Miracle-dz/UniFAR-FTUs huggingface dataset:
A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

Identification of drug–target interactions (DTIs) is critical for discovering potential target protein candidates for new drugs. However, traditional experimental methods have limitations in discovering DTIs. They are time‐consuming, tedious, and expensive, and often suffer from high false‐positive rates and false‐negative rates. Therefore, using computational methods to predict DTIs has received extensive attention from many researchers in recent years. To address this issue, in this paper, an effective prediction model is presented which is based on the information of drug molecular structure data and protein sequence data. It performs prediction with the following procedures. First, we transform the sequences of each target into a position‐specific scoring matrix (PSSM), such that the features can retain biological evolutionary information. We then use a feature vector of molecular substructure fingerprints to describe the chemical structure information of the drug compounds. Second, the Legendre moments algorithm is used to extract new features from the PSSM. Finally, a classification algorithm called rotation forest is used to perform prediction, we tested its prediction performance on four golden standard data sets: enzymes, G‐protein‐coupled receptors, ion channels, and nuclear receptors. As a result, the proposed method achieves average accuracies of 0.9026, 0.8260, 0.8703, and 0.7444 on these four data sets using five‐fold cross‐validation. We also compare the proposed method with the support vector machine and other existing approaches. The proposed model is proved to be superior to comparative methods, showing that it is feasible, effective, and robust for predicting potential DTI.
The analysis of dislocation pipe radius for diffusion

Dislocation pipe radius for diffusion and the distribution are analyzed using combined an equivalent boundary slab model and a dislocation pipe model. Both models were applied to oxygen self-diffusion in MgO single crystal. The lattice diffusion coefficient Dl and the diffusion coefficient for oxygen diffusion along a dislocation, Dd, at 1350°C are obtained as Dl=1.17×10−20 and Dd=4.44×10−16 m2sec−1, respectively. Oxygen diffusion along the dislocation takes place within a pipe of radius 0.18 nm surrounding the dislocation. The theoretical results of dislocation structures, dislocation density d=3.2×1014 m−2 and subgrain size b=1.15 mum, are found to agree well with the electron-microscopic experiments by Moriyoshi and Ikegami (1985).
Nonorthogonal Multiple Access for Next-Generation Mobile Networks: A Technical Aspect for Research Direction

5G mobile communications offer several benefits, which include providing extremely low latency, very high data rates, significant improvement in the number of users, and increase in base station capacity and perceived quality of service. This may be achieved at the cost of an increased receiver complexity by nonorthogonal access of users. Nonorthogonal multiple access (NOMA) is one of the capable contenders to achieve the vision of 5G wireless communications. Supporting a higher number of users than available orthogonal resources is the key feather of NOMA. In this article, the basic principle of NOMA has been reviewed and compared with other orthogonal multiple access (OMA). A comprehensive survey is presented in the latest NOMA scheme. The distinguished NOMA schemes design principle features, and recent deployments are discussed. Furthermore, the performance is compared in terms of the bit error rate, system capacity, and energy efficiency. The performance results show that NOMA can achieve the required goals, in terms of the user data rate, system capacity, interference cancellation scheme, and reception complexity.
Research network propagation: The impact of PhD students’ temporary international mobility

As the global mobility of researchers increases, many of whom are supported by national funding agencies’ mobility schemes, there is growing interest in understanding the impact of this overseas mobility on knowledge production and networking. This study addresses a relatively understudied mobility—the temporary international mobility of PhD students in STEM fields—and its relation to the establishment of research collaborations between mobile PhD students and researchers at the host university and with other researchers overseas. First, we find that 55% of the participants established relevant international collaborations (i.e., with hosting supervisors and/or others at the hosting university), and we explore these collaboration patterns in detail by taking a novel research propagation approach. Second, we identify features of the visiting period that influence the formation of research collaborations abroad, such as the prestige of the host university, the duration of the international mobility period, the cultural distance, and the number of peer PhD students at the host university. Previous research collaborations between the home and host supervisors are also found to play a crucial role in research collaboration development. Age at the time of mobility is not found to be particularly relevant. We find that female PhD students are less able to benefit from collaborative research efforts than male students. These findings advance the knowledge of global research networks and provide important insights for research funding agencies aiming to promote international research mobility at the doctoral level.
1-(3-Tert-Butylphenyl)-2,2,2-Trifluoroethanone as a Potent Transition-State Analogue Slow-Binding Inhibitor of Human Acetylcholinesterase: Kinetic, MD and QM/MM Studies

