Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta Continual Learning

Using neural networks in practical settings would benefit from the ability of the networks to learn new tasks throughout their lifetimes without forgetting the previous tasks. This ability is limited in the current deep neural networks by a problem called catastrophic forgetting, where training on new tasks tends to severely degrade performance on previous tasks. One way to lessen the impact of the forgetting problem is to constrain parameters that are important to previous tasks to stay close to the optimal parameters. Recently, multiple competitive approaches for computing the importance of the parameters with respect to the previous tasks have been presented. In this paper, we propose a learning to optimize algorithm for mitigating catastrophic forgetting. Instead of trying to formulate a new constraint function ourselves, we propose to train another neural network to predict parameter update steps that respect the importance of parameters to the previous tasks. In the proposed meta-training scheme, the update predictor is trained to minimize loss on a combination of current and past tasks. We show experimentally that the proposed approach works in the continual learning setting.
Introduction to MAchine Learning & Knowledge Extraction (MAKE)

The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community.
Aggregative information retrieval on social media stream : Tweet summarization

This paper addresses the challenge of tweet stream filtering and summarization, which is an important task for keeping users up to date on topics they care about without overwhelming them with irrelevant and redundant posts. To cut down the noise and shield users from unwanted posts, tweet stream is filtered and a concise summary containing relevant and non-redundant posts is generated. Rather than rely on traditional threshold filter based only on tweet content, we exploit social signals as well as query dependent features to train a binary classifier in attempts to filter out irrelevant tweets with respect to the topic of interest. The core intuition is that the use of machine learning algorithm allows to overcome the issue of threshold setting and to examine how effective is the use of social signals in tweet filtering. Unlike existing approaches that generate a summary by selecting iteratively top weighted tweets, we formulate the summary generation as an optimization problem to select a subset of tweets that maximizes the global summary relevance and fulfills constraints related to non-redundancy, coverage, temporal diversity and summary length. Our experiments were conducted on TREC RTF 2015 and TREC RTS 2016 datasets. The former was used to train the classifier, while the latter was used to evaluate the proposed approach. The obtained results have shown the effectiveness of our approach.
Distributional Advantage Actor-Critic

In traditional reinforcement learning, an agent maximizes the reward collected during its interaction with the environment by approximating the optimal policy through the estimation of value functions. Typically, given a state s and action a, the corresponding value is the expected discounted sum of rewards. The optimal action is then chosen to be the action a with the largest value estimated by value function. However, recent developments have shown both theoretical and experimental evidence of superior performance when value function is replaced with value distribution in context of deep Q learning [1]. In this paper, we develop a new algorithm that combines advantage actor-critic with value distribution estimated by quantile regression. We evaluated this new algorithm, termed Distributional Advantage Actor-Critic (DA2C or QR-A2C) on a variety of tasks, and observed it to achieve at least as good as baseline algorithms, and outperforming baseline in some tasks with smaller variance and increased stability.
The Prevalence of Virulence Genes and Virulotypes of Escherichia coli Strains Isolated from Hospital Wastewaters in Tehran, Iran

Background: Due to the widespread different pathogenic strains, Escherichia coli lead many severe to normal diseases worldwide. Finding the relation of clones with genomic content and clinical features is a key point to recognize the high potential-invasive strains. Specific virulence factors include adhesions, invasions, toxins, and capsule are the main determinants of pathogenic factors of E. coli strains. Methods: From Jun 2014 to Jun 2016, E. coli isolates recovered using standard bacteriological methods from wastewater sources in different hospitals in Tehran, Iran, were monitored to recognize the virulence genes by polymerase chain reaction (PCR) assay. Results: The high and low presences of virulence factors were fimH, 76% and afa, 13%, respectively. Conclusion: The results indicated the potential pathogenicity of E. coli strains circulating in hospital wastewaters in Tehran, Iran.
Galleria mellonella as a model system to study virulence potential of mucormycetes and evaluation of antifungal treatment

