Miracle-dz/UniFAR-FTUs huggingface dataset:
Basal Contamination of Sequencing: Lessons from the GTEx dataset

One of the challenges of next generation sequencing (NGS) is read contamination. We used the Genotype-Tissue Expression (GTEx) project, a large, diverse, and robustly generated dataset, to understand the factors that contribute to contamination. We obtained GTEx datasets and technical metadata and validating RNA-Seq from other studies. Of 48 analyzed tissues in GTEx, 26 had variant co-expression clusters of four known highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicated contamination. Sample contamination by non-native genes was associated with a sample being sequenced on the same day as a tissue that natively expressed those genes. This was highly significant for pancreas and esophagus genes (linear model, p=9.5e-237 and p=5e-260 respectively). Nine SNPs in four genes shown to contaminate non-native tissues demonstrated allelic differences between DNA-based genotypes and contaminated sample RNA-based genotypes, validating the contamination. Low-level contamination affected 4,497 (39.6%) samples (defined as 10 PRSS1 TPM). It also led ≥ to eQTL assignments in inappropriate tissues among these 18 genes. We note this type of contamination occurs widely, impacting bulk and single cell data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses. Awareness of this process is necessary to avoid assigning inaccurate importance to low-level gene expression in inappropriate tissues and cells.
Management of Early Implant Failure Associated with A Remnant: A Case Report

Implant restoration, a predictive procedure for edentulous areas, can suffer from early failure due to several factors. While thorough debridement of the extraction socket is standard practice for implant placement, unintentional remnant can occur. This case report presents an early implant failure following placement and guided bone regeneration. Radiological examination revealed radiopaque material around the implant, prompting material removal and subsequent biopsy. The histological analysis identified the area surrounding the bone material as granulation tissue containing chronic inflammatory cells, while the hard tissue area exhibited hypercementosis or periapical cemento-osseoeous dysplasia. These findings suggest unintentional placement of the implant into a remnant following the previous extraction. This case highlights the importance of thorough debridement after tooth extraction. Moreover, pre-implant placement radiological evaluation is necessary to avoid implant failure associated with unintentionally retaining lesion.
Multimodal Fake News Detection Based on Multi-source Heterogeneous Data Fusion

In the era of information explosion, people are constantly exposed to a wealth of unreliable information. Nevertheless, societal stability and public trust may be seriously threatened by such unverified or even fabricated false information. In order to discriminate the fake news, several fake news detection approaches have been proposed. However, most of the current multimodal fake news detection methods only rely on news text and images from a single data source. Owing to the scarcity of multimodal information and the limited volume of data, the effectiveness of the majority of these methods is low. As a result, we embrace the approach of amalgamating multi-source heterogeneous data and propose a new network structure FusionNet. In this network structure, we creatively use web crawler technology to expand the data. We achieve double amplification of text and image as opposed to the prior single-mode amplification of text, and by data screening and cleaning, we create multi-source and multimodal datasets for various viewpoints on the same event. We also propose a novel modal fusion approach that leverages the inherent information in news content, integrates an attention mechanism, and explores the shared relevant features between original news and multi-source news. Through empirical results and analysis, our approach is superior to other methods.
Machine Learning Approach for Malware Detection and Classification using Bio Inspired Algorithms

The prevalence and sophistication of malware today pose a challenge to conventional signature-based malware detection techniques. This paper provides an approach to the identification and classification of malware that makes use of bio-inspired algorithms in conjunction with machine learning (ML) approaches. The main purpose of this paper is to design a system that can accurately recognize and classify malware samples as they are encountered in real time. This paper uses different bio-inspired algorithms, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), to achieve this. These algorithms take cues from biological processes. These techniques are used to fine-tune the feature selection process, which plays a major role in determining the quality of the detection and classification models. These methods improve the system’s overall performance by simulating natural evolutionary processes to find the most discriminative and relevant features among a large set of candidate features. To predict the classification accuracy different ML algorithms are used like Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbour, Decision Tree, Multilayer Perceptron, Support Vector Machine, JRip, Random Tree, REPTree along with ACO and PSO. The results shows that bio-inspired algorithms improved the accuracy of the malware detection as compared to the basic ML algorithms.
A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

