bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bilateral pseudo-disc changes in a minipig model for unilateral anterior disc displacement of the temporomandibular joint
Authors: Liu Wei , Bai Xiaofeng , Yuan Wenlin , Wang Cunyi , Hu Yijie , Yu Jianghang , Zhang Yi , Shi Jiejun 
Published: December 2024

Abstract

Objective
Current animal models of anterior disc displacement (ADD) of the temporomandibular joint (TMJ) have primarily been developed in small animals, using sutures or elastic bands to pull the disc anteriorly. These models often exhibit severe osteoarthritis (OA) and rapid progression to late-stage ADD, which do not accurately reflect the natural course of ADD in humans. This study aimed to establish a unilateral ADD-induced surgical protocol in a minipig model and evaluate the early changes of ADD.
Methods
Six Bama minipigs (12 joints) were divided into 3 groups: control group (CG), ADD experimental group (ADD-EG), and contralateral experimental group (CL-EG). Nickel-titanium coil springs were used to induce mild, sustained traction to anteriorly displace the TMJ discs. After a 5-week observation period, in-vivo magnetic resonance imaging (MRI) scans confirmed the anterior position of the TMJ discs.
Results
Micro-computed tomography showed minimal condylar bone changes, while histological analysis revealed mild OA-like condylar cartilage changes. MRI identified hypointense lesions posterior to the TMJ discs, indicative of pseudo-disc changes in the posterior attachments (PAs). Histological staining further confirmed chondroid metaplasia in the PAs surrounded by transitional zones in both ADD-EG and CL-EG.
Conclusion
This study successfully established a unilateral ADD-induced minipig model and identified pseudo-disc changes in the PAs under abnormal mechanical stress, which may offer new perspectives for ADD therapies. To our knowledge, this is the first study to observe bilateral pseudo-disc changes in a unilateral ADD-induced model.
A Single-Cell Atlas of Coral Bleaching
Authors: Bonacolta Anthony , Snyder Grace , Karp Richard , Yeager Emily , Wen Alexandra , Dennison Caroline , Nonell Jordi , Traylor-Knowles Nikki , Baker Andrew , Campo Javier del 
Published: December 2024

Abstract

The symbiosis between corals and dinoflagellate algae is disrupted by heat stress, leading to bleaching. Recurring bleaching events, driven by the climate crisis, are causing massive coral mortality and threatening reefs worldwide. Despite its planetary scale impact, bleaching occurs at the cellular level. While much is known about the physiological and genomic responses of corals to bleaching, our understanding of it at the cellular level remains limited. Using single-cell transcriptomics, we show that individual coral and symbiont cells respond differently to thermal stress. In heat stress experiments with the coral Orbicella faveolata, hosting two co-dominant algal symbionts, we found that calicoblastic cells and cnidocytes were particularly susceptible, with nitrogen metabolism disrupted across all cell types. We also provide the first evidence of differing transcriptomic responses between symbionts with varying heat tolerances within the same coral host. Furthermore, we discovered that symbionts suppress the expression of heat stress related genes of their host cells. These findings reveal that coral cellular responses to bleaching are more complex and varied than previously thought. This study marks a starting point toward understanding the cellular dynamics of coral holobionts, shedding light on the mechanisms behind symbiosis breakdown, coral mortality, and ultimately, reef decline.
A Novel Hybrid Approach for Remaining Useful Life and Short Term Capacity Prediction of Lithium- ion Batteries
Authors: K Manimala , M Kalpana A 
Published: December 2024

