bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of bed clay on surface water-wave reconstruction from ripples
Authors: Malarkey Jonathan , Pollard Ellen M. , Fernández Roberto , Wu Xuxu , Baas Jaco H. , Parsons Daniel R.
Published: December 2024

Wave ripples can provide valuable information on their formative hydrodynamic conditions in past subaqueous environments by inverting dimension predictors. However, these inversions do not usually take the mixed non-cohesive/cohesive nature of sediment beds into account. Recent experiments involving sand–kaolinite mixtures have demonstrated that wave-ripple dimensions and the threshold of motion are affected by bed clay content. Here, a clean-sand method to determine wave climate from orbital ripple wavelength has been adapted to include the effect of clay and a consistent shear-stress threshold parameterisation. From present-day examples with known wave conditions, the results show that the largest clay effect occurs for coarse sand with median grain diameters over 0.45 mm. For a 7.4% volumetric clay concentration, the range of possible water-surface wavelengths and water depths can be reduced significantly, by factors of three and four compared to clean sand, indicating that neglecting clay when present will underestimate the wave climate.
Outdoor nitrogen dioxide exposure and longitudinal health status trajectory in the Canadian National Population Health Survey
Authors: Stieb David M. , Chen Li , Hystad Perry , Rittmaster Robyn , Lavigne Eric
Published: December 2024

Few studies have examined the association between air pollution and the trajectory of global health status measures related to the functional impacts of chronic disease. To address this gap, we examined the trajectory of the Health Utilities Index (HUI) over 17 years of follow-up among Canadian National Population Health Survey (NPHS) participants. Annual average nitrogen dioxide (NO2) exposures from a national land use regression surface were mapped to 15,631 NPHS participants at their place of residence provided at each follow-up. We modelled HUI trajectory as a cubic polynomial function of age in relation to air pollution and selected covariates using random growth curve models to account for longitudinal repeated measures. Adjusting for covariates selected based on a directed acyclic graph, we found that NO2 exposure exhibited a significant negative association with HUI in females. It also exhibited a significant positive interaction with the linear age term, and a significant negative interaction with the quadratic age term, resulting in a small non-significant decrease in quality adjusted life years lived after age 20 among females. Our analysis provides a proof of concept for examining the influence of built environment variables on the trajectory of health related quality of life in Canada.
Help-seeking intention for mental illness and associated factors among Dessie town residents in Northeast Ethiopia
Authors: Gelaye Habtam , Andualem Atsedemariam , Beyene Abeba , Gezie Hailemariam
Published: December 2024

Despite the increasing global burden of mental illness and the availability of effective evidence-based treatments, many individuals with mental illness do not seek professional help. Therefore, this study aimed to assess help-seeking intention for mental illness and associated factors among Dessie town residents, Northeast Ethiopia, 2021. A community-based cross-sectional study was conducted among 501 Dessie town residents. The data were collected by face-to-face interview. The General Help Seeking Questionnaire was used to assess help-seeking intention. Bivariable and multivariable logistic regression analysis techniques were used. Finally, a statistically significant level was declared at a p-value less than 0.05. Among 501 participants, 67.5% were likely to seek help from health professionals. Being female (AOR = 4.695, 95% CI = 1.63– 13.50), being single (AOR = 0.330, 95% CI = 0.12–0.89), and having good knowledge (AOR = 3.030, 95% CI = 1.25–7.35) were significantly associated with help-seeking intention. This study indicated that the participants’ help-seeking intention was inadequate. Sex, marital status, monthly income, and knowledge of mental illness were found to be associated with help-seeking intentions for mental illnesses. Therefore, community healthcare workers, healthcare administrators, and religious and community leaders should work to enhance the help-seeking intention of the community.
A randomised controlled pilot trial protocol for patient led cognitive gamified training during haemodialysis
Authors: Aksoy Murat , Hunter Samantha , Asghar Aziz U. R. , Bhandari Sunil
Published: December 2024

