Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of network pharmacology and molecular docking technology reveals the mechanism of the herbal pairing of Codonopsis Pilosula (Franch.) Nannf and Astragalus Membranaceus (Fisch.) Bge on chronic heart failure.

BACKGROUND The herbal pairing of Dangshen (DS) [Codonopsis pilosula (Franch.) Nannf. ] and Huangqi (HQ) [Astragalus membranaceus (Fisch.) Bge. ] (DHP) is a traditional Chinese herbal medicine that is frequently used to treat chronic heart failure (CHF) in China. However, the pharmacological mechanism of DHP has not been fully elucidated. This is the first study aimed to reveal the active mechanism of DHP in the treatment of CHF by using network pharmacology methods. METHODS The active ingredients of DHP were obtained from the TCMSP database, and the potential targets of DHP were predicted using the SwissTargetPrediction database. CHF-related targets were searched by the DisGeNET and GeneCards databases. The common targets between the disease and herbs were obtained using a Venn diagram. The STRING database was utilized to obtain the protein-protein interaction data. Next, we used Cytoscape 3.7. 2 software to construct and analyze the herb-ingredient-potential targets-disease network. Topology analysis was used to identify the key ingredients and hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Metascape database to reveal the mechanism. Furthermore, molecular docking simulation was performed using AutoDock Vina software to assess the affinity of the key ingredients and hub genes. RESULTS Five key ingredients and six hub genes were screened. The six hub genes were closely related to PI3K /AKT or ERK1/2 pathways. The KEGG pathways mainly involved the TNF signaling pathway, calcium signaling pathway, and cancer-related pathways. The GO enrichment analysis results showed that DHP might act on biological processes including positive regulation of kinase activity and cellular response to nitrogen compound via the three above-mentioned pathways in the treatment of CHF. Finally, the molecular docking results showed that the five key ingredients exhibited strong affinities to the six hub genes. CONCLUSIONS This study revealed the molecular mechanism that the flavonoids in DHP may alleviate endothelial dysfunction and cardiac hypertrophy via regulation of the TNF pathway and its downstream PI3K/Akt or ERK1/2 signaling pathways, or improve excitation-contraction coupling by regulating calcium signaling pathway, thereby improving CHF. These results provide insights for further experimentation on its pharmacological effects.
1 COVID-19 and Hypertension

The COVID-19 pandemic has taken us unprecedented, and management of comorbidities has become challenging as a consequence. Presence of comorbidities has shown to be an increased risk factor for critical illness. Since it’s a relatively new disease, there is a lack of robust clinical data concerning COVID-19 infection and presence of comorbidities. Among the COVID-19 patients who died in Italy, it was observed that those with comorbidities including hypertension (69%), diabetes (31%), ischemic heart disease (27%), atrial fibrillation (21%), and heart failure (16%) were the most important factors for the increased mortality rates observed (Gruppo Della database, 2020). 1 COVID-19 and Hypertension
Denitrification of groundwater: pilot-plant testing of cotton-packed bioreactor and post-microfiltration

The use of raw cotton as carbon source in the denitrification of drinking water was tested in a field pilot-plant. The reactor treated water from a well in which the concentration of nitrate varied from 22 mg N l –1 in summer to a minimum of 9 mg l –1 in winter. The experimental reactor had a capacity of approximately 9 m 3 and could be packed with up to 1500 kg of unprocessed cotton. The highest rate of denitrification observed was 0.36 kg N m –3 d –1 , at a feed rate of 6 m 3 h –1 . However, this performance could be sustained only temporarily as the relatively high water pressure caused serious compression of the bed. The long-term (six months) performance of the system was studied at feed rates of 0.8 and 1.5 m 3 h –1 . The process was stable and 80–100% of the influent nitrogen was removed. The increase in DOC at the outlet was usually less than 7 mg l –1 , and the number of bacteria was in the order of 10 5 –10 6 CFU ml –1 . Crossflow microfiltration was an effective post-treatment for the removal of bacteria and elimination of turbidity.
Longitudinal tracking of neuronal mitochondria delineates PINK1/Parkin-dependent mechanisms of mitochondrial recycling and degradation

