Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved geodetic European very-long-baseline interferometry solution using models of antenna gravitational deformation

Very-long-baseline interferometry (VLBI) is used for establishing global geodetic networks where the coordinates attain a 1-mm level of precision. Technique-dependent bias can degrade the VLBI positioning accuracy if it is present and unaccounted for. Among the potential bias, gravitational flexure of VLBI telescopes can vary the path traveled by the incoming radio signal and induce a bias in the height component of the station position. We process here more than 100 European VLBI sessions spanning 1990-2009 with VLBI time delay/Solve software, as the only VLBI analysis package that can be used to correct signal-path variation (SPV) due to gravitational flexure of VLBI telescopes. Currently, SPV models are neglected in VLBI data analysis. To determine the kinematics of the European area over the last 20 years and to assess the effects of telescope gravitational deformation on geodetic VLBI estimates, we perform two VLBI solutions with and without SPV models for telescopes in Medicina (northern Italy) and Noto (southern Italy). The two solutions differ by 8.8 mm and 7.2 mm in their height components, with this bias being one order of magnitude larger than the formal errors of the estimated heights. SPV models impact uniquely on the height component of stations where SPVs are modeled. Velocities are not affected by the use of the Medicina and Noto SPV models, and we show that the crustal kinematics derived from VLBI does not suffer from a lack of information with regard to the flexure of other telescopes. Normal 0 14 false false false MicrosoftInternetExplorer4
Multi-Omics Revealed Peanut Root Metabolism Regulated by Exogenous Calcium under Salt Stress

High salinity severely inhibits plant seedling root development and metabolism. Although plant salt tolerance can be improved by exogenous calcium supplementation, the metabolism molecular mechanisms involved remain unclear. In this study, we integrated three types of omics data (transcriptome, metabolome, and phytohormone absolute quantification) to analyze the metabolic profiles of peanut seedling roots as regulated by exogenous calcium under salt stress. (1) exogenous calcium supplementation enhanced the allocation of carbohydrates to the TCA cycle and plant cell wall biosynthesis rather than the shikimate pathway influenced by up-regulating the gene expression of antioxidant enzymes under salt stress; (2) exogenous calcium induced further ABA accumulation under salt stress by up-regulating the gene expression of ABA biosynthesis key enzymes AAO2 and AAO3 while down-regulating ABA glycosylation enzyme UGT71C5 expression; (3) exogenous calcium supplementation under salt stress restored the trans-zeatin absolute content to unstressed levels while inhibiting the root cis-zeatin biosynthesis.
Mixing Self-Attention and Convolution: A Unified Framework for Multisource Remote Sensing Data Classification

Convolution and self-attention are two powerful techniques for multisource remote sensing (RS) data fusion that have been widely adopted in Earth observation tasks. However, convolutional neural networks (CNNs) are inadequate for fully mining contextual information and representing the sequence attributes of spectral signatures. In addition, the specific self-attention mechanism often comes with high-computational costs, which hinders its application in the field of RS. To overcome the above limitations, this article proposes a unified framework called “mixing self-attention and convolution network (MACN)” for comprehensive feature extraction and efficient feature fusion. First, the proposed MACN utilizes two adaptive CNN encoders (ACEs) to extract shallow convolutional features from multisource RS data. Second, taking the complexity and varying scales of RS data into account, the proposed mixing self-attention and convolution Transformer (MACT) layer achieves local and global multiscale perception through an elegant integration of self-attention and convolution. MACT can extract abundant spatial and high-dimensional information (e.g., spectral and elevation information) while maintaining minimal computational overhead compared with pure convolution or self-attention counterparts. Finally, a multisource cross-guided fusion (MCGF) module is designed to achieve deep fusion of multisource RS data features. MCGF utilizes a carefully designed cross-modal attention mechanism to capture the interaction between multisource data and aggregate contextual information. Extensive tests on six public RS datasets have shown that our method outperforms other multisource fusion models, delivering state-of-the-art (SOTA) results on multiple RS data fusion tasks without specific tuning. The source code of the proposed method will be available publicly at https://github.com/like413/MACN.
Pannexin1: insight into inflammatory conditions and its potential involvement in multiple organ dysfunction syndrome

