Miracle-dz/UniFAR-FTUs huggingface dataset:
Dementia And Physical Activity (DAPA) trial of moderate to high intensity exercise training for people with dementia: randomised controlled trial

Abstract Objective To estimate the effect of a moderate to high intensity aerobic and strength exercise training programme on cognitive impairment and other outcomes in people with mild to moderate dementia. Design Multicentre, pragmatic, investigator masked, randomised controlled trial. Setting National Health Service primary care, community and memory services, dementia research registers, and voluntary sector providers in 15 English regions. Participants 494 people with dementia: 329 were assigned to an aerobic and strength exercise programme and 165 were assigned to usual care. Random allocation was 2:1 in favour of the exercise arm. Interventions Usual care plus four months of supervised exercise and support for ongoing physical activity, or usual care only. Interventions were delivered in community gym facilities and NHS premises. Main outcome measures The primary outcome was score on the Alzheimer’s disease assessment scale-cognitive subscale (ADAS-cog) at 12 months. Secondary outcomes included activities of daily living, neuropsychiatric symptoms, health related quality of life, and carer quality of life and burden. Physical fitness (including the six minute walk test) was measured in the exercise arm during the intervention. Results The average age of participants was 77 (SD 7.9) years and 301/494 (61%) were men. By 12 months the mean ADAS-cog score had increased to 25.2 (SD 12.3) in the exercise arm and 23.8 (SD 10.4) in the usual care arm (adjusted between group difference −1.4, 95% confidence interval −2.6 to −0.2, P=0.03). This indicates greater cognitive impairment in the exercise group, although the average difference is small and clinical relevance uncertain. No differences were found in secondary outcomes or preplanned subgroup analyses by dementia type (Alzheimer’s disease or other), severity of cognitive impairment, sex, and mobility. Compliance with exercise was good. Over 65% of participants (214/329) attended more than three quarters of scheduled sessions. Six minute walking distance improved over six weeks (mean change 18.1 m, 95% confidence interval 11.6 m to 24.6 m). Conclusion A moderate to high intensity aerobic and strength exercise training programme does not slow cognitive impairment in people with mild to moderate dementia. The exercise training programme improved physical fitness, but there were no noticeable improvements in other clinical outcomes. Trial registration Current Controlled Trials ISRCTN10416500.
On a Drawing-Frequency based Layered Canvas Mechanism for Collaborative Paper Editing Support Systems

We propose a Drawing-Frequency based Layered (DFL) Canvas approach to reduce a synchronization delay in collaborative web applications. The synchronization delay consists of a network delay and a drawing delay. The network delay is well-argued topic by various researches. We focus on the drawing delay, which is a primary bottleneck of Canvas objects synchronizations. We introduce the DFL Canvas approach in order to solve the drawing problem. The DFL Canvas divides Canvas objects by drawingfrequency. We implemented a collaborative paper editing support system with the DFL Canvas. Users can edit the same document and share Canvas objects on papers in real-time on web browsers. We present an evaluation result that indicates the approach is suited for collaborative paper editing support systems.
Interaction of low molecular weight hyaluronan with CD44 and toll‐like receptors promotes the actin filament‐associated protein 110‐actin binding and MyD88‐NFκB signaling leading to proinflammatory cytokine/chemokine production and breast tumor invasion

Both high and low molecular weight hyaluronan (HMW‐HA vs. LMW‐HA) exist in various tissues and cells. In this study, we investigated LMW‐HA‐mediated CD44 interaction with Toll‐like receptors (TLRs), the actin filament‐associated protein (AFAP‐110), and a myeloid differentiation factor (MyD88) in breast tumor cells (MDA‐MB‐231 cells). Our data indicate that LMW‐HA (but not HMW‐HA) preferentially stimulates a physical association between CD44 and TLRs followed by a concomitant recruitment of AFAP‐110 and MyD88 into receptor‐containing complexes in breast tumor cells. LMW‐HA‐activated AFAP‐110 then binds to filamentous actin (F‐actin) resulting in MyD88/nuclear factor‐κB (NF‐κB) nuclear translocation, NF‐κB‐specific transcription, and target gene [interleukine 1β and interleukine‐8 (IL‐1β and IL‐8)] expression. These signaling events lead to proinflammatory cytokine/chemokine production in the breast tumor cells. AFAP‐110‐F‐actin (activated by LMW‐HA) also promotes tumor cell invasion. Downregulation of AFAP‐110 or MyD88 by transfecting breast tumor cells with AFAP‐110 siRNA or MyD88 siRNA, respectively not only blocks the ability of LMW‐HA to stimulate AFAP‐110‐actin function, but also impairs MyD88‐NF‐κB nuclear translocation and NF‐κB transcriptional activation. Consequently, both IL‐1β/IL‐8 production and tumor cell invasion are impaired. Taken together, these findings suggest that LMW‐HA plays an important role in CD44‐TLR‐associated AFAP‐110‐actin interaction and MyD88‐NF‐κB signaling required for tumor cell behaviors, which may contribute to the progression of breast cancer. © 2011 Wiley Periodicals, Inc.
Industrial Internet education: Issues and opportunities

