bluuebunny/crossref_metadata_2025 huggingface dataset:
Riesz Energy with a Radial External Field: When is the Equilibrium Support a Sphere?
Authors: Chafaï Djalil , Matzke Ryan W. , Saff Edward B. , Vu Minh Quan H. , Womersley Robert S.
Published: December 2024

We consider Riesz energy problems with radial external fields. We study the question of whether or not the equilibrium measure is the uniform distribution on a sphere. We develop general necessary and general sufficient conditions on the external field that apply to powers of the Euclidean norm as well as certain Lennard – Jones type fields. Additionally, in the former case, we completely characterize the values of the power for which a certain dimension reduction phenomenon occurs: the support of the equilibrium measure becomes a sphere. We also briefly discuss the relationship between these problems and certain constrained optimization problems. Our approach involves the Frostman characterization, the Funk–Hecke formula, and the calculus of hypergeometric functions.
NPGPT: natural product-like compound generation with GPT-based chemical language models
Authors: Sakano Koh , Furui Kairi , Ohue Masahito
Published: December 2024

Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the intricate structures of these compounds present challenges in terms of structure determination and synthesis, particularly compared to the efficiency of high-throughput screening of synthetic compounds. In recent years, deep learning-based methods have been applied to the generation of molecules. In this study, we trained chemical language models on a natural product dataset and generated natural product-like compounds and verified the performance of the generated compounds as a drug candidate library. The results showed that the distribution of the compounds generated was similar to that of natural products. We also evaluated the effectiveness of the generated compounds as drug candidates. Our method can be used to explore the vast chemical space and reduce the time and cost of drug discovery of natural products.
Updated imaging markers in cerebral amyloid angiopathy: What radiologists need to know
Authors: Tanaka Fumine , Maeda Masayuki , Kishi Seiya , Kogue Ryota , Umino Maki , Ishikawa Hidehiro , Ii Yuichiro , Shindo Akihiro , Sakuma Hajime
Published: December 2024

Cerebral amyloid angiopathy (CAA) is an age-related small vessel disease pathologically characterized by the progressive accumulation of amyloid-beta (Aβ) peptide in cerebrovascular walls, affecting both cortical and leptomeningeal vessels. Amyloid deposition results in fragile vessels, which may lead to lobar intracerebral hemorrhage (ICH) and cognitive impairment. To evaluate the probability and severity of CAA, the imaging markers depicted on CT and MRI techniques are crucial, as brain pathological examination is highly invasive. Although the Boston criteria have established diagnostic value and have been updated to version 2.0, due to an aging population, the patients with CAA should also be assessed for their risk of future ICH or cognitive impairment. Furthermore, an increased awareness of CAA is essential when introducing anticoagulants for infarct in elderly patients or anti-amyloid antibodies for Alzheimer’s disease, as these may worsen CAA-related hemorrhagic lesions. However, the radiological literature on CAA has not been comprehensively updated. Here, we review the imaging markers of CAA and clinical significance. We also discuss the clinical and imaging characteristics of CAA-related inflammation, amyloid-related imaging abnormalities, and iatrogenic-CAA.
Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features
Authors: Nakajo Masatoyo , Hirahara Daisuke , Jinguji Megumi , Idichi Tetsuya , Hirahara Mitsuho , Tani Atsushi , Takumi Koji , Kamimura Kiyohisa , Ohtsuka Takao , Yoshiura Takashi
Published: December 2024

Objectives

This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-radiomic features for predicting outcomes in gallbladder cancer patients.

Materials and methods

The study analyzed 52 gallbladder cancer patients who underwent pre-treatment [18F]-FDG-PET/CT scans between January 2011 and December 2021. Twenty-seven patients were assigned to the training cohort between January 2011 and January 2018, and the data randomly split into training (70%) and validation (30%) sets. The independent test cohort consisted of 25 patients between February 2018 and December 2021. Eight clinical features (T stage, N stage, M stage, Union for International Cancer Control [UICC] stage, histology, tumor size, carcinoembryonic antigen level, and carbohydrate antigen 19-9 level) and 49 radiomic features were used to forecast progression-free survival (PFS). Three feature selection methods were applied including the univariate statistical feature selection test method, least absolute shrinkage and selection operator Cox regression method and recursive feature elimination method, and two ML algorithms (Cox proportional hazard and random survival forest [RSF]) were employed. Predictive performance was assessed using the concordance index (C-index).

Results

Two clinical variables (UICC stage, N stage) and three radiomic features (total lesion glycolysis, grey-level size-zone matrix_grey level non-uniformity and grey-level run-length matrix_run-length non-uniformity) were identified by the statistical feature selection method as significant for PFS prediction. The RSF model incorporating these features demonstrated strong predictive performance, with C-indices above 0.80 in both training and testing sets (training 0.81, testing 0.89). This model almost closely matched the actual and predicted progression timelines with a low mean absolute error of 1.435, a median absolute error of 0.082, and a root mean square error of 2.359.

