Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction of Continuous Theta Burst Stimulation-Induced Motor Plasticity in Healthy Elderly With COMT Val158Met Polymorphism

Objective To delineate whether cortical plasticity induced by continuous theta burst stimulation (cTBS) differed according to catechol-O-methyltransferase (COMT) gene polymorphism in healthy older adults. Methods Eighteen healthy older volunteers (mean age 73.78±5.04; 12 females and 6 males) were recruited. Volunteers randomly assigned in either a sham-first or real cTBS first group participated in two separate TMS visits with at least a 2-day wash-out period. Genotyping was carried out at baseline by a separate researcher who was blinded. cTBS was delivered in a hot spot over M1 at an active motor threshold of 80%. Motor evoked potentials (MEPs) were obtained at 120% of the resting motor threshold before and after sham/cTBS. Results The relative MEP to baseline was significantly decreased 0 and 10 minutes post-stimulation and increased 40 minutes post-stimulation, as compared with the sham condition. Immediately after cTBS, the Val/Val group had a significantly reduced relative MEP value, as compared with the MET carrier group. Conclusion In healthy older persons, cTBS-induced motor plasticity was reduced in the COMT Val/Val group as compared with the 158Met carrier group.
Reversible limbic encephalitis with antibodies against the membranes of neurones of the hippocampus

The authors report a patient with limbic encephalitis associated with an ovarian teratoma. An antibody against the membranes of neurones of the hippocampus was identified in both serum and CSF. Immunosuppressive treatments such as plasmapheresis and high-dose intravenous immunoglobulin administration resulted in a rapid clinical recovery.
Power, Performance Modeling and Optimization for Mobile System and Applications

Smartphone usage has experienced significant growth in the recent years. Despite of its popularity, there is a tension between the increasing demand for smartphone performance, e.g., lower response time, and the limited resource provided by smartphones, in particular energy. Unfortunately, the situation has been made even worse due to two major challenges. On the energy side, software developers do not necessarily understand the energy implication of their design decisions. On the performance side, the traditional approach to optimize performance is not necessarily applicable to mobile device due to the difference in workloads and performance bottlenecks. Combined, these difficulties made balancing between energy and performance for mobile systems and applications even more challenging. As a result, many mobile application, and even those developed by mature companies, can make poor decisions, either on performance or on energy. My thesis is dedicated to address these challenges by providing a practical, automatic, efficient, and effective framework to help mobile system and application developers to monitor, understand, and optimize the performance and energy of their target designs. My approach consists of three major steps. (1) We first enable developers’ understanding of energy implication by providing power models and its construction framework. The provided tool, PowerTutor has demonstrated great value by helping a number of developers to monitor the energy usage of the system and applications. We also enable developer’s understanding of performance in the mobile paradigm by providing Panappticon, a lightweight, system-wide, fine-grained event tracing system that automatically identifies
The Transcultural Diabetes Nutrition Algorithm: A Canadian Perspective

The Transcultural Diabetes Nutrition Algorithm (tDNA) is a clinical tool designed to facilitate implementation of therapeutic lifestyle recommendations for people with or at risk for type 2 diabetes. Cultural adaptation of evidence-based clinical practice guidelines (CPG) recommendations is essential to address varied patient populations within and among diverse regions worldwide. The Canadian version of tDNA supports and targets behavioural changes to improve nutritional quality and to promote regular daily physical activity consistent with Canadian Diabetes Association CPG, as well as channelling the concomitant management of obesity, hypertension, dyslipidemia, and dysglycaemia in primary care. Assessing glycaemic index (GI) (the ranking of foods by effects on postprandial blood glucose levels) and glycaemic load (GL) (the product of mean GI and the total carbohydrate content of a meal) will be a central part of the Canadian tDNA and complement nutrition therapy by facilitating glycaemic control using specific food selections. This component can also enhance other metabolic interventions, such as reducing the need for antihyperglycaemic medication and improving the effectiveness of weight loss programs. This tDNA strategy will be adapted to the cultural specificities of the Canadian population and incorporated into the tDNA validation methodology.
Experimental Evidence of Threat-Sensitive Collective Avoidance Responses in a Large Wild-Caught Herring School

