Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship of average volume of alcohol consumption and patterns of drinking to burden of disease: an overview.

AIMS As part of a larger study to estimate the global burden of disease attributable to alcohol: to quantify the relationships between average volume of alcohol consumption, patterns of drinking and disease and injury outcomes, and to combine exposure and risk estimates to determine regional and global alcohol-attributable fractions (AAFs) for major disease and injury categories. DESIGN, METHODS, SETTING: Systematic literature reviews were used to select diseases related to alcohol consumption. Meta-analyses of the relationship between alcohol consumption and disease and multi-level analyses of aggregate data to fill alcohol-disease relationships not currently covered by individual-level data were used to determine the risk relationships between alcohol and disease. AAFs were estimated as a function of prevalence of exposure and relative risk, or from combining the aggregate multi-level analyses with prevalence data. FINDINGS Average volume of alcohol consumption was found to increase risk for the following major chronic diseases: mouth and oropharyngeal cancer; oesophageal cancer; liver cancer; breast cancer; unipolar major depression; epilepsy; alcohol use disorders; hypertensive disease; hemorrhagic stroke; and cirrhosis of the liver. Coronary heart disease (CHD), unintentional and intentional injuries were found to depend on patterns of drinking in addition to average volume of alcohol consumption. Most effects of alcohol on disease were detrimental, but for certain patterns of drinking, a beneficial influence on CHD, stroke and diabetes mellitus was observed. CONCLUSIONS Alcohol is related to many major disease outcomes, mainly in a detrimental fashion. While average volume of consumption was related to all disease and injury categories under consideration, pattern of drinking was found to be an additional influencing factor for CHD and injury. The influence of patterns of drinking may be underestimated because pattern measures have not been included in many epidemiologic studies. Generalizability of the results is limited by methodological problems of the underlying studies used in the present analyses. Future studies need to address these methodological issues in order to obtain more accurate risk estimates.
A hybrid feature-based segmentation and classification system for the computer aided self-diagnosis of otitis media

We propose a novel hybrid otitis media (OM) computer aided detection (CAD) system, designed to aid in the self-diagnosis of various forms of OM. OM is a prevalent disease in both children and adults. Our system is able to differentiate normal ear from acute otitis media (AOM), otitis media with effusion (OME) and the multi-categories of chronic otitis media including perforation, retraction, cholesteatoma, etc. We propose a modified double active contour segmentation method designed for use with otoscope images, and enabled to handle user acquired data. To describe the visual symptoms (e.g., red, bulging, effusion, perforation, retraction, etc.) of otitis media accurately, we extract color, geometric and texture features by grid color moment, Gabor filter, local binary pattern and histogram of oriented gradients. A powerful classification structure based on Adaboost is used to select the most useful features and build a strong classifier. Our system achieves classification accuracy as high as 88.06% and is suitable for real use. In addition, some interesting observations about OM otoscope images are also discussed.
Control of Neuronal Network in Caenorhabditis elegans

Caenorhabditis elegans, a soil dwelling nematode, is evolutionarily rudimentary and contains only ∼ 300 neurons which are connected to each other via chemical synapses and gap junctions. This structural connectivity can be perceived as nodes and edges of a graph. Controlling complex networked systems (such as nervous system) has been an area of excitement for mankind. Various methods have been developed to identify specific brain regions, which when controlled by external input can lead to achievement of control over the state of the system. But in case of neuronal connectivity network the properties of neurons identified as driver nodes is of much importance because nervous system can produce a variety of states (behaviour of the animal). Hence to gain insight on the type of control achieved in nervous system we implemented the notion of structural control from graph theory to C. elegans neuronal network. We identified ‘driver neurons’ which can provide full control over the network. We studied phenotypic properties of these neurons which are referred to as ‘phenoframe’ as well as the ‘genoframe’ which represents their genetic correlates. We find that the driver neurons are primarily motor neurons located in the ventral nerve cord and contribute to biological reproduction of the animal. Identification of driver neurons and its characterization adds a new dimension in controllability of C. elegans neuronal network. This study suggests the importance of driver neurons and their utility to control the behaviour of the organism.
Wind power generation blade

