bluuebunny/crossref_metadata_2025 huggingface dataset:
Perioperative outcomes in an age-adapted analysis of the German StuDoQ|Pancreas registry for PDAC
Authors: Tschaidse Tengis , Hofmann Felix O. , Renz Bernhard , Hungbauer Maximilian , Klinger Carsten , Buhr Heinz J , Uhl Waldemar , Mees Sören Torge , Reissfelder Christoph , Ghadimi Michael , D'Haese Jan G. , Werner Jens , Ilmer Matthias , Keck Tobias
Published: May 2024

Background:
Pancreatic ductal adenocarcinoma (PDAC) typically occurs in an older patient population. Yet, early-onset pancreatic cancer (EOPC) has one of the fastest growing incidence rates. This study investigated the influence of age and tumor location on postoperative morbidity and mortality in a large, real-world dataset.
Methods:
Patients with confirmed PDAC undergoing pancreatic surgery between 01/01/2014 and 31/12/2019 were identified from the German StuDoQ|Pancreas registry. After categorization into early- (EOPC), middle- (MOPC), and late-onset (LOPC), and stratification into pancreaticoduodenectomy (PD) or distal pancreatectomy (DP), differences in morbidity and mortality as well as clinicopathologic parameters were analyzed.
Results:
In total, 3011 identified patients were identified. No difference in the occurrence of POPF, PPH or DGE between different age groups and resection techniques was detected. However, in patients undergoing PD, major complications (Clavien-Dindo ³ 3a) were observed more frequently in LOPC (30,7%) than in MOPC (26,2%) and EOPC (16,9%; p < 0,01). Mortality almost tripled from EOPC (2,4%) to MOPC (3,6%) to LOPC (6,6%, p < 0,01) and significantly higher FTR rates could be observed (EOPC 14,3%, MOPC 13,6%; LOPC 21,6%; p < 0,05). In centers with DGAV certification for pancreatic surgery, the risk of complications was significantly decreased in PD (OR 0,79; 95% CI 0,65-0,94; p = 0,010).
Conclusion:
Age has a pronounced impact on the perioperative outcomes after pancreatic resections of PDAC. This effect is more prevalent in PD compared to DP. Pancreatic surgery-specific complications, such as POPF, DGE or PPH do not occur more frequently in the elderly. Overall, the risk of major complications and mortality increases in elderly patients mainly secondary to higher FTR rates. In contrast, certified centers (DGAV) reduced the rate of major complications in PD.
Centralization of pancreatic surgery in high-volume centers with certified quality management is key to improve the outcomes of pancreatic surgery.
The implementation of speckle tracking echocardiography for cardiac resynchronization therapy optimization. A rotational myocardial mechanics interpretation
Authors: Stefanidis Alexandros , Korlou Paraskevi , Margos Panagiotis , Ikonomidis Ignatios , Paraskevaidis Ioannis , Gatzoulis Konstantinos , Aivalioti Evmorfia , Kostopoulos Konstantinos
Published: May 2024

Background: Cardiac resynchronization therapy (CRT) has an additive therapeutic influence on left ventricular function in heart failure patients, but the underlying mechanisms through which it works are not completely explained. Our aim was to further elucidate the role of this intervention via rotational mechanics using 2D speckle tracking echocardiography (2D-STE).
Results: We investigated 46 patients (65 ± 9 years) who received CRT. All enrolled patients were assessed on admission by 2D-STE and 6 minute walk test (6 min WT) and followed in the outpatient device clinic by 2D-STE (at one week and 6-month post-implantation) and 6 min WT (at 6 months post-implantation). On their first appointment all biventricular systems were optimized by atrioventricular delay optimization and by changing the temporal activation of ventricular electrodes aiming to reach the highest left ventricular effective stroke volume across all activation options. A new 2D-STE based index (twist integral) targeting to assess the rotational mechanics of the whole cardiac cycle was also measured to further explain the CRT response.
Twenty-two (48%) patients with dilated cardiomyopathy as the predominant aetiology of heart failure were responders at 6-month follow-up. The commonest selected mode that was related with the greatest left ventricular performance response was the simultaneous activation of the 2 ventricular leads (39%). The strongest predictor of CRT response was the improvement of effective stroke volume between admission and first appointment at clinic, followed by the improvement of twist integral, the non-existence of coronary artery disease, and the improvement of peak systolic twist.
Conclusions: Additional CRT optimization via changing the temporal activation of ventricular electrodes is beneficial for left ventricular performance in heart failure patients. Rotational mechanics essentially explain the beneficial CRT contribution to these patients.
Evaluation of efficacy and effect of topical low-dose testosterone (3 mg/mL) on estradiol levels by mass spectrometry in breast cancer patients receiving ovarian suppression and aromatase inhibitor therapy
Authors: Taranto Patrícia , Sales Diogo de Brito , Maluf Fernando Cotait , Guendelmann Rafael Aliosha Kaliks , Pompei Luciano de Melo , Leal Alessandro , Buzaid Antonio Carlos , Schvartsman Gustavo
Published: May 2024