Sepsis represents a global health concern, and patients with severe sepsis are at risk of experiencing MODS (multiple organ dysfunction syndrome), which is associated with elevated mortality rates and a poorer prognosis. The development of sepsis involves hyperactive inflammation, immune disorder, and disrupted microcirculation. It is crucial to identify targets within these processes to develop therapeutic interventions. One such potential target is Panx1 (pannexin-1), a widely expressed transmembrane protein that facilitates the passage of molecules smaller than 1 KDa, such as ATP. Accumulating evidence has implicated the involvement of Panx1 in sepsis-associated MODS. It attracts immune cells via the purinergic signaling pathway, mediates immune responses via the Panx1-IL-33 axis, promotes immune cell apoptosis, regulates blood flow by modulating VSMCs’ and vascular endothelial cells’ tension, and disrupts microcirculation by elevating endothelial permeability and promoting microthrombosis. At the level of organs, Panx1 contributes to inflammatory injury in multiple organs. Panx1 primarily exacerbates injury and hinders recovery, making it a potential target for sepsis-induced MODS. While no drugs have been developed explicitly against Panx1, some compounds that inhibit Panx1 hemichannels have been used extensively in experiments. However, given that Panx1’s role may vary during different phases of sepsis, more investigations are required before interventions against Panx1 can be applied in clinical. Overall, Panx1 may be a promising target for sepsis-induced MODS. Nevertheless, further research is needed to understand its complex role in different stages of sepsis fully and to develop suitable pharmaceutical interventions for clinical use.
Firn Densification: An Empirical Model

Abstract An empirical model of firn densification from the surface to the zone of pore close-off has been constructed. Fundamental rate equations have been derived for the first two stages of densification. In the first stage, for densities less than 0.55 Mg m−3, the densification rate is proportional to the mean annual accumulation times the term (ρ i − ρ), where ρ is the density of the snow and ρ i is the density of pure ice. The densification rate in the second stage, where 0.55 Mg m −3 < ρ < 0.8 Mg m−3, is proportional to the square root of the accumulation rate and to (ρi− ρ). Depth–density and depth–age calculations from this model are compared with observation. Model accumulation rates are within about 20% of values obtained by other techniques. It is suggested that depth intervals of constant density in some Antarctic cores may represent a synchronous event in the 1880 ’s when ten times the normal accumulation fell within a year or two.
Synchronization in simplicial complexes of memristive Rulkov neurons

Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure pairwise hybrid (electrical and chemical) synapses alongside a network with two-node electrical and multi-node chemical connections. The findings provide good insight into the impact of incorporating higher-order interaction in a network. Compared to two-node chemical synapses, higher-order interactions adjust the synchronization patterns to lower multi-node chemical coupling parameter values. Furthermore, the effect of altering higher-order coupling parameter value on the dynamics of neurons in the synchronization state is researched. It is also shown how increasing network size can enhance synchronization by lowering the value of coupling parameters whereby synchronization occurs. Except for complete synchronization, cluster synchronization is detected for higher electrical coupling strength values wherein the neurons are out of the completed synchronization state.
Intermediate pressure-normalized principal wall strain values are associated with increased abdominal aortic aneurysmal growth rates

