Large-scale Data Research Articles

Optimizing Hadoop Data Locality: Performance Enhancement Strategies in Heterogeneous Computing Environments

As organizations increasingly harness big data for analytics and decision-making, the efficient processing of massive datasets becomes paramount. Hadoop, a widely adopted distributed computing framework, excels in processing large-scale data. However, its performance is contingent on effective data locality, which becomes challenging in heterogeneous computing environments comprising diverse hardware resources. This research addresses the imperative of enhancing Hadoop’s data locality performance in heterogeneous computing environments. The study explores strategies to optimize data placement and task scheduling, considering the diverse characteristics of nodes within the infrastructure. Through a comprehensive analysis of Hadoop’s data locality algorithms and their impact on performance, this work proposes novel approaches to mitigate challenges associated with disparate hardware capabilities. Weighted Extreme Learning Machine Technique (Weighted ELM) with the Firefly Algorithm (WELM-FF) is used in the proposed work. The integration of Weighted Extreme Learning Machine (WELM) with the Firefly Algorithm holds promise for enhancing machine learning models in the context of large-scale data processing. The research employs a combination of theoretical analysis and practical experiments to evaluate the effectiveness of the proposed enhancements. Factors such as network latency, disk I/O, and CPU capabilities are taken into account to develop a holistic framework for improving data locality and, consequently, overall Hadoop performance. The findings presented in this study contribute valuable insights to the field of distributed computing, offering practical recommendations for organizations seeking to maximize the efficiency of their Hadoop deployments in heterogeneous computing environments. By addressing the intricacies of data locality, this research strives to enhance the scalability and performance of Hadoop clusters, thereby facilitating more effective utilization of big data resources.

Extraordinarily Time- and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep.

Canonical correlation analysis (CCA) is a correlation analysis technique that is widely used in statistics and the machine-learning community. However, the high complexity involved in the training process lays a heavy burden on the processing units and memory system, making CCA nearly impractical in large-scale data. To overcome this issue, a novel CCA method that tries to carry out analysis on the dataset in the Fourier domain is developed in this article. Appling Fourier transform on the data, we can convert the traditional eigenvector computation of CCA into finding some predefined discriminative Fourier bases that can be learned with only element-wise dot product and sum operations, without complex time-consuming calculations. As the eigenvalues come from the sum of individual sample products, they can be estimated in parallel. Besides, thanks to the data characteristic of pattern repeatability, the eigenvalues can be well estimated with partial samples. Accordingly, a progressive estimate scheme is proposed, in which the eigenvalues are estimated through feeding data batch by batch until the eigenvalues sequence is stable in order. As a result, the proposed method shows its characteristics of extraordinarily fast and memory efficiencies. Furthermore, we extend this idea to the nonlinear kernel and deep models and obtained satisfactory accuracy and extremely fast training time consumption as expected. An extensive discussion on the fast Fourier transform (FFT)-CCA is made in terms of time and memory efficiencies. Experimental results on several large-scale correlation datasets, such as MNIST8M, X-RAY MICROBEAM SPEECH, and Twitter Users Data, demonstrate the superiority of the proposed algorithm over state-of-the-art (SOTA) large-scale CCA methods, as our proposed method achieves almost same accuracy with the training time of our proposed method being 1000 times faster. This makes our proposed models best practice models for dealing with large-scale correlation datasets. The source code is available at https://github.com/Mrxuzhao/FFTCCA.

Data Fusion for Enhancing Urban Air Quality Modeling using Large-Scale Citizen Science

Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.

Natural History and Clinicopathological Associations of TRPC6-Associated Podocytopathy.

