bluuebunny/crossref_metadata_2025 huggingface dataset:
Delayed immune-related adverse events profile associated with immune checkpoint inhibitors: a real-world analysis
Authors: Yang Yana , Li Linman , Tian Jing , Ma Linwen , Wu Yaoxin , Luo Qian , Luo Yan
Published: November 2024

Background

Immune-related adverse events (irAEs) typically occur within 3 months of initiating immune-checkpoint inhibitors (ICIs), which has been extensively documented. But the clinical profiles of late-onset irAEs remain inadequately characterized. Therefore, this study aims to quantify the correlation between delayed irAEs and ICIs, and to delineate the profiles of delayed toxicities associated with ICIs using data from the Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods

Data from the January 2011 to December 2023 in FAERS database were extracted. Four signal detection indices, reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS), were employed to evaluate the associations between ICIs and delayed irAEs.

Results

A total of 147,854 cases were included in this study, of which 3,738 cases related to delayed irAEs were identified. Generally, 8 signals at System Organ Class (SOC) level were found to be associated with ICIs. Males had a slightly higher reporting frequencies for respiratory disorders (ROR975 = 0.95) and blood and lymphatic system disorders (ROR025 = 1.22), but lower reporting frequencies for immune system disorders (ROR025 = 1.16). Three monotherapy (anti-PD-1, anti-PD-L1 and anti-CTLA-4) were all associated with significant increasing gastrointestinal disorders (ROR025 = 1.66, 1.16, 1.99) and metabolism disorders (ROR025 = 2.26, 1.74, 3.13). Anti-PD-1 therapy exhibited higher rates of respiratory toxicities (ROR025 = 1.46 versus 0.82) and skin toxicities (ROR025 = 1.27 versus 0.94) compared with anti-CTLA-4 therapy. At PT levels, pneumonitis (ROR025: from 11.85 to 29.27) and colitis (ROR025: from 2.11 to 24.84) were the most notable PT signals associated with all three ICI regimens. For outcomes of delayed irAEs, gastrointestinal disorders showed the highest proportion (51.06%) of death.

Conclusion

Our pharmacovigilance analysis indicates that a small percentage of patients receiving ICIs therapy experience delayed irAEs, which are challenging to manage and may result in severe consequences. Prompt identification and intervention of these delayed irAEs are crucial in clinical practice.
Boosting of fusion reactions initiated by laser accelerated proton beam in a non-thermal neutral and non-neutral proton-boron plasma
Authors: Nissim N. , Henis Z. , Eliezer S. , Schweitzer Y. , Daponta C. , Moustaizis S.
Published: November 2024

In this paper we explore the possibility of boosting the reactivity of non-thermal proton-boron fusion triggered by an external proton beam in a plasma at densities near and lower than solid density and temperature characteristic to laser plasma interaction. Suprathermal protons generated by collisions with alpha particles, as well as energetic protons created by the beam protons that do not undergo fusion during the stopping down in the bulk plasma, are accounted for. In addition, we conduct calculations for non-neutral plasma, motivated by recent suggestion that the number of fusion events in such system may be increased.
SRPNet: stroke risk prediction based on two-level feature selection and deep fusion network
Authors: Zhang Daoliang , Yu Na , Yang Xiaodan , De Marinis Yang , Liu Zhi-Ping , Gao Rui
Published: November 2024

Background

Stroke is one of the major chronic non-communicable diseases (NCDs) with high morbidity, disability and mortality. The key to preventing stroke lies in controlling risk factors. However, screening risk factors and quantifying stroke risk levels remain challenging.

Methods

A novel prediction model for stroke risk based on two-level feature selection and deep fusion network (SRPNet) is proposed to solve the problem mentioned above. First, the two-level feature selection method is used to screen comprehensive features related to stroke risk, enabling accurate identification of significant risk factors while eliminating redundant information. Next, the deep fusion network integrating Transformer and fully connected neural network (FCN) is utilized to establish the risk prediction model SRPNet for stroke patients.

