Learning methods

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations...

The long-term monitoring stability of electronic current transformers is crucial for accurately obtaining the current signal of the power grid. However, it is difficult to accurately distinguish between the fluctuation of non-stationary random signals on the primary side of the power grid and the gradual error of the transformers themselves. A current transformer error prediction model, CNN-MHA-BiLSTM, based on the golden jackal optimization (GJO) algorithm, which is used to obtain the optimal parameter values, bidirectional long short-term memory (BiLSTM) network, convolutional neural networks (CNNs), and multi-head attention (MHA), is proposed to address the difficulty of measuring error evaluation...

Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevating cancer risk, yet research rarely focuses on related immunological mechanisms, which play an important role in the occurrence and development of cancer. We investigated the association between blood chromium (Cr) levels and genetic damage biomarkers as well as the immune regulatory mechanism involved, such as costimulatory molecules, in 120 workers exposed to chromates. Higher blood Cr levels were linearly correlated with higher genetic damage, reflected by urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) and blood micronucleus frequency (MNF)...

The presence of global synchronization of vasomotion induced by oscillating visual stimuli was identified in the mouse brain. Endogenous autofluorescence was used and the vessel 'shadow' was quantified to evaluate the magnitude of the frequency-locked vasomotion. This method allows vasomotion to be easily quantified in non-transgenic wild-type mice using either the wide-field macro-zoom microscopy or the deep-brain fiber photometry methods. Vertical stripes horizontally oscillating at a low temporal frequency (0...

BACKGROUND: Hylurgus ligniperda, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it has caused significant damage in China. While traps have been effective in monitoring and controlling pests, manual inspections are labor-intensive and require expertise in insect classification. To address this, we applied a two-stage cascade convolutional neural network, YOLOX-MobileNetV2 (YOLOX-Mnet), for identifying H. ligniperda and other pests captured in traps...

Transanal minimally invasive surgery (TAMIS) is an effective procedure that plays an important role in the care of patients with significant rectal neoplasia and polyps including early-stage cancers. However, it is perhaps underutilised and under threat from both advanced flexible endoscopic procedures and proceduralists (who often act as gatekeepers for referral to colorectal surgeons), as well as from robotic surgery proponents. TAMIS advocates can learn and adopt practice insights from both these fields and incorporate available technological innovations building on the huge accomplishments already delivered in this area...

Background: The severity of antipsychotic-induced cervical dystonia has traditionally been evaluated visually. However, recent advances in information technology made quantification possible in this field through the introduction of engineering methodologies like machine learning. Methods: This study was conducted from June 2021 to March 2023. Psychiatrists rated the severity of cervical dystonia into 4 levels (0: none, 1: minimal, 2: mild, and 3: moderate) for 101 videoclips, recorded from 87 psychiatric patients receiving antipsychotics...

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect...

Soil organic matter is an important indicator of soil fertility, and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study, we analyzed the organic matter content of 1 690 soil surface layers (0-20 cm) and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics, deterministic interpolation, geostatistical interpolation, and machine learning, respectively, and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models...

This study selected five typical types of chemical industry volatile organic compounds (VOCs) emission characteristics in China for analysis. The results from 70 source samples showed that alkanes were the dominant VOCs category from synthetic material industry sources, petrochemical industry sources, and coating industry sources (accounting for 43%, 63%, and 68%, respectively); olefins were the main VOCs category from the daily supplies chemical industry (46%); and halogenated hydrocarbons were the dominate VOCs category from specialty chemicals industry account source emissions (43%)...

<b><br>Indroduction:</b> Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.</br> <b><br>Aim:</b> Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method.</br> <b><br>Materials and methods:</b> Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed...

Migration research poses several unique challenges and opportunities. Conducting ethical global health practice, especially when studying migrant mental health, is of particular concern. This article explores seven challenges and lessons learned in our mixed-methods study conducted to assess the impact of the migration experience on Haitian migrants' mental health in Santiago, Chile. The primary challenges were recruiting in a highly mobile population, building trust and community participation, overcoming language barriers, safety considerations during the Covid-19 pandemic, mitigating potential negative impacts of research on the community, providing psychological support, and finding meaningful ways to benefit the community...

Purpose: The purpose of this study was to explore the lived experiences of nursing and social work students who were taking courses during the COVID-19 pandemic. Focus group discussions gave students a chance to express the pandemic's effects on their education and life. Methods: A hermeneutic phenomenological approach using Van Manen's Four Lifeworld Existentials guided this study. Using an open-ended format, interviews were conducted in 6 small groups ranging from 2 to 9 individuals, in person or via Zoom...

Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning...

The root system plays a vital role in plants' ability to absorb water and nutrients. In situ root research offers an intuitive approach to exploring root phenotypes and their dynamics. Deep-learning-based root segmentation methods have gained popularity, but they require large labeled datasets for training. This paper presents an expansion method for in situ root datasets using an improved CycleGAN generator. In addition, spatial-coordinate-based target background separation method is proposed, which solves the issue of background pixel variations caused by generator errors...

The number of flowers is essential for evaluating the growth status of litchi trees and enables researchers to estimate flowering rates and conduct various phenotypic studies, particularly focusing on the information of individual panicles. However, manual counting remains the primary method for quantifying flowers, and there has been insufficient emphasis on the advancement of reliable deep learning methods for estimation and their integration into research. Furthermore, the current density map-based methods are susceptible to background interference...

Recent years have seen the development of novel, rapid, and inexpensive techniques for collecting plant data to monitor the nutritional status of crops. These techniques include hyperspectral imaging, which has been widely used in combination with machine learning models to predict element concentrations in plants. When there are multiple elements, the machine learning models are trained with spectral features to predict individual element concentrations; this type of single-target prediction is known as single-target regression...

BACKGROUND: T cell exhaustion in the tumor microenvironment has been demonstrated as a substantial contributor to tumor immunosuppression and progression. However, the correlation between T cell exhaustion and osteosarcoma (OS) remains unclear. METHODS: In our present study, single-cell RNA-seq data for OS from the GEO database was analysed to identify CD8+ T cells and discern CD8+ T cell subsets objectively. Subgroup differentiation trajectory was then used to pinpoint genes altered in response to T cell exhaustion...

BACKGROUND: Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD. METHODS: GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes...

Qualitative research conducted in a multilingual setting is an arduous, yet essential, endeavour. As part of my PhD research program, I set out to conduct qualitative process evaluation of a stroke trial in 11 languages in the Indian subcontinent. In this article, I reflect upon the challenges, oversights, and successes that I experienced in the hope of offering insight of use to fellow researchers conducting healthcare fieldwork in multicultural contexts where many languages are spoken. My account starts with a description of the setting's context and the necessity of conducting research in multiple languages...