Miracle-dz/UniFAR-FTUs huggingface dataset:
Centriole signaling restricts hepatocyte ploidy to maintain liver integrity

In this study, Sladky et al. investigated how the PIDDosome–TP53 pathway triggers hepatocyte polyploidization, a tightly controlled process that is initiated at weaning and increases with age. They show that hepatocytes accumulate centrioles during cycles of polyploidization in vivo, and that the presence of excess mature centrioles containing ANKRD26 was required to activate the PIDDosome in polyploid cells.
SANC: precision calculations for the SM processes

E-mail:Gizo.Nanava@ifj.edu. plWe describe the present status of the computersystem SANC (Supportof Analytic and Numericalcalculations for Colliders) v.1. 10, intended for precision calculations of the event distributions forvarious decays and processes of the high energy elementary particle interactions in the NLO ofthe Standard Model (SM). At present, the system realises a semi-automatic chain of calculationsfrom the SM Lagrangian up to a Monte Carlo (MC) event simulation for many 1 →2 decays and2→2 processes. Numerous comparisonswith existing in the literature calculations are presented.XI International Workshop on Advanced Computing and Analysis Techniques in Physics ResearchApril 23-27 2007Amsterdam, the Netherlands
Empirical investigation of e-health intervention in cervical cancer screening: A systematic literature review

Cervical cancer (CC) screening can detect the cancer early but is underutilized, especially among the developing countries and low- to middle-income countries. Electronic health (e-health) has the potential for disseminating health education and is widely used in the developed countries. This systematic literature review investigates the effectiveness of e-health intervention for improving knowledge of CC and the intention or uptake for CC screening. We followed the PRISMA 2020 guideline and registered with PROSPERO (registration ID CRD42021276036). We searched the Web of Science, Scopus and EBSCO Medline Complete databases for eligible studies. Studies that conveyed informational material through e-health intervention were selected. The results were analyzed using narrative synthesis, and the pooled estimates were calculated using meta-analysis. A total of six studies involving 1886 women were included in this review. The use of e-health aids alone led to increased knowledge. The meta-analysis demonstrated that the mixed-education method of e-health movies and video education with didactic sessions increased CC screening uptake. A random-effects model revealed that CC screening uptake following e-health interventions were almost double of that of their comparison (odds ratio = 2.29, 95% confidence interval: 1.28–4.10, p < 0.05). Various areas of study demonstrated e-health intervention effectiveness (minority communities, urban areas, rural areas). Health education through e-health intervention has huge potential for promoting CC screening in the community. Nevertheless, the use of appropriate frameworks, user engagement and culturally tailored e-health need to be prioritized.
CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity

Abstract Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co‐expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA–miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine‐learning algorithm was used to compute the gene importance in AA. Finally, drug‐target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA‐approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co‐expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.
Comparison of different tyre models for tyre/road noise applications

In terms of safety and environment, the reduction of noise generated by tire vibrations on roads has a significant importance. In order to study the vibration properties of a tire, various models have been presented in the literature. The main purpose of the current study is a brief review of the characteristics of some models. It is supposed that the tire is subjected to an excitation caused by the contact between the tire and the road. Subsequently, the dynamic behaviour of these models are studied and compared with each other. The effects of inflation pressure and the tread patterns on the dynamic behaviour of the mentioned models are examined. For verification, the dynamical behaviour of the tire is studied experimentally. Application of the present study can be contemplated in the prediction of rolling noise and rolling resistance.
High expression of E2F transcription factors 7: An independent predictor of poor prognosis in patients with lung adenocarcinoma

Adenocarcinoma is the most common pathological type of lung cancer. The E2F7 transcription factor has been confirmed to be related to the occurrence and development of a variety of solid tumors, but the relationship with the prognosis of lung cancer is still unclear. Therefore, we conducted this study to explore the prognostic value of E2F7 for lung adenocarcinoma (LUAD) patients. In this study, we analyzed samples from the Cancer Genome Atlas (TCGA) to study the correlation between the expression of E2F7 and clinical features, the difference in expression between tumors and normal tissues, the prognostic and diagnostic value, and Enrichment analysis of related genes. All statistical analysis uses R statistical software (version 3.6.3). The result shows that the expression level of E2F7 in LUAD was significantly higher than that of normal lung tissue (P = 1e-34). High expression of E2F7 was significantly correlated with gender (P = .034), pathologic stage (P = .046) and M stage (P = .025). Multivariate Cox analysis confirmed that E2F7 is an independent risk factor for OS in LUAD patients (P = .027). Genes related to cell cycle checkpoints, DNA damage telomere stress-induced senescence, DNA methylation, chromosome maintenance and mitotic prophase showed differential enrichment in the E2F7 high expression group. In short, high expression of E2F7 is an independent risk factor for OS in LUAD patients and has a high diagnostic value.
Adverse childhood experiences, bullying, inflammation and BMI in 10-year-old children: The biological embodiment

