Browse Articles

A versatile and efficient method to isolate nuclei from low-input cryopreserved tissues for single-nuclei transcriptomics

Role of functional genes for seed vigor related traits through genome-wide association mapping in finger millet (Eleusine coracana L. Gaertn.)

Efficient WSI classification with sequence reduction and transformers pretrained on text

Abstract From computer vision to protein fold prediction, Language Models (LMs) have proven successful in transferring their representation of sequential data to a broad spectrum of tasks beyond the domain of natural language processing. Whole Slide Image (WSI) analysis in digital pathology naturally fits to transformer-based architectures. In a pre-processing step analogous to text tokenization, large microscopy images are tessellated into smaller image patches. However, due to the massive size of WSIs comprising thousands of such patches, the problem of WSI classification has not been addressed via deep transformer architectures, let alone via available text-pre-trained deep transformer language models. We introduce SeqShort, a multi-head attention-based sequence shortening layer that summarizes a large WSI into a fixed- and short-sized sequence of feature vectors by removing redundant visual information. Our sequence shortening mechanism not only reduces the computational costs of self-attention on large inputs, it also allows to include standard positional encodings to the previously unordered bag of patches that compose a WSI. We use SeqShort to effectively classify WSIs in different digital pathology tasks using a deep, text pre-trained transformer model while fine-tuning less than 0.1% of its parameters, demonstrating that their knowledge about natural language transfers well to this domain.

Assessing delimiting strategies to identify the infested zones of quarantine plant pests and diseases

Abstract Following the discovery of a quarantine plant pest or disease, delimitation is urgently conducted to define the boundaries of the infested area, typically through surveys that detect the presence or absence of the pest. Swift and accurate delimitation is crucial after a pest or pathogen enters a new region for containment or eradication. Delimiting an area that is too small allows the pest to spread uncontrollably, while delimited areas that are too large can lead to excessive economic costs, making eradication cost-prohibitive. Despite its significance, there is a lack of comprehensive reviews on delimiting strategies and their effectiveness in managing plant pests; many current practices are ad-hoc and not scientifically based. In this study, we used an individual-based model to simulate the spread of Huanglongbing (citrus greening), a priority EU pest, and evaluated three delimiting strategies across various host distribution landscapes. We found that an adaptive strategy was most effective, especially when tailored to the polycyclic nature of the pest. This underscored the need for specific delimiting approaches based on the epidemiological characteristics of the target pest.

Factors influencing partnerships between care workers and families in nursing homes in South Korea

Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques

Neuromodulation perception by the general public

Abstract The development of neurotechnologies offers exciting opportunities for novel brain interventions. Public perception plays a crucial role in determining the success and acceptance of these interventions. This study aimed to understand the general non-expert population’s representation of neuromodulation and their preferences for common methods such as pharmaceutical drugs, brain implants, ultrasound, magnetic, and electrical stimulations. We conducted a comprehensive online survey with 784 participants to assess their perception of neuromodulation before and after providing information. We also asked the participants to rank their preferences for different neuromodulation techniques after being provided with information. Statistical analyses included inferential non-linear models and free-text data mining. Our findings revealed that overall, neuromodulation was positively perceived by the participants. Furthermore, providing information resulted in a significant improvement in participants’ perception of neuromodulation. Ultrasound stimulation emerged as the most preferred treatment choice, while pharmaceutical drugs were considered a middle-choice option and brain implants ranked last due to safety concerns. Healthcare providers could benefit from enhancing patient education and awareness to promote informed decision-making and improve treatment adherence. Additionally, stakeholders have to address the existing distrust surrounding pharmaceutical drugs and prioritize the development and promotion of safe, non-invasive neuromodulation treatments.

The Burden of adolescent depression and the impact of COVID-19 across 204 countries and regions from 1990 to 2021: results from the 2021 global burden of disease study

Altitudinal influence on survival mechanisms, nutritional composition, and antimicrobial activity of Moringa Peregrina in the summer climate of Fujairah, UAE

Changes of riparian soil-plant system phosphorus responding to hydrological alternations of Three Gorges Reservoir

Association between serum high-sensitivity C-reactive protein levels and osteoarthritis in adults from NHANES 2015 to 2018

Epidemiological patterns and therapeutic approaches of toad toxin poisoning in a retrospective case study

Exploring the genetics of social behaviour in C. calcarata

Identification of ECE2 signaling in promoting non-small lung cancer progression through ET1/YAP1/MAGEA3 axis

Temporal stability and lack of variance in microbiome composition and functionality in fit recreational athletes

2.05 μm high-energy thulium-doped fibre amplifier based on backward pumping

Optimizing nutritional strategies in term NEC and perforation infants after intestinal operation: a retrospective study

Machine learning modeling for predicting adherence to physical activity guideline

Exploration of contemporary modernization in UWSNs in the context of localization including opportunities for future research in machine learning and deep learning

Fertilizer types and nitrogen rates integrated strategy for achieving sustainable quinoa yield and dynamic soil nutrient-water distribution at high altitude

Abstract Quinoa (Chenopodium quinoa Willd.) is a crop particularly adapted to high-altitude environments characterized by significant variability in climate and soil conditions Fertilization is essential for providing nutrients and influencing soil nutrient cycling and hydrological dynamics. This study aimed to optimize fertilizer type and nitrogen (N) application rates to improve soil nutrient availability, moisture retention, and quinoa yield. We examined three fertilizer types: compound fertilizer (NPK), bio-microbial fertilizer (BM), and slow-release fertilizer (SRF), with nitrogen application rates of 90, 120, and 150 kg ha− 1, compared to a control group (CK) with no fertilization. Our results revealed that applying 120 kg ha− 1 of nitrogen with SRF significantly reduced soil bulk density, improved water retention beyond 60 cm depth, and enhanced water use efficiency by 9.2–16.2%, alleviating water stress. In conjunction with BM, this nitrogen application increased soil organic matter, alkali-hydrolyzed nitrogen, and the availability of phosphorus and potassium, especially during the grain-filling stage, promoting quinoa growth. Elevated nitrogen rates (120 and 150 kg ha− 1) with BM maximized soil urease and sucrase activities, correlating positively with key soil chemical parameters. Additionally, 120 kg ha− 1 of SRF notably boosted quinoa biomass and yield components. Economic analysis indicated that SRF at 120 kg ha− 1 nitrogen provided the highest productivity. These results highlight the importance of fertilizer type and nitrogen rates in enhancing soil nutrient status and optimizing water infiltration in high-altitude soils, offering a drought-resistant strategy for quinoa cultivation.