Miracle-dz/UniFAR-FTUs huggingface dataset:
Workplace determinants of endotoxin exposure in dental healthcare facilities in South Africa.

OBJECTIVES Aerosols generated during dental procedures have been reported to contain endotoxin as a result of bacterial contamination of dental unit water lines. This study investigated the determinants of airborne endotoxin exposure in dental healthcare settings. METHODS The study population included dental personnel (n = 454) from five academic dental institutions in South Africa. Personal air samples (n = 413) in various dental jobs and water samples (n = 403) from dental handpieces and basin taps were collected. The chromogenic-1000 limulus amebocyte lysate assay was used to determine endotoxin levels. Exposure metrics were developed on the basis of individually measured exposures and average levels within each job category. Analysis of variance and multivariate linear regression models were constructed to ascertain the determinants of exposure in the dental group. RESULTS There was a 2-fold variation in personal airborne endotoxin from the least exposed (administration) to the most exposed (laboratory) jobs (geometric mean levels: 2.38 versus 5.63 EU m(-3)). Three percent of personal samples were above DECOS recommended exposure limit (50 EU m(-3)). In the univariate linear models, the age of the dental units explained the most variability observed in the personal air samples (R(2) = 0.20, P < 0.001), followed by the season of the year (R(2) = 0.11, P < 0.001). Other variables such as institution and total number of dental units per institution also explained a modest degree of variability. A multivariate model explaining the greatest variability (adjusted R(2) = 0.40, P < 0.001) included: the age of institution buildings, total number of dental units per institution, ambient temperature, ambient air velocity, endotoxin levels in water, job category (staff versus students), dental unit model type and age of dental unit. CONCLUSIONS Apart from job type, dental unit characteristics are important predictors of airborne endotoxin levels in this setting.
Approximate modeling of continuous context in factorization algorithms

Factorization based algorithms -- such as matrix or tensor factorization -- are widely used in the field of recommender systems. These methods model the relations between the entities of two or more dimensions. The entity based approach is suitable for dimensions such as users, items and several context types, where the domain of the context is nominal. Continuous and ordinal context dimensions are usually discretized and their values are used as nominal entities. While this enables the usage of continuous context in factorization methods, still much information is lost during the process. In this paper we propose two approaches for better modeling of the continuous context dimensions. Fuzzy event modeling tackles the problem through the uncertainty of the value of the observation in the context dimension. Fuzzy context modeling, on the other hand, enables context-states to overlap, thus certain observations are influenced by multiple context-states. Throughout the paper seasonality is used as an example of continuous context. We incorporate the modeling concepts into the iTALS algorithm, without degrading its scalability. The effect of the two approaches on recommendation accuracy is measured on five implicit feedback databases.
Pulmonary Complication of Novel Influenza A (H1N1) Infection: Imaging Features in Two Patients

Novel influenza A (H1N1) virus is the pathogen of recent global outbreaks of febrile respiratory infection. We herein report the imaging findings of pulmonary complication in two patients with novel influenza A (H1N1) infection. The first patient without secondary infection showed the ill-defined ground-glass opacity nodules and patch areas of ground-glass opacities. The second patient with secondary pneumococcal pneumonia showed areas of lobar consolidation in the right middle lobe and left lower lobe and ground-glass opacities.
signal from noise When are randomised trials unnecessary? Picking

http://bmj.com/cgi/content/full/334/7589/349 Updated information and services can be found at: These include: Data supplement http://bmj.com/cgi/content/full/334/7589/349/DC1 "References" References http://bmj.com/cgi/content/full/334/7589/349#otherarticles 16 online articles that cite this article can be accessed at: http://bmj.com/cgi/content/full/334/7589/349#BIBL This article cites 13 articles, 1 of which can be accessed free at:
Predicting People's 3D Poses from Short Sequences

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Instead of computing candidate poses in individual frames and then linking them, as is often done, we regress directly from a spatio-temporal block of frames to a 3D pose in the central one. We will demonstrate that this approach allows us to effectively overcome ambiguities and to improve upon the state-of-the-art on challenging sequences.
Seed dormancy is a dynamic state: variable responses to pre- and post-shedding environmental signals in seeds of contrasting Arabidopsis ecotypes

