Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term health of children conceived after assisted reproductive technology

Abstract The aim of this narrative review is to summarize the present knowledge on long-term outcome of children born after assisted reproductive technologies (ART). The main outcomes covered are neurodevelopment including cerebral palsy, cognitive development, attention deficit hyperactivity disorder and autism spectrum disease, growth, cardiovascular function, diabetes type 1, asthma, malignancies, and reproductive health. Results have mainly been obtained from systematic reviews/meta-analyses and large registry studies. It has been shown that children born after ART, when restricted to singletons, have a similar outcome for many health conditions as their spontaneously conceived peers. For some outcomes, particularly cardiovascular function and diabetes, studies show some higher risk for ART singletons or subgroup of ART singletons. The fast introduction of new ART techniques emphasizes the importance of continuous surveillance of children born after ART.
Experimental Studies of Front-of-Package Nutrient Warning Labels on Sugar-Sweetened Beverages and Ultra-Processed Foods: A Scoping Review

Policies that require front-of-package (FoP) nutrient warnings are becoming increasingly common across the globe as a strategy to discourage excess consumption of sugary drinks and ultra-processed food. However, a better understanding of the pathway through which FoP nutrient warnings work, as well as a review of how outcomes being measured in recent studies map onto this pathway, are needed in order to inform policy on the most effective FoP label design for reducing purchases of ultra-processed foods. This scoping review describes a conceptual model for how FoP nutrient warnings affect consumer behavior, examines which of these outcomes are currently being measured, and summarizes evidence from randomized controlled experiments. Twenty-two studies which experimentally tested nutrient warnings against a control label or other labeling systems were included for full-text review. Our conceptual model includes attention; comprehension, cognitive elaboration, and message acceptance; negative affect and risk perception; behavioral intentions, and behavioral response, along with other elements such as external factors and interpersonal communications. We found that many studies focused on outcomes such as attention, comprehension, and behavioral intentions, but considerable gaps in the evidence remain, particularly for intermediary steps on the pathway to behavioral change, such as negative affect and social interactions. FoP nutrient warnings were visually attended to by consumers, easy to understand, helped consumers identify products high in nutrients of concern, and discouraged them from purchasing these products, although other labeling systems were perceived as containing more information and performed better at helping consumers rank the healthfulness of products. More research is needed to understand whether and how nutrient warnings work in the real world to discourage consumer purchases of sugary drinks and ultra-processed food.
Decoding Protein-protein Interactions: An Overview.

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions which determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine receptor 3 B-isoform and predictive modeling with IP10 and PF4.
Novel Therapeutic Approach to Induce Autophagy in a Drosophila Model for Huntington’s Disease

Autophagy induction is an attractive therapeutic approach to ameliorate aggregate accumulation in many neurodegenerative diseases. In Huntington's disease (HD) in vivo models, a number of genetic and pharmacological mechanisms aimed to induce autophagy have been successfully tested [1], demonstrating the role of autophagy in promoting the elimination of mutant huntingtin (mHTT) aggregates and its neuroprotective effect. In their recent report in Cells, Vernizzi and colleagues [2] presented a totally new mechanism to induce autophagy, promote the elimination of mHTT aggregates, and ultimately achieve neuroprotection. This novel therapy is based on the overexpression of glutamine synthetase 1 (GS1), an enzyme that catalyzes the synthesis of L-glutamine from L-glutamate as part of the glutamate glutamine cycle (GGC), a physiological process between glia and neurons that controls glutamate homeostasis [3]. [. ].
“The Financial Impact Is Depressing and Anxiety Inducing”: A Qualitative Exploration of the Personal Financial Toll of Arthritis

The financial experience faced by working‐age people with arthritis includes living below the poverty line for many. Financial distress among people with arthritis is known to contribute to poorer health outcomes, including high psychological distress and more severe pain. Despite the demonstrated societal cost of arthritis care and management, the personal costs borne by the individual are not well understood. The aim of this study was to explore the perceived financial impacts of living with arthritis among younger adults (defined as those ages 18–50 years).
HER2 Heterogeneity in Personalized Therapy of Gastro-Oesophageal Malignancies: An Overview by Different Methodologies