Results

We evaluate the performance of the SRPNet using screening data from the China Stroke Data Center (CSDC), and further validate its effectiveness with census data on stroke collected in affiliated hospital of Jining Medical University. The experimental results demonstrate that the SRPNet model selects features closely related to stroke and achieves superior risk prediction performance over benchmark methods.

Conclusions

SRPNet can rapidly identify high-quality stroke risk factors, improve the accuracy of stroke prediction, and provide a powerful tool for clinical diagnosis.
Phenotypic and functional disparities in perivascular adipose tissue
Authors: Guo Erling , Liu Dan , Zhu Ziming
Published: November 2024

The adipose tissue surrounding blood vessels is known as perivascular adipose tissue (PVAT), which represents a distinct ectopic fat depot that adheres to the majority of the vasculature. In recent years, owing to its unique location and function, PVAT has been regarded as a new type of adipose tissue distinct from traditional visceral fat. It releases adipokines with vasoconstrictive functions, which regulate vascular function through paracrine and endocrine mechanisms. Interestingly, PVAT can be categorized as white, brown or a mixture of both depending on its anatomical location. Brown adipose tissue (BAT) is located adjacent to the thoracic aorta in rodents, while a mix of brown and white tissue surrounds the abdominal aorta. PVAT exhibits regional phenotypic differences in different parts of the vasculature bed, which may lead to heterogeneity in the secretion profiles and norepinephrine (NE) content in regional PVAT and subsequently affect the regulation of specific adipokine signaling pathways in regional PVAT, resulting in differences in the regulation of vascular function. The aim of this review was to explore the potential factors that influence the anticontractile function of regional PVAT in the vasculature, including the heterogeneity of regional PVAT, the anticontractile function mediated by endothelial nitric oxide synthase (eNOS) in regional PVAT, the activity of the adiponectin-eNOS pathway in regional PVAT adipocytes, and the concentration of the sympathetic neurotransmitter NE in regional PVAT.
Development of high-speed precision maize metering device for dense planting pattern with standard ridges
Authors: Chen Junzhi , Guan Tianyuan , Yuan Zixin , Xu Fudong , Zhao Kuangyu , Tang Han , Zhao Jiale , Wang Jinwu
Published: November 2024

This study proposed a high-speed precision dual-chamber maize metering device for the dense planting pattern with standard ridge promoted in China. Through theoretical analysis of the sowing process, parameters for the key components have been designed. The metering device is capable of planting two rows in a single pass for high-speed precision seeding. The effect of operating speed and negative pressure on seed metering quality was investigated. A high-speed camera was used to capture the trajectory of maize seed at different operating speeds, and it was found that intra-row shifts were caused by collisions at the mouth of the seed guide tube and rotation of the seed as it fell. Employing a two-factor, five-level orthogonal rotation test, Investigated the optimal operating parameters for the maize metering device. Response surface analysis showed that optimum seed metering quality was achieved at 14.2 km/h and 13.5 kPa. Validation tests showed a qualified rate of 97.93%, with a coefficient variation of 8.97% for this method. Additionally, an energy consumption analysis indicated a reduction in operating energy consumption of approximately 32% compared to conventional air suction seed metering devices for dense planting with large ridges on the same area of farmland. This study provides insights for reducing energy consumption in the seeding process, contributing to the sustainable development of agricultural resources.
Comparative analysis of chloroplast genomes and phylogenetic relationships in the endemic Chinese bamboo Gelidocalamus (Bambusoideae)
Authors: Wang Chengkun , Li Yonglong , Yang Guangyao , Zhang Wengen , Guo Chunce
Published: November 2024

Introduction

Gelidocalamus Wen is a small yet taxonomically challenging genus within the Arundinarieae tribe. Recent molecular studies have suggested it may not be monophyletic. However, limited species sampling and insufficient molecular marker information have resulted in poorly resolved phylogenetic relationships within this genus.

Methods

The complete chloroplast genomes covering all 16 species and one variant of Gelidocalamus were sequenced, and comparative analyses were conducted. Phylogenetic analyses were performed using different molecular markers, including chloroplast data, the nuclear ribosomal DNA (nrDNA) repeats region, and 29 mitochondrial protein-coding genes. Additionally, the divergence times of Gelidocalamus were estimated to reveal their evolutionary history.