Driver assistance systems and autonomous vehicle navigation have become important topics in vehicular technology. Among all of the functions, lane detection is one of the most important. A variety of approaches have been proposed in this field. While learning-based methods achieve impressive accuracy in detecting complex lane markings under clear daylight conditions, adapting these models to diverse weather conditions remains a challenge. On the other hand, geometric-based approaches require parameter tuning for different scenarios but require fewer computational resources. A self-tuned algorithm with high generalizability across diverse weather conditions is proposed in this paper. The algorithm integrates fuzzy logic-based adaptive functions with edge identification and line detection modules, enabling image adjustments in response to challenging weather conditions. The proposed tracking function utilizes previous detection results to fine-tune the selected Range of Interest (ROI), optimizing both accuracy and processing time. By incorporating these adaptive features into common geometric-based frameworks, the algorithm achieves higher detection rates compared to previous studies during challenging weather conditions. Furthermore, the proposed work exhibits better generalizability and significantly shorter processing time when compared to state-of-the-art learning-based models, as demonstrated through extensive testing on multiple datasets.
CAR-T Cell Therapy in Diffuse Large B Cell Lymphoma: Hype and Hope

Abstract Patients with non-Hodgkin lymphomas (NHLs) resistant to standard therapies have a dismal prognosis. The outcome is even poorer in patients relapsing after autologous stem cell transplantation. Most of these patients do not qualify for an allogeneic hematopoietic cell transplantation (HCT) due to refractory disease, lack of a suitable allogeneic donor, higher age, or cumulative toxicity of previous chemotherapy. Despite patients undergoing allogeneic HCT normally profit from a graft-versus-lymphoma effect, overall survival in patients with NHL after HCT remains short. Therefore, novel treatment modalities are urgently needed. Chimeric antigen receptor (CAR)-T cells, a new class of cellular immunotherapy involving ex vivo genetic modification of T cells to incorporate an engineered CAR have been used in clinical trials. In the majority of studies, B cell malignancies treated with CD19 targeting CAR-T cells have been analyzed. Recently, results from 2 CD19 directed CAR-T cell trials with an increased follow-up of patients led to Food and Drug Administration and European Medicines Agency approval of tisagenlecleucel and axicabtagene ciloleucel. Common adverse events (AEs) include cytokine release syndrome and neurological toxicity, which may require admission to an intensive care unit, B cell aplasia and hemophagocytic lymphohistiocytosis. These AEs are manageable when treated by an appropriately trained team following established algorithm. In this review, we summarize the results of 3 large phase II CD19 CAR-T cell trials and focus on AEs. We also provide a perspective of ongoing activity in this field with the intend to improve the potency of this emerging novel therapy.
Post-Capillary Pulmonary Hypertension: Clinical Review

Pulmonary hypertension (PH) caused by left heart disease, also known as post-capillary PH, is the most common etiology of PH. Left heart disease due to systolic dysfunction or heart failure with preserved ejection fraction, valvular heart disease, and left atrial myopathy due to atrial fibrillation are causes of post-capillary PH. Elevated left-sided filling pressures cause pulmonary venous congestion due to backward transmission of pressures and post-capillary PH. In advanced left-sided heart disease or valvular heart disease, chronic uncontrolled venous congestion may lead to remodeling of the pulmonary arterial system, causing combined pre-capillary and post-capillary PH. The hemodynamic definition of post-capillary PH includes a mean pulmonary arterial pressure > 20 mmHg, pulmonary vascular resistance < 3 Wood units, and pulmonary capillary wedge pressure > 15 mmHg. Echocardiography is important in the identification and management of the underlying cause of post-capillary PH. Management of post-capillary PH is focused on the treatment of the underlying condition. Strategies are geared towards pharmacotherapy and guideline-directed medical therapy for heart failure, surgical or percutaneous management of valvular disorders, and control of modifiable risk factors and comorbid conditions. Referral to centers with advanced heart and pulmonary teams has shown to improve morbidity and mortality. There is emerging interest in the use of targeted agents classically used in pulmonary arterial hypertension, but current data remain limited and conflicting. This review aims to serve as a comprehensive summary of postcapillary PH and its etiologies, pathophysiology, diagnosis, and management, particularly as it pertains to advanced heart failure.
Bad-Boy Bryozoan Biomarkers : Cheilostome Distribution Patterns Along a Bahamian Depth Gradient

