Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute and chronic psychosocial stress by the brain-derived neurotrophic factor in male humans: a highly standardized and controlled study

Objective: The neurotrophic protein brain-derived neurotrophic factor (BDNF) plays a pivotal role in brain function and is affected by acute and chronic stress. We here investigate the patterns of BDNF and cortisol stress reactivity and recovery under the standardized stress protocol of the TSST and the effect of perceived chronic stress on the basal BDNF levels in healthy young men. Methods: Twenty-nine lean young men underwent the Trier Social Stress Test (TSST) and a resting condition. Serum BDNF and cortisol were measured before and repeatedly after both conditions. The perception of chronic stress was assessed by the Trier Inventory for Chronic Stress (TICS). Results: After the TSST, there was a significant increase over time for BDNF and cortisol. Stronger increase in cortisol in response to stress was linked to an accelerated BDNF decline after stress. Basal resting levels of BDNF was significantly predicted by chronic stress perception. Conclusions: The increased BDNF level following psychosocial stress suggest a stress-induced neuroprotective mechanism. The presumed interplay between BDNF and the HPA-axis indicates an antagonistic relationship of cortisol on BDNF recovery post-stress. Chronically elevated high cortisol levels, as present in chronic stress, could thereby contribute to reduced neurogenesis, and an increased risk of neurodegenerative conditions in persons suffering from chronic stress.
Fusion of Visible and Infrared Images Using Efficient Online Convolutional Dictionary Learning

To balance fusion performance and time consumption, this paper proposes a fast and effective infrared and visible fusion method using online convolutional dictionary learning (OCDL). First, source images are decomposed into low-pass layers and a detail layers using low-pass filtering technique. Then low-pass fused layer is obtained using ‘average’ rule. The dictionary trained on Flicker-large dataset using OCDL is applied to detail layer fusion, which uses ADMM-based CSC and ‘maximum’ strategy to calculate fused high-pass components. Finally, fused image is reconstructed based on fused low-pass components and high-pass components. The subjective results and objective metrics of the infrared-visible image fusion experiments show that our designed fusion method ensures quality of fused images and outperforms advanced online convolutional sparse coding (OCSC) based fusion methods in terms of computational cost and memory requirements. Our method averagely increases objective metrics (Cross entropy, NMI, PSNR, NABF and CC) by 13.66%, 6.42%, 0.03%, 74.77% and 0.21% compared to OCSC. Meanwhile, our method improved 10.1% beyond OCSC in terms of time consumption.
Molecular screening reveals Variolin B as a multitargeted inhibitor of lung cancer: a molecular docking-based fingerprinting and molecular dynamics simulation study

Abstract Lung Cancer is the topmost death causing cancer and results from smoking, air pollution, cigar, exposure to asbestos or radon-like substances, and genetic factors. The cases of Lung Cancer in south Asian developing nations are being seen most due to heavy pollution and unbalanced lifestyle and putting a considerable burden on healthcare systems. The Food and Drug Administration of the USA has approved almost 100 drugs against SCLC and NSLC and a few drugs that are given to minimise the side effect of anticancer drugs. However, the drugs are shown to be resistant at significantly higher stages and non-affective on cancerous cells and have long-term side effects due to designing the drug by keeping one protein/gene target while designing or repurposing the drugs. In this study, we have taken five main lung cancer protein targets- Nerve growth factor protein (1SG1), Apoptosis inhibitor survivin (1XOX), Heat shock protein (3IUC), Protein tyrosine phosphate (3ZM3), Aldo-keto reductase (4XZL) and screened the complete prepared Drug Bank library of 155888 compounds and identified Variolin B (DB08694) as a multitargeted inhibitor against lung cancer using HTVS, SP and XP sampling algorithms followed by MM\GBSA calculation to sort the best pose. Variolin B is a natural marine antitumor and antiviral compound, so we analysed the ADMET properties and interaction patterns and then simulated all five P-L complexes for 100 ns in water using the NPT ensemble to check its selves against lung cancer. The docking results, ADMET and fingerprints have shown a good performance, and RMSD and RMSF results were with least deviation and fluctuations (<2Å) and produced a huge contact with other residues making the complex stable. The complexes initially fluctuated and deviated due to changes in the solute medium and sudden heat and stabilise after a few ns. However, extensive experimental validation is required before human use. Communicated by Ramaswamy H. Sarma
The activation of SIRT1 by resveratrol reduces breast cancer metastasis to lung through inhibiting neutrophil extracellular traps