The utility model relates to a wind power generation blade, which comprises a rotation shaft body, a plurality of blades which are distributed on the periphery of the rotation shaft body at equal interval with the rotation shaft body serving as the centre of a circle and at least one turbulent flow portion. Each blade comprises a windward side and a leeward side, each windward side is provided with a wind outlet area and a wind inlet area which receives wind and is far away from the wind outlet area, and the turbulent flow portion is arranged in the wind outlet area. When the blade rotates, the turbulent flow portion has the wind resistance effect to enable the wind to be free of rapid flowing to promote force to promote the blade to rotate, and a generator is effectively driven to generate electric energy.
Compressive sampling based MVDR spectrum sensing

We propose a compressive sampling (CS) based MVDR spectrum estimator, which estimates the wideband spectrum from the compressed signals with sub-Nyquist-rate sampling. To analyze detection performance, we derive the statistics of the estimated CS MVDR spectrum considering finite samples. We also show that different compression matrices produce different levels of signal leakage and influence the computation of detection thresholds.
A novel meta predictor design for hybrid branch prediction

Recent systems have been paved the way for being high-performance due to the super-pipelining, dynamic scheduling and superscalar processor technologies. The performance of the system is greatly affected by the accuracy of the branch prediction because the overhead of each misprediction has grown due to greater number of instructions per cycle and the deepened pipeline. Hybrid branch prediction is usually used to increase the prediction accuracy on such high-performance systems. Normally hybrid branch prediction uses several branch predictors. A meta-predictor selects which branch predictor should be used corresponding to the program context of the branch instruction instance for the branch prediction. In this paper, we discuss about the saturating counter within meta predictor. The design of the saturating counter which selects a predictor that has high-prediction ratio has brought out the high accuracy of the prediction for the branch predictor.
Multimodal semi-supervised image classification by combining tag refinement, graph-based learning and support vector regression

We investigate an image classification task where the training images come along with tags, but only a subset being labeled, and the goal is to predict the class label of test images without tags. This task is crucial for image search engine on photo sharing Web sites. In previous work, it is handled by first learning a multiple kernel learning classifier using both image content and tags to score unlabeled training images, and then building up a least-squares regression (LSR) model on visual features to predict the label of test images. However, there exist three important issues in the task: (1) Image tags on photo sharing Web sites tend to be inaccurate and incomplete, and thus refining them is beneficial; (2) Supervised learning with a limited number of labeled samples may be unreliable to some extent, while a graph-based semi-supervised approach can be adopted by also considering similarities of unlabeled data; (3) LSR is established upon centered visual kernel columns and breaks the symmetry of kernel matrix, whereas support vector regression can readily use the original visual kernel and thus leverage its full power. To handle the task more effectively, we propose to combine tag refinement, graph-based learning and support vector regression together. Experimental results on the PASCAL VOC'07 and MIR Flickr datasets show the superior performance of the proposed approach.
Stimulus novelty and emotion perception: the near absence of habituation in the visual cortex

In rapid serial visual presentation of pictures, an early event-related brain potential component shows enlarged negativity over occipital regions for emotional pictures compared with neutral pictures. The present study examined whether the processing of emotional target pictures varies as a function of stimulus repetition. Accordingly, pictures of erotica, neutral contents, and mutilations were repeatedly presented (90 times) while the electroencephalogram was recorded with a 129 dense sensor array. As in previous studies, emotional pictures were associated with a larger posterior negativity than neutral pictures. Furthermore, differential emotion processing did not vary as a function of stimulus repetition and was similarly expressed across blocks of picture presentation. These findings suggest the near absence of habituation in differential emotion processing during perceptual processing.
Atomic resolution structural characterization of recognition of histo-blood group antigens by Norwalk virus

