MethodsX最新文献

{"title":"Quantitative assessment of brain metabolism in mice using non-contrast MRI at 11.7T","authors":"Xiuli Yang,&nbsp;Yuguo Li,&nbsp;Hanzhang Lu,&nbsp;Zhiliang Wei","doi":"10.1016/j.mex.2025.103175","DOIUrl":"10.1016/j.mex.2025.103175","url":null,"abstract":"<div><div>Brain oxygen metabolism indicates the rate of energy consumption and is a potential marker of pathological changes. Positron emission tomography (PET) is the gold standard for measuring metabolic rates using radioactive tracers. However, its application in preclinical studies, particularly with rodent animals, is constrained by the need for arterial input function measurements and on-site cyclotron facilities for tracer preparation. As an alternative, non-invasive, non-contrast MRI techniques, such as T2-relaxation-under-spin-tagging (TRUST) and phase-contrast (PC) MRI, can be used for evaluating brain metabolism in vivo. This study outlines a step-by-step method for implementing TRUST and PC MRI in mice at 11.7T scanner. The proposed method yields non-invasive, non-contrast quantitative measurements of global brain metabolism in approximately 20 min, paving the way for broader applications in future pathophysiological studies.<ul><li><span>•</span><span><div>Non-invasive and non-contrast assessment of brain metabolism in mice.</div></span></li><li><span>•</span><span><div>Quantitative measurement of metabolic rate in approximately 20 min.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103175"},"PeriodicalIF":1.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for siRNA-mediated knockdown of target genes in RA-induced neurogenesis using P19 cells","authors":"Hossein Khodadadi ,&nbsp;Hiroaki Taniguchi","doi":"10.1016/j.mex.2025.103177","DOIUrl":"10.1016/j.mex.2025.103177","url":null,"abstract":"<div><div>This study presents a comprehensive protocol for siRNA-mediated knockdown in the differentiation of P19 cells into neuronal-like cells. Utilizing a retinoic acid (RA)-induced neurogenesis model, P19 cells were cultured under specific conditions that facilitated the formation of embryoid bodies (EBs), which were subsequently differentiated into neuronal-like cells. In this investigation, we specifically targeted the Nfe2l1 gene using siRNA transfection to assess the efficiency and effectiveness of our protocol throughout the neuronal differentiation process. Validation of the differentiation was performed through quantitative reverse transcription PCR (RT-qPCR) analysis, measuring the expression levels of key neuronal markers, including Map2 and Pax6 along with the pluripotency marker Oct4. Additionally, the efficiency of the siRNA-mediated knockdown was confirmed by western blot analysis, which demonstrated significant gene silencing at protein levels. These findings underscore the potential of siRNA technology in elucidating gene function during neuronal differentiation and highlight the critical role of targeted gene silencing in advancing neurogenesis research. Furthermore, this study provides a robust and reliable protocol for gene knockdown in neuronal-like cells derived from P19 cells, thereby facilitating further investigations into the intricate molecular mechanisms that govern neurogenesis, neuronal maturation, and overall brain development.<ul><li><span>•</span><span><div>Developed a novel protocol for targeted gene knockdown in P19 cells during neuronal differentiation.</div></span></li><li><span>•</span><span><div>Successful silencing of the Nfe2l1 gene during neuronal differentiation, validated by western blot.</div></span></li><li><span>•</span><span><div>This study provides a reliable protocol for gene knockdown in neuronal differentiation, aiding functional studies of genes in neurogenesis.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103177"},"PeriodicalIF":1.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-enhanced corn disease diagnosis using few-shot learning and VGG16","authors":"Ruchi Rani ,&nbsp;Jayakrushna Sahoo ,&nbsp;Sivaiah Bellamkonda ,&nbsp;Sumit Kumar","doi":"10.1016/j.mex.2025.103172","DOIUrl":"10.1016/j.mex.2025.103172","url":null,"abstract":"<div><div>Plant Disease Detection in the early stage is paramount. Traditionally, it was done manually by the farmers, which is a laborious and time-intensive task. With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. However, they rely on extensive datasets for accurate prediction, which is impracticable to acquire and annotate. Thus, Few Shot Learning is the state-of-the-art model in machine learning, which requires minimum examples to train the model for generalization. As humans need a few examples to recognize things, Few-shot Learning mimics the same human brain process. The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. An attention module is integrated with the backbone, and further, prototypical few-shot learning is used for corn disease prediction and classification with an accuracy of 98.25 %.<ul><li><span>•</span><span><div>The proposed model intends to identify the diseases early, so the insights generated would be relevant for farmers, and probable losses can be reduced.</div></span></li><li><span>•</span><span><div>By applying Few-Shot Learning, the system avoids the significant challenges of requiring extensively annotated datasets, making it feasible for real-world agricultural applications.