Abstract

Accurately determining the remaining usable life of lithium-ion batteries is essential for effective battery management. This paper presents a novel hybrid method for predicting both the Remaining Useful Life (RUL) and short-term capacity of batteries, known as ZOA-MSVM, which integrates the Zebra Optimization Algorithm (ZOA) with Multi-kernel Support Vector Machine (MSVM). The objective of the paper is to provide precise forecasts of future capacities and RUL for lithium-ion batteries (LIBs) while effectively managing uncertainty. The ZOA-MSVM model is trained using data from LIBs, leveraging the Zebra Optimization Algorithm to identify optimal kernel parameters, penalty factors, and weight coefficients for the MSVM model. This trained model predicts battery capacity and RUL by considering key factors such as capacity, current, and voltage during discharge operations. When compared to existing techniques, including artificial neural networks (ANN), ant lion optimizer-optimized ANN, and adaptive network-based fuzzy inference systems, the ZOA-MSVM method demonstrates improved performance with reduced errors during both charging and discharging. Notably, the uncertainty intervals produced by the ZOA-MSVM technique often encompass the actual remaining life of the battery, and its charging error is lower than that of other current methods. Utilizing the NASA battery dataset, an observed data sequence is generated for regression prediction. Results indicate that the enhanced algorithm significantly improves prediction accuracy and generalization ability, while also reducing training time and computational complexity.
A 4×256 Gbps Silicon Transmitter with On-Chip Adaptive Dispersion Compensation
Authors: Li Yu , Ran Shihuan , Guo Yu , Liu Yuanbin , Miao Ting , Wu Yangbo , Qin Yang , Guo Yuyao , Lu Liangjun , Zhu Yixiao , Zhuge Qunbi , Chen Jianping , Zhou Linjie 
Published: December 2024

Abstract

The exponential growth of data traffic propelled by cloud computing, artificial intelligence, and the Internet of Things demands innovative advancements in optical communication technology. Wavelength division multiplexing (WDM) has been instrumental in enhancing transmission rates within high-speed optical modules. However, as single-lane data rates surpass 200 Gbps in O-band transmission, chromatic dispersion in optical fibers emerges as a critical barrier, especially at wavelengths distant from the zero-dispersion point, such as 1271 nm. Here, we present a novel 4-channel silicon photonic transmitter capable of 1 Tbps aggregate data rate, featuring integrated on-chip adaptive dispersion compensation. This transmitter utilizes Mach-Zehnder modulators (MZMs) with adjustable input intensity splitting ratios, enabling precise control over the chirp magnitude and sign of the transmitted signal. By dynamically tuning the input splitting ratio between the MZM branches, we generate chirped signals that effectively counteract fiber dispersion at specific WDM wavelengths. Experimental demonstrations show successful transmission of 4 × 256 Gbps (4 × 200 Gbps) four-level pulse amplitude modulation (PAM-4) signals over 5 km (10 km) of standard single-mode fiber at the high-dispersion wavelength of 1271 nm. The bit error ratios achieved are below the 7% hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10-3, accomplished without resorting to power-intensive pre-emphasis techniques. To the best of our knowledge, this work represents the first demonstration of a silicon-based 1 Tbps transmitter with integrated on-chip adaptive dispersion compensation. Our results highlight a significant leap towards scalable, energy-efficient, and high-capacity optical interconnects, underscoring the potential of this technology in future local area network WDM (LAN-WDM) applications within data centers and high-performance computing systems.
ALW-YOLOV8n:A Lightweight underwater detector Enhanced by Attention mechanism, ADown Block and Wise-WIoU on YOLOv8n
Authors: Cai Shaobin , Zhou Xin , Wei Liansuo , Mo Yuchang 
Published: December 2024

Abstract

To date, general object detection methods have made significant progress in recognizing terrestrial scenes; however, identifying underwater organisms in underwater environments presents numerous challenges. The quality of underwater images is often affected by light attenuation and scattering, leading to blurring and loss of detail in the images. To address these challenges and further improve the accuracy of underwater object detection, this paper proposes an underwater object detection model based on an improved YOLOv8n, called ALW-YOLOv8n. Firstly, the ADown module is used to replace certain convolutional modules in the network, effectively reducing feature loss during the down-sampling process while also lowering computational costs. Secondly, in the backbone network, the LSKA module is integrated into the SPPF module, further enhancing the model's feature fusion capability. Finally, to address the limitations of the loss function, the CIoU loss function is replaced with the Wise-WIoU loss function to improve detection accuracy.The experimental results show that ALW-YOLOv8n performs exceptionally well on the URPC dataset, achieving an mAP@0.5 of 82.1%, which is 2.0% higher than the original YOLOv8n model, while reducing the number of parameters by 4.81%. Additionally, on the S-UODAC2020 dataset and the Aquarium dataset, ALW-YOLOv8n achieved 68.8% and 71.7% mAP@0.5, respectively. Finally, extensive experiments were conducted, and the results demonstrate that the model has broad applicability and generalization capabilities across different underwater datasets.
Efficient Photothermal Annealing of Carbon Quantum Dots
Authors: Lee Jae-Won , Yang Imjeong , Hong K. S. , Kim M. S. , Jung H. J. 
Published: December 2024

