Miracle-dz/UniFAR-FTUs huggingface dataset:
Plant cytogenetics tests can predict toxic effects on human cells: genotoxic and mutagenic effects of Tityus serrulatus scorpion venom on vegetal and human cells

The effects of Tityus serrulatus venom was accessed on the cell cycle and genetic material of the Lactuca sativa L. and compared with the damages to human leukocytes, in order to evaluate the genotoxic and mutagenic potential of toxins on cell types very different. The occurrence of cell cycle alterations and DNA fragmentation was evaluated in Lactuca sativa. Incubations of venom with human leukocytes were also held with subsequent evaluation of cell proliferation index, micronucleus and fragmented DNA. The T. serrulatus venom showed cytogenotoxic, reducing mitotic index and induced alterations in the cell cycle and micronuclei formation. The occurrence of cell death was evidenced by the detection of condensed nuclei, positive TUNEL signals, and presence of DNA fragmentation on lettuce cells. The scorpion venom induced DNA fragmentation and micronuclei in leukocytes. Further, the role of peptides and proteases from this venom in inducing the observed damage was discussed.
Overview of fabric defect detection techniques based on computer vision

In modern industrial development and the era of intelligent production, fabric defects have become a crucial aspect of the textile industry. The accurate identification of defects on fabrics through computer vision technology holds significant research value. This article provides an overview of machine vision detection techniques for fabric defects. It classifies and analyzes existing defect detection techniques, summarizes achievements and innovations in recent years, and examines the research progress in defect detection. The article discusses various models and theories in detail, compares commonly used defect detection techniques, and identifies their main shortcomings. It analyzes the research status in the field of defect detection both domestically and internationally in recent years, provides a comprehensive discussion on relevant models and theories, compares them with current common defect detection techniques, highlights the imperfections in mainstream fabric defect detection, and offers predictions for future development trends.
Molecular Biology and Clinical Management of Esophageal Adenocarcinoma

Simple Summary Esophageal adenocarcinoma (EAC) is one of the two main subtypes of esophageal cancer. EAC is a highly lethal disease with rising incidence in Western countries. EAC is associated with chronic gastroesophageal reflux disease and Barrett’s esophagus and mostly occurs in the distal esophagus. In the past decades, much effort has been made to improve treatment strategies, including regimens with chemoradiotherapy, targeted and immune therapies. Despite the multi-modal therapies, the survival of EAC patients has improved only marginally, and major breakthroughs in EAC treatment have not been achieved. We aim to summarize the literature on the comprehensive molecular landscape of EAC to elucidate factors underlying the EAC malignant behavior and poor outcomes. We discuss in detail the etiology, genetics, epigenetics and histology of EAC, as well as the currently employed therapies. Better knowledge about the molecular biology of EAC learned from this review may provide leads for developing novel therapies in the future. Abstract Esophageal adenocarcinoma (EAC) is a highly lethal malignancy. Due to its rising incidence, EAC has become a severe health challenge in Western countries. Current treatment strategies are mainly chosen based on disease stage and clinical features, whereas the biological background is hardly considered. In this study, we performed a comprehensive review of existing studies and discussed how etiology, genetics and epigenetic characteristics, together with the tumor microenvironment, contribute to the malignant behavior and dismal prognosis of EAC. During the development of EAC, several intestinal-type proteins and signaling cascades are induced. The anti-inflammatory and immunosuppressive microenvironment is associated with poor survival. The accumulation of somatic mutations at the early phase and chromosomal structural rearrangements at relatively later time points contribute to the dynamic and heterogeneous genetic landscape of EAC. EAC is also characterized by frequent DNA methylation and dysregulation of microRNAs. We summarize the findings of dysregulations of specific cytokines, chemokines and immune cells in the tumor microenvironment and conclude that DNA methylation and microRNAs vary with each different phase of BE, LGD, HGD, early EAC and invasive EAC. Furthermore, we discuss the suitability of the currently employed therapies in the clinic and possible new therapies in the future. The development of targeted and immune therapies has been hampered by the heterogeneous genetic characteristics of EAC. In view of this, the up-to-date knowledge revealed by this work is absolutely important for future EAC studies and the discovery of new therapeutics.
Fault Location of the Renewable Energy Sources Connected Distribution Networks Based on Time Differences of the Modal Traveling Waves