Human epidermal growth factor receptor-2 (HER2)-expression gastro-oesophageal adenocarcinomas (GEA) gained interest as an important target for therapy with trastuzumab. In the current review, we focused the current knowledge on HER2 status in dysplastic and neoplastic gastric conditions, analyzing the methodological procedures to identify HER2 expression/amplification, as well as the proposed scoring recommendations. One of the most relevant questions to evaluate the useful impact of HER2 status on therapeutic choice in GEAs is represented by the significant heterogeneity of HER2 protein and gene expression that may affect the targeted treatment selection. Future development of biotechnology will continue to evolve in order to offer more powerful detection systems for the assessment of HER2 status. Finally, liquid biopsy as well as mutation/amplification of several additional genes may furnish an early detection of secondary HER2 resistance mechanisms in GEAs with a better monitoring of the treatment response.
Individual variation in glucocorticoid plasticity: considerations & future directions.

The hypothalamic-pituitary-adrenal (HPA) axis regulates the secretion of glucocorticoids, hormones with diverse roles ranging from regulating daily metabolic demand to coping with sudden perturbations. As a result, glucocorticoids are thought to help vertebrates track their changing environments and coordinate plasticity in diverse phenotypes. While this endocrine system is highly plastic-where one individual can produce multiple phenotypes across varying environmental conditions-little is understood about the degree to which individuals, populations, or species differ in circulating glucocorticoid plasticity. Empirical research quantifying individual variation in glucocorticoid plasticity has increased in recent years, though the multiple complex roles of the HPA-axis make it challenging to generalize the extent to which individual variation in plasticity exists. I provide an overview of current findings on variation in glucocorticoids plasticity, and outline multiple types of glucocorticoid plasticity researchers should consider in future work to advance our understanding of the causes and consequences of individual variation in glucocorticoid plasticity.
Secure D2D Communication in 5G Networks

-5G technology is presently the most prominent technologies in network domain. Although, it is said to be available by year 2020, but still there are lot of views about the features and benefits which 5G will offer to the world in comparison with 4G.Also there are lot of discussions about its resource requirements with which our world will enter into a new era of mobile technology after implementing these requirements. With the concept of Internet of Things brought together by 5G, we can expect that this technology will go one step ahead as future network technology. As different application areas as well as new architecture and technologies will grow with 5G, so we can also expect that we will face different security and privacy protection challenges. One of the most promising area of 5G technology is device to device communication. With the growth of such promising technology, we know that since 2G, user privacy and security has been a very important issue. In this paper, we broadly review D2D communication security in 5G networks. At last, future research directions are presented which can strengthen the security of 5G technology.
The systematics of the Cervidae: a total evidence approach

Systematic relationships of cervids have been controversial for decades. Despite new input from molecular systematics, consensus could only be partially reached. The initial, gross (sub) classification based on morphology and comparative anatomy was mostly supported by molecular data. The rich fossil record of cervids has never been extensively tested in phylogenetic frameworks concerning potential systematic relationships of fossil cervids to extant cervids. The aim of this work was to investigate the systematic relationships of extant and fossil cervids using molecular and morphological characters and make implications about their evolutionary history based on the phylogenetic reconstructions. To achieve these objectives, molecular data were compiled consisting of five nuclear markers and the complete mitochondrial genome of 50 extant and one fossil cervids. Several analyses using different data partitions, taxon sampling, partitioning schemes, and optimality criteria were undertaken. In addition, the most extensive morphological character matrix for such a broad cervid taxon sampling was compiled including 168 cranial and dental characters of 41 extant and 29 fossil cervids. The morphological and molecular data were analysed in a combined approach and other comprehensive phylogenetic reconstructions. The results showed that most Miocene cervids were more closely related to each other than to any other cervids. They were often positioned between the outgroup and all other cervids or as the sister taxon to Muntiacini. Two Miocene cervids were frequently placed within Muntiacini. Plio- and Pleistocene cervids could often be affiliated to Cervini, Odocoileini or Capreolini. The phylogenetic analyses provide new insights into the evolutionary history of cervids. Several fossil cervids could be successfully related to living representatives, confirming previously assumed affiliations based on comparative morphology and introducing new hypotheses. New systematic relationships were observed, some uncertainties persisted and resolving systematics within certain taxa remained challenging.
ImmersiveArt A Study of Virtual Reality and the Benefits of Natural User Interface

