bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facile preparation of 3D porous graphene-based composite hydrogels doped with different ratios of MXene for MB adsorption
Authors: Wang Yaning , Yan Xiaoxia , Wang Yuxi , Yang Kai , Lin Fei , Shen Jiefen , Sun Xuming , Chen Zhigang , Jiang Wenshuai 
Published: August 2024

Abstract

The 3D porous structure of graphene functional materials possesses characteristics such as high porosity and large specific surface area, enabling dye molecules to easily enter and diffuse within the 3D network, thereby emerging as a promising solution for treating dye wastewater pollution. In this study, graphene@MXene composite hydrogels with a 3D porous structure were prepared using the hydrothermal reduction method with MXene and graphene oxide as raw materials, and their adsorption performance towards MB was investigated. The morphology and structure of the graphene@MXene composite hydrogels were examined through SEM, EDX,FT-IR, and BET analysis techniques. The graphene@MXene composite hydrogels doped with 20 wt% MXene exhibited the best removal efficiency for MB, showcasing excellent adsorption performance following the introduction of MXene. It is noteworthy, however, that as the proportion of MXene increased, there was a noticeable decrease in the adsorption performance of the composite hydrogels towards MB. During the preparation of the composite hydrogel, the introduction of Ca2+ as a cross-linking agent not only reduced the preparation time but also adjusted the specific surface area and pore size distribution of the hydrogel. As a result, there was an approximately 30% increase in the MB adsorption rate. The adsorption kinetics results revealed that the adsorption of MB on the graphene@MXene composite hydrogels, both pre- and post-introduction of calcium ions, conformed to a pseudo-second-order kinetic model. This observation suggests that the adsorption process is influenced by various mechanisms such as external diffusion, surface adsorption, and internal diffusion. Our study further revealed that the microwave irradiation spectra of 3D graphene@MXene composite aerogels changed before and after MB adsorption, exhibiting characteristic features of graphene or MXene. This finding confirms the adsorption of MB, suggesting a potential new method for its detection. This research demonstrates promising applications in both dye wastewater treatment and the detection of MB.
Body Image and Its Relationship with Shyness Among Female Students of the College of Physical Education and Sports Sciences - University of Basra
Authors: Abdul-Hussain Abbas Zaki , Ashour Aya Hashem , Nouri Ali Sabah 
Published: August 2024

Abstract

The study aimed to investigate the nature of the relationship between body image and shyness among a sample of female students from the College of Physical Education and Sports Science. Additionally, it aimed to determine the level of shyness among these students and explore the statistically significant relationship between body image and shyness. The study sample consisted of 60 female students from the College of Physical Education and Sports Science at the University of Basra, To achieve the study's objectives, the researcher employed a descriptive-analytical method. Questionnaires were used as the primary tool for data collection, with two specific instruments: one to measure body image, comprising 29 items, and another to measure shyness, comprising 26 items. These tools were initially applied to a pilot sample of 15 students from the study population. After verifying the validity and reliability of the instruments and ensuring they had good psychometric properties, they were distributed to a random sample of 60 students. Multiple statistical methods were used to analyze the responses of the sample members
Study Results:
1.Body image is not constant but changes according to life events. It varies between illness and health and across different life stages, as well as according to economic and social conditions.
Key Recommendations:
1.Guide students who suffer from negative thoughts about their body image and help them overcome the psychological difficulties they face.
A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study
Authors: Zheng Zijian , Wang He , Wang Zhicheng , Fu Jun , Fan Wenjian , Lin Yuanxiang , Kang Dezhi , Jiang Changzhen , Lin Zhangya , Yan Xiaorong , chen qianxi 
Published: August 2024

