bluuebunny/crossref_metadata_2025_split huggingface dataset:
Atypical lateralization of visuospatial attention can be associated with better or worse performance on line bisection
Authors: Villar-Rodríguez Esteban , Davydova Tatiana , Marin-Marin Lidón , Avila César 
Published: April 2024

Abstract
The causal and statistical hypotheses diverge in determining whether the lateralization of language function in one cerebral hemisphere entails the lateralization of visuospatial function in the opposite hemisphere. Additionally, it remains unclear if the atypical organization of these functions could influence cognitive performance. This study addresses these questions by examining the hemispheric lateralization of visuospatial attention during a line bisection judgement (landmark) task in three groups of healthy non-right-handed individuals with different language production organizations: left (typical), ambilateral (atypical), and right (atypical). Consistent with the causal hypothesis, results indicate that the groups with left and right language lateralization primarily utilize the opposite hemisphere for visuospatial attention. The ambilateral group, however, displays a pattern compatible with an independent organization, supporting the statistical hypothesis. Behavioral analyses reveal that atypical lateralization of visuospatial attention (non-right) can lead to either better or worse performance during the landmark task, depending on the specific pattern. Bilateral organization is associated with reduced overall accuracy, whereas the left organization results in improved performance during the most challenging trials. These findings suggest the existence of diverse pathways to lateralization, akin to either the causal or statistical hypothesis, which can result in cognitive advantages or disadvantages.
Effect of Length of stay with Post-operative Robotic Partial Nephrectomy care
Authors: Garcia Kyle , Suppanuntaroek Sappaya , Combates Christopher , Paster Irasema Concepción , Deal Carly , Lee Benjamin 
Published: April 2024

Abstract
Background
To compare the 30-day readmission rates and emergency department visit rates for patients who underwent robotic partial nephrectomy before and after March 31st, 2020.
Methods
Patients who were treated at the University of Arizona Department of Urology for renal cell carcinoma from 2017–2023 were reviewed. The patients were assigned into 2 groups, Group A underwent surgery between February 1st 2017 and March 31st 2020, and Group B underwent surgery between April 1st 2020 through May 2023. All the data were collected using REDCAP database.
Results
A total of 210 cases were reviewed (105 cases in Group A, and 105 cases in Group B). There was no statistically significant difference between Emergency department visits and re-admission rates between Group A and Group B. Group B had a significantly shorter length of stay than Group A (2.32 days and 1.34 days, p < 0.001) with no other differences between the groups.
Conclusion
There was no increase in 30-day emergency department visits and readmission rates when patients who underwent robotic partial nephrectomy were discharged on post-operative day one, when compared with those discharged on post-operative day two. Thus, our preliminary data supports that this is a safe and effective change that will allow for more open hospital beds.
Local existence of the solution to the fractional tropical climate model
Authors: Hu Meiqi , Jiang Zaihong , Zhang Jingjun 
Published: April 2024

Abstract
In this paper, we consider Cauchy problem of the $n$-dimensional $(n\geq2) $ tropical climate model with fractional velocity diffusion $\alpha>\frac{1}{2}$. We prove the local existence of solutions in $H^s$ with $s>\max\{\frac{n}{2}+1-2\alpha, 0\}$, which improves the known existence theory for this system.
Mathematics Subject Classification (2010). Primary 99Z99; Secondary 00A00.
“We will never catch up unless we look forward”: A qualitative analysis of barriers to healthcare access faced by racial and ethnic minority older adults in low-income neighborhoods of Chicago
Authors: Bella Gretchen , Borowski Elisa , Stathopoulos Amanda 
Published: April 2024

