Miracle-dz/UniFAR-FTUs huggingface dataset:
Soluble endoglin regulates expression of angiogenesis-related proteins and induction of arteriovenous malformations in a mouse model of hereditary hemorrhagic telangiectasia

ABSTRACT Endoglin is a transmembrane glycoprotein expressed in vascular endothelium that plays a key role in angiogenesis. Mutations in the endoglin gene (ENG) cause hereditary hemorrhagic telangiectasia type 1 (HHT1), characterized by arteriovenous malformations (AVMs) in different organs. These vascular lesions derive from abnormal processes of angiogenesis, whereby aberrant vascular remodeling leads to focal loss of capillaries. Current treatments for HHT1 include antiangiogenic therapies. Interestingly, a circulating form of endoglin (also known as soluble endoglin, sEng), proteolytically released from the membrane-bound protein and displaying antiangiogenic activity, has been described in several endothelial-related pathological conditions. Using human and mouse endothelial cells, we find that sEng downregulates several pro-angiogenic and pro-migratory proteins involved in angiogenesis. However, this effect is much reduced in endothelial cells that lack endogenous transmembrane endoglin, suggesting that the antiangiogenic activity of sEng is dependent on the presence of endogenous transmembrane endoglin protein. In fact, sEng partially restores the phenotype of endoglin-silenced endothelial cells to that of normal endothelial cells. Moreover, using an established neonatal retinal model of HHT1 with depleted endoglin in the vascular endothelium, sEng treatment decreases the number of AVMs and has a normalizing effect on the vascular phenotype with respect to vessel branching, vascular density and migration of the vascular plexus towards the retinal periphery. Taken together, these data show that circulating sEng can influence vascular development and AVMs by modulating angiogenesis, and that its effect on endothelial cells depends on the expression of endogenous endoglin. This article has an associated First Person interview with the first author of the paper. Summary: Soluble endoglin regulates vascular development and arteriovenous malformations by modulating angiogenesis, and its effect on endothelial cells depends on expression of endogenous membrane-bound endoglin.
Effects of Juniperus phoenicea Hydroalcoholic Extract on Inflammatory Mediators and Oxidative Stress Markers in Carrageenan-Induced Paw Oedema in Mice

Juniperus phoenicea (J. phoenicea) is a wild tree belonging to the Cupressaceae family, commonly used for the treatment of several disorders. This study aimed to evaluate the potential protective effects of J. phoenicea hydroethanolic extract (EtOH-H2OE) against oxidation, acute inflammation, and pain in mice models. For the purpose, chemical compounds of J. phoenicea EtOH-H2OE were also analyzed by GC-MS. The J. phoenicea EtOH-H2OE showed a potent antioxidant activity in vitro, thanks to its richness in phenolic and flavonoid compounds. Mice treated with EtOH-H2OE (100 mg/kg BW) showed reduced paw oedema formation and decreased malondialdehyde (MDA) content. The evaluation of antioxidant enzyme activities in paw oedema tissue after five hours of carrageenan induction showed a significant increase (P < 0.05). Inflammatory biomarkers explorations of J. phoenicea EtOH-H2OE-treated mice showed a restoration of the studied parameters to near-normal values. Furthermore, EtOH-H2OE of J. phoenicea produced a significant reduction of the number of abdominal writhes (P < 0.05) in a dose-dependent way. Phytochemical analysis of the J. phoenicea EtOH-H2OE by GC-MS showed the presence of hexadecanoic and stearic acids known as anti-inflammatory and analgesic compounds. Our investigation provided evidence that J. phoenicea EtOH-H2OE can effectively reduce the inflammation and pain in mice models.
Direction of arrival estimation method for multiple UHF partial discharge sources based on virtual array extension

The localization method of multiple partial discharge (PD) of power equipment in airinsulated substation based on traditional ultra-high frequency (UHF) sensor array suffer from the problem of low accuracy, due to the lack of enough sensors. Thus, a novel virtual array extension method based on Compressive Sensing (CS) is proposed in this paper. The traditional 2×2 UHF array is virtually extended to a N×N (N>2) array, with the steering vector matrix as the basis transformation matrix, the random Gaussian matrix as the measurement matrix, and the orthogonal matching pursuit (OMP) algorithm as the signal reconstruction algorithm. The PD signal received by the extended array is used to construct the PD spatial spectrum, on the basis of wide-band signal focusing. The directional angle and pitch angle of PD sources are obtained by performing peak search of the PD spatial spectrum. The simulation results show that the extended 4×4 array can detect up to 3 PD sources simultaneously. Moreover, the directional of arrival (DOA) estimation accuracy is greatly improved. Compared to the traditional 2×2 array, the directional angle estimation error is reduced from 10° to 4°. The experiment results further verified the effectiveness of the proposed method.
Assessing Learning Analytics Systems Impact by Summative Measures