Exposure to adversity during the first years of life might already be biologically embedded well before adult life. Thus, the impact of different stressful experiences needs to be explored. This study aims to examine if the association between being victimized (adverse childhood experiences—ACEs and bullying) and (hs-) C-Reactive Protein (CRP) is explained by the influence of adversity on the body mass index (BMI) of the child. We included children from the Portuguese birth cohort Generation XXI (n = 3712) that at 10 years of age completed a questionnaire on the exposure to ACEs and bullying victimization, assessed by an adaptation from the original ACEs study and an adaptation of The Bully Scale Survey, respectively. Following an overnight fast, a venous blood sample was collected by trained nurses and hs-CRP was assayed in fresh blood samples. Weight and height were measured with the child in underwear and bare feet. Weight was measured to the nearest one-tenth of a kilogram with the use of a digital scale (Tanita), and height was measured to the nearest one-tenth of a centimetre with the use of a wall stadiometer (seca®). BMI was calculated as the value of weight (kg) over squared height (m), and computed as an age- and sex-specific BMI standard deviation (SD) score (z-score), according to the World Health Organization Child Growth Standards (5–19 years). Regression coefficients and respective 95% Confidence Interval [β(95%CI)] were computed using path analysis. We observed that ACEs had a positive total effect on hs-CRP at the age of 10 years (β = 0.06; 95%CI: -0.02; 0.15). A direct effect (β = 0.02; 95%CI: -0.01; 0.06) accounted for 66.1% of the association between ACEs and hs-CRP. A positive total effect of bullying victimization on hs-CRP (β = 0.20; 95%CI: 0.06; 0.34) was observed. A direct effect (β = 0.08; 95%CI: -0.05; 0.21) accounted for 40.0% of the association, while an indirect effect through BMI (β = 0.12; 95%CI: 0.06; 0.18) explained 60.0% of the pathway between bullying victimization and hs-CRP. Results suggest that there might be different mechanisms involved in the biological embedding of childhood experiences. BMI seems to explain a great part of the association between exposure to bullying victimization and hs-CRP at 10 years of age. Further research is still needed to better understand the mechanisms explaining the emergence and persistence of health poorer outcomes later in life for victims of abuse.
Identifying Potential Gamification Elements for A New Chatbot for Families With Neurodevelopmental Disorders: User-Centered Design Approach

Background Chatbots have been increasingly considered for applications in the health care field. However, it remains unclear how a chatbot can assist users with complex health needs, such as parents of children with neurodevelopmental disorders (NDDs) who need ongoing support. Often, this population must deal with complex and overwhelming health information, which can make parents less likely to use a software that may be very helpful. An approach to enhance user engagement is incorporating game elements in nongame contexts, known as gamification. Gamification needs to be tailored to users; however, there has been no previous assessment of gamification use in chatbots for NDDs. Objective We sought to examine how gamification elements are perceived and whether their implementation in chatbots will be well received among parents of children with NDDs. We have discussed some elements in detail as the initial step of the project. Methods We performed a narrative literature review of gamification elements, specifically those used in health and education. Among the elements identified in the literature, our health and social science experts in NDDs prioritized five elements for in-depth discussion: goal setting, customization, rewards, social networking, and unlockable content. We used a qualitative approach, which included focus groups and interviews with parents of children with NDDs (N=21), to assess the acceptability of the potential implementation of these elements in an NDD-focused chatbot. Parents were asked about their opinions on the 5 elements and to rate them. Video and audio recordings were transcribed and summarized for emerging themes, using deductive and inductive thematic approaches. Results From the responses obtained from 21 participants, we identified three main themes: parents of children with NDDs were familiar with and had positive experiences with gamification; a specific element (goal setting) was important to all parents, whereas others (customization, rewards, and unlockable content) received mixed opinions; and the social networking element received positive feedback, but concerns about information accuracy were raised. Conclusions We showed for the first time that parents of children with NDDs support gamification use in a chatbot for NDDs. Our study illustrates the need for a user-centered design in the medical domain and provides a foundation for researchers interested in developing chatbots for populations that are medically vulnerable. Future studies exploring wide range of gamification elements with large number of potential users are needed to understand the impact of gamification elements in enhancing knowledge mobilization.
Evaluation of the effect of green tea and its constituents on embryo development in a zebrafish model