Time-lapse imaging delineates the mitochondrial life cycle in neurons regulated by PINK1 and Parkin. Altered mitochondrial quality control and dynamics may contribute to neurodegenerative diseases, including Parkinson’s disease, but we understand little about these processes in neurons. We combined time-lapse microscopy and correlative light and electron microscopy to track individual mitochondria in neurons lacking the fission-promoting protein dynamin-related protein 1 (Drp1) and delineate the kinetics of PINK1-dependent pathways of mitochondrial quality control. Depolarized mitochondria recruit Parkin to the outer mitochondrial membrane, triggering autophagosome formation, rapid lysosomal fusion, and Parkin redistribution. Unexpectedly, these mitolysosomes are dynamic and persist for hours. Some are engulfed by healthy mitochondria, and others are deacidified before bursting. In other cases, Parkin is directly recruited to the matrix of polarized mitochondria. Loss of PINK1 blocks Parkin recruitment, causes LC3 accumulation within mitochondria, and exacerbates Drp1KO toxicity to dopamine neurons. These results define a distinct neuronal mitochondrial life cycle, revealing potential mechanisms of mitochondrial recycling and signaling relevant to neurodegeneration.
Rapid generation of maternal mutants via oocyte transgenic expression of CRISPR-Cas9 and sgRNAs in zebrafish

A time-saving and deletion-prone method facilitates functional study of maternal factors in zebrafish. Maternal products are exclusive factors to drive oogenesis and early embryonic development. As disrupting maternal gene functions is either time-consuming or technically challenging, early developmental programs regulated by maternal factors remain mostly elusive. We provide a transgenic approach to inactivate maternal genes in zebrafish primary oocytes. By introducing three tandem single guide RNA (sgRNA) expression cassettes and a green fluorescent protein (GFP) reporter into Tg(zpc:zcas9) embryos, we efficiently obtained maternal nanog and ctnnb2 mutants among GFP-positive F1 offspring. Notably, most of these maternal mutants displayed either sgRNA site–spanning genomic deletions or unintended large deletions extending distantly from the sgRNA targets, suggesting a prominent deletion-prone tendency of genome editing in the oocyte. Thus, our method allows maternal gene knockout in the absence of viable and fertile homozygous mutant adults. This approach is particularly time-saving and can be applied for functional screening of maternal factors and generating genomic deletions in zebrafish.
On learning deep domain-invariant features from 2D synthetic images for industrial visual inspection

Deep learning resulted in a huge advancement in computer vision. However, deep models require a large amount of manually annotated data, which is not easy to obtain, especially in a context of sensitive industries. Rendering of Computer Aided Design (CAD) models to generate synthetic training data could be an attractive workaround. This paper focuses on using Deep Convolutional Neural Networks (DCNN) for automatic industrial inspection of mechanical assemblies, where training images are limited and hard to collect. The ultimate goal of this work is to obtain a DCNN classification model trained on synthetic renders, and deploy it to verify the presence of target objects in never-seen-before real images collected by RGB cameras. Two approaches are adopted to close the domain gap between synthetic and real images. First, Domain Randomization technique is applied to generate synthetic data for training. Second, a novel approach is proposed to learn better features representations by means of self-supervision: we used an Augmented Auto-Encoder (AAE) and achieved results competitive to our baseline model trained on real images. In addition, this approach outperformed baseline results when the problem was simplified to binary classification for each object individually.
Experiment Assisting System with Local Augmented Body (EASY-LAB) for Subject Experiments under the COVID-19 Pandemic

Since it is challenging to perceive space and objects with a video conferencing system, which communicates using only video and audio, there are difficulties in testing subjects in the COVID-19 pandemic. We propose the EASY-LAB system that allows an experimenter to perform observation and physical interaction with the subject even from a remote location actively. The proposed system displays the camera image on a HMD worn by the experimenter, which camera is mounted on a small 6 DOF robot arm end, allowing observation from an easy-to-see perspective. The experimenter can also instruct the subject using another robot arm with a laser pointer. The robot’s joint angles are calculated by Inverse Kinematics from the experimenter’s head movements, then reflected in the actual robot. Photon Unity Networking component was used for the synchronization process with remote locations. These devices are affordable, effortless to set up, and can be delivered to the subject’s home. Finally, the proposed system was evaluated by four subjects, As a preliminary result, the mean pointing error was 1.1 cm, and the operation time was reduced by 60% compared with the conventional video conferencing system. This result indicated the EASY-LAB’s capability, at least in tasks that require pointing and observation from various angles. The statistical study with more subjects will be conducted in the follow-up study.
Accelerating the Training Process of Generative Query Network

GQN (Generative Query Network) is a novel unsupervised generative model which is intended for neural rendering tasks. However, the time-consuming training process makes it difficult to deploy GQN. In this paper, we propose two methods to boost its training speed. Computationally, a two-phased method is proposed so that the training time is greatly reduced due to a much smaller parameter set without significant loss of image quality. We also adopt the gradient accumulation technique and avoid redundant communication. Experiments are carried out on a typical dense GPGPU platform and the result shows that, remaining similar image quality, our two-phase training approach can reduce 56.92% training time and the gradient accumulation gains 9.86% speedup.
An Examination of View-Settings for Long Texts in VR Reading