Conclusion

This study highlights the potential of using ML approaches with clinical and pre-treatment [18F]-FDG-PET radiomic data for predicting the prognosis of gallbladder cancer.
SWI brush sign of cerebral parenchymal veins in central nervous system diseases
Authors: kishi Seiya , Maeda Masayuki , Kogue Ryota , Tanaka Fumine , Umino Maki , Toma Naoki , Sakuma Hajime
Published: December 2024

Brush sign (BS) was first reported as prominent hypointensity of deep medullary veins and subependymal veins on T2*-weighted images at 3 T MRI in patients with acute stroke in the territory of the middle cerebral artery. Subsequently, BS in central nervous system (CNS) diseases such as moyamoya disease, cerebral venous thrombosis, and Sturge–Weber syndrome was also described on susceptibility-weighted imaging (SWI), and the clinical implications of BS were discussed. The purpose of this review is to demonstrate BS on SWI in various CNS diseases and its mechanisms in the above-mentioned diseases. We also explain the clinical implications of this finding in each disease.
Prediction of grading of ovarian endometrioid carcinoma using conventional MRI features
Authors: Kawaguchi Masaya , Kato Hiroki , Furui Tatsuro , Isobe Masanori , Noda Yoshifumi , Hyodo Fuminori , Miyazaki Tatsuhiko , Matsuo Masayuki
Published: December 2024

Objective

The purpose of this study was to evaluate MRI findings of ovarian endometrioid carcinoma (OEC) as a predictor of histological grade.

Materials and methods

This study included 60 patients with histopathologically confirmed OEC (20, 30, and 10 with grades 1, 2, and 3, respectively). Clinical and MRI results were retrospectively reviewed. We compared the following parameters between the three grades: age, tumor markers, presence of uterine corpus cancer, bilaterality, configuration, peritoneal dissemination, abnormal ascites, signal intensities of cystic and solid components, tumor size, and apparent diffusion coefficient (ADC) values of solid components.

Results

T1-hyperintense cysts were more common in grade 1 than in grades 2–3 OEC (80% vs. 60%, vs. 40%, p < 0.05). The signal intensity ratio between the cystic components with the largest solid component and muscle (1.49 vs. 1.08 vs. 0.98, p < 0.05) was higher in grade 1 than in grades 2–3 OEC. Necrosis within solid components was less common in grade 1 than in grades 2–3 OEC (31% vs. 68% vs. 88%, p < 0.05), and the ADC values of solid components were higher in grade 1 than in grades 2–3 OEC (1.10 vs. 0.99 vs. 0.79 × 10−3 mm2/sec, p < 0.05). There were no significant differences in other factors.

Conclusion

On T1-weighted images, grade 1 OEC showed a higher signal intensity in the cystic components than grades 2–3 OEC. Necrosis and lower ADC values were more frequently observed in grades 2–3 than in grade 1 OEC.
The Matricial Szegő Mapping from the Perspective of Schur Analysis
Authors: Fritzsche Bernd , Kirstein Bernd , Mädler Conrad
Published: December 2024

We study the interrelations between non-negative Hermitian

$${q\times q}$$

q
×
q

 measures on the unit circle and non-negative Hermitian

$${q\times q}$$

q
×
q

 measures on a symmetric interval

$${[-2r,2r]}$$

[
-
2
r
,
2
r
]

where

$$r \in (0, \infty )$$

r
∈
(
0
,
∞
)

. Since both types of non-negative Hermitian

$${q\times q}$$

q
×
q

 measures are uniquely determined by their sequences of Fourier coefficients and power moments, respectively, we also express these interrelations in terms of moment sequences. Our approach significantly utilizes previous investigations by the authors on the inner structure of these sequences. Specifically, previous work by the authors employed Schur analysis methods to describe the intrinsic structure of

$$\mathbb {T}$$

T

-non-negative definite sequences of complex

$${q\times q}$$

q
×
q

 matrices and

$${[\alpha ,\beta ]}$$

[
α
,
β
]

-non-negative definite sequences of complex

$${q\times q}$$

q
×
q

 matrices. The primary aim of this paper is to uncover the relationships between corresponding parameters of sequences connected by a matrix version of the Szegő mapping, which was introduced by Szegő in the context of orthogonal polynomials. In essence, a complete concordance between both objects is achieved in the symmetric case.
Optimal time recommendation model for home appliances: HSB living lab + dishwasher study
Authors: Malakhatka Elena , Hagejärd Sofie , Mazidi Mohammadreza , Corcoran Katja , Röderer Kathrin , Wallbaum Holger
Published: December 2024

