Miracle-dz/UniFAR-FTUs huggingface dataset:
Exercise and cardioprotection against ischemia reperfusion injury: A Review

Cardiovascular disease (CVD) has been the leading cause of death in the United States for nearly a century. Despite relatively recent progress in cardiovascular health interventions, CVD and particularly ischemic heart disease remain a significant global health problem. Various approaches to evoke a preconditioned phenotype has largely been unsuccessful, highlighting several logistical problems in pharmacologic approaches to cardioprotection. Issues include the unpredictable nature of ischemic events, the cost of chronically delivering pharmacologic countermeasures, and the fact that many exogenous stimuli to cardiac preconditioning result in rapid cellular habituation and a waning of cardioprotection. Accordingly, many lifestyle approaches to cardiac preconditioning have been investigated, including moderate intensity aerobic exercise. Regular exercise represents a pragmatic, sustainable, and cost-effective measure for reducing CVD risk and for provoking robust levels of cardioprotection against ischemic events. The scientific sub-field borne from this research line is known as exercise-induced cardiac preconditioning. Key discoveries of the nature of exercise preconditioning reveal that clinically relevant protection can be obtained with moderate intensity exercise in as little as 1-3 days and occurs independent of sex and age. The short-term nature of the exercise preconditioning stimulus highlights the importance of biochemical alterations as a key mechanistic process for this protection. Importantly, mechanisms central to exercise-induced cardiac preconditioning include a concert of up-regulated, or allosterically altered, factors that mitigate calcium overload, free radical damage and oxidative stress, and bioenergetic dyshomeostasis. Finally, the efficacy of exercise-induced cardiac preconditioning highlights the value of repetitive physical activity as a scientific approach to drug discovery.
Alleviation of heat stress in broiler chicken using turmeric (Curcuma longa) - a short review

Heat stress is one of the most crucial issues in broiler production, particularly in tropical regions. Thermal stress may pose several problems related to growth, feed intake, nutrient utilization, physiological condition, immune function, intestinal ecology, and morphology as well as the antioxidant system in the body of chickens. Turmeric (Curcuma longa) is rich in curcumin, which can serve as an antioxidant. Being part of the nutritional interventions, treatment using turmeric has been documented to alleviate the negative impact of heat stress on broiler chickens, in terms of production, physiology, immunology, and antioxidant status of broilers. The present review elucidates the alleviation of heat stress in broiler chicken using turmeric based on the most recent literature.
Getting a GRIP on work-related stress: Designing services with users and other committed stakeholders

Can design improve services targeting work-related stress? This question is tackled in GRIP, a project that focuses on the role of design in the development of a product-service system (PSS) for work-related stress. In the case of PSS the control of designers over the process and impact of design is reduced due to less formalised planning and a higher level of co-creation by stakeholders. GRIP proposes new designs that are co-created with different stakeholders, and that address the potential causes and effects of work-related stress. As active designers and design researchers, clearly our expertise is in design not stress. Therefore, we collaborate with end users, companies, and stress and vitality consultancies, utilising each other’s skills and resources to offer more effective solutions in tackling work-related stress. Our approach is formalised in the GRIP Service Model, which helps stakeholders to develop new PSS targeting work-related stress. This model allows committed partners to bring in their expertise, and to foresee and exploit their personal/commercial opportunities. Our unique approach to the design of PSS can be seen as a combination between a classical User Centred Design process, and a collaborative process of New Service Design. While focusing on one specific case around work-related stress, we will share our experiences and discuss implications for the design of co-created multi-stakeholder PSS. In particular, we will discuss the Grip Service Model in terms of how it helps designers to strike a better balance between flexibility and control over the process and impact of their work.
An Improved Algorithm for Match Point Purification

: Aiming at the problem that the RANSAC algorithm has high computational complexity and low efficiency in image matching. A fast coarse-to-fine strategy is introduced in this paper: the RANSAC algorithm has been used in the inliers sub-set to estimate the point transformation matrix between two images accurately by using least square method. It filters the noisy or wrong input data iteratively, and then estimates the model parameters through pure data, so it has strong error-tolerant capacity for the image noise and inaccuracy of feature extraction.
CORPORATE GOVERNANCE AND EARNINGS QUALITY: EVIDENCE FROM DISTRESS LIKELIHOOD QUOTED FIRMS

