bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of nutritional status on occurrence of pneumonia after traumatic cervical spinal cord injury
Authors: Hayashi Tetsuo , Fujiwara Yuichi , Irie Momo , Masuda Muneaki , Sakai Hiroaki , Kawano Osamu , Maeda Takeshi 
Published: September 2023

Abstract

Pneumonia after cervical spinal cord injury (CSCI) is a common and serious complication; however, its nutrition-related etiology has not yet been elucidated. This study aimed to elucidate the effects of nutritional factors on pneumonia after CSCI.
Patients with acute traumatic CSCI who were admitted within 3 days after injury and followed up for at least 3 months were retrospectively examined. Occurrence of pneumonia, nutritional status, severity of dysphagia, vital capacity, use of respirators, and motor scores for paralysis were evaluated.
Of 182 patients included in this study, 33 (18%) developed pneumonia. Multiple logistic regression analysis revealed that low nutritional status, severe paralysis, and low vital capacity were significant risk factors for pneumonia.
The severity of paralysis, respiratory dysfunction, and poor nutritional status can affect the occurrence of pneumonia after CSCI. In addition to respiratory management, nutritional assessment and intervention may play key roles in preventing pneumonia associated with spinal cord injury-induced immune depression. Nutritional care should be provided as soon as possible when the nutritional status of a patient worsens after an injury.
Non-invasive self-adaptive information states acquisition inside dynamic scattering spaces
Authors: Li Erping , Li Ruifeng , Ma Jinyan , Li Da , Wu Yunlong , Qian Chao , Zhang Ling , Chen Hongsheng , Kottos Tsampikos 
Published: September 2023

Abstract

Pushing the information states acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies point out that the maximum information states can be achieved by engineered modes with specific matrices and operators, however, they typically necessitate time-consuming iterations or partial intrusion for multi-targets. Whereas non-invasive designs have been substantially explored across various physical scenarios, acquiring information states non-invasively inside dynamic scattering spaces remains challenging as it involves intractable non-unique mapping problem. Here, we first demonstrate that non-invasive information states acquisition is feasible and propose a tandem-generated adversarial network framework that can perform self-adaptive wavefield control inside dynamic scattering spaces. As an example, we have carried out a proof-of-principle experiment in the microwave regime, and prove that by using only the external scattering matrix of the system, information states acquisition for multi-targets can achieve the Fisher information limit efficiently. Our work demonstrates the unprecedented potential for real-time, efficient, and complex wave manipulation, providing explicable perspectives for precise measurements inside dynamic complex systems, such as medical micro-manipulation, optical Internet of Things, and intelligent communications.
Growth curve establishment of Cratylia argentea for harvest timing
Authors: Climaco Leonardo Campos Teatini , Teixeira Elaine Cristina , Abreu Lucas Freires , Souza Fernando Antônio , Silva Karina Toledo , Matrangolo Walter José Rodrigues , Lana Angela Maria Quintão 
Published: September 2023

Abstract
The objectives of this study were to evaluate the growth curve and chemical composition of Cratylia (Cratylia argentea). The experiment was conducted with Cratylia planted in two-line spacings of 2 or 3 meters between rows and 0,5 m between plants. Cratylia plants were evaluated in a cycle of 126 days, with pruning events every 21 days, and a total of 6 assessments for each treatment. Parameters of bromatological composition (DM, CP, NDF, ADF) of plant edible fraction (EF) and productivity (total productivity and productivity of the EF in DM) were evaluated. The study was performed in a completely randomized design with 2 treatments (spacing) and 6 replications. The variations over days were evaluated using regression analysis, and the results were compared by Fisher test (p<0,05). The models that had the best fit for productivity was exponential and logistic, whereas, for the bromatological composition it was Gompertz and polynomial. The 3 m spacing had lower productivity and CP content. The Cratylia growth rate reduced with increased plant maturity, which suggested its prune at 86 days for the 2 m spacing and 91 days for 3 m spacing. Based on the results found in this research, the 2 m showed greater yield and CP content. Both spacings presented values of CP, NDF, and ADF of 20.26, 60.29 and 40.63% for 2 m spacing and 18.97, 60.61 and 42.11% for 3 m spacing, respectively for 105 days of pruning, which was less productive for both spacings.
Postmortem homeostatic maintenance with three stepwise pH progressions in the stratum corneum of the skin
Authors: Amagai Masayuki , Fukuda Keitaro , Ito Yoshihiro , Furuichi Yuki , Matsui Takeshi , Horikawa Hiroto , Miyano Takuya , Okada Takaharu , Logtestijn Mark van , Tanaka Reiko , Miyawaki Atsushi 
Published: September 2023