Disruptions in cognitive function have been reported in individuals undergoing haemodialysis and those with chronic kidney disease. This pilot study protocol primarily assesses the feasibility and acceptability of using mobile cognitive gaming apps for patient-led cognitive training during haemodialysis sessions. The protocol consists of three phases: (1) reviewing and evaluating available cognitive gaming apps, (2) conducting focus groups/interviews with people with kidney disease to determine app preferences, and (3) undertaking a quasi-experimental randomised controlled trial to compare cognitive outcomes between a patient-led app intervention group and a standard care control group over four months. Primary outcomes will include changes in cognitive test scores [Montreal Cognitive Assessment (MoCA), Modified Mini-Mental State Exam (3MSE), Rapid Objective Working Memory Assessment (ROWMA)], while secondary outcomes will encompass quality of life measures [Patient-Reported Outcomes Measurement (PROM) Kidney Disease Quality of Life Short Form (KDQoL-SF™) v 1.3, Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health Instrument, European Quality of Life Five Dimension (EQ-5D)]. If demonstrated to be effective, this novel method of utilising gamified cognitive training applications could potentially mitigate cognitive decline and improve the well-being of people receiving haemodialysis without necessitating significant clinical resources. The findings from this research will guide the development of a larger definitive randomised trial in the future.
Direct detection of 4-dimensions of SARS-CoV-2: infection (vRNA), infectivity (antigen), binding antibody, and functional neutralizing antibody in saliva
Authors: Mohammadi Aida , Chiang Samantha , Li Feng , Wei Fang , Lau Chang S. , Aziz Mohammad , Ibarrondo Francisco J. , Fulcher Jennifer A. , Yang Otto O. , Chia David , Kim Yong , Wong David T.W.
Published: December 2024

We developed a 4-parameter clinical assay using Electric Field Induced Release and Measurement (EFIRM) technology to simultaneously assess SARS-CoV-2 RNA (vRNA), nucleocapsid antigen, host binding (BAb) and neutralizing antibody (NAb) levels from a drop of saliva with performance that equals or surpasses current EUA-approved tests. The vRNA and antigen assays achieved lower limit of detection (LOD) of 100 copies/reaction and 3.5 TCID₅₀/mL, respectively. The vRNA assay differentiated between acutely infected (n = 10) and infection-naïve patients (n = 33) with an AUC of 0.9818, sensitivity of 90%, and specificity of 100%. The antigen assay similarly differentiated these patient populations with an AUC of 1.000. The BAb assay detected BAbs with an LOD of 39 pg/mL and distinguished acutely infected (n = 35), vaccinated with prior infection (n = 13), and vaccinated infection-naïve patients (n = 13) from pre-pandemic (n = 81) with AUC of 0.9481, 1.000, and 0.9962, respectively. The NAb assay detected NAbs with a LOD of 31.6 Unit/mL and differentiated between COVID-19 recovered or vaccinated patients (n = 31) and pre-pandemic controls (n = 60) with an AUC 0.923, sensitivity of 87.10%, and specificity of 86.67%. Our combo assay represents a significant technological advancement to simultaneously address SARS-CoV-2 infection and immunity, and it lays the foundation for tackling potential future pandemics.
Spatiotemporal patterns and drivers of orchard meadow loss in South Tyrol, Italy
Authors: Schönafinger Alexander , Egarter Vigl Lukas , Tasser Erich
Published: December 2024

Orchard meadows, a specific agroforestry system characterised by scattered high-stem fruit trees, are a traditional element of several cultural landscapes in Central Europe and provide important ecosystem services. Since the middle of the 20th century, orchard meadows have drastically declined across Europe. Spatial information on the drivers and patterns of such a decline in several regions in Central Europe is lacking. In this study, we aimed to provide the first detailed insights into the development of orchard meadows in South Tyrol. We assessed the current distribution of orchard meadows in South Tyrol (Northern Italy) and analysed the patterns and drivers of their decline over the last 75 years. Aerial images of the orchard meadows in South Tyrol from the 1950s up to the 2020s were examined, and changes in their distribution were statistically analysed. The results revealed that the historical orchard meadow area in South Tyrol has decreased by more than 6,000 ha (−95%) over the past 75 years, showing one of the largest reductions in Central Europe. Orchard meadow loss was highest at lower elevations (–5,155 ha), shallow slopes (–4,313 ha), and expositions towards the south (–5,035 ha). Orchard meadows have declined due to agricultural intensification towards the establishment of modern fruit orchards (56%) and intensively managed grasslands (8%), urbanisation (23%), and reforestation (6%), and other types of land use/land cover (6.5%). Only 0.5% of the traditional orchard meadows have been preserved. The results suggest that orchard meadows are at a high risk of disappearing from the South Tyrolean landscape, resulting in the loss of genetic diversity of many local or indigenous fruit varieties (especially apples and pears). Immediate action is needed to safeguard the current orchard meadow population and appropriate management measures are needed to highlight the multiple values of orchard meadows and provide a valuable alternative to intensive arable farming.
Numerical proceeding to calculate impurity states in 2D semiconductor heterostructures
Authors: Akimov Volodymyr , Tulupenko Viktor , Demediuk Roman , Tiutiunnyk Anton , Duque Carlos A. , Morales Alvaro L. , Laroze David , Mora-Ramos Miguel Eduardo
Published: December 2024

