Miracle-dz/UniFAR-FTUs huggingface dataset:
Falls, Cognitive Function, and Balance Profiles of Singapore Community-Dwelling Elderly Individuals: Key Risk Factors

Objective: This study compared occurrence of falls, cognitive function, and balance profiles across participants in elderly age categories, investigating associations between the 3 aspects in a sample of Singapore’s elderly population. Method: Community-dwelling elderly individuals (N = 385) were randomly recruited and grouped into “young-old (65-74 years),” “medium-old (75-84 years),” and “oldest-old (above 85 years)” groups. The Fallproof Health and Activity questionnaire, adapted Mini-Mental State Examination (MMSE), and Berg Balance Scale (BBS) tests were used to survey information related to falls, cognition, and balance profiles. Results: Findings revealed significant differences in MMSE and BBS scores across the age groups. Participants with mild cognitive impairment (odds ratio [OR] = 1.87, 95% confidence interval [CI] = 1.08-3.25) and BBS score ≤40 (OR = 0.25, 95% CI = 0.14-0.46) were at the highest risk of falling. Conclusion: Community-dwelling elderly individuals with subtle cognitive impairment and BBS scores ≤40 displayed an increased risk of falling.
Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications

Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to train an efficient and robust supervised-learning classifier for fMRI data. Among various classification techniques, sparse representation classifier (SRC) exhibits a state-of-the-art classification performance in image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to improve SRC using unlabeled testing samples to allow it to be effectively applied to fMRI-based decoding. We proposed a semisupervised-learning SRC with an average coefficient (semiSRC-AVE) method that performed the classification using the average coefficient of each class instead of the reconstruction error and selectively updated the training dataset using new testing data with high confidence to improve the performance of SRC. Simulated and real fMRI experiments were performed to investigate the feasibility and robustness of semiSRC-AVE. The results of the simulated and real fMRI experiments showed that semiSRC-AVE significantly outperformed supervised learning SRC with an average coefficient (SRC-AVE) method and showed better performance than the other three semisupervised learning methods.
AMR dependency parsing with a typed semantic algebra

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.
Double-Sided SIW Leaky-Wave Antenna With Increased Directivity in the  $E$ -Plane

The control of the radiation properties of a double-sided leaky-wave antenna (LWA) is demonstrated in this communication. To this aim, a standard substrate integrated waveguide (SIW) transmission line working with the TE<sub>20</sub> mode is used. Due to the asymmetry of the radiated fields at both sides of the SIW, the equivalent magnetic currents generated are added in-phase at the center of the antenna, thus contributing to the radiation and improving the directivity in the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane. Moreover, the complex propagation constant of the SIW LWA is properly controlled by means of the separation <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> between posts, which mainly determines the leakage rate, and by the width <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> of SIW, which mainly determines the pointing angle. In order to demonstrate this control, three prototypes radiating at different pointing angles (10°, 30°, and 50°) and same beamwidth (10°) have been manufactured and measured at the design frequency of 15 GHz.
Augmented reality implementation as reinforcement tool for public textbooks education in Ecuador

The present work releases on the methodology followed for the implementation and experimentation of an Augmented Reality (AR) application as a reinforcement tool for teaching-learning activities. The study case was carried out on a sample of 44 participants, who are students of the History subject of the second level of a public high school in Ecuador. The sample was divided into two groups. The AR tool was tested on the Experimental Group (EG), and its performance was compared with that of the Control Group (CG). Results obtained demonstrated that introduction of AR contents can efficiently support both activities and experiences academic, allowing for the enhancement of students grades significantly. In fact, the average grade of the EG was 17.0% higher than that of the CG.
Systematic review and meta-analysis of Spanish studies regarding the association between maternal 25-hydroxyvitamin D levels and perinatal outcomes