Abstract

Objective: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery.
Methods:Patients undergoing endoscopic transsphenoidal surgery (ETS) for pituitary adenoma were included in this retrospective and prospective study. Preoperative MRI, visual acuity, visual field, and postoperative visual recovery data were collected. Logistic regression analysis was used to assess the importance of clinical and MRI features, and a prediction model was developed.
Results: The cohort included 198 patients (150 retrospective, 48 prospective). In the retrospective data, visual recovery was observed in 111 patients (74.0%), while non-recovery was observed in 39 patients (26.0%). In the prospective data, visual recovery was observed in 27 patients (56.25%) and non-recovery in 21 patients (43.75%). Blindness, headache, adenoma area, and adenoma upward growth distance were negatively correlated with visual recovery (p < 0.05), while the pituitary gland area was positively correlated (p = 0.001). Logistic regression selected three clinical features: blindness, headache, and visual impairment course. Two additional imaging features, pituitary gland maximum area, and adenoma maximum area, were incorporated into the prediction model. The area under the curve of the prediction model was 0.944 in the retrospective cohort and 0.857 in the prospective cohort. Accuracy was 88% and 81.25%, respectively.
Conclusion: This study successfully developed a clinical practical model combining clinical and radiological features to preoperatively predict visual recovery for patients with pituitary adenoma. The model has the potential to provide personalized counseling for individual patients.
Opto-electrical decoupled phototransistor for starlight detection
Authors: Zhang Zhiyong , Zhou Shaoyuan , Zhang Xinyue , Wang Ying , Lin dongyi , zou shoubin , Wang Jingwen , Xiao luna , Zhang Dijie , Jiang Jianhua , Zhang Panpan , Zhang Jianbing , Tang Jiang 
Published: August 2024

Abstract

Highly sensitive shortwave infrared (SWIR) detectors are essential for detecting weak radiation (typically below 10− 8 W·Sr− 1·cm− 2·µm− 1) with high-end passive image sensors. However, mainstream SWIR detection technology is based on epitaxial photodiodes, which cannot effectively detect ultraweak infrared radiation due to the lack of inherent gain. Here, we developed a heterojunction-gated field-effect transistor (HGFET) consisting of a colloidal quantum dot (CQD)-based p-i-n heterojunction and a carbon nanotube (CNT) field-effect transistor, which achieves a high inherent gain based on an opto-electric decoupling mechanism for suppressing noise. The stacked heterojunction absorbs infrared radiation and separates electron-hole pairs. Then, the generated photovoltage tunes the drain current of the CNT FET through an Y2O3 gate insulator. As a result, the HGFET significantly detects and amplifies SWIR signals with a high inherent gain while minimally amplifying noise, leading to a recorded specific detectivity above 1014 Jones at 1300 nm and a recorded maximum gain-bandwidth product of 69.2 THz. Direct comparative testing indicated that the HGFET can detect weak infrared radiation at 0.46 nW/cm2 levels; thus, compared to commercial and reported SWIR detectors, this detector is much more sensitive and enables starlight detection or vision. As the fabrication process is very compatible with CMOS readout integrated circuits, the HGFET is a promising SWIR detector for realizing passive night vision imaging sensors with high resolutions that are high-end, highly sensitive, and inexpensive.
Functional ultrasound and brain connectivity reveal central nervous system compromise in Trembler-J mice, model of Charcot-Marie-Tooth disease
Authors: Fialho Maximiliano Anzibar , Barreiro Mariana Martínez , Alberdi Lucia Vázquez , Damián Juan Pablo , Tomaso Maria Vittoria Di , Baranger Jérôme , Tanter Mickael , Calero Miguel , Negreira Carlos , Rubido Nicolás , Kun Alejandra , Brum Javier 
Published: August 2024