Abstract
This paper seeks to understand barriers to healthcare access faced by socially vulnerable older adults in Chicago using qualitative analysis. As the U.S. population ages, ease of healthcare access by older adults grows in importance. Yet, current literature lacks an examination of pain points and coping strategies in accessing healthcare, specifically by socially vulnerable older adults. This is especially relevant as virtual telehealth access and transportation options that shape healthcare access evolve rapidly. In this work, we conduct three listening sessions with 40 total participants to understand the lived experiences of Black and Latino older adults living in low-income areas of Chicago. Transcript data was analyzed using qualitative concept and emotion coding to identify vulnerabilities that older patients face in accessing care. This work reveals unique insight on the interfaces between transportation, healthcare, and communication systems used in healthcare access, and provides a recommendation for seeking solutions that prioritize autonomy for older adults by offering physical accessibility and customized technology.
The Enhancement of Unsupervised Cross-Modality Domain Adaptive Cardiac Segmentation: Self-Training++ and FC-CRF Optimization
Authors: Li Yongbo , Qi Xiaoyi , Wang Jinjin , Yang Fei 
Published: April 2024

Abstract
The training of deep convolutional neural networks requires a substantial amount of labeled data. Nevertheless, the available kinds of labels that are implemented currently to segment medical images cause high-quality image annotations that bring costly and time-consuming procedures. Moreover, distinct modalities such as CT and MRI-based cardiac images make it difficult to train a model. So, one set of image data in one modality cannot be segmented into image data in another modality efficiently. Thus, an unsupervised domain-adaptive cardiac segmentation algorithm implementing a Self-training + + algorithm is suggested to conduct a process called the cross-modality medical-image segmentation known as modality data. The algorithm produces pseudo-labels for images utilized in the target domain and introduces rare class sampling (RCS) by implementing the Self-training + + approach. Additionally, the network called the Mix Transformer semantic segmentation is enhanced by incorporating the module called the atrous spatial pyramid pooling to acquire multiscale semantic information as if it were a teacher and student network to advance the robustness of the segmentation network. Then, fully connected conditional random fields (FC-CRF) are implemented to raise the confidence level of pseudo-labeling. The proposed algorithm substantially decreases the domain bias impact on the outcomes of cardiac image segmentation when distinct modalities exist and alleviates the dependence on expert-labeled data. Then, domain-adaptive training for full cardiac medical-image segmentation is performed in two directions, namely, CT to MRI and MRI to CT cardiac images, and promising experimental outcomes are achieved even though a limited number of data is employed.
Examining the Prevalence and Risk Factors of Genital Warts among HIV-Infected and HIV-Negative Women: A Cross-Sectional Study in Cameroon
Authors: Fougang Daina Charnelle , Martin Kuete 
Published: April 2024

Abstract
Background
Current research primarily focuses on high-risk strains of HPV associated with cervical cancer, overlooking risk factors for low-risk HPV infections like genital warts among vulnerable women, especially concerning their HIV serology. Understanding the interplay between genital warts, HIV status, and socio-cultural factors is crucial for informing targeted prevention to alleviate the burden of genital warts in vulnerable populations.
Aims
The purpose of this study is to determine the prevalence and risk factors of genital warts among women based on their HIV serostatus.
Materials and Methods
A cross-sectional study was conducted among women seeking gynecological consultation at a public Hospital in Cameroon. Data were collected through a survey and medical records, then analyzed using SPSS version 18.0.
Results
Among 257 women attending the hospital, 60 had genital warts, with the majority being HIV-positive (63.33%). Notably, 67% of these women sought gynecology consultation for the first time for genital warts. The location and types of genital warts were associated with HIV status, with papule genital warts being more prevalent among HIV-infected women and acuminate warts among non-infected women. Additionally, associations were found between smoking, multiple sexual partners, and genital warts among HIV-positive women. The reliance on traditional medicine or homemade remedies (85%) and the high prevalence of self-medication (75%) were also observed.
Conclusions
Addressing socio-cultural barriers is essential to enhance healthcare-seeking behaviour, facilitate early detection and treatment, and alleviate the burden of genital warts and HIV. Efforts should prioritize awareness-raising, enhancing healthcare access, and integrating traditional medicine into comprehensive healthcare systems.
Structural and FE analysis of HAp used in biomedical implants
Authors: BHATTACHARYYA ARNAB SANKAR , Bhattacharyya Kushal 
Published: April 2024

