bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feasibility of a prospective, longitudinal study of resilience among young military recruits with embedded laboratory sub-study: the ARMOR pilot trial
Authors: Hitz Andrea C , Hubbling Shelly Bachelors Degree , Hodges Annika , Campbell Emily M Hagel , Bangerter Ann , Polusny Melissa A. 
Published: July 2023

Abstract
Background
Multilevel, longitudinal studies are integral to resilience research; however, they are costly and present unique methodological challenges. The objective of this study was to examine the feasibility of study methods (recruitment, retention, data collection) for a large-scale prospective, longitudinal study of resilience among young National Guard recruits.
Methods
This feasibility trial used a pre-test/post-test design with embedded laboratory sub-study. Participants were young military recruits who had recently enlisted in the Army National Guard and had not yet shipped to Basic Combat Training (BCT). Recruitment and baseline data collection (Time 1), which included a battery of computerized self-report measures and neurocognitive tests, were conducted at local armories. Participants completed an online follow-up (Time 2) survey outside of drill training after returning from BCT. A subset of participants was recruited to complete extensive laboratory procedures pre-and post-BCT, including clinical interview, additional self-report measures, and performance on a series of neurobehavioral tasks during electroencephalogram recordings and, at pre-BCT only, magnetic resonances imaging. Feasibility outcomes assessed our ability to recruit, retain, and collect data from participants. Analysis of outcomes was based on descriptive statistics and evaluation of the feasibility of the larger study was based on pre-determined go/no go progression criteria.
Results
All pre-determined progression criteria were met. A total of 102 (97.1%) of eligible military service members consented to participate. Of these, 73 (73.7%) completed the Time 2 survey. Of the 24 participants approached, 14 agreed to participate in the laboratory sub-study, 13 completed follow-up laboratory visits. Overall, completion of online surveys and laboratory tasks was excellent. However, participants had difficulties completing online surveys during BCT and the computerized neurocognitive testing battery at Time 2.
Conclusions
Study methods were feasible, and all predetermined criteria for progression to the large-scale longitudinal study were met. Some minor protocol adaptations were identified from this feasibility study. Lessons learned and recommendations for future research are discussed.
Decadal response of atmospheric inorganic nitrogen dry deposition into offshore areas to policy controls
Authors: Yan Jinpei , Wang Shanshan , Xu Suqing , Zhao Shuhui , Zhang Miming , Dai Siying , Shi Jun , Yang Hang , Lin Qi , Jiao Liping 
Published: July 2023

Abstract
Accurately assessing the dry deposition fluxes (DDF) of inorganic nitrogen aerosol (aerosol-IN) is crucial for mitigating the ecological damage caused by excessive nitrogen to oceanic equilibria. We developed a dry deposition model to assess the DDF of aerosol-IN into Chinese offshore areas from 2011 to 2020, ranging from 3.95×106 g/(km2 year) to 6.00×107 g/(km2 year). Our findings showed a clear decline in the DDF of aerosol-IN following the implementation of atmospheric laws and policies. Promoting the use of new energy vehicles, the rational use of nitrogenous fertilizers, and standardized straw burning will all reduce pollution caused by excessive nitrogen deposition. Aerosol-IN is a more significant source of inorganic nitrogen than river transport in Chinese offshore areas. The reduction of aerosol-IN dry deposition is a beneficial factor in mitigating offshore eutrophication, which should be considered when devising strategies for the protection and development of China's Beautiful Bay.
Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study
Authors: Wang Chenxi , Feng Xinyue , Qi Xingang , Hong Zilong , Dun Wanghuan , Zhang Ming , Liu Jixin 
Published: July 2023