Understanding the genetic basis of human diseases has become integral to drug development and precision medicine. Recent advancements have enabled the identification of molecular pathways driving diseases, leading to targeted treatment strategies. The increasing investment in rare diseases by the biotech industry underscores the importance of genetic evidence in drug discovery and approval processes. Here we studied a monogenic Mendelian kidney disease, TRPC6-associated podocytopathy (TRPC6-AP), to present its natural history, genetic spectrum, and clinicopathological associations in a large cohort of patients with causal variants in TRPC6, in order to help define the specific features of disease and further facilitate drug development and clinical trials design. the study involved 64 individuals from 39 families with TRPC6 causal missense variants. Clinical data, including age of onset, laboratory results, response to treatment, kidney biopsy findings, and genetic information, were collected from multiple centers nationally and internationally. Exome or targeted sequencing was performed and variant classification was based on strict criteria. Structural and functional analyses of TRPC6 variants were conducted to understand their impact on protein function. In depth re-analysis of light and electron microscopy specimens for 9 available kidney biopsies was conducted to identify pathological features and correlates of TRPC6-AP. Large-scale sequencing data did not support causality for TRPC6 protein-truncating variants. We identified 21 unique TRPC6 missense variants, clustering in three distinct regions of the protein, and with different effects on TRPC6 3D protein structure. Kidney biopsy analysis revealed FSGS patterns of injury in most cases, along with distinctive podocyte features including diffuse foot process effacement and swollen cell bodies. The majority of patients presented in adolescence or early adulthood but with ample variation (average 22, SD ± 14 years), with frequent progression to kidney failure but with variability in time between presentation and ESKD. This study provides insights into the genetic spectrum, clinicopathological associations, and natural history of TRPC6-AP.

Unveiling Digital Mirrors: Decoding Gendered Body Poses in Instagram Imagery

Social media platforms have had a significant impact on people's everyday lives worldwide and imagery on social media has become a vital means of practicing views of the self and communicating them to others. Albeit a significant proportion of people frequently upload self-representations on social media, this source of information on individuals, groups, and societies remains under-examined within the social sciences and neighboring disciplines. In our study, we focus on gender-stereotypical body-posing in self-portraits on social media. While sociology has examined gender stereotypes for decades, research lacks empirical evidence on representations in digital contexts as well as novel forms of stereotyping. We present a scalable and transferable methodology for analyzing body poses in images on the social media platform Instagram by combining neural network pose detection with an unsupervised learning approach. Based on a clustering algorithm applied to gender-annotated imagery, we identify 150 body posing clusters. Our results reveal significant gender differences in 20 percent of clusters, many of which represent gender-stereotypical body poses addressed in sociological literature. Moreover, we can identify new stereotypical poses related to smartphone technology and social media trends. This study represents a novel approach to utilizing large-scale image data for social science research and contributes to a better understanding of the consolidation and reproduction of gender stereotypes in digital realms.

Association between Changes in Menarcheal Age and Adolescent Idiopathic Scoliosis: An Analysis of 38,879 Patients over 20 Years.

Menarche has a significant impact on the progression of adolescent idiopathic scoliosis (AIS); however, studies in this area are insufficient. This study used large-scale school screening data to investigate the relationship between menarcheal age and AIS, especially the severity of scoliosis. Of 2,326,577 students who participated in school screening for scoliosis (SSS) in South Korea from 2001 to 2021, 38,879 girls with AIS, who experienced menarche, were included. Data including the patient's demographics, such as menarcheal age, Cobb angle, Risser stage, and the interval between menarche and the time of screening were retrieved from the SSS database. Pearson correlation coefficient was used to identify the changes in menarcheal age according to the birth year and to investigate the relationship between menarcheal age and each variable of interest. Based on the birth year, the mean menarcheal ages in girls with AIS from 1988 to 2008 demonstrated a steadily decreasing trend over time (r = -0.857, p < 0.001). Girls with AIS and late menarche demonstrated a higher Cobb angle at the time of screening (r = 0.095, p < 0.001). Other variables did not significantly correlate with menarcheal age. Based on the SSS, a large-scale school screening dataset, menarcheal age in girls with AIS demonstrated an ongoing downward trend in the recent 20 years in South Korea. Notably, girls with AIS and late menarche had a higher Cobb angle at screening. Our findings indicate the need for earlier screening of AIS in girls who have not undergone menarche.

