Miracle-dz/UniFAR-FTUs huggingface dataset:
INCREASED BRAIN LEVELS OF HYDROGEN PEROXIDE IN A TRANSGENIC MOUSE MODEL OF HUNTINGTON ’ S DISEASE

Huntington’s disease (HD) is an inherited autosomal neurodegenerative disorder characterized by neuronal loss in specific brain regions, notably the striatum and the cortex. HD is associated with a dynamic mutation, an expansion in CAG repeats, located in the exon 1 of the HD gene. When the CAG expansion exceeds 35 repeats, progressive motor disability, including chorea and rigidity, cognitive impairment and psychiatric symptoms become apparent. The HD gene codes for a 350 kDa protein with still unknown function, huntingtin. The expanded CAG tract is translated into an elongated polyglutamine (polyQ) domain at the N-terminus of huntingtin. The polyQ domain confers mutated huntingtin with new cellular toxic properties and a higher propensity for protein aggregation. Several pathological mechanisms have been proposed for HD pathogenesis. Previous data suggested a contribution of oxidative stress in HD neurodegeneration, however the precise role of these mechanisms in HD are still not entirely understood. To clarify this role, we isolated cortical brain synaptosomes and mitochondria from YAC128 HD transgenic (expressing full-length human mutant huntingtin) and age-matched wild-type mice with 9 and 12 months of age. Oxygen consumption was evaluated in freshly isolated cortical synaptosomes and no differences were observed between YAC128 and wild-type mice, at 9 and 12 months of age. Moreover, wild-
Enhancing Farmers’ Engagement in Biomass Supply Chain through Risk Perception-Motivation-Behavior Change Model: A Case Study of National Bioenergy Power Industry Area, Northeast China

The growth of huge external costs caused by pollution, along with the shortage of fossil fuel reserves, create additional concerns that represent strong motivations for the development of a new type of power plants assumed to be environmentally friendly and based on endogenous resources. The use of biomass is a promising alternative to fossil fuels, which would mitigate environmental pollution and optimize energy structures. In China, a major application of biomass is combustion to generate electricity and heat. However, the current straw-based biomass power plants face difficulty in development. Because farmers have various risk perceptions, not a few of them are willing to cooperate with the middleman, who takes on the responsibility to collect straw from the farmers. Their risk perceptions decrease the motivation in participating crop straw collection activities. To solve the problem of insufficient collected straw for the biomass power industry, the National Bio-energy Power industry area in Wangkui County, China was selected as a case study to investigate the empirical problems. This study was divided into eight chapters. Chapter I introduces the current situation of biomass supply chain and the barrier of development of biomass power plants in China. The dilemma in developing biomass power plant is derived from the situation introduction. In Chapter II, the external cost of coal-fired and biomass power plants was compared, using the lifecycle approach. The results highlight that the external costs of a coal-fired plant are 0.072 US $/kWh, which are much higher than that of a biomass power plant, 0.00012 US$/kWh. The external cost of coal-fired power generation is as much as 90% of the current price of electricity generated by coal, while the external cost of a biomass power plant is 1/1000 of the current price of electricity generated by biomass. The external cost of coal-fired power plants could be resource to incentive biomass power plants.
Harnessing the Power of Big Data Analytics

