bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pursuit of Precision: Lobar Split Cone-Shaped Subsegmentectomy for Lung Nodules in Complex Positioning
Authors: Cao Kexin , Zhu Jian , Zheng Jianan , Wei Ke , Li Zhihua , Chen Zhijun , Chen Liang , Wu Weibing 
Published: May 2024

Abstract

Background
Thoracoscopic segmentectomy is the main surgical method for the treatment of early lung cancer. With the promotion of technology and increasingly accurate criteria for lung subsegments, lung nodules with complex positions involving intersegmental and multisegments have become technical bottlenecks. This study aimed to verify whether lobar split cone-shaped subsegmentectomy could solve this bottleneck problem.
Methods
The clinical data of patients with lung nodules ≤ 2 cm located in the complex position in the Department of Thoracic Surgery of Jiangsu Provincial People's Hospital from January 2019 to August 2023 were collected. We analyzed the characteristics of patients who underwent lobar split cone-shaped subsegmentectomy and compared the surgical outcomes and complications between these patients and lobectomy patients at similar locations.
Results
A total of 22 patients underwent lobar split cone-shaped subsegmentectomy, and 47 patients were treated with lobectomy. Lobar split cone-shaped subsegmentectomy for patients with lung nodules in complex positions can yield satisfactory surgical margins under some conditions (in this study, the nodule diameter was 0.98 ± 0.31 mm, the depth ratio was 0.43 ± 0.13, and the consolidation tumor ratio was 0.27 ± 0.19). Compared to those in the lobectomy group, surgical outcomes were better (p > 0.05 for surgical complications and p < 0.05 for postoperative hospital stay and the number of resected subsegments).
Conclusion
Lobar split cone-shaped subsegmentectomy is a promising method for performing true lung segmentectomy with safe resection margins for patients with lung nodules in complex positions involving multiple segments. It can be used as a true lung segmentectomy method.
Struma ovarii and sarcomatoid carcinoma：A case report and review of the literature
Authors: qin haojie , CHEN dan , JIN shan , Liu Jia , Gao Bo , Wang Yongpeng 
Published: May 2024

Abstract

This is a rare case of struma ovarii combined with sarcomatoid carcinoma. Whether it's struma ovarii or ovarian sarcomatoid carcinoma, the incidence of both is extremely low and rarely reported, so there are no clear guidelines for either disease and to our knowledge this may be the first case of combined occurrence of both. Therefore, this report describes its clinical manifestations, diagnosis and treatment, analyzes the pathogenesis, and summarizes the previous literature in the hope that it can be helpful to other tumor-related medical personnel and provide material support for the formation of guidelines in this disease.
Investigating Proxemics Behaviors Towards Individuals, Pairs, and Groups in Virtual Reality
Authors: Nolte Debora , Hjoj Reem , Pacheco Tracy Sánchez , Huang Ann , König Peter 
Published: May 2024

Abstract

The proxemics theory explains the consistent social boundaries surrounding individuals (Hall, 1966), yet little is known about the social boundaries surrounding pairs or groups of people. The current study explored interpersonal proxemics behavior in a virtual environment, focusing on distances maintained towards individual pedestrians, pairs, and groups. Using virtual reality to simulate a city center, participants freely navigated it while their movements and gazes were captured. Importantly, the city was populated by pedestrians in different social configurations. Eye movements identified interactions defined by gaze-onsets towards a pedestrian's head. Our results indicate that participants approached individuals with a median distance of 3.18 unity units aligned with the social space boundary (Hall, 1966). Distances kept from pairs and groups were similarly centered within the social space, revealing no significant difference in approaching behavior across different social configurations. The consistency in approaching distances suggests that personal and social spaces are not substantially altered, irrespective of the social context.
Where do waterbirds settle down under heavy rain: Waterbird habitat planning and design from the perspective of rain and flood resilience
Authors: Chen xinyi , Gu jinjin 
Published: May 2024