Abstract Neutrophil extracellular traps (NETs) play a crucial role in breast cancer metastasis. However, the therapeutic target of NETs in breast cancer metastasis is still unknown. Using a natural metabolite library and single-cell sequencing data analysis, we identified resveratrol (RES), a polyphenolic natural phytoalexin, and agonist of silent information regulator-1 (SIRT1) that suppressed NETs formation after cathepsin C (CTSC) treatment. In vivo, RES significantly hindered breast cancer metastasis in a murine orthotopic 4T1 breast cancer model. Serum levels of myeloperoxidase-DNA and neutrophil elastase-DNA in mouse breast cancer model were significantly lower after RES treatment. Correspondingly, the tumour infiltrated CD8+T cells in the lungs increased after the treatment. Mechanistically, RES targets SIRT1 in neutrophils and significantly inhibits the citrullination of histones H3, which is essential for chromatin decondensation and NETs formation. Furthermore, we identified that the NETs were suppressed by RES in bone marrow neutrophils after CTSC treatment, while specific deficiency of SIRT1 in neutrophils promoted NETs formation and breast cancer to lung metastasis. Thus, our results revealed that RES could be potentially identified as a viable therapeutic drug to prevent neutrophil cell death and breast cancer metastasis. Graphical Abstract
Necessary Optimality Conditions for Optimization Problems with Variational Inequality Constraints

In this paper we study optimization problems with variational inequality constraints in finite dimensional spaces. Kuhn-Tucker type necessary optimality conditions involving coderivatives are given under certain constraint qualifications including one that ensures nonexistence of non-trivial abnormal multipliers. The result is applied to bilevel programming problems to obtain Kuhn-Tucker type necessary optimality conditions. The Kuhn-Tucker type necessary optimality conditions are shown to be satisfied without any constraint qualification by the class of bilevel programming problems where the lower level is a parametric linear quadratic problem.
SELEÇÃO DE PLANTAS QUANTIFICADORAS DE HERBICIDAS RESIDUAIS

The determination of the concentration of compounds in the soil through plants quantifiers show as the main advantage to detect only biologically active residues, no need for expensive instruments and previous extraction of residues from the soil. Thus, this work aims to select indicator plants the presence of residual herbicides for use in bioassays. The experimental design used was the completely randomized, using the bifactorial arrangement 8x6, with five repetitions. The factor A consisted of the crops and factor B consisted of the preemergence herbicides application. The results show that the sensibility in the detection of herbicide in soil depends on the crop used. The sensibility of species Lactuca sativa L. and Raphanus sativus var. sativus L. don’t permit conditions to quantify the presence of herbicides atrazine, cloransulam, imazaquin, metribuzin and S-metolachlor. Raphanus sativus var. oleiferus Metzger is potencial quantified of imazaquin and S-metolachlor. Plants of Curcubita pepo L. are promising in the bioevaluation of metribuzin. The specie Cucumis sativus L. is potential bioindicator of cloransulan and imazaquin. Avena sativa is potential to quantify imazaquin and metribuzin. Hordeum vulgare L. can quantify metribuzin and Triticum aestivum L. is promising in the detection of atrazine bioavailability
Research on information quantity of urban traffic guide signs based on wayfinding

Based on the statistics of guide signs in 31 provincial capitals in China, this paper analyzes the factors that affects the quantity of information on guide signs, and proposes a real-time driving simulation based cognitive time test method for guide signs; establishes a driving simulation test platform and compiles road test scenarios with different amount of sign information; studies and tests the influence of multi-objective road name information on drivers' cognitive time. At the same time, the effects of speed and the number of word of road name on cognitive time were also tested. The results show that: the information quantity of 7 on a guide sign information board is the most suitable for cognitive needs; the factors influencing the search time of target road name from high to low are number of word of each road name, number of road names on each board and speed.
Effect of flow rate on clogging processes in small diameter aquifer storage and recovery injection wells

This study investigates injection well clogging at low injection rates (<0.38 m 3 / min or 100 gpm) through laboratory column experiments and geochemical analyses. Clogging in recharge wells is a major concern for aquifer storage and recovery (ASR) systems. Many of the current methods used to predict injection well clogging assume a higher injection rate. These methods may not adequately identify the clogging processes occurring at low flow rates. An ongoing project at the Kansas Geological Survey (KGS) investigates a low-cost, slow flow alternative to traditional ASR recharge systems. The project utilizes gravity-induced recharge and small diameter wells installed with direct-push technology to recharge and store ground water. The KGS ASR recharge system will have significantly lower injection rates than traditional ASR wells due to increased frictional losses in small-diameter wells and the absence of injection pumps. To examine clogging processes at low flow rates, laboratory columns packed with sand and gravel cores, taken from the Pleistocene Belleville formation during direct-push well installation at the Lower Republican River field site, were used for clogging experiments. Changes in geochemistry and hydraulic conductivity were monitored in three column sets running at flow rates of 1 m/day, 2 m/day, and 3 m/day, for seventeen days. Each set contains one column each of native aquifer water and one column of treated surface water, respectively. Hydraulic conductivity for all columns in this experiment increased above initial levels by at least one order of magnitude. Columns with the highest pumping speed had the greatest increase in hydraulic conductivity, suggesting sufficient pressure existed to mobilize clay particles and remove them from the iv columns. Experimental results and geochemical analyses indicate clay dispersion is the primary factor influencing hydraulic conductivity changes at the Republic ASR site. Observations of biofilm in effluent tubing suggest bacterial clogging of medium and low flow treated surface water columns, but bacterial abundance was not quantified in this study. Further understanding of clogging factors at low flow rates will aid in the selection of the most beneficial redevelopment and pretreatment methods for slow recharge ASR systems.
Ensemble prediction and intercomparison analysis of GRACE time‐variable gravity field models