This study investigates the effectiveness of an Optimal Time Recommendation model (OTR) in encouraging citizens to shift the usage of their home appliances, such as dishwasher to off-peak hours. The research was conducted at the HSB Living Lab + in Gothenburg city, involving 74 participants from diverse social groups, including students, one-person households, couples, and families with kids. The study employed a mixed-methods approach, combining surveys, interviews, and data from self-reporting QR-code or iPad-based web-interface. Participants were provided with personalised recommendations generated by the OTR model, which considered factors such as energy demand, grid load, electricity pricing and level of CO2. The recommendations aimed to assist users in identifying the optimal time slots for operating their home appliances during off-peak, motivated by the lower price, lower CO2 emission or both. Results indicated a positive response from participants across all social groups. Most participants reported an increased awareness of their energy consumption patterns and a willingness to adopt delay shifting practices. However, some frictions and obstacles to adopt shifting time of the behaviour were highlighted as well. The findings from this case study contribute to the existing knowledge on flexibility and Demand-Side Management (DSM). These findings can inform home appliances producers to increase the delay start function usability, policymakers to emphasise the eco-design of the white goods, and researchers in developing effective strategies to encourage energy conservation practices on a larger scale.
A Comparison between the Methodology of the Mainstream in (Neuro-)Psychology, Holzkamp’s and Vygotsky’s Approach
Authors: Nigrini Leonard , Amici Federica , Llorente Miquel
Published: December 2024

The present paper treats the issue of methodological assumptions in mainstream neuropsychology and, as counter-concepts, in Vygotsky’s approach and Holzkamp’s critical psychology. The analysis identifies four main assumptions concerning the methodology of mainstream neuropsychology, which are contrasted with the positions of other approaches. The methodologies of the mainstream neuropsychology vs. Holzkamp’s and Vygotsky’s approach assume: (1) mechanistic vs. dialectical materialism; (2) formal vs. dialectical logic; (3) decomposition into elements vs. units; (4) reductionism of psychic processes to the brain vs. activity as a unity of environmental and organism-pole. Despite the vast coincidence in their main assumptions, we also discuss nuances of difference between Holzkamp’s and Vygotsky’s approaches. The former, possibly due to its reference to cultural-historical activity theory (CHAT) and its theoretical neglection of the organism-pole of psychic functions, falls short of structural considerations in its accounts on phylogenetic emergence. On the other hand, Vygotsky’s neuropsychology does not fully explore the phylogenetic emergence of basic units of functional psychic organisation. This might be due to certain implications of Vygotsky’s initial accounts, which seem to highlight cultural development to the detriment of phylogenetic one.
Grounding Causal Closure or Something Near Enough
Authors: Saad Bradford
Published: December 2024

A causal argument for physicalism is widely held to pose a problem for dualism. This view has an unobvious presupposition, namely that the causal closure of the physical has a special sort of ground. The requisite sort of ground must distinguish the causal argument for physicalism from many defective causal arguments. On behalf of physicalists, I develop an account of the ground for the causal closure of the physical, thereby putting the causal argument for physicalism back in the business of causally problematizing dualism. One consequence of my account is that physicalists can pose a causal problem for dualism using a much weaker closure premise than is generally assumed.
Exploring sensitivity and initiative in a mother-baby dyad with Video Intervention Therapy (VIT): a case study in a high-risk population
Authors: Pérez-Burriel Marc , Herreman Christian , Montuori Eliana , Sánchez-Núñez Maria Julia , Downing George
Published: December 2024

This preliminary report is a single case study aiming to evaluate the feasibility of Video Intervention Therapy (VIT) in difficult scenarios involving high-risk mother-infant dyads in a housing program for homeless families. The study conducted in Clermont-Ferrand, France, used the Coding Interactive Behavior (CIB) system. Ruth Feldman's framework and Stuart et al.'s 4-EFA model was applied. Three VIT sessions showcased personalized interventions, aided by the CIB's role in observing and detailing changes. The results showed in this case that maternal insensitivity, characterized by high scores in intrusiveness, interference and directiveness, goes parallel to infant’s affective disconnection and social withdrawal. The VIT and CIB mutually reinforce the team's analysis and intervention perspectives pointing towards the feasibility of the video-intervention. The findings are discussed in relation to the design of future research and the need to include video intervention (VIT) work within the service's team of direct care professionals.
Differential Regulatory Effects of Probiotics on Bone Metabolism by the Status of Bone Health and Delivery Route
Authors: Park Chaeyeon , Park Ok-Jin , Kwon Yeongkag , Lee Jueun , Yun Cheol-Heui , Han Seung Hyun
Published: December 2024

Probiotics are known to have favorable effects on human health. Nevertheless, probiotics are not always beneficial and can cause unintended adverse effects such as bacteremia and/or inflammation in immunocompromised patients. In the present study, we investigated the effects of probiotics on the regulation of bone metabolism under different health conditions and delivery routes. Intragastric administration of Lactiplantibacillus plantarum to ovariectomized mouse models for mimicking post-menopausal osteoporosis in humans substantially ameliorated osteoporosis by increasing bone and mineral density. In contrast, such effects did not occur in normal healthy mice under the same condition. Interestingly, however, intraperitoneal administration of L. plantarum induced bone destruction by increasing osteoclast differentiation and decreasing osteoblast differentiation. Furthermore, when L. plantarum was implanted into mouse calvarial bone, it potently augmented bone resorption. Concordantly, L. plantarum upregulated osteoclastogenesis and downregulated osteoblastogenesis in in vitro experiments. These results suggest that L. plantarum can have distinct roles in the regulation of bone metabolism depending on bone health and the delivery route.
Your visualisations are going places: SciVis on gaming consoles
Authors: Becher Michael , Müller Christoph , Sellenthin Dominik , Ertl Thomas , Reina Guido , Weiskopf Daniel
Published: December 2024

