Miracle-dz/UniFAR-FTUs huggingface dataset:
Women with Preterm Birth Have Evidence of Subclinical Atherosclerosis a Decade After Delivery.

Background: Women with preterm birth (PTB) have excess risk of cardiovascular disease (CVD) and metabolic dysregulation after delivery, but vascular mechanisms are poorly understood. We considered that women with PTB may have evidence of subclinical atherosclerosis after delivery, perhaps related to cardiometabolic risk factors. Materials and Methods: The Pregnancy Outcomes and Community Health Moms (POUCHmoms) study followed women from pregnancy through 7 to 15 years after delivery (n = 678). Women underwent B-mode ultrasound to measure the average intima-media thickness (IMT) across the common carotid, bulb, and internal carotid artery segments at follow-up (n = 605). Linear regression estimated the overall and segment-specific difference in IMT between women with preterm and term births. Results: Women were, on average, 38 years old (SD 5.7) at the follow-up visit. Those with a prior preterm versus term birth had thicker mean IMT (average of eight segments, 0.592 mm vs. 0.575, p = 0.04). Differences persisted after accounting for age, race, smoking, and body mass index (difference = +0.018 mm, p = 0.019) and were attenuated after adjustment for blood pressure, medication use, and total cholesterol (difference = +0.014, p = 0.052). Thicker mean bulb IMT in women with PTB was robust to cardiovascular risk factor adjustments (fully adjusted difference = +0.033, p = 0.029). Excluding cases of prepregnancy hypertension or preeclampsia did not change results. Conclusions: Mechanisms leading to subclinical atherosclerosis may link PTB with future CVD. PTB differences in maternal vessel remodeling in the carotid bulb, an arterial segment more prone to early development of atherosclerosis, were independent of traditional risk factors suggesting that novel processes may be involved.
Electrical coupling and its channels

&NA; As the physiology of synapses began to be explored in the 1950s, it became clear that electrical communication between neurons could not always be explained by chemical transmission. Instead, careful studies pointed to a direct intercellular pathway of current flow and to the anatomical structure that was (eventually) called the gap junction. The mechanism of intercellular current flow was simple compared with chemical transmission, but the consequences of electrical signaling in excitable tissues were not. With the recognition that channels were a means of passive ion movement across membranes, the character and behavior of gap junction channels came under scrutiny. It became evident that these gated channels mediated intercellular transfer of small molecules as well as atomic ions, thereby mediating chemical, as well as electrical, signaling. Members of the responsible protein family in vertebrates—connexins—were cloned and their channels studied by many of the increasingly biophysical techniques that were being applied to other channels. As described here, much of the evolution of the field, from electrical coupling to channel structure‐function, has appeared in the pages of the Journal of General Physiology. &NA; Harris explores the development of our current understanding of electrical coupling between cells and the channels that mediate it, highlighting the contributions of the Journal of General Physiology.
The plant hormone auxin beats the time for oscillating light-regulated lateral root induction

ABSTRACT The molecular mechanism underlying the periodic induction of lateral roots, a paradigmatic example of clock-driven organ formation in plant development, is a matter of ongoing, controversial debate. Here, we provide experimental evidence that this clock is frequency modulated by light and that auxin serves as a mediator for translating continuous light signals into discontinuous gene activation signals preceding the initiation of lateral roots in Arabidopsis seedlings. Based on this evidence, we propose a molecular model of an ultradian biological clock involving auxin-dependent degradation of an AUX/IAA-type transcription repressor as a flexible, frequency-controlling delay element. This model widens the bandwidth of biological clocks by adding a new type that allows the pace of organ formation to adapt to the changing environmental demands of the growing plant. Highlighted Article: Light controls periodic organ formation in Arabidopsis via a hormone-driven molecular oscillator with adjustable frequency.
Higher Genetic Risk For Schizophrenia Is Associated With Living In Urban And Populated Areas