Abstract Seeds have evolved to be highly efficient environmental sensors that respond not only to their prevailing environment, but also their environmental history, to regulate dormancy and the initiation of germination. In the present work we investigate the combined impact of a number of environmental signals (temperature, nitrate, light) during seed development on the mother plant, during post-shedding imbibition and during prolonged post-shedding exposure in both dry and imbibed states, simulating time in the soil seed bank. The differing response to these environments was observed in contrasting winter (Cvi, Ler) and summer (Bur) annual Arabidopsis ecotypes. Results presented show that environmental signals both pre- and post-shedding determine the depth of physiological dormancy and therefore the germination response to the ambient environment. The ecotype differences in seed response to ambient germination conditions are greatly enhanced by seed maturation in different environments. Further variation in response develops following shedding when seeds do not receive the full complement of environmental signals required for germination and enter the soil seed bank in either dry or imbibed states. Species seed dormancy characteristics cannot therefore be easily defined, as seed dormancy is a dynamic state subject to within-species adaptation to local environments.
Memory modification in the brain: Computational and experimental investigations

Any aspect of cognition that is concerned with learning faces the same basic question: when are old memories modified, and when are new memories formed? For example, since Pavlov’s first studies of classical conditioning, it has been believed that extinction of an associative memory entails new learning rather than modification of an old memory (Pavlov, 1927; Bouton, 2004). The same question arises in the domain of episodic memory, where the notion of an ‘episode’ hinges crucially on the ability to parse the stream of experience into distinct chunks. The question attains vivid clinical significance in the psychopathology of fear and addiction, where patients struggle to modify or erase a persistent, maladaptive memory.
Caching Strategies for Information Centric Networking: Opportunities and Challenges

Internet usage has shifted from host-centric end-to-end communication to a content-centric approach mainly used for content delivery. Information Centric Networking (ICN) was proposed as a promising novel content delivery architecture. ICN includes in-network caching features at every node which has a major impact on content retrieval. For instance, the ICN efficiency depends drastically on the management of the caching resources. The management of caching resources is achieved with caching strategies. Caching strategies decide what, where and when content is stored in the network. In this paper, we revisit the recent technical contributions on caching strategies, we compare them and discuss opportunities and challenges for improving content delivery in the upcoming years.
Protein Kinase C Inhibition Rescues Manic-Like Behaviors and Hippocampal Cell Proliferation Deficits in the Sleep Deprivation Model of Mania

Background: Recent studies revealed that bipolar disorder may be associated with deficits of neuroplasticity. Additionally, accumulating evidence has implicated alterations of the intracellular signaling molecule protein kinase C (PKC) in mania. Methods: Using sleep deprivation (SD) as an animal model of mania, this study aimed to examine the possible relationship between PKC and neuroplasticity in mania. Rats were subjected to SD for 72h and tested behaviorally. In parallel, SD-induced changes in hippocampal cell proliferation were evaluated with bromodeoxyuridine (BrdU) labeling. We then examined the effects of the mood stabilizer lithium, the antipsychotic agent aripiprazole, and the PKC inhibitors chelerythrine and tamoxifen on both behavioral and cell proliferation impairments induced by SD. The antidepressant fluoxetine was used as a negative control. Results: We found that SD triggered the manic-like behaviors such as hyperlocomotion and increased sleep latency, and reduced hippocampal cell proliferation. These alterations were counteracted by an acute administration of lithium and aripiprazole but not of fluoxetine, and only a single administration of aripiprazole increased cell proliferation on its own. Importantly, SD rats exhibited increased levels of phosphorylated synaptosomal-associated protein 25 (SNAP-25) in the hippocampus and prefrontal cortex, suggesting PKC overactivity. Moreover, PKC inhibitors attenuated manic-like behaviors and rescued cell proliferation deficits induced by SD. Conclusions: Our findings confirm the relevance of SD as a model of mania, and provide evidence that antimanic agents are also able to prevent SD-induced decrease of hippocampal cell proliferation. Furthermore, they emphasize the therapeutic potential of PKC inhibitors, as revealed by their antimanic-like and pro-proliferative properties.
Prevalence and determinants of non-alcoholic fatty liver disease in lifelines: A large Dutch population cohort

