Miracle-dz/UniFAR-FTUs huggingface dataset:
The APOE ε4 allele is associated with a reduction in FEV1/FVC in women: A cross-sectional analysis of the Long Life Family Study

Introduction Murine studies have shown that apolipoprotein E modulates pulmonary function during development, aging, and allergen-induced airway disease. It is not known whether the polymorphic human APOE gene influences pulmonary function. Objectives We assessed whether an association exists between the polymorphic human APOE ε2, ε3, and ε4 alleles and pulmonary function among participants in the Long Life Family Study. Methods Data from 4,468 Caucasian subjects who had genotyping performed for the APOE ε2, ε3, and ε4 alleles were analyzed, with and without stratification by sex. Statistical models were fitted considering the effects of the ε2 allele, defined as ε2/2 or ε2/3 genotypes, and the ε4 allele, defined as ε3/4 or ε4/4 genotypes, which were compared to the ε3/3 genotype. Results The mean FEV1/FVC ratio (the forced expiratory volume in one second divided by the forced vital capacity) was lower among women with the ε4 allele as compared to women with the ε3/3 genotype or the ε2 allele. Carriage of the APOE ε4 allele was associated with FEV1/FVC, which implied lower values. Further analysis showed that the association primarily reflected women without lung disease who were older than 70 years. The association was not mediated by lipid levels, smoking status, body mass index, or cardiovascular disease. Conclusions This study for the first time identifies that the APOE gene is associated with modified lung physiology in women. This suggests that a link may exist between the APOE ε4 allele, female sex, and a reduction in the FEV1/FVC ratio in older individuals.
Pharmacodynamics of Minocycline against Acinetobacter baumannii in a Rat Pneumonia Model

Minocycline is currently approved in the United States for the treatment of infections caused by susceptible isolates of Acinetobacter spp. The objective of these studies was to determine the minocycline exposures associated with an antibacterial effect against Acinetobacter baumannii in a rat pneumonia model. ABSTRACT Minocycline is currently approved in the United States for the treatment of infections caused by susceptible isolates of Acinetobacter spp. The objective of these studies was to determine the minocycline exposures associated with an antibacterial effect against Acinetobacter baumannii in a rat pneumonia model. Rats received minocycline doses as 30-min intravenous infusions. In the rat pneumonia model, six clinical isolates of A. baumannii with MICs ranging from 0.03 to 4 mg/liter were studied. In this model, minocycline produced a bacteriostatic effect with a free 24-h area under the concentration-time curve (AUC)/MIC ratio of 10 to 16 and produced 1 log of bacterial killing with a free 24-h AUC/MIC of 13 to 24. These exposures can be achieved with the current FDA-approved dosage regimens of intravenous minocycline.
Developmental pathways of adolescent cannabis use: Risk factors, outcomes and sex-specific differences

Abstract Background: Characterizing patterns of adolescent cannabis use (CU), as well as risk factors and outcomes uniquely associated with these pathways is essential for informing treatment and prevention efforts. Yet, few studies have examined these issues among youth at-risk of engaging in problematic cannabis use. Further, research accounting for use of other substances or sex differences in patterns of CU remains exceedingly sparse. Methods: Trajectory-based modeling was used to identify underlying CU pathways among a predominantly Hispanic (90%) sample of at-risk youth (n = 401; 46% female) across adolescence (ages∼14–18), controlling for baseline substance use and participant demographics. Adolescent psychopathology (i.e., conduct disorder, attention deficit hyperactivity disorder, anxiety, and depression) was examined as a predictor and outcome of CU. Results: Three trajectories of adolescent CU were identified, with most youth (74%) engaging in relatively “low” levels of use, followed by ∼12% exhibiting an early-initiating “chronic” course, and 14% “escalating” in use. Although boys and girls both experienced increased levels of CU across adolescence, boys were more likely to exhibit escalating and chronic patterns of use. Findings revealed unique associations between adolescent CU pathways and facets of psychopathology; most notably, the relatively robust and bidirectional association between CU trajectories and conduct problem symptoms. Conclusions: Specific facets of psychopathology may confer unique associations with CU across development, including the initiation and exacerbation of CU during adolescence.
Antenna Selection Symbol-Level Precoding for Low Complexity Large-Scale Antenna Array Systems