The age of big data is upon us. Data is being collected by businesses at a rate never encountered before, through Web sources, cellular phones and social media. The growth of Internet businesses has led to a whole new scale of data processing challenges. Companies such as Google, Facebook, Yahoo, Twitter, and Amazon now routinely collect and process hundreds to thousands of terabytes of data on a daily basis. This represents a significant change in the volume of data which can be processed, a major reduction in processing time required, and reduction of the cost required to store data. Organizations have been collecting data for years, but the digital age has brought with it a substantial increase in the amount of data that is available to the modern-day business. For example, the genealogy site Ancestry.com stores about 2.5 petabytes of data (White, 2012). Twitter collects 7 terabytes of new data each day (Soffer & Heid, 2012). This data size growth rate can be attributed to several factors. The first is a more prominent presence in the online community. Many of the major retailers such as Apple, Wal-Mart, Target, Macy’s, Best Buy, Kohl’s, and Walgreens have much more of an online presence than they did 10 years ago. This online retail presence increases the amount of data each company has access to and can collect. From the financial services and healthcare sectors more data is being produced from a business protection standpoint. That is, more backup, recovery, and monitoring of customer or patient records. In 2011, researchers from MIT Sloan Management Review and IBM asked 3,000 executives, managers and analysts how they obtain value from their massive amounts of data. The study found that organizations that used business information and analytics outperformed organizations who did not. Specifically, these researchers found that top-performing businesses were twice as likely to use analytics to guide future strategies and guide day-to-day operations as their lower-performing counterparts (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). In order to extract value from big data, companies need to be able to easily work with terabytes and petabytes of data constantly generated by employees, customers, competitors, and Websites. It is not only the size of the data sets that distinguishes the big data movement, but also the differing types of data that must be handled. The scope of data collected by organizations is more diverse than ever. Data comes in a variety of different forms, such as structured and unstructured, spread across internal and external sources. Data is also more dynamic in the age of big data. Data constantly changes and evolves in real-time, making the window for taking action considerably shorter than in the past (McAfee & Brynjolfsson, 2012). This chapter will define big data and big data analytics. Emerging data architectures that can handle vast amounts of data such as Hadoop will be examined. Hive, the new programming language developed by Facebook that makes Hadoop more accessible, will be explained. A survey of how Billie Anderson Bryant University, USA
Snowflake: A Model Agnostic Accelerator for Convolutional Neural Networks

Deep convolutional neural networks (CNNs) are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processing tasks. CNNs require billions of operations to process a frame. This computational complexity, combined with the inherent parallelism of the convolution operation make CNNs an excellent target for custom accelerators. However, when optimizing for different CNN hierarchies and data access patterns, it is difficult for custom accelerators to achieve close to 100% computational efficiency. In this work, we present Snowflake, a scalable and efficient accelerator that is agnostic to CNN workloads, and was designed to always perform at near-peak hardware utilization. Snowflake is able to achieve a computational efficiency of over 91% on modern CNN models. Snowflake, implemented on a Xilinx Zynq XC7Z045 SoC is capable of achieving a peak throughput of 128G-ops/s and a measured throughput of 100 frames per second and 120 G-ops/s on the AlexNet CNN model, 36 frames per second and 116G- ops/s on the GoogLeNet CNN model and 17 frames per second and 122 G-ops/s on the ResNet-50 CNN model. To the best of our knowledge, Snowflake is the only implemented system capable of achieving over 91% efficiency on modern CNNs and the only implemented system with GoogLeNet and ResNet as part of the benchmark suite.
G A ] 12 J an 2 01 6 Lyman continuum leaking from the compact star-forming dwarf galaxy J 0925 + 1403

Y. I. Izotov, I. Orlitová, D. Schaerer , T. X. Thuan, A. Verhamme, N. G. Guseva 1 & G. Worseck 1 Main Astronomical Observatory, National Academy of Scienc es of Ukraine, 27 Zabolotnoho str. , Kyiv 03680, Ukraine 2 Astronomical Institute, Czech Academy of Sciences, Bočn ́ ı II 1401, 141 00, Prague, Czech Republic 3 Observatoire de Genève, Université de Genève, 51 Ch. des Maillettes, 1290, Versoix, Switzerland 4 CNRS, IRAP, 14 Avenue E. Belin, 31400 Toulouse, France 5 Astronomy Department, University of Virginia, PO Box 40032 5, Charlottesville, VA 22904, USA 6 Max-Planck-Institut für Astronomie, Königstuhl 17, 691 17 Heidelberg, Germany
Cross-domain recommender system through tag-based models

