Miracle-dz/UniFAR-FTUs huggingface dataset:
Global donor and acceptor splicing site kinetics in human cells

RNA splicing is an essential part of eukaryotic gene expression. Although the mechanism of splicing has been extensively studied in vitro, in vivo kinetics for the two-step splicing reaction remain poorly understood. Here, we combine transient transcriptome sequencing (TT-seq) and mathematical modeling to quantify RNA metabolic rates at donor and acceptor splice sites across the human genome. Splicing occurs in the range of minutes and is limited by the speed of RNA polymerase elongation. Splicing kinetics strongly depends on the position and nature of nucleotides flanking splice sites, and on structural interactions between unspliced RNA and small nuclear RNAs in spliceosomal intermediates. Finally, we introduce the ‘yield’ of splicing as the efficiency of converting unspliced to spliced RNA and show that it is highest for mRNAs and independent of splicing kinetics. These results lead to quantitative models describing how splicing rates and yield are encoded in the human genome.
Oxaliplatin-Based Chemotherapy in Patients with Metastatic Colorectal Cancer Aged at Least 75 Years: A Post-Hoc Subgroup Analysis of Three Phase II Trials

Patients older than 75 years of age are usually excluded from metastatic colorectal cancer studies based on a combination chemotherapy containing oxaliplatin. Our group conducted three phase II trials in elderly patients in recent years. A post-hoc subgroup analysis of 67 patients aged at least 75 years was included in this study. Oxaliplatin was combined with capecitabine in two trials and with uracil-tegafur (UFT) plus folinic acid in the third trial. In one study, bevacizumab was also added to chemotherapy. The median age of patients was 77 years, and all had a good performance status (0 to 1). The observed overall response rate was 45%, comparable to younger patients (51%, p = 0.49). The estimated median progression-free survival (PFS) time and overall survival (OS) time were 8.7 and 19.3 months, respectively. These results did not significantly differ from those in younger patients (8.0 months for PFS (p = 0.58) and 19.7 months for OS (p = 0.94), respectively). The most common grade 3–4 adverse events included diarrhea (13%), fatigue (13%), peripheral neuropathy (10%), and neutropenia (7%). Moreover, the toxicity was never statistically different from that in younger patients. The efficacy of oxaliplatin-based combination was maintained in fit elderly patients ≥75 years.
Medial Temporal Lobe Atrophy is Related to Learning Strategy Changes in Amnestic Mild Cognitive Impairment

Abstract Objective: Deficits in the semantic learning strategy were observed in subjects with amnestic mild cognitive impairment (aMCI) in our previous study. In the present study, we explored the contributions of executive function and brain structure changes to the decline in the semantic learning strategy in aMCI. Methods: A neuropsychological battery was used to test memory and executive function in 96 aMCI subjects and 90 age- and gender-matched healthy controls (HCs). The semantic clustering ratio on the verbal learning test was calculated to evaluate learning strategy. Medial temporal lobe atrophy (MTA) and white matter hyperintensities (WMH) were measured on MRI with the MTA and Fazekas visual rating scales, respectively. Results: Compared to HCs, aMCI subjects had poorer performance in terms of memory, executive function, and the semantic clustering ratio (P < .001). In aMCI subjects, no significant correlation between learning strategy and executive function was observed. aMCI subjects with obvious MTA demonstrated a lower semantic clustering ratio than those without MTA (P < .001). There was no significant difference in the learning strategies between subjects with high-grade WMH and subjects with low-grade WMH. Conclusion: aMCI subjects showed obvious impairment in the semantic learning strategy, which was attributable to MTA but independent of executive dysfunction and subcortical WMH. These findings need to be further validated in large cohorts with biomarkers identified using volumetric brain measurements. (JINS, 2019, 25, 706–717)
Discordancy for a Villous Maturation Defect in a Dizygotic Twin Placenta

Abstract Background: Dizygotic twin pregnancies with discordant manifestation of abnormalities with unclear etiology are of interest because they arise in the same environment. Case report: We present a dizygotic third trimester twin placenta with discordant villous maturation, one placenta lacking developed syncytiocapillary membranes. The twins were eutrophic with no perinatal or postnatal complications. Conclusions: Discordant manifestation of villous maturation in dizygotic twin placentas could be a hint for a genetic rather than an environmental etiology. Villous maturation defect may be underrecognized and has been associated with perinatal morbidity and stillbirth in the late third trimester. Proper recognition is important because of the increased recurrence risk of villous dysmaturity.
MDDCMA: A Distributed Image Fusion Framework Based on Multiscale Dense Dilated Convolution and Coordinate Mean Attention

