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Summary Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the ‘Rule of Three’. Availability and implementation https://github.com/yydiao1025/MacFrag. Supplementary information Supplementary data are available at Bioinformatics online.

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Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target–disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug–target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target–disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

We report bulk magnetization measurements and spatially resolved measurements of magnetic domains inCo3Sn2S2single crystals. The results indicate that a previously reported magnetic anomaly around 130 K is due to an anomalous domain wall depinning upon cooling. Our measurements also reveal a hysteresis between field-cooled-cooling and field-cooled-warming magnetization curves acquired under a constant magnetic field below 300 Oe. This observation rules out the possibility that the anomaly stems from a second-order phase transition. Our results further suggest that changes in the shape of hysteresis loops from 5 to 170 K are caused by an unusual temperature-dependent domain nucleation field that changes sign around 130 K. The Kerr rotation images of the magnetic domains confirm that the domain walls depin between 120 and 140 K.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of ‘near-drug’ properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords ‘A61P, compound, structure’ in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97–98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

The nexus approach is a promising method used to address issues regarding environmental dilemmas. However, effective consumption-based and production-based nexus governance strategies are not well understood. Using data envelopment analysis and China’s provincial data for 2017, this study analysed the input–output efficiency of the water-energy-food nexus by considering production-based intensity, consumption-based intensity, and the quantity index system. The results show that policies involving consumption-based intensity metrics can be more efficient, and the efficiency of both production-based intensity (0.482) and consumption-based intensity (0.682) are much higher than the efficiency of the quantity (0.378) index system. The results also indicate that province-specific consumption-based governance strategies are crucial for 30 provinces in China. Finally, three policy directions for nexus governance in China are proposed, namely, shifting policy attention from production-based governance to consumption-based governance, focusing intensity metrics on scale efficiency and aiming quantity metrics on pure technical efficiency, and localizing province-specific management strategies. This paper compares consumption-based and production-based nexus approaches, and the results indicate that a call for consumption-based approaches in future nexus modelling and governance is appropriate. This paper also has implications for China’s nexus governance.

To solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of information points is analyzed by using the characteristics of Dirichlet graph, and the anonymous set of users is generated and sent to LBS server. Second, the relationship matrix is generated, and the proximity relationship between the user position and the target information point is obtained by calculation. Then, the private information retrieval model is applied to ensure the privacy of users’ target information points. Finally, the experimental results show that the KPDR method not only satisfies the diversity of l 3 / 4 , but also increases the anonymous space, reduces the communication overhead, ensures the anonymous success rate of users, and effectively prevents the disclosure of user privacy.

One of the most advanced, promising, and commercially viable research issues in the world of hydrogel dressing is gaining functionality to achieve improved therapeutic impact or even intelligent wound repair. In addition to the merits of ordinary hydrogel dressings, functional hydrogel dressings can adjust their chemical/physical properties to satisfy different wound types, carry out the corresponding reactions to actively create a healing environment conducive to wound repair, and can also control drug release to provide a long-lasting benefit. Although a lot of in-depth research has been conducted over the last few decades, very few studies have been properly summarized. In order to give researchers a basic blueprint for designing functional hydrogel dressings and to motivate them to develop ever-more intelligent wound dressings, we summarized the development of functional hydrogel dressings in recent years, as well as the current situation and future trends, in light of their preparation mechanisms and functional effects.

Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.

Bruton's tyrosine kinase (BTK) is an attractive therapeutic target in the treatment of cancer, inflammation, and autoimmune diseases. Covalent and noncovalent BTK inhibitors have been developed, among which covalent BTK inhibitors have shown great clinical efficacy. However, some of them could produce adverse effects, such as diarrhea, rash, and platelet dysfunction, which are associated with the off-target inhibition of ITK and EGFR. In this study, we disclosed a series of pteridine-7(8

We report bulk magnetization measurements and spatially resolved measurements of magnetic domains in Co3Sn2S2 single crystals. The results indicate that a previously reported magnetic anomaly around 130 K is due to an anomalous domain wall depinning upon cooling. Our measurements also reveal a hysteresis between field-cooled-cooling (FCC) and field-cooled-warming (FCW) magnetization curves acquired under a constant magnetic field below 300 Oe. This observation rules out the possibility that the anomaly stems from a second-order phase transition. Our results further suggest that changes in the shape of hysteresis loops from 5 K to 170 K are caused by an unusual temperature-dependent domain nucleation field that changes sign around 130 K. The Kerr rotation images of the magnetic domains confirm that the domain walls depin between 120 K and 140 K., Comment: 8 pages, 7 figures