Abstract

Stratum corneum (SC), the outermost skin layer comprising dead keratinocytes (corneocytes), plays a vital role in skin barrier function and maintains its thickness by shedding corneocytes, although mechanisms safeguarding SC homeostasis remained unclear. Ex vivo studies reported that monotonous neutral-to-acidic pH distribution exists in the SC; however, we present by intravital SC-pH imaging at the single-corneocyte resolution that corneocytes undergo postmortem differentiation and develop three-tiered zonation: the lower-moderately acidic, middle-acidic, and upper-neutral zones. The upper SC-pH zone possesses acclimation capacity, changing its pH according to the external environment and having a neutral pH owing to skin microbiota under steady-state. The middle SC-pH zone represents the acid mantle layer, providing a defensive barrier against pathogens. Mathematical model incorporating the three-tiered zonation simulated the high catalytic activity of kallikrein-related peptidases on the SC surface, resulting in proper corneocyte shedding. Thus, the three-tiered SC-pH zonation provides a crucial platform for maintaining SC homeostasis.
Decoding Peak Performance : Decipher BIOS as bio-wearables potentiated biomarker for hyper-performance compared to traditional field tests
Authors: Shaikh Ateeb , Baluni Sameer , Yezdani haleema , Saraogi Pratik 
Published: September 2023

Abstract
This study investigates the potential benefits of Acotfit's BIOS performance tool in the context of cricketers compared to traditional nutritional strategies. Continuous monitoring and data collection encompassing metabolic health, cognitive function, physiological metrics, and wearable data were conducted. The results demonstrate significant improvements in metabolic health, cognitive function, and overall well-being when incorporating Acotfit's BIOS. Moreover, a comparative analysis against traditional nutritional strategies underscores the potential advantages of this innovative approach. These findings suggest that Acotfit's BIOS may offer a promising avenue for cricketers looking to optimize their athletic performance and well-being.
A parallel topology optimization framework based on unstructured 3D FEA using PETSc and Eigen
Authors: Wang Yu , Li Renfu , Wang Kun 
Published: September 2023

Abstract
This paper presents a framework based on unstructured meshes for three-dimensional large-scale parallel topology optimization using PETSc and Eigen, which is easy to use and expand. The framework solves the minimum compliance problem using diagonally numbered eight-node hexahedral elements. The method of moving asymptotes (MMA) is chosen as the optimization solver. The validity of the framework is demonstrated by a classical cantilever beam problem and two more examples of wheel rim and rotator illustrate the expansibility of the framework. The code can be found from https://github.com/aiwanzhe/Unstructured-FEA-based-Topology-Optimization-in-PETSc-and-Eigen.
Deep-learning based 3-year survival prediction of pineoblastoma patients
Authors: Li Xuanzi , Yang Shuai , Liu Qiaodan , Wang Siyang , Zha Dasong , Zhang Shuyuan , Peng Yingpeng , Deng Chuntao 
Published: September 2023