Background Premenopausal, high-risk, hormone receptor-positive breast cancer patients are often treated with ovarian suppression in combination with aromatase inhibitors. This combination has important adverse effects, particularly in sexual function, such as vaginal dryness and loss of libido. There is no effective therapy for reduced sexual function in this setting. Our study aimed to determine the efficacy and safety, particularly regarding sexual function, of a low-dose, topical testosterone gel administration.Methods This is a pilot, single-center study, designed to evaluate the efficacy of topical testosterone gel (3 mg/day) in improving sexual function in 29 premenopausal patients on ovarian suppression in combination with an aromatase inhibitor. The primary safety endpoint was to determine serum estradiol, measured by liquid chromatography-mass spectrometry monthly for three consecutive months. The primary efficacy endpoint was assessed by means of the Female Sexual Function Index questionnaire, which include various domains of sexual function such as libido, sexual satisfaction and vaginal lubrication.Results We report the results on 29 patients. Twenty-two patients (75%) completed the 3-month treatment, and seven discontinued treatment. One was excluded after the first visit because she was postmenopausal, one had a mild skin reaction and five discontinued treatment over the three months mainly due to logistical difficulties related to the COVID-19 pandemic. A total of 29 patients maintained the value of baseline mass spectrometry assay for estradiol of less than 2.7 pg/mL during the 3-month treatment in all three measurements. We observed a significant improvement in Female Sexual Function Index measures over the visits, with an increase from a mean of 11.7 at baseline to 19.1 in the third month (p < 0.001), with the greatest improvement observed between the second and third months. Regarding the domains of the questionnaire evaluated separately, desire, excitement, lubrication, orgasm and satisfaction all showed significant improvement over three months of the protocol.Conclusions Our findings suggest that topical testosterone seems to be safe and may be effective in improving sexual function in patients on ovarian suppression and AI. A randomized phase 2 study is warranted.Trial registration The project was submitted and approved through the hospital's SGPP platform in 11/26/2019 (Project No. SGPP: 3938-19) and CAAE (Research Ethics Committee) (CAAE No: 25609719.5.0000.007)
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Pay As You Earn (PAYE) Tax as Panacea to Backlog of Gratuities and Pensions of Civil Servants in Nigeria
Authors: Mohammed Sani Damamisau , Garba Sunusi , Sani Armaya’u Alhaji , Ibrahim Rukayya Tijjani , Muhammad Yusra Musa , Kassim Ibrahim Ahmad
Published: May 2024