As our technologies have evolved, they have allowed us the opportunity to access and manipulate increasingly large, complex pieces of data. Gaming has become an incredibly involved experience with its hyper-detailed environments. The power of mobile computing has put a vast amount of information constantly at our fingertips; and the recent movements in augmented and virtual reality are allowing the physical and virtual worlds to bleed into each other in unique ways. As the information we interact with comes to us in an ever-increasing variety of forms and complexities, so too must our ways of interaction with said data evolve and become a less noticeable obstacle. In this paper, we study improvements on usability through use of natural interfaces, specifically the improvement of experience and user efficiency whilst using muscle computer interaction as opposed to more traditional forms of interaction.
Design and Implementation of a Cluster Based Approach for Software Verification

Automatic software verification is a resource intensive process that often exceeds the capacity of a single machine. To tackle bigger programs, a way to add more computing power is needed. But hardware can not be upgraded indefinitely. One solution is to form a cluster out of several computers and use the combined computing power. To make the best use of the clustered resources, algorithms must be designed with increased latency and non-local data in mind. This thesis evaluates different ways of adding cluster support to an existing parallel verification algorithm that is part of the configurable software verification platform CPAchecker .
Securing Operating System Audit Logs

—System auditing mechanisms are an important concern when investigating and responding to security incidents. Unfortunately, attackers regularly engage in anti-forensic activities after an intrusion, erasing or falsifying system logs to cover their tracks. While a variety of tamper-evident logging solutions have appeared in the industry and the literature, these techniques have seen limited usage because of impractical limitations such as requiring expensive cryptographic computations, fully trusted servers or specialized no-rewrite storage. In this poster, we introduce a novel framework for scalable tamper-evident system logging. Our system leverages features of increasingly available trusted execution environments (TEE) to enable the veriﬁcation of log integrity while being minimally invasive to the underlying logging framework. We further present a security analysis of our system and evaluate its performance overhead on the standard Linux’s audit framework.
An Advanced Criterion of Permutation Set for SCMA

—For Sparse Code Multiple Access (SCMA) with the dimension distance-based codebook design, the convergence reliability of some detected layers in their corresponding decision processes was unsatisfactory at the Message Passing Algorithm (MPA) receiver. Driven by this problem, in the dimension distance-based codebook design of SCMA, an advanced criterion of permutation set is proposed to increase the variance of the set of absolute differences between the sums of distances between interfering dimensions of transmitted codewords multiplexed on all resource nodes. Simulation results show that the Bit Error Rate (BER) performance of the proposed criterion outperforms that of traditional criterion under the same spectral efficiency. 
Expos´e Master’s Thesis: A Synthetic Motif Generator

In colloquial language a time series is a (mostly long) sequence of reals and a subsequence is a sequence of consecutive values in a time series. Two sequences of reals are matching if both have the same number of values and a very similar shape. A motif can be considered as a repetitive pattern in a time series and is represented by a sequence of reals that matches multiple subsequences in a time series. Motif discovery denotes the problem of finding a previously unknown motif in a time series. In chronological order the motif discovery problem was first tackled by Lin, Keogh, Lonardi, and Patel, 2002. They search for the subsequence in a time series that matches the largest number of subsequences of that time series within a given similarity range to discover a repeating pattern in a time series. We call this problem set motif problem. Next Mueen et al., 2009, faced the motif discovery problem. They search for the most similar pair(s) of subsequences in a time series to discover a repeating pattern. We call this problem pair motif problem. After that Grabocka et al., 2015, dealed with the motif discovery problem. They search for a sequence of reals, not necessarily a subsequence of the time series, that matches the largest number of subseqences in a time series within a given similarity range. We call this problem latent set motif problem. As the pair motif problem defines the motif as the most similar pair(s) of subsequences in a time series, the set motif defines potentially more subsequences in the same time series. The latent set motif matches potentially even more subsequences in the same time series than the set motif, since a repeating pattern in a time series occurs noisily under real conditions and may not be a subsequence of the time series. Therefore, the motif discovery problem is divided into the pair motif, set motif and latent set motif problem. Figure 1 illustrates all three motif discovery problems. In our previous student research project “A Synthetic Motif Generator” we
Experimental study of the neuron-level mechanisms emerging from backpropagation