Kinetic studies and molecular modeling of human acetylcholinesterase (AChE) inhibition by a fluorinated acetophenone derivative, 1-(3-tert-butylphenyl)-2,2,2-trifluoroethanone (TFK), were performed. Fast reversible inhibition of AChE by TFK is of competitive type with Ki = 5.15 nM. However, steady state of inhibition is reached slowly. Kinetic analysis showed that TFK is a slow-binding inhibitor (SBI) of type B with Ki* = 0.53 nM. Reversible binding of TFK provides a long residence time, τ = 20 min, on AChE. After binding, TFK acylates the active serine, forming an hemiketal. Then, disruption of hemiketal (deacylation) is slow. AChE recovers full activity in approximately 40 min. Molecular docking and MD simulations depicted the different steps. It was shown that TFK binds first to the peripheral anionic site. Then, subsequent slow induced-fit step enlarged the gorge, allowing tight adjustment into the catalytic active site. Modeling of interactions between TFK and AChE active site by QM/MM showed that the “isomerization” step of enzyme-inhibitor complex leads to a complex similar to substrate tetrahedral intermediate, a so-called “transition state analog”, followed by a labile covalent intermediate. SBIs of AChE show prolonged pharmacological efficacy. Thus, this fluoroalkylketone intended for neuroimaging, could be of interest in palliative therapy of Alzheimer’s disease and protection of central AChE against organophosphorus compounds.
A Semi-supervised Deep Auto-encoder Based Intrusion Detection for IoT

The main problem facing the Internet of Things (IoT) today is the identification of attacks due to the constrained nature of IoT devices. To address this problem, we present a lightweight intrusion detection system (IDS) which acts as a second line of defense allowing the reinforcement of the access control mechanism. The proposed method is based on a Deep Auto-Encoder (DAE), which learns the pattern of a normal process using only the features of the user’s normal behavior. Whatever deviation from the expected behavior is considered an anomaly. We validate our approach using two well-known network datasets, namely, the NSL-KDD and CIDDS-001. The experimental results demonstrate that our approach provides promising results in terms of accuracy, detection rate and false alarm rate.
Lamellar Diblock Copolymer Grain Boundary Morphology. 3. Helicoid Section Twist Boundary Energy

The helicoid section morphology allows a diblock copolymer lamellar phase to maintain microphase separation across a twist grain boundary. The interface between the two microphases in the grain boundary region approximates a stack of sections of the helicoid minimal surface. Grain boundary energies were calculated for the helicoid section morphology both as a function of diblock chain characteristics and as a function of grain boundary twist angle. The basic approach to grain boundary energy calculation is to formulate a general expression for local free energy density as a function both of chain characteristics and of the local curvature of the interface. The local energy density is then integrated over the mathematical model for the Scherk grain boundary. Two general methods of calculation were used, and the results where then compared. First, a self-consistent field model was formulated in which average energies per chain were calculated for all the possible interfacial curvature environments encounter.
Electrically Transduced Gas Sensors Based on Semiconducting Metal Oxide Nanowires

