Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular mechanism of a large conformational change of the quinone cofactor in the semiquinone intermediate of bacterial copper amine oxidase

Copper amine oxidase from Arthrobacter globiformis (AGAO) catalyses the oxidative deamination of primary amines via a large conformational change of a topaquinone (TPQ) cofactor during the semiquinone formation step. This conformational change of TPQ occurs in the presence of strong hydrogen bonds and neighboring bulky amino acids, especially the conserved Asn381, which restricts TPQ conformational changes over the catalytic cycle. Whether such a semiquinone intermediate is catalytically active or inert has been a matter of debate in copper amine oxidases. Here, we show that the reaction rate of the Asn381Ala mutant decreases 160-fold, and the X-ray crystal structures of the mutant reveals a TPQ-flipped conformation in both the oxidized and reduced states, preceding semiquinone formation. Our hybrid quantum mechanics/molecular mechanics (QM/MM) simulations show that the TPQ conformational change is realized through the sequential steps of the TPQ ring-rotation and slide. We determine that the bulky side chain of Asn381 hinders the undesired TPQ ring-rotation in the oxidized form, favoring the TPQ ring-rotation in reduced TPQ by a further stabilization leading to the TPQ semiquinone form. The acquired conformational flexibility of TPQ semiquinone promotes a high reactivity of Cu(i) to O2, suggesting that the semiquinone form is catalytically active for the subsequent oxidative half-reaction in AGAO. The ingenious molecular mechanism exerted by TPQ to achieve the “state-specific” reaction sheds new light on a drastic environmental transformation around the catalytic center.
Memetic algorithm for the dynamic vehicle routing problem with simultaneous delivery and pickup

In recent years, the Vehicle Routing Problem (VRP) has become an important issue for distribution companies. Also, the rapid development of communication means and the appearance of reverse logistics have given rise to new variants of the VRP. This article deals with an important variant of the VRP which is Dynamic Vehicle Routing Problem with Simultaneous Delivery and Pickup (DVRPSDP), in which new customers appear during the working day and each customer requires simultaneous delivery and pickup. A Memetic Algorithm (MA) that combines Genetic Algorithm (GA) and local search procedure have been proposed to solve the problem. The performance of the algorithm is evaluated with the tests carried out on a set of benchmarks found in the literature. The proposed memetic algorithm is very efficient and gives many good solutions.
SIRT1/FOXO1 Axis-Mediated Hippocampal Angiogenesis is Involved in the Antidepressant Effect of Chaihu Shugan San

Objective Chaihu Shugan San (CSS) has a long history for treating major depressive disorder (MDD), which has been verified effectively and safely in clinical studies. Deficient angiogenesis plays important roles in MDD. However, the underlying mechanisms of CSS on angiogenesis remain poorly understood. Methods Network pharmacology analysis was applied to explore the potential angiogenic targets and pathways between CSS and MDD. These targets would be validated in chronic unpredictable mild stress (CUMS)-induced depressive-like mice by Western blots, immunofluorescence, and immunohistochemistry. Then, the underlying molecular mechanisms were further investigated in brain microvascular endothelial cells (BMVECs) with CSS-containing serum by Western blots and immunofluorescence. Results Network pharmacology analysis showed that the antidepressant role of CSS was closely associated with Silent information regulator protein 1 (SIRT1)/Forkhead box O1 (FOXO1) axis-mediated angiogenesis. This prediction was confirmed in the following experiments. CSS induced angiogenesis, increased SIRT1 expression, and decreased FOXO1 expression in the hippocampus of CUMS mice. Five percent CSS-containing serum produced a significant increase in BMVECs proliferation, migration, and tube formation, but these effects were reduced by SIRT1 silencing. CSS serum could also promote FOXO1 translocation to the cytoplasm through SIRT1 signaling, which triggered FOXO1 protein degradation. What is more, CSS upregulated VEGFA and BDNF expressions not only in the hippocampus of depressive mice but also in BMVECs supernatants. Of note, these trophic factors play important roles in promoting neurogenesis. Conclusion The study showed that CSS could promote angiogenesis and neurogenesis in the hippocampus of CUMS-induced mice. The underlying molecular mechanism involves the SIRT1/FOXO1 axis and subsequent regulation of VEGFA and BDNF. These findings provide novel insight into CSS drug development, and targeting the SIRT1/FOXO1 axis might be a promising strategy to treat MDD.
Interaction between Phage T4 Protein RIII and Host Ribosomal Protein S1 Inhibits Endoribonuclease RegB Activation