Abstract
Pain empathy enables us to understand and share how others feel in the context of pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, chronic pain patients have an increased risk for abnormal pain empathy, and the association between the whole-brain functional network, chronic pain, and pain empathy remains unclear. Using resting state functional magnetic resonance imaging (fMRI) and machine learning analysis, we investigated the static and dynamic functional network connectivity (FNC) in predicting pain empathy scores in 41 healthy controls (HCs) and 45 women with primary dysmenorrhea (PDM). In addition, a classification analysis was performed to study the FNC differences between HCs and PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state menstrual pain were recorded. In study 1, results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. This finding was validated in an independent dataset. In study 2, PDM exhibited a distinctive prediction network for pain empathy. The predictive features were concentrated in the sensorimotor network (SMN) and exhibited a significant association with trait menstrual pain. Moreover, the SMN-related dynamic FNC could accurately distinguish PDM from HCs. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that chronic pain may affect pain empathy through the maladaptive dynamic interaction between brain networks.
Action research in agricultural development projects: Case study of a dairy project in Ethiopia
Authors: Ayantunde Augustine A. , Ebro Abule , Berhanu Tinsae , Moges Demewez , Lee Jan van der 
Published: July 2023

Abstract
For development projects, an action research approach is often encouraged to enhance participation of end-users or beneficiaries at various stages of the project to realize the desired social transformation in the target communities. The project under consideration in this study adopted action research for some of its activities. A review of the project action research activities was conducted as part of the strategic reflection for lesson learning and to inform modification where necessary. The objectives of this study were to assess the relevance of action research approach to implementation of project activities and to draw lessons for agricultural development projects applying participatory approaches. Fifty-one participants involved in the project action research activities were interviewed on their understanding of action research, its strengths, weaknesses, opportunities and threats, and level of participation in action research activities. The general impression of the participants interviewed regarding action research approach was that it is relevant and important for the project, and that it has provided a good platform for learning by all participants. Some key lessons from this study for development projects when applying action research approach include the need to focus on a few key issues or activities that can produce social transformation in the target communities, necessity of adequate planning for monitoring and documentation of learning by participants, and that caution should be exercised that action research does not become a “fashion”, particularly in development projects, whereby it is used unreflectively.
Indian standard time dissemination using precision time protocol: Towards resilient time synchronization using optical fibers for critical infrastructure in India
Authors: Kumar Kuldeep , Ghosh Sandip Kumar , Neelam  , Pandey Shailesh Chandra , Vattikonda Bharath , Panja Subhasis , Agarwal Ashish , Das Manoj 
Published: July 2023

Abstract

National time dissemination using terrestrial ethernet and optical fiber cables is an alternative to satellite-based systems and provides redundancy to reliable time services for critical infrastructure in the country. Precision time protocol based on IEEE 1588 standards using such cables and fibers can provide sub-microsecond time synchronization between a provider and the client. The method employs hardware time stamping, dedicated traffic-free fiber links, and PTP-aware network elements. We present a proof of concept experiment for the distribution of Indian standard time using the IEEE protocol in a laboratory and the effect of introducing a PTP-supported switch on the distribution performance. Further, we report on the observed synchronization when an underground dark telecom optical fiber lying outside the laboratory in the central Delhi region, is used as the medium of communication. The one-second pulse-pulse jitter is found to be within 2.5 ns.
BBR-With Adaptive Pacing Gain Rates (BBR-APG): An improved TCP-BBR Congestion Control Algorithm on RTT Fairness Design Problem
Authors: Njogu Charles Kihungi , Yang Wang , Njogu Humphrey Waita , Bosire Adrian 
Published: July 2023