Gaming consoles, whether stationary or handheld, are designed to provide a reasonably high level of computing power to run contemporary video games at an attractive price point, a compact form factor and modest energy consumption. While consoles have traditionally been closed-off systems, recent versions of the Xbox allow the development of apps for the Universal Windows Platform (UWP) on retail devices, making it potentially a suitable platform for bringing scientific visualisation (SciVis) applications to the masses. We describe how to run such applications, namely volume rendering and ray casting of spherical glyphs, on commodity gaming systems, not only on the Xbox Series X/S, but also on handheld devices like the Steam Deck. We detail the challenges and limitations we encountered during the implementation and provide the results of an extensive study of rendering performance, not only proving the viability of the approach but also allowing for a cost and benefit evaluation compared to standard desktop computers.

Graphical abstract
Quantifying performance gains of DirectStorage for the visualisation of time-dependent particle datasets
Authors: Müller Christoph , Ertl Thomas
Published: December 2024

Visualisation is an essential tool for analysing unstructured particle data as they are produced, for instance, in molecular dynamics or astrodynamics simulations. Such simulations often comprise multiple time steps, and scientists produce datasets with more and more particles in each step. To handle the steadily increasing size of these simulations, many solutions based on organising the data efficiently or reducing their amount using compression have been proposed over the years. Recently, a new storage API called DirectStorage has been introduced on Xbox consoles and Windows. DirectStorage promises a dramatic reduction of loading times in games by making the transfer from NVMe drives to graphics memory more efficient. That begs the question of whether DirectStorage is also beneficial for visualising time-dependent particle data that have to be streamed from disc to the GPU and whether a potential improvement in throughput enables interactive streaming of frames that traditional APIs cannot handle. For that, we implemented a benchmarking application that supports different streaming methods. We report on the results of an extensive series of tests with varying parameters that influence the streaming performance, which show that the performance of DirectStorage is highly dependent on the choice of various parameters.

Graphic Abstract

We provide an empirical evaluation for the performance gains to be expected when using the DirectStorage API for rendering dynamic particle data set like the one on the right side. For that, we compare the overlapping DirectStorage-based I/O scheme depicted on at the bottom with traditional methods
Digital light processing 3D printing of dual crosslinked meniscal scaffolds with enhanced physical and biological properties
Authors: Menon Abhay , Elkhoury Kamil , Zahraa Amer , Sapudom Jiranuwat , Rahic Zerina , Gunsalus Kristin C. , Teo Jeremy , Gupta Nikhil , Vijayavenkataraman Sanjairaj
Published: December 2024

Regenerating damaged meniscal tissue remains a significant challenge due to the meniscus’ limited capacity for self-repair. Photocrosslinkable hydrogels, like gelatin methacryloyl (GelMA), offer a promising solution for meniscal regeneration by providing structural flexibility to accommodate the meniscus’ complex geometry while enabling the incorporation of bioactive molecules and cells. However, GelMA alone often lacks the mechanical robustness required for load-bearing applications. In this study, we introduce a dual-crosslinked GelMA scaffold, enhanced with tannic acid (TA), designed to replicate the mechanical properties of the native meniscus. By adjusting TA concentrations, we successfully fine-tuned the scaffold’s compressive modulus to match that of human meniscal tissue. This dual crosslinking not only improved mechanical strength but also resulted in a denser matrix with smaller pore sizes and reduced degradation and swelling rates. The optimized GelMA-TA formulation was 3D-printed into complex shapes, demonstrating its potential for producing patient-specific scaffolds. Beyond its mechanical benefits, the GelMA-TA scaffold exhibited excellent antioxidant and antibacterial properties. Human mesenchymal stem cells seeded onto the scaffold showed high viability, increased proliferation, and successful chondrogenic differentiation. Additionally, the GelMA-TA scaffold acted as an immunomodulatory biomaterial, suppressing pro-inflammatory responses in monocytes while promoting an anti-inflammatory, pro-regenerative M2a macrophage phenotype. These findings suggest that the GelMA-TA scaffold holds strong potential as a viable solution for meniscal tissue repair, offering both structural integrity and enhanced biological functionality.

