Large Datasets Research Articles

Application of a variational autoencoder for clustering and analyzing in situ articular cartilage cellular response to mechanical stimuli.

In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge-such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method-the Variational Autoencoder (VAE)-to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.

Domain Specific Named Entity Recognition Using Natural Language Processing (NLP) and Software Engineering in Production World

This research is concerned with domain-specific named entity recognition in which certain keywords are withdrawn from the phrase and sentences of the words or paragraph. The project in hand has been effective in different sectors like the insurance companies and hospitals for withdrawing the required keywords in a small amount of time. For instance, a medicine that belongs to a specific keyword is extracted easily and the same occurs with necessary keywords in the case of insurance companies. Advancements in deep neural networks have been established to build the named entity recognition model (NER Model). We have used Spacy and natural language processing for keyword extraction. The future scope of the project is to use LSTM for domain-specific keyword extraction and transfer learning using NLP. A large dataset is required to build the system for keyword extraction since dataset have always been the challenging part of such a system.

Revolutionizing Cybersecurity with Machine Learning: A Comprehensive Review and Future Directions

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

A combined lipidomic and proteomic profiling of Arabidopsis thaliana plasma membrane.

The plant plasma membrane (PM) plays a key role in perception of environmental signals, and set-up of adaptive responses. An exhaustive and quantitative description of the whole set of lipids and proteins constituting the PM is necessary to understand how these components allow to fulfill such essential physiological functions. Here we provide by state-of-the-art approaches the first combined reference of the plant PM lipidome and proteome from Arabidopsis thaliana suspension cell culture. We identified and quantified a reproducible core set of 2165 proteins, which is by far the largest set of available data concerning this plant PM proteome. Using the same samples, combined lipidomic approaches, allowing the identification and quantification of an unprecedented repertoire of 414 molecular species of lipids showed that sterols, phospholipids, and sphingolipids are present in similar proportions in the plant PM. Within each lipid class, the precise amount of each lipid family and the relative proportion of each molecular species were further determined, allowing to establish the complete lipidome of Arabidopsis PM, and highlighting specific characteristics of the different molecular species of lipids. Results obtained point to a finely tuned adjustment of the molecular characteristics of lipids and proteins. More than a hundred proteins related to lipid metabolism, transport, or signaling have been identified and put in perspective of the lipids with which they are associated. This set of data represents an innovative resource to guide further research relative to the organization and functions of the plant PM.

Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.

Most studies on surgical activity recognition utilizing artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical video datasets. It remains speculative whether those models would generalize to other centers. In this work, we introduce a large multi-centric multi-activity dataset consisting of 140 surgical videos (MultiBypass140) of laparoscopic Roux-en-Y gastric bypass (LRYGB) surgeries performed at two medical centers, i.e., the University Hospital of Strasbourg, France (StrasBypass70) and Inselspital, Bern University Hospital, Switzerland (BernBypass70). The dataset has been fully annotated with phases and steps by two board-certified surgeons. Furthermore, we assess the generalizability and benchmark different deep learning models for the task of phase and step recognition in 7 experimental studies: (1) Training and evaluation on BernBypass70; (2) Training and evaluation on StrasBypass70; (3) Training and evaluation on the joint MultiBypass140 dataset; (4) Training on BernBypass70, evaluation on StrasBypass70; (5) Training on StrasBypass70, evaluation on BernBypass70; Training on MultiBypass140, (6) evaluation on BernBypass70 and (7) evaluation on StrasBypass70. The model's performance is markedly influenced by the training data. The worst results were obtained in experiments (4) and (5) confirming the limited generalization capabilities of models trained on mono-centric data. The use of multi-centric training data, experiments (6) and (7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments (1) and (2)). MultiBypass140 shows considerable variation in surgical technique and workflow of LRYGB procedures between centers. Therefore, generalization experiments demonstrate a remarkable difference in model performance. These results highlight the importance of multi-centric datasets for AI model generalization to account for variance in surgical technique and workflows. The dataset and code are publicly available at https://github.com/CAMMA-public/MultiBypass140.