In the task of fusing infrared and visible images, the extraction of features and fusion strategy significantly impacts the outcome of the fusion. However, prevailing fusion methods are often manually designed, unlearnable, and neglect to consider context adequately. To address these issues, this paper introduces a distributed architecture network based on attention mechanism and dense dilated convolution, realizing three-channel data fusion. This network employs a distributed fusion framework to fully utilize the fusion output of the previous step, capitalizing effectively on the target and texture information in infrared and visible images. Initially, two channels gather ample context from the source images through a dense dilated convolution module with multiscale channel attention. Subsequently, a fusion strategy based on coordinate mean attention is adopted to facilitate the fusion of results between the two channels. Then, the fused features and the preceding fusion results are fed into the fusion channel, minimizing loss of target and texture information in infrared and visible images. Furthermore, we incorporate an edge correction block, capable of refining the edge details of the fusion results and effectively suppressing noise. The proposed method demonstrates good fusion performance and extensive ablation experiments validate the effectiveness of the proposed methodology. Simultaneously, both subjective qualitative and objective quantitative comparison results, conducted on public datasets such as RoadScene, TNO, and MSRS, indicate that the visual quality and evaluation metrics of our fusion images are comparable to those achieved by the state-of-the-art image fusion methods.
Skin Disease Prediction Using Machine Learning Techniques

An innovative machine learning-based decision support system for the accurate classification of various skin conditions like eczema, psoriasis, melanoma, and dermatitis. It involves preprocessing images, feature extraction, model training, and rigorous evaluation using metrics like accuracy and precision. A user-friendly interface facilitates seamless integration into clinical practice, with plans to expand the dataset, enhance interpretability, and incorporate real-time data for improved skin disease predictions, benefiting both dermatologists and patients.
Marathi Extractive Text Summarizer Using Graph Based Model

Manual summarization of large documents of texts is tedious and error prone. Also, the results in such kind of summarization may lead to different results for a particular document. Thus, Automatic text summarization has become important due to the tremendous growth of information and data. It chooses the most informative part of text and forms summaries that reveal the main purpose of the given document. It yields summary produced by summarization system which allows readers to comprehend the content of document instead for reading each and every individual document. So, the overall intention of Text Summarizer is to provide the meaning of text in less words and sentences. Summarization can be categorized as: Abstractive summarization and Extractive summarization. This case study is based on an extractive concept implemented on the studied models. Numerous automatic text summarization systems are handy today for English and other foreign languages. But when it comes to Indian languages, we observe inadequate number of automatic summarizers. Our efforts in this direction are mainly for developing automatic text summarizer for marathi Language. We look forward to evaluate the obtained summaries using ROUGE metric. This paper describes a multi document marathi extractive summarizer.
Molecular sexing assays in 114 mammalian species: In silico sequence reanalysis and a unified graphical visualization of diagnostic tests

Abstract Molecular‐based methods for identifying sex in mammals have a wide range of applications, from embryo manipulation to ecological studies. Various sex‐specific or homologous genes can be used for this purpose, PCR amplification being a common method. Over the years, the number of reported tests and the range of tested species have increased greatly. The aim of the present analysis was to retrieve PCR‐based sexing assays for a range of mammalian species, gathering the gene sequences from either the articles or online databases, and visualize the molecular design in a uniform manner. For nucleotide alignment and diagnostic test visualization, the following genomic databases and tools were used: NCBI, Ensembl Nucleotide BLAST, ClustalW2, and NEBcutter V2.0. In the 45 gathered articles, 59 different diagnostic tests based on eight different PCR‐based methods were developed for 114 mammalian species. Most commonly used genes for the analysis were ZFX, ZFY, AMELX, and AMELY. The tests were most commonly based on sex‐specific insertions and deletions (SSIndels) and sex‐specific sequence polymorphisms (SSSP). This review provides an overview of PCR‐based sexing methods developed for mammals. This information will facilitate more efficient development of novel molecular sexing assays and reuse of previously developed tests. Development of many novel and improvement of previously developed tests is also expected with the rapid increase in the quantity and quality of available genetic information.
Does quality disconfirmation in tourism e-marketplace lead to negative tourist emotions and behaviors?