Nowadays, data pertaining to clients are generated at such a rapid rate it is completely beyond the processing ability of a human, which leads to a problem called information explosion. How to quickly and automatically provide personalized choices for someone from a large collection of resources has become a key factor in determining the success of many commercial activities. In this context, recommender systems have been developed as a type of software that aims to predict and suggest items which are relevant to a specific user by analyzing the user’s previous interaction data with certain items. Recommender systems have a broad application in our daily life, such as product recommendation in Amazon, video and movie recommendation in Youtube, music recommendation in Spotify. A fundamental brick in building most recommender systems is the collaborative filtering-based model, which has been widely adopted due to its outstanding performance and flexible deployment. However, this model together and its variations suffer from the so-called data sparsity problem, which results when user sonly rate a limited number of items. With the development of the transfer learning technique in recent years, cross-domain recommendation has emerged as an effective way to address data sparsity in recommender systems. The principle of cross-domain recommendation is to exploit knowledge from auxiliary source domains to assist recommendation making in a sparse target domain. In the development of cross-domain recommender systems, the most important step is to build a bridge between the domains in order to transfer knowledge. This task becomes more challenging in disjoint domains where users and items in both domains are completely non-overlapping. In this respect, tags are studied and utilized to establish explicit correspondence between domains. However, how to effectively exploit tags to increase domain overlap and ultimate recommendation quality remains as an open challenge which needs to be addressed. This thesis aims to develop novel tag-based cross-domain recommendation models in disjoint domains. First, it review the existing state-of-the-art techniques related to this research. It then provides three solutions by exploiting domainspecific tags, tag-inferred structural knowledge and tag semantics, respectively. To evaluate the proposed models, this thesis conducts a series of experiments on public datasets and compare them with state-of-the-art baseline approaches. The experimental results show the superior performance achieved by our models in different recommendation tasks under sparse settings. The findings of this research not only contribute to the state-of-the-art on cross-domain recommender systems, but also provide practical guidance for handling unstructured tag data in recommendation tasks.
Digital Forensic Readiness Architecture for Cloud Computing Systems

Cloud computing underpins many of the current emergent and established technologies. As a result, cloud computing has an impact on many components of our daily lives, be it from online shopping and banking to usage of mobile apps. Because of this ubiquity, crime related to cloud systems is an ongoing concern. There are, however, many factors that, while enabling cloud systems to function, also make digital forensic investigations on such systems very challenging. While processes and standards are defined for digital forensics, these processes often do not work when applied to cloud systems. Forensic investigations are, by their nature, very disruptive to the operation of a system. This is often unacceptable in a cloud environment. One way to mitigate the risk of a forensic investigation is to proactively prepare for such an event by achieving forensic readiness. This leads to the research conducted for this dissertation. The central question is whether it possible to achieve forensic readiness in a cloud environment, so that a digital forensic investigation can be conducted with minimal or no disruption to the operation of said cloud environment. This dissertation examines the background information of cloud computing, digital forensics and software architecture in order to get a clear understanding of the various research domains. Five possible models for the acquisition of data in a cloud environment are proposed, using the NIST cloud reference architecture as a baseline. A full, technology neutral, architecture for a cloud forensics system is i then generated. This architecture allows for the acquisition of forensic data within a cloud environment. The architecture ensures that the data is kept forensically stable and enables the proactive analysis of the captured data. Using one of the acquisition models, a proof of concept implementation is done of the architecture. Experiments are run to determine whether the system meets the set functional requirements and quality attributes to enable forensic readiness in a cloud system. The architecture and implementation are evaluated against the experimental results and possible improvements are suggested. The research is then concluded and possible future avenues of research in the field of cloud forensics are suggested.
Weakly-Supervised Degree of Eye-Closeness Estimation

Following recent technological advances there is a growing interest in building non-intrusive methods that help us communicate with computing devices. In this regard, accurate information from eye is a promising input medium between a user and computing devices. In this paper we propose a method that captures the degree of eye closeness. Although many methods exist for detection of eyelid openness, they are inherently unable to satisfactorily perform in real world applications. Detailed eye state estimation is more important, in extracting meaningful information, than estimating whether eyes are open or closed. However, learning reliable eye state estimator requires accurate annotations which is cost prohibitive. In this work, we leverage synthetic face images which can be generated via computer graphics rendering techniques and automatically annotated with different levels of eye openness. These synthesized training data images, however, have a domain shift from real-world data. To alleviate this issue, we propose a weakly-supervised method which utilizes the accurate annotation from the synthetic data set, to learn accurate degree of eye openness, and the weakly labeled (open or closed) real world eye data set to control the domain shift. We introduce a data set of 1.3M synthetic face images with detail eye openness and eye gaze information, and 21k real-world images with open/closed annotation. The dataset will be released online upon acceptance. Extensive experiments validate the effectiveness of the proposed approach.
Conditional Random Fields: Integration of Local Information and Global Information for Image Labeling