Data Plants: Growing Insights in Agriculture through Data Science

Data Science blends different scientific methods to extract valuable insights from large sets of data. Agriculture is crucial for India's economy because more than half of its population relies on it as their main source of income. Research and Extension systems are key in developing and spreading agricultural technologies to boost farmers' earnings. Recently, the use of data has become a game-changer in farming, transforming traditional practices and promoting sustainability efforts. This paper investigates how data-driven methods like machine learning, remote sensing, and predictive analytics are reshaping farming operations and decision-making processes. It also discusses the challenges hindering the widespread adoption of data-driven approaches in farming, such as data security issues and lack of infrastructure. Despite these challenges, there are many opportunities for further exploration and innovation, emphasizing the importance of collaboration across disciplines and policy interventions to maximize the benefits of data-driven approaches for sustainable food production. In summary, this study highlights the significant role of data in farming and its importance in addressing global food security and environmental sustainability challenges.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation.

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Automated Scoring of Alzheimer’s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation

Background: Application of visual scoring scales for regional atrophy in Alzheimer’s disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

‘In the company of cheerful ladies’: whether female entrepreneurs are more productive?

AbstractAfter over two decades, the debate on the female underperformance hypothesis remains not concluded. This study sheds some new light on the hypothesis by (i) showing that surrounding institutional forces play an important role in determining how female businesses perform and (ii) arguing that to understand gender differences (or lack of those) in performance, we need to look at productivity alongside profitability, revenues, and growth. Specifically, we posit that, in certain developing countries, female entrepreneurs devise specific strategies to cope with challenging institutional contexts. In such contexts, female entrepreneurs have less opportunity to realize economic rents compared to males, but they respond to these constraints by becoming more efficient in resource use through relying on female employment. Investigating a large set of longitudinal data from Vietnam, we find that female businesses are more productive than male businesses, and that this effect is stronger when female owner-managers employ more female employees, or even female employees only. However, we also find that these positive effects are weakened with increased corruption. This provides important implications for female entrepreneurs and policymakers in developing countries.

Universality of phenotypic distributions in bacteria

Some phenotypic properties in bacteria exhibit universal statistics, with distributions collapsing under scaling. The extent and origins of such universality are not well understood. Using phenomenological modeling of growth and division, we identify compound “shape factors” that describe the distributions throughout a large set of single-cell data. We find that the emergence of universal distributions is associated with the robustness of shape factors across conditions, explaining the universality of cell size and highly expressed protein content and the nonuniversality of times between consecutive divisions. A wide range of experimental data sets support our theory quantitatively. Published by the American Physical Society 2024

Discovery of an endemism hotspot of freshwater mussels (Bivalvia: Unionidae) in Assam, with a description of two new genera

Abstract The Indian subcontinent houses a unique fauna of freshwater mussels. Tectonic and biogeographic reconstructions indicate that this fauna represents a derivative of Mesozoic Gondwanan biota and that it arrived in Asia on the Indian Plate, using this tectonic block as a ‘biotic ferry’. Though a preliminary integrative revision of the Indian Unionidae was published recently, the level of endemism and taxonomic richness of regional assemblages are unsatisfactorily known. Here, we revise the taxonomy of freshwater mussels from the Brahmaputra River basin based on a large DNA-sequence and morphological dataset that was sampled in Assam, north-eastern India. We managed to collect and sequence topotypes for nearly all nominal species described from the region, except for Lamellidens friersoni and Parreysia smaragdites. Our phylogenetic and morphology-based research reveals that the regional Unionidae assemblage contains nine species, belonging to a single subfamily, the Parreysiinae. Seven species and three genera (Balwantia, Pseudoscabies Bolotov, Sonowal, Kardong, Pasupuleti and Subba Rao gen. nov., and Assamnaia Bolotov, Sonowal, Kardong, Pasupuleti and Subba Rao gen. nov.) appear to be endemic to the region, indicating that the Brahmaputra River may represent an endemism hotspot of global significance. Our findings highlight that this region should be considered a high-priority area for freshwater conservation management.