</div></span></li><li><span>•</span><span><div>Incorporating a fine-tuned VGG16 backbone along with an attention mechanism and prototypical Few-Shot Learning results in a robust and scalable solution with high accuracy for classifying corn diseases.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103172"},"PeriodicalIF":1.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On derived t-path, t=2,3 signed graph and t-distance signed graph","authors":"Deepa Sinha,&nbsp;Sachin Somra","doi":"10.1016/j.mex.2025.103160","DOIUrl":"10.1016/j.mex.2025.103160","url":null,"abstract":"&lt;div&gt;&lt;div&gt;A signed graph &lt;span&gt;&lt;math&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/math&gt;&lt;/span&gt; is a pair &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mrow&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;/msup&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;σ&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;that consists of a graph &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mrow&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/mrow&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;/msup&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and a sign mapping called signature &lt;span&gt;&lt;math&gt;&lt;mi&gt;σ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; from &lt;em&gt;E&lt;/em&gt; to the sign group &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;{&lt;/mo&gt;&lt;mrow&gt;&lt;mo&gt;+&lt;/mo&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;}&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. In this paper, we discuss the &lt;em&gt;t&lt;/em&gt;-path product signed graph &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;^&lt;/mo&gt;&lt;/mover&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/msub&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;where vertex set of &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;^&lt;/mo&gt;&lt;/mover&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is the same as that of &lt;span&gt;&lt;math&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/math&gt;&lt;/span&gt; and two vertices are adjacent if there is a path of length &lt;em&gt;t&lt;/em&gt;, between them in the signed graph &lt;span&gt;&lt;math&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/math&gt;&lt;/span&gt;. The sign of an edge in the &lt;em&gt;t&lt;/em&gt;-path product signed graph is determined by the product of marks of the vertices in the signed graph &lt;span&gt;&lt;math&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/math&gt;&lt;/span&gt;, where the mark of a vertex is the product of signs of all edges incident to it. In this paper, we provide a characterization of &lt;span&gt;&lt;math&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/math&gt;&lt;/span&gt; which are switching equivalent to &lt;em&gt;t&lt;/em&gt;-path product signed graphs &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;^&lt;/mo&gt;&lt;/mover&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; which are switching equivalent to &lt;span&gt;&lt;math&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;/math&gt;&lt;/span&gt; and also the negation of the signed graph ŋ&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; that are switching equivalent to &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;^&lt;/mo&gt;&lt;/mover&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. We also characterize signed graphs that are switching equivalent to &lt;span&gt;&lt;math&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;-distance signed graph &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mover&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; where 2-distance signed graph &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mover&gt;&lt;mstyle&gt;&lt;mi&gt;Σ&lt;/mi&gt;&lt;/mstyle&gt;&lt;mo&gt;¯&lt;/mo&gt;&lt;/mover&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mrow&gt;&lt;msup&gt;&lt;","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103160"},"PeriodicalIF":1.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast screening of gold in rock samples using polyurethane foam extraction and inductively coupled plasma mass spectrometry determination","authors":"Jalal Hassan ,&nbsp;Naeemeh Zari ,&nbsp;Mohammad-Hadi Karbasi","doi":"10.1016/j.mex.2025.103170","DOIUrl":"10.1016/j.mex.2025.103170","url":null,"abstract":"<div><div>In this work, the volume of sample solution and concentration of gold was optimized for extraction with foam in dimensions (1 × 1 × 1) and then was used for extraction from soil samples. The results showed that the proposed technique has a good analytical efficiency compared to the standard fire assay method and the accuracy of work is in the range of 74–125 %. The equation of linear calibration curve was obtained with regression coefficient better than 0.9997, and the detection and quantification limit of the gold in aqueous and soil sample obtained 0.25 and 0.8 µg kg ⁻¹, respectively.<ul><li><span>•</span><span><div>This method is inexpensive and fast for determination of gold in various samples.</div></span></li><li><span>•</span><span><div>This method has high thought of sample determination.</div></span></li><li><span>•</span><span><div>This method is green chemistry method.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103170"},"PeriodicalIF":1.