In 1993 and 1994, the Shelf and Slope Experimental Taphonomy Initiative (SSETI) deployed thirty-five samples of sea urchins along the continental shelf/slope of the Bahamas in an effort to explore the paleoecology and taphonomic potentials of shallow water carbonate environments. Samples were retrieved at 1-, 2-, and 6-year intervals for examination and comparison of epibiont accumulation. Tests and spines of the sea urchin Eucidaris were examined for encrusting cheilostome Bryozoa. Specimens were identified to the genus level. Assessment of abundance and distribution patterns with water depth shows that cheilostomes are prevalent in photic waters, and lacking at depth. Burial of substrates limits bryozoan settlement patterns in shallow waters but not below the photic zone. Preliminary results indicate that cheilostomes may be useful biomarkers, at least in modern environments.
A New Machine-Learning-Based Calibration Scheme for MODIS Thermal Infrared Water Vapor Product Using BPNN, GBDT, GRNN, KNN, MLPNN, RF, and XGBoost

The knowledge of atmospheric water vapor distribution is vital to our understanding of weather and climate. In this article, we propose a new calibration scheme based on machine learning to enhance the observational performance of official all-weather precipitable water vapor (PWV) data records from thermal infrared (IR) measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The calibration scheme takes several influence factors into consideration, which are linked with the performance of satellite-retrieved IR PWV measurements. The ground-based water vapor data, acquired from 214 global positioning system (GPS) sites across China in 2016, are regarded as reference PWV to train the machine-learning-based calibration approaches. The evaluation result during 2017–2019 across China shows that the calibrated MODIS IR all-weather PWV product agrees better with GPS-retrieved reference PWV observations, with $R^{2}$ of 0.88–0.94, root-mean-square error (RMSE) of 2.79–4.08 mm, and mean bias (MB) of 0.16–0.52 mm. The RMSE between water vapor measurements from MODIS and GPS can be reduced by 41.74%, 45.76%, 44.29%, and 49.04% in confident-clear, probably-clear, probably-cloudy, and confident-cloudy conditions, respectively. Our methods, developed based on the new calibration scheme, could be a promising tool to the calibration of other satellite-derived IR all-weather water vapor products, which could be also extended to other regions or time periods.
Applications and Challenges of Machine Learning Techniques for Smart Manufacturing in Industry 4.0

The fourth industrial revolution, Industry 4.0, is transforming the manufacturing industry by integrating advanced technologies such as IoT, AI, and ML into the manufacturing process. In particular, the application of machine learning techniques in smart manufacturing has the potential to revolutionize the way manufacturers approach production. This paper reviews the various applications of machine learning techniques in smart manufacturing, including predictive maintenance, quality control, and supply chain management. We also discuss the challenges associated with implementing machine learning techniques in manufacturing, such as data management and privacy concerns, lack of skilled personnel, and the need for effective collaboration between manufacturers and technology providers. Additionally, we provide insights into the future of machine learning in manufacturing, including the potential for increased automation, improved operational efficiency, and the development of new business models. Overall, this paper highlights the potential benefits and challenges associated with the application of machine learning techniques in smart manufacturing, providing valuable insights for manufacturers looking to integrate these technologies into their operations.
Customer Engagement, Customer Equity, and Their Influence on Consumer Repurchase Intention in E-Commerce Mobile Applications

The focus of this research is to determine the effect of customer engagement on repurchase intention using the customer equity dimension mediating the mobile e-commerce application at Shopee Indonesia company. Customer equity has three dimensions: value equity, brand equity, and relationship equity. The research approach is quantitative, with a purposive sampling technique, and the research sample is 160 respondents. This study identified modeling with Structural Equations Analysis (SEM) with SmartPLS 3.29. The findings of this study are that the variable customer engagement has a positive and significant effect on the dimensions of customer equity (value equity, brand equity, and relationship equity). Dimensions of customer equity (value equity, brand equity, and relationship equity) positively and significantly affect repurchase intention. Customer engagement has an indirect and significant positive effect on repurchase intentions mediated by customer equity dimensions (value equity, brand equity, and relationship equity).
Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks

Background Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking. Methods We downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking. Results PMRI, which consists of four anti-PD-L1 immunotherapy-associated genes (IGF2BP3, P4HB, RAC3, and CLK2), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of IGF2BP3 inhibited BCa cell proliferation and migration, and IGF2BP3 was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins. Conclusions Our PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa.
FLEXURAL-SLIP DURING VISCO-ELASTIC BUCKLE FOLDING

