title: PeerJ
description:  Articles published in PeerJ
link: https://peerj.com/articles/index.rss3?journal=peerj&page=356
creator: info@peerj.com PeerJ
errorsTo: info@peerj.com PeerJ
language: en

title: Construction of a predictive model for bone metastasis from first primary lung adenocarcinoma within 3 cm based on machine learning algorithm: a retrospective study
link: https://peerj.com/articles/17098
last-modified: 2024-03-14
description: BackgroundAdenocarcinoma, the most prevalent histological subtype of non-small cell lung cancer, is associated with a significantly higher likelihood of bone metastasis compared to other subtypes. The presence of bone metastasis has a profound adverse impact on patient prognosis. However, to date, there is a lack of accurate bone metastasis prediction models. As a result, this study aims to employ machine learning algorithms for predicting the risk of bone metastasis in patients.MethodWe collected a dataset comprising 19,454 cases of solitary, primary lung adenocarcinoma with pulmonary nodules measuring less than 3 cm. These cases were diagnosed between 2010 and 2015 and were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing clinical feature indicators, we developed predictive models using seven machine learning algorithms, namely extreme gradient boosting (XGBoost), logistic regression (LR), light gradient boosting machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP) and support vector machine (SVM).ResultsThe results demonstrated that XGBoost exhibited superior performance among the four algorithms (training set: AUC: 0.913; test set: AUC: 0.853). Furthermore, for convenient application, we created an online scoring system accessible at the following URL: https://www.xsmartanalysis.com/model/predict/?mid=731symbol=7Fr16wX56AR9Mk233917, which is based on the highest performing model.ConclusionXGBoost proves to be an effective algorithm for predicting the occurrence of bone metastasis in patients with solitary, primary lung adenocarcinoma featuring pulmonary nodules below 3 cm in size. Moreover, its robust clinical applicability enhances its potential utility.
creator: Yu Zhang
creator: Lixia Xiao
creator: Lan LYu
creator: Liwei Zhang
uri: https://doi.org/10.7717/peerj.17098
license: https://creativecommons.org/licenses/by/4.0/
rights: ©2024 Zhang et al.

title: Evaluation of different Kabuli chickpea genotypes against Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) in relation to biotic and abiotic factors
link: https://peerj.com/articles/16944
last-modified: 2024-03-13
description: BackgroundThe chickpea pod borer Helicoverpa armigera (Hübner) is a significant insect pest of chickpea crops, causing substantial global losses.MethodsField experiments were conducted in Central Punjab, Pakistan, to investigate the impact of biotic and abiotic factors on pod borer population dynamics and infestation in nine kabuli chickpea genotypes during two cropping seasons (2020–2021 and 2021–2022). The crops were sown in November in both years, with row-to-row and plant-to-plant distances of 30 and 15 cm, respectively, following a randomized complete block design (RCBD).ResultsResults showed a significant difference among the tested genotypes in trichome density, pod wall thickness, and leaf chlorophyll contents. Significantly lower larval population (0.85 and 1.10 larvae per plant) and percent damage (10.65% and 14.25%) were observed in genotype Noor-2019 during 2020–2021 and 2021–2022, respectively. Pod trichome density, pod wall thickness, and chlorophyll content of leaves also showed significant variation among the tested genotypes. Pod trichome density and pod wall thickness correlated negatively with larval infestation, while chlorophyll content in leaves showed a positive correlation. Additionally, the larval population positively correlated with minimum and maximum temperatures, while relative humidity negatively correlated with the larval population. Study results explore natural enemies as potential biological control agents and reduce reliance on chemical pesticides.
creator: Hafiz Muhammad Bilal Yousuf
creator: Muhammad Yasin
creator: Habib Ali
creator: Khalid Naveed
creator: Ammara Riaz
creator: Amal Mohamed AlGarawi
creator: Ashraf Atef Hatamleh
creator: Yunfeng Shan
uri: https://doi.org/10.7717/peerj.16944
license: https://creativecommons.org/licenses/by/4.0/
rights: © 2024 Muhammad Bilal Yousuf et al.