Large-Scale Antenna Array Systems may be used to serve multiple users in the same time-frequency resource block which results to harmful multi-user interference (MUI). In the literature precoding techniques have been proposed as a way to mitigate the induced MUI, by designing the transmitted signals using the knowledge of the Channel State Information (CSI), in block-level precoding (BLP) or both the CSI and the information-bearing symbols, in symbol-level precoding (SLP). However, the proposed SLP techniques require fully digital baseband processing which is infeasible in large-scale antenna array systems because of the high cost and power consumption of radio frequency (RF) components. In order to reduce the number of y-RF chains, we address an Antenna Selection Symbol-Level Precoding (AS-SLP) scheme, which minimizes the MUI by activating only a subset of the available antennas. For this scheme we develop an efficient algorithm, based on Coordinate Descent. Simulations provide an insight on the efficiency of the proposed approach and its improvement with respect to the fully digitally approaches.
Inducing and maintaining the intentional stance by showing interactions between multiple agents

In human-computer interaction, it is first necessary for the human to recognize that there is a meaning beyond information provision while interacting with an agent. One of the critical barriers to social human-computer interaction is the mental state of people with respect to agents. Intentional stance is defined to be an appropriate mental stance in human-agent interaction. To induce intentional stance, we showed interactions between multiple agents in this study. The aim of this study is to investigate whether observing interactions between multiple agents can encourage and maintain the intentional stance toward the agents. We conducted experiments in which participants performed a series of tasks to build a house in a virtual world. In this task, two agents helped and interacted with the participants. The participants were divided into two groups; a group that observed the interaction(observation-group) and a group that directly interacted with the agents(direct-group). From the data obtained in the experiment, we investigated whether the participants tried to interact with the agent that analyzed the participant's behavior and questions. As a result, we could confirm that observing the interaction induced the intentional stance, but the effect was weakened in some cases.
EvalD Reference-Less Discourse Evaluation for WMT18

We present the results of automatic evaluation of discourse in machine translation (MT) outputs using the EVALD tool. EVALD was originally designed and trained to assess the quality of human writing, for native speakers and foreign-language learners. MT has seen a tremendous leap in translation quality at the level of sentences and it is thus interesting to see if the human-level evaluation is becoming relevant.
Karayolu Kaplamalarının Sonlu Elemanlar Yöntemi İle Analizinde Gerilme-Birim Şekil Değiştirme Davranışına Etki Eden Parametrelerin İncelenmesi

Esnek kaplamalar bitum ve granuler malzemeler ile insa edilen bir ust yapi turudur. Esnek ust yapilarin projelendirilmesinde kullanilan yontemler; ampirik yontemler, kayma gocmesi sinirlama yontemi, deplasman sinirlama yontemi, regresyon yontemi ve mekanistik ampirik yontemler olarak bes kategoriye ayrilabilir. Bu yontemler arasindan geleneksel ampirik yontemlerden mekanistik ampirik dizayn yontemlerine dogru bir yonelme soz konusudur. Kaplama tabakalarinin heterojenligi, dinamik ve tekrarli yukleme kosullari gibi sebeplerle arastirmacilar calismalarini sonlu elemanlar yontemi ile yapmayi tercih etmektedir ancak sonlu elemanlar modelinin 2 veya 3 boyutlu olarak tanimlanmasi, yukleme kosullari, tabakalar arasi temas durumlari ve formulasyonlari, izgara boyutlari, sinir kosullari ve model boyutlari gibi parametreler sonuclar uzerinde onemli bir etkiye sahiptir. Bu calismada kaplama, temel ve alt temel tabakalarindan olusan geleneksel bir ust yapi kesiti uzerinde statik yukleme kosullari altinda kaplama tepkilerini etkileyen parametreler incelenmistir.
Mantra: a novel imputation measure for disease classification and prediction

Medical record instances can have missing values which makes them unsuitable for learning process. Data Imputation is normally done to fill one or more missing data attribute values. Imputation helps to perform supervised or un-supervised learning after the dataset is free from missing data. Learning process helps the discovery of hidden, valuable and important information that can provide insightful results. Imputation is a data pre-processing task that requires applying distance function to find missing values. In this paper, a distance function named MANTRA is proposed to impute missing data values. The distance function is also called as the imputation measure since it is designed for imputation of missing values. A working example is demonstrated that shows how imputation is achieved using proposed distance function, MANTRA. It is proved that the nominal value that is filled after imputation is same as the original.
OntoREA© Accounting and Finance Model: Including Option Contracts in the MDD-Context