We propose an approach for addressing the challenges and meeting the needs of industrial networking education. The approach is based on using open source resources, key networking technologies, and an experimental framework to revolutionize the way industrial networking ought to be taught at higher learning institutions around the world. Based on the approach a new type of industrial networking course has been designed, developed, and offered with positive outcomes.
Towards QoS-enabled SDN networks

SDN networks have become increasingly popular, especially for real time systems. Optimizing the self-defined network cost and performance is thereby primordial. This requires the network to meet real-time requirements and provide guarantees related to bandwidth and packet loss, and associated with the end-to-end delay and jitter which are essential indicators of QoS. This paper is an effort towards QoS-enabled SDN networks that allows establishing the formulation for both delay and jitter. Furthermore, we evaluate the effect of end-to-end delay and jitter in SDN networks as QoS metrics to finally establish their respective behavior.
Inhibition of Protein Kinase Mζ Disrupts the Stable Spatial Discharge of Hippocampal Place Cells in a Familiar Environment

It is widely held that spatial computations in the rodent hippocampus require the location-specific discharge of place cells that together form a stable cognitive map used to solve and perform spatial tasks. It is not known, however, if map stability requires persistent hippocampal synaptic strength changes that are vulnerable to blockade of protein kinase Mζ (PKMζ) phosphorylation activity, a manipulation that reverses hippocampal LTP and disrupts multiple forms of long-term memory. Here we report that acute intrahippocampal inhibition of PKMζ disrupts place cell activity in a familiar environment, where the map is expected to be stable. After this disruption, new, stable spatial firing patterns can later form, but the new and original maps are unrelated even though the rat is exposed to a constant environment. We therefore propose that the previously demonstrated erasure of stored spatial memory and the disruption of place cell firing are parallel effects of PKMζ blockade. We similarly propose that the known sparing of new spatial memory formation depends on the sparing of new map formation. On these bases, we argue that the loss of the map used to perform a practiced spatial task leads to behavioral performance deficits, and that synaptic plasticity maintained by PKMζ, which stabilizes the map, is essential for the proper expression of spatial memory.
User Preference-Based Dual-Memory Neural Model With Memory Consolidation Approach

Memory modeling has been a popular topic of research for improving the performance of autonomous agents in cognition related problems. Apart from learning distinct experiences correctly, significant or recurring experiences are expected to be learned better and be retrieved easier. In order to achieve this objective, this paper proposes a user preference-based dual-memory adaptive resonance theory network model, which makes use of a user preference to encode memories with various strengths and to learn and forget at various rates. Over a period of time, memories undergo a consolidation-like process at a rate proportional to the user preference at the time of encoding and the frequency of recall of a particular memory. Consolidated memories are easier to recall and are more stable. This dual-memory neural model generates distinct episodic memories and a flexible semantic-like memory component. This leads to an enhanced retrieval mechanism of experiences through two routes. The simulation results are presented to evaluate the proposed memory model based on various kinds of cues over a number of trials. The experimental results on Mybot are also presented. The results verify that not only are distinct experiences learned correctly but also that experiences associated with higher user preference and recall frequency are consolidated earlier. Thus, these experiences are recalled more easily relative to the unconsolidated experiences.
The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs

This paper introduces the three SSIX corpora for sentiment analysis. These corpora address the need to provide annotated data for supervised learning methods. They focus on stock-market related messages extracted from two financial microblog platforms, i.e., StockTwits and Twitter. In total they include 2,886 messages with opinion targets. These messages are provided with polarity annotation set on a continuous scale by three or four experts in each language. The annotation information identifies the targets with a sentiment score. The annotation process includes manual annotation verified and consolidated by financial experts. The creation of the annotated corpora took into account principled sampling strategies as well as inter-annotator agreement before consolidation in order to maximize data quality
MRI‐Guided Thrombolysis for Stroke with Unknown Time of Onset