Aggregation is commonly thought to improve animals' security. Within aquatic ecosystems, group-living prey can learn about immediate threats using cues perceived directly from predators, or from collective behaviours, for example, by reacting to the escape behaviours of companions. Combining cues from different modalities may improve the accuracy of prey antipredatory decisions. In this study, we explored the sensory modalities that mediate collective antipredatory responses of herring (Clupea harengus) when in a large school (approximately 60 000 individuals). By conducting a simulated predator encounter experiment in a semi-controlled environment (a sea cage), we tested the hypothesis that the collective responses of herring are threat-sensitive. We investigated whether cues from potential threats obtained visually or from the perception of water displacement, used independently or in an additive way, affected the strength of the collective avoidance reactions. We modified the sensory nature of the simulated threat by exposing the herring to 4 predator models differing in shape and transparency. The collective vertical avoidance response was observed and quantified using active acoustics. The combination of sensory cues elicited the strongest avoidance reactions, suggesting that collective antipredator responses in herring are mediated by the sensory modalities involved during threat detection in an additive fashion. Thus, this study provides evidence for magnitude-graded threat responses in a large school of wild-caught herring which is consistent with the “threat-sensitive hypothesis”.
The influence of study characteristics on reporting of subgroup analyses in randomised controlled trials: systematic review

Objective To investigate the impact of industry funding on reporting of subgroup analyses in randomised controlled trials. Design Systematic review. Data sources Medline. Study selection Randomised controlled trials published in 118 core clinical journals (defined by the National Library of Medicine) in 2007. 1140 study reports in a 1:1 ratio by high (five general medicine journals with largest number of total citations in 2007) versus lower impact journals, were randomly sampled. Two reviewers, independently and in duplicate, used standardised, piloted forms to screen study reports for eligibility and to extract data. They also used explicit criteria to determine whether a randomised controlled trial reported subgroup analyses. Logistic regression was used to examine the association of prespecified study characteristics with reporting versus not reporting of subgroup analyses. Results 469 randomised controlled trials were included, of which 207 (44%) reported subgroup analyses. High impact journals (adjusted odds ratio 2.64, 95% confidence interval 1.62 to 4.33), non-surgical (versus surgical) trials (2.10, 1.26 to 3.50), and larger sample size (3.38, 1.64 to 6.99) were associated with more frequent reporting of subgroup analyses. The strength of association between trial funding and reporting of subgroups differed in trials with and without statistically significant primary outcomes (interaction P=0.02). In trials without statistically significant results for the primary outcome, industry funded trials were more likely to report subgroup analyses (2.29, 1.30 to 4.72) than non-industry funded trials. This was not true for trials with a statistically significant primary outcome (0.79, 0.46 to 1.36). Industry funded trials were associated with less frequent prespecification of subgroup hypotheses (31.3% v 38.0%, adjusted odds ratio 0.49, 0.26 to 0.94), and less use of the interaction test for analyses of subgroup effects (41.4% v 49.1%, 0.52, 0.28 to 0.97) than non-industry funded trials. Conclusion Industry funded randomised controlled trials, in the absence of statistically significant primary outcomes, are more likely to report subgroup analyses than non-industry funded trials. Industry funded trials less frequently prespecify subgroup hypotheses and less frequently test for interaction than non-industry funded trials. Subgroup analyses from industry funded trials with negative results for the primary outcome should be viewed with caution.
Classifying network complexity

Growing complexity in the Internet is the subject of many debates. While there is extensive research on complexity in some specific areas such as graph theory or software design, the complexity of a real life network is not very well defined or understood. This position paper provides a proposed classification of network complexity, some observations, and an overview of approaches to control network complexity. The goal is to stimulate and aid future discussions on the subject.
MRI and EEG as long-term seizure outcome predictors in familial mesial temporal lobe epilepsy