Graphical abstract
Improving Global Reservoir Parameterizations by Incorporating Flood Storage Capacity Data and Satellite Observations
Authors: Shen Youjiang , Yamazaki Dai , Pokhrel Yadu , Zhao Gang
Published: December 2024

Accurate reservoir representation in large‐scale river models remains challenging owing to limited access to data on reservoir operations. We contribute to model development by introducing a global machine‐learning based flood storage capacity (FSC) data set and a satellite‐based target storage reservoir operation scheme (SBTS). The FSC data set for 1,178 flood control reservoirs is constructed using multiple reservoir attributes and reported FSC data. Integrating these FSCs into SBTS enables its global applicability with generic formulations of reservoir zoning. Then, we develop SBTS by introducing monthly median values of satellite storage data as target storage parameters. With these seasonal patterns as constrains, improvements in simulation results are achieved. When simulated with observed inflow, SBTS performed significantly better (median Kling‐Gupta efficiency values of 0.52 and 0.17 for outflow and storage simulations among 289 reservoirs), compared to the previous reservoir operation scheme with linearly interpolated target storage parameter (0.41 and −0.19). Compared to two existing global schemes without seasonal target storages, SBTS demonstrates improved performance for many reservoirs whose inflow seasonal pattern is more regular. When coupled with a global river model, it improved discharge simulations across 293 downstream gauges, with overall performance, peak, and low flow improving at 40%, 21%, and 35% of gauges, respectively, compared to simulations without reservoirs. However, reservoir simulations do not improve notably due to the biases in simulated inflow to reservoirs. We demonstrated that machine‐learning FSC and satellite observations help improve reservoir parameterizations, and found that improvements in other aspects of river modeling are essential for accurately reproducing discharge patterns.
Whole-body angular momentum during stair ascent and descent in individuals with and without knee osteoarthritis
Authors: Chan Daisy O.M. , Subasinghe Arachchige Ransi S.S. , Wang Sizhong , Chan Peter P.K. , Cheung Roy T.H.
Published: December 2024

Given the higher fall risk and the fatal sequelae of falls on stairs, it is worthwhile to investigate the mechanism of dynamic balance control in individuals with knee osteoarthritis during stair negotiation. Whole-body angular momentum ($$\:\overrightarrow{H}$$) is widely used as a surrogate to reflect dynamic balance and failure to constrain $$\:\overrightarrow{H}$$ may increase the fall risk. This study aimed to compare the range of $$\:\overrightarrow{H}$$ between people with and without knee osteoarthritis during stair ascent and descent. Seven participants with symptomatic knee osteoarthritis and eight asymptomatic controls were instructed to ascend and descend an instrumented staircase at a fixed cadence. Kinematic and kinetic data were collected and range of $$\:\overrightarrow{H}$$ in sagittal, frontal, and transverse planes were computed. The knee osteoarthritis group exhibited greater $$\:\overrightarrow{H}$$ in the sagittal plane during both stair ascent (P = 0.005, Cohen’s d = 1.7) and descent (P = 0.020, Cohen’s d = 1.3) as well as in the transverse plane during stair descent (P = 0.015, Cohen’s d = 1.3), than the control group. These observations may be explained by greater hip flexion (P < 0.05, Cohen’s d > 1.12) and reduced knee flexion moment (P < 0.001, Cohen’s d<-2.77) during stair ascent and descent, and decreased foot progression angle (P = 0.038, Cohen’s d=-1.2) during stair descent, in individuals with knee osteoarthritis. No significant difference in frontal plane $$\:\overrightarrow{H}$$ was found between the two groups (P > 0.05). Individuals with knee osteoarthritis exhibited greater whole-body angular momentum during stair negotiation when compared to asymptomatic controls. Our findings may provide mechanistic rationale for a greater fall risk among people with knee osteoarthritis.
Cyclic quantum annealing: searching for deep low-energy states in 5000-qubit spin glass
Authors: Zhang Hao , Boothby Kelly , Kamenev Alex
Published: December 2024

Quantum computers promise a qualitative speedup in solving a broad spectrum of practical optimization problems. The latter can be mapped onto the task of finding low-energy states of spin glasses, which is known to be exceedingly difficult. Using D-Wave’s 5000-qubit quantum processor, we demonstrate that a recently proposed iterative cyclic quantum annealing algorithm can find deep low-energy states in record time. We also find intricate structures in a low-energy landscape of spin glasses, such as a power-law distribution of connected clusters with a small surface energy. These observations offer guidance for further improvement of the optimization algorithms.
Heart rate variability parameters indicate altered autonomic tone in subjects with COVID-19
Authors: Gruionu Gabriel , Aktaruzzaman Md , Gupta Anita , Nowak Thomas V. , Ward Matthew , Everett Thomas H.
Published: December 2024