Abstract

Urban wetlands have ecological functions such as maintaining biodiversity and regulating runoff, and wetland waterbirds are also important biological indicators of wetland ecosystems, which can reflect the ecological conditions of wetlands and cities. In recent years, extreme weather occurs frequently, urban waterlogging is serious, rivers play the role of flood drainage, and rainwater backflow will have a serious impact on urban rivers, wetlands and aquatic organisms. Therefore, it is necessary to explore the inundation of urban wetlands under extreme rainfall conditions. In the past, most of the research on waterlogging was from the perspective of disaster prevention, and there was little research on the degree of habitat destruction of waterbirds. In this paper, the HEC-RAS hydrological model was used to simulate the extreme rainfall of the Shiwuli River in the study area of Hefei City, and the results showed that once a rainstorm occurred, the downstream wetland would be flooded in a large area and the inundation height would exceed the flood defense level, which would have a serious impact on the swimming birds such as coots nesting in the downstream wetland. Therefore, the landscape, foraging and habitat conditions of Coots were selected as the design factors, and the floating bird island was designed to alleviate the invasion of rain and flood waterlogging on Coots. The innovation of this study is to propose an optimization strategy for the impact of extreme weather on waterfowl habitat, which can provide a reference for waterfowl protection and wetland management, and improve the impact of environmental changes such as waterlogging caused by extreme rainfall on downstream lakes on waterfowl populations.
Scheduling with Speed Predictions
Authors: Balkanski Eric , Ou Tingting , Stein Clifford , Wei Hao-Ting 
Published: May 2024

Abstract

Algorithms with predictions is a recent framework that has been used to overcome pessimistic worst-case bounds in incomplete information settings. In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initially unknown. In this paper, we study the speed-robust scheduling problem where the speeds of the machines, instead of the processing times of the jobs, are unknown and augment this problem with predictions.
Our main result is an algorithm that achieves a $\min\{\eta^2(1+\alpha), (2 + 2/\alpha)\}$ approximation, for any $\alpha \in (0,1)$, where $\eta \geq 1$ is the prediction error. When the predictions are accurate, this approximation outperforms the best known approximation for speed-robust scheduling without predictions of $2-1/m$, where $m$ is the number of machines, while simultaneously maintaining a worst-case approximation of $2 + 2/\alpha$ even when the predictions are arbitrarily wrong. In addition, we obtain improved approximations for three special cases: equal job sizes, infinitesimal job sizes, and binary machine speeds. We also complement our algorithmic results with lower bounds. Finally, we empirically evaluate our algorithm against existing algorithms for speed-robust scheduling.
Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks: A Case Study in China
Authors: Zhong Chunling , Peng Jin 
Published: May 2024

Abstract

This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform probabilistic analysis on quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the "low professional level of designers," "poor production refinement," and "poor storage conditions for finished products" have a significant impact on quality risks. This research offers a novel risk assessment tool for steel structural component production, effectively assisting enterprises in identifying potential risks, formulating risk reduction strategies, and enhancing production quality.
Research on Short term Rainfall Prediction Technology Based on Deep Learning
Authors: Ji Chongxing , Xu Yuan 
Published: May 2024

Abstract

Short-term rainfall prediction is a crucial and practical research area, with the accuracy of rainfall prediction, particularly for heavy rainfall, significantly impacting people's lives, property, and even their safety. Deep learning and RNN cyclic convolutional networks have emerged as important research avenues to address this issue; however, each approach has its limitations. This article integrates their respective key advantages and further optimizes them from the perspectives of model framework and training loss function. Finally, we demonstrate the effectiveness of our optimization measures through experiments.
Crystal structure of L-2-keto-3-deoxyfuconate 4-dehydrogenase reveals a unique binding mode as a α-furanosyl hemiketal of substrates
Authors: Akagashi Miyu , Watanabe Seiya , Kwiatkowski Sebastian , Drozak Jakub , Terawaki Shin-ichi , Watanabe Yasunori 
Published: May 2024