Abstract Objective: This systematic review and meta-analysis of Spanish studies assessed the association of maternal 25-hydroxyvitamin D [25(OH)D] levels on perinatal outcomes. Methods: PubMed, Cochrane Library, Embase, Scielo, Scopus, and Web of Science research databases were searched from inception through December 30 2017 using the terms ‘vitamin D’, ‘pregnancy’, and ‘Spain’. Studies that compared first or second half of pregnancy normal 25(OH)D (≥30.0 ng/mL) versus insufficient (20.0–29.9 ng/mL) or deficient (<20.0 ng/mL) circulating levels and perinatal outcomes were systematically extracted. Data are presented as pooled odds ratios and their 95% confidence intervals (CIs) for categorical variables or mean differences and CIs for continuous variables. Risk of bias was evaluated with the Newcastle–Ottawa Scale. Results: Five cohort studies met inclusion criteria. The risk of gestational diabetes mellitus, preeclampsia, preterm birth, and small-for-gestational-age infants, and birthweight was not influenced by first half of pregnancy maternal 25(OH)D levels. In addition, second half of pregnancy 25(OH) levels did not affect birthweight. Conclusion: Maternal 25(OH)D levels during pregnancy did not affect studied perinatal outcomes and birthweight. 摘要 目的：通过对西班牙研究的系统综述和荟萃分析评估母体25-羟基维生素D [25（OH）D]水平与围产期结局的关系。 方法：使用术语“维生素D”, “妊娠”和“西班牙”在PubMed, Cochrane图书馆, Embase, Scielo, Scopus和Web of Science研究数据库检索2017年12月30日后的文献。 系统提取了在妊娠期前半期或后半期正常25（OH）D循环水平（≥30.0ng/ mL）与不足（20.0-29.9ng / mL）或缺乏（<20.0ng / mL）对围产期妊娠结局影响的研究。 数据表示为合并比值比, 其分类变量的95％置信区间（CI）或连续变量的均值差异和置信区间。 使用Newcastle–Ottawa量表评估偏倚风险。 结果：五项队列研究均符合纳入标准。 妊娠期糖尿病, 先兆子痫, 早产, 小于胎龄儿和出生体重的风险不受产妇妊娠前半期25（OH）D水平的影响。 此外, 妊娠后半期25（OH）水平不影响出生体重。 结论：妊娠期母体25（OH）D水平不影响围产期结局和出生体重。
Neonatal Alexander Disease: Novel GFAP Mutation and Comparison to Previously Published Cases

Abstract Alexander disease (AxD) is a genetic leukodystrophy caused by GFAP mutations leading to astrocyte dysfunction. Neonatal AxD is a rare phenotype with onset in the first month of life. The proband, belonging to a large pedigree with dominantly inherited benign familial neonatal epilepsy (BFNE), had a phenotype distinct from the rest of the family, with hypotonia and macrocephaly in addition to drug-resistant neonatal seizures. The patient deteriorated and passed away at 6 weeks of age. The pathological and neuroimaging data were consistent with the diagnosis of AxD. Genetic analysis of the proband identified a novel de novo GFAP missense mutation and a KCNQ2 splice site mutation segregating with the BFNE phenotype in the family. The GFAP mutation was located in the coil 2B region of GFAP protein, similar to most neonatal-onset AxD cases with an early death. The clinical and neuroradiological features of the previously published neonatal AxD patients are presented. This study further supports the classification of neonatal-onset AxD as a distinct phenotype based on the age of onset.
Basolateral amygdala, nicotinic cholinergic receptors, and nicotine: Pharmacological effects and addiction in animal models and humans

The amygdala is involved in processing incoming information about rewarding stimuli and emotions that denote danger such as anxiety and fear. Bi‐directional neural connections between basolateral amygdala (BLA) and brain regions such as nucleus accumbens, prefrontal cortex, hippocampus, and hindbrain regions regulate motivation, cognition, and responses to stress. Altered local regulation of BLA excitability is pivotal to the behavioral disturbances characteristic of posttraumatic stress disorder, and relapse to drug use induced by stress. Herein, we review the physiological regulation of BLA by cholinergic inputs, emphasizing the role of BLA nicotinic receptors. We review BLA‐dependent effects of nicotine on cognition, motivated behaviors, and emotional states, including memory, taking and seeking drugs, and anxiety and fear in humans and animal models. The alterations in BLA activity observed in animal studies inform human behavioral and brain imaging research by enabling a more exact understanding of altered BLA function. Converging evidence indicates that cholinergic signaling from basal forebrain projections to local nicotinic receptors is an important physiological regulator of BLA and that nicotine alters BLA function. In essence, BLA is necessary for behavioral responses to stimuli that evoke anxiety and fear; reinstatement of cue‐induced drug seeking; responding to second‐order cues conditioned to abused drugs; reacquisition of amplified nicotine self‐administration due to chronic stress during abstinence; and to promote responding for natural reward.
The Impact of Intraclass Correlation on the Effectiveness of Level-Specific Fit Indices in Multilevel Structural Equation Modeling