COVID-19 is associated with long-term cardiovascular complications. Heart Rate Variability (HRV), a measure of sympathetic (SNS) and parasympathetic (PNS) control, has been shown to predict COVID-19 outcomes and correlate with disease progression but a comprehensive analysis that includes demographic influences has been lacking. The objective of this study was to determine the balance between SNS, PNS and heart rhythm regulation in hospitalized COVID-19 patients and compare it with similar measurements in healthy volunteers and individuals with cardiovascular diseases (CVD), while also investigating the effects of age, Body Mass Index (BMI), gender and race. Lead I ECG recordings were acquired from 50 COVID-19 patients, 31 healthy volunteers, and 51 individuals with cardiovascular diseases (CVD) without COVID-19. Fourteen HRV parameters were calculated, including time-domain, frequency-domain, nonlinear, and regularity metrics. The study population included a balanced demographic profile, with 55% of participants being under 65 years of age, 54% identifying as male, and 68% identifying as White. Among the COVID-19 patients, 52% had a BMI ≥ 30 compared to 29% of healthy volunteers and 33% of CVD patients. COVID-19 and CVD patients exhibited significantly reduced time-domain HRV parameters, including SDNN and RMSSD, compared to healthy volunteers (SDNN: 0.02 ± 0.02 s vs. 0.06 ± 0.03 s, p < 0.001; RMSSD: 0.02 ± 0.02 s vs. 0.05 ± 0.03 s, p = 0.08). In the frequency domain, both COVID-19 and CVD patients showed increased low-frequency (LF) power and lower high-frequency (HF) power compared to healthy volunteers (COVID-19 LF: 18.47 ± 18.18%, HF: 13.69 ± 25.80%; Healthy LF: 23.30 ± 11.79%, HF: 22.91 ± 21.86%, p < 0.01). The LF/HF ratio was similar in COVID-19 patients (1.038 ± 1.54) and healthy volunteers (1.03 ± 0.78). Nonlinear parameters such as SD1 were significantly lower in COVID-19 patients (0.04 ± 0.04 s vs. 0.08 ± 0.05 s, p < 0.01), indicating altered autonomic regulation. Variations in HRV were observed based on demographic factors, with younger patients, females, and non-white individuals showing more pronounced autonomic dysfunction. COVID-19 patients exhibit significant alterations in HRV, indicating autonomic dysfunction, characterized by decreased vagal tone and sympathetic dominance, similar to patients with severe cardiovascular comorbidities. Despite higher heart rates, the HRV analysis suggests COVID-19 is associated with substantial disruption in autonomic regulation, particularly in patients with specific demographic risk factors.
Examining the impact of globalization and natural resources on environmental sustainability in G20 countries
Authors: Wang Caihua , Mahmood Hamid , Khalid Samia
Published: December 2024

The G20 countries are responsible for around 75% of the world’s greenhouse gas (GHG) emissions, including the use of natural resources. In this regard, the role of globalization in achieving environmental sustainability is a relatively new topic of concern. As a result, the present study considers how globalization and natural resources affect GHG emissions, as well as the roles that renewable energy consumption and urbanization play in the G20 countries between 1990 and 2020. Moreover, this research utilizes advanced panel data estimation methods to assess these interrelationships, like CUP-FM and CUP-BC methods. The study finds that globalization and natural resource use contribute to increased GHG emissions, with a more pronounced effect in high-income countries because most of the countries have extended carbon taxes or emissions trading systems in the last few years. Conversely, the adoption of renewable energy is associated with a reduction in emissions, underscoring the importance of transitioning to cleaner energy sources. Urbanization trends also correlate positively with GHG emissions, highlighting the environmental impact of rapid urban growth, particularly in middle-income countries. These results underscore the need for targeted policy interventions. High-income countries should intensify efforts to reduce emissions through renewable energy and stricter regulations on resource consumption. While, middle-income countries require support to manage urbanization and adopt sustainable practices. This study provides crucial insights into the complex dynamics of globalization, resource use, and sustainability, offering valuable guidance for policymakers and researchers engaged in environmental management and climate change mitigation.
Road terrain recognition based on tire noise for autonomous vehicle
Authors: Yang Dongsheng , Zhang Dongmin , Yuan Yi , Lei Zhaoyu , Ding Binlei , Bo Lei
Published: December 2024

Effective road terrain recognition is crucial for enhancing the driving safety, passability, and comfort of autonomous vehicles. This study addresses the challenges of accurately identifying diverse road surfaces using deep learning in complex environments. We introduce a novel end-to-end Tire Noise Recognition Residual Network (TNResNet) integrated with a time-frequency attention module, designed to capture and leverage time-frequency information from tire noise signals for road terrain classification. Our method was evaluated on five distinct road types: asphalt, cement, grass, mud, and sand. The performance of TNResNet was rigorously compared against traditional machine learning techniques, including Decision Trees, K-Nearest Neighbors, and Support Vector Machines, as well as advanced deep learning models like Long Short-Term Memory and Convolutional Neural Networks. Experimental results demonstrate that TNResNet achieves superior classification accuracy of 99.48%, outperforming all comparative methods. This work not only establishes a robust framework for road terrain identification but also showcases the significant practical implications of TNResNet in the realm of autonomous vehicle navigation.
Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy
Authors: Logeshwaran J. , Patel Shobhit K. , Kumar Om Prakash , Al-Zahrani Fahah Ahmed
Published: December 2024

Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes. To address this issue, we proposed the Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) to enhance the efficiency and stability of clustering. Improved the sustainability and durability of the network by incorporating Dynamic CH selection employing Deep Reinforcement Learning (DRL). Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. Simulation results were implemented using the ns-3 simulation tool, and the proposed model outperformed the traditional methods in deployment coverage (95%), cluster stability index (0.97), and CH selection efficiency (95%). This work is expected to study the 6G communication systems as a key enabler for IoT applications and as the title legible name explains, the solutions smartly done in a practical and scalable way gives a systematic approach towards solving the IoT traffic, and multi-routing challenges that are intended to be addressed in 6G era delivering a robust IoT ecosystem in securing the process.
Extraction and chemical characterization of humic acid produced from lignite coals of arid region of Gujarat, Western India
Authors: Rani Juhi , Kumari Sushmita , Paul Biswajit
Published: December 2024