Objective: To evaluate the natural history and outcome predictors in familial mesial temporal lobe epilepsy (FMTLE). Methods: We conducted a longitudinal study of 103 individuals from 17 FMTLE families (mean follow-up: 7.6 years). We divided subjects into 3 groups: FMTLE (n = 53), unclassified seizure (n = 18), and asymptomatics (n = 32). We divided FMTLE patients into 3 subgroups: seizure-free (n = 19), infrequent (n = 17) seizures, and frequent (n = 17) seizures and further reclassified them into favorable and poor outcome. We defined hippocampal atrophy (HA) by visual MRI analysis and performed volumetry in those who had 2 MRIs. Results: FMTLE patients with infrequent seizures evolved to either frequent seizures (17.6%) or seizure freedom (23.5%). In the seizure-free group, most remained seizure-free and 21% developed infrequent seizures. All patients with frequent seizures remained in the same status or underwent surgery. Twelve percent of the asymptomatics and 22% of the unclassified-seizure group evolved to FMTLE with infrequent seizures. Predictive factors of poor outcome were presence of HA (p = 0.0192) and interictal epileptiform discharges (p = 0.0174). The relationship between initial precipitating incidents and clinical outcome was not significant although a tendency was observed (p = 0.055). Use of antiepileptic drugs and secondary generalized seizures during the patient’s lifetime did not predict poor outcome. We observed progression of HA only in the group with frequent seizures. Conclusion: Most patients with FMTLE continued in the same clinical status. However, patients with frequent seizures had progression of HA and none improved except those who underwent surgery. Interictal epileptiform discharges and HA predicted poorer outcome in FMTLE, and there was a tendency in favor of initial precipitating incidents as outcome predictors.
Journal of Insect Science: Vol. 2006 | Article 16

Effects of egg retention on movement and dispersal activity of Trichogramma principium (Hymenoptera, Trichogrammatidae) females were investigated under laboratory conditions. Individual females were observed during one minute in the absence of hosts. Movement activity and dispersal rate were estimated by the length of the track and by the distance from the start point, respectively. Before the test, all wasps during 2 – 4 days were presented with a possibility to parasitize a factitious laboratory host, Sitotroga cerealella Oliv. (Lepidoptera, Gelechiidae). Wasps that had parasitized before the test show significant reduction of spontaneous walking activity and dispersal rate when compared with females that refused to parasitize the non-preferred host (i.e. manifested egg retention). This effect cannot be considered as a direct arrestment reaction to the host because during the test period, no hosts were provided. Thus, egg retention results not only in temporal spread, but also in more intensive spatial dispersal of a group of simultaneously emerged females.
VizStruct: exploratory visualization for gene expression profiling

MOTIVATION DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse. buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.
POTENTIAL OF SILYBUM MARIANUM L. FOR PHYTOREMEDIATION OF SOILS CONTAMINATED WITH HEAVY METALS

Comparative research has been conducted to allow us to determine the accumulation of heavy metals, micro and macroelements in the vegetative and reproductive organs of milk thistle, the quality of milk thistle oil, as well as the possibilities to use the plant for phytoremediation of heavy metal contaminated soils. The experiment was performed on an agricultural field contaminated by the NonFerrous-Metal Works (NFMW) near Plovdiv, Bulgaria. The experimental plots were situated at different distances (0.5, and 15 km) from the source of pollution. The contents of heavy metals in plant materials (roots, stems, leaves, seeds and head seeds) were determined. The quality of milk thistle oils (heavy metals and fatty acid composition) was also determined. The quantitative measurements were carried out with inductively-coupled plasma (ICP). Mllk thistle is a plant that is tolerant to heavy metals and can be referred to the hyperaccumulators of lead and the accumulators of cadmium and zinc. The plant can be successfully used in the phytoremediation of heavy metal contaminated soils. Heavy metal soil contamination has no significant effect on the oil content of the milk thistle seeds and the fatty acid composition of the oil. Oil of milk thistle grown in contaminated soils is characterized by a lower content of saturated acids (with the exception of arachidonic acid) and polyunsaturated acids, and a higher monounsaturated acid content compared to the oil from the uncontaminated area. The processing of milk thistle seeds into oil and the use of the obtained oil will greatly reduce the cost of phytoremediation.
Learning from Partial Labels