あらまし 本研究では画像中のすべての画素にカテゴリーラベルを割り当てる画像ラベリングを行う. 提案手 法では従来手法のように局所的な特徴のみを用いるのではなく,大域的な特徴も用いてラベルの推定を行う. ま た,カテゴリー間の同時に起こりやすい関係や起こりにくい関係もモデル化し,ラベルの推定に利用する. 局 所的な推定と大域的な推定の統合には条件付確率場(Conditional Random Field: CRF)を利用する. 従来の CRF は局所的な関係をモデル化するのには優れているが,大域的な関係は十分にモデル化できない. そこで複 数の確率場を用いたモデル化が行われているが,モデル構造が複雑となるという欠点がある. これに対し提案手 法では一つの確率場において推定を統合するためモデル構造が簡単である. また,従来手法では局所的な特徴へ の依存が大きいという問題があったが,提案手法では大域的な特徴も利用するためこの問題にも対応している. 提案手法の有効性は 2 種類の屋外シーン画像のラベリング実験により確認した. 大域的な特徴を用いることで認 識精度は 10%程度向上し,大域的な視点から明らかな誤りは大きく減少した. キーワード 画像ラベリング,シーン画像,条件付確率場,局所情報,大域情報
Prevalence of Depression Among Primary Caregivers of Stroke Survivors in Nigeria

Background: Stroke has a great adverse effect not only on the lives of stroke survivors but also on the informal caregivers, as they provide care ranging from assistance in activities of daily living, leading to a high risk of depression and other psychological morbidities. Objectives: This study explored the prevalence of depression and associated factors among primary caregivers of stroke survivors in Nigeria. Methods: A cross sectional survey on the prevalence of depression among primary caregivers of stroke survivors was conducted at selected hospitals in Maiduguri, Nigeria. A sample of convenience was used to recruit participants in this study. The Beck depression inventory was used to screen for depression. Data form and patients folders were used to obtain socio-demographics and clinical characteristics of participants and stroke survivors. Result: A total of 115 primary caregivers participated, out of which 53% were male and 47% were female. The mean age and overall beck depression inventory score were 30.78 ± 9.66 and 15.42 ± 7.84, respectively. The overall prevalence of depression was found to be 46.1%. Type of stroke (P = 0.03) and post stroke duration (P = 0.02) of stroke survivors cared for was found to be associated with depression among caregivers of stroke survivors. Conclusions: Caring for the patients with stroke presents increased in the psychological impact of caregiving for stroke survivors, it is therefore recommended that information, education, and support should be provided to primary caregivers as they are key in sustaining rehabilitation gains and long term overall well-being of stroke survivors.
Requirement of Autophagy Proteins Beclin-1 and Map1-LC3b in the Induction of Innate and Adaptive Immune Responses to Respiratory Syncytial Virus Infection.

viii Chapter 1 – Introduction . 1 1a. Respiratory Syncytial Virus . 1 1a.i. Virology and immune evasion by RSV . 1 1a.ii. Epidemiology of RSV infection . 4 1a.iii. Immune-mediated lung pathology in patients during RSV infection . 5 1a.iv. Environmental and genetic risk factors associated with severe RSV infection 6 1b. Human and murine immune responses to RSV . 9 1b.i. Detection of RSV by pattern recognition receptors . 10 1b.ii. Innate cytokine production and immune cell recruitment in response to RSV 13 1b.iii. Induction of innate and adaptive immune responses to RSV by lung dendritic cells . 16 1b.iv. Involvement of lung dendritic cells in protective and pathogenic responses to RSV . 18 1c. Autophagy in immune defense against viruses . 20 1c.i. Autophagosome formation and maturation . 21 1c.ii. Host autophagy and defense against intracellular pathogens . 24 1c.iii. Involvement of autophagy in DC function . 26
Bacterial and eukaryotic porins :structure, function, mechanism