In the current study, extraction of humic acid (HAs) from lignite fines of kutch basin of Gujarat (western India) were reported. The extraction was done by International Humic Substances Society (IHSS) method. Several analytical and spectroscopic techniques were used to characterize of extracted HAs. The gravimetric analysis showed (28.5% and 26.4%) of humic acid extraction from panandhro mines (PM) and mata-No-Madh mines (MNMM) respectively. UV–Vis spectroscopy revealed a high degree of humification, higher stability and aromatic nature. FTIR peaks demonstrated the presence of carboxylic, alcoholic, and phenolic functional groups. SEM/EDX analysis revealed that surface were smooth, non-porous and like loose sponge that showed the presence of major elements like C, O, F, Al, Na, S, Si, Ca, Ti and Fe. The combination of results gives a better and improved understanding of the nature of lignite coals of western India. It may be helpful in choosing suitable coals for the extraction of humic acid and using it for enhancing plant growth condition, soil enrichment and creating green energy solution. This study not only helps in the research related to extraction of humic acid from lignite but also creates a new avenue for the efficient and clean use of lignite.
Performance metrics outperform physiological indicators in robotic teleoperation workload assessment
Authors: Odoh Gift , Landowska Aleksandra , Crowe Emily M. , Benali Khairidine , Cobb Sue , Wilson Max L. , Maior Horia A. , Kucukyilmaz Ayse
Published: December 2024

Robotics holds the potential to streamline the execution of repetitive and dangerous tasks, which are difficult or impossible for a human operator. However, in complex scenarios, such as nuclear waste management or disaster response, full automation often proves unfeasible due to the diverse and intricate nature of tasks, coupled with the unpredictable hazards, and is typically prevented by stringent regulatory frameworks. Consequently, the predominant approach to managing activities in such settings remains human teleoperation. Teleoperation can be demanding, especially in high-stress situations, and involves a complex blend of both cognitive and physical workload. We present an experiment to explore a range of physiological and performance-related metrics for workload assessment during robotic teleoperation. Thirty-five participants performed a teleoperation task, during which we manipulated cognitive and physical workload conditions. We recorded multiple metrics, including brain activity using functional Near-Infrared Spectroscopy, galvanic skin responses, cardiovascular responses, subjective workload ratings, task and robot performance data. Our results suggest that robotic teleoperation performance may be the most robust metric for distinguishing between different levels of workload experienced during teleoperation, with most physiological measures becoming insignificant to distinguish high cognitive workload.
Neratinib enhances the efficacy of CDK4/6 inhibitor plus endocrine therapy in HR+/HER2-low breast cancer cell line ZR-75-1 via hsa-miR-23a-5p
Authors: Chen Liushan , Ye Lingling , Liang Yuqi , Luo Wei , Zuo Qian , Huang Ping , Hu Yuyu , Dai Yan , Wu Yingchao , Guo Qianqian , Chen Qianjun
Published: December 2024

HR+/HER2-low breast cancer is a significant subgroup of conventional HR+/HER2-negative breast cancer, and combination of CDK4/6 inhibitor and endocrine therapy is the standard first-line and second-line treatments for advanced HR+/HER2-low breast cancer. Nevertheless, it remains uncertain whether HER2 signaling affects the effectiveness of CDK4/6 inhibitor administered in combination with endocrine therapy for HR+/HER2-low breast cancer and suitable intervention measures. This study revealed poor efficacy for CDK4/6 inhibitor combined with endocrine therapy for HR+/HER2-low breast cancer in vitro and in vivo models. Secondly, suppression of HER2 gene expression in HR+/HER2-low breast cancer cells resulted in significantly improved efficacy for CDK4/6 inhibitor combined with endocrine therapy. Furthermore, the anti-HER inhibitor neratinib was administered to enhance the effectiveness of CDK4/6 inhibitor combined with endocrine therapy in HR+/HER2-low breast cancer by inhibiting the HER2 pathway and lowering HER2 mRNA expression. Strikingly, neratinib reversed the efficacy of CDK4/6 inhibitor and endocrine therapy by reducing HER2 mRNA stability in HR+/HER2-low breast cancer through the interaction of HER2 3’-UTR region with hsa-miR-23a-5p. Even after reducing neratinib dosage to the standard 1/2 dose (20 mg/kg), it remained highly effective and well-tolerated. This study provides a viable and well-tolerated triple combination therapy for clinical HR+/HER2-low breast cancer.
Cognitive abilities predict naturalistic speech length in older adults
Authors: Neff Patrick , Demiray Burcu , Martin Mike , Röcke Christina
Published: December 2024