Members of Norovirus, a genus in the family Caliciviridae, are causative agents of epidemic diarrhea in humans. Susceptibility to several noroviruses is linked to human histo-blood type, and its determinant histo-blood group antigens (HBGAs) are regarded as receptors for these viruses. Specificity for these carbohydrates is strain-dependent. Norwalk virus (NV) is the prototype genogroup I norovirus that specifically recognizes A- and H-type HBGA, in contrast to genogroup II noroviruses that exhibit a more diverse HBGA binding pattern. To understand the structural basis for how HBGAs interact with the NV capsid protein, and how the specificity is achieved, we carried out x-ray crystallographic analysis of the capsid protein domain by itself and in complex with A- and H-type HBGA at a resolution of ≈1.4 Å. Despite differences in their carbohydrate sequence and linkage, both HBGAs bind to the same surface-exposed site in the capsid protein and project outward from the capsid surface, substantiating their possible role in initiating cell attachment. Precisely juxtaposed polar side chains that engage the sugar hydroxyls in a cooperative hydrogen bonding and a His/Trp pair involved in a cation–π interaction contribute to selective and specific recognition of A- and H-type HBGAs. This unique binding epitope, confirmed by mutational analysis, is highly conserved, but only in the genogroup I noroviruses, suggesting that a mechanism by which noroviruses infect broader human populations is by evolving different sites with altered HBGA specificities.
G-commerce: market formulations controlling resource allocation on the computational grid

In this paper we investigate G-commerce-computational economies for controlling resource allocation in Computational Grid settings. We define hypothetical resource consumers (representing users and Grid-aware applications) and resource producers (representing resource owners who "sell" their resources to the Grid). We then measure the efficiency of resource allocation under two different market conditions: commodities markets and auctions. We compare both market strategies in terms of price stability, market equilibrium, consumer efficiency, and producer efficiency. Our results indicate that commodities markets are a better choice for controlling Grid resources than previously defined auction strategies.
Myod and H19-Igf2 locus interactions are required for diaphragm formation in the mouse

The myogenic regulatory factor Myod and insulin-like growth factor 2 (Igf2) have been shown to interact in vitro during myogenic differentiation. In order to understand how they interact in vivo, we produced double-mutant mice lacking both the Myod and Igf2 genes. Surprisingly, these mice display neonatal lethality due to severe diaphragm atrophy. Alteration of diaphragm muscle development occurs as early as 15.5 days post-coitum in the double-mutant embryos and leads to a defect in the terminal differentiation of muscle progenitor cells. A negative-feedback loop was detected between Myod and Igf2 in embryonic muscles. Igf2 belongs to the imprinted H19-Igf2 locus. Molecular analyses show binding of Myod on a mesodermal enhancer (CS9) of the H19 gene. Chromatin conformation capture experiments reveal direct interaction of CS9 with the H19 promoter, leading to increased H19 expression in the presence of Myod. In turn, the non-coding H19 RNA represses Igf2 expression in trans. In addition, Igf2 also negatively regulates Myod expression, possibly by reducing the expression of the Srf transcription factor, a known Myod activator. In conclusion, Igf2 and Myod are tightly co-regulated in skeletal muscles and act in parallel pathways in the diaphragm, where they affect the progression of myogenic differentiation. Igf2 is therefore an essential player in the formation of a functional diaphragm in the absence of Myod.
Scaling Properties of the Redshift Power Spectrum: Theoretical Models

