Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the understanding of neural mechanisms and guiding targeted therapy for clinical syndrome after thalamic infarction with advanced neuroimaging

Thalamic infarction can result in a diverse array of symptoms, including motor and sensory deficits, memory and attention difficulties, and alterations in mood and behavior; these symptoms are collectively referred to as thalamic infarction syndrome. The neural mechanisms underlying these symptoms are not yet fully understood, hampering the development of effective and individualized treatments. The current understanding of the pathology of thalamic infarction syndrome is mainly based on conventional computed tomography/magnetic resonance imaging scans, which reveal blood supply to various nuclei groups and clinical features. Thalamic infarction syndrome can be categorized into four groups according to the affected territory and associated vascular syndrome. Recent advancements in neuroimaging techniques, which enable the precise identification of affected pivotal thalamic subnuclei, altered brain structures, white matter pathway integrity, abnormal neural activity, and maladaptive states of brain networks, can enhance our understanding of the clinical mechanisms and inform the development of more effective therapeutic strategies. This review summarizes research on the pathological neural mechanisms of thalamic infarction syndrome and highlights future directions.
Intravascular Large B-Cell Lymphoma (IVLBCL) Presenting with CNS Involvement in Patient with Chronic Lymphocytic Leukemia

Background: Intravascular lymphoma (IVL) is a rare, often fatal disease characterized by intraluminal proliferation of lymphoid cells within blood vessels. Intravascular Large B-Cell Lymphoma (IVLBCL) is frequently found in different organs, but the skin and central nervous system (CNS) are the most affected ones. Routine radiological examination and additional diagnostic tools, such as high-resolution, three-dimensional (3D) FLAIR sequence, T2-weighted (T2W) and T1-weighted (T1W) contrast-enhanced magnetic resonance imaging (MRI), MRI diffusion and perfusion imaging studies are useful to confirm the diagnosis in cases where it is not easy to perform histopathological examinations [1]. 1.2. Case Report: We present a clinical case of a 63-year-old female patient who presented with progressive cognitive deficit as mild motor aphasia, acalculia, agraphia, dyslexia and mild ideomotor apraxia without any signs of other organ involvement. It was known that the patient had 5-year anamnesis of chronic lymphocytic leukemia, stage II and splanchnic venous thrombosis 2 months ago. Blood biochemical analysis without any significant change, except leukocytosis in full blood count (leukocytes 15,2 x 10*9/L, where neutrophils were in normal range, but lymphocytes were increased). Head magnetic resonance imaging (MRI) showed pathological signal areas on the left parietal lobe with hypointense signal on contrast-enhanced T1-weighted, hyperintense signal on contrast-enhanced T2-weighted and FLAIR-weighted MRI images with restricted diffusion and slightly lower ADC value. Magnetic resonance perfusion imaging studies did not show an increase in cerebral blood flow or in cerebral blood volume. In this case, the patient’s diagnosis was made based on typical radiological features. 1.3. Conclusions: Intravascular large B-cell lymphoma is a rare subtype of extra nodal large B-cell lymphoma with an aggressive clinical course. The diagnosis is challenging, as it presents without any obvious tumour mass or lymphadenopathy. [2] As the clinical presentation is variable without specific characteristics, mostly biopsy or even autopsy confirms the definite diagnosis. But in some cases, the diagnosis can be made by typical radiological findings in the restricted availability of brain biopsy.
An ENG resonator-based microwave sensor for the characterization of aqueous glucose