Background Under current guidelines, intravenous thrombolysis is used to treat acute stroke only if it can be ascertained that the time since the onset of symptoms was less than 4.5 hours. We sought to determine whether patients with stroke with an unknown time of onset and features suggesting recent cerebral infarction on magnetic resonance imaging (MRI) would benefit from thrombolysis with the use of intravenous alteplase. Methods In a multicenter trial, we randomly assigned patients who had an unknown time of onset of stroke to receive either intravenous alteplase or placebo. All the patients had an ischemic lesion that was visible on MRI diffusion‐weighted imaging but no parenchymal hyperintensity on fluid‐attenuated inversion recovery (FLAIR), which indicated that the stroke had occurred approximately within the previous 4.5 hours. We excluded patients for whom thrombectomy was planned. The primary end point was favorable outcome, as defined by a score of 0 or 1 on the modified Rankin scale of neurologic disability (which ranges from 0 [no symptoms] to 6 [death]) at 90 days. A secondary outcome was the likelihood that alteplase would lead to lower ordinal scores on the modified Rankin scale than would placebo (shift analysis). Results The trial was stopped early owing to cessation of funding after the enrollment of 503 of an anticipated 800 patients. Of these patients, 254 were randomly assigned to receive alteplase and 249 to receive placebo. A favorable outcome at 90 days was reported in 131 of 246 patients (53.3%) in the alteplase group and in 102 of 244 patients (41.8%) in the placebo group (adjusted odds ratio, 1.61; 95% confidence interval [CI], 1.09 to 2.36; P=0.02). The median score on the modified Rankin scale at 90 days was 1 in the alteplase group and 2 in the placebo group (adjusted common odds ratio, 1.62; 95% CI, 1.17 to 2.23; P=0.003). There were 10 deaths (4.1%) in the alteplase group and 3 (1.2%) in the placebo group (odds ratio, 3.38; 95% CI, 0.92 to 12.52; P=0.07). The rate of symptomatic intracranial hemorrhage was 2.0% in the alteplase group and 0.4% in the placebo group (odds ratio, 4.95; 95% CI, 0.57 to 42.87; P=0.15). Conclusions In patients with acute stroke with an unknown time of onset, intravenous alteplase guided by a mismatch between diffusion‐weighted imaging and FLAIR in the region of ischemia resulted in a significantly better functional outcome and numerically more intracranial hemorrhages than placebo at 90 days. (Funded by the European Union Seventh Framework Program; WAKE‐UP ClinicalTrials.gov number, NCT01525290; and EudraCT number, 2011‐005906‐32.)
Facial ageing and rejuvenation modeling including lifestyle behaviours, using biometrics-based approaches. (Modélisation par approches biométriques du vieillissement et du rajeunissement numérique du visage, intégration de facteurs comportementaux liés au mode de vie)

The main focus of this thesis is to model the evolution trajectory of human face from infancy to senility using the biometrics facial features.The manifestation of facial changes caused by ageing depends on different factors such as genetic, ethnicity and lifestyle. Nevertheless, individuals in the same age group share some facial similarities. These resemblances can be employed to approximate the facial appearance of an individual in the bygone or the forthcoming years.Unlike numerous studies dealing with predictive face ageing models, for the first time, this thesis proposes the first Backward Facial Ageing Model aiming at digitally rejuvenate an adult face appearance down to its early childhood. We also present the Forward Facial Ageing Model to predict the adult face appearance in its future by taking into account the naturalageing trajectory. The main purpose of Forward Facial Ageing Model is to have a base model for the supplementary ageing models such as behavioural models.In this thesis for the first time in face ageing studies, the effects of different lifestyle behaviours are integrated into the facial ageing models. The Behavioural Facial Ageing Models predict the feature of a young face in case of having the high-risk lifestyle habits. The main attempt of these models is to illustrate the adverse effects of unsafe lifestyle behaviourson the senility of the face, aiming to prevent the youth from becoming involved in these habits. The Facial Ageing Modeling Database, contains over 1600 facial images, is collected to construct the models and 30 Face Templates for the purpose of the face ageing studies.Besides, the Face Time-Machine Database from 120 subjects is created and published to testand evaluate the results. For the proposed approach face contour and different components are modified non-linearly based on an estimated geometrical model related to the trajectory of growth or ageing. Moreover, the face texture is adapted by mapping a Face Template to the estimated geometrical model. Then, the effects of each lifestyle habit are set up to the primal predictive model.The evaluations of the results indicate that the proposed models are remarkably accurate to estimate the correct face appearance of an individual in the target age. While the simulated facial images are realistic and have the appearance, geometrical and textural characteristics of the target age, the personal identity and details of the input face images are preserved
Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies

Domain adaptation is the task of transforming a model trained using data from a source domain to a different target domain. In Unsupervised Domain Adaptation (UDA), we do not assume any labelled training data from the target domain. In this paper, we consider the problem of UDA in the contact of Part-of-Speech (POS). Specifically, we study the effect of data imbalance on UDA of POS, and compare different pivot selection strategies for accurately adapting a POS tagger trained using some source domain data to a target domain. We propose the use of F-score to select pivots using available labelled data in the source domain. Our experimental results on using benchmark dataset for cross-domain POS tagging, show that using frequency combined with Fscores for selecting pivots in the source labelled data produces the best results.
Generating a Gold Standard for a Swedish Sentiment Lexicon