Abstract Mucorales can cause cutaneous to deep-seated infections, mainly in the immunocompromised host, resulting in high mortality rates due to late and inefficient treatment. In this study, Galleria mellonella larvae were evaluated as a heterologous invertebrate host to study pathogenicity of clinically relevant mucormycetes (Rhizopus spp., Rhizomucor spp., Lichtheimia spp., Mucor spp.). All tested species were able to infect G. mellonella larvae. Virulence potential was species-specific and correlated to clinical relevance. Survival of infected larvae was dependent on (a) the species (growth speed and spore size), (b) the infection dose, (c) the incubation temperature, (d) oxidative stress tolerance, and (e) iron availability in the growth medium. Moreover, we exploited the G. mellonella system to determine antifungal efficacy of liposomal amphotericin B, posaconazole, isavuconazole, and nystatin-intralipid. Outcome of in vivo treatment was strongly dependent upon the drug applied and the species tested. Nystatin-intralipid exhibited best activity against Mucorales, followed by posaconazole, while limited efficacy was seen for liposomal amphotericin B and isavuconazole. Pharmacokinetic properties of the tested antifungals within this alternative host system partly explain the limited treatment efficacy. In conclusion, G. mellonella represents a useful invertebrate infection model for studying virulence of mucormycetes, while evaluation of treatment response was limited.
Randomized controlled trials ( RCTs ) for the evaluation of the efficacy and safety of adalimumab in the treatment of HS early RCTs

php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Clinical, Cosmetic and Investigational Dermatology 2016:9 367–372 Clinical, Cosmetic and Investigational Dermatology Dovepress
Comparison of Pediatric Cardiopulmonary Resuscitation Quality in Classic Cardiopulmonary Resuscitation and Extracorporeal Cardiopulmonary Resuscitation Events Using Video Review*

Objectives: To assess differences in cardiopulmonary resuscitation quality in classic cardiopulmonary resuscitation versus extracorporeal cardiopulmonary resuscitation events using video recordings of actual pediatric cardiac arrest events. Design: Single-center, prospective, observational trial. Setting: Tertiary-care pediatric teaching hospital, cardiac ICU. Patients: All patients admitted to the pediatric cardiac ICU with cardiopulmonary resuscitation events lasting greater than 2 minutes captured on video. Interventions: None. Measurements and Main Results: Seventeen events comprising 264.5 minutes of cardiopulmonary resuscitation were included: 11 classic cardiopulmonary resuscitation events (87.5 min) and six extracorporeal cardiopulmonary resuscitation events (177 min). Events were divided into 30-second epochs, and cardiopulmonary resuscitation quality markers were assessed using video and telemetry data review of goal endpoints: end-tidal carbon dioxide greater than or equal to 15 mm Hg, diastolic blood pressure greater than or equal to 30 mm Hg, chest compression fraction greater than 80% per epoch, and chest compression rate between 100 and 120 chest compression per minute. Additionally, each chest compression pause (hands-off event) was recorded and timed. When compared with extracorporeal cardiopulmonary resuscitation, classic cardiopulmonary resuscitation epochs were more likely to have end-tidal carbon dioxide greater than or equal to 15 mm Hg (56% vs 6.2%; p = 0.01) and provide chest compression between 100 and 120 times per minute (112 vs 134 chest compression per minute; p < 0.001). No difference was found between classic cardiopulmonary resuscitation and extracorporeal cardiopulmonary resuscitation in compliance with diastolic blood pressure greater than or equal to 30 mm Hg (38% classic cardiopulmonary resuscitation vs 30% extracorporeal cardiopulmonary resuscitation). There were 135 hands-off events: 52 in classic cardiopulmonary resuscitation and 83 in extracorporeal cardiopulmonary resuscitation (p = 0.12). Conclusions: Classic cardiopulmonary resuscitation had superior adherence to end-tidal carbon dioxide goals and chest compression rate guidelines than extracorporeal cardiopulmonary resuscitation.
Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non‐neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular‐level, cellular‐level, and organ‐level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient‐specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.
Delay from symptom onset to treatment start among tuberculosis patients in England, 2012–2015