This paper introduces a randomized study conducted among a group of 48 student Java programmers to assess the impact of learning analytics (LA) on their academic performance. The LA system design incorporated both cognitive and metacognitive tools to help learners take possession of their learning processes. Participation was voluntary and data about potential confounding factors were also collected to minimize bias by blocking on two or more factors (future work). This paper summarily explores the relationships between students' programming expertise, coding assignments, user experience and satisfaction, and academic performance. The results of this preliminary exploration are inconclusive as to whether the LA system made a difference in academic performance. Nevertheless, they seem to indicate that LA was beneficial to student programmers and that summative measures such as grades are not a proper metric to measure the usefulness of LA systems.
Microglia in Health and Disease: A Unique Immune Cell Population

Microglia are essential for the development and function of the adult brain. Microglia arise from erythro-myeloid precursors in the yolk sac and populate the embryonic brain early during development. Unlike monocytes, which are constantly renewed via the differentiation of bone marrow-derived hematopoietic stem cells throughout life, resident microglia in the healthy brain are thought to persist during adulthood via constant self-renewal. Their ontogeny, together with the absence of turnover from the periphery and the singular environment of the central nervous system (CNS), make microglia a unique cell population. Supporting this notion, recent transcriptional studies have revealed that microglia display specific gene expression signatures that are clearly distinct from other brain and peripheral immune cell populations in the healthy and diseased CNS. The unique properties of microglial cells during development, as well as in the adult CNS, such as their role in synaptic stripping or the exceptional capacity to scan the brain parenchyma and rapidly react to its perturbations, have emerged in recent years. In the coming years, understanding (i) how microglia acquire and maintain their unique profiles in order to fulfill distinct tasks in the healthy CNS and (ii) how these are altered in perturbed environments will be essential to develop strategies to diagnose or treat CNS disorders with an immunological component. This Research Topic will provide a comprehensive overview of our current understanding of the cellular and molecular mechanisms that make microglia a unique immune cell population within the healthy CNS as well as in inflammatory, neurodegenerative and tumorigenic processes. We welcome the submission of Original Research, Protocol, Review and Mini-Review articles that cover, but are not limited to, the following topics.
Burden and Determinants of Anemia in a Rural Population in South India: A Cross-Sectional Study

Background/Objectives To determine the prevalence and determinants of blood haemoglobin level and mild, moderate, and severe anemia in a sample of adults from rural Tamil Nadu, India. Subjects/Methods We recruited a sample of men and nonpregnant women aged 20 years and older. Clinical health measures included blood haemoglobin concentration and body mass index. We assessed associations between anemia outcomes and sociodemographic and dietary factors using linear and logistic regression modeling. Results A total of 753 individuals (412 women and 341 men) participated in this study. The prevalence of anemia was 57.2% among women and 39.3% among men (P<0.001). Prevalence of anemia increased with age among men (P<0.001) but not women (P>0.05). Iron intake was low; 11.7% women and 24.1% of men reported iron intakes above recommended dietary allowances (P<0.001). Factors (OR (95% CI)) associated with mild or moderate anemia among women included television ownership (0.27 (0.13, 0.58)), livestock ownership (0.46 (0.28, 0.75)), refined grain consumption (1.32 (1.02, 1.72)), meat consumption (0.84 (0.71, 0.99)), and commercial agriculture production (mild: 4.6 (1.1, 18.8); moderate: 6.8 (1.98, 23.1)). Factors associated with mild, moderate, or severe anemia among men included rurality (0.50 (0.25, 0.99)), sugar consumption (1.04 (1.01, 1.06)), egg consumption (0.80 (0.65, 0.99)), and high caste (7.3 (1.02, 52.3)). Conclusion Both women and men in this region may be particularly vulnerable to anemia, and future research must expand beyond dietary risk factors to examine the impacts of sociodemographic and environmental factors.
Correction to “Identification of Vital Nodes in Complex Network via Belief Propagation and Node Reinsertion”