Background. Non-small-cell lung cancer (NSCLC) is a major component of lung cancer and is significantly correlated with poor prognosis. N6-methyladenosine (m6A) RNA methylation is closely related to the occurrence, progression, and prognosis of cancer. The potential biological functions and mechanisms of m6A RNA methylation in the immune microenvironment are still unclear. Methods. We assessed m6A RNA methylation modification patterns in 1326 NSCLC patient samples based on 20 m6A regulators, linking these clusters to the tumor microenvironment and immune cell infiltration. The m6Ascore was created to quantify the m6A modification patterns of individual tumors. We then assessed the value of NSCLC patients in terms of clinical prognosis and immunotherapy response. Results. According to different mRNA expression levels, two different m6A clusters were identified. m6A aggregation was significantly associated with clinical prognostic characteristics, the tumor microenvironment, and immune-related biological processes. Fifteen differential genes were screened based on these two m6A clusters, and to further investigate the mechanisms of action of these differential genes, they were subjected to unsupervised clustering analysis, which classified them into four different genomic isoforms. Prognostic analysis indicated that the survival advantage of the m6A gene cluster A modification mode was significantly prominent. We continued to construct the m6Ascore, which was used as a scoring tool to evaluate tumor typing, immunity, and prognosis. Patients with a low m6Ascore showed a significant survival advantage, and the group with a low m6Ascore had a better prognosis predicted by immunotherapy. The anti-PD-1/L1 immunotherapy cohort showed that a lower m6Ascore was associated with higher efficacy of immunotherapy. Conclusions. The results suggest that m6A RNA methylation regulators make an important difference in the tumor immune microenvironment of patients with NSCLC. m6A gene characterization and the construction of the m6Ascore provide us with a richer understanding of m6A RNA methylation modification patterns in NSCLC patients and help to predict clinical prognosis and immunotherapeutic response.

Among the three existing targeted gene editing technologies, zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats-CRISPR-associated 9 (CRISPR-Cas9), the latter is widely used owing to its simplicity, efficiency, and low cost. Here, we routinely infected A172 and U251 cells with lentiviral vectors, in which aquaporin-8 (AQP8) was knocked out using CRISPR/Cas9. Our results indicated that cryopreservation did not significantly alter the viral infection efficiency, but influenced AQP8 expression in the infected cells at both protein and mRNA levels compared with the non-cryopreserved samples. Further, AQP8 expression at protein and mRNA levels in recovered cryopreserved infected cells did not significantly differ from those in the blank and negative controls, indicating that the lentivirus was still infectious at low temperatures. However, it failed to release the AQP8-targeting guide RNA in the infected cells, or the guide RNA was released, but underwent changes that caused it to malfunction in the cells with CRISPR/Cas9-mediated AQP8 knock-out. Our findings possibly provide some insights into the reliability of lentiviruses as CRISPR/Cas9 vectors.

${\mathrm{Co}}_{3}{\mathrm{Sn}}_{2}{\mathrm{S}}_{2}$ is a magnetic Weyl semimetal, in which ferromagnetic ordering at 177 K is predicted to stabilize Weyl points. We perform temperature and spatial dependent angle-resolved photoemission spectroscopy measurements through the Curie temperature (${T}_{c}$), which show large band shifts and renormalization concomitant with the onset of magnetism. We argue that ${\mathrm{Co}}_{3}{\mathrm{Sn}}_{2}{\mathrm{S}}_{2}$ evolves from a Mott ferromagnet below ${T}_{c}$ to a correlated metallic state above ${T}_{c}$. To understand the magnetism, we derive a tight-binding model of Co-$3{d}_{{x}^{2}\ensuremath{-}{y}^{2}}$ orbitals on the kagome lattice. At the filling obtained by first-principles calculations, this model reproduces the ferromagnetic ground state, and results in the reduction of Coulomb interactions due to cluster effects. Using a disordered local moment simulation, we show how this reduced Hubbard $U$ leads to a collapse of the bands across the magnetic transition, resulting in a correlated state, which carries associated characteristic photoemission signatures that are distinct from those of a simple lifting of exchange splitting. The behavior of topology across ${T}_{c}$ is discussed in the context of this description of the magnetism.

Large-scale COVID-19 vaccinations are currently underway in many countries in response to the COVID-19 pandemic. Here, we report, besides generation of neutralizing antibodies, consistent alterations in hemoglobin A1c, serum sodium and potassium levels, coagulation profiles, and renal functions in healthy volunteers after vaccination with an inactivated SARS-CoV-2 vaccine. Similar changes had also been reported in COVID-19 patients, suggesting that vaccination mimicked an infection. Single-cell mRNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) before and 28 days after the first inoculation also revealed consistent alterations in gene expression of many different immune cell types. Reduction of CD8+ T cells and increase in classic monocyte contents were exemplary. Moreover, scRNA-seq revealed increased NF-κB signaling and reduced type I interferon responses, which were confirmed by biological assays and also had been reported to occur after SARS-CoV-2 infection with aggravating symptoms. Altogether, our study recommends additional caution when vaccinating people with pre-existing clinical conditions, including diabetes, electrolyte imbalances, renal dysfunction, and coagulation disorders.

Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase selectivity profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4,219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. For each top-ranked kinase, the additional classification attributes and the phylogenetic tree are given simultaneously.