Abstract

The Charcot-Marie-Tooth-1E (CMT1E) disease is typically described as a peripheral neuropathy in humans, causing decreased nerve conduction, spastic paralysis, and tremor. The Trembler-J (TrJ) mice serve as a high fidelity model of this disease. Here, we use functional ultrasound (fUS) and functional connectivity (FC) to analyze TrJ mice’s brain activity during sensory stimulation and resting-state experiments against Wild Type (WT) mice – the healthy counterpart. fUS is an imaging technique that measures cerebral blood volume (CBV) temporal changes. We study these changes in the primary somatosensory cortex barrel field (S1BF) of both mice populations during periodic vibrissae stimulation, measuring the number of pixels that correlate to the stimulation (i.e., the size of the activation area), the average correlation of these pixels (i.e., the response strength), and the CBV’s rate of change for each stimulation (i.e., the hemodynamic response). Then, we construct a FC matrix for each genotype and experiment by correlating the CBV signals from the 8 cortical regions defined by the Paxinos & Franklin atlas. Our results show that TrJ mice have significantly diminished neurovascular responses and altered brain connectivity with respect to WT mice, pointing to central nervous system effects that could shift our understanding of the CMT1E disease.
ICAT-net:Integration of Coordinate Attention and Transformer network for seismic signal detection and phase arrival picking
Authors: Li XueNing , Chen Fangjiong , Lai YePing , Tang Peng , Liang XiaoJun 
Published: August 2024

Abstract

Seismic signal detection is a crucial technology for enhancing the efficiency of earthquake early warning systems.However, existing deep learning-based seismic signal detection models often face limitations in resource-constrained seismic monitoring engineering environments due to their high computational resource demands. To address this challenge, we introduce an innovative seismic signal detection network, which integrates the advantages of Coordinate Attention modules and Transformer attention mechanisms (ICAT-net). It aims to reduce computational resource consumption while maintaining or enhancing the multitask recognition performance of seismic waveform detection and phase picking. Specifically, ICAT-net employs a Downsampling module to reduce data dimensions, while meticulously controlling the spatial relationships of features through the Coordinate Attention module. Coupled with the capacity of the Transformer to capture long-range dependencies, a significant enhancement is observed in the accuracy of earthquake event detection and phases picking. By using concatenation operations between encoders and decoders, the model retains rich contextual information and gradually restores the spatial resolution of the signal during the decoding process. The study trained the ICAT-net using the global seismic dataset Stanford Earthquake Dataset (STEAD) and employed multidimensional performance metrics, including precision, recall, F1-score, mean absolute error, floating-point operations, and model parameters, to ensure comprehensive and accurate evaluation. Extensive experiments demonstrate that the ICAT-net can generate more accurate responses in various seismic scenarios, achieving higher detection accuracy with lower computational power consumption, providing a highly valuable tool for earthquake monitoring and disaster risk assessment.
Morphometric description of the feline tibia using three – dimensional computed tomography
Authors: Pantangco Marie J.A.P.V. , Chui Matthew , Johnson Kenneth A. , Basa Rachel M. 
Published: August 2024

Abstract

Background: Previous studies have described bone length and surface area of the feline tibia, however information regarding three-dimensional (3D) and internal morphometry is lacking. The objective of this study was to describe the length, external and internal bone diameter, cortical thickness, cancellous bone volume and mechanical joint angles in 8 domestic adult feline cadaver tibiae using computed tomography (CT). Measurements of the paired tibiae were performed in triplicate and the average was recorded.
Results: Overall tibia length measured 111.61mm (95% CI 107.89 – 115.53mm).
The proximal tibia had a mean cancellous bone volume of 12.45mm3 (95% CI 11.4 – 13.49mm3). The distal tibia had a mean cancellous bone volume of 2.09mm3 (95% CI 1.62 – 2.55mm3). The narrowest internal bone diameter mediolaterally was at 50% tibial length (4.23mm 95% CI 4.05 – 4.42mm) and craniocaudally at 75% tibial length (3.77mm 95% CI 3.57 – 3.97mm). Mean mechanical joint angles and 95% CI were: tibial plateau angle 31.42° (30.09 – 32.75°); mechanical medial proximal tibia angle 95.15° (94.63 – 95.68°); mechanical medial distal tibia angle 94.08° (93.36 – 94.79°); mechanical cranial distal tibia angle 88.69° (87.04 – 90.32°); mechanical caudal proximal tibia angle 58.53° (57.20 – 59.86°) and sagittal plane alignment 30.16° (28.47 – 31.84°).
Conclusions: Use of computed tomography allowed the estimation of internal bone morphometry and joint geometry in a heterogeneous sample of feline tibias. This provided data that would be valuable in planning and developing new techniques for internal fracture fixation.
Cardiovascular performance of neonates with hypoxic-ischemic encephalopathy under therapeutic hypothermia evaluated by conventional and advanced echocardiographic techniques
Authors: Nunes Vanessa A. C. , Assef Jorge E. , Bihan David C. S. Le , Barretto Rodrigo B. M. , Magalhães Maurício , Pedra Simone R. F. F. 
Published: August 2024