The article provides and discusses details of numerical proceeding for the expansion method to calculate energy positions and wave functions of the localized and resonant electronic states emerging in quantum well-type semiconductor nanostructures because of perturbation of confined states by the Coulomb potential of the hydrogenic impurity center. Effective mass approximation is used. Several excited both resonant and non-resonant states are calculated and classified for the case of a simple rectangular GaAs/AlGaAs quantum well. Results are compared to the ones in literature.
Analysis of plasmon modes in Bi2Se3/graphene heterostructures via electron energy loss spectroscopy
Authors: Moorsom Timothy , McCauley Mairi , Nizamuddin Bin Muhammad Mustafa Ahmad , Ramadan Sami , Burton Joel , Sasaki Satoshi , MacLaren Donald A. , Petrov Peter K.
Published: December 2024

Topological Insulators (TIs) are promising platforms for Quantum Technology due to their topologically protected surface states (TSS). Plasmonic excitations in TIs are especially interesting both as a method of characterisation for TI heterostructures, and as potential routes to couple optical and spin signals in low-loss devices. Since the electrical properties of the TI surface are critical, tuning TI surfaces is a vital step in developing TI structures that can be applied in real world plasmonic devices. Here, we present a study of Bi2Se3/graphene heterostructures, prepared using a low-cost transfer method that reliably produces mono-layer graphene coatings on Bi2Se3 flakes. Using both Raman spectroscopy and electron energy loss spectroscopy (EELS), we show that the graphene layer redshifts the energy of the $$\pi$$ plasmon mode in Bi2Se3, creating a distinct surface plasmon that differs significantly from the behaviour of a TI-trivial insulator boundary. We demonstrate that this is likely due to band-bending and electron transfer between the TI surface and the graphene layer. Based on these results, we outline how graphene overlayers can be used to create tuneable, stable plasmonic materials based on topological insulators.
Investigation of factors affecting the sound absorption behaviour of 3D printed hexagonal prism lattice polyamide structures
Authors: Vašina Martin , Měsíček Jakub , Mánek Martin , Ma Quoc-Phu , Hajnyš Jiří , Petrů Jana
Published: December 2024

The aim of this work is to investigate the sound absorption properties of open-porous polyamide 12 (PA12) structures produced using Selective Laser Sintering (SLS) technology. The examined 3D-printed samples, fabricated with hexagonal prism lattice structures, featured varying thicknesses, cell sizes, and orientations. Additionally, some samples were produced with an outer shell to evaluate its impact on sound absorption. Experiments were conducted using the transfer function method with an acoustic impedance tube in the frequency range of 250 Hz and 6400 Hz. The results showed that the studied geometric factors significantly affected the sound absorption of the PA12 samples. In some cases, the hexagonal prism lattice structures demonstrated relatively high sound absorption properties. Thanks to their properties such as lower weight, recyclability, and resistance to moisture and chemicals, these structures become competitive with commonly used sound-insulating materials, making them promising candidates for sound absorption. Furthermore, numerical simulations using Ansys software confirmed that the sound absorption properties of the open-porous material structures generally increased with higher specific airflow resistance. The findings highlight the advantages of 3D printing technology in producing complex, highly customizable porous structures for noise reduction applications.
Complexity measure in natural time analysis identifying the accumulation of stresses before major earthquakes
Authors: Varotsos Panayiotis A. , Sarlis Nicholas V. , Nagao Toshiyasu
Published: December 2024

Here, we suggest a procedure through which one can identify when the accumulation of stresses before major earthquakes (EQs) (of magnitude M 8.2 or larger) occurs. Analyzing the seismicity in natural time, which is a new concept of time, we study the evolution of the fluctuations of the entropy change of seismicity under time reversal for various scales of different length i (number of events). Although the stress might be accumulating throughout the entire process of EQ preparation due to tectonic loading, here we find that the proposed complexity measure reveals different stress accumulation characteristics from those in the long-term background when the system approaches the critical stage. Specifically, we find that anomalous intersections between scales of different i are observed upon approaching a major EQ occurrence. The investigation is presented for the seismicity in Japan since 1984 including the M9 Tohoku EQ on 11 March 2011, which is the largest EQ ever recorded there, as well as for the seismicity before 2017 Chiapas M8.2 EQ, which is Mexico’s largest EQ in more than a century. Based on this new complexity measure, a preprint submitted on 5 December 2023 anticipated the 1 January 2024 M7.6 EQ in Japan.
Constitutive androstane receptor (CAR) functions as a tumor suppressor via regulating stemness in liver cancer
Authors: Bae Sarah Da Won , Nguyen Romario , Yuen Lawrence , Lam Vincent , George Jacob , Qiao Liang
Published: December 2024