We report the results of an analysis of the redshift power spectrum PS(k,μ) in three typical cold dark matter (CDM) cosmological models, where μ is the cosine of the angle between the wavevector and the line of sight. Two distinct biased tracers derived from the primordial density peaks of Bardeen et al. and the cluster-underweight model of Jing, Mo, & Börner are considered in addition to the pure dark matter models. Based on a large set of high-resolution simulations, we have measured the redshift power spectrum for the three tracers from the linear to the nonlinear regime. We investigate the validity of the relation (guessed from linear theory) in the nonlinear regime, PS(k,μ) = PR(k)[1 + βμ2]2D(k,μ,σ12(k)), where PR(k) is the real-space power spectrum and β = Ω/bl. The damping function D, which should generally depend on k, μ, and σ12(k), is found to be a function of only one variable, kμσ12(k). This scaling behavior extends into the nonlinear regime, while D can be accurately expressed as a Lorentz function, well known from linear theory, for values of D > 0.1. The difference between σ12(k) and the pairwise velocity dispersion defined by the three-dimensional peculiar velocity of the simulations (taking r = 1/k) is about 15%. Therefore, σ12(k) is a good indicator of the pairwise velocity dispersion. The exact functional form of D depends on the cosmological model and on the bias scheme. We have given an accurate fitting formula for the functional form of D for the models studied.
Draft Genome Sequence of Coniochaeta ligniaria NRRL 30616, a Lignocellulolytic Fungus for Bioabatement of Inhibitors in Plant Biomass Hydrolysates

ABSTRACT Here, we report the first draft genome sequence (42.38 Mb containing 13,657 genes) of Coniochaeta ligniaria NRRL 30616, an ascomycete with biotechnological relevance in the bioenergy field given its high potential for bioabatement of toxic furanic compounds in plant biomass hydrolysates and its capacity to degrade lignocellulosic material.
Two ways of using artificial neural networks in knowledge discovery from chemical materials data

In the application area of chemical materials, data mining methods have been used for more than a decade. By far most popular have from the very beginning been methods based on artificial neural networks. However, they are frequently used without awareness of the difference between the numeric nature of knowledge obtained from data by neural network regression, and the symbolic nature of knowledge obtained by some other data mining methods. This paper explains that within the surrogate modelling approach, which plays an important role in this area, using numeric knowledge is justified. At the same time, it recalls the possibility to obtain symbolic knowledge from neural networks in the form of logical rules and describes a recently proposed method for the extraction of Boolean rules in disjunctive normal form. Both ways of using neural networks are illustrated on examples from this application area.
Paris-Harrington Tautologies

We study the proof complexity of Paris-Harrington's Large Ramsey Theorem for bi-colorings of graphs. We prove a non-trivial conditional lower bound in Resolution and a quasi-polynomial upper bound in bounded-depth Frege. The lower bound is conditional on a (very reasonable) hardness assumption for a weak (quasi-polynomial) Pigeonhole principle in Res(2). We show that under such assumption, there is no refutation of the Paris-Harrington formulas of size quasi-polynomial in the number of propositional variables. The proof technique for the lower bound extends the idea of using a combinatorial principle to blow-up a counterexample for another combinatorial principle beyond the threshold of inconsistency. A strong link with the proof complexity of an unbalanced Ramsey principle for triangles is established. This is obtained by adapting some constructions due to Erdos and Mills.
Evaluation of an Outpatient Rehabilitative Program to Address Mobility Limitations Among Older Adults

Abstract Live Long Walk Strong is a clinical demonstration program for community-dwelling older patients. It was designed to be consistent with current fall prevention guidelines and reimbursed under the Medicare model. Patients were screened within primary care and referred to a physiatrist followed by systematic assessment and treatment within an outpatient rehabilitative care setting. The treatment included behavioral modification, fall prevention education, community/home exercise integration, and exercise targeting strength, power, flexibility, balance, and endurance. Treatment duration and frequency varied with each patient based on baseline presentation, clinical judgment, and patient preference. Program feasibility and preliminary effectiveness were evaluated by assessing participation and changes in physical performance, respectively. There were 266 patients referred to the program, and 147 were willing to participate. Of these, 116 patients completed all scheduled visits (10.8 ± 3.9 visits). The noncompleters (n = 31) had a higher rate of falls in the previous 6 months and lower baseline Short Physical Performance Battery composite score. At the completion of care, the adjusted mean change in Short Physical Performance Battery was 1.66 units, surpassing a large clinically meaningful threshold (1 unit). The Live Long Walk Strong program appears to be feasible to implement and demonstrates preliminary effectiveness in enhancing mobility among older adults.
A theoretical model for opportunistic routing in ad hoc networks