Abstract

This study aimed to evaluate hemodynamic and ventricular performance of neonates with hypoxic-ischemic encephalopathy under therapeutic hypothermia using conventional and advanced echocardiographic techniques. This was an observational, prospective study including 22 neonates with hypoxic-ischemic encephalopathy matched with 22 healthy neonates. The echocardiographic studies were performed during hypothermia and after rewarming. Echo parameters included ejection and shortening fractions, right ventricular fractional area change, biventricular Tei index, right ventricular s´ wave velocity, tricuspid annular plane systolic excursion, biventricular stroke volume and cardiac output, left and right ventricular global longitudinal strain, left ventricular circumferential and radial strain, twist and torsion.
Results: Left ventricular ejection and shortening fractions did not change during therapeutic hypothermia (hypothermia:72±7% and 39±6%, rewarming:74±5% and 41±5%, p=0.21 and p=0.25) however they were higher after rewarming compared to the control group (70±5%, p=0.003 and 36±4%, p=0.002 respectively). There was no difference on left ventricular global longitudinal, circumferential and radial strain, twist and torsion during the therapeutic process. Higher pulmonary artery systolic pressure and worse right ventricular global longitudinal strain were observed in the study group (hypothermia:44±24mmHg and 17,9±4,9%, rewarming:53±24mmHg and 19,2±3,8%, control group:29±11mmHg and 20,5±2,2% respectively).
Conclusion: Left ventricular parameters remain stable during the two phases of therapeutic hypothermia what suggests no impact of the induced cooling on left ventricular systolic function. Right ventricular global longitudinal strain was the only echocardiographic tool able to identify some degree of systolic function impairment during therapeutic hypothermia. This could be explained by the pulmonary hypertension identified in the study group during this period.
Akkermansia modulates microglia and astrocytes in a strain specific manner
Authors: Schwerdtfeger Luke A. , Lanser Toby B. , Montini Federico , Moreira Thais , LeServe Danielle S. , Cox Laura M. , Weiner Howard L. 
Published: August 2024

Abstract

Microglia and astrocytes are the primary glial cells in the central nervous system (CNS) and their function is shaped by multiple factors. Regulation of CNS glia by the microbiota have been reported, although the role of specific bacteria has not been identified. We colonized germ-free mice with the type strain Akkermansia muciniphila (AmT) and a novel strain of A. muciniphila (BWH-H3) isolated from a subject with multiple sclerosis and compared to mice colonized with Bacteroides cellulosilyticus (BWH-E5) isolated from a healthy control subject. We then investigated the effect of these bacteria on microglia and astrocyte gene expression by RNA sequencing. We found altered gene expression profiles in brain microglia, with Akkermansia downregulating genes related to antigen presentation and cell migration. Furthermore, we observed strain specific effects, with Akkermansia H3 upregulating histone and protein binding associated genes and downregulating channel and ion transport genes. Astrocyte pathways that were altered by Akkermansia H3 mono-colonization included upregulation of proliferation pathways and downregulation in cytoskeletal associated genes. Furthermore, animals colonized with type strain Akkermansia and strain H3 had effects on the immune system including elevated splenic γδ-T cells and increased IFNg production in CD4 + T cells. We also measured intestinal short chain fatty acids and found that both A. muciniphila strains produced proprionate while B. cellulosilyticus produced acetate, proprionate, and isovalerate. Taken together, our study shows that specific members of the intestinal microbiota influence both microglial and astroyctes which may be mediated by changes in short chain fatty acids and peripheral immune signaling.
The role of stakeholders in the development of ChAdOx1-S Covid-19 vaccine: reflection for future pandemics
Authors: Ayres Ana Beatriz Nascimento , Travassos Xisto Lucas , Savino Wilson , Sampaio Renelson Ribeiro 
Published: August 2024