Constitutive androstane receptor (CAR) is a xenosensor that is almost exclusively expressed in the liver. Studies in rodents suggest an oncogenic role for CAR in liver cancer, but its role in human liver cancer is unclear. We aimed to investigate the functional roles of CAR in human liver cancer with a focus on the liver cancer stem cells. We used bioinformatics to increase our understanding of CAR in human liver cancer and associated stem cell markers. We studied the functional roles of CAR in human liver cancer with a focus on the liver cancer stem cell using siRNA, modulation of CAR activity, and tumorsphere formation assays. We have revealed significant associations between CAR and a wide variety of signalling pathways including stemness signalling. Further in vitro studies have shown that activation of CAR significantly reduces cancer cell stemness and represses proliferation, migration, invasion, and the tumorsphere-forming abilities of liver cancer cells (p < 0.05). Our data demonstrates the unequivocal tumor-suppressive role of CAR in liver cancer. While more detailed mechanistic studies are warranted, the efficacy of CAR xeno-activators in the treatment of advanced hepatocellular carcinoma (HCC) may potentially open a new avenue for liver cancer therapy.
The plausibility of claimed induced seismicity
Authors: Wilkinson Max , Mhana Najwa , Wilson Miles P. , Foulger Gillian R. , Tezel Timur , Gluyas Jon G.
Published: December 2024

Claims of industrially induced seismicity vary from indisputable to unpersuasive and yet the veracity of industrial induction is vital for regulatory and operational practice. Assessment schemes have been developed in response to this need. We report here an initial assessment of the reliability of all globally known cases of proposed human-induced earthquakes and invite specialists on particular cases to refine these results. 1235 cases were assessed, requiring over 1000 h of work. From the 881 cases for which scorable evidence is available, we class 87% as ‘Confidently Induced’, 10% as ‘Probably Induced’, 2% as ‘Equivocal’ and < 1% as ‘Confidently Natural’. The most seismogenic activities are fracking, research, geothermal, water reservoir impoundment, conventional oil and gas. Least seismogenic activities are construction, deep penetrating bombs, coal bed methane. 354 cases (29%) lack enough information to be assessable. Future work could include applying data mining techniques including natural language processing and AI to uncover new evidence. Future best practice for rapid assessment of cases would ideally involve an independent panel of scientists who rapidly apply a questionnaire scheme, reach consensus, and inform a response.
Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis
Authors: B. Pavihaa Lakshmi , S. Vidhya
Published: December 2024

A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel algorithm, the Modified-Net architecture, which integrates a broad spectrum of architectural components tailored to detect the intricate patterns and variances in DVT imaging data. Our work integrates advanced components such as dilated convolutions for larger receptive fields, spatial pyramid pooling for context, residual and inception blocks for multiscale feature extraction, and attention mechanisms for highlighting key features. Our framework enhances precision of DVT region identification, attaining an accuracy of 98.92%, with a loss of 0.0269. The model also validates sensitivity 96.55%, specificity 96.70%, precision 98.61%, dice 97.48% and Intersection over Union (IoU) 95.10% offering valuable insights into DVT segmentation. Our framework significantly improves segmentation performance over traditional methods such as Convolutional Neural Network , Sequential, U-Net, Schematic. The management of DVT can be improved through enhanced segmentation techniques, which can improve clinical observation, treatment planning, and ultimately patient outcomes.
Deep reinforcement learning and fuzzy logic controller codesign for energy management of hydrogen fuel cell powered electric vehicles
Authors: Rostami Seyed Mehdi Rakhtala , Al-Shibaany Zeyad , Kay Peter , Karimi Hamid Reza
Published: December 2024

Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration. More specifically, this research paper establishes a power system model with three DC-DC converters, which includes a hierarchical energy management framework employed in a two-layer control strategy. Three loop control strategies for hybrid electric vehicles based on reinforcement learning are designed in the low-level layer control strategy. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) is used with a network. Three DRL controllers are designed using the hierarchical energy optimization control architecture. The comparative results between the two strategies, Deep Reinforcement Learning and Fuzzy logic supervisory control (DRL-F) and Super-Twisting algorithm and Fuzzy logic supervisory control (STW-F) under the EUDC driving cycle indicate that the proposed model DRL-F can ensure the Root Mean Square Error (RMSE) reduction for 21.05% compared to the STW-F and the Mean Error reduction for 8.31% compared to the STW-F method. The results demonstrate a more robust, accurate and precise system alongside uncertainties and disturbances in the Energy Management System (EMS) of FCHEV based on an advanced learning method.
Loss of Hoxa5 function affects Hox gene expression in different biological contexts
Authors: Frenette Béatrice , Guéno Josselin , Houde Nicolas , Landry-Truchon Kim , Giguère Anthony , Ashok Theyjasvi , Ryckman Abigail , Morton Brian R. , Mansfield Jennifer H. , Jeannotte Lucie
Published: December 2024