Cross-view action recognition understanding from exocentric to egocentric perspective

Understanding action recognition in egocentric videos has emerged as a vital research topic with numerous practical applications. With the limitation in the scale of egocentric data collection, learning robust deep learning-based action recognition models remains difficult. Transferring knowledge learned from the large-scale exocentric data to the egocentric data is challenging due to the difference in videos across views. Our work introduces a novel cross-view learning approach to action recognition (CVAR) that effectively transfers knowledge from the exocentric to the selfish view. First, we present a novel geometric-based constraint into the self-attention mechanism in Transformer based on analyzing the camera positions between two views. Then, we propose a new cross-view self-attention loss learned on unpaired cross-view data to enforce the self-attention mechanism learning to transfer knowledge across views. Finally, to further improve the performance of our cross-view learning approach, we present the metrics to measure the correlations in videos and attention maps effectively. Experimental results on standard egocentric action recognition benchmarks, i.e., Charades-Ego, EPIC-Kitchens-55, and EPIC-Kitchens-100, have shown our approach’s effectiveness and state-of-the-art performance.

Mechanism-free repurposing of drugs for C9orf72-related ALS/FTD using large-scale genomic data.

Repeat expansions in the C9orf72 gene are the most common genetic cause of (ALS) and frontotemporal dementia (FTD). Like other genetic forms of neurodegeneration, pinpointing the precise mechanism(s) by which this mutation leads to neuronal death remains elusive, and this lack of knowledge hampers the development of therapy for C9orf72-related disease. We used an agnostic approach based on genomic data (n= 41,273 ALS and healthy samples, and n= 1,516 C9orf72 carriers) to overcome these bottlenecks. Our drug-repurposing screen, based on gene- and expression-pattern matching and information about the genetic variants influencing onset age among C9orf72 carriers, identified acamprosate, a γ-aminobutyric acid analog, as a potentially repurposable treatment for patients carrying C9orf72 repeat expansions. We validated its neuroprotective effect in cell models and showed comparable efficacy to riluzole, the current standard of care. Our work highlights the potential value of genomics in repurposing drugs insituations where the underlying pathomechanisms are inherently complex. VIDEO ABSTRACT.

Cascaded capsule twin attentional dilated convolutional network for malicious URL detection

Malware is one of the most popular cyber-attacks, and it is becoming more common on the network every day. In contrast to benign transmission, which typically exhibits symmetrical patterns, malware communication often shows asymmetrical behaviours, making detection a complex challenge. Fortunately, malware can be distinguished and identified for actual activities utilizing a variety of artificial intelligence methods. However, insufficient work has been allocated to the problem of handling high-dimensional and huge data. This paper proposes a novel deep learning-based approach to identify malicious Uniform Resource Locators (URLs) specifically designed to handle the challenges posed by large-scale and complex data. Initially, input data is sourced from a comprehensive Kaggle dataset, which includes diverse and large-scale URL samples. The URLs are then transformed into vector representations using a Vector Embedding Module, which employs a character-level word embedding technique to capture intricate patterns within the URLs. To further refine the data, the Chaotic Kookaburra Efficient-Bo Network (CKEBO-Net) is applied to extract the most significant features from these vectors, effectively reducing the dimensionality and computational burden. Subsequently, the Cascaded Capsule Twin Attentional Dilated Convolutional Network (C2TA_DiCN) model is introduced to classify and identify malicious URLs with high precision. This model leverages the unique strengths of capsule networks and attentional mechanisms, enhancing its capability to capture subtle dependencies within the data. Furthermore, the Lyrebird Meta-heuristic Optimization (LMO) algorithm is used to fine-tune the model parameters appropriately, ensuring that the training process is efficient and robust. The proposed approach is implemented using Python and rigorously evaluated on the Kaggle dataset. Simulation results demonstrate that the proposed method significantly outperforms existing models, achieving a malicious URL detection accuracy of 99.7%.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.

Progressive Supervision via Label Decomposition: An long-term and large-scale wireless traffic forecasting method