Abstract
Google scientists published Bottleneck Bandwidth and Round-trip propagation time (BBR), a novel TCP congestion control algorithm variant. Unlike traditional congestion control algorithms, BBR adjusts its transmission rates by periodically measuring the network traffic based on round-trip propagation time (RTprop) and available bottleneck bandwidth (Btlbw). This results in BBR achieving higher delivery rates and minimizing the latency on our modern-day network infrastructure. However, some recent experimental evaluations and analyses have shown severe RTT (round-trip time) fairness concerns in the TCP-BBR algorithm. When two competing flows with different RTTs coexist on a shared bottleneck link, BBR tends to prefer and favor longer RTT flows, therefore consuming more bandwidth share than short RTT counterparts, which can result in short RTT flows being starved even to death. This preference nature of the BBR in favor of long RTT flows results in an acute throughput imbalance and an extreme BBR and network vulnerability that malevolent individuals can easily exploit by increasing the delay (RTTs) in their flows to create unwarranted long RTTs on their TCP flows to unfairly obtain and enjoy the lion's share of the available bandwidth than the rest of users on the network. The BBR-With Adaptive Pacing Gain Rates (BBR-APG) algorithm is proposed in this study as an alternative solution to the TCP-BBR's RTT fairness problem. Our proposed approach adaptively adjusts the pacing gain rates of each TCP flow based on the bottleneck inflight queue status during the bandwidth estimation phase instead of the default fixed pacing gain rates as provided by the original version of the BBR algorithm, which only works perfectly when only a single flow is involved. With the BBR-APG algorithm, different RTT flows can fairly compete for the available bottleneck bandwidth, thus enhancing the RTT fairness issue that has been compromised in the original version of the BBR algorithm. Through our extensive simulation analysis on NS3, we identified that our proposed methodology improves RTT fairness by more than 18\%, a reduction of more than 24\% and 20\% on packets retransmission rates and queuing delay, respectively, compared to other recently published BBR variants like an adaptive congestion window of BBR (BBR-ACW) on various network conditions.
Multi-output Ensembles for Multi-step Forecasting
Authors: Cerqueira Vitor , Torgo Luis 
Published: July 2023

Abstract

This paper studies the application of ensembles composed of multi-output models for multi-step ahead forecasting problems. Dynamic ensembles have been commonly used for forecasting. However, these are typically designed for one-step ahead tasks. On the other hand, the literature regarding the application of dynamic ensembles for multi-step aheadforecasting is scarce. Moreover, it is not clear how the combination rule is applied across the forecasting horizon. We carried out extensive experiments to analyze the application of dynamic ensembles for multi-step forecasting. We resorted to a case study with 3568 time series and an ensemble of 30 multi-output models. We discovered that dynamic ensembles based on arbitrating and windowing present the best performance according to average rank. Moreover, as the horizon increases, most approaches struggle to outperform a static ensemble that assigns equal weights to all models. The experiments are publicly available in a repository.
A Y-incision to Enlarge the Aortic Root for Aortic Valve Stenosis with Anomalous Aortic Origin of the Right Coronary Artery
Authors: Yoshida Keisuke , Miura Yujiro , Fukunaga Yukiko , Mtsuishi Atsuyuki 
Published: July 2023

Abstract
Background
Anomalous aortic origin of the coronary artery (AAOCA) is a rare congenital heart disease. Thus, optimal indications for surgery in patients with severe aortic valve stenosis (AS) complicated by AAOCA remain uncertain.
Case presentation
We report the case of a 57-year-old man who underwent aortic valve replacement (AVR) and aortic root enlargement using a Y-incision procedure for severe AS with an anomalous aortic origin of the right coronary artery (AAORCA). As preoperative single-photon emission computed tomography revealed no ischaemic lesions, an aortic root enlargement with a Y-incision was performed to avoid the potential compression of the prosthetic valve on the AAOCA and prosthesis-patient mismatch.
Conclusions
Preoperative evaluation of the coronary anatomy and myocardial ischaemia using advanced imaging modalities and aortic root enlargement with the Y-incision procedure is an effective strategy for preventing ischaemic complicationsin cases of severe AS with AAORCA.
Incorporating slow NMDA-type receptors with nonlinear voltage-dependent magnesium block in a next generation neural mass model: derivation and dynamics
Authors: Sheheitli Hiba , Jirsa Viktor 
Published: July 2023