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific fit indices. Our study proposed to fill this gap in the methodological literature. A Monte Carlo study was conducted to investigate the performance of (a) level-specific fit indices derived by a partially saturated model method (e.g., CF I PS _ B and CF I PS _ W ) and (b) SRM R W and SRM R B in terms of their performance in multilevel structural equation models across varying ICCs. The design factors included intraclass correlation (ICC: ICC1 = 0.091 to ICC6 = 0.500), numbers of groups in between-level models (NG: 50, 100, 200, and 1,000), group size (GS: 30, 50, and 100), and type of misspecification (no misspecification, between-level misspecification, and within-level misspecification). Our simulation findings raise a concern regarding the performance of between-level-specific partial saturated fit indices in low ICC conditions: the performances of both TL I PS _ B and RMSE A PS _ B were more influenced by ICC compared with CF I PS _ B and SRMRB. However, when traditional cutoff values (RMSEA≤ 0.06; CFI, TLI≥ 0.95; SRMR≤ 0.08) were applied, CF I PS _ B and TL I PS _ B were still able to detect misspecified between-level models even when ICC was as low as 0.091 (ICC1). On the other hand, both RMSE A PS _ B and SRM R B were not recommended under low ICC conditions.
Identification of a novel long noncoding RNA that promotes osteoblast differentiation

Long noncoding RNAs (lncRNAs) are a heterogeneous class of transcripts, longer than 200 nucleotides, 5′‐capped, polyadenylated, and poorly conserved among mammalian species. Several studies have shown the contribution of lncRNAs to different cellular processes, including regulation of the chromatin structure, control of messenger RNA translation, regulation of gene transcription, regulation of embryonic pluripotency, and differentiation. Although limited numbers of functional lncRNAs have been identified so far, the immense regulatory potential of these RNAs is already evident, indicating that a functional characterization of lncRNAs is needed. In this study, mouse preosteoblastic cells were induced to differentiate into osteoblasts. At 3 sequential differentiation stages, total RNA was isolated and libraries were constructed for Illumina sequencing. The resulting sequences were aligned and transcript abundances were determined. New lncRNA candidates that displayed differential expression patterns during osteoblast differentiation were identified by combining bioinformatics and reverse transcription polymerase chain reaction analyses. Among these, lncRNA‐1 that exhibited increased expression during osteogenesis and was downregulated during myogenesis. Importantly, knockdown of lncRNA‐1 expression in primary mouse preosteoblasts was found to inhibit osteogenic differentiation, reflected by a reduced transcription of the Runx2/p57 and Sp7 bone master genes. Together, our results indicate that lncRNA‐1 represents a new regulatory RNA that plays a relevant role during the early stages of osteogenesis.
Connectome priors in deep neural networks to predict autism

We propose a novel element-wise layer for deep neural networks that incorporates general priors designed for connectomes. In contrast to regular images, connectomes, expressed as adjacency matrices, are composed of elements that capture a relationship between two brain regions. As each element in the connectome has an anatomical meaning that is consistent across samples, prior knowledge about the structure of the connectome can be encoded and used to regularize learning based approaches. Thus in this work, we introduce a novel trainable element-wise layer for deep neural networks, with data-dependent anatomically-informed prior regularization terms designed for connectomes. We validate our approach on 1013 functional connectomes from the autism brain imaging data exchange (ABIDE) dataset, and show that our proposed layer and regularization terms improves the accuracy of predicting patients with autism spectrum disorder from controls within a deep learning framework.
Finding Seeds and Relevant Tags Jointly: For Targeted Influence Maximization in Social Networks

-1mmWe study the novel problem of jointly finding the top- k seed nodes and the top- r relevant tags for targeted influence maximization in a social network. The bulk of the research on influence maximization assumes that the influence diffusion probabilities across edges are fixed, and the top- k seed users are identified to maximize the cascade in the entire graph. However, in real-world applications, edge probabilities typically depend on the information being cascaded, e.g., in social influence networks, the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In addition, a campaigner often has a specific group of target customers in mind. In this work, we model such practical constraints, and investigate the novel problem of jointly finding the top-k seed nodes and the top- r relevant tags that maximize the influence inside a target set of users. Due to the hardness of the influence maximization problem, we develop heuristic solutions --- with smart indexing, iterative algorithms, and good initial conditions, which target high-quality, efficiency, and scalability. -1mm
Pregnancy‐associated plasma protein A and mortality in haemodialysis

Elevated pregnancy‐associated plasma protein A (PAPP‐A) levels are associated with increased risk of death in ischaemic heart disease as well as in haemodialysis patients. Previous research indicates that the prognostic value of PAPP‐A may be stronger in patients with concomitant diabetes mellitus or signs of inflammation. We studied the association between PAPP‐A and outcomes in prevalent haemodialysis patients and hypothesized that diabetes mellitus and inflammation status act as effect modifiers.
Single-Channel Speech Enhancement With Phase Reconstruction Based on Phase Distortion Averaging