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

In order to solve the problem of track plate cracks detection, night low light environments and crack growth irregularity, this paper proposes an improved YOLO (you only look once). In view of the problem that the YOLO has poor detection effect on small targets and overlapping targets, this paper refers to the RPN-RCN, removes the fully connected layer based on the original YOLO network, Anchor Box and a new convolution layer added to it. The improved network is used to superimpose the lower characteristics of shallow convolutional structure in the original YOLO and the abstract semantic features extracted from the deep convolutional network. Experiments show that the improved YOLO effectively enhances the detection ability of small targets and overlapping targets, and improves the detection accuracy of track plate cracks. In addition, the real-time performance of the improved algorithm also meets the engineering requirements.

Multicore fibers (MCFs) offer a fascinating solution to the need to increase the fiber density and thus meet the exponentially growing demand for capacity in optical communication networks. Despite overwhelming research into MCFs, the desire for a general fusion splicing scheme between dissimilar MCFs remains unanswered. Here, we propose a tapering technique to reshape MCFs that includes both reverse-tapering and down-tapering schemes and can be exploited to tailor the core-to-core spacing and modify the modal property of MCFs. By matching both the spacing and the mode field diameter, we demonstrated a low-loss (0.18 ± 0.10 dB) and low-crosstalk (–68 ± 3 dB) fusion splice between two spacing-mismatched MCFs with a spacing difference of up to 26 μm. The proposed novel schemes are also suitable for splicing between MCFs with slightly different spacings and can provide a unique perspective for fabricating MCF devices and boosting various MCF applications.

Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. The additional classification attributes and phylogenetic tree are also given for each top-ranked kinase.

Microstructured optical fibers (MOFs) have attracted intensive research interest in fiber-based optofluidics owing to their ability to have high-efficient light-microfluid interactions over a long distance. However, there lacks an exquisite design guidance for the utilization of MOFs in subwavelength-scale optofluidics. Here we propose a tapered hollow-core MOF structure with both light and fluid confined inside the central hole and investigate its optofluidic guiding properties by varying the diameter using the full vector finite element method. The basic optical modal properties, the effective sensitivity, and the nonlinearity characteristics are studied. Our miniature optofluidic waveguide achieves a maximum fraction of power inside the core at 99.7%, an ultra-small effective mode area of 0.38 µm2, an ultra-low confinement loss, and a controllable group velocity dispersion. It can serve as a promising platform in the subwavelength-scale optical devices for optical sensing and nonlinear optics.

With the rapid development of high-speed railways today, CRTSII type track slabs have caused diseases due to their own structural aging and various external factors. This paper presents a crack detection method for track slabs in complex environments. Due to the uneven illumination in the nighttime, the shooting environment is complex and shadows are easily generated. Firstly, the histogram equalization is used to reduce the effect of uneven illumination on the detection of cracks. Secondly, the improved Canny edge detection algorithm is used to draw a continuous crack profile with adaptive dual thresholds. Then, an algorithm based on the contour of the fracture shape is used to extract the fracture characteristics and achieve accurate crack location. Finally, a criterion for determining the level of cracking faults based on the width of cracks is proposed. The algorithm can effectively improve the accuracy of track plate crack detection under complex environment, quantify the level of crack disease, and provide maintenance basis for track maintenance personnel. Experimental results show that the method has high robustness and accuracy.

With the continual rising of oil price and the intensification of greenhouse effect, how to reduce operational fuel consumption and decrease air pollution is very meaningful. This paper establishes a fuel consumption prediction model and presents an improved A∗ algorithm for fuel-efficient ship weather routing. The improved A∗ algorithm measures the angle between a segment from the starting node to the ending node and the true north line. Then, it takes the angle as heuristic information to determine which nodes need to be searched at each current node. Eventually, it selects the next optimal node from these searched nodes. This improved algorithm is applicable for convex obstacles. The simulation results of improved A∗ algorithm show that when the obstacles are convex obstacles, this new method can find the fuel-efficient route and reduce the amount of total searched nodes effectively.

As a heuristic search algorithm, the A∗ algorithm is often used to solve the problem of ships routing optimization. For convex obstacles, the A∗ algorithm is improved in the local optimal path planning of ships, when the increased number of waypoints lead a lower efficiency. The improved algorithm uses the angle between the starting point, the ending point and north direction as the heuristic information to reduce the number of route points in the path planning process. After simulation and verification, the number of the improved algorithms waypoint searched is reduced by about 30 percent compared with the classical A∗ algorithm, significantly less than the classical A∗ algorithm.

Triclosan is an antimicrobial agent which is frequently found in the aquatic environment. Photolysis is an important transformation pathway for triclosan in surface water. Though a lot of studies have been conducted on the toxicity of triclosan, few of them focused on the ecological risk of the mixture after sunlight irradiation. The aim of the present study was to investigate the potential toxicity of triclosan under light irradiation and the influence of the coexisting humic acid by bioassay. Photo-induced acute toxicity and genetic toxicity were observed in the triclosan solutions after 24 h of light irradiation. The addition of humic acid at the concentration of 1 and 5 mg/L both resulted in a significant decrease (p