In the published version of our paper <xref ref-type="bibr" rid="ref1">[1]</xref>, because of the first author’s lack of experience, we did not clearly state that the Belief Propagation (BP) algorithm and the underlying spin glass model has been originally developed by Zhou <italic>et al.</italic> <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref>. Even if we did include <xref ref-type="bibr" rid="ref2">[2]</xref> and <xref ref-type="bibr" rid="ref3">[3]</xref> in the reference list of <xref ref-type="bibr" rid="ref1">[1]</xref>, this ambiguity may mislead the reader into thinking that the BP algorithm is our original contribution. In this Correction, the contributions of <xref ref-type="bibr" rid="ref2">[2]</xref> and <xref ref-type="bibr" rid="ref3">[3]</xref> are explicitly acknowledged. The main results and conclusion of our paper <xref ref-type="bibr" rid="ref1">[1]</xref> remain the same.
Movement Disorder Detection via Adaptively Fused Gait Analysis Based on Kinect Sensors

This paper presents a novel method for movement disorder detection using a single Kinect sensor. Numerous recent studies have focused on gait analysis involving spatiotemporal parameters, such as the step length and gait cycle. However, these methods ignore significant kinematic information, e.g., leg swing characteristics. Therefore, in this paper, we 1) introduce a new concept called gait symmetry to measure the similarity of leg swing motions using the correlation among the angles formed by the hip, knee, and ankle joints; 2) extract the step length and gait cycle using a zero-crossing detection method; and 3) explore the K-means and Bayesian methods to conduct a combined gait analysis using the three extracted gait parameters. In addition, to reduce the effects of walking differences related to age and gender, we conduct a gait analysis of different age and gender groups. In our experiments, we collect a data set consisting of data from 120 participants (50 with disordered walking patterns and 70 with normal walking patterns). The experimental results show that 1) including the proposed gait symmetry parameter greatly improves detection accuracy, 2) joint utilization of the three gait parameters can enable significantly better performance, and 3) considering age and gender information can enable more robust detection.
${L_{2,1}}$ -Norm Discriminant Manifold Learning

Recently, <inline-formula> <tex-math notation="LaTeX">${L_{1}}$ </tex-math></inline-formula>-norm-based robust discriminant feature extraction technique has been attracted much attention in dimensionality reduction and pattern recognition. However, it does not relate to the scatter matrix which well characterizes the geometric structure of data. In this paper, we propose a robust formulation of graph embedding framework for dimensionality reduction. In this robust framework, we use <inline-formula> <tex-math notation="LaTeX">${L_{2}}$ </tex-math></inline-formula>-norm to measure the distance along space dimension and <inline-formula> <tex-math notation="LaTeX">${L_{1}}$ </tex-math></inline-formula>-norm to sum overall data points. The proposed robust graph embedding framework retains the traditional framework’s desirable properties, such as rotational invariance and well geometric structure, and simultaneously suppresses outliers. Based on this framework, we develop a simple and robust feature extraction method, namely <inline-formula> <tex-math notation="LaTeX">${L_{2,1}}$ </tex-math></inline-formula>-norm-based discriminant locality preserving projections (<inline-formula> <tex-math notation="LaTeX">${L_{2,1}}$ </tex-math></inline-formula>-DLPP) and provide an effective iterative algorithm to solve <inline-formula> <tex-math notation="LaTeX">${L_{2,1}}$ </tex-math></inline-formula>-DLPP. Extensive experiments in artificial data and three popular face databases illustrate the effectiveness of our proposed method.
Monitoring of birth registry coverage and data quality utilizing lot quality assurance sampling methodology: A pilot study

Background: Effectively monitoring the coverage and quality of data in low-resource settings is challenging. Lot quality assurance sampling (LQAS) is a method to classify coverage as adequate or inadequate. The aim of this pilot study is sought to determine the coverage and quality of a birth registry in a rural district in Pakistan. Methods: This survey was conducted in 14 clusters of Thatta, Pakistan. LQAS methodology was used to monitor the birth registry from December 2015 to February 2016. We randomly selected 19 villages from each cluster. We used a short questionnaire to review the quality of data collection for select variables. Frequency and percentages were reported for categorical variables. For data validation, Kappa statistics (κ) were applied to assess the agreement between categorical observations, and the Bland–Altman test was used to assess agreement for continuous data. Results: Of the 14 clusters sampled, 12 clusters had adequate coverage. Agreement of hemoglobin performance between the women's response and information in birth registry data was good (κ = 0.718) (95% confidence interval [CI]: 0.58–0.82); agreement on birth outcome recorded by the workers in the registry and as mentioned by women was very good (κ = 1.0); and agreement whether birth weight was assessed within 48 h of delivery was good (κ = 0.648) (0.37–0.92). Conclusion: LQAS is a powerful tool to monitor coverage and data quality of the birth registry maintained by the global network for women's and children's health in Pakistan and potentially for data from other surveillance systems.
Screen content image quality assessment based on the most preferred structure feature