Results

The plastomes of Gelidocalamus ranged in size from 139,500 bp to 139,801 bp, with a total of 137 identified genes, including 90 protein-coding genes, 39 tRNA genes, and 8 rRNA genes. The size of the nrDNA repeats ranged from 5,802 bp to 5,804 bp. Phylogenetic analysis based on chloroplast data revealed that Gelidocalamus is polyphyletic, with different subclades distributed within the IV and V clades. However, phylogenetic analysis based on nrDNA and mitochondrial genes did not effectively resolve the relationships within the genus.

Discussion

Comparative analysis of chloroplast genomes indicated that Gelidocalamus shares a high degree of similarity with closely related genera in terms of chloroplast genome collinearity, codon usage bias, and repetitive sequences. Divergence time estimation suggests that it is a relatively young group, with all members appearing successively over the past four million years. The complex phylogenetic patterns may arise from the rapid radiation of Arundinarieae. This study provides a preliminary foundation for further in-depth research on the phylogeny, genomic structural features, and divergence times of this genus.
Influence of false beliefs and empathy on white lies among children with mild intellectual disabilities: focusing on trait and state perspectives
Authors: Zhen Zeng , Liming Meng
Published: November 2024

Children with mild intellectual disabilities often exhibit poor social skills owing to intellectual impairments. This makes it essential to enhance their communication abilities. This study provides a novel contribution by systematically investigating the effects of false beliefs and empathy on white lie behavior among children with mild intellectual disabilities, considering both state and trait dimensions. Experiment 1 examined the impact of trait-level false beliefs and trait-level empathy on trait-level white lies. The results demonstrated that trait-level false beliefs and trait-level empathy both significantly promoted white lie behavior. Experiment 2 explored the influence of state-level false beliefs and state-level empathy on state-level white lies and found similar positive effects. By integrating both trait and state perspectives, this research fills a gap in the literature on white lie behavior in children with mild intellectual disabilities and uncovers the mechanisms through which false beliefs and empathy operate in different contexts. These findings offer comprehensive educational and intervention strategies to improve social adaptation in children with mild intellectual disabilities.
Are the functions of non-suicidal self-injury associated with its persistence and suicide risk in university students? Insights from a network analysis
Authors: Szewczuk-Bogusławska Monika , Kowalski Krzysztof , Bogudzińska Bogna , Misiak Błażej
Published: November 2024

Background

To date, a number of intra- and interpersonal functions of non-suicidal self-injury (NSSI) have been identified. Yet, their association with persistence of NSSI and suicide risk remains unknown. The study aimed to investigate which functions of NSSI are associated with its persistence and suicide risk in university students using a network analysis.

Methods

Altogether, 830 university students reporting a lifetime history of NSSI were enrolled. The persistence of NSSI was defined as its presence over preceding 12 months.

Results

Persistent NSSI was directly connected to the nodes representing two functions of NSSI: affect regulation and self-punishment. Suicide risk was directly connected to the anti-suicide function of NSSI. The shortest pathway from persistent NSSI to suicide risk led through depressive symptoms. Other likely pathways (three mediating nodes) led through the functions of NSSI (affect regulation or self-punishment, anti-dissociation, and anti-suicide). Depressive symptoms had the highest centrality. However, it did not differ significantly compared to some functions of NSSI (marking distress, anti-dissociation, toughness, and affect regulation).