Abstract

To synthesize highly crystalline carbon quantum dots (CQDs), we established a two-step process that integrates hydrothermal reactions and photothermal annealing with a xenon flash lamp. A xenon flash lamp enabled rapid and uniform heating, effectively enhancing crystallinity in a way that contrasts with conventional furnace annealing, which requires extended processing times. Transmission electron microscopy analysis revealed that the high-temperature environment with a xenon flash lamp facilitated the conversion of the amorphous carbon structure into a more ordered crystalline form of CQDs. A comparison of Raman spectra and photoluminescence (PL) of amorphous CQDs and crystalline CQDs revealed significant differences between the two states. PL intensity of the crystalline CQDs was approximately ten times higher than that of amorphous CQDs, and this notable difference highlights an advantage of crystallizing CQDs: their optical performance is significantly enhanced.
Refining linear trend estimates from one dimensional time series data with autoregressive covariance modelling - An application to GRACE total water storage time series data
Authors: Jendges Lukas , Brockmann Jan Martin 
Published: December 2024

Abstract

Deriving trend functions from observed time series is one of the main tasks in the field of signal processing. Separating the observation into a deterministic function, a stochastic residual signal and a stochastic noise component using dedicated model representations enables to further study these individual components, e.g. screening for climate signals.
Whereas the deterministic part is modelled as a linear combination of basis functions, the use of autoregressive processes to model the noise and signal is proposed. Within an iterative estimation scheme, the uncertainty information of the observed variables is properly modelled and carefully propagated to the resulting parameters. This enables the use of statistical testing and Least-Squares Collocation in further investigations of the separated signal components.
In this study, the proposed iterative procedure is applied to relatively short total water storage time series derived from measurements of the satellite mission GRACE. The trend in total water storage is for instance relevant for climate studies, identifying regions getting drier or wetter. Accounting for the aforementioned covariance information based on autoregressive processes allows to use Hypothesis tests to identify regions with significant trends. On the contrary, the smoothed stochastic signal components are required to identify extreme/anomalous events like floods and droughts in the observed time series.
Additionally, to improve the estimation of the stochastic signal, data from numerical models are used to estimate the process characteristics.
A low-protein calorie-restricted diet mitigates kidney injury in diabetic mice by modulating the gut-kidney axis
Authors: Zhang Ruixiang , Wei Xiao , Xu Yijiao , Han Chunrong , Cai Xiangzeng , Wu Yinling , Geng Yan , Liu Chao 
Published: December 2024

Abstract

Background: Dietary interventions are a promising strategy for restoring microbial balance in chronic kidney disease. Research indicates that a low-protein calorie-restricted (LPCR) diet can reduce renal injury in diabetic rodents. However, it is unclear whether the beneficial effects of LPCR diet in mice with diabetic kidney disease (DKD) are mediated through the modulation of the gut microbiota.
Methods: A mouse model of diabetes was established using high-fat diet combined with streptozotocin injection. Diabetic mice were randomly divided into four groups: normal protein (NP, 20% protein), caloric restriction (CR, 30% restriction), low protein (LP, 13% protein), and LPCR (13% protein, 30% restriction). After five weeks of intervention, blood and urine samples were collected for relevant analyses, faecal samples for gut microbiota analysis, and kidney tissues for histological and immunohistochemical assays，as well as Western blot analysis.
Results: LPCR diet significantly improved fasting blood glucose levels and lipid profiles (p < 0.01) and mitigated kidney damage in diabetic mice. Additionally, LPCR diet ameliorated gut microbiota dysbiosis, significantly suppressing the increase in Firmicutes/Bacteroidetes ratio (p < 0.05) and decreasing serum trimethylamine oxide(TMAO) levels (p < 0.01). Compared to the NP group, the LPCR group exhibited significant reductions in serum TNF-α levels and the expression of ASC, NLRP3, and IL-1β in kidney tissue (p < 0.01).
Conclusion: LPCR diet exerts renoprotective effects in mice with DKD, possibly by modulating the gut-kidney axis to reduce circulating TMAO levels, thereby inhibiting NLRP3 inflammasome activation in kidney tissue.
Gender-Specific Determinants of Severe Obesity Among US Adults: Evidence from the NHANES Data
Authors: Budhathoki Nirajan , Inungu Joseph N. 
Published: December 2024