As one of the most popular beverages, green tea has attracted much interest for its beneficial effects on human health. However, the toxicity of green tea and its underlying mechanism are still poorly understood. Here, we evaluated the effect of green tea and its constituents on development by exposing zebrafish embryos to them. Morphologic results demonstrated that 0.1% and 0.2% green tea increased mortality, delayed epiboly of gastrulation, and shortened body length. Green tea altered the expression pattern of dlx3, cstlb, myod, and papc and decreased the expression levels of wnt5 and wnt11, suggesting that green tea disturbed convergence and extension movement through the downregulation of wnt5 and wnt11. The increased expression of the dorsal gene chordin and reduced expression of wnt8 and its target genes vox and vent in embryos exposed to 0.1% and 0.2% green tea indicated that green tea could affect dorsoventral differentiation by inhibiting the wnt8 signaling pathway. Additionally, green tea could inhibit epiboly progression by disrupting F‐actin organization or removing F‐actin in vegetal yolks during gastrulation. However, no malformation was caused by exposure to the five catechins and gallic acid individually. The mixture of constituents showed a similar effect to green tea solution on the embryos, such as smaller eyes and head, shorter body length, and slower heart rate, which indicated that the effect of green tea solution on embryo development was mainly due to the comprehensive effect of multiple components in the green tea solution.
Prediction of the Structural Properties of Powder Materials by 3D Modeling Methods

This article examines the main problems of modelling spherical (circular) particles. The main method of the initial process of filling lobules using the Cauchy and Reynolds problem is substantiated. An image of an object-oriented complex of free fall of a spherical particle and their many non-collision spheres is presented. Based on the obtained research results, the main parameters of the process of filling particles of heterogeneous materials. An example of visualization of the developed software product for filling material particles is given, taking into account a number of cross-sections of a cylindrical hopper in height. A histogram of the distribution of material particles from porosity over the volume of a cylindrical hopper is also constructed.
Bioactive natural products in COVID-19 therapy

The devastating COVID-19 pandemic has caused more than six million deaths worldwide during the last 2 years. Effective therapeutic agents are greatly needed, yet promising magic bullets still do not exist. Numerous natural products (cordycepin, gallinamide A, plitidepsin, telocinobufagin, and tylophorine) have been widely studied and play a potential function in treating COVID-19. In this paper, we reviewed published studies (from May 2021 to April 2022) relating closely to bioactive natural products (isolated from medicinal plants, animals products, and marine organisms) in COVID-19 therapy in vitro to provide some essential guidance for anti-SARS-CoV-2 drug research and development.
Geographical differences in the diet of Dissostichus mawsoni revealed by metabarcoding

The diet of the Antarctic toothfish (Dissostichus mawsoni) was analyzed using metabarcoding to determine whether spatiotemporal factors are related to its feeding ecology. A diet metabarcoding analysis was conducted for five years from 2016 to 2020 using 1,777 samples collected from two distantly located subareas, 88.3 and 58.4. 1. Metabarcoding results revealed 105 prey haplotypes (29 families, 45 genera) in the stomach contents of D. mawsoni, which can serve as valuable genetic information for the accurate identification of piscine species inhabiting the Southern Ocean. Most of the stomach contents of D. mawsoni consisted of fish taxa, comprising 99.61% of read count, which is consistent with other studies indicating that D. mawsoni is piscivorous. The prey compositions were highly different between the two subareas (88.3 and 58.4.1) regardless of the year, indicating that the diet of D. mawsoni strongly reflects the fish assemblages in geographically different habitats. These results strongly suggest that the stomach contents are good ecological indicators for monitoring any changes in the marine ecosystem caused by either the fishery of D. mawsoni, the most voracious piscine predator in its habitat, or environmental changes. In addition, quantitative polymerase chain reaction results of the two most abundant Macrourus prey species of Antarctic toothfish, M. caml and M. whitsoni, showed that the distribution of these two species may be related to the dynamics of gyres, which flow along the Antarctic continent.
Explore Deep Feature Learning to Power Equipment Monitoring and Defect Detection

The research of power equipment defect detection based on image feature has become a hot issue nowadays. In order to solve the problems of low efficiency and accuracy in traditional power equipment defect detection methods, a defect detection method of power metering equipment based on image deep learning is proposed in this work. We train the deep feature learning network model and obtain the optimal solution of network weights in right of training. The association rules are designed and the defect detection mechanism is designed in combination with the collected meter reading dataset. Based on the designed deep network model, defects are identified with the preprocessed images. In the meantime, in order to reduce the power consumption and time delay of data transmission in the process of defect recognition, we introduce the idea of edge computing, so that part of the defect recognition tasks can realize end-to-end intelligence while taking images. Experimental results show that the proposed method can improve defect detection capability and guarantee the normal operation of power metering equipment largely.
Research on Medical Image Classification Based on Image Segmentation and Feature Fusion