Discussion

In university students, persistent NSSI might be directly associated with its functions related to affect regulation and self-punishment, while suicide risk might be directly associated with the anti-suicide function. The observations posit a role of intrapersonal functions in shaping the outcomes of NSSI. Depressive symptoms and some NSSI functions might be the most promising targets for interventions in this population.
Imitative inhibitory control is associated with psychotic experiences in a sample from the general population
Authors: Ulloa José Luis , Núñez Daniel E. , Gaspar Pablo A. , Brass Marcel
Published: November 2024

Psychotic experiences (PE) are prevalent and associated with several negative mental health outcomes in both clinical and general population, particularly in young people. A promising avenue to understand the mechanisms underlying PE is to investigate functions that may be related to specific neural systems. One of these key cognitive mechanisms is the ability to control our imitative responses, which is strongly linked to an adequate social functioning. Emergent evidence suggests that impairments in this function might be involved in the early expressions of psychosis, but few studies have investigated its association with PE in a sample from the general population. Using an imitation-inhibition paradigm we examined this relationship in a community sample of young healthy individuals (N=204) and found that increased levels of PE levels were associated with lower imitative inhibitory control. These effects seem to be specific to imitation-inhibition as no correlation was found for a more general cognitive control as addressed by a Stroop-like task. In addition, these effects seem to be more evident for paranoid ideations. Overall, our results suggest that imitative inhibitory control can serve as a proxy to detect abnormalities associated with psychotic experiences.
Quantitative EEG and its relationship with attentional control in patients with anxiety disorders
Authors: Yuan Danfeng , Yang Xiangyun , Wang Pengchong , Yang Lijuan , Yang Ting , He Fang , Xu Yi , Li Zhanjiang
Published: November 2024

Introduction

Attentional control is crucial in the development and maintenance of anxiety disorders. Understanding the underlying mechanisms of attentional control can help to shed light on the neuropathological processes in anxiety disorders (ANX). Quantitative electroencephalography (QEEG) offers a cost-effective, noninvasive method for examining the neuropathological mechanisms of mental disorders.

Methods

In this study, 67 patients with ANX and 45 healthy controls (HC) were recruited. EEG recordings were obtained for 5 minutes in an eyes-closed condition. QEEG was employed to evaluate the mechanisms of attentional control in ANX.

Results

Neurophysiological measures indicated that anxiety patients exhibited a more frontal topographic pattern of theta/beta ratio (TBR) compared to HC. Additionally, a significant decrease in temporal beta power was observed in the ANX group. Correlation analysis revealed that decreased beta power and increased TBR were significant association between attentional control deficits in ANX.

Discussion

These findings provide electrophysiological evidence of impaired attentional control processing in anxiety patients, characterized by decreased temporal beta power and increased frontal TBR. Temporal beta power and frontal TBR may serve as promising biomarkers for attentional control in ANX.
Correlations between problematic internet use and suicidal behavior among Chinese adolescents: a systematic review and meta-analysis
Authors: He Xubin , Chen Si , Yu Qinyao , Yang Ping , Yang Bo
Published: November 2024

Background

Problematic Internet Use (PIU) has been increasingly linked to suicidal behavior among adolescents, raising significant public health concerns, particularly in Chinese youth. This study aimed to systematically review and meta-analyze the correlation between PIU and suicidal behavior in Chinese adolescents to provide a clearer understanding of this association.

Methods

A comprehensive search was conducted across seven databases up to July 1, 2024. Studies investigating the relationship between PIU and suicidal behavior among Chinese adolescents were included. A random-effects meta-analysis was employed to assess pooled effect sizes, with subgroup analyses conducted to explore potential moderators, such as geographic region, age, gender, assessment tools for PIU and suicidal ideation, and the presence of depression. Data analysis was performed using STATA software (version 16).

Results

This meta-analysis, comprising 23 studies with 353,904 participants, identified significant associations between PIU and suicidal behavior among Chinese adolescents. PIU was associated with increased risks of suicidal ideation (OR = 1.72, 95% CI: 1.45, 2.03), suicidal plans (OR = 1.50, 95% CI: 1.02, 2.20), and suicidal attempts (OR = 1.48, 95% CI: 1.16, 1.89). Subgroup analyses indicated higher risks in specific groups: adolescents from Central China (OR = 1.84, 95% CI: 1.46, 2.32), college students (OR = 2.09, 95% CI: 1.66, 2.62). The risk of suicidal ideation was particularly elevated when depression was not controlled (OR = 1.86, 95% CI: 1.53, 2.25). These findings underscore the need for targeted interventions in vulnerable populations.