We address the problem of partially-labeled multiclass classification, where instead of a single label per instance, the algorithm is given a candidate set of labels, only one of which is correct. Our setting is motivated by a common scenario in many image and video collections, where only partial access to labels is available. The goal is to learn a classifier that can disambiguate the partially-labeled training instances, and generalize to unseen data. We define an intuitive property of the data distribution that sharply characterizes the ability to learn in this setting and show that effective learning is possible even when all the data is only partially labeled. Exploiting this property of the data, we propose a convex learning formulation based on minimization of a loss function appropriate for the partial label setting. We analyze the conditions under which our loss function is asymptotically consistent, as well as its generalization and transductive performance. We apply our framework to identifying faces culled from web news sources and to naming characters in TV series and movies; in particular, we annotated and experimented on a very large video data set and achieve 6% error for character naming on 16 episodes of the TV series Lost.
UML Specialization for Fault Tolerant Component Based Architectures

Architecture-based reasoning about reliability and fault tolerance is gaining increasing importance as component-based software architectures become more widespread. Architectural description languages (ADLs) are used to specify high-level views of software design. ADLs usually involve a static, structural view of a system together with a dynamic, state-transition-style semantics, facilitating specification and analysis of distributed and event-based systems. The aim is a compositional syntax and semantics: overall component behavior is understood in terms of subcomponent behavior. ADLs have been successful in understanding architecture functionality. However, it remains to be investigated how to equip an ADL with a compositional semantics for specification and analysis of extra-functional properties such as reliability and fault-tolerance. This paper combines architecture definition with probabilistic finite state machines suitable to model reliability and fault-tolerance aspects. We present a compositional approach to specifying fault tolerance through parameterization of architectures. Using Probabilistic Real Time Computational Tree Logic (PCTL) we can specify and check statements about reliability of such architectures.
Olfactory Impairment and Subjective Olfactory Complaints Independently Predict Conversion to Dementia: A Longitudinal, Population-Based Study

Abstract We examined whether conversion to dementia can be predicted by self-reported olfactory impairment and/or by an inability to identify odors. Common forms of dementia involve an impaired sense of smell, and poor olfactory performance predicts cognitive decline among the elderly. We followed a sample of 1529 participants, who were within a normal range of overall cognitive function at baseline, over a 10-year period during which 159 were classified as having a dementia disorder. Dementia conversion was predicted from demographic variables, Mini-Mental State Examination score, and olfactory assessments. Self-reported olfactory impairment emerged as an independent predictor of dementia. After adjusting for effects of other predictors, individuals who rated their olfactory sensitivity as “worse than normal” were more likely to convert to dementia than those who reported normal olfactory sensitivity (odds ratio [OR] = 2.17; 95% confidence interval [CI] [1.40, 3.37]). Additionally, low scores on an odor identification test also predicted conversion to dementia (OR per 1 point increase = 0.89; 95% CI [0.81, 0.98]), but these two effects were additive. We suggest that assessing subjective olfactory complaints might supplement other assessments when evaluating the risk of conversion to dementia. Future studies should investigate which combination of olfactory assessments is most useful in predicting dementia conversion. (JINS, 2014, 20, 1–9)
Activity-Dependent Plasticity of Astroglial Potassium and Glutamate Clearance

Recent evidence has shown that astrocytes play essential roles in synaptic transmission and plasticity. Nevertheless, how neuronal activity alters astroglial functional properties and whether such properties also display specific forms of plasticity still remain elusive. Here, we review research findings supporting this aspect of astrocytes, focusing on their roles in the clearance of extracellular potassium and glutamate, two neuroactive substances promptly released during excitatory synaptic transmission. Their subsequent removal, which is primarily carried out by glial potassium channels and glutamate transporters, is essential for proper functioning of the brain. Similar to neurons, different forms of short- and long-term plasticity in astroglial uptake have been reported. In addition, we also present novel findings showing robust potentiation of astrocytic inward currents in response to repetitive stimulations at mild frequencies, as low as 0.75 Hz, in acute hippocampal slices. Interestingly, neurotransmission was hardly affected at this frequency range, suggesting that astrocytes may be more sensitive to low frequency stimulation and may exhibit stronger plasticity than neurons to prevent hyperexcitability. Taken together, these important findings strongly indicate that astrocytes display both short- and long-term plasticity in their clearance of excess neuroactive substances from the extracellular space, thereby regulating neuronal activity and brain homeostasis.
A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.