There is an increasing demand for multilingual sentiment analysis, and most work on sentiment lexicons is still carried out based on English lexicons like WordNet. In addition, many of the non-English sentiment lexicons that do exist have been compiled by (machine) translation from English resources, thereby arguably obscuring possible language-speciﬁc characteristics of sentiment-loaded vocabulary. In this paper we describe the creation of a gold standard for the sentiment annotation of Swedish terms as a ﬁrst step towards the creation of a full-ﬂedged sentiment lexicon for Swedish – i.e., a lexicon containing information about prior sentiment (also called polarity) values of lexical items (words or disambiguated word senses), along a scale negative–positive. We create a gold standard for sentiment annotation of Swedish terms, using the freely available SALDO lexicon and the Gigaword corpus. For this purpose, we employ a multi-stage approach combining corpus-based frequency sampling and two stages of human annotation: direct score annotation followed by Best-Worst Scaling. In addition to obtaining a gold standard, we analyze the data from our process and we draw conclusions about the optimal sentiment model.
IVD: Automatic Learning and Enforcement of Authorization Rules in Online Social Networks

Authorization bugs, when present in online social networks, are usually caused by missing or incorrect authorization checks and can allow attackers to bypass the online social network's protections. Unfortunately, there is no practical way to fully guarantee that an authorization bug will never be introduced—even with good engineering practices—as a web application and its data model become more complex. Unlike other web application vulnerabilities such as XSS and CSRF, there is no practical general solution to prevent missing or incorrect authorization checks. In this paper we propose Invariant Detector (IVD), a defense-in-depth system that automatically learns authorization rules from normal data manipulation patterns and distills them into likely invariants. These invariants, usually learned during the testing or pre-release stages of new features, are then used to block any requests that may attempt to exploit bugs in the social network's authorization logic. IVD acts as an additional layer of defense, working behind the scenes, complementary to privacy frameworks and testing. We have designed and implemented IVD to handle the unique challenges posed by modern online social networks. IVD is currently running at Facebook, where it infers and evaluates daily more than 200,000 invariants from a sample of roughly 500 million client requests, and checks the resulting invariants every second against millions of writes made to a graph database containing trillions of entities. Thus far IVD has detected several high impact authorization bugs and has successfully blocked attempts to exploit them before code fixes were deployed.
Single mode step-index polymer optical fiber for humidity insensitive high temperature fiber Bragg grating sensors.

We have fabricated the first single-mode step-index and humidity insensitive polymer optical fiber operating in the 850 nm wavelength ranges. The step-index preform is fabricated using injection molding, which is an efficient method for cost effective, flexible and fast preparation of the fiber preform. The fabricated single-mode step-index (SI) polymer optical fiber (POF) has a 4. 8µm core made from TOPAS grade 5013S-04 with a glass transition temperature of 134°C and a 150 µm cladding made from ZEONEX grade 480R with a glass transition temperature of 138°C. The key advantages of the proposed SIPOF are low water absorption, high operating temperature and chemical inertness to acids and bases and many polar solvents as compared to the conventional poly-methyl-methacrylate (PMMA) and polystyrene based POFs. In addition, the fiber Bragg grating writing time is short compared to microstructured POFs.
Serotonergic Neuroplasticity in Alcohol Addiction

Alcohol addiction is a debilitating disorder producing maladaptive changes in the brain, leading drinkers to become more sensitive to stress and anxiety. These changes are key factors contributing to alcohol craving and maintaining a persistent vulnerability to relapse. Serotonin (5-Hydroxytryptamine, 5-HT) is a monoamine neurotransmitter widely expressed in the central nervous system where it plays an important role in the regulation of mood. The serotonin system has been extensively implicated in the regulation of stress and anxiety, as well as the reinforcing properties of all of the major classes of drugs of abuse, including alcohol. Dysregulation within the 5-HT system has been postulated to underlie the negative mood states associated with alcohol use disorders. This review will describe the serotonergic (5-HTergic) neuroplastic changes observed in animal models throughout the alcohol addiction cycle, from prenatal to adulthood exposure. The first section will focus on alcohol-induced 5-HTergic neuroadaptations in offspring prenatally exposed to alcohol and the consequences on the regulation of stress/anxiety. The second section will compare alterations in 5-HT signalling induced by acute or chronic alcohol exposure during adulthood and following alcohol withdrawal, highlighting the impact on the regulation of stress/anxiety signalling pathways. The third section will outline 5-HTergic neuroadaptations observed in various genetically-selected ethanol preferring rat lines. Finally, we will discuss the pharmacological manipulation of the 5-HTergic system on ethanol- and anxiety/stress-related behaviours demonstrated by clinical trials, with an emphasis on current and potential treatments.