Gratuities and pensions are payments due to retired civil servants; however, payments of these have not being timely and consistent to the extent that 23 state governments are owing 836.40 billion as gratuities and pension liabilities as at end of 2022. Non-payment of these retirement benefits is often accompanied with social and health problems leading to premature death of retirees. Consequently, the aim of this study is to examine the potentials of monthly Pay As You Earn (PAYE) tax to eliminate the backlog of retirement benefits owed retired civil servants. To achieve this aim, quantitative secondary data of PAYE tax collections by state governments is collected from the annual Internally Generated Revenue reports of states published by National Bureau of Statistics (NBS) 2010–2022. Descriptive statistical tools of analysis are employed to analyze collected data while Burgess’s activity theory and public policy analytical framework guided the conduct of the study. On the overall, results from the study revealed that while the 23 state governments owe 836.40 billion as liabilities of retirement as at 2022, the same states have collected the sum of 5,672 billion as PAYE tax 2010–2022. On individual states, 20 states have collected PAYE tax 2010–2022 more than their liabilities of retirement benefits while only three states collected PAYE tax less than their accumulated liabilities of retirement benefit. The short run policy implication is for states to set aside collected PAYE tax to settle the backlog of benefits and adopt the Contributory Pension Scheme (CPS) to avoid future recurrence of the backlog.
Data-Driven Optimisation Based Machine Learning for Thermal Comfort in Building Environment
Authors: Alarab Ismail , Efstratiou Christos , Prakoonwit Simant , Otero Fernando , Nikolopoulou Marialena , Barker Matthew , Baldwin Andrew , Clarke Malcolm
Published: May 2024

Achieving thermal comfort under minimum energy considerations has become a hot topic in the field of energy building management. The existing studies have suggested several methods to predict thermal comfort and accordingly adjust the temperature setpoints to reduce the energy. The two drawbacks of the existing studies involve the increased complexity in optimisation methods using multiple models and the lack of these methods in the optimisation approach. Our novel work proposes a data-driven optimisation solution based on machine learning (ML) to maintain thermal comfort under energy efficiency considerations. We simply infer the input from a desired output using supervised learning models as an optimisation solution. We discuss the efficiency and cost-effectiveness of the proposed solution using a public UK-schools dataset from the ASHRAE database.
Virtual Reality Rehabilitation using Hand Tracking
Authors: Coox Aaron , McKittrick Andrea , Tornatore Giovanna , Desselle Mathilde , Laracy Sue , Bo Antonio Padilha Lanari
Published: May 2024

Virtual reality rehabilitation (VR) can complement traditional rehabilitation therapy to increase motivation and thus the quality of exercise, while transferring effectively to real-world tasks. Hand tracking can significantly improve usability and immersion. A VR hand-tracking-based interaction system has been developed that supports various hand interactions, including grasping, pinching, and gesturing to complete virtual exercises. Interactions support modifiable difficulty and requirements, metrics, and visual error augmentation (EA). Audio-visual signifiers and feedback to guide interactions are also applied. A proof-of-concept rehabilitation game was tested with non-expert (N=7) and expert occupational therapy (N=5) participants. Participants completed tasks including grabbing objects, pinching, pulling handles, tracing paths, and making hand gestures. The system's usability was the primary outcome of the study. The results have shown this system is highly usable, with a modified System Usability Scale (SUS) of 88.93 (non-expert) and 74.5 (expert). A preliminary performance analysis was also conducted, indicating a limited number of unsuccessful hand-object interactions, although the accuracy in some interactions was impacted by tracking occlusion.
Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects
Authors: Eldesouky Mohamed Tamer , Raslan Atef Tayh Nour El-Din
Published: May 2024

Focusing on identifying hidden patterns and dependencies between software defects that are difficult to detect using traditional analysis methods, this study employs Association Rule Mining (ARM) to analyze over 140,000 open-source GitHub issues. By leveraging ARM, we have been able to extract explicit association rules that illustrate the interrelations among various issue attributes such as labels and release versions. Our findings indicate strong, meaningful associations that equip developers and quality assurance teams with the information necessary to strategically prioritize issues. This prioritization significantly influences bug fixing and system enhancements. Offering a marked improvement over conventional analysis techniques, ARM provides a clearer and more precise understanding of defect dynamics, thereby enhancing the efficiency and effectiveness of software development practices.
QuantForecast - Navigating the Financial Future
Authors: Aggrawal Niyati , Rathi Megha , Kansal Riya , Jamwal Aditya , Agarwal Swati
Published: May 2024