title: Filling the gaps in ecology of tropical tiger beetles (Coleoptera: Cicindelidae): first quantitative data of sexual dimorphism in semi-arboreal Therates from the Philippine biodiversity hotspot
link: https://peerj.com/articles/16956
last-modified: 2024-03-13
description: BackgroundSexual dimorphism, driven by sexual selection, leads to varied morphological distinctions in male and female insects, providing insights into selection pressures across species. However, research on the morphometric variability within specific taxa of tiger beetles (Coleoptera: Cicindelidae), particularly arboreal and semi-arboreal species, remains very limited.MethodsWe investigate sexual dimorphism in six semi-arboreal Therates tiger beetle taxa from the Philippines, focusing on morphological traits. We employed morphometric measurements and multivariate analyses to reveal patterns of sexual dimorphism between sexes within the taxa.ResultsOur results indicate significant sexual dimorphism in elytra width, with females consistently displaying broader elytra, potentially enhancing fecundity. Notable sexual size dimorphism was observed in Therates fulvipennis bidentatus and T. coracinus coracinus, suggesting heightened sexual selection pressures on male body size. Ecological factors, mating behavior, and female mate choice might contribute to the observed morphological variation. These findings emphasize the need for further studies to comprehend mating dynamics, mate choice, and ecological influences on morphological variations in semi-arboreal and arboreal tiger beetles.
creator: Dale Ann Acal
creator: Anna Sulikowska-Drozd
creator: Radomir Jaskuła
uri: https://doi.org/10.7717/peerj.16956
license: https://creativecommons.org/licenses/by/4.0/
rights: © 2024 Acal et al.

title: Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution
link: https://peerj.com/articles/16972
last-modified: 2024-03-13
description: The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.
creator: Julia Hackländer
creator: Leandro Parente
creator: Yu-Feng Ho
creator: Tomislav Hengl
creator: Rolf Simoes
creator: Davide Consoli
creator: Murat Şahin
creator: Xuemeng Tian
creator: Martin Jung
creator: Martin Herold
creator: Gregory Duveiller
creator: Melanie Weynants
creator: Ichsani Wheeler
uri: https://doi.org/10.7717/peerj.16972
license: https://creativecommons.org/licenses/by/4.0/
rights: © 2024 Hackländer et al.

title: EPI-SF: essential protein identification in protein interaction networks using sequence features
link: https://peerj.com/articles/17010
last-modified: 2024-03-13
description: Proteins are considered indispensable for facilitating an organism’s viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.
creator: Sovan Saha
creator: Piyali Chatterjee
creator: Subhadip Basu
creator: Mita Nasipuri
uri: https://doi.org/10.7717/peerj.17010
license: https://creativecommons.org/licenses/by/4.0/
rights: ©2024 Saha et al.

title: Gut microbiota and its metabolites in Alzheimer’s disease: from pathogenesis to treatment
link: https://peerj.com/articles/17061
last-modified: 2024-03-13
description: IntroductionAn increasing number of studies have demonstrated that altered microbial diversity and function (such as metabolites), or ecological disorders, regulate bowel–brain axis involvement in the pathophysiologic processes in Alzheimer’s disease (AD). The dysregulation of microbes and their metabolites can be a double-edged sword in AD, presenting the possibility of microbiome-based treatment options. This review describes the link between ecological imbalances and AD, the interactions between AD treatment modalities and the microbiota, and the potential of interventions such as prebiotics, probiotics, synbiotics, fecal microbiota transplantation, and dietary interventions as complementary therapeutic strategies targeting AD pathogenesis and progression.Survey methodologyArticles from PubMed and china.com on intestinal flora and AD were summarized to analyze the data and conclusions carefully to ensure the comprehensiveness, completeness, and accuracy of this review.ConclusionsRegulating the gut flora ecological balance upregulates neurotrophic factor expression, regulates the microbiota-gut-brain (MGB) axis, and suppresses the inflammatory responses. Based on emerging research, this review explored novel directions for future AD research and clinical interventions, injecting new vitality into microbiota research development.
creator: Xinfu Zou
creator: Guoqiang Zou
creator: Xinyan Zou
creator: Kangfeng Wang
creator: Zetao Chen
uri: https://doi.org/10.7717/peerj.17061
license: https://creativecommons.org/licenses/by/4.0/
rights: ©2024 Zou et al.

title: On population structure and breeding biology of burrowing crab Dotilla blanfordi Alcock, 1900
link: https://peerj.com/articles/17065
last-modified: 2024-03-13
description: BackgroundThe present study investigated the population structure and breeding biology of the burrowing brachyuran crab species Dotilla blanfordi Alcock, 1900, which is commonly found on the sandy beach of Bhavnagar, located on the Gulf of Kachchh, Gujarat coast, India.MethodsMonthly sampling was conducted from February 2021 to January 2022 at the time of low tide using three line transects perpendicular to the water line, intercepted by a quadrate (0.25 m2) each at three different levels of the middle intertidal region: 20 m, 70 m, and 120 m. The quadrate area was excavated up to 30 cm and sieved for specimen collection. The collected specimens were categorised into different sexes viz., male, non-ovigerous female, or ovigerous female. For the fecundity study of D. blanfordi, the carapace width (mm) as a measure of size as well as their wet weight (g), size, number, and mass of their eggs were also recorded.ResultsThe study revealed sexual dimorphism among the population, with females having significantly smaller sizes as compared to males. The overall population was skewed towards females, with a bimodal distribution of males and females. The occurrence of ovigerous females throughout the year suggests that the population breeds incessantly throughout the year, with the highest occurrence in August and September. A positive correlation was observed between the morphology of crabs (carapace width and wet body weight) and the size, number, and mass of eggs.
creator: Krupal Patel
creator: Heris Patel
creator: Daoud Ali
creator: Swapnil Gosavi
creator: Nisha Choudhary
creator: Virendra Kumar Yadav
creator: Kauresh Vachhrajani
creator: Ashish Patel
creator: Dipak Kumar Sahoo
creator: Jigneshkumar Trivedi
uri: https://doi.org/10.7717/peerj.17065
license: https://creativecommons.org/licenses/by/4.0/
rights: ©2024 Patel et al.