Abstract
Purpose
Pineoblastoma (PB) is an extremely uncommon and highly aggressive malignancy that originates from the pineal gland, more frequently occurs in young children. Due to the rare nature, little is known about its prognostic implications and survival outcomes. Existing methods for prognostication based on traditional statistical approaches such as Cox proportional hazards (CPH) models, which have less-than-ideal predictive accuracy. Recently, deep learning algorithms has unlocked unprecedented advancements in diverse domains and has been applied extensively in medical fields. Thus, we sought to develop and compare deep learning models with CPH models in predicting 3-year overall (OS) and disease-specific survival (DSS) for patients with pineoblastoma.
Methods
We utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify patients diagnosed with pineoblastoma between 1975 and 2019. The dataset divided into training and testing sets (70:30 split) for training and evaluating deep neural networks (DNN) models, while 5-fold cross-validation was employed. Additionlly, multivariable CPH models were established for comparison. The primary endpoint was 3-year overall survival (OS) and disease-specific survival (DSS). The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curve.
Results
A total of 145 patients were included in the study. The AUC value for the DNN models was 0.92 for OS and 0.91 for DSS. In comparison, the AUC value for the CPH models was 0.641 for OS and 0.685 for DSS. Meanwhile, the DNN models demonstrated good calibration: OS model (slope = 0.94, intercept = 0.07) and DSS model (slope = 0.81, intercept = 0.20).
Conclusions
The DNN models that we constructed exhibited excellent predictive capabilities in forecasting the 3-year survival of pineoblastoma patients, outperforming the CPH models. Deep learning is expected to aid clinicians predict the prognosis effectively and accurately for patients with rare tumors.
Mapping the Supportive Care Needs of Adult Survivors of Childhood Cancer at a Comprehensive Cancer Center in the Middle East
Authors: Al-Rawashdeh Nedal , Damsees Rana , Abaza Haneen , Ammar Khawlah , Alananzeh Ibrahim , Ghosh Amal Abu , Al-Awady Shireen , Serhan Hashem Abu , Al-Ja’fari Khaled , Awadallah Omar , Al-Ja’fari Zaid , Serhan Leen Abu , Irshaidat Sarah , Obeidat Emad , Al-Omari Amal 
Published: September 2023

Abstract
Assessing unmet needs is crucial to achieving quality care and patient satisfaction. Between September and December 2021, we assessed unmet supportive care needs in a consecutive sample of adult survivors of childhood cancer at KHCC. Two hundred and ninety-seven adult survivors of childhood cancer completed the study questionnaire. Overall needs score across all domains was (24.80$\pm$19.65), with the financial domain scoring the highest (30.39$\pm$31.95) and sexuality scoring the lowest (7.67$\pm$19.67). Using a multivariate linear regression model, female gender was independently associated with significantly high scores in all need domains (p$<$0.001), except for sexuality. Monthly income, comorbidities, socioeconomic challenges, time since diagnosis, and age at diagnosis have emerged as predictors of needs in many domains. Mean quality of life (QoL) was significantly and inversely associated with the mean score in multiple domains: psychological (p$<$0.001), sexuality (p=0.038), financial (p$<$0.001), and overall needs (p=0.004). Following a thematic analysis of qualitative data, educational difficulties and work-related challenges were identified as other unmet needs. Cancer experiences during childhood significantly influence supportive care needs in adulthood. There is a need for more tailored studies assessing different populations of cancer survivors and avoiding the “one size fits all” model when it comes to survivorship care.
Environmental Life Cycle Analysis of Manufacturing Options for Humanitarian Supplies: Drinking water containers
Authors: Handler Robert M. , Roy Riya , Pearce Joshua Michael 
Published: September 2023

Abstract

PurposeThe purpose of this life cycle assessment (LCA) study was to determine the life cycle impacts for production and distribution of a humanitarian supply item under various supply chain paradigms in order to illustrate the potential environmental benefits of organizing production and supply operations for these items in novel ways. To do this a case study is used on a family-size water storage and dispensing bucket, such as the 14L-capacity polyethylene (PE) bucket commonly produced by Oxfam International.MethodsThe LCA is cradle-to-gate including production and transportation of PE plastic feedstock, fabrication of the water bucket, and transportation of the bucket to a common distribution site representative of a humanitarian aid location. Three different humanitarian aid locations are used to illustrate the range of potential impacts for each processing and supply system: Nepal, South Sudan, and Peru. Six processing and supply scenarios were investigated: 1) centralized Oxfam traditional system, 2) centralized commercial Chinese supply and distribution, 3) quasi-centralized Field Ready supply and distribution, 4) distributed supply and distribution system with 3-D printing, 5) distributed supply and distribution system with 3-D printing and local waste feedstock, and 6) distributed supply and distribution system with extrusion molding and local waste feedstock.Results and discussionThe results found the major contribution to total GHG emissions are electricity usage for manufacturing and shipping feedstock and final product. Among Systems 1–3, System-1 and System 2 are environmentally poor as the electricity emissions in Pakistan and China are high. System 3 was an improvement as the products are manufactured locally. Decentralized supply and distribution system with 3-D printing (System 4) is less compatible with regions of high grid emissions. In System 5, the same equipment has been used, but with local waste feedstock, which shows an improvement of 67.7% for Nepal and 65.5% for Peru because of the reduced shipping emissions, even if the manufacturing emission is the highest among all of the systems. System 6 is feasible for all three locations.ConclusionsIt is concluded that manufacturing should be prioritized on grids where the electricity emission is lower using local waste feedstock as it is the most efficient approach; however, a further study should be done on operating the FPF/FGF 3-D printer or extrusion molding systems powered with distributed photovoltaic systems in order to complement this process and produce the most environmentally responsible production.
Artificial Intelligence/Machine Learning Screening for COVID-19 using a US-Patent- Pending Technology known as iDetect COVID-19 Testing Application
Authors: Peterson Telyn , Hohlbein Joshua , Chong Paul , Lewis Fred , Enzenauer Robert W. 
Published: September 2023