Conclusion

This meta-analysis demonstrated significant associations between PIU and suicidal behaviors among Chinese adolescents. The findings emphasize the need for targeted interventions, particularly for adolescents from Central and Western China, college students, and those with untreated depression. Focused strategies are required to mitigate the risks associated with PIU and to effectively address suicidal behaviors in these high-risk populations.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024577593, identifier CRD42024577593.
Molecular physiology unlocks the mystery that relates cognitive impairment with the retina in schizophrenia and autism spectrum disorders: a perspective review
Authors: Al-Mazidi Sarah
Published: November 2024

Schizophrenia and Autism spectrum disorders (SSD and ASD) are neurodevelopmental disorders involving cognitive impairment. Timely diagnosis is important for early intervention; currently, no tools are available to help with early diagnosis. Molecular biomarkers of cognitive impairment have been extensively studied, but clinical correlation is crucial in screening for cognitive impairment in SSD and ASD. There has been growing interest in examining the retina to scan for neurological disorders since the retina is the only part of the central nervous system that can be directly imaged non-invasively and in a timely manner. This review discusses biomarkers of cognitive impairment and their correlation to the retina in SSD and ASD. It also discusses the possible involvement of the retina and molecular biomarkers, specifically Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) and ciliary neurotrophic factor (CNTF) in the pathophysiology of SSD and ASD. A protocol for early diagnosing cognitive impairment and its severity in SSD and ASD is also suggested. This review also mentions insights into the potential use of molecular biomarkers of cognitive impairment to enhance cognitive performance in ASD and SSD and areas where more research is needed to solve the mystery of the relationship between the retina and cognitive impairment in neurodevelopmental psychiatric disorders.
Association between elevated glycosylated hemoglobin and cognitive impairment in older Korean adults: 2009–2010 Ansan cohort of the Korean genome and epidemiology study
Authors: Kim Jung Sook , Chun Byung Chul , Lee Kyoungho
Published: November 2024

Objective

Cohort studies on the risk of cognitive impairment in the older population of S. Korea based on glycosylated hemoglobin (HbA1c) levels are exceedingly rare. This study aimed to analyze the association between HbA1c levels and cognitive impairment in older Korean adults without dementia.

Methods

We conducted a cross-sectional study using data from a community-based Ansan cohort (2009–2010), which was part of the Korea Genome Epidemiology Study. The study included 853 cohort participants aged ≥59 years living in Ansan city. Cognitive function was evaluated using the Korean version of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The MMSE and MoCA scores were categorized into normal cognition (≥24 and ≥ 23, respectively) and cognitive impairment (≤23 and ≤ 22, respectively). Multiple logistic regression analysis was used to estimate the association between HbA1c levels and cognitive impairment, with adjustments for covariates.

Results

The mean age of the participants was 66 years, and 433 (50.8%) were female. Cognitive impairment was observed in 12.5 and 44.3% of participants, based on the MMSE and MoCA, respectively. Regarding the MMSE scores, HbA1c level was a risk factor for cognitive impairment in women. Compared to normal HbA1c (≤5.6%) levels, adjusted odds ratios of MMSE decline for HbA1c 5.7–6.4% and HbA1c ≥6.5% were high: 2.16 (95% confidence interval [CI] 1.04–4.49) and 2.96 (95% CI, 1.04–8.39), respectively.

Conclusion

By improving glycemic control, the risk of cognitive impairment in the older population can be reduced. Further research on the role of sex differences in cognitive impairment is needed.
Leveraging digital infrastructure for sustainable grain production: evidence from China
Authors: Han Jiabin , Ge Wenting , Chou Yixiu
Published: November 2024

Introduction

Agriculture faces significant challenges in ensuring global food security while minimizing resource costs and environmental impacts. The development of digital infrastructure offers transformative potential for agricultural systems and aligns with the United Nations Sustainable Development Goals. This study examines the role of digital infrastructure in enhancing grain production capacity in China, a key player in the global food system.