Social stress in urban life has been proposed as an environmental risk factor associated with the increased prevalence of schizophrenia in urban compared to rural areas. However, the potential genetic contributions to this relationship have been largely ignored. Using a community-based sample of 15,544 adults living in Australia, we found higher genetic loading for schizophrenia in participants living in more densely populated (p-value=5.69*10-5) or less remote areas (p-value=0.003). Mendelian Randomization suggested that high schizophrenia genetic risk is a causal factor in chosing to live in denser (p-value=0.046) and less remote areas (p-value=0.044). Our results support the hypothesis of selective migration to more urban environments by people at higher genetic risk for schizophrenia and suggest a need to refine the social stress model for schizophrenia by including genetic influences on where people choose to live.
Emergence of staphylococcal scalded skin syndrome associated with a new toxinogenic, methicillin‐susceptible Staphylococcus aureus clone

&NA; A sharp increase in staphylococcal scalded skin syndrome (SSSS) cases has been recorded in our settings since 2015, with 31 cases having been documented during the period 2014–2017. The molecular investigation of strains from the above period showed the emergence of a methicillin‐susceptible, mupirocin‐ and fusidic acid‐resistant Staphyloccocus aureus clone that belongs to the ST121 complex and carries both epidermolysin (eta/etb) genes. We concluded that the SSSS caused by the newly emerged, highly virulent community‐associated‐methicillin sensitive S. aureus strains that have been encountered lately is more severe than impetigo. Physicians should be aware of the probability of SSSS epidemics from strains that are resistant to mupirocin and fusidic acid, which have been used irrationally and excessively.
PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network

Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music audio from musical scores. Directly synthesizing audio with sound sample libraries often leads to mechanical and deadpan results, since musical scores do not contain performance-level information, such as subtle changes in timing and dynamics. Moreover, while the task may sound like a text-to-speech synthesis problem, there are fundamental differences since music audio has rich polyphonic sounds. To build such an AI performer, we propose in this paper a deep convolutional model that learns in an end-to-end manner the score-to-audio mapping between a symbolic representation of music called the pianorolls and an audio representation of music called the spectrograms. The model consists of two subnets: the ContourNet, which uses a U-Net structure to learn the correspondence between pianorolls and spectrograms and to give an initial result; and the TextureNet, which further uses a multi-band residual network to refine the result by adding the spectral texture of overtones and timbre. We train the model to generate music clips of the violin, cello, and flute, with a dataset of moderate size. We also present the result of a user study that shows our model achieves higher mean opinion score (MOS) in naturalness and emotional expressivity than a WaveNet-based model and two off-the-shelf synthesizers. We open our source code at https://github.com/bwang514/PerformanceNet
Development of Resynthesis Flow for Improving Logical Masking Features of Combinational Circuits

This paper is devoted to development of combinational circuits resynthesis flow aimed at soft error tolerance improvement. Sensitivity factor, i.e. average number of non-reliable elements, is used as fault-tolerance metric, as well as circuit's “sensitive area” which additionally considers areas of standard cells. Re-synthesis algorithm involves iterative replacement of some sections of circuit by functionally equivalent blocks with better masking properties. Result can be achieved by adding redundancy, or by implementing more optimal structure with respect to fault tolerance. When estimating masking properties, circuit section is considered to be separate circuit, which speeds up program performance. We use input test patterns for subcircuit in accordance with their probabilities and observability of subcircuit outputs at primary circuit outputs to take into account influence of surrounding gates during optimal structure selection. The algorithm was tested on circuits from ISCAS'85 and LGSynth'89 benchmarks synthesized with different optimization parameters using two different standard digital libraries.
Connectivity Times in Vehicular Networks

We propose an analytical model that accurately captures the service advertisement and access mechanisms in IEEE 1609.4/IEEE 802.11p multi-channel vehicular networks where road-side units announce non-safety services to passing vehicles on a dedicated channel. For a drive-thru scenario, we calculate various performance measures including the service discovery probability, the mean time till discovery, and the channel utilization.
Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.
Tackling mixed messages: Practitioner reflections on working with adolescents with atypical anorexia and their families