A privacy-preserving publicly verifiable quantum random number generator

Verifying the quality of a random number generator involves performing computationally intensive statistical tests on large data sets commonly in the range of gigabytes. Limitations on computing power can restrict an end-user’s ability to perform such verification. There are also random number-based applications where an honest user needs to publicly demonstrate that the random bits they are using pass the statistical tests without the bits being revealed. Here, we report the implementation of an entanglement-based protocol that allows a third party to publicly perform statistical tests without compromising the privacy of the random bits.

Evaluation of the Impact of ChatGPT on the Selection of Surgical Technique in Bariatric Surgery.

With the growing interest in artificial intelligence (AI) applications in medicine, this study explores ChatGPT's potential to influence surgical technique selection in metabolic and bariatric surgery (MBS), contrasting AI recommendations with established clinical guidelines and expert consensus. Conducting a single-center retrospective analysis, the study involved 161 patients who underwent MBS between January 2022 and December 2023. ChatGPT4 was used to analyze patient data, including demographics, pathological history, and BMI, to recommend the most suitable surgical technique. These AI recommendations were then compared with the hospital's algorithm-based decisions. ChatGPT recommended Roux-en-Y gastric bypass in over half of the cases. However, a significant difference was observed between AI suggestions and actual surgical techniques applied, with only a 34.16% match rate. Further analysis revealed any significant correlation between ChatGPT recommendations and the established surgical algorithm. Despite ChatGPT's ability to process and analyze large datasets, its recommendations for MBS techniques do not align closely with those determined by expert surgical teams using a high success rate algorithm. Consequently, the study concludes that ChatGPT4 should not replace expert consultation in selecting MBS techniques.

Enhancing Social Media Insights: Leveraging Artificial Intelligence for Sentiment Analysis

Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.

Six-minute walk test in healthy British service personnel

IntroductionThe 6 min walk test (6MWT) is a widely used, safe and effective submaximal exercise test. The primary outcome is the distance walked, but additional physiological and patient-reported metrics can...

Indian Preference and the status of American Indians and Alaska Natives in the federal service: Employment, earnings, authority, and perceptions of fairness

AbstractPublic administration scholars have largely ignored American Indians and Alaska Natives in their studies of racial disparities in the federal service, despite strong reasons to believe they face discrimination. Using three large federal data sets (the American Community Survey, federal personnel records, and the Federal Employee Viewpoint Survey), we compare the status of American Indians and Alaska Natives in the federal service to both Whites and other minorities. We find that, largely due to Indian Preference, American Indians and Alaska Natives are much more likely than Blacks, Whites, Latinos, and Asians to hold federal jobs, but they are highly concentrated in the Bureau of Indian Affairs and the Indian Health Service, agencies with which they have a sordid and fraught history. This concentration allows them to work in positions that may advance the interests of their communities and increases their probabilities of being supervisors, but it does not prevent them from being among the poorest‐paid and least‐satisfied employees in the federal service.

Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments

For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (kobs) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log kobs, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (ADmax), average absolute relative deviation (AARD%), and R-squared (R2) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, ADmax: 3.795/3.732, AARD%: 641.0/821.2, and R2: 0.003/0.447. For the test set, AAD, AARD%, ADmax, and R2 values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log kobs, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.