Semiconducting metal oxide-based nanowires (SMO-NWs) for gas sensors have been extensively studied for their extraordinary surface-to-volume ratio, high chemical and thermal stabilities, high sensitivity, and unique electronic, photonic and mechanical properties. In addition to improving the sensor response, vast developments have recently focused on the fundamental sensing mechanism, low power consumption, as well as novel applications. Herein, this review provides a state-of-art overview of electrically transduced gas sensors based on SMO-NWs. We first discuss the advanced synthesis and assembly techniques for high-quality SMO-NWs, the detailed sensor architectures, as well as the important gas-sensing performance. Relationships between the NWs structure and gas sensing performance are established by understanding general sensitization models related to size and shape, crystal defect, doped and loaded additive, and contact parameters. Moreover, major strategies for low-power gas sensors are proposed, including integrating NWs into microhotplates, self-heating operation, and designing room-temperature gas sensors. Emerging application areas of SMO-NWs-based gas sensors in disease diagnosis, environmental engineering, safety and security, flexible and wearable technology have also been studied. In the end, some insights into new challenges and future prospects for commercialization are highlighted.
Stress-induced phase transformations in thermally cycled superelastic NiTi alloys: in situ X-ray diffraction studies

In situ laboratory-based and in situ synchrotron X-ray diffraction techniques were employed to study quantitatively the strain-induced austenite-to-martensite (A–M) transformation in thermally cycled (TC) superelastic NiTi alloys. The propagation of the A–M interfaces and the evolution of the microstructure were traced during uniaxial tensile loading. It was shown that the TC material exhibits localized transformation via the propagation of transformation bands. The amount of the martensite phase depends approximately linearly on the applied strain. Analysis of the broadening of the austenite diffraction lines indicates the presence of highly deformed austenite grains within the transformation bands. Analysis of the austenite diffraction-line shifts indicates that the overall lattice strain in the (retained) austenite in the transformation bands differs from that of the austenite in the adjacent untransformed regions.
Sequence Features of Mitochondrial Transporter Protein Families

Mitochondrial carriers facilitate the transfer of small molecules across the inner mitochondrial membrane (IMM) to support mitochondrial function and core cellular processes. In addition to the classical SLC25 (solute carrier family 25) mitochondrial carriers, the past decade has led to the discovery of additional protein families with numerous members that exhibit IMM localization and transporter-like properties. These include mitochondrial pyruvate carriers, sideroflexins, and mitochondrial cation/H+ exchangers. These transport proteins were linked to vital physiological functions and disease. Their structures and transport mechanisms are, however, still largely unknown and understudied. Protein sequence analysis per se can often pinpoint hotspots that are of functional or structural importance. In this review, we summarize current knowledge about the sequence features of mitochondrial transporters with a special focus on the newly included SLC54, SLC55 and SLC56 families of the SLC solute carrier superfamily. Taking a step further, we combine sequence conservation analysis with transmembrane segment and secondary structure prediction methods to extract residue positions and sequence motifs that likely play a role in substrate binding, binding site gating or structural stability. We hope that our review will help guide future experimental efforts by the scientific community to unravel the transport mechanisms and structures of these novel mitochondrial carriers.
Genome Survey Sequencing of In Vivo Mother Plant and In Vitro Plantlets of Mikania cordata

Mikania cordata, the only native congener of the invasive weed Mikania micrantha in China, is an ideal species for comparative study to reveal the invasion mechanism. However, its genome resources are lagging far behind its congener, which limits the comparative genomic analysis. Our goal is to characterize the genome of M. cordata by next-generation sequencing and propose a scheme for long-read genome sequencing. Previous studies have shown that the genomic resources of the host plant would be affected by the endophytic microbial DNA. An aseptic sample of M. cordata will ensure the proper genome in downstream analysis. Because endophytes are ubiquitous in the greenhouse-grown M. cordata, the in vitro culture with cefotaxime or timentin treatment was undertaken to obtain the aseptic plantlets. The in vivo mother plant and in vitro plantlets were used to survey the genome. The microbial contamination in M. cordata was recognized by blast search and eliminated from the raw reads. The decontaminated sequencing reads were used to predict the genome size, heterozygosity, and repetitive rate. The in vivo plant was so contaminated that microbes occupied substantial sequencing resources and misled the scaffold assembly. Compared with cefotaxime, treatment with timentin performed better in cultivating robust in vitro plantlets. The survey result from the in vitro plantlets was more accurate due to low levels of contamination. The genome size was estimated to be 1.80 Gb with 0.50% heterozygosity and 78.35% repetitive rate. Additionally, 289,831 SSRs were identified in the genome. The genome is heavily contaminated and repetitive; therefore, the in vitro culture technique and long-read sequencing technology are recommended to generate a high-quality and highly contiguous genome.
Peroxide crosslinking of rigid poly(vinyl chloride)