In this visionary work, we embark on a journey to revolutionize stock price prediction. Beginning with an insightful immaterial, we emphasize the critical role of predictive modeling in deciphering the ever-evolving dynamics of financial markets. Our work meticulously curates a spectrum of technical indicators, including renowned ones like RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence), tailored to empower both astute investors and discerning traders. Delving deeper, we navigate the intricate landscape of trading algorithms, carefully weighing their merits and demerits. From this analytical exploration, we pinpoint the algorithm that showcases unparalleled promise, thus setting the stage for groundbreaking advancements. The work showcases not only the prowess of machine learning and deep learning but also the art of visualization. By elegantly presenting the model's accuracy scores and visually encapsulating its performance, we bring forth an intuitive understanding of its potential. Infused with sophistication, we seamlessly blend time-honored time series analysis techniques such as ARIMA, LSTM, and jump diffusion into our models. Every step is a masterpiece, meticulously executed to ensure optimal epochs and stellar forecasting. Our models are rigorously tested, their predictions meticulously juxtaposed with real-world stock prices, and quantified using industry-standard metrics like RMSE and MAPE. In essence, this work isn't merely a quest for the best model—it's a testament to our dedication to empower investors and traders, providing them with a cutting-edge tool to navigate the volatile world of financial markets with unmatched precision and confidence.
Exploring Sentence-level Revision Capabilities of LLMs in English for Academic Purposes Writing Assistance
Authors: Du Zhendong , Hashimoto Kenji
Published: May 2024

The English for Academic Purposes (EAP) is pivotal for scholarly communication; however, it poses significant challenges for non-native English speakers. Recently, Large Language Models (LLMs) have been extensively utilized in EAP to assist with writing tasks. EAP writing assistance typically encompasses several downstream tasks in natural language processing (NLP), such as Grammatical Error Correction (GEC). Non\-etheless, some studies have revealed that the performance of LLMs in GEC tasks is inferior to traditional GEC solutions. To explore the capabilities of LLMs more thoroughly in aspects like deep semantic and syntactic structures, this study aims to rigorously assess the performance of LLMs in the Sentence-level Revision (SentRev) task. We designed three sets of meticulous experiments to evaluate the efficacy of different LLMs. The first experiment assessed LLMs using prompts in ten different languages, finding that the SentRev performance of LLMs was heavily influenced by the language of the prompt and the quality of the input text. The second experiment investigated the performance of English LLMs with minimal prompting in the SentRev task, yet the results showed no significant changes, contradicting some prior studies. In the third experiment, we devised an innovative and straightforward method that significantly enhanced the performance of multiple LLMs by integrating academic phrases from the Formulaic Language (FL) Academic Phrasebank\footnote{\url{https://www.phrasebank.manchester.ac.uk/}}, thus overcoming the performance limitations imposed by different languages on LLMs. Additionally, our study highlights the deficiencies in existing evaluation benchmarks and suggests that higher-level, discourse-based EAP text evaluation benchmarks merit deeper exploration.
Enhanced Network Security against SQL Injection Attack Using Machine Learning
Authors: Joshi Seema , Oza Neel
Published: May 2024

SQL injection attacks pose a significant risk to the security of computer networks. These attacks have the potential to gain unauthorized access to sensitive data, modify or remove data, or even render entire websites and databases inoperable. Conventional techniques for identifying and stopping SQL injection attacks are frequently resource-intensive, rendering them unfeasible for devices that manage substantial amounts of traffic. The objective of the proposed research is to improve the identification and prevention of structured query language injection attacks (SQLIAs) in web applications, with a specific focus on attaining a high rate of detection while minimizing false alarms. We utilized flow data obtained from several SQL injection attack scenarios that specifically targeted widely-used database engines. We obtained a high level of accuracy in identifying and mitigating these assaults by utilizing machine learning methods, specifically logistic regression. The method we used showed a detection rate over 98% and a false alarm rate below 0.029%. The results demonstrate substantial enhancements in the prevention of unauthorized access and the protection of sensitive information within databases. The results of our research align with or surpass current techniques, offering enhanced security for web applications. The model’s exceptional accuracy and minimal false alarm rate provide a groundbreaking method for detecting SQL injection attacks.
Timed Automata Verification and Synthesis via Finite Automata Learning
Authors: Sankur Ocan
Published: May 2024

