the prognosis and immune microenvironment in BRCA. [ABSTRACT FROM AUTHOR]

In this paper, we report a wide band spectrum tri-receptor quaternary organic photodetector (OPD) based on the additional two non-fullerene acceptor materials, ITIC and IEICO-4F, with the polymer material P3HT as donor and the fullerene PC61BM as the first acceptor. The second acceptor ITIC and the third accptor IEICO-4F, with different molecular structures and absorption, were introduced into the conventional system P3HT:PC61BM to improve narrow response spectral range and the uneven response of OPDs. The experiments results show, that proposed non-fullerene acceptor materials of the narrow optical band gap were successful to broaden the absorption spectral range to near infrared. Respectively, the responses (R) and external quantum efficiency at 630, 530, and 460 nm, are up to 0.293 A W−1, 0.288 A W−1, 0. 243 A W−1, and 58%, 67%, and 66%. The quaternary OPDs achieve significant response spectrum across the 400–950 nm range with the specific detection rate (D *) exceeding 1012 Jones. It demonstrates that expanding spectrum non-fullerene materials ITIC and IEICO-4F can improve the mixing degree of the mixed materials, decrease agglomeration brought on by single widening excess, and increase spectrum absorption and moderate non-uniform response. The exciton dissociation efficiency is considerably increased by simultaneous charge transfer between the receptors and the receptors in the active layer. Moreover, the carrier mobility is improved, bimolecular recombination is suppressed. [ABSTRACT FROM AUTHOR]

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions. [ABSTRACT FROM AUTHOR]

• A method for generating point-contact conjugate tooth surfaces is proposed. • The mechanism of point-contact conjugate tooth surfaces is revealed. • A simplified method of calculating contact points is given. • Two novel types of point-contact conjugate worm drives are proposed. • The proposed method is verified by numerical simulations and meshing tests. Point-contact conjugate tooth surfaces are insensitive to manufacturing errors and assembly errors and have no transmission errors. However, design conditions for the current method of generating point-contact conjugate tooth surfaces are strict or lacking. This study proposed a method for point-contact conjugate tooth surfaces with a constant instantaneous transmission ratio generated by point-contact conjugate involute helicoids. According to relative motions of single- and two-degree-of-freedom in generating, design criteria were given and a mathematical model of the method was established. The mechanism of point-contact conjugate tooth surfaces was obtained by investigating the relationship between the engagement lines/surfaces, and kinematics was studied, in which the mechanism with a constant instantaneous transmission ratio was geometrically analysed. Based on this method, four types of point-contact conjugate worm drives were studied, in which two novel types of worm drives were developed, i.e., a point-contact conjugate toroidal involute (TI) worm drive, and a point-contact conjugate involute cylindrical (ZI) worm drive. The method was validated for correctness using numerical simulations and meshing tests and the influence of adjustable parameters on the meshing performance was outlined. [ABSTRACT FROM AUTHOR]

[Display omitted] • Chitosan, TiO 2 and CEO nanocomposites can prolong the shelf life of strawberry. • TiO 2 and CEO destroy bacterial cell wall in different ways resulting death. • TiO 2 and CEO significantly improve the properties of chitosan in many aspects. The freshness and safety of fruits have always been crucial issues in the development of the industry. However, the existing fresh-keeping methods have limited effect, meanwhile, the preservation mechanism of different materials. In this study, Cinnamon essential oil (CEO), TiO 2, and chitosan (CS) were compounded to prepare safe and renewable nanocomposites (CS-T-C) for fruit preservation. The results showed that CEO mainly destroyed the bacterial cell wall through penetration, while TiO 2 is through destruction. The strawberry coated with CS-T-C showed better hardness, lower weight loss and mildew rate, and the shelf-life at 20℃ was extended for four days compared with the control. And all four nanocomposites were not cytotoxic. In summary, nanocomposites can deal with many problems through different mechanisms to maximize the fresh-keeping effect, and the nanocomposites developed in this work might be a good choice for fruit preservation. [ABSTRACT FROM AUTHOR]