Hoxa5 plays numerous roles in development, but its downstream molecular effects are mostly unknown. We applied bulk RNA-seq assays to characterize the transcriptional impact of the loss of Hoxa5 gene function in seven different biological contexts, including developing respiratory and musculoskeletal tissues that present phenotypes in Hoxa5 mouse mutants. This global analysis revealed few common transcriptional changes, suggesting that HOXA5 acts mainly via the regulation of context-specific effectors. However, Hox genes themselves appeared as potentially conserved targets of HOXA5 across tissues. Notably, a trend toward reduced expression of HoxA genes was observed in Hoxa5 null mutants in several tissue contexts. Comparative analysis of epigenetic marks along the HoxA cluster in lung tissue from two different Hoxa5 mutant mouse lines revealed limited effect of either mutation indicating that Hoxa5 gene targeting did not significantly perturb the chromatin landscape of the surrounding HoxA cluster. Combined with the shared impact of the two Hoxa5 mutant alleles on phenotype and Hox expression, these data argue against the contribution of local cis effects to Hoxa5 mutant phenotypes and support the notion that the HOXA5 protein acts in trans in the control of Hox gene expression.
Childhood adversity and peer influence in adolescent bullying perpetration
Authors: Ugwu Lawrence E. , Ramadie Kedibone J. , Ajele Wojujutari Kenni , Idemudia Erhabor Sunday
Published: December 2024

Bullying among South African adolescents is a critical public health issue. This study explores the relationship between childhood adversity, peer influence, and personality traits in predicting bullying perpetration. Data from 769 high school learners were analysed using Structural Equation Modelling. Findings indicate that childhood adversity predicts bullying perpetration, mediated by peer influence and moderated by personality traits like conscientiousness, extraversion, and emotional stability. These results support Social Learning Theory, emphasising observed behaviours and peer dynamics in bullying. Practical implications include targeted interventions addressing childhood adversity and fostering positive peer interactions and personality development.
Analysing the support mechanisms of the vaginal ring pessary on supine and upright MRI
Authors: van den Noort Frieda , de Alba Alvarez I. , van der Steen A. , Smelt A. D. , Simonis F. F. J. , Grob A. T. M.
Published: December 2024

Vaginal pessaries have been used for millennia to alleviate symptoms of pelvic organ prolapse (POP). Despite their long-standing use, the success rate of pessary treatment is approximately 60%, and the underlying mechanisms of support are not well understood. This study aims to investigate three previously proposed hypotheses regarding the support mechanisms of pessaries, utilizing supine and upright magnetic resonance imaging (MRI): (1) support by bony structures, (2) support by levator ani muscles (LAM), and (3) the uterus keeping the pessary in place by acting as a lever. Thirty POP patients, with a successful pessary fit for more than 3 months, underwent MRI scans in both upright and supine position. The position of the pelvic bony structures, LAM, uterus, and pessary were analysed by placing corresponding points on these structures and comparing the differences between supine and upright. Also, the angle of the pessary with the horizontal plane of the pelvic inclination corrections system was analysed. Principal component analysis was applied to evaluate how the positions of the LAM and the pessary changed between the two positions. The lowest point of the pessary descends below the lowest point of the bony structures in upright position, disproving the first hypothesis. The pessary significantly descends towards the LAM from supine to upright, but does not drop below it, strengthening the second hypothesis. The pessary angle is below 90° in upright position, the uterus is positioned in the pessary centre and remains constant from supine to upright, strengthening the third hypothesis.
Understanding and overcoming geographical barriers for scaling up dog vaccinations against rabies
Authors: Sambo Maganga , Hampson Katie , Johnson Paul C. D. , Johnson Olatunji O.
Published: December 2024

Rabies causes 59,000 human deaths annually in over 150 countries. Mass dog vaccination (MDV) is key to controlling dog rabies, requiring 70% coverage in the susceptible dog population to eliminate rabies deaths. MDV campaigns must achieve geographical homogeneity of coverage. Although rabies elimination is feasible, operation challenges exist, especially in hard-to-reach areas. We conducted geospatial modelling to identify geographical factors affecting MDV success in terms of campaign completeness and vaccination coverage across 25 districts in south-eastern Tanzania. From October 2016 to January 2017, about 81,000 dogs were vaccinated in 1,379 (68%) villages within these districts. Multivariable regression analysis revealed that land cover, residence, poverty, and elevation were associated with campaign completeness. The odds of achieving completeness in croplands were 1.75 times higher compared to forests. Vaccination coverage was influenced by residence, area, poverty index, and elevation, with urban areas having lower odds of achieving coverage compared to rural areas. Coverage probabilities exceeding 70% were lower on islands, highlands, urban areas, and areas bordering protected areas. As the 2030 deadline for "zero dog-mediated human rabies deaths" approaches, operational and logistical challenges in MDV campaigns persist. Our data provide insights into MDV success and failure, guiding future control efforts to improve their effectiveness.
Omics-based analysis of mitochondrial dysfunction and BBB integrity in post-COVID-19 sequelae
Authors: Dhariwal Rupal , Dave Kirtan , Jain Mukul
Published: December 2024

