Miracle-dz/UniFAR-FTUs huggingface dataset:
Cigarette smoking affects microRNAs and inflammatory biomarkers in healthy individuals and an association to single nucleotide polymorphisms is indicated

Abstract Background: Cigarette smoke induces inflammation and remodels immune response. Genetic and epigenetic alterations might be involved in the pathogenesis of smoking related diseases. In this study, we investigated the effect of smoking on systemic inflammation biomarkers and epigenetic changes at microRNA (miRNA) expression level. We also examined if the levels of inflammatory biomarkers were associated with selected single nucleotide polymorphisms (SNPs). Method: From 39 smokers and 101 non-smokers, levels of total white blood cells (WBCs) and its subpopulations, plasma cytokines/chemokines/proteins and miRNAs were analysed. For three biomarkers, C-reactive protein (CRP), MCP-1 and IFN-γ that were affected by smoking, the influence of SNPs was analyzed. Result: Elevated levels of total WBCs, neutrophils, monocytes, lymphocytes, CRP, MCP-1, IFN-γ and lower levels of miR-21 were detected in smokers. The elevated levels of IFN-γ in smokers was only statistically significantly associated with rs2069705 AG/GG SNP-genotype. Conclusions: A lower level of oncomir miRNA-21 and a higher level of immune modelling cytokine IFN-γ detected in smokers could be a protective immune response to cigarette smoke. The higher level of IFN-γ in smokers with a specific SNP genotype also suggests that a genetic interaction with smoking might predict the pathobiology of smoking related disease.
Chronic Nicotine Exposure Induces Murine Aortic Remodeling and Stiffness Segmentation—Implications for Abdominal Aortic Aneurysm Susceptibility

Aim: Arterial stiffness is a significant risk factor for many cardiovascular diseases, including abdominal aortic aneurysms (AAA). Nicotine, the major active ingredient of e-cigarettes and tobacco smoke, induces acute vasomotor effects that may temporarily increase arterial stiffness. Here, we investigated the effects of long-term nicotine exposure on structural aortic stiffness. Methods: Mice (C57BL/6) were infused with nicotine for 40 days (20 mg/kg/day). Arterial stiffness of the thoracic (TS) and abdominal (AS) aortic segments was analyzed using ultrasound (PWV, pulse wave velocity) and ex vivo pressure myograph measurements. For mechanistic studies, aortic matrix-metalloproteinase (MMP) expression and activity as well as medial elastin architecture were analyzed. Results: Global aortic stiffness increased with nicotine. In particular, local stiffening of the abdominal segment occurred after 10 days, while thoracic aortic stiffness was only increased after 40 days, resulting in aortic stiffness segmentation. Mechanistically, nicotine exposure enhanced expression of MMP-2/-9 and elastolytic activity in both aortic segments. Elastin degradation occurred in both segments; however, basal elastin levels were higher in the thoracic aorta. Finally, MMP-inhibition significantly reduced nicotine-induced MMP activity, elastin destruction, and aortic stiffening. Conclusion: Chronic nicotine exposure induces aortic MMP expression and structural aortic damage (elastin fragmentation), irreversibly increasing aortic stiffness. This process predominantly affects the abdominal aortic segment, presumably due in part to a lower basal elastin content. This novel phenomenon may help to explain the role of nicotine as a major risk factor for AAA formation and has health implications for ECIGs and other modes of nicotine delivery.
Splitability Annotations: Optimizing Black-Box Function Composition in Existing Libraries