Abstract
The COVID-19 pandemic necessitated the development of accurate diagnostics in order to control and minimize viral propagation; however, accurate and remote means of COVID-19 diagnosis have yet to be developed. The objective of this research was to explore the potential of external eye imaging and machine learning (ML) algorithms for early COVID-19 diagnosis via a patent-pending protocol known as iDetect. iDetect employs deep learning techniques to analyze external eye images captured using mobile eye imaging devices. Images were collected through rigorous data collection procedures, including polymerase chain reaction (PCR) testing and image acquisition from participants in multiple countries. Local interpretable model-agnostic explanations (LIME) analysis was utilized for ML model interpretability. The developed convolutional neural network (CNN) and support vector machine (SVM) models demonstrated high diagnostic performance, with the CNN models achieving an area-under-receiver operating characteristic (AUROC) of 92% and exhibiting sensitivity and specificity values exceeding 90% in most eye positions. The SVM models also showed promising performance, with an AUROC of 89%. The findings of this study present a practical and scalable solution for accurate diagnosis of COVID-19, contributing to ongoing efforts to explore innovative diagnostic methods and enhance early detection and management of infectious diseases, such as COVID-19.
Gut microbiota and central nervous system: a bidirectional two-sample Mendelian randomized analysis
Authors: An Jia , Ye Mingtang , Yu Di , Wang Qingfeng , Wang Qiang , Wu Kede , Yang Zhaocong , Mo Xuming 
Published: September 2023

Abstract
Background
Previous studies have shown that alterations in the gut microbiota are associated with the progression of central nervous system(CNS)disorders. Whether this connection reflects a causal relationship still unclear. We aimed to reveal a causal relationship between the gut microbiota and CNS diseases such as anoxic brain injury (ABI) and bacterial meningitis (BM).
Methods
A two-sample bi-directional Mendelian randomization (MR) analysis was performed by using genetic variants from genome-wide association studies as instruments variables for gut microbiota, ABI and BM. This study used inverse variance weighted, weighted median, MR-Egger and weighted mode methods to evaluate the causal relationship among gut microbiota, ABI and BM. Sensitivity analyses including horizontal pleiotropy analysis, Cochran's Q test, and leave-one-out method were subsequently performed to assess the reliability of the results.
Results
We found that the increased abundance of Lachnospiraceae family and Butyricoccus genus was positively associated with the risk of ABI. The increased abundance of Lactococcus, Ruminococcus gauvreauii and Desulfovibrionales genera were positively associated with the risk of BM, while Eubacterium ventriosum genus, Erysipelatoclostridium genus and NB1n order were negatively associated with the risk of BM. On the other hand, CNS disorders altered the composition of the gut microbiota.
Conclusion
MR analysis have shown a bidirectional causal relationship between the abundance of specific bacteria and ABI and BM, providing evidence for gut microecological therapies for ABI and BM.
Dietary fermented soybean meal affects rainbow trout (Oncorhynchus mykiss) growth performance, digestive enzymes, and liver function
Authors: Sadeghi Khaled , Kowsar Rasoul 
Published: September 2023