title: Academic stress in college students: descriptive analyses and scoring of the SISCO-II inventory
link: https://peerj.com/articles/16980
last-modified: 2024-03-12
description: In a competitive and demanding world, academic stress is of increasing concern to students. This systemic, adaptive, and psychological process is composed of stressful stimuli, imbalance symptoms, and coping strategies. The SISCO-II Academic Stress Inventory (SISCO-II-AS) is a psychometric instrument validated in Chile. It evaluates stressors, symptoms, and coping, both individually and globally. For its practical interpretation, a scale is required. Therefore, this study aims to descriptively analyze the SISCO-II-AS and to obtain its corresponding scales. Employing a non-experimental quantitative approach, we administered the SISCO-II-AS to 1,049 second and third-year students from three Chilean universities, with a disproportionate gender representation of 75.21% female to 24.79% male participants. Through descriptive and bivariate analysis, we established norms based on percentiles. For the complete instrument and its subscales, significant differences by sex were identified, with magnitudes varying from small to moderate. For the full instrument and its subscales, bar scale norms by percentile and sex are presented. Each subscale (stressors, physical and psychological reactions, social behavioural reactions, total reaction, and coping) has score ranges defined for low, medium, and high levels. These ranges vary according to the sex of the respondent, with notable differences in stressors and physical, psychological, and social behavioural reactions. This study stands out for its broad and heterogeneous sample, which enriches the representativeness of the data. It offers a comprehensive view of academic stress in college students, identifying distinctive factors and highlighting the importance of gender-sensitive approaches. Its findings contribute to understanding and guide future interventions. By offering a descriptive analysis of the SISCO-II-AS inventory and establishing bar norms, this research aids health professionals and educators in better assessing and addressing academic stress in the student population.
creator: Juan-Luis Castillo-Navarrete
creator: Claudio Bustos
creator: Alejandra Guzman-Castillo
creator: Walter Zavala
uri: https://doi.org/10.7717/peerj.16980
license: https://creativecommons.org/licenses/by/4.0/
rights: ©2024 Castillo-Navarrete et al.

title: First evidence of sexual dimorphism in olfactory organs of deep-sea lanternfishes (Myctophidae)
link: https://peerj.com/articles/17075
last-modified: 2024-03-12
description: Finding a mate is of the utmost importance for organisms, and the traits associated with successfully finding one can be under strong selective pressures. In habitats where biomass and population density is often low, like the enormous open spaces of the deep sea, animals have evolved many adaptations for finding mates. One convergent adaptation seen in many deep-sea fishes is sexual dimorphism in olfactory organs, where, relative to body size, males have evolved greatly enlarged olfactory organs compared to females. Females are known to give off chemical cues such as pheromones, and these chemical stimuli can traverse long distances in the stable, stratified water of the deep sea and be picked up by the olfactory organs of males. This adaptation is believed to help males in multiple lineages of fishes find mates in deep-sea habitats. In this study, we describe the first morphological evidence of sexual dimorphism in the olfactory organs of lanternfishes (Myctophidae) in the genus Loweina. Lanternfishes are one of the most abundant vertebrates in the deep sea and are hypothesized to use visual signals from bioluminescence for mate recognition or mate detection. Bioluminescent cues that are readily visible at distances as far as 10 m in the aphotic deep sea are likely important for high population density lanternfish species that have high mate encounter rates. In contrast, myctophids found in lower density environments where species encounter rates are lower, like those in Loweina, likely benefit from longer-range chemical or olfactory cues for finding and identifying mates.
creator: Rene P. Martin
creator: W. Leo Smith
uri: https://doi.org/10.7717/peerj.17075
license: https://creativecommons.org/licenses/by/4.0/
rights: © 2024 Martin and Smith

title: Impact of the coronavirus disease 2019 pandemic on the diversity of notifiable infectious diseases: a case study in Shanghai, China
link: https://peerj.com/articles/17124
last-modified: 2024-03-12
description: The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.
creator: Yongfang Zhang
creator: Wenli Feng
uri: https://doi.org/10.7717/peerj.17124
license: https://creativecommons.org/licenses/by/4.0/
rights: © 2024 Zhang and Feng