The study is a longitudinal survey of quoted non-financial firms with the likelihood of being distressed, unlike most previous studies that neglect distress as a potential reason for earnings quality. The total quoted firms in Nigeria Stock Exchange as at December 2016 is 178 which constituted the population of the study out of which a target population of 96 non-financial quoted firms were considered for the study while the sample size of 84 out of the 96 quoted nonfinancial firms with Altman’s Z-Score of less than 1.8 were studied within a period of 2011 to 2016 for the study. Purposive sampling technique was employed in selecting sampled firms that fell below the required standard. Historical data were obtained from the financial statements and accounts of sampled firms. Corporate governance variables were regressed on the dependent variables, namely, Earnings quality using Jones Model (JMODEL) and Modified Jones Model (MJOHNES) while pooled data regression was employed. The findings revealed that Chairman Ownership (CAMSH), CEO ownership (CEOSH) and Directors Ownership (DHOLD) were statistically significant, while Board Size, Board independence and Board gender have no significant effect on earnings quality of likelihood distress companies in Nigeria. The study, therefore, recommends that companies should not expand the Board size of likelihood distress companies as a means to managing earnings quality; Board chairman and CEO shares ownership should be discouraged for likelihood distress companies if reduced earnings management tendencies are to be checked. Board gender, if correctly used, can help to reduce earnings management in likelihood distress companies.
Genomic 8-oxo-7, 8-dihydro-2'-deoxyguanosine Quantification

[Abstract] 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dGuo) is among the most common reactive oxygen species-induced DNA lesions and can be used as a biomarker for oxidative stress. The lesion has been linked to several biological processes and diseases, including colorectal cancer, Huntington’s disease, estrogen-induced gene expression, and thymine dimer repair (reviewed in Delaney et al., 2012). The following assay is used to quantify 8-oxo-dGuo levels in DNA as described in Sousa et al. (2013).
Comparing Audience Appreciation to Fact-Checking Across Political Communities on Reddit

As a countermeasure to disinformation, many fact-checking websites, such as Snopes.com, provide valuable resources to verify news stories or claims. In this paper, we study how such fact-checking resources are used in online political discussions on Reddit, and how audiences or readers respond to their use in the context of the 2016 US Presidential Election. We first characterize the role of fact-checking resources by developing a typology for labeling instances in which they are employed in three political subreddits, r/politics, r/The_Donald and r/hillaryclinton. We find that fact-checking, when used as a correction to false information, is more prevalent on r/politics than on r/The_Donald or r/hillaryclinton. Next, we quantify audience responses to fact-checking by using comment score as a measure of popularity and find that the correction of facts is also more appreciated in r/politics than the other subreddits. Finally, we estimate the impact of corrections, and other uses of fact-checks, on the sustainability of a conversational thread and find that presence of corrections in r/politics appear to be correlated with short conversations. Overall, these findings indicate that the use of fact-checking resources within r/politics is distinct from more partisan subreddits.
Arabidopsis flavonoid glycosylation impacts on phenylpropanoid biosynthesis and plant growth

i SUMMARY Flavonols, a class of phenylpropanoids, have protective and regulatory functions in plants and are important nutritional ingredients. The hydrophobic compounds are glycosylated consecutively at their 3-OH and 7-OH positions in Arabidopsis thaliana. UGT78D1 and UGT78D2 are the major glycosyltransferases involved in flavonol 3-O-glycosylation in leaves of this plant. Accordingly, the ugt78d1 ugt78d2 double mutant was strongly compromised in the primary conjugation of flavonol aglycones. Instead of alternative modifications or aglycone accumulation, flavonol biosynthesis was repressed in ugt78d1 ugt78d2 retaining only one third of the wild-type flavonol level. The reduced flavonol amounts were associated with a repressed transcription of many flavonol-biosynthetic key genes including the committed step of flavonoid biosynthesis catalyzed by CHALCONE SYNTHASE (TT4). In addition, the expression of PHENYLALANINE AMMONIA LYASE (PAL), which constitutes the committed step of the general phenylpropanoid pathway, was repressed at the level of gene transcription and enzyme activity. However, a strong repression of total flavonol biosynthesis in the flavonol synthase 1 mutant did not negatively affect PAL expression nor was it repressed by a compromised downstream 7-O-glycosylation. PAL activity was also reverted back to wild-type level in tt4 ugt78d1 ugt78d2 lines eliminating flavonoid biosynthesis in addition to the repressed 3-O-glycosylation capacity. Chemical complementation of tt4 ugt78d1 ugt78d2 plants using naringenin, a flavonoid intermediate, could restore the reduction in PAL expression. Thus, these studies revealed a novel feedback inhibition of the flavonol-biosynthetic pathway, which was specifically linked to a compromised flavonol 3-O-glycosylation, but at the same time dependent on flavonol formation itself. Although the molecular mechanism of flavonol-dependent feedback inhibition remains obscure, this regulation may serve as a mean to avoid the accumulation of hydrophobic and potentially toxic flavonol aglycones. The ugt78d2 mutant possesses an altered flavonol glycoside pattern and showed severe shoot growth defects including dwarfism and loss of apical dominance. The ugt78d2 growth defects were eliminated by blocking flavonoid biosynthesis, suggesting that they were flavonoid-dependent. The modular combination of mutants in flavonoid biosynthesis or glycosylation and their flavonol glycoside profiles identified kaempferol 3-O-rhamnoside-7-Orhamnoside (k1) as the ugt78d2 phenotype-inducing metabolite. Flavonoids and in particular flavonols have been postulated to modulate auxin transport. However, the active flavonoids involved in this process have not yet been identified in plants. Basipetal auxin transport was reduced in the k1-overaccumulating ugt78d2 mutant. Preliminary data showed that auxin transport was reverted close to wild-type level in an ugt78d2 mutant complemented line. The
Constructive Approach for Early Extraction of Viral Spreading Social Issues from Twitter