It is important for tourism online marketplace to ensure potential tourists continually use their services as an alternative to find and purchase travel packages. Hence, the factors that influence customers’ negative behaviors, such as switching intention, complaints, and negative WOM are critical to be understood. This research explores the antecedents of tourist customers’ negative behaviors, using expectancy disconfirmation theory (EDT) as its theoretical lens, and IS success model as the initial quality precursors. The research gathered 81 sample, whom were active users of a tourism eMarketplace from the greater Jakarta region. The quantitative data was then analyzed using PLS-SEM. The results showed that quality disconfirmation served as a key antecedent of customers’ negative emotions, which in turn will lead to their negative behaviors. More specifically, disconfirmation in information quality may prompt customers’ regret, which then leads to negative WOM. Finally, systems quality disconfirmation triggers customers’ anger, which then elicits a number of negative behaviors such as switching intention, complaints and negative WOM.
Drowsiness Detection Using CNN Architecture

Nowadays, road accidents occur owing to the distractions of people who drives the vehicles by Internal or External factors. Using of Mobile phones and driving continuously for several hours are some of the reasons of driver distraction. Among these, the main distractions faced by drivers are drowsiness. Specifically, a driver turns to sleep while driving causes an accident. Latest studies reveal that approximately 20% of vehicle hits have caused by drowsy drivers. And now, these types of accidents can be detected by using modern software technologies. In that, Deep learning is a category of Artificial Intelligence (AI) concepts, which imitate the way humans, gain certain types of knowledge. Deep Learning plays a vital role in health care industry also. The main characteristic of deep learning is, it can built a model automatically by self-learning. In deep learning, Convolutional Neural Network (CNN) is a type of deep neural networks, which is applied to investigate visual imagery. In this research work, a CNN model will be built to identify the drowsiness of the driver by observing the driver's face reactions. The driver drowsiness dataset is downloaded from data-flair website and trained with CNN architecture. In this work, an image of driver's face will be captured through web camera and compared with trained images . If matches occur, an alarm will be given to make awareness to the driver and to avoid accidents.
Secure Data Storage in Cloud Using Cryptographic Algorithms

Cloud computing security refers to protection of data in cloud, its applications and the underlying infrastructure using some set of policies, controls and technologies. Information security or secure data storage, a part of cloud security aims to prevent unauthorized users to access the data and avoids destruction or modification of one's personal information. When one's data is taken to the cloud, lot of security concerns arises and assuring security for the data is still questionable. This paper proposes ways to secure data in cloud using strong cryptographic algorithms like RSA, AES and One Time Pad. Strong cryptographic algorithms are generally mature and unbreakable. Among the existing classical or modern algorithms, one-time pad is an unbreakable algorithm. This paper proposes combination of above three models and analyzes the algorithms by comparing them for storing the data in cloud. The analysis suggests the use of RSA and OTP with variations due to its less time complexity and space complexity.
Rural-urban disparities in age trajectories of depression caseness in later life: The China Health and Retirement Longitudinal Study

Background No consensus has been reached on whether depression decreases or increases with age in later life. Majority of the evidence comes from Western societies, while little is known about this relationship and its rural-urban disparities in the Chinese context. Methods Three waves of data from 15,501 Chinese adults aged 45–85 years from the China Health and Retirement Longitudinal Study, Chinese sister study of Health and Retirement Study, were used. Depression caseness was identified using the 10-item Center for Epidemiologic Studies Depression Scale (score ≥12). Urbanisation levels were determined by combining rural-urban residence and rural-urban Hukou (a household registration system). Odds ratios and predicted probabilities of depression caseness were estimated using generalised linear mixed models. Results For both men and women and across all ages, the crude predicted probability of depression caseness was the highest in the rural group, followed by the semi-urban group, and the lowest in the urban group. The probability was stable over age among urban men (around 0.05), but it increased at an accelerated rate with age among semi-urban men (0.25 at age 85, 95% confidence interval [CI]: 0.13–0.44) and rural men (0.29 at age 85, 95% CI: 0.22–0.39). Among women the age pattern was similar between the urbanisation groups: the probability increased with age, reached a peak at ages 75–80 (urban women: 0.16, 95% CI: 0.13–0.20; semi-urban women: 0.28, 95% CI: 0.20–0.39; rural women: 0.41, 95% CI: 0.36–0.46), and decreased slightly afterwards. These differences were significantly attenuated when socio-demographic characteristics and physical disability, but not when behaviour-related factors, were controlled for. Conclusion The age trajectories of later-life depression caseness varied by gender and urbanisation levels, and were not U-shaped as in many Western societies. The increasing depression caseness with age and the large rural disadvantage were substantially driven by socio-demographic characteristics and physical disability.
Effects of esmolol on QTc interval changes during tracheal intubation: a systematic review