Traditional routing strategies for multi-hop wireless networks forward packets by selecting at the sender side the next hop for each packet. Recently, such a paradigm has been called into question by a new approach, namely the Opportunistic Routing. It exploits the broadcast nature of wireless transmissions to take advantage from spatial diversity by routing the packets according to the propagation conditions, i.e. by selecting the next hop at the receiver side. Although numerous opportunistic algorithms and protocols have been proposed in the last years, very few works have used an analytical approach to analyze the opportunistic routing behavior so as to provide a guideline for future protocol design. In this paper, we propose an analytical model to describe any routing procedures operating according to the opportunistic paradigm. It applies in a very general multi-hop scenario and is not restricted to any specific network topology or opportunistic protocol. The model requires the knowledge of both the delivery ratios and node priority, which is based on the adopted routing metric (Expected Transmission Count (ETX), geographic distance, etc). In this paper we exploit such a model to derive a closed-form expression of the average number of data-link transmissions needed to successfully deliver a packet.
Measuring the efficiency of voltage reduction at Hydro-Québec distribution

The goal of this paper is to describe the methodology used to assess the effectiveness of conservation voltage reduction in the Hydro-Quebec distribution network. The motivation for attempting to control the load is presented as well as different procedures that have lead to conclude to the economic feasibility of the concept. The rational for the deployment plan is presented.
SUSTAINABLE DEVELOPMENT GOALS (SDGs) DAN PENGENTASAN KEMISKINAN

Isu kemiskinan tetap menjadi isu penting bagi negara-negara berkembang, demikian pula dengan Indonesia. Penanganan persoalan kemiskinan harus dimengerti dan dipahami sebagai persoalan dunia, sehingga harus ditangani dalam konteks global pula. Sehingga setiap program penanganan kemiskinan harus dipahami secara menyeluruh dan saling interdependen dengan beberapa program kegiatan lainnya. Dalam SDGs dinyatakan no poverty (tanpa kemiskinan) sebagai poin pertama prioritas. Hal ini berarti dunia bersepakat untuk meniadakan kemiskinan dalam bentuk apapun di seluruh penjuru dunia, tidak terkecuali Indonesia. Pengentasan kemiskinan akan sangat terkait dengan tujuan global lainnya, yaitu lainnya, dunia tanpa kelaparan, kesehatan yang baik dan kesejahteraan, pendidikan berkualitas, kesetaraan jender, air bersih dan sanitasi, energy bersih dan terjangkau; dan seterusnya hingga pentingnya kemitraan untuk mencapai tujuan-tujuan tersebut.
RITM: Revocation in the Middle

Although TLS is used on a daily basis by many critical applications, the public-key infrastructure that it relies on still lacks an adequate revocation mechanism. An ideal revocation mechanism should be inexpensive, efficient, secure, and privacypreserving. Moreover, rising trends in pervasive encryption pose new scalability challenges that a modern revocation system should address. In this paper, we investigate how network nodes can deliver certificate-validity information to clients. We present RITM, a framework in which middleboxes (as opposed to clients, servers, or certification authorities) store revocation-related data. RITM provides a secure revocation-checking mechanism that preserves user privacy. We also propose to take advantage of content-delivery networks (CDNs) and argue that they would constitute a fast and cost-effective way to disseminate revocations. Additionally, RITM keeps certification authorities accountable for the revocations that they have issued, and it minimizes overhead at clients and servers, as they have to neither store nor download any messages. We also describe feasible deployment models and present an evaluation of RITM to demonstrate its feasibility and benefits in a real-world deployment.