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of 3‑chloro-7‑hydroxy-4-methylcoumarin (CHMC) in urine as a biomarker of coumaphos exposure by high-performance liquid chromatography-fluorescence detection (HPLC-FLD)","authors":"Zachary G. Robbins ,&nbsp;Cynthia A. Striley ,&nbsp;Lee Wugofski","doi":"10.1016/j.mex.2025.103171","DOIUrl":"10.1016/j.mex.2025.103171","url":null,"abstract":"<div><div>The organophosphate pesticide coumaphos is used to control Cattle Tick Fever carried by multiple species of ticks and is a known hazard for workers treating livestock. The USDA Cattle Fever Tick Eradication Program requires regular blood draws to measure depressed cholinesterase levels as biomarkers of effect of long-term coumaphos exposure, however, the gap between blood draws may miss intermittent high exposures. Urine biomonitoring can supplement blood draws, offering personnel a sensitive and cost-effective method to monitor short-term exposures. Our objective was to improve and validate a previously published method to analyze the coumaphos metabolite 3‑chloro-7‑hydroxy-4-methylcoumarin (CHMC). Urine samples were hydrolyzed with glucuronidase and then extracted prior to analysis with high-performance liquid chromatography-fluorescence detection. Calibration curves were linear over a wide CHMC range (0.49 – 250.07 ng/mL) with a method detection limit of 0.06 ng/mL. This research will help establish an accessible urine biomonitoring method for assessing coumaphos exposures.<ul><li><span>•</span><span><div>The modified bioanalytical method maintained high sensitivity and specificity while reducing duration of the sample treatment steps and the chromatographic program.</div></span></li><li><span>•</span><span><div>Method validation tests followed the acceptance criteria guidelines in the NIOSH Manual of Analytical Methods.</div></span></li><li><span>•</span><span><div>CHMC levels were measured in workers exposed to coumaphos during livestock treatment.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103171"},"PeriodicalIF":1.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modification of an automated precision farming robot for high temporal resolution measurement of leaf angle dynamics using stereo vision","authors":"Frederik Hennecke ,&nbsp;Jonas Bömer ,&nbsp;René H.J. Heim","doi":"10.1016/j.mex.2025.103169","DOIUrl":"10.1016/j.mex.2025.103169","url":null,"abstract":"<div><div>In agriculture, the plant leaf angle influences light use efficiency and photosynthesis and, consequently, the overall crop performance. Leaf angle measurements are used in plant phenotyping, plant breeding, and remote sensing to study plant function and structure. Traditional manual leaf angle measurements have limited precision as they are labor- and time-intensive due to challenging environmental conditions and highly dynamic plant processes. To enable more detailed studies on leaf angles, we modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision. We demonstrate the system's accuracy and reliability, with minimal deviation from reference values. The method can be utilized by other researchers to gather data on leaf angles and other structural plant traits at regular intervals to access the dynamics of leaves, plants, and canopies. The system's low cost and adaptability can enhance the efficiency of crop monitoring in plant breeding and phenotyping experiments. Detailed documentation and code are available on <span><span>GitHub</span><svg><path></path></svg></span>.<ul><li><span>•</span><span><div>An open-source farming robot is retrofitted to function as an automatic data collection platform</div></span></li><li><span>•</span><span><div>Hard to access leaf angles can be retrieved with high accuracy</div></span></li><li><span>•</span><span><div>Leaf angle dynamics can be observed with high temporal resolution</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103169"},"PeriodicalIF":1.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NIRS as an alternative method for table grapes Seedlessness sorting","authors":"Chaorai Kanchanomai ,&nbsp;Parichat Theanjumpol ,&nbsp;Phonkrit Maniwara ,&nbsp;Sila Kittiwachana ,&nbsp;Sujitra Funsueb ,&nbsp;Shintaroh Ohashi ,&nbsp;Daruni Naphrom","doi":"10.1016/j.mex.2024.103089","DOIUrl":"10.1016/j.mex.2024.103089","url":null,"abstract":"<div><div>Seedlessness in table grapes is a desirable trait for consumers. Plant growth regulators (PGRs) have been extensively utilized to induce seedlessness. However, the efficacy of these PGRs is not uniformly successful. In addition, the seedlessness is difficult to detect by cutting and counting technique. The shortwave-near infrared spectroscopy (SW-NIRS), coupled with suitable chemometric analysis, is a non-destructive method for sorting and prediction of seedlessness grapes. The NIRS is higher efficiency than original technique in term of accuracy, measuring time and waste reduction.<ul><li><span>•</span><span><div>The SW-NIR spectra of 240 grape berries were recorded. Each reflectance spectrum was acquired in the wavenumber of 3996–12,489 cm⁻¹. After that all grape berries were cut and count for seedlessness sorting.All spectral together with seedlessness sorting were be analysis by chemometrics.</div></span></li><li><span>•</span><span><div>The NIR spectral data were analyzed using principal component analysis (PCA). In addition, supervised self-organizing map (SSOM) and quadratic discriminant analysis (QDA) were applied to classify the seedlessness.