Abstract

L-2-Keto-3-deoxyfuconate 4-dehydrogenase (L-KDFDH) catalyzes the NAD+-dependent oxidization of L-2-keto-3-deoxyfuconate (L-KDF) to L-2,4-diketo-3-deoxyfuconate (L-2,4-DKDF) in the non-phosphorylating L-fucose pathway from bacteria, and its substrate was previously considered to be the acyclic α-keto form of L-KDF. On the other hand, BDH2, a mammalian homolog with L-KDFDH, functions as a dehydrogenase for cis-4-hydroxy-L-proline (C4LHyp) with the cyclic structure. We found that L-KDFDH and BDH2 utilize C4LHyp and L-KDF, respectively. Therefore, to elucidate unique substrate specificity at the atomic level, we herein investigated for the first time the crystal structures of L-KDFDH from Herbaspirillum huttiense in the ligand-free, L-KDF and L-2,4-DKDF, D-KDP (D-2-keto-3-deoxypentonate; additional substrate), or L-2,4-DKDF and NADH bound forms. In complexed structures, L-KDF, L-2,4-DKDF, and D-KDP commonly bound as a α-furanosyl hemiketal. Furthermore, L-KDFDH showed no activity for L-KDF and D-KDP analogs without the C5 hydroxyl group, which form only the acyclic α-keto form. The C1 carboxyl and α-anomeric C2 hydroxyl groups and O5 oxygen atom of the substrate (and product) were specifically recognized by Arg148, Arg192, and Arg214. The side chain of Trp252 was important for hydrophobically recognizing the C6 methyl group of L-KDF. This is the first example showing the physiological role of the hemiketal of 2-keto-3-deoxysugar acid.
How does short-term air pollution exposure influence worker performance? Evidence from soccer players in China
Authors: Zhang Ning , Wei Xiahai , He Pan , Liu Yunxiao , Liu Hongyou , Lin Tao , Huang Xuhui , Shi Xin , Liu Zhu , Freeman Richard B. 
Published: May 2024

Abstract

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.
Airborne metal nanoparticles released by azides detonation: determination and potential public exposure
Authors: Testino Andrea , Cen Tianyu , Ludwig Christian , Mueller Elisabeth , Laura Torrent , Zhang Zheyu 
Published: May 2024

Abstract

Metal azides are highly energetic materials that release a large amount of gas upon detonation. They also release metal particles, generating an aerosol. The most common azide is sodium azide (NaN3), which is used nowadays in car airbags. If the decomposition is not complete, harmful azide particles might be inhaled. Heavy metal azides find application as a primary explosive (primer) in ammunition. Public health officials have raised concerns about heavy metal particles released during training in shooting ranges. We identify a lack of knowledge on airborne metal particles properties released from azide detonation and on the analytical methods applied to characterize them. As a case study, we detonate milligram amount of silver azide, copper azide, and a mixture of them in a glove box. We then analyze the airborne particles with a hyphenated analytical setup, able to measure real-time their particle size distribution and chemical composition. We detect spherical metal nanoparticles in the range of 2-500 nm. These findings and the developed analytical tools may allow identifying airborne nanoparticles in car passengers compartment after airbag activation as well as in indoor shooting ranges, contributing to the evaluation of public health risks.
BMAL1/REV-ERBα loop as a novel inflammatory sensor to drive NF-κB-mediated inflammation in vascular smooth muscle cells by modulating oxidative stress
Authors: Ge Yiling , Li Pei-Jie , Bu Ying-Rui , Zhang Bin , Xu Jin , He Si-Yuan , Cao Qing-Lin , Bai Yun-Gang , Ma Jin , Zhang Lin , Zhao Xing-Cheng , Xie Man-Jiang 
Published: May 2024