ABSTRACT The treatment of atypical anorexia nervosa (AN) poses new research and practice challenges for the field of eating disorders. The objective of this study was to describe frontline practitioners’ perceptions of differences between adolescents living with atypical versus typical AN, as well as the intervention challenges they experience when working with these adolescents and their families. We followed the principles of fundamental qualitative description and recruited a purposeful sample of practitioners treating adolescent eating disorders to complete a one-on-one semi-structured interview. Conventional content analysis and the constant comparison technique were used for data analysis. A total of 23 practitioners from four countries participated in this study. Practitioners described that adolescents with atypical AN present with higher pre-morbid weights and rates of weight-based teasing compared to their AN peers. Clinical challenges perceived by practitioners to be specific to working with adolescents with atypical AN included: addressing conflicting messages about eating disorders and weight loss, empathizing with a justified fear of weight gain, and increased risk for parental and therapist collusion with the eating disorder. Findings have implications for delivering interventions to adolescents seeking care for atypical AN.
Unsupervised Transformation Network Based on GANs for Target-Domain Oriented Image Translation

Image-to-image translation usually refers to the task of translating an input image from the source domain to the target domain while preserving the structure in the source domain. Recently, generative adversarial networks (GANs) using paired images for this task have made great progress. However, paired training data will not be available for many tasks. In this paper, a GAN-based unsupervised transformation network (UTN-GAN) is proposed for image-to-image translation. Importantly, UTN-GAN employs hierarchical representations and weight-sharing mechanism to translate images from the source domain to the target domain without paired images. We employ two groups of unsupervised GANs to generate the images in different domains first, and then discriminate them. In UTN-GAN, an auto-encoder reconstruction network is designed to extract the hierarchical representations of the images in the source domain by minimizing the reconstruction loss. In particular, the high-level representations (semantics) are shared with a translation network to guarantee that the input image and the output image are paired up in the different domains. All network structures are trained together by using a joint loss function. The experimental studies in qualitative and quantitative aspects on several image translation tasks show that the proposed algorithm is effective and competitive compared with some state-of-the-art algorithms.
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation

This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE consists of an auto-encoder where the internal code is normalized on the unit sphere and corrupted by additive noise. Simultaneously, a replica of the decoder (sharing the same parameters as the AE decoder) is used as the generator and fed with random latent vectors. An adversarial discriminator is trained to distinguish training samples reconstructed from the AE from samples produced through the random-input generator, making the entire generator-discriminator path differentiable for discrete data like text. The combined effect of noise injection in the code and shared weights between the decoder and the generator can prevent the mode collapsing phenomenon commonly observed in GANs. Since perplexity cannot be applied to non-sequential text generation, we propose a new evaluation method using the total variance distance between frequencies of hash-coded byte-level n-grams (NGTVD). NGTVD is a single benchmark that can characterize both the quality and the diversity of the generated texts. Experiments are offered in 6 large-scale datasets in Arabic, Chinese and English, with comparisons against n-gram baselines and recurrent neural networks (RNNs). Ablation study on both the noise level and the discriminator is performed. We find that RNNs have trouble competing with the n-gram baselines, and the ATNNFAE results are generally competitive.
Deep learning super-diffusion in multiplex networks

Complex network theory has shown success in understanding the emergent and collective behavior of complex systems Newman 2010 Networks: An Introduction (Oxford: Oxford University Press). Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks Bianconi 2018 Multilayer Networks: Structure and Function (Oxford: Oxford University Press); Boccaletti et al 2014 Phys. Rep. 544 1–122; Lee et al 2015 Eur. Phys. J. B 88 48; Kivelä et al 2014 J. Complex Netw. 2 203–71; De Domenico et al 2013 Phys. Rev. X 3 041022—in which each interaction type is mapped to its own network layer; e.g. multi-layer transportation networks, coupled social networks, metabolic and regulatory networks, etc. A salient physical phenomena emerging from multiplexity is super-diffusion: exhibited by an accelerated diffusion admitted by the multi-layer structure as compared to any single layer. Theoretically super-diffusion was only known to be predicted using the spectral gap of the full Laplacian of a multiplex network and its interacting layers. Here we turn to machine learning (ML) which has developed techniques to recognize, classify, and characterize complex sets of data. We show that modern ML architectures, such as fully connected and convolutional neural networks (CNN), can classify and predict the presence of super-diffusion in multiplex networks with 94.12% accuracy. Such predictions can be done in situ, without the need to determine spectral properties of a network.
Localized Provoked Vulvodynia: Association With Nerve Growth Factor and Transient Receptor Potential Vanilloid Type 1 Genes Polymorphisms