Pneumonia has always been the leading infectious disease leading to the death of children under five years old. X-ray images of the lungs have become the key to the diagnosis of this disease. If computer-aided medical diagnosis is used to automatically detect lung abnormalities, the accuracy of the diagnosis will be improved.This article aims to introduce a deep learning technology based on the combination of image segmentation and feature fusion, which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, use residuals to achieve image segmentation to more accurately divide the lung area. Secondly, the Xception network is used to extract the in-depth features of the data, and the extracted features are passed to the LSTM model to detect the extracted features, and classify the two cases of pneumonia and no pneumonia. This research combines Pearson's feature selection ideas and fuses the correlation between the two loss functions. Experimental results show that the accuracy of this paper is 98%, and the accuracy of AUC is 99%. Compared with the existing technical methods, the accuracy of the model designed in this paper is greatly improved. The model we designed has achieved excellent. experimental results on the currently available data sets. I hope our research can help doctors in the detection of pneumonia in children.
MVideoIndex: Querying and Indexing of Geo-referenced Videos

The geo-referenced video consists of space-temporal information such as time, spatial location, camera shooting direction, camera viewing angle, viewable distance, etc. This type of video is widely applicable with sensors loaded on video capture devices. The application of geo-referenced video queries is increasingly popular recently (e.g., travel recommendation, intelligent transportation, road anomaly detection). And each of them needs to realize the query process of geo-referenced video at a specific time or spatial location. However, existing mobile video indexing methods still have room for improvement. There still exist problems with efficiency and accuracy. In this paper, we proposed a novel indexing method named MVideoIndex. MVideoIndex can process point or range queries quickly by utilizing Minimum Bounding Tilted Rectangle (MBTR) in leaf nodes based on the linear change of movement direction in geo-referenced videos. For representing the viewable regions of geo-referenced videos along the trajectory better, we constructed the index with a memory buffer limit to avoid the situation, where the query target falls into a large index and is inconvenient to query. We experimentally analyzed the performance of MVideoIndex and the state-of-art video index method GeoVideoIndex to verify our theory. The performance shows that MVideoIndex is capable of reducing the index construction time and query time, presenting a better performance than other methods. We further compared the impact of the memory buffer threshold size on query efficiency and found that the optimal memory buffer threshold size is about 8-kilometer Byte. We also conducted experiments to explore the effect of MVideoIndex and GeoVideoIndex on different datasets and found a more suitable application scenario for MVideoIndex.
Measurement of soft tissue deformation at discomfort and pain threshold in different regions of the head

Abstract Understanding of product-soft tissue interface and related discomfort is essential while designing wearable devices. Although pressure thresholds at the perception of discomfort and pain have been measured in the past, associated tissue deformation is yet to be studied. This data can provide a holistic understanding of user discomfort and be a valuable reference for ergonomic product design. Hence, in the current study, tissue deformation at discomfort and pain threshold was measured using an ultrasound indentation device at 18 landmarks for 83 Chinese adults on the head and face. Results show that deformation was higher in the facial region than the scalp and forehead, with maximum deformation in the cheek area and minimum in the forehead region for both thresholds. Also, for most landmarks, the tissue deformation data showed no significant relationship with age and Body Mass Index (BMI). Nearly half of the landmarks exhibited significant gender-based differences. Overall, the measured data showed acceptable within-session and between-session reliability. Practitioner Summary: In this study, tissue deformation was measured in different head regions for discomfort and pain thresholds, and corresponding deformation maps were developed. Measured tissue deformation data showed no significant relationship with BMI and age. This data can be a useful reference in the design, testing, and evaluation of headgears.
Large-scale parallel exact diagonalization algorithm of the Hubbard model on Tianhe-2 supercomputer

We propose a parallel exact diagonalization method for solving the large-scale Hubbard model. The core of this algorithm is the parallelization of the Lanczos algorithm, for which we propose a hierarchical communication model and a fast strategy for finding nonzero elements of large-scale matrix, starting only from the symmetry of Hamiltonian matrix. The effect of our parallel algorithm was tested on the Tianhe-2 supercomputer, where the strong scaling efficiency could reach 53% for 30,000 cores in a 140-billion dimensional matrix, and the weak scaling efficiency remained above 40% for 60,000 cores in a 730-billion dimensional matrix.