Background & aims Non-alcoholic fatty liver disease is an increasing health issue that develops rather unnoticed with obesity, type 2 diabetes mellitus and metabolic syndrome. We investigated prevalence, determinants and associated metabolic abnormalities of non-alcoholic fatty liver disease in the largest population-based cohort to date. Methods Biochemical characteristics, type 2 diabetes mellitus and metabolic syndrome were determined in the Lifelines Cohort Study (N = 167,729), a population-based cohort in the North of the Netherlands. Non-alcoholic fatty liver disease was defined as Fatty Liver Index (FLI)≥60. Exclusion criteria were age <18 years, immigrants, missing data to assess FLI and metabolic syndrome, excessive alcohol use, previous-diagnosed hepatitis or cirrhosis and non-fasting blood sampling. Results Out of 37,496 included participants (median age 44 years, 62.1% female), 8,259 (22.0%) had a FLI≥60. Individuals with a FLI≥60 were more often male, older, obese, had higher levels of hemoglobinA1c, fasting glucose, liver enzymes, total cholesterol, low-density lipoprotein cholesterol, triglycerides, c-reactive protein and leucocytes and lower high-density lipoprotein cholesterol (all P<0.0001). Participants with a FLI≥60 showed higher prevalence of type 2 diabetes mellitus (9.3% vs. 1.4%), metabolic syndrome (54.2% vs. 6.2%), impaired renal function (20.1% vs. 8.7%) and cardiovascular disease (4.6% vs. 1.6%) (all P<0.0001). Multivariable logistic analysis showed that smoking, hemoglobin, leucocytes, c-reactive protein, platelets, alanine aminotransferase, alkaline phosphatase, albumin, impaired renal function (OR 1.27, 95%CI 1.15–1.41), metabolic syndrome (OR 11.89, 95%CI 11.03–12.82) and its individual components hyperglycemia (OR 2.53, 95%CI 2.34–2.72), hypertension (OR 1.89, 95%CI 1.77–2.01) and reduced high-density lipoprotein cholesterol (OR 3.44, 95%CI 3.22–3.68) were independently associated with suspected non-alcoholic fatty liver disease (all P<0.0001). Conclusion Twenty-two percent (22.0%) of the population in the North of the Netherlands is suspected to suffer from non-alcoholic fatty liver disease, coinciding with a significant increased risk of type 2 diabetes mellitus, metabolic syndrome, cardiovascular disease and impaired renal function.
Hybrid Ant Colony Optimization and Simulated Annealing for Rule Induction

This paper proposes a hybrid of ant colony optimization and simulated annealing for rule induction. The hybrid algorithm is part of the sequential covering algorithm which is the commonly used algorithm to extract classification rules directly from data. The hybrid algorithm will minimize the problem of low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule. Simulated Annealing will be used to produce a rule for each ant. The best rule for a colony will then be chosen and later the best rule among the colonies will be included in the rule set. The ordered rule set is arranged in decreasing order of generation. Thirteen data sets which consist of discrete and continuous data from UCI repository were used to evaluate the performance of the proposed algorithm. Promising results were obtained when compared to the Ant-Miner algorithm in terms of accuracy, number of rules and number of terms in the rules.
Mechanical design, implementation and tracking control of a seven degree of freedom redundant manipulator using discontineous Lyapunov-based controller

One of the main objectives of designing robotic systems is to design structures, capable of performing multi tasks. Redundant manipulators by having high degrees of freedom, expand the end-effectors' workspace and are widely used as multi-task systems. In this paper, mechanical design, kinematic and dynamic analyses, implementation of mechanical part and electronic hardware and control of a redundant 7-DOF robotic manipulator, based on the upper extremity structure of the human body has been addressed. The discontinuous Lyapunov-based controller is used, which comprises of integral operator and tracking controller. The goal is to improve the tracking performance of the robot manipulator in the presence of bounded and time variant disturbances. The proposed trajectory, relaxes the need of having knowledge about nonlinear dynamics. Based on the disturbance rejection scheme, tracking controller has been constructed which was asymptotically stabilizing in the sense of Lyapunov. Simulation results showed that the closed loop system strongly rejects disturbances.
Hexapod locomotion: A nonlinear dynamical systems approach

The ability of walking in a wide variety of terrains is one of the most important features of hexapod insects. In this paper we describe a bio-inspired controller able to generate locomotion and switch between different type of gaits for an hexapod robot. Motor patterns are generated by coupled Central Pattern Generators formulated as nonlinear oscillators. These patterns are modulated by a drive signal, proportionally changing the oscillators frequency, amplitude and the coupling parameters among the oscillators. Locomotion initiation, stopping and smooth gait switching is achieved by changing the drive signal. We also demonstrate a posture controller for hexapod robots using the dynamical systems approach. Results from simulation using a model of the Chiara hexapod robot demonstrate the capability of the controller both to locomotion generation and smooth gait transition. The postural controller is also tested in different situations in which the hexapod robot is expected to maintain balance. The presented results prove its reliability.
Image Relighting Using Depth ECE 547-Final Paper

This paper explores the new image processing domain involving images from depth cameras. While depth camera are a 2D array of data, the information they store is three dimensional in nature. Given a good depth image, the question now becomes what can we do with this added information. This paper explores the specific application of image relighting. Signal processing techniques of Guassian and Bilateral Filtering are shown to be necessary to reduce noise in the surface normal estimation, critical for model based lighting. The results include a Dual Bilateral Filter that uses information from both the depth and color image to estimate the surface normals.
Stochastic Cellular Fate Decision Making by Multiple Infecting Lambda Phage