Data movement is a major bottleneck in parallel data-intensive applications. In response to this problem, researchers have proposed new runtimes and intermediate representations (IRs) that apply optimizations such as loop fusion under existing library APIs. Even though these runtimes generally do no require changes to user code, they require intrusive changes to the library itself: often, all the library functions need to be rewritten for a new IR or virtual machine. In this paper, we propose a new abstraction called splitability annotations (SAs) that enables key data movement optimizations on black-box library functions. SAs only require that users add an annotation for existing, unmodified functions and implement a small API to split data values in the library. Together, this interface describes how to partition values that are passed among functions to enable data pipelining and automatic parallelization while respecting each library's correctness constraints. We implement SAs in a system called Mozart. Without modifying any library function, on workloads using NumPy and Pandas in Python and Intel MKL in C, Mozart provides performance competitive with intrusive solutions that require rewriting libraries in many cases, can sometimes improve performance over past systems by up to 2x, and accelerates workloads by up to 30x.
Adherence of Malaysian Adults’ Energy and Macronutrient Intakes to National Recommendations: A Review and Meta-Analysis

The present study examined the best available evidence regarding energy and macronutrient intake during adulthood (age 19 to 59 years) in Malaysia and assessed whether intakes adhere to national recommendations, in order to develop recommendations for dietary improvement based on population consumption patterns. A literature review and meta-analysis evaluated intake based on the following characteristics, using information from food balance sheets, national surveys, and individual studies: (1) levels of intake, (2) proportion of the population whose diets adhere to/exceed/fail to meet Malaysian Recommended Nutrient Intake (RNI) levels, and (3) sources of macronutrients observed in these studies. Food balance data suggested high levels of available energy, animal source protein, vegetable fat, and refined carbohydrates. Twenty studies (five nationwide, 15 individual) indicated that Malaysian adults generally met or exceeded recommendations for fat and protein, but were inconsistent with respect to energy and carbohydrates. Information on dietary sources was limited. Due to methodological limitations, insufficient evidence exists regarding energy and macronutrient intakes of Malaysian adults. Improved dietary assessment methods (including use of biomarkers), better data analysis, and updated food composition data, will provide more reliable information on which to base policy decisions and recommendations for improvement.
On the security of the hierarchical attribute based encryption scheme proposed by Wang et al

Ciphertext-policy hierarchical attribute-based encryption (CP-HABE) is a promising cryptographic primitive for enforcing the fine-grained access control with scalable key delegation and user revocation mechanisms on the outsourced encrypted data in a cloud. Wang et al. (2011) proposed the first CP-HABE scheme and showed that the scheme is semantically secure in the random oracle model [4, 5]. Due to some weakness in its key delegation mechanism, by presenting two attacks, we demonstrate the scheme does not offer any confidentiality and fine-grained access control. In this way, anyone who has just one attribute can recover any outsourced encrypted data in the cloud.
Ineffectiveness of Dictionary Coding to Infer Predictability Limits of Human Mobility

Recently, a series of models have been proposed to predict future movements of people. Meanwhile, dictionary coding algorithms are used to estimate the predictability limit of human mobility. Although dictionary coding is optimal, it takes long time to converge. Consequently, it is ineffective to infer predictability through dictionary coding algorithms. In this report, we illustrate this ineffectiveness on the basis of human movements in urban space.
Energy Efficiency Fairness for Multi-Pair Wireless-Powered Relaying Systems

We consider a multi-pair amplify-and-forward relay network where the energy-constrained relays adopting the time-switching protocol harvest energy from the radio-frequency signals transmitted by the users for assisting user data transmission. Both one-way and two-way relaying techniques are investigated. Aiming at energy efficiency (EE) fairness among the user pairs, we construct an energy consumption model incorporating rate-dependent signal processing power, the dependence on output power level of power amplifiers’ efficiency, and nonlinear energy harvesting (EH) circuits. Then, we formulate the max-min EE fairness problems in which the data rates, users’ transmit power, relays’ processing coefficient, and EH time are jointly optimized under the constraints on the quality of service and users’ maximum transmit power. To achieve efficient suboptimal solutions to these nonconvex problems, we devise monotonic descent algorithms based on the inner approximation (IA) framework, which solve a second-order-cone program in each iteration. To further simplify the designs, we propose an approach combining IA and zero-forcing beamforming, which eliminates inter-pair interference and reduces the numbers of variables and required iterations. Finally, extensive numerical results are presented to validate the proposed approaches. More specifically, the results demonstrate that ignoring the realistic aspects of power consumption might degrade the performance remarkably, and jointly designing parameters involved could significantly enhance the EE.
Metabolomes of mitochondrial diseases and inclusion body myositis patients: treatment targets and biomarkers