The topology of the distribution network and direction of the power flow will change when distributed generators (DGs) are connected to it, making it difficult to locate faults using conventional techniques like the impedance approach. Aiming at the two-phase short-circuit grounding faults of active distribution networks, this paper proposes a fault location method based on the time difference of the traveling wave modulus. The first step is the proposal of a zero-mode time-of-arrival calibration method for the ideal frequency band through the analysis of the attenuation of zero-mode traveling wave transmission. Next, define the relative wave velocity, research the quantitative relationship between the modulus transmission time difference and the zero-mode and aerial-mode wave velocities, and establish equation constraints between the modulus transmission time difference, relative wave velocity, and transmission distance. Then, time bounds and dynamic inequality constraints that establish relative wave velocities by fitting. Finally, combined with the abnormal data processing strategy, with the goal of minimizing the weighted deviation of the modulus time difference, the particle swarm optimization (PSO) algorithm is used to solve the fault distance. The PSCAD simulation result demonstrates that the method proposed in this paper has the advantages of high accuracy, strong error tolerance, and strong adaptability, and can quickly and accurately locate faults.
Influence of PVC and sun/view geometry on NDVI and SAVI in the upstream regions of Shule river basin

As the solar elevation angular and the viewing angular changes,the same surface produces different reflectance and radiation.In this research we mainly studied the impacts of the solar / view geometry on the normalized difference vegetation index(NDVI) and soil adjusted vegetation index(SAVI) in the different percetage of vegetation cover(PVC). We selected different plots of PVC as 17%,59%,73% and bare plot in the upstream regions of Shule river basin.As variation with solar geometry,we used ASD to collect spectral data in three view angles,the range of waveband is 350nm-1100nm.We used spectral data to simulate the red and nir of Landsat TM,and then calculated NDVI and SAVI.We drew a conclusion: changes in view angle caused variations in the indics to be as high as 61.7% in relation to nadir.There was an opposite view angle behavior between NDVI and SAVI,with the former increasing from antisolar to forward scattering view direction.A derivative analysis of the indices showed the SAVI exhibited a more linear relationship with the individual bands than NDVI.
Maternal cardiovascular and haematological complications alter the risk associations between environmental exposure and adverse pregnancy outcomes

Given China's recent introduction of the "three-child policy" in response to population ageing, safeguarding perinatal health has become an urgent priority. Previous epidemiological research seldom explored the risk factors of maternal cardiovascular and haematological diseases, or its impact on adverse pregnancy outcomes (APO). To fill the literature gap, here we conducted systematic epidemiological analyses on 121,090 pregnant women and their neonates from the ZEBRA Chinese prospective maternity cohort. We find that incremental exposure in PM2.5, O3, and green space modify the risks of APO, including congenital heart disease, by 11.2%, 7.8%, and -5.5%, respectively. Maternal cardiovascular and haematological complications during pregnancy significantly aggravate the risk of APO by 66.2%, and also modify the environment-APO risk associations by amplifying the hazards of air pollution and weakening the protective effect of greenness accessibility. Our research findings support the Sustainable Development Goals (e.g. SDG3) by providing first-hand epidemiological evidence and clinical guidance for protecting maternal and neonatal health.
Modeling the West African Monsoon Using a Superparameterized GCM and a High-resolution Cloud Resolving Model

1. Introduction/Motivation The West African Monsoon (WAM) is the term commonly used to describe the seasonal rains that occur in West Africa from May to October. Since rain-fed agriculture is one of the main sources of food and income for the people in West Africa, societies in this region are highly vulnerable to variability in monsoon rains (Baron et al. 2005). Over the past 40 years, sub-Saharan Africa has also been in the throes of a severe drought which has had devastating agricultural, economic and societal consequences for the region (Nicholson et al. 2000). As our planet warms due to increasing greenhouse gas concentrations, it is probable that we can expect changes in the African monsoon circulation, which could further influence water resources in the Sahel. Unfortunately, the global circulation models (GCMs) that are used to make climate projections are currently unable to represent the timing, spatial patterns and magnitude of the monsoon precipitation over West Africa (Yang and Slingo 2001; Dia and Trenberth 2004; Meehl et al. 2006). The inability of these models to capture the observed monsoon system, greatly undermines their ability to represent potential changes to the monsoon in a warmer climate. The WAM is a complicated system which involves many interactions between the atmosphere, ocean and land surface. The WAM is also influenced by processes that occur over a range of temporal and spatial scales (Hall and Peyrille, 2006). Traditional GCMs have difficulty capturing the monsoon because they are unable to represent the complex multi-scale interactions that are known to be associated with the monsoon (Yang and Slingo 2001). However, it is possible that GCMs that have implemented the multi-scale modeling framework (MMF) may be able to better capture the WAM. MMFs have been uniquely designed to examine the multi-scale interactions between small-scale circulations and large-scale dynamics. While traditional GCMs must parameterize small-scale physical features such as dry and moist convective processes, in MMFs, each GCM grid cell has been embedded with a row of cloud resolving models (CRMs) which allows for explicit simulation of small-scale cloud and boundary-layer processes (Randall et al. 2003). This approach of embedding CRMs within a GCM is sometimes referred to as " superparameterization ". In this study, I propose to examine the ability of the Superparameterised-Community Atmosphere Model (SP-CAM, Khairoutdinov et al., 2005), a MMF, to simulate the West African monsoon. Specifically I will examine the model's ability to represent …
Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records