This work proposes a microwave filter with a notched frequency of transmission using an epsilon negative (ENG) unit-cell resonator as a sensor device. The device finds important application for the characterization of life-saving samples such as glucose. The ENG structure consists of two complementary geometries in the shape of ring and horn. The structure efficiently inhibits the incoming RF signal and creates a stopband resonance at 2.074 GHz. The printed circuit board of the layout was realized using FR-4 substrate of relative permittivity εr = 4.4, and height of 1.6 mm. It is experimentally seen that in the complementary area of horn and circular ring, the glucose sample perturbs the air–dielectric fringing fields which exist over the complementary area and modifies the frequency of stopband resonance. A change in sensor resonance was recorded and calibrated for different concentrations of glucose sample. The sensor exhibits a linear response for glucose concentration ranging from 20 to 100 mg ml−1 in the sensing area.
CPVD: Cross Project Vulnerability Detection Based on Graph Attention Network and Domain Adaptation

Code vulnerability detection is critical for software security prevention. Vulnerability annotation in large-scale software code is quite tedious and challenging, which requires domain experts to spend a lot of time annotating. This work offers CPVD, a cross-domain vulnerability detection approach based on the challenge of ”learning to predict the vulnerability labels of another item quickly using one item with rich vulnerability labels. ” CPVD uses the code property graph to represent the code and uses the Graph Attention Network and Convolution Pooling Network to extract the graph feature vector. It reduces the distribution between the source domain and target domain data in the Domain Adaptation Representation Learning stage for cross-domain vulnerability detection. In this paper, we test each other on different real-world project codes. Compared with methods without domain adaptation and domain adaptation methods based on natural language processing, CPVD is more general and performs better in cross-domain vulnerability detection tasks. Specifically, for the four datasets of chr_deb, qemu, libav, and sard, they achieved the best results of 70.2%, 81.1%, 59.7%, and 78.1% respectively on the F1-Score, and 88.4%,86.3%, 85.2%, and 88.6% on the AUC.
Cover sheet : Title : PET-guided MR Spectroscopy in Alzheimer ’ s disease . Running Head : MR Spectroscopy in Alzheimer ’ s disease

1 STAHA FACILITY EXIT QUESTIONNAIRE COVERSHEET Interviewer Name District 1 Korogwe 2 Muheza Facility name 1 Magunga Hospital 2 Teule Hospital ID number (district code + interviewer code + respondent code) Interview date & starting time (AUTOMATIC STAMP) Interview ending time (AUTOMATIC STAMP) INTRODUCTION “Hello my name is.and I am working with Ifakara Health Institute and Columbia University in the United States. The reason I am here is because we are conducting a survey on health in Tanga region and I would like to ask a few questions of women who have given birth in this health care facility. I will be asking for basic information about yourself, your household, and your health. I will also ask questions about your use of health care in general and about your experiences with child delivery. Let me assure you that whatever information is give to me will not be shared with anyone and will only be used for research purposes. ”
A Survey on Crop Yield Prediction Models

Objectives : To analysis various models to improve the prediction of crop yield production. Methods : In this paper, there are different methods has been analyzed to predict the crop yield. The methods such as artificial neural network, Adaptive Neuro-Fuzzy inference System, Fuzzy Logic and Multi Linear Regression are analyzed to know the best methods for crop yield prediction. The prediction of crop yield varied by internal factors and external factors of crop an environment. The internal factors such as pesticides, water level, spacing and fertilizers and the external factors such as temperature, humidity, and rainfall. There are various models were developed to predict the crop yield prediction. This paper provides detailed information about the different models for crop yield prediction. Findings : In this paper various models for crop yield prediction are compared through their parameters such as Root Mean Square Error (RMSE), R 2 ,correlation coefficient and Mean Square Error (MSE) to prove Adaptive NeuroFuzzy Inference System (ANFIS)prediction model is better than other techniques. Application/improvements : The findings of this work prove that the Adaptive Neuro Fuzzy Inference System (ANFIS) prediction model provides better result than other approaches.
A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion

This study proposes an improvement in the performance of reduced-order models (ROMs) based on dynamic mode decomposition to model the flow dynamics of the attractor from a transient solution. By combining higher order dynamic mode decomposition (HODMD) with an efficient mode selection criterion, the HODMD with criterion (HODMDc) ROM is able to identify dominant flow patterns with high accuracy. This helps us to develop a more parsimonious ROM structure, allowing better predictions of the attractor dynamics. The method is tested in the solution of a NACA0012 airfoil buffeting in a transonic flow, and its good performance in both the reconstruction of the original solution and the prediction of the permanent dynamics is shown. In addition, the robustness of the method has been successfully tested using different types of parameters, indicating that the proposed ROM approach is a tool promising for using in both numerical simulations and experimental data.
P-branes and the field theory limit

We investigate Domain–Wall (DW)/Quantum Field Theory (QFT) correspondences in various dimensions. Our general analysis does not only cover the well–studied cases in ten and eleven dimensions but also enables us to discuss DW/QFT correspondences in lower dimensions. New examples include ‘d–branes’ in six dimensions preserving 8 supersymmetries.
Abstract 11851: Acetate Rescues the Gut Microbial Metabolites PAA/PAG-induced Vascular Senescence by Epigenetic and SASP Regulation

Introduction: Age-related gut microbiota alteration may underpin vascular disease. Yet, little is known about the role of cardiovascular disease-linked gut microbial metabolite phenylacetyl glutamine (PAG) in vascular aging. Hypothesis: 1) PAG promotes endothelial senescence via epigenetic and SASP modulation; 2) Acetate rescues PAG-induced endothelial senescence and impaired angiogenesis. Methods: We quantitated plasma concentrations of PAG and its precursor phenylacetic acid (PAA) in old and young healthy humans (>65 vs. 18-35 years, n=41-45) and mice (24 vs. 3 months, n=6) by LC-MS/MS. Fecal acetate was measured by HPLC-RI. We then treated proliferating human aortic endothelial cells (PEC) with PAA+Glutamine (for PAG production) to examine its effects on cellular senescence, epigenetic and SASP state, mitochondrial respiration, and angiogenesis. Results: We showed markedly higher plasma PAA and PAG concentrations in old humans and mice vs. young ones. Yet, fecal acetate level was significantly lower in old vs. young mice. PAG significantly induced senescence (increased SA-β-gal positive cells and transcripts p16, p19, p21) and mitochondrial ROS production in PEC associated with upregulated SASP (IL1α, IL1β, TNF-α and adhesion molecule VCAM-1), and reduced mitochondrial respiration. PAG markedly reduced angiogenesis (endothelial sprouting and tube formation) accompanied by decreases in CaMKKβ T286 and histone deacetylase 4 (HDAC4 S632 ) phosphorylation, histone 3 (H3) acetylation, and the subsequent Mef2A-mediated eNOS S1177 phosphorylation. By contrast, acetate (3 μM) rescued senescence and impaired angiogenesis and mitochondrial function and reversed SASP and epigenetic alteration induced by PAG in PEC. Conclusions: We conclude that acetate regulates the aging gut microbial metabolite PAG-induced SASP and epigenetic alteration, by which rescues endothelial senescence and represents a potential vascular regenerative strategy.
High performance of graphene photodetector using double-cavity structure

Abstract. A double-cavity graphene photodetector consisting of filtering cavity and absorption cavity with high performance is proposed. The device has an ultranarrow response linewidth and high quantum efficiency as well as high response speed. By taking full advantage of the double cavity, the structure parameters of the photodetector are deeply optimized, and 88% peak quantum efficiency with 0.75-nm linewidth is obtained. It is believed that the double-cavity graphene photodetector has promising and potential application in a high-speed density wavelength-division multiplexing system.
High Dimensional Covariance Estimation for Spatio-Temporal Processes