Introduction and aims Esmolol is an ultra-short-acting β1 antagonist that has been shown to attenuate the corrected QT (QTc) interval prolongation associated with laryngoscopy and endotracheal intubation (LTI). Prolongation of the QTc interval can precipitate arrhythmias, the most serious of which is torsades de pointes . The aim of this systematic review was to compare esmolol and placebo on QTc changes occurring during LTI. Materials and methods PubMed, EMBASE, Cochrane Registry of Clinical Trials and CINAHL databases (up to August 2018) were screened for randomised controlled trials comparing esmolol and placebo on QTc changes during LTI in cardiac and non-cardiac surgeries. The primary outcome was QTc changes during LTI and secondary outcome was related to adverse effects from esmolol such as bradycardia and hypotension. Results Seven trials were identified involving 320 patients, 160 patients receiving esmolol or placebo apiece. A shortening of the QTc post-LTI was evident in the esmolol group compared with the placebo in four studies. Compared with the baseline, the QTc was reduced post-LTI in the esmolol group. In the placebo group, the QTc was prolonged compared with the baseline post LTI. Nonetheless, esmolol did not prevent QTc prolongation in the remaining three studies, and much of this was attributed to employing QTc prolonging agents for premedication and anaesthetic induction. No significant adverse events were noted. Conclusion Compared with placebo, esmolol reduced the LTI-induced QTc prolongation when current non-QTc prolonging agents were chosen for tracheal intubation. Future studies should explore whether transmural dispersion (a marker of torsadogenicity) is also affected during LTI by analysing parameters such as the Tp-e interval (interval between the peak to the end of the T-wave) and Tp-e/QTc (rate corrected Tp-e interval). Trial registration number CRD42018090282.
Effect of Bone Marrow-Derived Mesenchymal Stem Cells on Ischaemic-Reperfused Hearts in Adult Rats with Established Chronic Kidney Disease

Background and Objectives Bone marrow-derived mesenchymal stem cells (BM-MSCs) are adult multipotent non-haematopoietic stem cells that have regeneration potential. The current study aimed to detect the ability of BM-MSCs to improve kidney and cardiac functions in adult rats with established chronic kidney disease. Methods Rats were divided into sham-operated control, untreated sub totally nephrectomised and treated sub totally nephrectomised groups. Body weight, kidney and cardiac tissue weights, plasma creatinine and urea levels and arterial blood pressure were measured. ECG was recorded, and an in vitro isolated heart study was performed. Results: Stem cell treatment decreased the elevated plasma creatinine and urea levels and decreased systolic, diastolic and mean arterial blood pressure values. These changes were accompanied by a decrease in glomerular hypertrophy with apparent normal renal parenchyma. Additionally, BM-MSCs shortened Q-To and Q-Tc intervals, all time to peak tension values, the half relaxation value at 30 min of reperfusion and the contraction time at 15 and 30 min of reperfusion. Moreover, stem cell treatment significantly increased the heart rate, QRS voltage, the peak tension at the 15- and 30-min reperfusion time points and the peak tension per left ventricle at the 30-min reperfusion time point compared to the pre-ischaemia baseline. BM-MSCs resolve inter muscular oedema and lead to the re-appearance of normal cardiomyocytes. This improvement occurs with the observations of BM-MSCs in renal and heart tissues. Conclusions BM-MSCs can attenuate chronic kidney disease progression and the associated cardiac electrophysiological and inotropic dysfunction.
Spatio–Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks †

Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio–temporal patterns of skeletal movements and how to learn their discriminative features for classification tasks. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.
Cell–cell fusion induced by reovirus FAST proteins enhances replication and pathogenicity of non-enveloped dsRNA viruses

Fusogenic reoviruses encode fusion-associated small transmembrane (FAST) protein, which induces cell–cell fusion. FAST protein is the only known fusogenic protein in non-enveloped viruses, and its role in virus replication is not yet known. We generated replication-competent, FAST protein-deficient pteropine orthoreovirus and demonstrated that FAST protein was not essential for viral replication, but enhanced viral replication in the early phase of infection. Addition of recombinant FAST protein enhanced replication of FAST-deficient virus and other non-fusogenic viruses in a fusion-dependent and FAST-species-independent manner. In a mouse model, replication and pathogenicity of FAST-deficient virus were severely impaired relative to wild-type virus, indicating that FAST protein is a major determinant of the high pathogenicity of fusogenic reovirus. FAST-deficient virus also conferred effective protection against challenge with lethal homologous virus strains in mice. Our results demonstrate a novel role of a viral fusogenic protein and the existence of a cell–cell fusion-dependent replication system in non-enveloped viruses.