Long-term and Large-scale Wireless Traffic Forecasting (LL-WTF) is pivotal for strategic network management and comprehensive planning on a macro scale. However, LL-WTF poses greater challenges than short-term ones due to the pronounced non-stationarity of extended wireless traffic and the vast number of nodes distributed at the city scale. To cope with this, we propose a Progressive Supervision method based on Label Decomposition (PSLD). Specifically, we first introduce a Random Subgraph Sampling (RSS) algorithm designed to sample a tractable subset from large-scale traffic data, thereby enabling efficient network training. Then, PSLD employs label decomposition to obtain multiple easy-to-learn components, which are learned progressively at shallow layers and combined at deep layers to effectively cope with the non-stationary problem raised by LL-WTF tasks. Finally, we compare the proposed method with various state-of-the-art (SOTA) methods on three large-scale WT datasets. Extensive experimental results demonstrate that the proposed PSLD significantly outperforms existing methods, with an average 2%, 4%, and 11% performance improvement on three WT datasets, respectively. In addition, we built an open source library for WT forecasting (WTFlib) to facilitate related research, which contains numerous SOTA methods and provides a strong benchmark. Experiments can be reproduced through https://github.com/Anoise/WTFlib.

Large-scale inventory in natural forests with mobile LiDAR point clouds

Large-scale forest inventory at the individual tree level is critical for natural resource management decision making. Terrestrial Laser Scanning (TLS) has been used for individual tree level inventory at plot scale However, due to the inflexibility of TLS and the complex scene of natural forests, it is still challenging to localize and measure every tree at large scale. In this paper, we present a framework to conduct large-scale natural forest inventory at the individual tree level by taking advantage of deep learning models and Mobile Laser Scanning (MLS) systems. First, a deep learning model, ForestSPG, was developed to perform large-scale semantic segmentation on MLS LiDAR data in natural forests. Then, the forest segmentation results were used for individual stem mapping. Finally, Diameter at Breast Height (DBH) was measured for each individual stem. Two natural forests mapped with backpack and Unmanned Aerial Vehicle (UAV) LiDAR systems were tested. The results showed that the proposed ForestSPG is able to segment large-scale forest LiDAR data into multiple ecologically meaningful classes. The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. The proposed framework can not only segment complex forest components with LiDAR data from different platforms but also demonstrate good performance on stem mapping and DBH measurement. Our research provides and automatic and scalable solution for large-scale natural forest inventory at individual tree level, which can be the basis for large-scale estimation of wood volume and biomass.

Proteome- and Transcriptome-Wide Genetic Analysis Identifies Biological Pathways and Candidate Drug Targets for Preeclampsia.

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited. In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry. We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1 [sulfotransferase 1A1], SH2B3 [SH2B adapter protein 3], SERPINE2 [serpin family E member 2], RGS18 [regulator of G-protein signaling 18], PZP [pregnancy zone protein], NOTUM [notum, palmitoleoyl-protein carboxylesterase], METAP1 [methionyl aminopeptidase 1], MANEA [mannosidase endo-alpha], jun-D [JunD proto-oncogene], GDF15 [growth differentiation factor 15], FGL1 [fibrinogen like 1], FGF5 [fibroblast growth factor 5], FES [FES proto-oncogene], APOBR [apolipoprotein B receptor], ANP [natriuretic peptide A], ALDH-E2 [aldehyde dehydrogenase 2 family member], ADAMTS13 [ADAM metallopeptidase with thrombospondin type 1 motif 13], and 3MG [N-methylpurine DNA glycosylase]), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation. This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

Support vector machine based on the quadratic unconstrained binary optimization model

Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.

A least-squares framework for developing interval type-2 fuzzy semantics

The developing of IT2 fuzzy semantics is critical for computing with words (CWW), but exiting approaches lack flexibility and fail to adapt user’s diversified demands. The study proposes a least-squares framework for designing CWW encoders that construct interval type-2 fuzzy sets (IT2 FSs) to represent the semantic meanings of linguistic words. In the least-squares framework, an CWW encoder is characterized by two elements: an intra-uncertain semantic mapping and an inter-uncertain semantic family. The intra-uncertain semantic mapping transforms data intervals into type-1 fuzzy sets (T1 FSs), then an optimal IT2 FS is derived from the inter-uncertain semantic family using the least-squares method. Furthermore, several intra-uncertain semantic mappings are introduced, and a compatibility measure is defined to facilitate model selection. The least-squares framework benefits from the flexible selection of intra-uncertain semantic mappings and least-squares optimization-based construction of IT2 FSs. In experiments, the least-squares framework is applied to handle real-world online survey data and the large-scale online review data set of a Chinese life service review site, Dianping.com. Compared to the enhanced interval approach and the Hao-Mendel approach, the least-squares framework shows its favorable efficiency in experiments and statistical tests, and adapts to user-defined intra- and inter-uncertain semantic families.