Abstract

Objectives and design:
As a pleiotropic inflammatory cytokine, TNF-α could act as a kind of zeitgeber mediator to integrate with circadian clock and modulate inflammatory signaling. We aimed to investigate how circadian gene Bmal1 regulating inflammation in vascular smooth muscle cells (VSMCs) upon TNF-α stimulation.
Methods
Circadian rhythmicity of Bmal1 expression was detected in the mouse VSMCs challenged with TNF-α, and then Bmal1 was knocked down or overexpressed by adenovirus transfection to investigate the effects and machines of Bmal1 on inflammatory signaling.
Results
1) TNF-α stimulated Bmal1 transcription and disrupted its circadian expression in VSMCs. 2) Transcriptional activation of Bmal1 furtherly activated TNF-α-induced- NF-κB signaling and exacerbated VSMCs inflammation by triggering oxidative stress. 3) TNF-α-activated JNK signaling enhanced REV-ERBα phosphorylation and degradation, and thus promoted Bmal1 transcription in VSMCs.
Conclusion
Our work identified a specific pathway by which the transcriptional activation of Bmal1, mediated by the TNF-α-induced REV-ERBα phosphorylation, triggered oxidative stress to exacerbate inflammatory response in VSMCs, which represents a new opportunity for clock gene Bmal1 being a potential diagnostic marker and therapeutic target for TNF-α mediated vascular inflammation.
Mediating effect of lipid levels during first trimester of pregnancy between COVID-19 lockdown and gestational diabetes mellitus
Authors: Peng Jing , Jiao Bo , Huang Zhen , Shao Feixue , Liu Yuan , Lin Cunjie , Hua Xiaolin 
Published: May 2024

Abstract

Background Higher triglycerides (TG) levels are associated with gestational diabetes mellitus (GDM) and higher prevalence of GDM was found in women experiencing COVID-19 pandemic lockdown in early pregnancy. This study aimed to explore the relationship among lockdown, lipid levels change in the first trimester and GDM.
Methods This was a population-based observational cohort study. Mean values of lipid levels were compared by Wilcoxon rank-sum test. Logistic regression analysis was used to analyze the effects of lockdown and lipid profile on GDM. Mediation analysis was performed to identify whether blood lipid level mediated the association between lockdown and GDM.
Results A total of 3997 pregnant women were included, among whom 614 (15.36%) were defined as GDM group and 3383 (84.64%) were defined as non-GDM group. Women who experienced lockdown in the first trimester had higher levels of serum total cholesterol (TC), TG, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) (p < 0.001) than others. Besides, experiencing lockdown (OR 1.84, 95% CI 1.50-2.26) and higher level of TG (OR 1.34, 95% CI 1.16-1.55) during the first trimester were both significantly and independently associated with the risk of GDM by multivariate logistic regression analysis. Furthermore, mediation analysis was conducted to explore the potential relationship among lockdown, lipid levels change during the first trimester and GDM,which showed TG played a mediating role in the association between lockdown and GDM (p < 0.001).
Conclusions Pregnant women who experienced lockdown had higher levels of TC, TG HDL and LDL in the first trimester.TG had a mediating effect between lockdown and GDM, indicating that lockdown which brought physical inactivity and poor diet in early pregnancy may have an effect on the development of GDM by affecting lipid profile. Therefore, exercise and diet interventions in early pregnancy could help prevent GDM.
Vulnerability detection under poisoning attacks through code and token features
Authors: González-Manzano Lorena , Garcia-Alfaro Joaquin 
Published: May 2024