Methods

We analyzed data from 277 prefecture-level cities in China from 2011 to 2021. A double machine learning model was employed to empirically assess the impact of digital infrastructure on grain production capacity, allowing for robust insights into causal relationships.

Results

Results reveal that digital infrastructure significantly enhances grain production. Mechanism analysis results indicate that digital infrastructure construction drives agricultural technological advancements and farmland scale, contributing to increased production capacity. Heterogeneity analysis results show that the impact of digital infrastructure construction is significant in major grain-producing regions and the central-eastern regions, while its effects are relatively limited in grain production-consumption balanced regions, main grain consumption regions, and the western regions.

Discussion

The results underscore the importance of strengthening digital infrastructure in rural areas to improve grain production capacity. Tailored policy implications are suggested to enhance sustainable food production and contribute to global food security, particularly in regions with varying agricultural dynamics.
Analysis of spatial and temporal characteristics and evolution of green total factor productivity in agriculture in the lower Yellow River basin
Authors: He Junru , Han Mingyang
Published: November 2024

The construction of ecological barriers in the Yellow River Basin represents a significant step toward reducing agricultural carbon emissions, achieving carbon neutrality, and reaching carbon peaking in China. The diverse agrarian development objectives of various regions within the basin have resulted in a heterogeneous approach to greening agriculture. Therefore, this paper will evaluate the development of carbon sink agriculture across 34 cities and municipalities in the lower Yellow River basin from 2008 to 2021 based on the EBM-GML model, and analyze the spatial-temporal evolution of agricultural green total factor productivity (AGTFP) in each region through the application of the Moran index, kernel density estimation, and spatial Markov chain analysis. The results demonstrate that agricultural carbon emissions in the Lower Yellow River Basin gradually decreased throughout the study period. Furthermore, overall carbon emission efficiency improved, indicating significant potential for further emission reduction. In addition, Agricultural Green Technology Progress (AGTC) has become a primary driver of AGTFP growth, while Agricultural Green Technology Efficiency (AGEC) has demonstrated a gradual upward trend. Locally, most areas are weakly connected and display an isolated development trend. The results of the kernel density analysis demonstrate a notable degree of mobility in the distributional dynamics of AGTFP growth, characterized by a gradual narrowing of the gap between locations. The transfer of (AGTFP) types in the lower reaches of the Yellow River Basin is stable, with a noticeable “club convergence” phenomenon, while geographical conditions significantly influence the transfer of AGTFP types in this region. Based on long-term trend predictions, the future trajectory of AGTFP in the lower Yellow River Basin appears optimistic and is expected to improve progressively, with the overall distribution tending toward equilibrium.
Risk factors for healing failure after arthroscopic rotator cuff repair in small to medium-sized tears: a retrospective cohort study
Authors: Wang Guangying , Liu Changli , Wang Jiansong , Li Haoran , Yu Guosheng
Published: November 2024

Objectives

To identify risk factors for tendon healing failure following arthroscopic rotator cuff repair (ARCR) in patients with small to medium-sized rotator cuff tears (RCTs).

Methods

A retrospective study was conducted on 320 patients with RCTs who underwent arthroscopic repair between June 2018 and June 2021. All patients had at least 2 years of postoperative follow-up, with MRI scans at the final assessment. Based on MRI results, patients were categorized into the healing success group (Group A: types I–III) or the healing failure group (Group B: types IV–V). Variables associated with rotator cuff healing, including patient characteristics, baseline symptoms, imaging data, and surgery-related factors, were analyzed using univariate and multivariate logistic regression.

Results

Healing failure occurred in 54 of the 320 patients (16.9%). Functional status improved significantly across all patients (P < 0.05), irrespective of healing outcomes. Multifactorial analysis identified smoking (OR = 1.931, P = 0.028), diabetes (OR = 3.517, P = 0.038), lower bone mineral density (BMD) (OR = 1.551, P = 0.018), higher fatty infiltration (FI) (OR = 4.025, P = 0.009), and smaller acromiohumeral distance (AHD) (OR = 2.546, P = 0.006) as independent risk factors for healing failure.