Abstract
We derive a next generation neural mass model of a population of quadratic-integrate-and-fire neurons, with slow adaptation, and conductance-based AMPAR, GABAR and nonlinear NMDAR synapses. We show that the Lorentzian ansatz assumption can be satisfied by introducing a piece-wise polynomial approximation of the nonlinear voltage-dependent magnesium block of NMDAR current. We study the dynamics of the resulting system for two example cases of excitatory cortical neurons and inhibitory striatal neurons. Bifurcation diagrams are presented comparing the different dynamical regimes as compared to the case of linear NMDAR currents, along with sample comparison simulation time series demonstrating different possible oscillatory solutions. The omission of the nonlinearity of NMDAR currents results in a shift in the range (and possible disappearance) of the constant high firing rate regime, along with a modulation in the amplitude and frequency power spectrum of oscillations. Moreover, nonlinear NMDAR action is seen to be state-dependent and can have opposite effects depending on the type of neurons involved and the level of input firing rate received. The presented model can serve as a computationally efficient building block in whole brain network models for investigating the differential modulation of different types of synapses under neuromodulatory influence or receptor specific malfunction.
The effect of afforestation on inorganic carbon in soils of arid and semi-arid lands of northwest China
Authors: Zhu Xinglin , Si Jianhua , He Xiaohui , Jia Bing , Zhou Dongmeng , Wang Chunlin , Qin Jie , Liu Zijin 
Published: July 2023

Abstract
Alxa is a region with severe land desertification and extreme ecological fragility in China. The plantation in the area has effectively curbed the desertification of the local land. However, Studies on soil inorganic carbon（SIC）dynamics after sandy land afforestation are still relatively few. Understanding SIC profiles' distribution and stock changes after afforestation is essential for assessing regional, continental, and global soil carbon sink potential. Using 5, 11, 22, and 46 years of Haloxylon ammodendron (H. ammodendron) plantations and control sands (MS) in the Alxa region, we studied the variation characteristics of SIC with increasing stand age from 0 to 300 cm. Within the 0-300 cm soil layer, SIC storage increased significantly after afforestation, with 46yrs increasing by 6.52 kg m-2 compared to MS. SIC sequestration rate (CSR) decreased with increasing stand age, in the order of 5yrs（0.054 kg m-2 yr-1）＞11yrs（0.025 kg m-2 yr-1）＞22yrs（0.016 kg m-2 yr-1）＞46yrs（0.009 kg m-2 yr-1）. The 100~300 cm SIC storage accounts for over 60% of the SIC pool. Soil carbon pool estimation will be largely underestimated if only the shallow SIC is considered.
The Liouville current of holomorphic quadratic differential metrics
Authors: Shi Jiajun 
Published: July 2023

Abstract

In this paper we study the Liouville current of flat cone metrics coming from a holomorphic quadratic differential. Anja Bankovic and Christopher J. Leininger proved in \cite{bankovic2018marked} that for a fixed closed surface, there is a injection map from the space of flat cone metrics to the space of geodesic currents. We manage to show that metrics coming from holomorphic quadratic differentials can be distinguished from other flat metrics by just looking at the geodesic currents. The key idea is to analyze the support of Liouville current, which is a topological invariant independent of the metric, and get information about cone angles and holonomy. The holonomy part involves some subtlety of relationship between singular foliation and geodesic lamination. We also obtain an interesting result that almost all geodesics of a quadratic differential metric will be dense in the surface, and no other flat metrics have this property, which gives another judgement for whether a metric comes from a holomorphic quadratic differential.
Mathematics Subject Classification (2020) 51H25 · 53C99
Development of an anoikis-related gene signature for lung squamous cell carcinoma to predict prognosis, immune landscape, and immunotherapy response
Authors: Liu Jian , Wei Li 
Published: July 2023