To reduce the burden from VR reading of long texts, this study finds the view-settings for better readability and less fatigue. As the view-settings, we focus on the font type, the font color, the font size, and the view-distance from the text. Our results show the relation among the view-settings, the readability, and the fatigue for long texts in VR reading.
“If they believe, then so shall I”: Perceived beliefs of the in-group predict conspiracy theory belief

Conspiracy beliefs are widespread and can have detrimental consequences. As perceived social norms can exert a powerful influence on individuals, we investigated the relationship between perceived conspiracy belief norms and personal endorsement, and whether others’ conspiracy belief is overestimated. In Study 1, UK university students (N = 111) completed measures of their personal conspiracy beliefs and estimations of others’ beliefs (an in-group and an out-group they chose, and a prescribed in-group). Perceived in-groups’ belief strongly predicted personal conspiracy belief; perceived out-group’s belief did not. Studies 2 and 3 replicated these findings in a British community sample (N = 177) and in a UK parent sample (N = 197), focusing on antivaccine conspiracy theories. All studies demonstrated that people overestimate the conspiracy beliefs of others. This is the first demonstration of the association between perceived in-group conspiracy belief social norms and individuals’ personal conspiracy beliefs. Interventions challenging misperceived norms could be effective in reducing conspiracy beliefs.
Self-Control at 220 Miles per Hour: Steering and Braking to Achieve Optimal Outcomes During Adolescence

Adolescence is a developmental period characterized by heightened attraction to rewards and risk-taking propensities. Dual-systems models portray the adolescent brain in terms of a maturational mismatch whereby brain systems involved in sensitivity to incentives become potentiated before impulse-control systems have matured. That perspective implies that relying on impulse inhibition to overcome temptation is likely to yield uneven success during adolescence. Using the analogy of practice driving a race car, we propose another process that leads to achieving healthy outcomes: steering aimed at limiting or preventing motivational conflict and thereby lessening reliance on impulse control (termed braking). The focal idea is that the more adolescents can avoid troublesome contexts, the less they will need to depend on their relatively weak impulse-control abilities to avert problems and danger. Recent work links dispositional differences in self-control to indicators of steering, such as situation selection, habit cultivation, and proactive responding. Steering to curb or avoid motivational conflict could be key to promoting healthy outcomes during adolescence, a developmental period characterized by vulnerability to risk, and could have lasting importance given that enduring patterns of unhealthy, dangerous, and self-defeating behaviors often start during this period.
Attention-based deep learning networks for identification of human gait using radar micro-Doppler spectrograms

Abstract Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.
X-Linked Hypophosphatemic Rickets: Multisystemic Disorder in Children Requiring Multidisciplinary Management

X-linked hypophosphatemic rickets (XLH) is the commonest inherited form of rickets. It is caused by an impaired regulation of fibroblast growth factor 23 (FGF23) due to a PHEX gene mutation, which leads to reduced tubular reabsorption of phosphate and renal 1α-hydroxylase activity and increased renal 24-hydroxylase activity. Hypophosphatemia associated with renal phosphate wasting, normal serum levels of calcium, parathyroid hormone, and 25-hydroxyvitamin D represents the main biochemical sign in affected patients. Patients with XLH show rickets and osteomalacia, severe deformities of the lower limbs, bone and muscular pain, stunted growth, and reduced quality of life. However, XLH is a multisystemic disorder requiring multidisciplinary approaches in specialized subdisciplines. Severe complications may occur in patients with XLH including craniosynostosis, hearing loss, progressive bone deformities, dental and periodontal recurrent lesions, and psychosocial distress. Moreover, long-term conventional treatment with active vitamin D metabolites and oral inorganic phosphate salts may cause endocrinological complications such as secondary or tertiary hyperparathyroidism, and adverse events in kidney as hypercalciuria, nephrocalcinosis, and nephrolithiasis. However, conventional treatment does not improve phosphate metabolism and it shows poor and slow effects in improving rickets lesions and linear growth. Recently, some trials of treatment with recombinant human IgG1 monoclonal antibody that targets FGF23 (burosumab) showed significant improvement of serum phosphate concentration and renal tubular reabsorption of phosphate that were associated with a rapid healing of radiologic signs of rickets, reduced muscular and osteoarticular pain, and improved physical function, being more effective for the treatment of patients with XLH in comparison with conventional therapy. Therefore, a global management of patients with XLH is strongly recommended and patients should be seen regularly by a multidisciplinary team of experts.
Fibril Surface-Dependent Amyloid Precursors Revealed by Coarse-Grained Molecular Dynamics Simulation