Lytic viruses of bacteria (bacteriophages, phages) are intracellular parasites that take over hosts’ biosynthetic processes for their propagation. Most of the knowledge on the host hijacking mechanisms has come from the studies of the lytic phage T4, which infects Escherichia coli. The integrity of T4 development is achieved by strict control over the host and phage processes and by adjusting them to the changing infection conditions. In this study, using in vitro and in vivo biochemical methods, we detected the direct interaction between the T4 protein RIII and ribosomal protein S1 of the host. Protein RIII is known as a cytoplasmic antiholin, which plays a role in the lysis inhibition function of T4. However, our results show that RIII also acts as a viral effector protein mainly targeting S1 RNA-binding domains that are central for all the activities of this multifunctional protein. We confirm that the S1–RIII interaction prevents the S1-dependent activation of endoribonuclease RegB. In addition, we propose that by modulating the multiple processes mediated by S1, RIII could act as a regulator of all stages of T4 infection including the lysis inhibition state.
Stability of Non-Concentric, Multilayer, and Fully Aligned Porous MoS2 Nanotubes

Nanotubes made of non-concentric and multiple small layers of porous MoS2 contain inner pores suitable for membrane applications. In this study, molecular dynamics simulations using reactive potentials were employed to estimate the stability of the nanotubes and how their stability compares to macroscopic single- (1L) and double-layer MoS2 flakes. The observed stability was explained in terms of several analyses that focused on the size of the area of full-covered layers, number of layers, polytype, and size of the holes in the 1L flakes. The reactive potential used in this work reproduced experimental results that have been previously reported, including the small dependency of the stability on the polytype, the formation of S–S bonds between inter- and intra-planes, and the limit of stability for two concentric rings forming a single ring-like flake.
Conservative numerical schemes for high-frequency wave propagation in heterogeneous media

The present work focuses on the numerical resolution of the acoustic or elastic wave equation in a piece-wise homogeneous medium, splitted by interfaces. We are interested in a high-frequency setting, introduced by strongly oscillating initial conditions, for which one computes the distribution of the energy density by a so-called kinetic approach (based on the use of a Wigner transform). This problem then reduces to a Liouville-type transport equation in a piece-wise homogeneous medium, supplemented by reflection and transmission laws at the interfaces. Several numerical techniques and ranges of application are also reviewed. The transport equation which describes the evolution of the energy density in the phase space positions _ wave vectors is numerically solved by finite differences. This technique raises several difficulties related to the conservation of the total energy in the medium and at the interfaces. They may be alleviated by dedicated numerical schemes allowing to reduce the numerical dissipation by either a global or a local approach. The improvements presented in this thesis concern the interpolation of the energy densities obtained by transmission on the grid of discrete wave vectors, and the correction of the difference of variation scales of the wave celerity on each side of the interfaces. The interest of the foregoing developments is to obtain conservative schemes that also satisfy the usual convergence properties of finite difference methods. The construction of such schemes and their effective implementation constitute the main achievement of the thesis. The relevance of the proposed methods is illustrated by several numerical simulations, that also emphasize their efficiency for rather coarse meshes.
Hallmarks of the Mott-Metal Crossover in the Hole Doped J=1/2 Mott insulator Sr2IrO4

The physics of doped Mott insulators remains controversial after decades of active research, hindered by the interplay among possible competing orders and fluctuations. It is thus highly desired to distinguish the intrinsic characters of the Mott-metal crossover from those of other origins. We investigate the evolution of electronic structure and dynamics of the hole-doped J=1/2 Mott insulator Sr2IrO4. The effective hole doping is achieved by replacing Ir with Rh atoms, with the chemical potential immediately jumping to or near the top of the lower Hubbard band. The doped iridates exhibit multiple exotic features previously observed in doped cuprates - pseudogaps, Fermi "arcs", and marginal-Fermi-liquid-like electronic scattering rates, despite different mechanisms that forbid electron double-occupancy. We argue these universal features of the Mott-metal crossover are not related to preformed electron pairing, quantum criticality or density-wave formation as most commonly discussed. Instead, short-range antiferromagnetic correlations may play an indispensible role.
Research on wear of Ni-Cr alloy milling based on residual network

With the development of the manufacturing industry and information technology, the quality requirements of products are getting higher and higher. A cutting tool is one of the important factors affecting product quality, so it is of great significance to study cutting tool wear. In this paper, the influence of Ni-Cr alloy on milling cutter wear was studied. Deep learning is widely used in the neighborhood of signal recognition. In this paper, a convolution neural network with residual structure is proposed to classify the wear state of cutting tools. The input of the model is the collected vibration signal, and the output is the classification of tool wear. A convolution neural network can automatically extract the characteristics of signals and identify different types of wear signals. The experimental results show that the convolution neural network with residual structure can converge faster and have higher accuracy than the traditional convolution neural network and the accuracy of tool wear classification is about 98.5%. The loss rate of the model is only about 0.25%.
Bright solitons in a quasi-one-dimensional dipolar Bose-Einstein condensate