Introduction Current abdominal aortic aneurysm (AAA) assessment relies on analysis of AAA diameter and growth rate. However, evidence demonstrates that AAA pathology varies among patients and morphometric analysis alone is insufficient to precisely predict individual rupture risk. Biomechanical parameters, such as pressure-normalized AAA principal wall strain (ερ+¯/PP, %/mmHg), can provide useful information for AAA assessment. Therefore, this study utilized a previously validated ultrasound elastography (USE) technique to correlate ερ+¯/PP with the current AAA assessment methods of maximal diameter and growth rate. Methods Our USE algorithm utilizes a finite element mesh, overlaid a 2D cross-sectional view of the user-defined AAA wall, at the location of maximum diameter, to track two-dimensional, frame-to-frame displacements over a full cardiac cycle, using a custom image registration algorithm to produce ερ+¯/PP. This metric was compared between patients with healthy aortas and AAAs (≥3 cm) and compared between small and large AAAs (≥5 cm). AAAs were then separated into terciles based on ερ+¯/PP values to further assess differences in our metric across maximal diameter and prospective growth rate. Non-parametric tests of hypotheses were used to assess statistical significance as appropriate. Results USE analysis was conducted on 129 patients, 16 healthy aortas and 113 AAAs, of which 86 were classified as small AAAs and 27 as large. Non-aneurysmal aortas showed higher ερ+¯/PP compared to AAAs (0.044 ± 0.015 vs. 0.034 ± 0.017%/mmHg, p = 0.01) indicating AAA walls to be stiffer. Small and large AAAs showed no difference in ερ+¯/PP. When divided into terciles based on ερ+¯/PP cutoffs of 0.0251 and 0.038%/mmHg, there was no difference in AAA diameter. There was a statistically significant difference in prospective growth rate between the intermediate tercile and the outer two terciles (1.46 ± 2.48 vs. 3.59 ± 3.83 vs. 1.78 ± 1.64 mm/yr, p = 0.014). Discussion There was no correlation between AAA diameter and ερ+¯/PP, indicating biomechanical markers of AAA pathology are likely independent of diameter. AAAs in the intermediate tercile of ερ+¯/PP values were found to have nearly double the growth rates than the highest or lowest tercile, indicating an intermediate range of ερ+¯/PP values for which patients are at risk for increased AAA expansion, likely necessitating more frequent imaging follow-up.
Molecular profiling and specific targeting of gemcitabine-resistant subclones in heterogeneous pancreatic cancer cell populations

Purpose Chemotherapy is pivotal in the multimodal treatment of pancreatic ductal adenocarcinoma (PDAC). Technical advances unveiled a high degree of inter- and intratumoral heterogeneity. We hypothesized that intratumoral heterogeneity (ITH) impacts response to gemcitabine treatment and demands specific targeting of resistant subclones. Methods Using single cell-derived cell lines (SCDCLs) from the classical cell line BxPC3 and the basal-like cell line Panc-1, we addressed the effect of ITH on response to gemcitabine treatment. Results Individual SCDCLs of both parental tumor cell populations showed considerable heterogeneity in response to gemcitabine. Unsupervised PCA including the 1,000 most variably expressed genes showed a clustering of the SCDCLs according to their respective sensitivity to gemcitabine treatment for BxPC3, while this was less clear for Panc-1. In BxPC3 SCDCLs, enriched signaling pathways EMT, TNF signaling via NfKB, and IL2STAT5 signaling correlated with more resistant behavior to gemcitabine. In Panc-1 SCDCLs MYC targets V1 and V2 as well as E2F targets were associated with stronger resistance. We used recursive feature elimination for Feature Selection in order to compute sets of proteins that showed strong association with the response to gemcitabine. The optimal protein set calculated for Panc-1 comprised fewer proteins in comparison to the protein set determined for BxPC3. Based on molecular profiles, we could show that the gemcitabine-resistant SCDCLs of both BxPC3 and Panc-1 are more sensitive to the BET inhibitor JQ1 compared to the respective gemcitabine-sensitive SCDCLs. Conclusion Our model system of SCDCLs identified gemcitabine-resistant subclones and provides evidence for the critical role of ITH for treatment response in PDAC. We exploited molecular differences as the basis for differential response and used these for more targeted therapy of resistant subclones.
Physical Education Content Knowledge and Physical Activity Behaviors of Mississippi High School Students

PHYSICAL EDUCATION CONTENT KNOWLEDGE AND PHYSICAL ACTIVITY BEHAVIORS OF MISSISSIPPI HIGH SCHOOL STUDENTS by Christina L. L. Martin Abstract of a Dissertation Submitted to the Graduate Studies Office of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophyof a Dissertation Submitted to the Graduate Studies Office of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
The Interaction of Research Methods for System Evaluation and Theory Testing: A New Vision of the Benefits of Multi-Methodological Information Systems Research