We present algorithms for model checking and controller synthesis of timed automata, seeing a timed automaton model as a parallel composition of a large finite-state machine and a relatively smaller timed automaton, and using compo-sitional reasoning on this composition. We use automata learning algorithms to learn finite automata approximations of the timed automaton component, in order to reduce the problem at hand to finite-state model checking or to finite-state controller synthesis. We present an experimental evaluation of our approach.
Quantifying the carbon footprint of academic clinical trials: building the evidence base and hotspot identification
Authors: Griffiths Jessica , Adshead Fiona , Salman Rustam Al-Shahi , Anderson Craig , Bedson Emma , Bliss Judith , Boshoff Ana , Chen Xiaoying , Cranley Denise , Doran Peter , Dunne Fidelma , Gamble Carrol , Gillies Katie , Hood Kerenza , Kavanagh Columb , Malone Julia , McGregor Naomi , McNamara Carolyn , Midha Elis , Moore Keith , Murphy Lucy , Newman Christine , O’Reilly Seamus , Perkins Alexis M , Pett Sarah , Sydes Matthew R , Whitty Laura , You Frank , Fox Lisa , Williamson Paula
Published: May 2024

Background
Clinical trials are fundamental to healthcare, however they also contribute to anthropogenic climate change. Following previous work to develop and test a method and guidance to calculate the carbon footprint of clinical trials, we have now applied the guidance to 10 further UK and international, academically-sponsored clinical trials to begin identification of hotspots and opportunities for lower carbon trial design.
Methods
10 collaborating Clinical Trial Units (CTUs) self-identified and a trial was selected from their portfolio to represent a variety of designs, health areas and interventions. Trial activity data was collated by trial teams across 10 modules spanning trial set up through to closure, then multiplied by emission factors provided in the guidance to calculate the carbon footprint. User feedback was collected on the process of applying the draft guidance.
Results
Six completed and four ongoing trials (two in follow up, two recruiting) were footprinted across a variety of interventions (6 IMP trials, one nutritional, one surgical, one health surveillance and one complex intervention trial). The carbon footprint of the 10 trials ranged from 15 to 765 tonnes CO2e. Common hotspots were identified as CTU emissions, trial-specific patient assessments and trial team meetings and travel. Hotspots for specific trial designs were also identified. Time taken to collate activity data and complete carbon calculations ranged from 5–60 hours. The draft guidance was updated to include new activities identified from the 10 trials, and in response to user feedback.
Discussion
There are opportunities to reduce the impact of trials across all modules, particularly trial specific meetings and travel, patient assessments and laboratory practice. A trial’s carbon footprint should be considered at the design stage, but work is required to make this common place.
Effects of T3SS-positive Pseudomonas isolates on sugar beet growth stimulation and pathogen resistance
Authors: Nedeljković Marija , Mesaroš Aleksandra , Rašić Vuk , Nikolić Ivan , Stanković Slaviša , Lozo Jelena , Atanasković Iva
Published: May 2024

The intricate interplay between plants and microorganisms in agricultural ecosystems holds immense potential for increasing crop productivity and resilience in the face of climate change and increasing pathogen pressure. This study addresses the function of the type 3 secretion system (T3SS) in nonpathogenic Pseudomonas strains associated with sugar beet (Beta vulgaris L.). We identified T3SS-positive isolates and characterised their genetic diversity and T3SS expression profiles. Our results show that T3SS is widely distributed among sugar beet-associated Pseudomonas, with variations in T3SS gene sequences and expression patterns. Notably, T3SS functionality was demonstrated in one isolate, P. marginalis OL141. In planta experiments with this isolate showed a correlation between T3SS and the growth of sugar beet and resistance to Pseudomonas syringae infections. The T3SS-mediated interactions in P. marginalis OL141 point to a novel mechanism underlying plant-microbe symbiosis and offer promising opportunities for sustainable agriculture. Future research directions include elucidating the mechanistic basis of T3SS-mediated plant-microbe interactions and exploring their broader implications for sustainable agriculture and global food security.