Objective The aim of the study was to study the associations between localized provoked vulvodynia (LPV) and several single-nucleotide polymorphisms (SNPs) in the transient receptor potential vanilloid type 1 (TRPV1), nerve growth factor (NGF), and the heparanase (HPSE) genes. Materials and Methods Prevalence of SNPs among 65 women with moderate or severe primary LPV (initial symptoms occur with first provoking physical contact) and 126 healthy, ethnically matched controls was analyzed in an observational case-control study. Each participant answered a questionnaire addressing familial LPV occurrence and comorbid pain conditions. Results Familial occurrences of LPV, temporomandibular joint (TMJ) symptoms, recurrent vaginitis, and irritable bowel syndrome were significantly higher among LPV women than healthy controls. Genotyping analyses revealed a novel, statistically significant high prevalence of polymorphism c.945G>C (rs222747) of TRPV1 and a SNP in the promoter region of NGF (rs11102930) in LPV women compared with controls. A logistic regression model for rs222747 and rs11102930 frequent alleles indicates significant LPV association within the entire study group and Ashkenazi Jewish women, respectively. Comparison of pain conditions with frequent alleles showed the rs222747 “CC” genotype of TRPV1 associated with women with TMJ, recurrent vaginitis, and LPV. Conclusions Our results suggest novel genetic susceptibility to primary LPV associated with specific alleles in genes TRPV1 and NGF and propose the rs222747 “C” allele of TRPV1 as a common genetic predisposition for other pain syndromes.
Energy-Efficient Routing and Link Adaptation for 2D Wireless Relay Networks in the Wideband Regime

We discuss the globally optimal energy-efficient design of a 2D relay network. Different from the existing routing protocols on energy saving, which finds the minimal energy route for a given data rate, the proposed algorithm jointly optimizes routing and data rate to maximize energy efficiency (EE) defined as the achievable data rate per power consumption. We propose a low-complexity algorithm to circumvent the huge complexity of the exhaustive search for the network EE maximization and prove its global optimality. Moreover, the proposed algorithm is implemented in a distributed fashion because each relay needs to send its routing information only to the relays in its adjacent tiers, which significantly reduces the signaling overhead of the centralized implementation. Our analysis on the worst-case complexity in a fading channel shows that the complexity of the proposed algorithm increases linearly while that of the exhaustive search increases exponentially as the tier index increases. Simulation results confirm that the proposed algorithm outperforms the existing routing protocols on energy saving and achieves the globally optimal network EE at a significantly lower complexity than the exhaustive search.
Geospatial Information Utilization in Indonesian Local Government

Government decisions and actions at national and local levels have, to varying degree, spatial footprints. At the local government level, this includes spatial planning, land management, taxation, and the issuance of building and site permits. Utilization of geospatial information in Indonesian local government was started more than 25 years ago. However, there was a high variation in the intensity, frequency, and types of geospatial information utilization. In a particular local government, there was also a variation of use among local government agencies. This paper aims to portray and analyses the current status of geospatial information utilization in Indonesian local government. Quantitatively, the information was obtained from questionnaires distributed to 24 districts and seven cities. The questionnaire covered areas of data availability, utilization, and management of geospatial information. For every district and city, around 20 to 30 questionnaires were sent, to gather information from all local governments agencies. Qualitatively, observations and in-depth interviews were conducted to collect information on the plans, progress, and challenges in geospatial information utilization. In general, the utilization of geospatial information is progressing steadily with some cities or districts made significant achievement while others still struggling in defining geospatial policies. Local governments have limited staffs with expertise in spatial data handling. Some recommendations to overcome the challenges and impediments are presented. Recent central government initiatives on national spatial data infrastructure and one map policy, are essential to speed up the process of creating better environment governance in local government.
Can Blood Oxygenation Level Dependent Functional Magnetic Resonance Imaging Be Used Accurately to Compare Older and Younger Populations? A Mini Literature Review

A wealth of research has investigated the aging brain using blood oxygenation level dependent functional MRI [Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI)]. However, many studies do not consider the aging of the cerebrovascular system, which can influence the BOLD signal independently from neural activity, limiting what can be inferred when comparing age groups. Here, we discuss the ways in which the aging neurovascular system can impact BOLD fMRI, the consequences for age-group comparisons and possible strategies for mitigation. While BOLD fMRI is a valuable tool in this context, this review highlights the importance of consideration of vascular confounds.