The optimum conditions for crosslinking rigid poly(vinyl chloride) with trimethylolpropane trimethacrylate (TMPTMA) and peroxide have been examined. The extent of crosslinking was measured by determining gel content by Soxhlet extraction in tetrahydrofuran. Mechanical properties were measured at 130°C and dynamic viscoelastic measurements were carried out to detect changes in the glass transition temperature (Tg). It was found that 15 phr of TMPTMA and 0.3 phr of peroxide were optimum concentrations for maximizing the extent of crosslinking, tensile strength, and Tg. The lower molding temperature of 170°C was preferred to minimize thermal degradation. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2904–2909, 2007
GhoMR: Multi-Receptive Lightweight Residual Modules for Hyperspectral Classification

In recent years, hyperspectral images (HSIs) have attained considerable attention in computer vision (CV) due to their wide utility in remote sensing. Unlike images with three or lesser channels, HSIs have a large number of spectral bands. Recent works demonstrate the use of modern deep learning based CV techniques like convolutional neural networks (CNNs) for analyzing HSI. CNNs have receptive fields (RFs) fueled by learnable weights, which are trained to extract useful features from images. In this work, a novel multi-receptive CNN module called GhoMR is proposed for HSI classification. GhoMR utilizes blocks containing several RFs, extracting features in a residual fashion. Each RF extracts features which are used by other RFs to extract more complex features in a hierarchical manner. However, the higher the number of RFs, the greater the associated weights, thus heavier is the network. Most complex architectures suffer from this shortcoming. To tackle this, the recently found Ghost module is used as the basic building unit. Ghost modules address the feature redundancy in CNNs by extracting only limited features and performing cheap transformations on them, thus reducing the overall parameters in the network. To test the discriminative potential of GhoMR, a simple network called GhoMR-Net is constructed using GhoMR modules, and experiments are performed on three public HSI data sets—Indian Pines, University of Pavia, and Salinas Scene. The classification performance is measured using three metrics—overall accuracy (OA), Kappa coefficient (Kappa), and average accuracy (AA). Comparisons with ten state-of-the-art architectures are shown to demonstrate the effectiveness of the method further. Although lightweight, the proposed GhoMR-Net provides comparable or better performance than other networks. The PyTorch code for this study is made available at the iamarijit/GhoMR GitHub repository.
Characterization of In Vitro 3D Cell Model Developed from Human Hepatocellular Carcinoma (HepG2) Cell Line

In genetic toxicology, there is a trend against the increased use of in vivo models as highlighted by the 3R strategy, thus encouraging the development and implementation of alternative models. Two-dimensional (2D) hepatic cell models, which are generally used for studying the adverse effects of chemicals and consumer products, are prone to giving misleading results. On the other hand, newly developed hepatic three-dimensional (3D) cell models provide an attractive alternative, which, due to improved cell interactions and a higher level of liver-specific functions, including metabolic enzymes, reflect in vivo conditions more accurately. We developed an in vitro 3D cell model from the human hepatocellular carcinoma (HepG2) cell line. The spheroids were cultured under static conditions and characterised by monitoring their growth, morphology, and cell viability during the time of cultivation. A time-dependent suppression of cell division was observed. Cell cycle analysis showed time-dependent accumulation of cells in the G0/G1 phase. Moreover, time-dependent downregulation of proliferation markers was shown at the mRNA level. Genes encoding hepatic markers, metabolic phase I/II enzymes, were time-dependently deregulated compared to monolayers. New knowledge on the characteristics of the 3D cell model is of great importance for its further development and application in the safety assessment of chemicals, food products, and complex mixtures.
Rhodamine B Doped ZnO Monodisperse Microcapsules: Droplet-Based Synthesis, Dynamics and Self-Organization of ZnO Nanoparticles and Dye Molecules