Mitochondrial disorders (MDs) are inherited multi‐organ diseases with variable phenotypes. Inclusion body myositis (IBM), a sporadic inflammatory muscle disease, also shows mitochondrial dysfunction. We investigated whether primary and secondary MDs modify metabolism to reveal pathogenic pathways and biomarkers. We investigated metabolomes of 25 mitochondrial myopathy or ataxias patients, 16 unaffected carriers, six IBM and 15 non‐mitochondrial neuromuscular disease (NMD) patients and 30 matched controls. MD and IBM metabolomes clustered separately from controls and NMDs. MDs and IBM showed transsulfuration pathway changes; creatine and niacinamide depletion marked NMDs, IBM and infantile‐onset spinocerebellar ataxia (IOSCA). Low blood and muscle arginine was specific for patients with m.3243A>G mutation. A four‐metabolite blood multi‐biomarker (sorbitol, alanine, myoinositol, cystathionine) distinguished primary MDs from others (76% sensitivity, 95% specificity). Our omics approach identified pathways currently used to treat NMDs and mitochondrial stroke‐like episodes and proposes nicotinamide riboside in MDs and IBM, and creatine in IOSCA and IBM as novel treatment targets. The disease‐specific metabolic fingerprints are valuable “multi‐biomarkers” for diagnosis and promising tools for follow‐up of disease progression and treatment effect.
Distributed functional reactive programming on actor-based runtime

Reactive programming over a network is a challenging task because efficient elimination of temporary violations of data flow invariants, known as glitches, in a distributed setting is still an open issue. In this paper, we propose a method for constructing a distributed reactive programming system of which runtime guarantees the properties of single source glitch-freedom and the robustness against out-of-order messages. Based on the method, we developed a purely functional reactive programming language XFRP whose compiler produces Erlang code. Using some examples, we show that the proposed method is beneficial for constructing distributed reactive applications without suffering from inconsistencies.
Impaired Spike Timing Dependent Cortico-Cortical Plasticity in Alzheimer's Disease Patients.

BACKGROUND Mechanisms of cortical plasticity have been recently investigated in Alzheimer's disease (AD) patients with transcranial magnetic stimulation protocols showing a clear impairment of long-term potentiation (LTP) cortical-like plasticity mechanisms. OBJECTIVE We aimed to investigate mechanisms of cortico-cortical spike-timing dependent plasticity (STDP) in AD patients investigating the connections between posterior parietal cortex (PPC) and primary motor cortex (M1). METHODS We used a cortico-cortical paired associative stimulation (cc-PAS) protocol to repeatedly activate the connection between PPC and M1 of the left-dominant hemisphere in a sample of fifteen AD patients and ten age-matched healthy subjects. PPC transcranial magnetic stimulation preceded (ccPAS +5) or followed M1 stimulation (ccPAS - 5) by 5 ms. Motor-evoked potentials (MEPs) were collected to assess the time course of the after effects of cc-PAS protocol measuring MEP amplitude as index of cortico-cortical associative plasticity. RESULTS In healthy subjects, ccPAS - 5 protocol induced the expected long-lasting increase of MEP amplitude compatible with LTP-like cortical plasticity while PAS +5 protocol induced the opposite effect. AD patients did not show any significant modification of the amplitude of MEP after both ccPAS protocols. CONCLUSIONS Our study shows that in AD patients the time-locked activation of human cortico-cortical connections is not able to form STDP, reflecting an impairment of a multi-factor plasticity process.
Stop Illegal Comments: A Multi-Task Deep Learning Approach

Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep learning community is the application of multi-task models that enable single deep neural networks to perform more than one task at the same time, for example classification and translation tasks. These powerful novel models are capable of transferring knowledge among different tasks or training sets and therefore could open up the legal domain for many deep learning applications. In this paper, we investigate the transfer learning capabilities of such a multi-task model on a classification task on the publicly available Kaggle toxic comment dataset for classifying illegal comments and we can report promising results.
Towards End-to-end Automatic Code-Switching Speech Recognition

Speech recognition in mixed language has difficulties to adapt end-to-end framework due to the lack of data and overlapping phone sets, for example in words such as "one" in English and "w\`an" in Chinese. We propose a CTC-based end-to-end automatic speech recognition model for intra-sentential English-Mandarin code-switching. The model is trained by joint training on monolingual datasets, and fine-tuning with the mixed-language corpus. During the decoding process, we apply a beam search and combine CTC predictions and language model score. The proposed method is effective in leveraging monolingual corpus and detecting language transitions and it improves the CER by 5%.
Tumor Microenvironment Targeted Nanotherapy

Recent developments in nanotechnology have brought new approaches to cancer diagnosis and therapy. While enhanced permeability and retention effect promotes nano-chemotherapeutics extravasation, the abnormal tumor vasculature, high interstitial pressure and dense stroma structure limit homogeneous intratumoral distribution of nano-chemotherapeutics and compromise their imaging and therapeutic effect. Moreover, heterogeneous distribution of nano-chemotherapeutics in non-tumor-stroma cells damages the non-tumor cells, and interferes with tumor-stroma crosstalk. This can lead not only to inhibition of tumor progression, but can also paradoxically induce acquired resistance and facilitate tumor cell proliferation and metastasis. Overall, the tumor microenvironment plays a vital role in regulating nano-chemotherapeutics distribution and their biological effects. In this review, the barriers in tumor microenvironment, its consequential effects on nano-chemotherapeutics, considerations to improve nano-chemotherapeutics delivery and combinatory strategies to overcome acquired resistance induced by tumor microenvironment have been summarized. The various strategies viz., nanotechnology based approach as well as ligand-mediated, redox-responsive, and enzyme-mediated based combinatorial nanoapproaches have been discussed in this review.
Cluster Size Management in Multi-Stage Agglomerative Hierarchical Clustering of Acoustic Speech Segments

Agglomerative hierarchical clustering (AHC) requires only the similarity between objects to be known. This is attractive when clustering signals of varying length, such as speech, which are not readily represented in fixed-dimensional vector space. However, AHC is characterised by $O(N^2)$ space and time complexity, making it infeasible for partitioning large datasets. This has recently been addressed by an approach based on the iterative re-clustering of independent subsets of the larger dataset. We show that, due to its iterative nature, this procedure can sometimes lead to unchecked growth of individual subsets, thereby compromising its effectiveness. We propose the integration of a simple space management strategy into the iterative process, and show experimentally that this leads to no loss in performance in terms of F-measure while guaranteeing that a threshold space complexity is not breached.
Recent developments and highlights in allergen immunotherapy

Allergen immunotherapy (AIT) is the only disease‐modifying treatment option for patients with IgE‐mediated inhalant allergies. Though used in clinical practice for more than 100 years, most innovations in AIT efficacy and safety have been developed in the last two decades. This expert review aimed to highlight the recent progress in AIT for both application routes, the sublingual (SLIT) and subcutaneous (SCIT) forms. As such, it covers recent aspects regarding efficacy and safety in clinical trials and real‐life data and outlines new concepts in consensus and position papers as well as in guidelines for AIT. Potential clinical and nonclinical biomarkers are discussed. This review also focuses on potential future perspectives in AIT, such as alternative application routes, immune‐modulating adjuvants, and recombinant vaccines. In conclusion, this state of the art review provides a comprehensive overview of AIT and highlights unmet needs for the future.
Interactive Roles for AMPK and Glycogen from Cellular Energy Sensing to Exercise Metabolism