Past research has demonstrated the association between social engagement and the maintenance of cognitive abilities. However, inconsistent definitions of social engagement have posed challenges to systematically investigate this association. This paper addresses the role of social relationships in cognitive functioning among older adults, focusing on the real-life communication indicator—length of own speech—as a measure of social activity. Utilizing advanced technology to unobtrusively measure older adults’ real-life speech, this study investigates its association with various cognitive abilities and sociodemographic factors. Differential cognitive measures, and sociodemographic data including factors like age, sex, education, income, persons living in the same household, loneliness, and subjective hearing status were included. Audio data of 83 participants are analyzed with a machine learning speaker identification algorithm. Using Elastic Net regularized regression, results indicate that higher levels of working memory, cognitive speed, and semantic fluency predict own speech in everyday life. While having no partner negatively predicted own speech length, we unexpectedly found that higher hearing status was related to lower speech frequency. Age was neither a relevant predictor in the regression nor correlated with any other variables. We discuss implications and future research applications based on the findings from our novel approach.
Conotoxin kM-RIIIJ reveals interplay between Kv1-channels and persistent sodium currents in proprioceptive DRG neurons
Authors: Raghuraman Shrinivasan , Carter Jackson , Walter Markel , Karthikeyan Manju , Chase Kevin , Giglio Matías L. , Giacobassi Mario , Teichert Russell W. , Terlau Heinrich , Olivera Baldomero M.
Published: December 2024

Voltage-gated potassium channels (VGKCs) comprise the largest and most complex families of ion channels. Approximately 70 genes encode VGKC alpha subunits, which assemble into functional tetrameric channel complexes. These subunits can also combine to form heteromeric channels, significantly expanding the potential diversity of VGKCs. The functional expression and physiological role of heteromeric K-channels have remained largely unexplored due to the lack of tools to probe their functions. Conotoxins, from predatory cone snails, have high affinity and specificity for heteromeric combinations of K-channels and show great promise for defining their physiological roles. In this work, using conotoxin kM-RIIIJ as a pharmacological probe, we explore the expression and physiological functions of heteromeric Kv1.2 channels using constellation pharmacology platform. We report that heteromers of Kv1.2/1.1 are highly expressed in proprioceptive neurons found in the dorsal root ganglion (DRG). Inhibition of Kv1.2/1.1 heteromers leads to an influx of calcium ions, suggesting that these channels regulate neuronal excitability. We also present evidence that Kv1.2/1.1 heteromers counteract persistent sodium currents, and that inhibiting these channels leads to tonic firing of action potentials. Additionally, kM-RIIIJ impaired proprioception in mice, uncovering a previously unrecognized physiological function of heteromeric Kv1.2/1.1 channels in proprioceptive sensory neurons of the DRG.
Exploring the role of the MUC1 mucin in human oral lubrication by tribological in vitro studies
Authors: Ammam Ianis , Pailler-Mattéi Cyril , Ouillon Lucas , Nivet Clément , Vargiolu Roberto , Neiers Fabrice , Canon Francis , Zahouani Hassan
Published: December 2024

In the context of the oral cavity, an organic layer known as the mucosal pellicle (MP) adheres to the surface of the oral epithelium, playing a pivotal role in lubricating and safeguarding oral tissues. The formation of the MP is driven by interactions between a transmembrane mucin known as MUC1, located on the oral epithelium, and salivary secreted mucin, namely MUC5B and MUC7. This study aimed to investigate the function of MUC1 and the influence of its structure on MP lubrication properties. We proposed a novel methodology to study oral lubrication based on four different models of oral epithelium on which we conducted in vitro tribological studies. These models expressed varying forms of MUC1, each possessing on of the distinct domain constituting the mucin. Mechanical parameters were used as indicators of lubrication efficiency and, consequently, of the role played by MUC1 in oral lubrication. The results from the tribological tests revealed that the presence of full MUC1 resulted in enhanced lubrication. Furthermore, the structure of MUC1 protein drive the lubrication. In conclusion, the mechanical tests conducted on our epithelium models demonstrated that MUC1 actively participates in epithelium lubrication by facilitating the formation of the MP.
Mathematical analysis of fractional Chlamydia pandemic model
Authors: Alqahtani Zuhur , Almuneef Areej , DarAssi Mahmoud H. , AbuHour Yousef , Al-arydah Mo’tassem , Safi Mohammad A. , Al-Hdaibat Bashir
Published: December 2024

In this study, we developed a Caputo-Fractional Chlamydia pandemic model to describe the disease’s spread. We demonstrated the model’s positivity and boundedness, ensuring biological relevance. The existence and uniqueness of the model’s solution were established, and we investigated the stability of the $$\alpha$$-fractional order model. Our analysis proved that the disease-free equilibrium point is locally asymptotically stable. Additionally, we showed that the model has a single endemic equilibrium point, which is globally asymptotically stable when $${\mathscr {R}}_0$$ exceeds 1. Using Latin Hypercube sampling and partial rank correlation coefficients (PRCCs), sensitivity analysis identified key parameters influencing $${\mathscr {R}}_0$$. Numerical simulations further illustrated the impact of parameter variations on disease dynamics.