In recent years, there has been a rapid increase in viral spreading social issues that emerge in the public consciousness through the fast spread of information online. With the advent of social media, they sometimes yield unexpected side effects such as product boycotts. Therefore, it is important to recognize them as the earliest and take preventive measures against them. Existing researches on social issue extraction have mainly focused on news channels and newspapers as the primary information sources. However, such approaches cannot be applied for the early extraction of viral spreading social issues because their epicenter is the online public’s opinion. In this study, we propose a constructive method inspired by a social issues research approach, called constructivism, for the early extraction of viral spreading social issues. It is characteristic that our method identifies the keywords related to social issues using information obtained from the claims-making activities on Twitter and Twitter-user clustering. We conducted experiments on a large Twitter dataset comprising tens of billions of tweets and the proposed method successfully extracted six out of the seven viral spreading social issues earlier than their first TV news coverage. Furthermore, the proposed method could identify such cases approximately two weeks earlier, on average, than the first national TV news coverage.
Analysing Privacy Leakage of Life Events on Twitter

People share a wide variety of information on Twitter, including the events in their lives, without understanding the size of their audience. While some of these events can be considered harmless such as getting a new pet, some of them can be sensitive such as gender-transition experiences. Every interaction increases the visibility of the tweets and even if the original tweet is protected or deleted, public replies to it will stay in the platform. These replies might signal the events in the original tweet which cannot be managed by the event subject. In this paper, we aim to understand the scope of life event disclosures for those with both public and protected (private) accounts. We collected 635k tweets with the phrase “happy for you” over four months. We found that roughly 10% of the tweets collected were celebrating a mentioned user’s life event, ranging from marriage to surgery recovery. 8% of these tweets were directed at protected accounts. The majority of mentioned users also interacted with these tweets by liking, retweeting, or replying.
Distributed Subgraph Enumeration via Backtracking-based Framework

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. The state-of-the-art distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework (B-BENU) for distributed subgraph enumeration. B-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. B-BENU does not shuffle any partial matching result. Instead, it stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand. To support dynamic data graphs, we propose the concept of incremental pattern graphs and turn continuous subgraph enumeration into enumerating incremental pattern graphs at each time step. We develop the Streaming-BENU framework (S-BENU) to enumerate their matches efficiently. We implement B-BENU and S-BENU with the local database cache and the task splitting techniques. The extensive experiments show that B-BENU and S-BENU can scale to big data graphs and complex pattern graphs. They outperform the state-of-the-art methods by up to one and two orders of magnitude, respectively.
Distance Correlation Sure Independence Screening for Accelerated Feature Selection in Parkinson’s Disease Vocal Data

With the abundance of machine learning methods and the temptation of using them all in an ensemble, having model-agnostic feature selection is incredibly alluring. Principal component analysis was developed in 1901 and has been a strong contender in this role since, but in the end is an unsupervised method. It offers no guarantee that the features selected have predictive power because it does not know what is being predicted. To this end, Peng et al. developed the minimum redundancy-maximum relevance (mRMR) method in 2005. It uses the mutual information not only between predictors but also with the response in its calculation. Estimating mutual information and entropy tend to be expensive and problematic endeavors, which leads to excessive processing times even for data that is approximately 750 by 750 in a Leave-One-Subject- Out jackknife situation. To remedy this, we use a method from 2012 called Distance Correlation Sure Independence Screening (DC-SIS) which uses the distance correlation measure of Székely et al. to select features that have the greatest dependence with the response. We show that this method produces statistically indistinguishable results to mRMR on Parkinson’s Disease vocal diagnosis data 90 times faster.
On Compression Principle and Bayesian Optimization for Neural Networks