Conclusions

Smoking, diabetes, lower BMD, higher FI, and smaller AHD are independent risk factors for healing failure following ARCR.
Allorecognition Unveiled: Integrating Recent Breakthroughs Into the Current Paradigm
Authors: Charmetant Xavier , Pettigrew Gavin J. , Thaunat Olivier
Published: November 2024

In transplantation, genetic differences between donor and recipient trigger immune responses that cause graft rejection. Allorecognition, the process by which the immune system discriminates allogeneic grafts, targets major histocompatibility complex (MHC) and minor histocompatibility antigens. Historically, it was believed that allorecognition was solely mediated by the recipient’s adaptive immune system recognizing donor-specific alloantigens. However, recent research has shown significant roles for innate immune components, such as lymphoid and myeloid cells, which are sometimes triggered by the mere absence of a self-protein in the graft. This review integrates recent breakthroughs into the current allorecognition paradigm based on the well-established direct and indirect pathways, emphasizing the semi-direct pathway where recipient antigen-presenting cells (APCs) acquire donor MHC molecules, and the inverted direct pathway where donor CD4+ T cells within the graft activate recipient B cells to produce donor-specific antibodies (DSAs). The review also explores the role of natural killer (NK) cells in both promoting and inhibiting graft rejection, highlighting their dual role in innate allorecognition. Additionally, it discusses the emerging understanding of myeloid cell-mediated allorecognition and its implications for initiating adaptive immune responses. These insights aim to provide a more comprehensive understanding of allorecognition, potentially leading to improved transplant outcomes.
Simulating Vertical Profiles of Optical Turbulence at the Special Astrophysical Observatory Site
Authors: Shikhovtsev Artem Y. , Potanin Sergey A. , Kopylov Evgeniy A. , Qian Xuan , Bolbasova Lidia A , Panchuk Asya V. , Kovadlo Pavel G.
Published: November 2024

In this paper, we used meteorological data to simulate vertical profiles of optical turbulence at the Special Astrophysical Observatory (SAO) (Russia, 43°40′19″ N 41°26′23″ E, 2100 m a.s.l.), site of the 6 m Big Telescope Alt-azimuthal. For the first time, the vertical profiles of optical turbulence are calculated for the SAO using ERA-5 reanalysis data. These profiles are corrected using DIMM measurements as well as estimations of atmospheric boundary layer heights. We may note that the method basically reconstructs the most important features of the shape of the measured profile under clear sky. Atmospheric turbulent layers were identified, and the strength of optical turbulence in these layers was estimated. The model hourly values of seeing corresponding to the obtained vertical profiles range from 0.40 to 3.40 arc sec; the values of the isoplanatic angle vary in the range from 1.00 to 3.00 arc sec (at λ = 500 nm). The calculated median of seeing is close to 1.21 arc sec. These estimations are close to the measured median of seeing (1.21 arc sec).
Hydrological Response to Climate Change: McGAN for Multi-Site Scenario Weather Series Generation and LSTM for Streamflow Modeling
Authors: Sha Jian , Chang Yaxin , Liu Yaxiu
Published: November 2024

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies.
Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania
Authors: Rimkus Egidijus , Stonevičius Edvinas , Gečaitė Indrė , Mačiulytė Viktorija , Valiukas Donatas
Published: November 2024

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid.
Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project
Authors: De Vito Saverio , Del Giudice Antonio , D’Elia Gerardo , Esposito Elena , Fattoruso Grazia , Ferlito Sergio , Formisano Fabrizio , Loffredo Giuseppe , Massera Ettore , D’Auria Paolo , Di Francia Girolamo
Published: November 2024

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue.
Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction
Authors: Agbehadji Israel Edem , Obagbuwa Ibidun Christiana
Published: November 2024

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O3 concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles.
The Kernel Density Estimation Technique for Spatio-Temporal Distribution and Mapping of Rain Heights over South Africa: The Effects on Rain-Induced Attenuation
Authors: Lawal Yusuf Babatunde , Owolawi Pius Adewale , Tu Chunling , Van Wyk Etienne , Ojo Joseph Sunday
Published: November 2024