A number of multi-methodological Information Systems research frameworks have been proposed to guide research in the IS field. In this paper, we describe a three-year program of research evaluating a computerbased training system for deception detection (Agent99 Trainer) using one such multi-methodological IS framework. The process and results of this research program clearly demonstrate the benefits of combining and triangulating multiple research methods. Moreover, through the experiences in the Agent99 Trainer study, we discover two different types of interactions between the research methods that were applied in this study. One of the interactions is between the results from different methods, while the other is between the results and the methods themselves. Hence, we propose an extended interpretation of multi-methodological IS research frameworks to incorporate these two types of interactions between research methods. We argue that this extended interpretation will provide a more specific explanation for why the triangulation among multiple research methods can provide benefits to IS research.
A qualitative study on traditional healers’ perceptions and management of epidermolysis bullosa

Background Epidermolysis bullosa (EB) is a rare, incurable genodermatosis causing blisters that can result in multisystemic complications and death. Limited data exists on EB in South Africa. Research indicates that the majority of African patients consult traditional health practitioners (THPs) before seeking allopathic healthcare. Aim This study aims to understand THPs belief systems, experiences, perceptions and management of EB patients and their families in the social and cultural context to improve the healthcare of EB patients. Setting The study setting is Nelson Mandela School of Medicine, Durban, and Grey’s hospital, Pietermaritzburg, KwaZulu-Natal. Methods Qualitative in-depth interviews were conducted with 10 THPs. A non-probability, purposive sampling method was used. A two-site qualitative study was guided by interpretative phenomenological analysis. Guba’s trustworthiness framework was used to ensure rigour. Results Three male and seven female THPs were interviewed, including sangoma, inyanga and umthandazi. The integration presented five global themes: (1) THP practices, (2) perceptions of THP, (3) experiences of THP with patients with EB, (4) diagnosis and management plans of THP and (5) vision and role of THPs. There were multiple divergent perspectives among the THPs with the shared African worldview. Conclusion Understanding THPs belief systems and therapeutic options is crucial for holistic patient management. Knowledge exchange can promote safe healthcare practices and facilitate collaboration between traditional and allopathic health practitioners. Contribution This is the first study to explore THPs perceptions and practices regarding EB, a rare disease.
Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. Methods In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. Results The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus. Conclusion Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.
Efficient Implementation of Many-Ported Memories by Using Standard-Cell Memory Approach

Multi-ported memories are widely used in many applications, such as for high-speed and high-performance parallel computations. While conventional SRAM-based memory macros are limited in both flexibility (e.g., to accommodate a large number of write inputs and read outputs) and performance, standard cell-based memories are much more flexible and can be parameterized. The physical implementation approaches of the existing tools have great difficulty in dealing with these memories due to the multiplicity of wiring and misunderstanding of the regular structure of these arrays, to the point of inability to converge under certain conditions. This paper presents novel methodologies for the logical and physical implementation of many-ported standard cell memories (MPSCMs). Two methodologies are proposed to replace the standard design flow by controlling and guiding the design tools to improve power consumption, area and performance of these arrays. A commercial 65 nm CMOS technology was used to evaluate and benchmark the two design methodologies on MPSCM macros of different sizes as compared to other equivalent macros designed with standard methodologies and state-of-the-art designs. Physical implementation results show that as compared to a standard RTL approach, the implementation of a 3-write, 5-read port (3W5R) register file with the tightly-controlled methodology leads a $2\times $ increase in placement density along with significant reductions in write power (–66%), read power (–37%) and leakage power (–80%), while also improving the access time (–6%). When considering an extreme case of a many-ported memory with 20-write and 20-read ports (20W/20R), the guided methodology leads to improvements in delay (–13%), write power (–62%), and leakage power (–51%). Both methodologies were implemented within an automation utility based on the “Salamandra” open source netlisting tool, enabling fast and easy migration to additional process nodes and standard cell libraries for generating MPSCMs with various sizes and features.