Abstract Flexural-slip is considered as an important mechanism during folding and a general conceptual and qualitative understanding has been provided by various field studies. However, quantitative evidence of the importance of the flexural-slip mechanism during fold evolution is sparse due to the lack of suitable strain markers. In this study, 2D finite element analysis is used to overcome these disadvantages and to simulate flexural-slip during visco-elastic buckle folding. Variations of single and multilayer layer fold configurations are investigated, showing that flexural-slip is most likely to occur in effective single layer buckle folds, where slip occurs between contacts of competent layers. Based on effective single layer buckle folds, the influence of the number of slip surfaces, the degree of mechanical coupling (based on the friction coefficient), and layer thickness, on the resulting slip distribution are investigated. The results are in agreement with the conceptual flexural-slip model and show that slip is initiated sequentially during the deformation history and is maximum along the central slip surface of the fold limb. The cumulative amount of slip increases as the number of slip surfaces is increased. For a lower degree of mechanical coupling increased slip results in different fold shapes, such as box folds, during buckling. In comparison with laboratory experiments, geometrical relationships and field observations, the numerical modeling results show similar slip magnitudes. It is concluded that flexural-slip should represent a significant contribution during buckle folding, affecting the resulting fold shape for increased amounts of slip.
Music Emotion Recognition using Convolutional Neural Networks for Regional Languages

Music emotion recognition is a challenging task that has been getting increasing kindness in the field of artificial intelligence. We propose a unique lD Convolutional Neural Network (lD CNN) method for music emotion recognition. Our proposed approach combines the advantages of convolutional neural networks with the lD temporal structure of music signals. Evaluate our model on two publicly available datasets and demonstrate its effectiveness in accurately recognizing different emotions from music signals. Our method presents a novel approach to abstracting raw audio signals into meaningful representations. It utilizes multiple lD convolutional and pooling layers to extract features from the raw audio signals, which can then be used for further analysis. We demonstrate the effectiveness of our approach by providing examples of applications that can benefit from this abstraction process, such as sound recognition and speech recognition. Additionally, we discuss the potential of this method to be applied in other areas such as natural language processing and music generation. We compare the performance of our model with traditional machine learning (ML) methods and demonstrate that our lD CNN outperforms these traditional ML methods in terms of accuracy. We also discuss potential use cases for this technology and its implications for future research in audio signal processing. We show that our model achieves superior results in terms of accuracy and computational efficiency, making it a promising solution for music emotion recognition tasks. Our results suggest that the lD CNN approach is an effective tool for recognizing musical emotions with high accuracy
Evaluating the Efficacy of Eccentric Half-Squats for Post-Activation Performance Enhancement in Jump Ability in Male Jumpers

The purpose of the present study was to investigate effect of post-activation performance enhancement (PAPE) induced by the eccentric half-squat exercise on vertical jump performance in male jumpers. The jumping height, peak power, and work were measured and evaluated in twenty male jumpers (age: 21.2 ± 1.7 years, height: 191.1 ± 3.3 cm, body mass: 81.56 ± 7.3 kg) who participated in the national championship last year. Participants performed five eccentric half-squats at 85% of their one-repetition maximum (1 RM), with a knee angle below 90°, followed by immediate and 2 min delayed jump assessments using the Squat Jump (SJ) and Countermovement Jump (CMJ) tests. Results showed that this specific PAPE protocol did not significantly improve jump performance for the SJ (Height: ES = 0.613, p = 0.462, Work: ES = 0.124, p = 0.231, Power: ES = 0.382, p = 0.125) or CMJ (Height: ES = 0.523, p = 0.368, Work ecc: ES = 0.133, p = 0.505 (only main effect time p < 0.05), Work con: ES = 0.114, p = 0.101, Power ecc: ES = 0.134, p = 0.177, Power con: ES = 0.182, p = 0.195, Leg stiffness: ES = 0.095, p = 0.358) tests. Factors such as stimulus specificity, rest intervals, muscle length, and the balance between potentiation and fatigue may explain these results. This study highlights the complexity of PAPE responses and suggests that a single set of eccentric squats with a short rest may not improve jump performance in male jumpers. Further research is required to optimize the interplay between conditioning stimuli and rest periods to maximize PAPE effects in athletic performance enhancement.