Abstract
Hydroxyapatite (HAp) was prepared from egg shells by various routes using hexane and acetic acid followed by heat treatment. HAp has a wide application in water treatment by removal of metal ions. XRD of the samples showed use of acetic acid followed by high temperature sintering leads to formation crystalline phases of HAp. Strong evidence of CaCO3 in calcite phase was obtained in other samples. Zinc oxide nanoparticles have also been synthesized by different methods such as sol-gel, co- precipitate, and green synthesis. The effect of different synthesis methods was investigated using X-Ray Diffraction (XRD). Finite Element Analysis (FEA) of nanoindentation were conducted to assess the fracture resistance of these materials
Windows deep transformer Q-networks: an extended variance reduction architecture for partially observable reinforcement learning
Authors: Wang Zijian , Wang Bin , Dou Hongbo , Liu Zhongyuan 
Published: April 2024

Abstract
Recently, one of the mainstream research directions in Reinforcement Learning involves Partial Observability(PO). One approach to addressing this task is the establishment of a sequence-tosequence model. Transformer is an excellent architecture for building a sequence-to-sequence model, and as the network of Deep Q-Networks, Transformer has achieved certain outperformed results in dealing with partial observability problems. However, Transformer relies heavily on the quality of input data. Overestimation exists in Deep Q-Networks(DQN), affecting the quality of input data to Transformer, resulting in poor training efficiency. In this work, we propose Windows Deep Transformer Q-Networks(Windows DTQN), which is an improved architecture that mitigates the overestimation issue in DQN by reducing the variance of Q-values, thereby enhancing the quality of input data and improving the training efficiency of Transformer. Our experiments demonstrate that our approach achieves better results than the current mainstream DQN algorithms in Partially Observable Markov Decision Process(POMDP).
How the brain can be trained to achieve an intermittent control strategy for stabilizing quiet stance by means of reinforcement learning?
Authors: Takazawa Tomoki , Suzuki Yasuyuki , Nakamura Akihiro , Matsuo Risa , Morasso Pietro , Nomura Taishin 
Published: April 2024

Abstract

The stabilization of human quiet stance is achieved by a combination of the intrinsic elastic properties of ankle muscles and an active closed-loop activation of the ankle muscles, driven by the delayed feedback of the ongoing sway angle and the corresponding angular velocity in a way of a delayed proportional (P) and derivative (D) feedback controller. It has been shown that the active component of the stabilization process is likely to operate in an intermittent manner rather than as a continuous controller: the switching policy is defined in the phase-plane, which is divided in dangerous and safe regions, separated by appropriate switching boundaries. When the state enters a dangerous region, the delayed PD control is activated, and it is switched off when it enters a safe region, leaving the system to evolve freely. In comparison with continuous feedback control, the intermittent mechanism is more robust and capable to better reproduce postural sway patterns in healthy people. However, the superior performance of the intermittent control paradigm as well as its biological plausibility, suggested by experimental evidence of the intermittent activation of the ankle muscles, leaves open the quest of a feasible learning process, by which the brain can identify the appropriate state-dependent switching policy and tune accordingly the P and D parameters. In this work, it is shown how such a goal can be achieved with a reinforcement motor learning paradigm, building upon the evidence that, in general, the basal ganglia are known to play a central role in reinforcement learning for action selection and, in particular, were found to be specifically involved in postural stabilization.
3D ultra-short echo time 31P-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI
Authors: Bozymski Brian , Emir Uzay , Dydak Ulrike , Shen Xin , Thomas M. Albert , Özen Ali , Chiew Mark , Clarke William , Sawiak Stephen 
Published: April 2024