The SARS-CoV-2 virus that resulted in the COVID-19 pandemic has been implicated in a range of neurological issues, such as encephalopathy, stroke, and cognitive decline. Although the precise mechanism causing these issues is unknown, mounting evidence shows that blood–brain barrier (BBB) disruption is probable2 a major factor. The integrity of the blood–brain barrier (BBB), a highly selective barrier that divides the brain from the systemic circulation, is crucial for preserving normal brain function. By analysing the multi-transcriptome data, this work explores the neurological impacts of the SARS-CoV-2 virus and provides insight into the molecular mechanisms behind BBB breakdown and neurological symptoms in COVID-19 patients. The endothelial cells of BBB expresses inflammatory genes in response to the systemic inflammation induced due to SARS-CoV-2 remnants in the body. This raises the possibility that systemic inflammation brought on by SARS-CoV-2 and BBB integrity are correlated. Furthermore, the study highlights the pathways involved in oxidative stress and endothelial cell activation, revealing their role in COVID-19 passage through BBB and induction of systemic inflammation and advancement toward neurological disorders. The article showcases the evidence that mitochondrial dysfunction is a major aftermath associated with SARS-CoV-2 infection as the impaired Mitochondria leads to an accumulation of reactive oxygen species (ROS), triggering endothelial dysfunction, and leading to the passage of harmful molecules across the BBB. This study offers insightful information that may open up the possibilities for new treatment plans by targeting biomarkers specifically associated with inflammation and BBB dysfunctioning conditions.
Clustering and classification for dry bean feature imbalanced data
Authors: Lee Chou-Yuan , Wang Wei , Huang Jian-Qiong
Published: December 2024

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features. The results show that the proposed algorithm BLSMOTE + K-means + SVM is superior to other traditional machine learning methods in classification and various performance indicators. The BLSMOTE + K-means + DT generates decision rules for the dry bean dataset and the the obesity levels dataset, and the BLSMOTE + K-means + RF ranks the importance of explanatory variables. These experimental results can provide scientific evidence for decision-makers.
Comparison of large single and small multiple doses of cyclophosphamide exposure in mice during early prepubertal age on fertility outcome
Authors: Salian Sujith Raj , Daddangadi Akshatha , Predheepan Dhakshanya , Bhagat Amonkar Divya Deeleep , Pandya Riddhi Kirit , Laxminarayana Sindhura Lakshmi Koulmane , Uppangala Shubhashree , Kalthur Guruprasad , Anderson Richard A. , Adiga Satish Kumar
Published: December 2024

Cyclophosphamide (CY) exposure is known to affect the ovary and impair fertility. Clinically, treatment is generally given over multiple doses, but research models have generally used single doses. The relative effects of administering multiple small doses of CY in the prepubertal period are not elucidated. Two-week-old early-prepubertal Swiss albino female mice were administered with either large single (200 mg/Kg x 1; CY200X1) or small multiple (75 mg/Kg x 4; CY75X4) CY doses, thus a 50% higher total dose. Surviving females were assessed for estrous cyclicity, ovarian follicle reserve, oocyte functional competence, and postnatal assessment of first-generation (F1) pups. Exposure to CY75X4 reduced the loss of ovarian follicles (p < 0.05), and body weight (p < 0.001), and resulted in a larger population of cycling females (p < 0.01) with higher oocyte yield (p < 0.05) compared to CY200X1. Although CY200X1 exposed cycling females had comparable oocyte quality, and fertility index, the postnatal mortality was higher in F1 pups (p < 0.05) in comparison to the CY75X4 group. Although both strategies affect oocyte quality and functional competence similarly, CY75X4, despite the higher overall dose, results in reduced follicle loss, produces higher oocyte/blastocyst yield, and exhibits lower postnatal mortality rates, suggesting a potential advantage over CY200X1 for later fertility and offspring health. The differences in effects of the two treatment models show the need for designing animal model studies that more closely mimic the clinical administration of gonadotoxic therapies such as cyclophosphamide.
Potato late blight leaf detection in complex environments
Authors: Li Jingtao , Wu Jiawei , Liu Rui , Shu Guofeng , Liu Xia , Zhu Kun , Wang Changyi , Zhu Tong
Published: December 2024