Abstract
A 56-day feeding trial was used to assess how the rainbow trout (Oncorhynchus mykiss) responded to diets containing various amounts of fermented soybean meal (FSBM). Rainbow trout was fed four isonitrogenous and isocaloric diets in which FSBM was used at 0 (FSBM0), 10 (FSBM10), 15 (FSBM15), or 20 (FSBM20) percent of the fish diet. With rising FSBM levels, there was a linear decrease in weight gain, as well as total cholesterol and triglyceride levels in the blood. In comparison to other treatments, the FSBM20 group had the least amount of weight gain, digestive enzyme activity (total protease, trypsin, chymotrypsin, amylase, and pepsin), and immune activity (serum lysozyme activity, serum bactericidal activity, serum complement activity, and anti-protease activity). When compared to the FSBM0 and FSBM10 groups, the FSBM15 and FSBM20 groups significantly reduced the body's lipid and protein compositions, respectively. In comparison to the control group, the addition of FSBM linearly decreased the liver enzymes aspartate transaminase and alanine transaminase. The PCA and sub-network enrichment analysis revealed a positive correlation between FSBM and feed conversion ratio. The current findings implied that use of FSBM in Rainbow trout’s diets may negatively affect the growth performance or digestive enzymes and should be used with caution.
Gastrointestinal Stromal Tumor (GIST) Presenting as Acute Abdomen: Images in surgery
Authors: TOUATI Med Dheker , BELHADJ Anis , IDANI Maroua , OTHMANE Mohamed Raouf BEN , SAIDANI Ahmed , CHEBBI Faouzi 
Published: September 2023

Abstract
Gastrointestinal stromal tumors originating from the small bowel are uncommon. we presente the case of a 45-year-old man with no prior medical or surgical history presented to our hospital's emergency department with abdominal pain, vomiting, inability to pass gas, and no bowel movements. Physical examination revealed abdominal distention and tenderness. His white blood cell count was 10.9x10^3/µl, and hemoglobin levels were 13.3 g/dl. A thoraco-abdomino-pelvic CT scan showed air-fluid levels in the ileo-jejunal loops, a soft tissue density mass measuring approximately 77x35 mm, and liver metastasis (Fig. A, B). The patient underwent surgery due to acute abdomen from mechanical ileus. Proximal small bowel dilatation and a 9x4 cm soft-tissue mass protruding from the serosa (Fig. C) were found without luminal obstruction, approximately 70 cm proximal to the ileocecal valve. Additionally, a flange (Fig. D) between the tumor and sigmoid caused ileum loop obstruction. Tumor resection with a linear surgical stapler was performed, and pathology confirmed a gastrointestinal stromal tumor. The patient was discharged on the fourth postoperative day without complications, and an Imatinib treatment plan was initiated.
Implicit Sensing Self-Supervised Learning Based on Graph Multi-pretext tasks for Traffic Flow Prediction
Authors: Sattarzadeh Ali Reza , Pathirana Pubudu N. , Palaniswami Marimuthu 
Published: September 2023

Abstract
In recent years, spatial-temporal graph neural networks (GNNs) have significantly improved traffic prediction by modeling intricate spatiotemporal dependencies in irregular traffic networks. However, these approaches may underutilize the intrinsic properties of traffic data and suffer from overfitting due to their local nature. This paper introduces the Implicit Sensing Self-Supervised learning model (ISSS) to address these issues. ISSS leverages a multi-pretext task framework for traffic flow prediction, integrating multiple self-supervised tasks, including contrastive learning and spatial jigsaw puzzles, to learn both specific and general representations. ISSS emphasizes capturing the intrinsic spatial relationships among sensor locations, particularly through spatial and temporal jigsaw puzzles. These tasks explore the correlation between traffic patterns at sensor locations and critical temporal periods, recognizing the significance of temporal information in traffic data. By transforming data into an alternative feature space, ISSS facilitates the adoption of contrastive learning tasks, enhancing regularization, and promoting a deeper understanding of learned traffic features, resulting in more specific representations. Comparative experiments on six datasets demonstrate ISSS's effectiveness in learning general and discriminative features in both supervised and unsupervised modes. ISSS outperforms existing models, showcasing its potential for improving traffic flow prediction while addressing challenges associated with local operations and overfitting.