Bacteriophage lambda is a classic system for the study of cellular decision making. Both experiments and mathematical models have demonstrated the importance of viral concentration in the lysis-lysogeny decision outcome in lambda phage. However, a recent experimental study using single cell and single phage resolution reported that cells with the same viral concentrations but different numbers of infecting phage (multiplicity of infection) can have markedly different rates of lysogeny. Thus the decision depends on not only viral concentration, but also directly on the number of infecting phage. Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network. Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated. We find that interplay between the level of intrinsic noise and viral protein decision threshold is a major factor that produces dependence on multiplicity of infection. However, simulations suggest spatial segregation of phage particles does not play a significant role. Cellular image processing is used to re-analyse the original time-lapse movies from the recent study and it is found that higher numbers of infecting phage reduce the cell elongation rate. This could also contribute to the observed phenomena as cellular growth rate can affect transcription rates. Our model further predicts that rate of lysogeny is dependent on bacterial growth rate, which can be experimentally tested. Our study provides new insight on the mechanisms of individual phage decision making. More generally, our results are relevant for the understanding of gene-dosage compensation in cellular systems.
Premenstrual Syndrome and Premenstrual Dysphoric Disorder: Symptoms and Cluster Influences

Many women in their reproductive years experience some mood, behavioral, or physical symptoms in the week prior to menses. Women experiencing mild symptoms may have a wide variability in the level of symptom burden, whereas a minority suffers severe and debilitating symptoms. Severe premenstrual syndrome (PMS) affects 3% to 5% of women of reproductive age and has been classified under the Diagnostic and Statistical Manual of Mental Disorders (DSM IV) as premenstrual dysphoric disorder (PMDD). Both disorders are characterized mainly by symptoms confined to the premenstrual period, which reduce not only patients' quality of life, but also their working activities. Women suffering PMDD experience severe dysphoric mood, and a greater desire and actual intake of certain foods, demonstrating impaired cognitive performance during the luteal phase. Several theories have been proposed to explain the underlying mechanisms of PMS and PMDD with complex bio-psycho-social factors involved. Although precise causes are unknown, the late luteal phase could be associated with diverse psychosomatic and behavioral symptoms appearing premenstrually which should be appropriately treated. Notwithstanding this high prevalence, no specific symptoms or signs appear, nor have any recognizable anatomical factors been identified in women suffering PMS or PMDD, and hence, no universal treatment yet exists. Despite this, therapeutic progress has been reached, although the ideal treatment has not yet been obtained due to the many clusters involved.
Interactive Learning of Top-down Attention Control and Motor Actions

Like humans and primates, artificial creatures like robots are limited in terms of allocation of their resources to huge sensory and perceptual information. Thus attention is regarded as the same solution as humans in this domain. While bottom-up attention is determined by the image statistics, top down attention is dependent the on behavior and the task an agent is doing. This work attempts to consider a task based top-down visual attention control when resources of the agent are limited. Particularly attention control is formulated as an optimization problem in which the agent has to gain maximum reward while satisfying a constraint which is its information processing bottleneck. Reinforcement learning is then used to solve that optimization problem. A driving environment is simulated in that agent has to learn how to drive safely by attending to the right spatial locations and performing appropriate motor actions.
A Novel Hybrid Approach for Web Caching

With the advancement of technology and development of many new applications now a days, the World Wide Web (WWW) suffers from scaling and reliability problems due to overloaded and congested proxy servers. Caching at local proxy servers helps, but cannot satisfy more than a third to half of requests, more requests are still sent to original origin servers. To address this issue, we propose a Hybrid Web Caching Algorithm (HWCA) for managing and searching in the cache of proxy servers. The algorithm uses Hierarchical Web Cache concepts along with static hierarchies with dynamic mechanism of proxy servers. Most of the requests are being satisfied through local proxy servers and very less requests are being fulfilled by remote origin servers. This mechanism keeps the congestion less on the network by using lesser bandwidth as most of the requests are satisfied from local sites. The proposed work is divided into three phases. The first phase is to characterize all the challenges in front of hybrid approach and all its viable study. In second phase, architecture is proposed for implementation of Hybrid Web Cache System. Third phase consists of the proposed algorithm for web caching. The performance of the proposed algorithm is evaluated using hit ratio and latency time of requests depending on varying cache sizes. The results obtained show that the proposed algorithm is effective in comparison to the existing techniques with respect to the above metrics.
Experimental analysis on cross domain preferences association and rating prediction

Cross domain recommendation and preferences association are emerging research topics. In this paper, we will study the two topics through experimental analysis methods: firstly, we use folksonamy to analyze the preferences association among different domains; secondly, we analyze the feasibility of cross domain rating prediction based on KNN model. The experimental results report the associative tag pairs of users' preferences on items across domains. In addition, we report the cross domain prediction results here.