High Dimensional Covariance Estimation for Spatio-Temporal Processes by Kristjan Greenewald Chairs: Alfred O. Hero III and Shuheng Zhou High dimensional time series and array-valued data are ubiquitous in signal processing, machine learning, and science. Due to the additional (temporal) direction, the total dimensionality of the data is often extremely high, requiring large numbers of training examples to learn the distribution using unstructured techniques. However, due to difficulties in sampling, small population sizes, and/or rapid system changes in time, it is often the case that very few relevant training samples are available, necessitating the imposition of structure on the data if learning is to be done. The mean and covariance are useful tools to describe high dimensional distributions because (via the Gaussian likelihood function) they are a data-efficient way to describe a general multivariate distribution, and allow for simple inference, prediction, and regression via classical techniques. In this work, we develop various forms of multidimensional covariance structure that explicitly exploit the array structure of the data, in a way analogous to the widely used low rank modeling of the mean. This allows dramatic reductions in the number of training samples required, in some cases to a single training sample.
Deep-Learning-Based System for Change Detection Onboard Earth Observation Small Satellites

In recent years, the important evolution in the number, potentiality, and diversity of Earth observation (EO) satellites has resulted in dramatic increases in the payload data volume and rate. However, these exponential increases in the generated data volume are creating a significant bottleneck onboard EO satellites due to transmission bandwidth limits and communication delays. Onboard imaging payload data processing can provide an appropriate solution to alleviate the induced data bottleneck. It can also facilitate rapid response for decision-making operations. Change detection is one of the most significant functions in onboard payload data processing systems that enable a real-time reaction to natural disasters, such as flooding, earthquakes, and volcanic eruptions. In this article, we address the problem of automatic change detection onboard EO satellites. This article aims to design an automatic onboard change detection system (OCDS) that can run on existing flight-proven hardware by taking advantage of the attractive features of a leading model in deep learning (DL) called convolutional neural network. The contribution of this article is twofold. An efficient algorithmic solution for change detection based on DL that fulfills space environment-induced constraints is first proposed. Second, a preliminary hardware architecture of the proposed OCDS is designed based on payload data processing flight-proven hardware. The experimental results demonstrate the efficiency of the proposed DL-based change detection approach and the suitability of the designed OCDS for onboarding on EO small satellites.
Big Graph Processing : Partitioning and Aggregated Querying

Avec l'avenement du « big data », de nombreuses repercussions ont eu lieu dans tous les domaines de la technologie de l'information, preconisant des solutions innovantes remportant le meilleur compromis entre couts et precision. En theorie des graphes, ou les graphes constituent un support de modelisation puissant qui permet de formaliser des problemes allant des plus simples aux plus complexes, la recherche pour des problemes NP-complet ou NP-difficils se tourne plutot vers des solutions approchees, mettant ainsi en avant les algorithmes d'approximations et les heuristiques alors que les solutions exactes deviennent extremement couteuses et impossible d'utilisation.Nous abordons dans cette these deux problematiques principales: dans un premier temps, le probleme du partitionnement des graphes est aborde d'une perspective « big data », ou les graphes massifs sont partitionnes en streaming. Nous etudions et proposons plusieurs modeles de partitionnement en streaming et nous evaluons leurs performances autant sur le plan theorique qu'empirique. Dans un second temps, nous nous interessons au requetage des graphes distribues/partitionnes. Dans ce cadre, nous etudions la problematique de la « recherche agregative dans les graphes » qui a pour but de repondre a des requetes interrogeant plusieurs fragments de graphes et qui se charge de la reconstruction de la reponse finale tel que l'on obtient un « matching approche » avec la requete initiale
Patient Satisfaction Level at the Outpatient in University of Muhammadiyah Malang Hospital