Abstract Improving access to tuberculosis (TB) care and ensuring early diagnosis are two major aims of the WHO End TB strategy and the Collaborative TB Strategy for England. This study describes risk factors associated with diagnostic delay among TB cases in England. We conducted a retrospective cohort study of TB cases notified to the Enhanced TB Surveillance System in England between 2012 and 2015. Diagnostic delay was defined as more than 4 months between symptom onset and treatment start date. Multivariable logistic regression was used to identify demographic and clinical factors associated with diagnostic delay. Between 2012 and 2015, 22 422 TB cases were notified in England and included in the study. A third (7612) of TB cases had a diagnostic delay of more than 4 months. Being female, aged 45 years and older, residing outside of London and having extra-pulmonary TB disease were significantly associated with a diagnostic delay in the multivariable model (aOR = 1.2, 1.2, 1.2, 1.3, 1.8, respectively). This study identifies demographic and clinical factors associated with diagnostic delay, which will inform targeted interventions to improve access to care and early diagnosis among these groups, with the ultimate aim of helping reduce transmission and improve treatment outcomes for TB cases in England.
INVESTIGATION OF THE ENERGY SAVING POTENTIAL FOR AN OFFICE BUILDING COMPLEX

The building stock in the European Union accounts for over 40% of final energy use, where the usage of non-residential buildings may be up to 40% higher than the residential sector. Improving building energy efficiency across all categories of buildings is one key goal of the European energy policies, made prominent by the Climate and Energy package, Energy Performance of Building Directive (EPBD) and Energy Efficiency Directive (EED). In this study, a simulation model of an office-building complex utilizing district heating was created using transient simulation software TRNSYS. The model was validated using consumption data provided by the facility owner, after which an investigation of the energy saving potential along with the economic viability of adapting a new heatand power supply system was conducted. The system designs were comprised of a geothermal energy system in combination with a PV-system and electricity storage. It was concluded that the systems were sufficient in maintaining an adequate indoor climate. Furthermore, the investments were ascertained as profitable and resulted in a decreased building specific energy demand.
Minimizing Turtle-Shell Matrix Based Stego Image Distortion Using Particle Swarm Optimization

The purpose of data hiding is to embed secret messages into cover media so that the receiver will not be aware of the existence of these important messages. Recently, Chang et al. proposed a novel data hiding scheme based on turtle shells, which provided a good visual quality and an acceptable embedding capacity. However, the matrix that is used in the scheme based on turtle shells is not an optimal matrix. This means that the distortion of the image can be reduced further. In this paper, we propose a method that uses particle swarm optimization (PSO) to further reduce the distortion of the cover image based on turtle shells. Our experimental results conﬁrmed that our scheme was eﬃcient in ﬁnding an optimal replacement table and achieved a better visual quality of the cover image. Also, our scheme enhanced the security of scheme based on the turtle-shell matrix.
A Classification System for Assessment and Home Monitoring of Tremor in Patients with Parkinson's Disease

Background: Tremor is one of the most common symptoms of Parkinson's disease (PD), which is widely being used in the diagnosis procedure. Accurate estimation of PD tremor based on Unified PD Rating Scale (UPDRS) provides aid for physicians in prescription and home monitoring. This article presents a robust design of a classification system to estimate PD patient's hand tremors and the results of the proposed system as compared to the UPDRS. Methods: A smartphone accelerometer sensor is used for accurate and noninvasive data acquisition. We applied short-time Fourier transform to time series data of 52 PD patients. Features were extracted based on the severity of PD patients' hand tremor. The wrapper method was employed to determine the most discriminative subset of the extracted features. Four different classifiers were implemented for achieving best possible accuracy in the estimation of PD hand tremor based on UPDRS. Of the four tested classifiers, the Naive Bayesian approach proved to be the most accurate one. Results: The classification result for the assessment of PD tremor achieved close to 100% accuracy by selecting an optimum combination of extracted features of the acceleration signal acquired. For home health-care monitoring, the proposed algorithm was also implemented on a cost-effective embedded system equipped with a microcontroller, and the implemented classification algorithm achieved 93.8% average accuracy. Conclusions: The accuracy result of both implemented systems on MATLAB and microcontroller is acceptable in comparison with the previous works.
Anterior Thalamic Excitation and Feedforward Inhibition of Presubicular Neurons Projecting to Medial Entorhinal Cortex