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design.
Artificial Insemination as a Possible Convenient Tool to Acquire Genome-Edited Mice via In Vivo Fertilization with Engineered Sperm
Authors: Sato Masahiro , Inada Emi , Saitoh Issei , Morohoshi Kazunori , Nakamura Shingo
Published: November 2024

Advances in genome editing technology have made it possible to create genome-edited (GE) animals, which are useful for identifying isolated genes and producing models of human diseases within a short period of time. The production of GE animals mainly relies on the gene manipulation of pre-implantation embryos, such as fertilized eggs and two-cell embryos, which can usually be achieved by the microinjection of nucleic acids, electroporation in the presence of nucleic acids, or infection with viral vectors, such as adeno-associated viruses. In contrast, GE animals can theoretically be generated by fertilizing ovulated oocytes with GE sperm. However, there are only a few reports showing the successful production of GE animals using GE sperm. Artificial insemination (AI) is an assisted reproduction technology based on the introduction of isolated sperm into the female reproductive tract, such as the uterine horn or oviductal lumen, for the in vivo fertilization of ovulated oocytes. This approach is simpler than the in vitro fertilization-based production of offspring, as the latter always requires an egg transfer to recipient females, which is labor-intensive and time-consuming. In this review, we summarize the various methods for AI reported so far, the history of sperm-mediated gene transfer, a technology to produce genetically engineered animals through in vivo fertilization with sperm carrying exogenous DNA, and finally describe the possibility of AI-mediated creation of GE animals using GE sperm.
Environmental Affordance for Physical Activity, Neurosustainability, and Brain Health: Quantifying the Built Environment’s Ability to Sustain BDNF Release by Reaching Metabolic Equivalents (METs)
Authors: Khalil Mohamed Hesham
Published: November 2024

Background/Objectives: Unlike enriched environments for rodents, human-built environments often hinder neuroplasticity through sedentary lifestyles, to which exercise can merely overcome its adverse effects. This paper introduces "environmental affordance for physical activity" to quantify the potential of spatial layout designs to stimulate activity and sustain neuroplasticity, mainly hippocampal neurogenesis. Methods: A novel framework links metabolic equivalents (METs) that can be afforded by the spatial layout of the built environment to its role in increasing the brain-derived neurotrophic factor (BDNF)—a biomarker that promotes and sustains adult hippocampal neurogenesis and synaptic plasticity. Equations are developed to assess the built environment’s affordance for physical activity through BDNF changes measurable after brief exposure to the built environment for 20–35 min. Results: The developed equations are evidenced to be feasible to cause BDNF release through low- to moderate-intensity physical activity. This model provides a feasible assessment tool to test the built environment’s effectiveness towards neurosustainability. Conclusions: By sustaining neurogenesis, the environmental affordance for physical activity holds promise for improving mental health and preventing cognitive decline.
Working with AI: The Effect of Job Stress on Hotel Employees’ Work Engagement
Authors: Hou Yong , Fan Liwei
Published: November 2024

Based on the Conservation of Resources (COR) theory and social support theory, this study focuses on the effects of AI-induced stress on hotel employees’ work engagement and examines the mediating role of psychological capital and the moderating role of perceived organizational support. A sample of five-star hotels in China was selected for the study, data were analyzed, and hypotheses were tested using SPSS 27.0 and Mplus 7.4 software. The results of the study revealed that AI-induced stress had a significant negative effect on work engagement and psychological capital mediated the relationship between AI-induced stress and work engagement. Perceived organizational support moderated the relationship between work stress and psychological capital. Specifically, the higher the perceived organizational support, the lower the negative effect of work stress on psychological capital; conversely, the lower the perceived organizational support, the higher the negative effect of work stress on psychological capital. The greater the negative impact of work stress on psychological capital, the higher the perceived organizational support, and the smaller the negative impact of work stress on psychological capital. The findings of the study not only enrich the research related to AI in the hotel industry but also have certain reference significance for managers in the hotel industry who introduce AI in managing their employees.