Speech enhancement has been widely investigated for several decades, but by modifying only the amplitude spectrum of a speech signal, ignoring the phase spectrum, which has been regarded as an unimportant feature. However, it was recently reported that the phase spectrum plays an important role in speech quality and intelligibility. In this paper, we propose a phase reconstruction method based on harmonic enhancement using the fundamental frequency and phase distortion feature. This feature is known to show fluctuations in the phase spectrum with respect to time and frequency. We estimate the speech phase spectrum by considering the relationship between harmonic phase spectra. Experimental evaluations indicate that the proposed phase reconstruction method improves speech quality in various noisy environments.
Correlation between levator ani muscle injuries on MRI and fecal incontinence , pelvic organ prolapse , and urinary incontinence in primiparous women

© 2010 Mosby, Inc. All rights reserved. Corresponding Author: Marta E. Heilbrun, MD, Dept. of Radiology, University of Utah, 30 North 1900 East #1A071, Salt Lake City, UT 84132-2140, Phone: 801-581-7553, Fax: 801-581-2414, marta.heilbrun@hsc.utah. edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Human Bone Marrow Contains Mesenchymal Stromal Stem Cells That Differentiate In Vitro into Contractile Myofibroblasts Controlling T Lymphocyte Proliferation

Mesenchymal stromal stem cells (MSC) that reside in the bone marrow (BM) can be amplified in vitro. In 2-dimension (D) cultures, MSC exhibit a morphology similar to fibroblasts, are able to inhibit T lymphocyte and natural killer cell proliferation, and can be differentiated into adipocytes, chondrocytes, or osteoblasts if exposed to specific media. Here we show that medullar MSC cultured in 2D formed an adherent stroma of cells expressing well-organized microfilaments containing α-smooth muscle actin and nonmuscle myosin heavy chain IIA. MSC could be grown in 3D in collagen membranes generating a structure which, upon exposition to 50 mM KCl or to an alternating electric current, developed a contractile strength that averaged 34 and 45 μN/mm2, respectively. Such mechanical tension was similar in intensity and in duration to that of human placenta and was annihilated by isosorbide dinitrate or 2,3-butanedione monoxime. Membranes devoid of MSC did not exhibit a significant contractility. Moreover, MSC nested in collagen membranes were able to control T lymphocyte proliferation, and differentiated into adipocytes, chondrocytes, or osteoblasts. Our observations show that BM-derived MSC cultured in collagen membranes spontaneously differentiate into contractile myofibroblasts exhibiting unexpected properties in terms of cell differentiation potential and of immunomodulatory function.
Inhibition and Updating, but Not Switching, Predict Developmental Dyslexia and Individual Variation in Reading Ability

To elucidate the core executive function profile (strengths and weaknesses in inhibition, updating, and switching) associated with dyslexia, this study explored executive function in 27 children with dyslexia and 29 age matched controls using sensitive z-mean measures of each ability and controlled for individual differences in processing speed. This study found that developmental dyslexia is associated with inhibition and updating, but not switching impairments, at the error z-mean composite level, whilst controlling for processing speed. Inhibition and updating (but not switching) error composites predicted both dyslexia likelihood and reading ability across the full range of variation from typical to atypical. The predictive relationships were such that those with poorer performance on inhibition and updating measures were significantly more likely to have a diagnosis of developmental dyslexia and also demonstrate poorer reading ability. These findings suggest that inhibition and updating abilities are associated with developmental dyslexia and predict reading ability. Future studies should explore executive function training as an intervention for children with dyslexia as core executive functions appear to be modifiable with training and may transfer to improved reading ability.
Kullanımlar ve Doyumlar Yaklaşımı Perspektifinden Üniversite Öğrencilerinin Medyaya Bakışı

Aktif izleyici kavramini ele alan kullanimlar ve doyumlar yaklasimi, insanlarin ihtiyaclarini karsilamak icin medyayi kullandigini ve bundan bir doyum sagladigini ileri surmektedir. Kullanimlar ve doyumlar yaklasimini temel olarak alan bu saha calismasi, Katz, Gurevitch ve Haas'in 1973 yilinda yaptiklari besli kategorilestirmeyi (bilissel ihtiyaclar, duygusal ihtiyaclar, kisisel butunlesme ihtiyaclari, sosyal butunlesme ihtiyaclari ve gerceklerden kacis ihtiyaclari) baz olarak alip, Ankara'da bulunan universitelerin ogrencileri uzerinde kullanimlar ve doyumlar yaklasimi perspektifinden test etmektedir.