Abstract
Background
Lung squamous cell carcinoma (LUSC) is a highly invasive malignancy associated with a poor prognosis. Anoikis plays a crucial role in the malignant progression of various cancers. However, studies focusing on the role of anoikis in LUSC are needed.
Methods
A total of 357 anoikis-related genes (ARGs) were obtained from the GeneCards database and Harmonizome portals. Univariate Cox regression was used to identify prognostic ARGs for patients with LUSC, and the R package ConsensusClusterPlus was used to determine the subtypes. Lasso regression was used to build a prognostic risk model called the ARG score. Functional enrichment analysis was used to investigate the potential biological function of the ARGs. The ESTIMATE, ssGSEA, and CIBERSOT algorithms were used to evaluate the pattern of immune infiltration of patients with different LUSC subtypes and risk groups. The R package timeROC, survival, and survminer were used to test the accuracy of the prognostic model and to construct a nomogram. RT-PCR was used to detect differential expression of the 9 hub genes in LUSC and normal lung epithelial cells. Finally, OncoPredict was used to identify drugs with therapeutic value for patients in the high and low-risk groups.
Results
A total of 138 differentially expressed ARGs were selected, among which 15 survival-related ARGs were identified, and 3 subtypes (A, B, and C) with significant differences in the immune infiltration and survival outcomes were determined. Nine prognostic ARGs were identified to construct prognostic models, and the predictive capacity was verified in the GSE73403 and GSE30219 datasets. Patients with a high-risk score had worse outcome, and were associated with Tregs, resting memory CD4 + T cells, neutrophils, immune escape phenotypes, inflammatory response, and epithelial–mesenchymal transition. A nomogram with excellent clinical diagnostic accuracy was established for LUSC patients. The 9 hub genes had lower expression in LUSC cell lines, except for CHEK2, FADD, and SPINK1. Finally, we identified several drugs for patients in different risk groups.
Conclusions
ARGs play a key role in LUSC, and the ARG score devised in this study may be a valuable tool for determining clinical prognosis and the immunotherapy response in patients with LUSC.
An amygdala-centered effective connectivity network in trait anxiety
Authors: Chang Jingjing , Liu Xin , Xue Song , Qiu Jiang 
Published: July 2023

Abstract

Previous studies have established that the amygdala plays an important role in trait anxiety. However, there remains limited knowledge regarding the changes in amygdala-centered effective connectivity network associated with this trait. The current study employed the Granger Causal analysis to investigate the directional connectivity patterns involving the amygdala in relation to trait anxiety in a large cohort of young adults. The results revealed a negative association between trait anxiety scores and the Granger causality from the left middle frontal gyrus and right superior frontal gyrus to the right amygdala. Conversely, higher trait anxiety levels were found to be associated with increased effective connectivity from the left amygdala to the left hippocampus. Moreover, the right superior frontal cortex-to-right amygdala Granger causality partly and negatively mediated the positive correlation between the left amygdala-to-left hippocampus Granger causality and trait anxiety. These results demonstrated the significance of the prefrontal cortex-amygdala-hippocampus neural circuitry in the neurobiological mechanisms underlying trait anxiety. Our findings might contribute to the supervision and mitigation of the potential transition from high trait anxiety to pathological states.
Naïve Bayes Classifier Based On Optimized Harmony Search Algorithm
Authors: Geng Daoqu , Wen Yunfei 
Published: July 2023

Abstract
Naïve Bayes classifiers have made a big splash in the field of data mining and supervised learning. However, the assumption of "conditional independence of attributes" is difficult to realize in reality, which affects the final classification results. To address this problem, this paper introduces a dynamic weighting mechanism to assign corresponding weights to each attribute according to its influence on the classification result, and constructs a weight-based naïve Bayes classifier. The classifier takes the classification error rate as the objective function, uses the Harmony Search Algorithm to calculate the global optimal weights, and assigns the weights to each attribute after normalization. To improve the optimal-seeking ability of the Harmony Search Algorithm, this paper optimizes its new solution generation method and reduces the probability of falling into a local optimum by using an optimization scheme that accepts worse solutions with a certain probability. Experimental results demonstrate that the algorithm proposed in this paper has some improvement in classification indicators such as correct rate and F1-Score compared with the classical naïve Bayes classifier and other optimized naïve Bayes classifiers.