The ultracold atomic gases have provided an important environment for studying quantum many-particle systems in the last two decades. In 2005 the experimental realization of a 52Cr Bose-Einstein condensate with inter-atomic magnetic dipole dipole interaction opened the door to a new level in the research of degenerate quantum gases. As opposed to the usual contact interaction, this new interaction is long-range and anisotropic being partially repulsive or attractive. In the mean-field approximation initially are introduced the main issues about non-dipolar condensates with particular interest in the attractive regime (a < 0) where is possible the formation of bright solitons and the existence of instability by collapse beyond a certain critical value. The study is carried out mainly using a numerical method and a variational one. Later, the dipolar Bose-Einstein condensate is depicted by means of the non-local Gross-Pitaevskii equation. From the non-dipolar scenario by means of the extension in the numerical and the variational method is determined the formation of bright solitons in the GPE in the three-dimensional model and the quasi-one-dimensional to three different dipolar condensates of experimental relevance, namely 52Cr, 168Er and 164Dy. Plots of chemical potential and rms sizes of solitons are obtained. Finally, it is studied collision dynamics of two bright solitons in the quasi-1D dipolar model of every condensate above.
Silicon Photonics Sensors for Biophotonic Applications—A Review

In the last two decades, biosensors have made a remarkable development in the area of food quality monitoring, drug discovery, environmental monitoring, soil quality monitoring, the healthcare industry, and medical point of care diagnosis. The integrated silicon photonic-based biosensors using the evanescent wave (EW) field and their advantages in terms of high sensitivity, high reliability, compactness, robustness, and miniaturization are discussed in this review. Specifically, different silicon photonic device configurations based on the EW field, such as interferometer-, resonator-, photonic crystal-, and Bragg grating-based biosensors, are discussed in detail. Moreover, various performance improvement strategies, such as advance and fundamental approaches, including Vernier effect-based systems in the cascaded form of the Mach–Zehnder interferometer (MZI)-ring resonator, are also reviewed. This article further reviews the usage of silicon-photonic biosensors for lab-on-chip (LOC) applications, including optoelectronic integrated circuits (OEICs). The state-of-the-art performance of different biosensors technologies is also comparatively discussed.
Assessment of the Causes and Consequeses of School Dropouts among Government Secondary School Students in Owerri Municipal Council of Imo State

Students are supposed to spend a particular number of years before graduation from school. When a student stops his attendance of the school before the time leading to the completion of the programme, he has dropout. The problem of school dropouts among secondary school students in Nigeria has become a thing of great concern to educationist in the country and the society at large. This study is, therefore, aimed at finding out the Causes and Consequence of School Dropout among Government Secondary Schools in Owerri Municipal Council of Imo Sate. In this study, descriptive survey was used in carrying out the research. Four research questions were formulated. Questionnaire was used as the instrument to collect information for the study. A sample size of 689 students drawn from 4 selected secondary schools was used as the respondents. The data collected were analysed with descriptive data using mean. The findings revealed that the causes of school dropout are as follows: high cost of education, peer influence, early marriage and pregnancies of unmarried girls and the quest to get rich quick. It also revealed that dropping out of school results to inferiority complex, engagement in antisocial behaviours, low ambition, marital discord and decline in national economic advancement. To stop this problem of school dropout, the researchers therefore recommended that the government should reduce the cost of education, parents should endeavour to carry out their responsibilities on their children, educational system should be well administered and guidance counsellors employed in secondary schools.
Policy framework for implementation of a circular economy in Croatia: Past, present, and future

This work aims to shed light on the evolution of the national policy framework in Croatia in its transition to circular economy (CE), and provide a better understanding of how Croatia stands in its transition to CE. The methodology employed in this research involves a systematic analysis of policy programmes, initiatives, and actions regarding Croatia’s transition to CE since 1991. This has resulted in a list of 31 policy documents, mainly laws, action plans, and policy proposals, grouped according to the eight components of the European Green Deal. Institutional stakeholders, funding opportunities, and CE performance are also discussed in order to complement the descriptions of the policy framework. The main contribution of this research is the insight it offers into the history of CE policy development in Croatia, which can be roughly divided into four phases, starting from environmental protection in the 1990s and moving towards a more dedicated approach to CE that recently emerged in policy agendas during 2019 and 2020, mostly due to European initiatives and regulations. Progress is expected to emerge as a result of external factors, primarily European programmes such as the Recovery and Resilience Facility (RRF) and Just Transition Mechanism (JTM), which demand clearly defined priorities and elaborated projects.
Generative Adversarial Networks Under CutMix Transformations for Multimodal Change Detection