The backpropagation algorithm [1] is the most successful learning algorithm for training deep artiﬁcial neural networks. Its inner workings are in stark contrast with other learning rules, as it is based on a global, black box optimization procedure rather than the repetition of a local, neuron-level procedure (e
Traffic-type Assignment for TSN-based Mixed-criticality Cyber-physical Systems

This article focuses on mixed-criticality applications with functions that have different timing requirements, i.e., hard real-time (HRT), soft real-time (SRT), and functions that are not time-critical (NC). The applications are implemented on distributed cyber-physical systems that use IEEE Time-sensitive Networking (TSN). TSN is the product of an IEEE effort to bring deterministic real-time capabilities to IEEE 802.3 Ethernet. TSN supports the convergence of multiple traffic types, i.e., critical, real-time, and regular “best-effort” traffic within a single network: Time-triggered (TT), where the messages are transmitted based on static schedule tables, Audio-video Bridging (AVB), for dynamically scheduled messages with a guaranteed bandwidth and bounded delays, and Best Effort (BE), for which no timing guarantees are provided. The HRT messages have deadlines, whereas we capture the quality-of-service for the SRT messages using “utility functions. ” Given the network topology, the set of application messages, including their routing, and the set of available AVB classes, we are interested in determining the traffic type of each message, such that all the HRT messages are schedulable and the total utility for the SRT messages is maximized. We propose a Tabu Search-based metaheuristic to solve this optimization problem. The proposed proof-of-concept tool has been evaluated using several benchmarks, including two realistic test cases.
A Modified Average Reward Reinforcement Learning Based on Fuzzy Reward Function

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Classification of Food Spices by Proximate Content: Principal Component, Cluster, Meta-Analyses

Proximate composition of six food spices commonly used in South-East Nigeria are classified by principal component analysis (PCAs) of constituents and spices cluster analysis (CAs). Samples are grouped into two classes. Compositional PCA and spice CA permit classificating them and group the similar ones. The first PCA axis explains 61% of the variance; first two, 93%; first three, 99; etc. Different behaviour of species depends on ash, fibre, fat, moisture, etc. Macronutrients (protein, carbohydrate, fat) contents are adequate. Carbohydrate amounts are high. Fat quantities are moderate. Fat is closer to protein than to carbohydrate.
Inﬂuence of Dropout and Dynamic Receptive Field Operations on Convolutional Networks

The method and the experiments which have been performed in order to struggle with coadaptation and to improve generalization abilities of networks with the help of two techniques: dynamic receptive ﬁelds and dropout have been presented of the article. It is an eﬀective approach for networks training. The use of the method, combining the dropout technique and dynamic receptive ﬁelds, allows to reduce the generalization error and prevents the co-adaptation of neurons
Phosphorus recovery and recycling with ecological engineering: A review

Phosphorus is essential to life on Earth and frequently limits the productivity of ecosystems, including agroecosystems. Currently, a substantial portion of the global human population relies on finite phosphate rock resources used for chemical fertilizer production. Concern over poor management of these vital resources and continued efforts to enhance soil fertility and food security have stimulated interest in phosphorus recovery and recycling. Existing heterogeneity in phosphorus waste flows, agricultural phosphorus needs, the availability of resources, and spatial patterns of land use calls for a diverse array of phosphorus recycling strategies. Ecological engineers working on phosphorus management have most commonly aimed to create phosphorus sinks on the landscape to help mitigate eutrophication. There is a growing need for ecological engineering approaches that go beyond phosphorus retention to create pathways for phosphorus recovery and recycling, supporting both eutrophication control and food