Nationwide Prevalence of Inherited Retinal Diseases in the Israeli Population

Data regarding the prevalence of various inherited retinal diseases (IRDs) are limited and vary across populations; moreover, nationwide prevalence studies may be limited to a specific IRD phenotype, potentially leading to inaccurate prevalence estimations. Therefore, nationwide prevalence data are needed. To determine the prevalence of 67 IRD phenotypes in the Israeli population. This cohort study collected nationwide data regarding the number of individuals affected with IRD phenotypes assessed in 10 clinical and academic centers in Israel as part of the research activity of the Israeli inherited retinal disease consortium. Data were collected in May 2023 on 9396 individuals residing in Israel who were diagnosed by an ophthalmologist with an IRD using either electroretinography or retinal imaging where included. Individuals with retinal diseases known to have a nonmendelian basis or without a clear genetic basis and those who were reported as deceased at the time of data collection were excluded from this study. Prevalence of 67 IRD phenotypes. Among the 9396 participants in our cohort, the most common IRD in Israel was retinitis pigmentosa with a disease prevalence of approximately 1:2400 individuals, followed by cone-rod dystrophy (approximately 1:14 000), Stargardt disease (approximately 1:16 000), Usher syndrome (approximately 1:16,000), and congenital stationary night blindness (approximately 1:18 000). The prevalence of all IRDs combined was 1:1043 individuals. The current study provides large prevalence dataset of 67 IRD phenotypes, some of which are extremely rare, with only a single identified case. This analysis highlights the potential importance of performing additional nationwide prevalence studies to potentially assist with determining the prevalence of IRDs worldwide.

Impact of osteoporotic risk in men undergoing transcatheter aortic valve replacement: a report from the LAPLACE-TAVI registry.

Low body weight and advanced age are among the best predictors of osteoporosis. Osteoporosis Self-Assessment Tool (OST) values are calculated by a simple formula [(body weight in kilograms - age in years) × 0.2] to identify patients at increased risk of osteoporosis. In our recent single-center study, we demonstrated an association between OST and poor outcomes in postmenopausal women after transcatheter aortic valve replacement (TAVR). We aimed to investigate the impact of osteoporotic risk in men with aortic stenosis who underwent TAVR in a large cohort. In this multi-center study, 1,339 men who underwent TAVR between April 2010 and July 2023 were retrospectively analyzed. Women were excluded from the present study. All patients were deemed appropriate for TAVR after a review by a multidisciplinary team. Baseline characteristics of patients were compared by dividing patients into three tertiles, based on the OST value: ≤ -6.16, -6.16 to -4.14, and -4.14 < for tertiles 1, 2, and 3, respectively. Primary endpoint was all-cause mortality after TAVR. Tertile 1 (patients with the lowest OST values) included older patients with smaller body mass index, lower hemoglobin and albumin levels. In addition, they had greater clinical frailty scale, slower 5-meter walk test, weaker hand grip strength, and more cognitive impairment, indicating increased frailty. They were more severely symptomatic, with lower ejection fractions, smaller aortic valve areas, and more tricuspid regurgitation than were patients in the other two groups. Multivariate analysis revealed that OST tertiles 3 was associated with decreased risk of all-cause mortality (hazard ratio, 0.66; 95% confidence interval, 0.48-0.90), compared with OST tertile 1 as a reference. For OST tertiles 1, 2, and 3, the estimated 1-year survival rates of all-cause mortality post-TAVR were 83.6% ± 1.9%, 91.1% ± 1.4%, and 93.1% ± 1.3%, respectively, (log-rank, p <0.001). In conclusions, in men as same as women, osteoporotic risk assessed by OST values was overlapped with increased frailty. The simple OST formula was useful for predicting all-cause mortality in patients undergoing TAVR in large registry datasets.

1q amplification and PHF19 expressing high-risk cells are associated with relapsed/refractory multiple myeloma

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers can be identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and is already studied using RNA-seq. In this study, we generate a large (325,025 cells and 49 patients) single cell multi-omic dataset and jointly quantify ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identify an association between one plasma cell subtype with myeloma progression that we call relapsed/refractory plasma cells (RRPCs). These cells are associated with chromosome 1q alterations, TP53 mutations, and higher expression of PHF19. We also identify downstream regulation of cell cycle inhibitors in these cells, possible regulation by the transcription factor (TF) PBX1 on chromosome 1q, and determine that PHF19 may be acting primarily through this subset of cells.