Abstract

COVID-19, an infectious disease caused by the virus SARS-CoV-2, has become a global pandemic and has been classified as a Public Health Emergency of International Concern. Considering vaccines as an effective action to shorten viral transmission and to reduce mortality, stakeholders have worked together to develop vaccines, which played a vital role in containing this pandemic worldwide. In the development of the adenovirus-based ChAdOx1-S vaccine, it is interesting to note the speed at which it was created, with safety, breaking paradigms, relying on connections and influences at various levels, showing that a vaccine can be produced in a very short period of time if there are specific stakeholder dynamics. Herein, we combined the concepts and methods of Stakeholder Theory and Social Network Analysis within this complex scenario, indeed, the positive results delivered to society reinforce the principles of the Stakeholder Theory. Moreover, Social Network Analysis revealed that the ChAdOx1-S vaccine ecosystem had 30 nodes of interconnection. Overall, the data summarized presently discussed indicate the need for preparedness within the vaccine development ecosystem, important for future research into the management and development process of vaccines designed to cope with future global or regional health emergencies. Key points related to the complexity of a health emergency, considering its risks to public health and society. A coordinated, interdisciplinary, and systemic response between the different actors is emphasized. This study focuses on analyzing the actors involved in the activities related to the development of the ChAdOx1-S vaccine against COVID-19, taking into account the paradigms of time and the pandemic emergency. Considering the network analysis, for this vaccine, new institutional arrangements have emerged, significantly expanding the limits of knowledge, since the initial research was based on more than a decade of knowledge production.
Active Constrained Motion Control for a Robot-Assisted Endoscope Manipulator in Pediatric Minimal Access Surgery
Authors: Li Hongbing 
Published: August 2024

Abstract

Robot-assisted laparoscopic surgery has three main system requirements: safety, simplicity, and intuitiveness. However, accidental movement of the endoscope due to body fatigue and misunderstanding of the verbal orders between the surgeon and assistant will contribute to highly unexpected tool-tissue interactions, particularly in pediatric minimal-access surgery with restricted working space. This study introduces a compact, lightweight endoscope manipulator with a mechanical remote-center-motion function. Using a custom-designed human-machine interface, the surgeon can intuitively control the movement of the endoscope manipulator over their view. In addition, an active constrained motion control algorithm is proposed to generate a forbidden-region constraint for avoiding collisions between the endoscope tip and surrounding organs in a pediatric abdominal cavity with restricted space. Simulations and experiments demonstrate the performance of the proposed compact endoscope manipulator and the active constrained surface tracking control scheme.
Similarity Pattern Enhancement for API Recommended Methods via Graph Representation Learning
Authors: Fang Yingtao , Wang Yong , Gao Cuiyun , Chen Linjun , Zhang Yifan 
Published: August 2024

Abstract

Developers often utilize application programming interfaces (API) to simplify their development tasks. API recommendation methods can automatically recommend the APIs that developers need to improve development quality and efficiency. Recent research combines collaborative filtering with graph neural networks for API recommendation. However, these approaches often face the problem of sub-optimal graph representation due to data sparsity. To address these issues, we propose an API recommendation method that enhances the raw data by fusing usage patterns in similar projects. Typically, similar projects share similar functional and structural features, including code modules with similar usage patterns, which include rich information such as invocation and structural details. Enhancing the raw data with this information can effectively alleviate the sparsity problem of the interaction matrix induced by the construction graph of the raw data. Specifically, our approach consists of matching similar items for each entry in the original dataset to form new classes. These new classes are then merged into code modules with similar usage patterns to augment the original dataset. Subsequently, the graph represents the learning model capturing the higher-order collaborative signals of the API calls in the augmented dataset and integrating the structural information of the items into the graph. To evaluate the effectiveness of our approach, we conducted experiments using data collected from large open source repositories. The experimental results show that the method is effective.