Abstract

Severe obesity results from a complex interplay of factors. Reports have revealed that women in the United States are disproportionately affected by severe obesity. This study examined the gender-specific risk factors contributing to severe obesity in men and women, emphasizing demographic, socioeconomic, personal and dietary habits, and underlying comorbidities. Data were obtained from the 2017–2020 cycle of National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression models were utilized to study gender-specific risk factors for severe obesity. The weighted prevalence of severe obesity among females aged 20 years and above was 11.9%, compared to 6.2% among males. Being a non-Hispanic Asian, having underlying high blood pressure, and arthritis were significantly associated with severe obesity for females. The 31–40 years age group, being a non-Hispanic Asian, and having underlying high blood pressure were significantly associated with severe obesity for males. Self-reported sleeping trouble was a risk factor when data for both sexes were combined. The study establishes several demographic characteristics and comorbidities as risk factors for severe obesity among US adults. Although sex itself is not a modifiable risk factor, understanding unique risk factors is important for designing gender-specific interventions to reduce the severe obesity burden.
Predicting Next-Day Rainfall Using Machine Learning Techniques
Authors: Asim Mohammed , Hassan Owais Ul , Naved Mohammed 
Published: December 2024

Abstract

Predicting rainfall using physical models is inherently complex due to involvement of a large number of variables. Accurate and timely predictions of rainfall have the potential to mitigate human and financial losses. Machine learning techniques capture non-linear relationships between variables more effectively than traditional statistical methods. The major objective of the present research is to develop machine learning (ML) models based on logistic regression, decision trees, random forests, and artificial neural networks (ANNs) for predicting next-day rainfall using a comprehensive set of environmental variables. The effectiveness of these ML models was tested using an extensive dataset that comprises of 145460 instances containing 21 quantitative variables from 49 weather stations in Australia. Evaluation of model efficacy was conducted employing an array of performance metrics based on accuracy, precision, recall, and F1 score. The findings indicate that the random forest model demonstrated superior performance compared to other techniques, with the ANN model exhibiting performance that was closely comparable. Random forest achieved the highest accuracy of 85.55% among the models considered and demonstrated strong precision (75.28%) along with a well-balanced F1-score of 56.11%. It can be concluded that the random forest model is an excellent choice for applications where both accuracy and robustness are essential.
Optimization of Growth Conditions for High-Ge-Content Si 1-x Ge x Epitaxial Layers Using Ultra-high-vacuum CVD for High-Performance Semiconductor Applications
Authors: Kim Ji-Hoon , Park Jea-Gun 
Published: December 2024

Abstract

As semiconductor devices scale down, integrating high Ge content Si1-xGex epitaxial layers has become crucial for enhancing device performance in Dynamic Random-Access Memory (DRAM) and logic structures like Fin Field-Effect Transistors (FETs) and Gate-All-Around (GAA) FETs. This study investigates the growth of defect-free Si1-xGex layers using ultra-high vacuum chemical vapor deposition (UHV-CVD) with a focus on optimizing growth parameters, including temperature, gas ratios, and boron doping levels, to achieve high Ge concentrations and smooth surfaces. Growth experiments were conducted across temperatures from 450 °C to 700 °C, with optimal conditions observed at 530°C and a Si2H6 gas ratio of 1:4.2, producing a defect-free Si1-xGex layer with 46 at% Ge. Temperature effects revealed that managing hydrogen desorption rates were critical for enhancing Ge incorporation while minimizing defects. Strain-induced roughness was mitigated through precise control of Si2H6 ratios, essential at high Ge levels.
Boron incorporation was adjusted using diborane (B2H6) flow rates, where an optimal rate of 50 sccm resulted in a boron concentration of 2.7 x 1020 atoms/cm³, achieving a balance between dopant levels and surface smoothness. These findings highlight the importance of managing growth parameters to ensure both material stability and electrical properties, supporting the advancement of high-performance semiconductor devices with superior scalability and reliability.
Cortico-thalamic communication for action coordination in a skilled motor sequence
Authors: Huang Z. Josh , Li Yi , An Xu , Mulcahey Patrick J. , Qian Yongjun , Xu X. Hermione , Zhao Shengli , Mohan Hemanth , M.Suryanarayana Shreyas , Bachschmid-Romano Ludovica , Brunel Nicolas , Whishaw Ian 
Published: December 2024