Potato late blight is a common disease affecting crops worldwide. To help detect this disease in complex environments, an improved YOLOv5 algorithm is proposed. First, ShuffleNetV2 is used as the backbone network to reduce the number of parameters and computational load, making the model more lightweight. Second, the coordinate attention mechanism is added to reduce missed detection for leaves that are overlapping, damaged, or hidden, thereby increasing detection accuracy under challenging conditions. Lastly, a bidirectional feature pyramid network is employed to fuse feature information of different scales. The study results show a significant improvement in the model’s performance. The number of parameters was reduced from 7.02 to 3.87 M, and the floating point operations dropped from 15.94 to 8.4 G. These reductions make the model lighter and more efficient. The detection speed increased by 16 %, enabling faster detection of potato late blight leaves. Additionally, the average precision improved by 3.22 %, indicating better detection accuracy. Overall, the improved model provides a robust solution for detecting potato late blight in complex environments. The study’s findings can be useful for applications and further research in controlling potato late blight in similar environments.
Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches
Authors: Arni Ankit M. , Fraser Diane P. , Sharp Seth A. , Oram Richard A. , Johnson Matthew B. , Weedon Michael N. , Patel Kashyap A.
Published: December 2024

A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10− 31), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10− 300). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10− 300) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10− 14) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 − 6). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10− 58) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10− 300). Notably, when applying the European ancestry-derived 90th centile risk threshold, only 0.71% (95% CI 0.41–1.13) of African ancestry individuals and 6.4% (95% CI 5.6–7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.
Personality traits and their correlation with behavior in the bonnet macaque (Macaca radiata) in southern India
Authors: Arlet Małgorzata E , Sakson-Obada Olga , Zakrzewska Marzena , Singh Mewa
Published: December 2024

The purpose of our research was to identify the primary personality traits of bonnet macaques using an observer-based questionnaire and to assess the accuracy of this method in predicting behavior. Additionally, we examined the relationship between personality traits and variables such as sex, age, and rank. We rated 25 males and 38 females on 50 personality questionnaire items using a 7-point scale, with reliable ratings and good agreement among raters (the lowest of the calculated ICC (3:k) index ≥ 0.62). Behavioral observations of 38 females over two-years were analyzed. Exploratory factor analysis revealed five traits: Friendliness, Aggression, Playfulness, Fearfulness, and Opportunism. Every trait correlated with a certain number of observed behaviors. Our results suggest that the questionnaire-based method is sufficiently valid as it confirmed high behavioral convergence with Fearfulness, Playfulness and Aggressiveness. Further studies are needed to understand the functional value of behaviors associated with Friendliness and Opportunism. Our study confirmed a tolerant social style of bonnet macaques, characterized by similar Aggression levels in both males and females, and lack of difference in Aggression level between subadult and adult males. Moreover, we identified a species-characteristic trait Opportunism which, unlike in other studied macaques, did not involve aggression-related characteristics.
In Vitro conservation and genetic diversity analysis of rare species Ribes janczewskii
Authors: Nurtaza Aidana , Dyussembekova Damira , Islamova Symbat , Samatova Indira , Zhanybekova Zhanargul , Umirzakova Alima , Magzumova Gulmira , Muranets Anna , Kakimzhanova Almagul
Published: December 2024

Ribes janczewskii is a rare and valuable plant known for its resistance to spring frosts, pests, and diseases. It is used in hybridization to develop resistant currant varieties but is on the verge of extinction, listed in Kazakhstan Red Book. This study developed a micropropagation and slow-growth storage protocol for conservation. Genotypes were identified through DNA barcode analysis (rbcL, ITS, and matK) and sequences uploaded to the National Center for Biotechnology Information database. Genetic diversity was assessed using iPBS primers, generating 98 fragments with 88–94% polymorphic bands. Biochemical analysis of fruits showed vitamin C content from 4.64 to 5.61 mg/100 g, vitamin E from 2.26 to 3.16 mg/100 g, vitamin B5 from 3.18 to 4.93 mg/100 g, and quercetin up to 12.5 mg/100 g. Micropropagation stages were optimized with 12% hydrogen peroxide for surface sterilization, achieving up to 73.3% explant viability. Effective hormonal combinations for in vitro culture included WPM with BAP 0.2 mg L−1 and GA 0.5 mg L−1, and for propagation, BAP 0.25 mg L−1, GA 0.5 mg L−1, and IBA 0.5 mg L−1. Mannitol (20 g L−1) was used for slow-growth storage, keeping explants viable for 4 months without re-cultivation.
The impact of transient air pollution exposure on worker performance in Chinese soccer players
Authors: Zhang Ning , Wei Xiahai , Huang Xuhui , He Pan , Liu Yunxiao , Liu Hongyou , Lin Tao , Shi Xin , Liu Zhu , Freeman Richard B.
Published: December 2024