Abstract

Phosphorus-31 magnetic resonance spectroscopic imaging (31P-MRSI) provides valuable non-invasive in vivo information on tissue metabolism but is burdened by poor sensitivity and prolonged scan duration. Ultra-short echo time (UTE) acquisitions minimize signal loss when probing signals with relatively short spin-spin relaxation time (T2), while also preventing first-order dephasing. Here, a three-dimensional (3D) UTE sequence with a rosette k-space trajectory is applied to 31P-MRSI at 3T. Conventional chemical shift imaging (CSI) employs highly regular Cartesian k-space sampling, susceptible to substantial artifacts when accelerated via undersampling. In contrast, this novel sequence’s “petal-like” pattern offers incoherent sampling more suitable for compressed sensing (CS). These results showcase the competitive performance of UTE rosette 31P-MRSI against conventional weighted CSI with simulation, phantom, and in vivo leg muscle comparisons.
Supervised Phenotype Discovery from Multimodal Brain Imaging
Authors: Gong Weikang , Bai Song , Zheng Ying-Qiu , Smith Stephen , Beckmann Christian 
Published: April 2024

Abstract
Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects' demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without use of information in nIDPs. In this paper, we proposed Supervised BigFLICA (SuperBigFLICA), a semi-supervised, multimodal, and multi-task fusion approach for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely bypasses the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension scores.
Utilizing waveform synthesis in harmonic oscillator seasonal trend model for short- and long-term streamflow drought modeling and forecasting
Authors: Raczyński Krzysztof , Dyer Jamie 
Published: April 2024

ABSTRACT
This study introduces an improved version of the harmonic oscillator seasonal trend (HOST) model framework to accurately simulate medium- and long-term changes in extreme events, focusing on streamflow droughts in the Mobile River catchment. The updated model incorporates new mathematical formulas and waveform synthesis, resulting in improved performance. The updated framework successfully captures long-term and seasonal patterns with a Kling -Gupta efficiency exceeding 0.5 for seasonal fluctuations and over 0.9 for trends. The best-fit model explains around 98% of long-term and approximately 55% of seasonal variance. Test sets show slightly lower accuracies, with about 20% of nodes underperforming due to the absence of drought during the test phase resulting in false-positive model forecasts. The newly developed weighted occurrence classification outperforms the binary classification occurrence model. In addition, application of an automatic period multiplier for decomposition using the seasonal trend decomposition using LOESS (locally estimated scatterplot smoothing) method improves test dataset performance and reduces false-positive forecasts. The improved framework provides valuable insights for extreme flow distribution, offering potential for improved water management planning, and the combination of the HOST model with physical models can address short-term drivers of extreme events, enhancing drought occurrence forecasting and water resource management strategies.
Evaluación de grupos fenotípicos de la familia sexual de papa ‘Chacasina’ y de cultivares comerciales de papa en la Costa Central
Authors: Villagómez C. Vidal , Rodríguez S. Gilberto 
Published: April 2024

Se realizó un experimento, bajo condiciones de producción en la costa central, con los objetivos de evaluar el comportamiento de cuatro grupos fenotípicos de la familia de semilla sexual de papa ‘Chacasina’ en comparación con los cv ‘Yungay’, ‘Tomasa Tito Condemayta’ y ‘Canchan - INIAA’, y determinar la factibilidad técnico- económica del uso de tubérculos-semilla de papa ‘Chacasina’ como alternativa para esta región. Luego de la evaluación realizada se encontró que “Chacasina bulk Huancayo” logró el rendimiento más alto, encontrándose en el mismo nivel de significación con ‘Yungay’, “Chacasina piel rosada”, “Chacasina blanca” y ‘Tomasa Tito Condemayta’. En la familia ´Chacasina´, “Chacasina bulk Huancayo”, “Chacasina piel rosada” y “Chacasina blanca”, presentan rendimientos significativamente superiores, para una siembra en costa central, que “Chacasina ojos rosados”. Los cambios en los Ingresos Netos (IN) para la familia de semilla sexual de papa ´Chacasina´ en comparación con los cv ‘Yungay’, ‘Tomasa Tito Condemayta’ y ‘Canchan - INIAA’ son positivos, en nueve de los doce casos, mientras que las diferencias entre los costos variables (CV) en todos los casos son negativas. Luego de realizar el análisis de presupuestos parciales, se concluye que la siembra de papa con tubérculos provenientes de semilla sexual de ‘Chacasina’ en condiciones de invierno en costa central es más beneficiosa que la siembra con tubérculos de variedades comerciales, convirtiéndose en una propuesta atractiva.
Una aproximación metodológica para la caracterización espacial de las áreas verdes publicas del distrito de La Molina, Lima
Authors: Peña G. Víctor 
Published: April 2024