Precise measurements of the Earth's time‐varying gravitational field from the NASA/Deutsches Zentrum für Luft‐ und Raumfahrt Gravity Recovery and Climate Experiment (GRACE) mission allow unprecedented tracking of the transport of mass across and underneath the surface of the Earth and give insight into secular, seasonal, and subseasonal variations in the global water supply. Several groups produce these estimates, and while the various gravity fields are similar, differences in processing strategies and tuning parameters result in solutions with regionally specific variations and error patterns. This study examined the spatial, temporal, and spectral variations between the different gravity field products and developed an ensemble gravity field solution from the products of four such analysis centers. The solutions were found to lie within a certain analysis scatter regardless of the local relative water height variation, and the ensemble model is clearly seen to reduce the noise in the gravity field solutions within the available scatter of the solutions.
Soil C:N ratio as a predictor of annual riverine DOC flux at local and global scales

Dissolved organic carbon (DOC) is important in a wide variety of chemical, physical, and biological processes in surface waters. We examined the relationship between DOC flux and soil C:N ratio on a biome basis. DOC fluxes for 164 rivers were subdivided into 15 biome types including tropical rain forest, coniferous forests, peatland, deciduous forests, mixed forests, and grasslands. A database of soil C:N ratios was constructed and subdivided into biome types. At a global scale, mean soil C:N ratio of a biome accounts for 99.2% of the variance in annual riverine DOC flux among biomes. The relationship between soil C:N ratio and DOC flux at the biome scale was used to predict annual riverine DOC flux at the watershed scale for three test watersheds not included in the original model. Predicted flux of each watershed was within 4.5% of the actual DOC flux. Using the C:N model, we estimated the total export of carbon from land to the oceans to be 3.6×1014 g yr−1. This empirical model should be useful in predicting changes in DOC flux under changing climatic conditions.
Attention-LSTM-Based Prediction Model of PM2.5 Hourly Concentration

As an important indicator to measure the concentration of air pollutants, the monitoring and prediction of PM2.5 concentration can effectively protect the atmospheric environment and further reduce the harm caused by air pollution. With the large-scale establishment of automatic air quality monitoring stations, the air quality prediction model built by traditional machine learning can no longer meet the current needs. In this paper, an attention-LSTM model based on Long Short Term Memory network (LSTM) and multi-head attention mechanism is proposed to predict PM2.5 hourly concentration, and the data from a monitoring station in Shenyang are trained and tested. This model takes into account the influence of PM2.5 concentration on other air quality data, uses the two-layer LSTM network to capture the complex time series correlation features between air quality data, and uses the multi-head attention mechanism to extract the time series correlation features of different subspaces, which can obtain more complete and effective characteristic information. Experiments prove that compared with mainstream air quality prediction methods such as ARIMA, AutoEncoder and LSTM, the Attention-LSTM model proposed in this paper effectively improves the prediction accuracy and can accurately predict PM2.5 concentration.
Hoax Detection on Social Media with Convolutional Neural Network (CNN) and Support Vector Machine (SVM)

Hoax news has long been a problem for society that is quite worrying because receiving hoax news can change a person's point of view to something that is not good, the impact of which is detrimental to many individuals and groups of people. Machine learning and deep learning can be implemented to detect hoax news. Examples of methods used in previous studies are SVM (Support Vector Machine) and CNN (Convolutional Neural Network). This research proposes the application of the CNN and SVM methods. In addition, this research develops a CNN-SVM hybrid model, which is the uniqueness of this research. The dataset is sourced from Twitter which focuses on the Ferdy Sambo Case and the Kanjuruhan Tragedy that will occur in 2022. The dataset amounts to 25,325 and is divided into two with a splitting ratio of 90:10. After three algorithms was trained, they achieved excellent performance. This matter can be seen from the accuracy scores for the two methods, which managed to improve their performance after feature extraction and expansion were applied with TF-IDF (Term Frequency Inverse Document Frequency) feature extraction, unigram + bigram weighting, and feature expansion with GloVe (Global Vector for Word Representation). The highest performance model is the SVM model with the similarity top 1 and Tweet corpus (95.95% accuracy), followed by the hybrid CNN-SVM model with the similarity top 10 and Tweet + News corpus (95.79% accuracy) and CNN model with the similarity top 15 with Tweet + News corpus (95.11% accuracy).
An Entropy- and Attention-Based Feature Extraction and Selection Network for Multi-Target Coupling Scenarios