Finding methods for making generalizable predictions is a fundamental problem of machine learning. By looking into similarities between the prediction problem for unknown data and the lossless compression we have found an approach that gives a solution. In this paper we propose a compression principle that states that an optimal predictive model is the one that minimizes a total compressed message length of all data and model definition while guarantees decodability. Following the compression principle we use Bayesian approach to build probabilistic models of data and network definitions. A method to approximate Bayesian integrals using a sequence of variational approximations is implemented as an optimizer for hyper-parameters: Bayesian Stochastic Gradient Descent (BSGD). Training with BSGD is completely defined by setting only three parameters: number of epochs, the size of the dataset and the size of the minibatch, which define a learning rate and a number of iterations. We show that dropout can be used for a continuous dimensionality reduction that allows to find optimal network dimensions as required by the compression principle.
Note: low-rank tensor train completion with side information based on Riemannian optimization

We consider the low-rank tensor train completion problem when additional side information is available in the form of subspaces that contain the mode-$k$ fiber spans. We propose an algorithm based on Riemannian optimization to solve the problem. Numerical experiments show that the proposed algorithm requires far fewer known entries to recover the tensor compared to standard tensor train completion methods.
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unseen scenarios, even when they are trained on many instances of semantically similar environments. Data augmentation has recently been shown to improve the sample efficiency and generalization of RL agents. However, different tasks tend to benefit from different kinds of data augmentation. In this paper, we compare three approaches for automatically finding an appropriate augmentation. These are combined with two novel regularization terms for the policy and value function, required to make the use of data augmentation theoretically sound for certain actor-critic algorithms. We evaluate our methods on the Procgen benchmark which consists of 16 procedurally-generated environments and show that it improves test performance by ~40% relative to standard RL algorithms. Our agent outperforms other baselines specifically designed to improve generalization in RL. In addition, we show that our agent learns policies and representations that are more robust to changes in the environment that do not affect the agent, such as the background. Our implementation is available at this https URL.
Hermes Attack: Steal DNN Models with Lossless Inference Accuracy

Deep Neural Networks (DNNs) models become one of the most valuable enterprise assets due to their critical roles in all aspects of applications. With the trend of privatization deployment of DNN models, the data leakage of the DNN models is becoming increasingly serious and widespread. All existing model-extraction attacks can only leak parts of targeted DNN models with low accuracy or high overhead. In this paper, we first identify a new attack surface -- unencrypted PCIe traffic, to leak DNN models. Based on this new attack surface, we propose a novel model-extraction attack, namely Hermes Attack, which is the first attack to fully steal the whole victim DNN model. The stolen DNN models have the same hyper-parameters, parameters, and semantically identical architecture as the original ones. It is challenging due to the closed-source CUDA runtime, driver, and GPU internals, as well as the undocumented data structures and the loss of some critical semantics in the PCIe traffic. Additionally, there are millions of PCIe packets with numerous noises and chaos orders. Our Hermes Attack addresses these issues by huge reverse engineering efforts and reliable semantic reconstruction, as well as skillful packet selection and order correction. We implement a prototype of the Hermes Attack, and evaluate two sequential DNN models (i.e., MINIST and VGG) and one consequential DNN model (i.e., ResNet) on three NVIDIA GPU platforms, i.e., NVIDIA Geforce GT 730, NVIDIA Geforce GTX 1080 Ti, and NVIDIA Geforce RTX 2080 Ti. The evaluation results indicate that our scheme is able to efficiently and completely reconstruct ALL of them with making inferences on any one image. Evaluated with Cifar10 test dataset that contains 10,000 images, the experiment results show that the stolen models have the same inference accuracy as the original ones (i.e., lossless inference accuracy).
Spring weed communities of rice agrocoenoses in central Nepal

Abstract Rice field weed communities occurring in central Nepal are presented in this study. The research was focussed on the classification of segetal plant communities occurring in paddy fields, which had been poorly investigated from a geobotanical standpoint. In all, 108 phytosociological relevés were sampled, using the Braun-Blanquet method. The analyses classified the vegetation into 9 communities, including 7 associations and one subassociation. Four new plant associations and one new subassociation were proposed: Elatinetum triandro-ambiguae, Mazo pumili-Lindernietum ciliatae, Mazo pumili-Lindernietum ciliatae caesulietosum axillaris, Rotaletum rotundifoliae and Ammanietum pygmeae. Due to species composition and habitat preferences all phytocoenoses were included into the Oryzetea sativae class and the Ludwigion hyssopifolio- octovalvis alliance. As in other rice field phytocoenoses, the main discrimination factors for the plots are depth of water, soil trophy and species richness. The altitudinal distribution also has a significant influence and separates the Rotaletum rotundifoliae and Elatinetum triandro-ambiguae associations. The study shows that anthropogenic rice fields can harbour relatively rich rush and water vegetation. More than 80 species were noted in the vegetation plots. Several of them are considered to be extremely rare and have been recorded on the world Red List.