Amyloid peptides are known to self-assemble into larger aggregates that are linked to the pathogenesis of many neurodegenerative disorders. In contrast to primary nucleation, recent experimental and theoretical studies have shown that many toxic oligomeric species are generated through secondary processes on a pre-existing fibrillar surface. Nucleation, for example, can also occur along the surface of a pre-existing fibril—secondary nucleation—as opposed to the primary one. However, explicit pathways are still not clear. In this study, we use molecular dynamics simulation to explore the free energy landscape of a free Abeta monomer binding to an existing fibrillar surface. We specifically look into several potential Abeta structural precursors that might precede some secondary events, including elongation and secondary nucleation. We find that the overall process of surface-dependent events can be described at least by the following three stages: 1. Free diffusion 2. Downhill guiding 3. Dock and lock. And we show that the outcome of adding a new monomer onto a pre-existing fibril is pathway-dependent, which leads to different secondary processes. To understand structural details, we have identified several monomeric amyloid precursors over the fibrillar surfaces and characterize their heterogeneity using a probability contact map analysis. Using the frustration analysis (a bioinformatics tool), we show that surface heterogeneity correlates with the energy frustration of specific local residues that form binding sites on the fibrillar structure. We further investigate the helical twisting of protofilaments of different sizes and observe a length dependence on the filament twisting. This work presents a comprehensive survey over the properties of fibril growth using a combination of several openMM-based platforms, including the GPU-enabled openAWSEM package for coarse-grained modeling, MDTraj for trajectory analysis, and pyEMMA for free energy calculation. This combined approach makes long-timescale simulation for aggregation systems as well as all-in-one analysis feasible. We show that this protocol allows us to explore fibril stability, surface binding affinity/heterogeneity, as well as fibrillar twisting. All these properties are important for understanding the molecular mechanism of surface-catalyzed secondary processes of fibril growth.
The use of web-scraped data to analyze the dynamics of footwear prices

Abstract Aim/purpose – Web-scraping is a technique used to automatically extract data from websites. After the rise-up of online shopping, it allows the acquisition of information about prices of goods sold by retailers such as supermarkets or internet shops. This study examines the possibility of using web-scrapped data from one clothing store. It aims at comparing known price index formulas being implemented to the web-scraping case and verifying their sensitivity on the choice of data filter type. Design/methodology/approach – The author uses the price data scrapped from one of the biggest online shops in Poland. The data were obtained as part of eCPI (electronic Consumer Price Index) project conducted by the National Bank of Poland. The author decided to select three types of products for this analysis – female ballerinas, male shoes, and male oxfords to compare their prices in over one-year time period. Six price indexes were used for calculation – The Jevons and Dutot indexes with their chain and GEKS (acronym from the names of creators – Gini–Éltető–Köves–Szulc) versions. Apart from the analysis conducted on a full data set, the author introduced filters to remove outliers. Findings – Clothing and footwear are considered one of the most difficult groups of goods to measure price change indexes due to high product churn, which undermines the possibility to use the traditional Jevons and Dutot indexes. However, it is possible to use chained indexes and GEKS indexes instead. Still, these indexes are fairly sensitive to large price changes. As observed in case of both product groups, the results provided by the GEKS and chained versions of indexes were different, which could lead to conclusion that even though they are lending promising results, they could be better suited for other COICOP (Classification of Individual Consumption by Purpose) groups. Research implications/limitations – The findings of the paper showed that usage of filters did not significantly reduce the difference between price indexes based on GEKS and chain formulas. Originality/value/contribution – The usage of web-scrapped data is a fairly new topic in the literature. Research on the possibility of using different price indexes provides useful insights for future usage of these data by statistics offices.
Recommendations and Cross-selling: Pricing Strategies when Personalizing Firms Cross-sell

ABSTRACT Recommender systems enable firms to target customers with products and services that better match their needs, as well as cross-sell products and services. Considering these factors in markets with monopoly and duopoly, we investigate (i) How do pricing strategies differ when firms cross-sell versus when they do not cross-sell, and (ii) How do these pricing strategies change when a firm improves its recommender system? We find that cross-selling can enable a monopolist to subsidize its price for the focal products, while maximizing its profit. In a duopoly, the price set by the firm with the inferior system (low-type firm) is always lower when the firms cross-sell than when the firms do not cross-sell; however, that does not necessarily hold for the high-type firm. When the high-type firm improves its recommender system, the low-type firm may decrease its price when firms cross-sell, which does not happen when firms do not cross-sell.