The AMP-activated protein kinase (AMPK) is a heterotrimeric complex with central roles in cellular energy sensing and the regulation of metabolism and exercise adaptations. AMPK regulatory β subunits contain a conserved carbohydrate-binding module (CBM) that binds glycogen, the major tissue storage form of glucose. Research over the past two decades has revealed that the regulation of AMPK is impacted by glycogen availability, and glycogen storage dynamics are concurrently regulated by AMPK activity. This growing body of research has uncovered new evidence of physical and functional interactive roles for AMPK and glycogen ranging from cellular energy sensing to the regulation of whole-body metabolism and exercise-induced adaptations. In this review, we discuss recent advancements in the understanding of molecular, cellular, and physiological processes impacted by AMPK-glycogen interactions. In addition, we appraise how novel research technologies and experimental models will continue to expand the repertoire of biological processes known to be regulated by AMPK and glycogen. These multidisciplinary research advances will aid the discovery of novel pathways and regulatory mechanisms that are central to the AMPK signaling network, beneficial effects of exercise and maintenance of metabolic homeostasis in health and disease.
Average-case quantum advantage with shallow circuits

Recently Bravyi, Gosset and König (Science 2018) proved an unconditional separation between the computational powers of small-depth quantum and classical circuits for a relation. In this paper we show a similar separation in the average-case setting that gives stronger evidence of the superiority of small-depth quantum computation: we construct a computational task that can be solved on all inputs by a quantum circuit of constant depth with bounded-fanin gates (a "shallow" quantum circuit) and show that any classical circuit with bounded-fanin gates solving this problem on a non-negligible fraction of the inputs must have logarithmic depth. Our results are obtained by introducing a technique to create quantum states exhibiting global quantum correlations from any graph, via a construction that we call the extended graph. Similar results have been very recently (and independently) obtained by Coudron, Stark and Vidick (arXiv:1810.04233), and Bene Watts, Kothari, Schaeffer and Tal (STOC 2019).
Pharmacogenetics of Parkinson's Disease in Clinical Practice

Pharmacogenetics aims to identify the genetic factors participating in the heterogeneity of drug response. The ultimate goal is to provide personalized treatment by identifying responders and non‐responders, individuals at risk of developing drug adverse effects, and by adjusting dosage. Several studies have been performed in Parkinson's disease (PD), to investigate drug response variability according to genetic factors for dopamine replacement therapies.
Decoding transcriptomic intra‐tumour heterogeneity to guide personalised medicine in ovarian cancer

The evaluation of intra‐tumour heterogeneity (ITH) from a transcriptomic point of view is limited. Single‐cell cancer studies reveal significant genomic and transcriptomic ITH within a tumour and it is no longer adequate to employ single‐subtype assignment as this does not acknowledge the ITH that exists. Molecular assessment of subtype heterogeneity (MASH) was developed to comprehensively report on the composition of all transcriptomic subtypes within a tumour lesion. Using MASH on 3431 ovarian cancer samples, correlation and association analyses with survival, metastasis and clinical outcomes were performed to assess the impact of subtype composition as a surrogate for ITH. The association was validated on two independent cohorts. We identified that 30% of ovarian tumours consist of two or more subtypes. When biological features of the subtype constituents were examined, we identified significant impact on clinical outcomes with the presence of poor prognostic subtypes (Mes or Stem‐A). Poorer outcomes correlated with having higher degrees of poor prognostic subtype populations within the tumour. Subtype prediction in several independent datasets reflected a similar prognostic trend. In addition, paired analysis of primary and recurrent/metastatic tumours demonstrated Mes and/or Stem‐A subtypes predominated in recurrent and metastatic tumours regardless of the original primary subtype. Given the biological and prognostic value in delineating individual subtypes within a tumour, a clinically applicable MASH assay using NanoString® technology was developed as a classification tool to comprehensively describe constituents of molecular subtypes. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.