</div></span></li><li><span>•</span><span><div>The PCA results represented a negative tendency to classify the seedlessness. Clear classification tendency can be obtained from SOMs. Good predictive results from SSOM were obtained, as it gave a percentage correctly classified of 97.14 and 94.64% for training and test sample sets, respectively.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103089"},"PeriodicalIF":1.6,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing large text data for vocabulary profiling in corpus-based studies of academic discourse","authors":"Ismail Xodabande,&nbsp;Mahmood Reza Atai,&nbsp;Mohammad R. Hashemi","doi":"10.1016/j.mex.2025.103168","DOIUrl":"10.1016/j.mex.2025.103168","url":null,"abstract":"<div><div>This article introduces a protocol designed to analyze large corpora for vocabulary profiling, aimed at enhancing corpus-based studies of academic discourse. Given the complexity and volume of data typical in academic fields, this protocol integrates advanced corpus compilation techniques with lexical analysis tools to effectively identify and categorize vocabulary suitable for academic use. The study details the systematic process of compiling a large corpus of academic texts, and describes the adaptations made to corpus linguistics tools to handle and analyze a corpus with 278 million running words efficiently. Validation of the mid-frequency word list demonstrated its strong relevance to chemistry, with 6.4% coverage in chemistry research articles and 2.5–3% coverage in related disciplines like biology and life sciences. However, the coverage was much lower in general corpora, highlighting its specialized nature. This methodology not only provides a framework for academic vocabulary profiling but also offers scalable solutions for educators and researchers dealing with extensive text datasets. The findings contribute to advancing vocabulary research in chemistry and related fields, offering practical applications for improving educational resources and designing more effective curricula for academic English. The resulting vocabulary lists have significant implications for the design of curricula and educational resources, aiming to improve both the precision and effectiveness of language instruction in specialized academic settings.<ul><li><span>•</span><span><div>Developed a scalable protocol for analyzing large text data for vocabulary profiling.</div></span></li><li><span>•</span><span><div>Applied advanced lexical analysis to a 278-million-word academic corpus.</div></span></li><li><span>•</span><span><div>The mid-frequency vocabulary list produced offers pedagogical value in academic discourse.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103168"},"PeriodicalIF":1.6,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging hybrid model of ConvNextBase and LightGBM for early ASD detection via eye-gaze analysis","authors":"Ranjeet Bidwe ,&nbsp;Sashikala Mishra ,&nbsp;Simi Bajaj ,&nbsp;Ketan Kotecha","doi":"10.1016/j.mex.2025.103166","DOIUrl":"10.1016/j.mex.2025.103166","url":null,"abstract":"<div><div>ASD is a mental developmental disorder that significantly impacts the behavioural and communicational abilities of the child. ASD is affecting the world hard, and its global presence continuously increases. One of the reasons for this trend may be a pandemic, which increases screen time for children and decreases communication with peers or family. A lengthy and subjective non-clinical procedure is currently placed for detecting ASD, which is followed by a series of therapy sessions to cure it. This research introduces a novel method for eye gaze analysis to identify autistic traits in children. This proposed work offers<ul><li><span>•</span><span><div>A novel method of ConvNextBase and LightGBM leveraging eye position as a feature for early detection of autistic traits.</div></span></li><li><span>•</span><span><div>A new ConvNextBase architecture proposed with few unfreezed layers and extra dense layers with units of 512 and 128, respectively, and dropout layers with a rate of 0.5 that extract rich, high-level, and more complex features from the images to improve generalization and mitigate overfitting.</div></span></li><li><span>•</span><span><div>A LightGBM model performed classification using 3-fold cross-validation and found the best parameters for bagging_function, feature_fraction, max_depth, Number_of_leaves and learning_rate with values of 0.8, 0.8, −1, 31 and 0.1 respectively, to improve the model's robustness on unseen data.</div></span></li></ul>This proposed method is trained and tested on the publicly available Kaggle dataset, and results are benchmarked with other state-of-the-art methods. The experimentation finding shows that the proposed systems outperform other cutting-edge techniques in accuracy and specificity by 95 % and 98 %, respectively. Furthermore, the model achieved a precision of 93 %, showing that the model effectively reduces false positives and identifies false positives correctly. The classification process yielded 91 % under the AUC-ROC curve, showing the model's strong classification capability.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103166"},"PeriodicalIF":1.6,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}