OntoREA© is a specification of the Accounting and Finance domain in the OntoUML language [1] which includes derivative instruments as well. The authors use a forward contract as running example to demonstrate the validity of the OntoREA© Accounting and Finance model within the design science resource methodology (DSRM) [2]. They claim but they do not proof that the model not only holds for forward but also for option contracts. In this article the missing proof is given by refining the relational MySQL model. In the model driven development (MDD) context [3] this model constitutes the platform specific model (PSM) which is derived from the platform independent model (PIM) in form of the OntoREA© model. The refined PSM model adequately represents European option contracts and it should be especially useful for business analysts as well as for educational purposes.
Federated Byzantine Quorum Systems (Extended Version)

Some of the recent blockchain proposals, such as Stellar and Ripple, use quorum-like structures typical for Byzantine consensus while allowing for open membership. This is achieved by constructing quorums in a decentralised way: each participant independently chooses whom to trust, and quorums arise from these individual decisions. Unfortunately, the theoretical foundations underlying such blockchains have not been thoroughly investigated. To close this gap, in this paper we study decentralised quorum construction by means of federated Byzantine quorum systems, used by Stellar. We rigorously prove the correctness of basic broadcast abstractions over federated quorum systems and establish their relationship to the classical Byzantine quorum systems. In particular, we prove correctness in the realistic setting where Byzantine nodes may lie about their trust choices. We show that this setting leads to a novel variant of Byzantine quorum systems where different nodes may have different understanding of what constitutes a quorum.
Cellular Targets for the Treatment of Flavivirus Infections

Classical antiviral therapy targets viral functions, mostly viral enzymes or receptors. Successful examples include precursor herpesvirus drugs, antiretroviral drugs that target reverse transcriptase and protease, influenza virus directed compounds as well as more recent direct antiviral agents (DAA) applied in the treatment of hepatitis C virus (HCV). However, from early times, the possibility of targeting the host cell to contain the infection has frequently re-emerged as an alternative and complementary antiviral strategy. Advantages of this approach include an increased threshold to the emergence of resistance and the possibility to target multiple viruses. Major pitfalls are related to important cellular side effects and cytotoxicity. In this mini-review, the concept of host directed antiviral therapy will be discussed with a focus on the most recent advances in the field of Flaviviruses, a family of important human pathogens for which we do not have antivirals available in the clinics.
The Prevalence of Abdominal Obesity and Its Correlates among the Adults in Dodoma Region, Tanzania: A Community-Based Cross-Sectional Study

Introduction Overweight and obesity are a threat to the public health following their association with noncommunicable diseases, such as type 2 diabetes mellitus, cardiovascular disease, and some cancers. Despite this fact, the information on overweight and obesity, particularly in most developing countries, is still scarce to address the problem. This article partly addresses the gap through the findings of a cross-sectional survey that was conducted in Dodoma Region, Central Tanzania, to determine the prevalence and correlates of abdominal obesity among adults. Methods Using a community-based cross-sectional survey, data were generated from the participants who aged 18 years and above. Simple random sampling and Kish selection table techniques were used to get the sample who responded through a face-to-face-administered questionnaire. Waist circumference was measured using the guideline of the WHO protocol of measuring waist and hip circumference. Abdominal obesity is defined as a condition with waist circumference >102 cm for men and >88 cm for women. Prevalence was computed with a 95% confidence interval. Simple and multiple logistic regression models were fitted to identify the risk factors associated with abdominal obesity. Results A total of 840 respondents took part in the study. The overall prevalence of abdominal obesity was found to be 24.88% (209/840). The prevalence of abdominal obesity was significantly higher among women than men (35.14% vs. 6.89%, p < 0.0001) and higher among urban dwellers (33.56%) than their rural counterparts (15.56%). Correlates of abdominal obesity was found to be gender, marital status, place of residence, age, education level, and the time used in watching television. Conclusion This study revealed a high prevalence of abdominal obesity among the people living in the Dodoma Region. Increased age, urban residence, more time spent on television, less walking per day, and being ever married were all associated with having abdominal obesity in this population.
Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches.
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks

We introduce a new scene graph generation method called image-level attentional context modeling (ILAC). Our model includes an attentional graph network that effectively propagates contextual information across the graph using image-level features. Whereas previous works use an object-centric context, we build an image-level context agent to encode the scene properties. The proposed method comprises a single-stream network that iteratively refines the scene graph with a nested graph neural network. We demonstrate that our approach achieves competitive performance with the state-of-the-art for scene graph generation on the Visual Genome dataset, while requiring fewer parameters than other methods. We also show that ILAC can improve regular object detectors by incorporating relational image-level information.
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases

The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners celebrate this as an economical way to get more synthetic data that can be used to train downstream classifiers, it is not clear that they recognize the inherent pitfalls of this technique. In this paper, we aim to exhort practitioners against deriving any false sense of security against data biases based on data augmentation. To drive this point home, we show that starting with a dataset consisting of head-shots of engineering researchers, GAN-based augmentation "imagines" synthetic engineers, most of whom have masculine features and white skin color (inferred from a human subject study conducted on Amazon Mechanical Turk). This demonstrates how biases inherent in the training data are reinforced, and sometimes even amplified, by GAN-based data augmentation; it should serve as a cautionary tale for the lay practitioners.
On the Statistical and Information-theoretic Characteristics of Deep Network Representations

It has been common to argue or imply that a regularizer can be used to alter a statistical property of a hidden layer's representation and thus improve generalization or performance of deep networks. For instance, dropout has been known to improve performance by reducing co-adaptation, and representational sparsity has been argued as a good characteristic because many data-generation processes have a small number of factors that are independent. In this work, we analytically and empirically investigate the popular characteristics of learned representations, including correlation, sparsity, dead unit, rank, and mutual information, and disprove many of the \textit{conventional wisdom}. We first show that infinitely many Identical Output Networks (IONs) can be constructed for any deep network with a linear layer, where any invertible affine transformation can be applied to alter the layer's representation characteristics. The existence of ION proves that the correlation characteristics of representation is irrelevant to the performance. Extensions to ReLU layers are provided, too. Then, we consider sparsity, dead unit, and rank to show that only loose relationships exist among the three characteristics. It is shown that a higher sparsity or additional dead units do not imply a better or worse performance when the rank of representation is fixed. We also develop a rank regularizer and show that neither representation sparsity nor lower rank is helpful for improving performance even when the data-generation process has a small number of independent factors. Mutual information $I(\mathbf{z}_l;\mathbf{x})$ and $I(\mathbf{z}_l;\mathbf{y})$ are investigated, and we show that regularizers can affect $I(\mathbf{z}_l;\mathbf{x})$ and thus indirectly influence the performance. Finally, we explain how a rich set of regularizers can be used as a powerful tool for performance tuning.
Preliminary Investigation of the Mechanical Anisotropy of the Normal Human Corneal Stroma

Purpose To investigate the anisotropic characteristics of the normal human corneal stroma using fresh corneal tissue. Methods Sixty-four corneal specimens extracted from stromal lenticules were included in this study. The specimens were cut in the temporal-nasal (horizontal) or superior-inferior (vertical) direction. Strip specimens were subjected to uniaxial tensile testing. The tensile properties of the specimens were measured and compared in the two directions. Results The low-strain tangent modulus was statistically significantly greater in the vertical direction than in the horizontal direction (1.32 ± 0.50 MPa vs 1.17 ± 0.43 MPa; P=0.035), as was the high-strain tangent modulus (51.26 ± 8.23 MPa vs 43.59 ± 7.96 MPa; P ≤ 0.001). The elastic modulus in the vertical direction was also higher than that in horizontal direction at stresses of 0.01, 0.02, and 0.03 MPa, but not statistically significant; so, P=0.338, 0.373, and 0.417, respectively. Conclusions The biomechanical behavior in normal human corneal stroma tissue is slightly stiffer in the vertical direction than in the horizontal direction. This information may aid our understanding of the biomechanical properties of the cornea and related diseases.
Changing the Image Memorability: From Basic Photo Editing to GANs

Memorability is considered to be an important characteristic of visual content, whereas for advertisement and educational purposes it is often crucial. Despite numerous studies on understanding and predicting image memorability, there are almost no achievements in memorability modification. In this work, we study two approaches to image editing – GAN and classical image processing – and show their impact on memorability. The visual features which influence memorability directly stay unknown till now, hence it is impossible to control it manually. As a solution, we let GAN learn it deeply using labeled data, and then use it for conditional generation of new images. By analogy with algorithms which edit facial attributes, we consider memorability as yet another attribute and operate with it in the same way. Obtained data is also interesting for analysis, simply because there are no real-world examples of successful change of image memorability while preserving its other attributes. We believe this may give many new answers to the question "what makes an image memorable? " Apart from that we also study the influence of conventional photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience on memorability. In this case, we start from real practical methods and study it using statistics and recent advances in memorability prediction. Photographers, designers, and advertisers will benefit from the results of this study directly.