Abstract

The coordination of forelimb and orofacial movements to compose an ethological reach-to-consume behavior likely involves neural communication across brain regions. Leveraging wide-field imaging and photo-inhibition to survey across the cortex, we identified a cortical network and a high-order motor area (MOs-c), which coordinate action progression in a mouse reach-and-withdraw-to-drink (RWD) behavior. Electrophysiology and photo-inhibition across multiple projection neuron types within the MOs-c revealed differential contributions of pyramidal tract and corticothalamic (CTMOs) output channels to action progression and hand-mouth coordination. Notably, CTMOs display sustained firing throughout RWD sequence and selectively enhance RWD-relevant activity in postsynaptic thalamus neurons, which also contribute to action coordination. CTMOs receive converging monosynaptic inputs from forelimb and orofacial sensorimotor areas and are reciprocally connected to thalamic neurons, which project back to the cortical network. Therefore, motor cortex corticothalamic channel may selectively amplify the thalamic integration of cortical and subcortical sensorimotor streams to coordinate a skilled motor sequence.
Prevalence of, and factors associated with hemodynamic instability among women presenting with incomplete abortion at a tertiary hospital in southwestern Uganda
Authors: Kakama Caxton , Agaba David Collins , Byamukama Onesmus , Kajabwangu Rogers , Akol Mathiang Agany , Muhumuza Amos , Businge Julius , Turanzomwe Stuart , Ainomugisha Brenda , Kalyebara Kato Paul , Kayondo Musa , Ngonzi Joseph , Muganwa Wasswa George , Lugobe Henry Mark 
Published: December 2024

Abstract

Background
Sub-Saharan Africa has a high abortion case-fatality rate and most of these maternal deaths are due to hemodynamic instability which results from hemorrhage, especially in cases of incomplete abortion. Timely identification and management of hemodynamic instability is an important strategy for addressing the morbidity and mortality associated with incomplete abortion. This study, therefore, aimed at determining the prevalence and factors associated with hemodynamic instability among women presenting with incomplete abortion at Mbarara Regional Referral Hospital (MRRH).
Methods
We conducted a cross-sectional study at the gynaecology ward of MRRH from January 2024 to April 2024. We consecutively enrolled women with incomplete abortion and collected data on socio-demographic, abortion- and medical-related factors, and measured their blood pressure and heart rate at admission. Obstetric shock index (OSI), the ratio of the heart rate to systolic blood pressure was used as a measure of haemodynamic stability. A participant whose OSI was ≥ 0.9 was considered haemodynamically unstable. We performed a modified Poisson regression analysis to determine the factors associated with hemodynamic instability.
Results
A total of 137 women with incomplete abortion were enrolled in this study with a mean age of 26.3 (± 5.93) years. The majority of the participants had first-trimester abortions (65.7%), were married (70.8%), and were from rural areas (60.0%). The prevalence of hemodynamic instability was 41.6% (95% CI: 33.6–50.1). At multivariable regression analysis, gestation age ≥ 13 weeks (aPR 1.67, 95% CI: 1.12–2.49) and post-abortion infection (aPR 1.75, 95%CI: 1.18–2.60) were significantly associated with hemodynamic instability.
Conclusion
A high proportion of women with incomplete abortion at MRRH present when they are hemodynamically unstable. Women with second trimester abortion and those with post-abortion infection are more likely to present with hemodynamic instability. We recommend strengthening routine assessment and management of hemodynamic instability among women with incomplete abortion.