Abstract. The quality of screen content images (SCIs) plays a crucial benchmark of displaying the system and directly affects the user experience of multidevice communication. However, accurately predicting the quality of SCIs with an objective algorithm still has many challenges. Hence, we propose a new no-reference method for evaluating the quality of SCIs effectively and efficiently. Specifically, we first propose a simple but effective strategy to partition the SCIs into the rough and smooth regions. Then, a most preferred structure (MPS) metric is derived to represent the meaningful part of each image patch according to the free-energy theory of human brain. Subsequently, the MPSs of the rough and smooth regions are pooled for constructing the rough and smooth features of SCIs, separately. Finally, to evaluate the final visual quality, the rough and smooth features are combined as the quality-aware features of SCIs, and a learning-based framework is employed to map the quality-aware features to objective quality scores. The experimental results show that the performance of the proposed method is highly consistent with the human perception and superior to the state-of-the-art quality assessment methods on the SCI-oriented databases. Furthermore, the proposed method delivers a high computational efficiency and is very suitable to be integrated into the displaying system.
Hierarchical Distributed Coordination for Economic Dispatch

The classical economic dispatch problem faces difficult challenges in a future smart grid with a great many distributed generators and fast power fluctuations due to high percentage of renewable energy. The traditional centralized scheme can not cope with a large number of generators, and the convergence rate of the purely distributed control decreases with the increase of the network size. A hierarchical distributed scheme based on a partition framework is proposed, in which the centralized local optimizations in regions and the distributed optimization among regions are coupled to solve the economic dispatch problem. Thus the number of agents in distributed part is greatly reduced by the partition. The simulation illustrate the effectiveness and good performance of the proposed protocol.
Projecting the Burden of Chronic Kidney Disease in a Developed Country and Its Implications on Public Health

Background Chronic Kidney Disease (CKD) is a major public health problem worldwide. There is limited literature on a model to project the number of people with CKD. This study projects the number of residents with CKD in Singapore by 2035 using a Markov model. Methods A Markov model with nine mutually exclusive health states was developed according to the clinical course of CKD, based on a discrete time interval of 1 year. The model simulated the transition of cohorts across different health states from 2007 to 2035 using prevalence, incidence, mortality, disease transition, and disease detection rates. Results From 2007 to 2035, the number of residents with CKD is projected to increase from 316,521 to 887,870 and the prevalence from 12.2% to 24.3%. Patients with CKD stages 1-2 constituted the largest proportion. The proportion of undiagnosed cases will decline from 72.1% to 56.4%, resulting from faster progression to higher CKD stages and its eventual detection. Conclusion By 2035, about one-quarter of the Singapore residents are expected to have CKD. National policies need to focus on primary disease prevention and early disease detection to avoid delayed treatment of CKD which eventually leads to end-stage renal disease.
An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.
Using Link Analysis Algorithms to Study the Role of Neurons in the Worm Connectome

In this work, we propose the use of measures based on link analysis algorithms like the Hyperlink-Induced Topic Search (HITS) or PageRank for analyzing the properties of connectomes. While PageRank has been an algorithm that was utilized in past work and is known to be very informative, the HITS algorithm has not seen much application. To our knowledge, this works represents the first major study utilizing HITS and PageRank for use on the connectome of the C.Elegans worm. Our work shows that the hubness and authority values, along with PageRank values, can produce a predictive model of the functional role of neurons, which is better than using many other topological measures. We show that hubness and authority, despite its similarity to PageRank, produce much different information about the connectome. We also propose the use of a new measure, called the hub-authority ratio, as a way to look at the data from a different perspective. Our experimental results show that including this feature increases the predictive capability of the functional role classification model. We believe this has the potential for major application to the analysis of other connectomes, including humans.