The presubiculum contains head direction cells that are crucial for spatial orientation. Here, we examined the connectivity and strengths of thalamic inputs to presubicular layer 3 neurons projecting to the medial entorhinal cortex in the mouse. We recorded pairs of projection neurons and interneurons while optogenetically stimulating afferent fibers from the anterior thalamic nuclei. Thalamic input differentially affects presubicular neurons: layer 3 pyramidal neurons and fast-spiking parvalbumin-expressing interneurons are directly and monosynaptically activated, with depressing dynamics, whereas somatostatin-expressing interneurons are indirectly excited, during repetitive anterior thalamic nuclei activity. This arrangement ensures that the thalamic excitation of layer 3 cells is often followed by disynaptic inhibition. Feedforward inhibition is largely mediated by parvalbumin interneurons, which have a high probability of connection to presubicular pyramidal cells, and it may enforce temporally precise head direction tuning during head turns. Our data point to the potential contribution of presubicular microcircuits for fine-tuning thalamic head direction signals transmitted to medial entorhinal cortex. SIGNIFICANCE STATEMENT How microcircuits participate in shaping neural inputs is crucial to understanding information processing in the brain. Here, we show how the presubiculum may process thalamic head directional information before transmitting it to the medial entorhinal cortex. Synaptic inputs from the anterior thalamic nuclei excite layer 3 pyramidal cells and parvalbumin interneurons, which mediate disynaptic feedforward inhibition. Somatostatin interneurons are excited indirectly. Presubicular circuits may switch between two regimens depending on the angular velocity of head movements. During immobility, somatostatin-pyramidal cell interactions could support maintained head directional firing with attractor-like dynamics. During rapid head turns, in contrast, parvalbumin-mediated feedforward inhibition may act to tune the head direction signal transmitted to medial entorhinal cortex.
Maternal folic acid supplementation and antibody persistence 5 years after hepatitis B vaccination among infants

ABSTRACT Background: Maternal exposure to dietary factors during pregnancy may modulate the immunity of offspring by epigenetic programming. But the relationship between intrauterine environment and persistence of protective antibody after hepatitis B vaccination has not been reported. This study was to investigate the 5-year persistence of protective antibody response after primary hepatitis B vaccination, and its relationship with maternal folic acid supplementation. Materials and Methods: A total of 1461 children who completed a 3-dose 10 μg recombinant hepatitis B vaccine at birth and did not infect hepatitis B virus were followed up. Logistic regression and mediation analysis was used to explore the relationship between 5-year persistence of protective antibody and maternal nutrition. Results: Of 1403 children who did not revaccinated during the follow-up, 76.1% had protective hepatitis B surface antibody (anti-HBs) levels. Twenty percent of mothers did not take folate during pregnancy. Mediation analysis showed a total effect of folic acid supplementation on good persistence (odds ratio: 1.10, 95% CI: 1.03-1.17, p = 0.0010), a direct effect was 1.07 (95% CI: 1.01-1.13, p = 0.0128) and an indirect effect was 1.03 (95% CI: 1.00-1.06, p = 0.0672); the proportion of good persistence mediated by primary response was 30.3%. Conclusion: This study indicated a good protective anti-HBs persistence at year 5 after 10 μg recombination hepatitis B vaccination in infants. Maternal folic acid supplementation may improve the persistence of protective antibodies through other pathways. Multi-center cohort studies should be conducted to verify this conclusion.
Identification of CD24 as a marker for tumorigenesis of melanoma

Objective Cutaneous melanoma (CM) is a common skin cancer. Surgery is still the primary treatment for CM, as melanoma is resistant to chemotherapy. In the recent years, it has been found that cancer stem-like cells (CSCs) are responsible for this drug resistance. CD24 is a widely used marker to isolate CSCs. In this study, we aimed to analyze the properties of CD24+ and CD24− subpopulation of melanoma cells. Materials and methods We isolated CD24+ cells CSCs using magnetic-activated cell sorting system. We extracted total RNA and carried out reverse transcription polymerase chain reaction analysis. We counted the cell colonies using soft agar assay and assessed the cell invasion using cell migration assay. We implanted CD24+ or CD24− cells into the flank of non-obese diabetic severe combined immunodeficiency mice, and measured the tumor volumes every 5 days until the end of the experiment. We carried out immunohistochemical analysis to study the tissue sections. Results We demonstrated that the CD24+ subpopulation has self-renewal properties in vitro and in vivo by using soft agar assay and xenograft tumor model. Furthermore, we confirmed that CD24 expression is accompanied by activation of Notch1 signaling pathway. Conclusion This study provides new knowledge on the role of CD24 in the tumorigenic ability of melanoma.