Abstract

The complexity of implementations and the interconnection of assorted systems and devices facilitates the emergence of vulnerabilities. Detection systems are developed to fight against this security issue, being the use of Artificial Intelligence (AI) a common practice. However, the use of AI is not without its problems, specially those affecting the training phase. This paper tackles this issue following a two-fold approach. First, an AI-based vulnerability detection system based on code and token metrics, dubbed VulCoT, is developed. It reaches state-of-the-art performance while being suitable for C#, C/C++ and PHP. Second, the impact of poisoning attacks on VulCoT is analysed. Results show that VulCoT is specially affected beyond 20% of false data. Remarkably, detecting some of the most frequent Common Weakness Enumeration is altered even with lower poison rates. Overall, KNN and SVM are more appropriate for system protection in C# and C/C++, while MLP in PHP. Indeed, PHP is the language which is less affected by attacks, while C# and C/C++ present comparable results.
Development of a scoring system to predict local recurrence in brain metastases following complete resection and observation
Authors: Ohno Makoto , Takahashi Masamichi , Yanagisawa Shunsuke , Osawa Sho , Tsuchiya Takahiro , Fujita Shohei , Igaki Hiroshi , Narita Yoshitaka 
Published: May 2024

Abstract

Purpose
Postoperative careful radiological observation could be a therapeutic option in patients with brain metastases after complete resection. However, there are no clear criteria to determine this indication. We investigated risk factors for local recurrence after complete resection and developed a scoring system to predict it.
Methods
We included 53 patients with 54 brain metastases, who underwent complete resection between January 2016 and December 2021. We identified risk factors for local recurrence and developed a scoring system to predict it using the extracted risk factors by assigning one point to each risk factor and calculating the total scores for each patient. We evaluated the correlation between the prognostic score and time to local recurrence.
Results
Local recurrence occurred in 37 of 54 tumors (68.5%), with a median follow-up duration of 21.0 months. The median time to local recurrence was 5.1 months, with a 6-month recurrence rate of 55.6%. Univariate and multivariate analyses revealed that non-lung adenocarcinoma, infratentorial tumors, and no postoperative systemic therapy were identified as significant risk factors for local recurrence (non-lung adenocarcinoma: odds ratio 2.59, p = 0.035; infratentorial tumors: odds ratio 2.27, p = 0.044; and no postoperative systemic therapy: odds ratio 2.62, p = 0.0069). A score ≥ 2 showed a median time to local recurrence of 2.1 months, contrasting starkly with 30.8 months for a score ≤ 1 (p = 0.0002).
Conclusions
Non-lung adenocarcinoma, infratentorial tumors, and no postoperative systemic therapy were risk factors for local recurrence. Our scoring system using the factors can predict local recurrence, potentially aiding treatment decisions.
Classification of Speech Arrests and Speech Impairments during Awake Craniotomy: a multi-databases analysis
Authors: Maoudj Ilias , Kuwano Atsushi , Panheleux Céline , Kubota Yuichi , Kawamata Takakazu , Muragaki Yoshihiro , Masamune Ken , Seizeur Romuald , Dardenne Guillaume , Tamura Manabu 
Published: May 2024

Abstract

Purpose: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The ability to accurately assess linguistic functions in real-time not only enhances surgical precision but also contributes significantly to improving postoperative outcomes. However, today, its evaluation is subjective as it relies on a clinician’s observations only. This paper explores the use of a deep learning based model for the objective assessment of speech arrest and speech impairments during awake craniotomy.
Methods: We extracted 1883 3-second audio clips containing the patient’s response following Direct Electrical Stimulation from 23 awake craniotomies recorded from two operating rooms of the Tokyo Women’s Medical University Hospital (Japan) and 2 awake craniotomies recorded from the University Hospital of Brest (France). A Wav2Vec2-based model has been trained and used to detect speech arrests and speech impairments. Experiments were performed with different datasets settings and preprocessing techniques and the performances of the model were evaluated using the F1-score.
Results: The F1-score was 84.12% when the model was trained and tested on Japanese data only. In a cross-language situation, the F1-score was 74.68% when the model was trained on Japanese data and tested on French data.
Conclusion: The results are encouraging even in a cross-language situation but further evaluation is required. The integration of preprocessing techniques, in particular noise reduction, improved the results significantly.