Preface.List of Contributors.1. Regulation of Porin Gene Expression by the Two-Component Regulatory System EnvZ/OmpR (D. Walthers, et al.). 2. The Structures of General Porins (G. Schulz). 3. Role of Bacterial Porins in Antibiotic Susceptibility of Gram-negative Bacteria (J. Pages). 4. Porins of the Outer Membrane of Pseudomonas aeruginosa (R. Hancock & S. Tamber). 5. Regulation of Bacterial Porin Function (A. Basle & A. Delcour). 6. Reconstitution of General Diffusion Pores from Bacterial Outer Membranes (C. Danelon & M. Winterhalter). 7. OmpA/OprF: Slow Porins or Channels Produced by Alternative Folding of Outer Membrane Proteins (E. Sugawara & H. Nikaido). 8. Drug Efflux and Protein Export through Channel-tunnels (C. Andersen). 9. Structure-Function Relationships in Sugar-Specific Porins (T. Schirmer). 10. Functional Reconstitution of Specific Porins (R. Benz & F. Orlik). 11. Energy-coupled Outer Membrane Iron Transporters (V. Braun & M. Braun). 12. Structural and Functional Aspects of the Vitamin B12 Receptor BtuB (R. Kadner, et al.). 13. Structure and Function of Mitochondrial (Eukaryotic) Porins (R. Benz). 14. Mitochondrial Porins in Mammals: Insights Into Functional Roles from Mutant Mice and Cells (K. Anflous & W. Craigen). 15. Gene Family Expression and Multitopological Localization of Eukaryotic Porin/Voltage Dependent Anion-selective Channel (VDAC): Intracellular Trafficking and Alternative Splicing (V. De Pinto & A. Messina). 16. Function of the Outer Mitochondrial Membrane Pore (Voltage-dependent Anion Channel) in Intracellular Signalling (M. Vyssokikh & D. Brdiczka). Index.
Editorial

INTERNATIONAL CRICKET COACHING When Bob Woolmer tragically passed away during this year’s Cricket World Cup, the noble game lost the man who was known as the “father of international cricket coaching” [1]. The role of the head coach in cricket has a much shorter history than in other major team sports, dating back only to 1986 when Australia named Bobby Simpson as its first full-time cricket coach (a move that was regarded with “considerable scepticism” [2, p. 38]). Later in the same year England appointed Mickey Stewart in a similar role, but with the designation of “team manager” [2, p. 39]. The appointments of Simpson and Stewart can be explained by the lack of success of the Australia and England cricket teams and the “resultant dislocation in leadership:”
Developing an effective regulatory framework for virtual currencies in mauritius

It is widely accepted that virtual currencies have the potential of revolutionising the global finance sector. The potential of bitcoin has been recognised by corporate organisations and governments. With the continuing growth of bitcoin as a cryptocurrency, its regulatory regime has become crucial in determining the fate of this novel system of exchange. The global economic crisis and pandemic situation of 2020 is turning industries towards adoption of digital and virtual currencies. Although bitcoins have been regarded as the pioneer of decentralized peer to peer virtual currencies, there remains uncertainties in various countries as to whether they should embrace, devise new regulations or completely outlaw the digital currency. It has been recognised that Mauritius has the potential of acting as a Financial Technology (Fintech) hub for the African and surrounding regions through the rapid development and adoption of technology.For this to become possible, the current regulatory framework of Mauritius must be updated to reflect the continuing innovating changes in this area. So far, Mauritius has not yet implemented any specific regulations to address the use of Blockchain, bitcoins and cryptocurrencies in its jurisdiction. The only related legal framework currently being used in this area is the Regulatory Sandbox Licensing Scheme implemented by the Board of Investment Mauritius ("BOI"). It offers the possibility for investors to conduct their businesses in an area where there exists no legal framework or inadequate provisions under existing Mauritian legislation. In this research paper, the different regulatory frameworks of United States,Canada and China will be assessed to serve as a comparative analysis to examine the nature of their regulatory regimes.These jurisdictions have been chosen as they have been in the forefront of bringing significant regulatory changes, which has not been the case in any countries in Africa or Indian Ocean. These will be used to address the question as to whether there are any existing legal frameworks from other jurisdictions that can be customised or adapted for incorporation into the jurisdiction of Mauritius.