Using a unique dataset on the performance of soccer players in China (retrieved from 632 matches involving 24 teams during the 2014 to 2016 seasons), we investigate the effect of air pollution on different performance indicators that rely on different mixtures of the physical and cognitive inputs of players. To ensure a causal interpretation, we implement an instrumental variable (IV) approach using thermal inversion as the instrument for air pollution. We found that players’ performance indicators, especially those more related to cognitive factors, are more strongly influenced by air pollution. One standard deviation (SD) increase in the Air Quality Index (AQI) leads to 2.5% decrease in the number of players’ passes and 5.1% increase in the number of fouls. However, for performance indicators that are more related to players’ physical condition, e.g., running distances, no such significant impact is identified. Overall, these findings suggest that the negative impact of short-term air pollution exposure on outdoor worker performance is mainly cognitive, which we believe could lead to important policy implications, not only for competitive sports but also across a much broader spectrum of business sectors.
Whole genome sequencing reveals candidate causal genetic variants for spastic syndrome in Holstein cattle
Authors: Jacinto Joana G.P. , Letko Anna , Häfliger Irene M. , Akyürek Eylem Emek , Sacchetto Roberta , Gentile Arcangelo , Drögemüller Cord
Published: December 2024

Bovine spastic syndrome (SS) is a progressive, adult-onset neuromuscular disorder (NMD). SS is inherited but the mode of inheritance is unclear. The aim of this study was to characterize the phenotype and to identify a possible genetic cause of SS by whole-genome sequencing (WGS) and focusing on protein-changing variants. Seven affected unrelated Holstein cattle of both sexes were referred for SS at a mean age of 5.3 years (S.D.±1.1) showing intermittent spasm of the skeletal muscles of the pelvic girdle. Assuming monogenic recessive inheritance, analysis of the WGS data did not reveal any private variants common to all cases. Searching for homozygous rare variants considering each case individually, allowed the identification of a rare recessive likely pathogenic missense variant in TOR3A for one case with an allele frequency of 1.69% in a global Holstein population. In the remaining six SS cases, we identified seven potentially dominant de novo mutations or inherited alleles as private heterozygous, mostly missense, variants of uncertain significance involving seven different NMD candidate genes: MPEG1, LHX8, WHAMM, NGRN, TTN, ATP1A1, PCDH1. All eight candidate causal variants identified were predicted to be deleterious. This study describes for the first time WGS findings in confirmed cases of bovine SS and provides evidence for a heterogeneous genetic cause of SS in cattle.
Data-driven assessment of well stimulation in unconventional gas reservoirs
Authors: Yang Jian , Li Song , Zeng Ji , Yang Zhaozhong , Li Xiaogang , He Tingting , Yi Liangping , Kong Bing
Published: December 2024

Unconventional gas reservoirs, characterized by their complex geologies and challenging extraction conditions, demand innovative approaches to enhance gas production and ensure economic viability. Well stimulation techniques, such as hydraulic fracturing and acidizing, have become indispensable tools in unlocking the potential of these tight formations. However, the effectiveness of these techniques can vary widely depending on the specific characteristics of the reservoir. In this context, a data-driven approach to assess well stimulation practices offers a promising avenue to optimize recovery processes and reduce uncertainties. This paper presents a comprehensive study that leverages the power of big data analytics and machine learning to analyze and improve well stimulation strategies in unconventional gas reservoirs. By systematically gathering and processing vast arrays of geological, operational, and production data, this study aims to identify patterns and correlations that can predict stimulation outcomes more accurately. The ultimate goal is to develop a robust framework that allows for tailored stimulation designs based on the unique properties of each reservoir, thereby maximizing efficiency and minimizing environmental impacts. This study introduces a new procedure for assessing well stimulation performance, which involves analyzing the EUR through Duong’s model, calculating the key performance indicator of the treatment, and establishing a data-driven model to predict the treatment KPI.
Explainable AI improves task performance in human–AI collaboration
Authors: Senoner Julian , Schallmoser Simon , Kratzwald Bernhard , Feuerriegel Stefan , Netland Torbjørn
Published: December 2024

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed $$N=9,600$$ assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed $$N=5,650$$ assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, $$P=0.001$$) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, $$P=0.010$$) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.