In the present work, droplet-based microfluidics and sol-gel techniques were combined to synthesize highly monodisperse zinc oxide (ZnO) microspheres, which can be doped easily and precisely with dyes, such as rhodamine B (RhB), and whose size can be finely tuned in the 10–30 μm range. The as-synthesized microparticles were analyzed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and confocal microscopy. The results reveal that the microspheres exhibit an excellent size monodispersity, hollow feature, and a porous shell with a thickness of about 0.6 μm, in good agreement with our calculations. We show in particular by means of fluorescence recovery after photobleaching (FRAP) analysis that the electric charges carried by ZnO nanoparticles primary units play a crucial role not just in the formation and structure of the synthesized ZnO microcapsules, but also in the confinement of dye molecules inside the microcapsules despite a demonstrated porosity of their shell in regards to the solvent (oil). Our results enable also the measurement of the diffusion coefficient of RhB molecules inside the microcapsules (DRhB=3.8×10−8 cm2/s), which is found two order of magnitude smaller than the literature value. We attribute such feature to a strong interaction between dye molecules and the electrical charges carried by ZnO nanoparticles. These results are important for potential applications in micro-thermometry (as shown recently in our previous study), photovoltaics, or photonics such as whispering gallery mode resonances.
Sex difference in the mouse BAT transcriptome reveals a role of progesterone.

Brown adipose tissue (BAT) is a metabolically active organ that exhibits sex-differential features, i.e., being generally more abundant and active in females than in males. Although sex steroids, particularly estrogens, have been shown to regulate BAT thermogenic function, the underlying molecular mechanisms contributing to sexual dimorphism in basal BAT activity have not been elucidated. Therefore, we assessed the transcriptome of interscapular BAT of male and female C57BL/6J mice by RNA sequencing and identified 295 genes showing ≥2-fold differential expression (adjusted P<0.05). In silico functional annotation clustering suggested an enrichment of genes encoding proteins involved in cell-cell contact, interaction, and adhesion. Ovariectomy reduced the expression of these genes in female BAT towards a male pattern whereas orchiectomy had marginal effects on the transcriptional pattern, indicating a prominent role of female gonadal hormones in this sex-differential expression pattern. Progesterone was identified as a possible upstream regulator of the sex-differentially expressed genes. Studying direct effects of progesterone in vitro in primary adipocytes showed that progesterone significantly altered the transcription of several of the identified genes, possibly via the glucocorticoid receptor. In conclusion, this study reveals a sexually dimorphic transcription profile in murine BAT at general housing conditions and demonstrates a role for progesterone in the regulation of the interscapular BAT transcriptome.
Bio- and Biomimetic Receptors for Electrochemical Sensing of Heavy Metal Ions

Heavy metals ions (HMI), if not properly handled, used and disposed, are a hazard for the ecosystem and pose serious risks for human health. They are counted among the most common environmental pollutants, mainly originating from anthropogenic sources, such as agricultural, industrial and/or domestic effluents, atmospheric emissions, etc. To face this issue, it is necessary not only to determine the origin, distribution and the concentration of HMI but also to rapidly (possibly in real-time) monitor their concentration levels in situ. Therefore, portable, low-cost and high performing analytical tools are urgently needed. Even though in the last decades many analytical tools and methodologies have been designed to this aim, there are still several open challenges. Compared with the traditional analytical techniques, such as atomic absorption/emission spectroscopy, inductively coupled plasma mass spectrometry and/or high-performance liquid chromatography coupled with electrochemical or UV–VIS detectors, bio- and biomimetic electrochemical sensors provide high sensitivity, selectivity and rapid responses within portable and user-friendly devices. In this review, the advances in HMI sensing in the last five years (2016–2020) are addressed. Key examples of bio and biomimetic electrochemical, impedimetric and electrochemiluminescence-based sensors for Hg2+, Cu2+, Pb2+, Cd2+, Cr6+, Zn2+ and Tl+ are described and discussed.