Sensor data analysis of machines' operating status plays essential roles in production and maintenance planning. Feature extraction and selection are critical procedures in sensor data analysis. However, existing methods for feature extraction and selection ignore the scenarios where multiple targets exist, and the obtained features cannot be fine-tuned to the specific target accordingly. A typical example of multiple targets is the remaining useful life (RUL) prediction under multiple failure modes of degraded machines. To address this issue, this paper develops an entropy- and attention-based feature extraction and selection network for multi-target coupling scenarios. Specifically, we proposed a multivariate symmetrical uncertainty-based filtering (MSUBF) algorithm to eliminate redundancy of originally extracted features by Tsfresh. Then, an attention-based joint learning network is proposed to automatically adapt features to different coupling targets. In a case study of degraded aircraft turbine engines with two coupling targets, i.e., failure mode classification and RUL prediction, the proposed method outperforms the benchmark methods that do not consider the interaction of coupling targets.
Learning Decision Variables in Many-Objective Optimization Problems

Traditional Multi-Objective Evolutionary Algorithms (MOEAs) have shown poor scalability in solving Many-Objective Optimization Problems (MaOPs). The use of machine learning techniques to enhance optimization algorithms applied to MaOPs has been drawing attention due to their ability to add domain knowledge during the search process. One method of this kind is inverse modeling, which uses machine learning models to enhance MOEAs differently, mapping the objective function values to the decision variables. The Decision Variable Learning (DVL) algorithm uses the inverse model in its concept and has shown good performance due to the ability to directly predict solutions closed to the Pareto-optimal front. The main goal of this work is to experimentally show the DVL as an optimization algorithm for MaOPs. Our results demonstrate that the DVL algorithm outperformed the NSGA-III, a well-known MOEA from the literature, in almost all scenarios with restriction on the number of objective functions with a high number of objectives.
Soil strength estimation and pore pressure dissipation for free-fall piezocone in soft clay

This paper considers centrifuge modelling of free-fall piezocones in soft clay, and interprets the test data to estimate the undrained shear strength and coefficient of consolidation. Data from the free-fall piezocones, which involved dynamic embedment in a normally consolidated kaolin clay sample from various drop heights, were compared with equivalent data from piezocone tests. The undrained shear strength, as interpreted from the acceleration trace of the free-fall piezocone, was found to be in good agreement with that derived from the piezocone penetration tests. The free-fall piezocone is also found to produce identical pore pressure dissipation behaviour as for a statically penetrated piezocone, despite a significant rise in the pore pressure at the start of the dissipation phase.
Weighted Non-Convex Penalty Minimization for Compressed Ultrasound Signal Reconstruction

The reconstruction of high quality images from a limited number of radio-frequency (RF) measurements is crucial for ultrafast plane wave ultrasound (PWUS) imaging system. The sparse and low rank model can efficiently exploit the correlation between different ultrasound channels. However, the reconstruction strategies derived from sparse models use convex relaxations of the matrix $l_{1}$ norm to induce sparsity, and convex approximations are widely known to result in biased estimators, which usually underestimates the coefficient amplitudes of RF signals in the frequency domain. In this work, we designed a weighted nonconvex penalty (WNCP) to extract sparsity more strongly than the $l_{1}$ norm to alleviate the bias issue, aiming at reducing reconstruction errors. An optimization algorithm based on simultaneous direction method of multipliers (SDMM) is proposed to evaluate the effectiveness through in vivo data from PICMUS dataset. Results show the proposed method obtains a B-mode image quality superior to that obtained through our previous method.
Image Captioning Using Deep Learning Techniques

: With the use of the internet, a lot of data is being collected in the form of images. This type of data can be used for further analysis or processing only if we are able to draw out what the image represents or depicts. Image captioning in simple words can be defined as the process of producing or generating a description in the form of text for an image. The results have many applications in real life. This paper presents an effort to leverage the benefits of the techniques of Deep Learning in producing the captions for images based on the objects present, the properties of the objects, the actions that are being performed by them and the interaction between the objects and with their surroundings using a CNN and RNN model.