The current technological developments lead to increased heterogeneity and variability in remote sensing imagery. In this context, unsupervised multimodal change detection techniques are mandatory to perform a continuous monitoring and rapid damage assessment by means of heterogeneous remote sensing data. Taking advantage of the latest advances in deep learning, we address multimodal change detection from an intermodality image translation perspective. Intermodality translation is achieved by means of generative adversarial networks (GANs) built over U-Net architectures at both generator and discriminator levels and trained under CutMix transformations. A change prior is used to guide the learning process of the neural network framework and to reduce the impact of changed locations over the learned model. The change prior is derived in an unsupervised manner from comparisons between the postevent locations and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN) locations determined in the preevent image. The experiments were conducted over several pairs of heterogeneous remote sensing images, and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed multimodal change detection framework.
Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network

: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder relating to speech complications, nonverbal and social communication, and repetitive behaviors. There is no remedy for ASD but early diagnosis, mediation, and supportive care can aid the development of language, conduct, and communication skills. As the cause of ASD is a neurodevelopmental disorder, its diagnosis based on brain function analyzing different brain signals, especially Electroencephalography (EEG), has drawn attention recently. Brain activity is recorded over time as an EEG signal from the scalp of a human and is used to investigate complicated neuropsychiatric disorders in the brain. In this study, the data from the EEG channels are translated into two-Dimensional (2D) form through correlation, and classification is performed using Convolutional Neural Networks (CNN), the well-known deep learning method for image analysis and classification. Two different CNN models are considered for classification purposes: Generic CNN and Residual Network (ResNet), a well-known deep CNN model. The proposed method with Resnet achieved 88% classification accuracy on a five-fold cross-validation mode, whereas it was 100 on 20% of test samples. Experimental evaluations using clinical EEG data revealed the efficacy of the proposed method outperforming other existing methods.
Health literacy dimensions among public health service users with chronic diseases in Piracicaba, Brazil, 2019

Aim: This study analyzes factors associated with dimensions of health literacy (HL) functional, communicative and critical among public health service users with chronic non-communicable diseases. Methods: A cross-sectional analytical research was carried out in Piracicaba, São Paulo, Brazil, with adults and older adults attending Family Health Units (FHU). Data were collected by oral exam (CPOD and CPI) and a questionnaire on systemic conditions, sociodemographic factors, health behaviors and HLS (HLS-14). The outcomes consisted of functional, communicative, and critical HL dimensions dichotomized by median (high and low), which were analyzed by chi-square test (p<0.05) to find associations with the variables studied. Results: The study sample comprised 238 FHU users with 62.7 (± 10.55) mean age, of which 47.5% (n=113) showed high functional HL, 50.0% (n=119) high communicative HL, and 46.2% (n=110) high critical HL. High functional HL was associated with men (p<0.05). Functional and communicative HL were associated with having higher education (p<0.001 and p=0.018, respectively). High communicative and critical HL were associated with regular use of dental and medical services (p<0.05). Individuals with low functional HL were more likely to present poor tooth brushing (p=0.020). High HL (in all three dimensions) was associated with regular flossing and having more teeth (p<0.05). Conclusion: Functional, communicative and critical HL were associated with health behaviors and clinical outcomes, whereas the functional dimension was also associated with sociodemographic factors. HL dimensions allowed to differentiate health-related factors.
Improved maps of surface water bodies, large dams, reservoirs, and lakes in China

Abstract. Data and knowledge of surface water bodies (SWB), including large lakes and reservoirs (surface water areas > 1 km2), are critical for the management and sustainability of water resources. However, the existing global or national dam datasets have large georeferenced coordinate offsets for many reservoirs, and some datasets have not reported reservoirs and lakes separately. In this study, we generated China's surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19 338 images) in Google Earth Engine and very-high spatial resolution imagery in Google Earth Pro. There were ∼ 3.52 × 106 yearlong SWB polygons in China for 2019, only 0.01 × 106 of them (0.43 %) were of large size (> 1 km2). The areas of these large SWB polygons accounted for 83.54 % of the total 214.92 × 103 km2 yearlong surface water area (SWA) in China. We identified 2418 large dams, including 624 off-stream dams and 1794 on-stream dams, 2194 large reservoirs (16.35 × 103 km2), and 3051 large lakes (73.38 × 103 km2). In general, most of the dams and reservoirs in China were distributed in South China, East China, and Northeast China, whereas most of lakes were located in West China, the lower Yangtze River basin, and Northeast China. The provision of the reliable, accurate China-LDRL dataset on large reservoirs/dams and lakes will enhance our understanding of water resources management and water security in China. The China-LDRL dataset is publicly available at https://doi.org/10.6084/m9.figshare. 16964656.v3 (Wang et al., 2021b).