El presente estudio examina las características de dimensión, forma y distribución de los fragmentos correspondientes a las áreas verdes de carácter público del distrito de La Molina. En este sentido el estudio busca explicar la situación presente a fin de proponer pautas para el planeamiento y manejo de estas áreas con fines de mejoramiento de la calidad ambiental en la zona urbana. En el presente estudio se propone una metodología para la caracterización espacial del conjunto de áreas verdes de uso público, en el distrito de La Molina, presentando resultados cuantificables de su composición y configuración. En el distrito de La Molina el proceso de urbanización ha resultado en la fragmentación de los espacios verdes de uso público, dispersos dentro de la matriz urbana. Las preguntas que el presente estudio intenta responder son: ¿Cómo están distribuidas las áreas verdes públicos en La Molina? y ¿existe un patrón en la forma y distribución de estos espacios verdes?. El conjunto de polígonos, que representan a los espacios verdes públicos, fueron procesados utilizando la extensión Patch Analist 2.2 (Rempel et al., 1998) para el programa ArcView GIS 3.3 (ESRI, 2002). Este proceso permitió hallar índices para el análisis de la fragmentación o dispersión de cada fragmento. Las áreas verdes publicas identificadas fueron 91 fragmentos, cuyos tamaños varían entre 0.04 Ha. y 2.85 Ha. con una mediana de 0.57 Ha. La curva de Lorenz muestra una distribución casi proporcional del área total entre los diferentes fragmentos, muy cercana a la distribución ideal del área total entre cada fragmento por partes iguales; el coeficiente de Gini (G=0.353) confirma la mencionada homogeneidad en la distribución. Los resultados, correspondientes al análisis en toda la superficie de La Molina, revelaron fragmentación de los espacios y dispersión a escala del territorio distrital. La densidad de fragmentos en el Distrito es de 1.9 fragmentos por cada 100 hectáreas de la matriz urbana. Este valor es menor comparado al valor de densidad para el sector de Camacho-Sta. Patricia (donde se reconoció visualmente una aglomeración de polígonos): 6.6 fragmentos por cada 100 hectáreas. En este sector los tamaños de los fragmentos varían desde 0.09 Ha hasta 1.96 Ha, siendo la mediana de 0.62 Ha. En las condiciones actuales, las áreas verdes remanentes son el conjunto de fragmentos que deberán acoger el funcionamiento de los procesos naturales dentro de la ciudad. La viabilidad y sostenibilidad de las intervenciones, orientadas a conservar y recuperar los espacios verdes y la biodiversidad, deberán ser evaluadas a través de análisis espacial del conjunto de fragmentos. El presente estudio explora las capacidades y ventajas de las herramientas de análisis espacial; y tanto los índices como los resultados se estudian en el marco de la ecología del paisaje tomando énfasis en el concepto de fragmentación para explicar lo observado en el estudio.
Predicción de caudales medios mensuales del Río Huancané utilizando Modelos de Redes Neuronales
Authors: Sánchez D. Miguel , Laqui V. Wilber 
Published: April 2024

Esta investigación centró sus esfuerzos en la predicción de caudales medios mensuales del río Huancané, desarrollo de modelos de redes neuronales a partir de datos de caudales, precipitación y evaporación, así como la evaluación de la capacidad de desempeño frente a modelos estocásticos. De esta manera, se desarrollaron 10 modelos de redes neuronales artificiales con distintas arquitecturas, cuyo entrenamiento se realizó con un primer grupo de datos correspondientes al periodo 1964 – 1996, y su validación con un segundo grupo de datos del periodo 1997 – 2002. Los modelos de redes neuronales artificiales mostraron comparativamente mejor desempeño en materia de predicción frente a un modelo autorregresivo periódico de primer orden PAR (1).