Introduction: The hospital is one of the health care facilities expected to provide appropriate health services and information for patients and the broader community. Patient satisfaction is one of the essential things for health care providers as a measuring tool to evaluate service quality. Satisfaction can increase the number of visits that impact increasing sources of income for hospitals and, at the same time, realize a healthy and prosperous society. Objectives: The purpose of this study was to identify patient satisfaction in the outpatient installation of the Muhammadiyah Malang University Hospital in indicators of conformity with expectations, ease of obtaining services, and willingness to recommend. Methods: The design of this research is descriptive observational. The sample in this study were patients who visited the outpatient installation of the University of Muhammadiyah Malang Hospital in November 2020; as many as 136 respondents were selected by accidental sampling. Data collection with a patient satisfaction questionnaire. Data analysis with descriptive analysis and presented in the frequency distribution. Results: The results showed that as many as 68.4% of respondents stated that the service was as expected, 52.2% were easy to access services, 50.7% were willing to recommend to other parties, and 54.4% said they were satisfied. Conclusions: Patient satisfaction with the University of Muhammadiyah Malang Hospital in several indicators is relatively high, but it needs improvement in several respects. Nurses, doctors, and other human resources need to maintain good service delivery by providing appropriate, fast, always smiling, and friendly service to patients.
The Effectof Strategic Orientationonthe Competitive Advantage Case Study : a Sample of Economic Companies in "Biskra

The aim of this paper is to determine the impact of the dimensions of strategic orientation at the competitive advantage level . A questionnaire was developed especially to achieve this objective, and distributed to 30 supervisor from 4 economic institutionsin ‘biskra’ representing the study sample.The study concludes several results of which the most important was the adoption of strategic orientations ( Analysis, Defensiveness, Proactiveness and reactor). The results showed that there is a positive relationship between those orientations and competitive advantages. and the relationship between the Proactive orientations and competitive advantages the strongest relationships.The study recommended conducting similar studies applied in other firm’s in biskra environment, In addition to addressing the strategic orientations with other variables not covered in the current study.
Fuzzy Fourier system for function mapping

This paper presents a new system called Fuzzy Fourier system (FFS) for periodic functions mapping. The FFS composes a set of fuzzy rules in fuzzy Takagi-Sugeno-Kan fuzzy models. We set the consequent of each rule to be a Fourier series with adjustable parameters (linear orthogonal cosine functions). Therefore, the input/output mapping of FFS is periodic. In addition, with minor revised, The FFS can be used to be a digital-filter for given magnitude specification. Simulation examples illustrate the effectiveness of our approach.
Study of neutron irradiated structures of ammonothermal GaN

Study of the radiation damage in GaN-based materials becomes an important aspect for possible application of the GaN detectors in the harsh radiation environment at the Large Hadron Collider and at other particle acceleration facilities. Intentionally doped and semi-insulating bulk ammonothermal GaN materials were studied to reveal the dominant defects introduced by reactor neutron irradiations. These radiation defects have been identified by combining electron spin resonance and transmission spectroscopy techniques. Characteristics of carrier lifetime dependence on neutron irradiation fluence were examined. Variations of the response of the capacitor-type sensors with neutron irradiation fluence have been correlated with the carrier lifetime changes. The measurements of the photoconductivity and photoluminescence transients have been used to study the variation of the parameters of radiative and non-radiative recombination. The examined characteristics indicate that AT GaN as a particle sensing material is radiation hard up to high hadron fluences ⩾1016 cm−2.
Polarization Stability of Spun Fiber Resonator for Resonant Fiber Optic Gyro

As an important inertial navigation sensor, resonant fiber optic gyro (RFOG) has become a research hotspot in recent years due to its advantages of lightweight and high stability. The optical effects in the fiber ring resonator (FRR), such as scattering effects, polarization mode crosstalk, and so on, are the bottleneck restricting the development of RFOG, and they are also the key problems that researchers are trying to solve. Aiming at the problem of polarization noise, this article conducts in-depth theoretical and experimental research on polarization stability using spun fiber resonator for the first time. Compared with a common polarization-maintaining fiber (PMF) resonator of the same length, the results show that the RFOG using the spun fiber resonator has improved temperature stability compared with the conventional resonator structure. This research provides a new idea for the polarization noise suppression method of RFOG and lays an important foundation for the practical application of spun fiber in RFOG.