Unleashing the Potential of Artificial Intelligence (AI) Tools in Phytogeographical studies

Phytogeography, the study of the geographic distribution of plants, is important for understanding ecosystem dynamics, biodiversity, and ecological processes. Over the past few years, advances in technology, especially artificial intelligence (AI), have revolutionized various scientific fields, including ecology and environmental science. In recent years, AI techniques have been increasingly applied in phytogeography, providing new opportunities to increase our understanding of plant distribution patterns and improve conservation efforts. The study of the role of artificial intelligence in phytogeography focuses on how AI techniques such as machine learning, remote sensing, and spatial analysis are being used to analyse large-scale plant distribution data. By leveraging AI, researchers can gain valuable insights from vast and complex datasets, identify patterns and predict future changes in plant distributions with greater accuracy. Furthermore, AI-driven approaches have the potential to address important challenges in phytogeography, such as species distribution modelling, habitat mapping, and biodiversity conservation. By integrating AI with traditional ecological methods, more effective strategies can be developed to manage and conserve plant species and their habitats. AI-driven phytogeography research, provides an overview of recent progress, discusses potential applications of AI techniques in ecological studies, and the opportunities and challenges associated with the use of AI in understanding and conserving plant biodiversity. Ultimately, the integration of AI with phytogeography has the potential to revolutionize our understanding of plant distributions and inform more sustainable conservation practices in the face of global environmental change.

Genome-scale analysis of Mycobacterium avium complex isolates from Portugal reveals extensive genetic diversity

Opportunist infections caused by nontuberculous mycobacteria (NTM) have emerged as a significant public health problem. Among these, species of the Mycobacterium avium complex (MAC) are the main responsible for the increase in the number of human disease cases. In order to address the current needs in the detection and surveillance of MAC disease cases, we evaluated different species classification methodologies (BLASTn-based marker-gene approach, Kraken v2, rMLST and MLST databases) and their congruence with a core-SNP phylogenetic approach, based on whole genome sequencing (WGS) data. For this purpose, we used a collection of 142 MAC isolates from Portuguese patients diagnosed between 2014 and 2022. The marker-gene approach (based on the rpoB, hsp65 and groEL genes), showed the best results, allowing the identification of the 142 MAC isolates to the species/subspecies level (M. avium subsp. hominissuis, M. intracellulare, M. intracellulare subsp. chimaera, M. intracellulare subsp. yongonense, M. marseillence and M. colombiense). Additionally, we performed drug susceptibility testing that confirmed clarithromycin efficacy as a first-line treatment for MAC disease, as 93 % of the Portuguese isolates were susceptible. Using a core-SNP approach we also performed an in-depth phylogenetic analysis within each identified species group, and despite the high genetic diversity within the MAC species, we were able to clearly distinguish all the species/subspecies and identify genetic clusters with epidemiological potential.We highlight not only the need for the standardization of an appropriate genotyping approach for species identification and management of MAC disease, but also a more robust large-scale WGS data analysis, in a One Health perspective, in order to identify potential routes of transmission.

Towards a comprehensive analysis of agricultural land systems in the EU and US: A critical view on publicly available datasets

Understanding agricultural land systems is paramount to preparing for future transitions under climate change. Systematic quantitative, multi-context analyses of agricultural land systems are a promising way of increasing our understanding but rely on the availability of large-scale and commensurable data. The available data, however, are outcomes of political priorities and processes and thus not neutral windows into agricultural realities. We map, describe, and compare publicly available and publicly curated data describing agricultural land systems—those devoted to the production of food, fuel, and fiber—in the United States (US) and European Union (EU) to identify underlying data collection norms and political priorities. We discuss how these priorities shape what we can(not) see in each region and articulate future data needs to support an in-depth understanding of agricultural land systems dynamics. This comprehensive understanding of how and why agricultural land systems change is imperative to promoting sustainable, resilient, and just agriculture futures.

Large-scale population data enrichment in mental health research

Large-scale population data enrichment in mental health research