Background: Deep learning models showed great success and potential when applied to many biomedical problems. However, the accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, and covariate imbalance when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. Objective: The goal of this study is to investigate the extent to which time-varying covariates, rare incidence, and covariate imbalance influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Methods: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. Results: We developed a novel backward masking scheme to evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. Conclusions: Devising proper strategies to address challenges from time-varying covariates, lack of data, and covariate imbalance can be key to counteracting data bias and accurately predicting disease occurrence using deep learning models. The novel strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.
Analysis of the Association Between the Number of Intensivists and the Use of Cardiovascular Agonists: An Ecological Study Using Data From National Databases of Japan

Background Previous studies have demonstrated a correlation between management by intensivists and a decrease in hospital stay and mortality, yet the underlying reason remains unknown. Using open data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) and other databses, the present study aimed to explore the relationship between inotrope and vasoconstrictor use and the number of intensivists. Materials and methods Cardiovascular agonists listed in the 2020 NDB for which the total dose was known were included for analysis. Trends in cardiovascular agonist use over six years were then graphically assessed, and a linear regression model with the use of each target drug per prefecture as the objective variable in the 2020 data was created to analyze the impact of intensivists on drug use. Results A total of 61 drugs were classified into eight groups based on their composition, and drug use in each of the 47 prefectures was tabulated. Both the rate of use and cost showed a yearly decrease for dopamine but a yearly increase for norepinephrine. Multivariable analysis indicated that the number of intensivists was only significant for dopamine, which had a coefficient of -310 (95% CI: -548 to -72, p = 0.01) but that no such trend was evident for the other drugs. Conclusions The results demonstrated that an increasing number of intensivists in each prefecture correlated with decreasing use of dopamine, possibly explaining the improved outcomes observed in closed ICUs led by intensivists. Further research is warranted to establish causality.
Synergistic effect of two antimicrobial peptides, BP203 and MAP-0403 J-2 with conventional antibiotics against colistin-resistant Escherichia coli and Klebsiella pneumoniae clinical isolates

Drug-resistant Enterobacterales infections are a great health concern due to the lack of effective treatments. Consequently, finding novel antimicrobials or combining therapies becomes a crucial approach in addressing this problem. BP203 and MAP-0403 J-2, novel antimicrobial peptides, have exhibited effectiveness against Gram-negative bacteria. In this study, we assessed the in vitro antibacterial activity of BP203 and MAP-0403 J-2, along with their synergistic interaction with conventional antibiotics including colistin, rifampicin, chloramphenicol, ceftazidime, meropenem, and ciprofloxacin against colistin-resistant Escherichia coli and Klebsiella pneumoniae clinical isolates. The minimal inhibitory concentrations (MIC) of BP203 and MAP-0403 J-2 against tested E. coli isolates were 2–16 and 8–32 μg/mL, respectively. However, for the majority of K. pneumoniae isolates, the MIC of BP203 and MAP-0403 J-2 were >128 μg/mL. Notably, our results demonstrated a synergistic effect when combining BP203 with rifampicin, meropenem, or chloramphenicol, primarily observed in most K. pneumoniae isolates. In contrast, no synergism was evident between BP203 and colistin, chloramphenicol, ceftazidime, rifampicin, or ciprofloxacin when tested against all E. coli isolates. Furthermore, synergistic effects between MAP-0403 J-2 and rifampicin, ceftazidime or colistin were observed against the majority of E. coli isolates. Similarly, the combined effect of MAP-0403 J-2 with rifampicin or chloramphenicol was synergistic in the majority of K. pneumoniae isolates. Importantly, these peptides displayed the stability at high temperatures, across a wide range of pH values, in specific serum concentrations and under physiological salt conditions. Both peptides also showed no significant hemolysis and cytotoxicity against mammalian cells. Our findings suggested that BP203 and MAP-0403 J-2 are promising candidates against colistin-resistant E. coli. Meanwhile, the synergism of these peptides and certain antibiotics could be of great therapeutic value as antimicrobial drugs against infections caused by colistin-resistant E. coli and K. pneumoniae.
Comparative Efficacy and Safety of Neoadjuvant Immunotherapy with Chemotherapy versus Chemotherapy Alone in Non-Small Cell Lung Cancer: A Propensity Score and Inverse Probability Treatment Weighting Analysis

Purpose This study aimed to compare the efficacy and safety of neoadjuvant chemotherapy (NCT) and neoadjuvant immunotherapy combined with chemotherapy (NICT) combined with radical lung cancer resection for the treatment of patients with resectable non-small cell lung cancer (NSCLC). To adjust for confounding factors, we innovatively adopted two matching methods: propensity score (PS) and inverse probability of treatment weighting (IPTW). Patients and Methods We conducted a retrospective analysis of the clinicopathological features and prognosis of patients with resectable NSCLC treated with NCT or NICT combined with radical lung cancer resection using propensity score matching (PSM) at a ratio of 1:1 and IPTW to balance potential bias. Results After PSM, 116 pairs of patients who had undergone NCT or NICT were selected for the final analysis. The pathological complete remission (pCR) and major pathological remission (MPR) rates were significantly better in the NICT group than in the NCT group (pCR rate of 44.8% vs 2.6%, P< 0.001; MPR rate of 66.4% vs 20.7%, P< 0.001). No significant difference was seen between the NICT and NCT groups in terms of postoperative complications (12.1% vs 9.5%, P=0.182). Patients in the NICT group had significantly better disease-free survival (DFS) and overall survival(OS) than those in the NCT group ([3-year DFS: 75.2% vs 43.3%, P< 0.001] and [3-year OS: 91.5% vs 58.0%, P< 0.001]). Among all patients, those with postoperative pathology of pCR had better DFS (P< 0.001) and OS (P= 0.009). Patients with postoperative pathology of MPR had better DFS (P< 0.001) and OS (P< 0.001). The IPTW method yielded similar pathologic and prognostic results. Conclusion Patients with resectable NSCLC treated with NICT had better pathological responses and prognosis, than those treated with NCT, and the safety profiles of NICT and NCT were similar.
Evaluation of the relation between the maxillary sinus and the posterior teeth using digital panoramic radiography

This research aimed to determine the relationship between the maxillary posterior teeth and maxillary sinus floor (MSF), as well as the impact of nearby tooth loss on the space between MSF and posterior maxillary roots. A number of 120 digital panoramic radiographs were obtained from the archives of several clinics in Al-Najaf, Iraq, with the overall teeth examined in these radiographs including 236 of the 1st premolars, 227 of the 2nd premolars, 227 of the 1st molars, and 231 of the 2nd molars, from the right and left sides. The distances between the apices of the teeth and the maxillary sinus were determined. There are three categories of relationships between upper posterior teeth roots and MSF. These include type Os (root apex exists below or outside MSF), Type Co (root apex in contact with the MSF), and Type Is (root apex above or inside MSF). Type Os is the most encountered among premolars, Type Co is mostly encountered among the 2nd molars, and Type Is, in the 1st and 2nd molars. The study finds no correlation between age, gender, and the distribution of maxillary posterior tooth roots attached to the MSF. The first premolars were the furthest from MS, while the first molars were the closest. The most frequent link between maxillary molar roots and the MS was the Co-relation for the 2nd maxillary molar and the Is relation for the 1st maxillary molar. There is a non-significant decrease in the distance between the apices of the 1st maxillary premolar, 2nd maxillary premolar, and 1st maxillary molar and the MS before and after extraction.
Robust small area estimation for unit level model with density power divergence

Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter estimation method in unit level model. However, this kind of method is sensitive to outliers, and EB estimation will lead to considerable inflation of the mean square error(MSE) when there are outliers in the responses yij. In this study, we propose a robust estimation method for the unit-level model with outliers based on the minimum density power divergence. Firstly, by introducing the minimum density power divergence function, we give the estimation equation of the parameters of the unit level model, and obtain the asymptotic distribution of the robust parameters. Considering the existence of tuning parameters in the robust estimator, an optimal parameter selection algorithm is proposed. Secondly, empirical Bayesian predictors of unit and area mean in finite populations are given, and the MSE of the proposed robust estimators of small area means is given by bootstrap method. Finally, we verify the superior performance of our proposed method through simulation data and real data. Through comparison, our proposed method can can solve the outlier situation better.