Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment Modalities for Acne

Acne is a common inflammatory skin disease which affects the pilosebaceous units of the skin. It can have severe psychological effects and can leave the patient with severe skin scarring. There are four well-recognized pathological factors responsible for acne which is also the target for acne therapy. In this review, different treatment options are discussed, including topical (i.e., retinoids, and antibiotics) and systemic (i.e., retinoids, antibiotics, and hormonal) treatments. Since the general public has been showing an increasing interest in more natural and generally safer treatment options, the use of complementary and alternative medicines (CAM) for treating acne was also discussed. The use of physical therapies such as comedone extraction, cryoslush therapy, cryotherapy, electrocauterization, intralesional corticosteroids and optical treatments are also mentioned. Acne has been extensively researched with regards to the disease mechanism as well as treatment options. However, due to the increasing resistance of Propionibacterium acnes towards the available antibiotics, there is a need for new treatment methods. Additionally, the lack of necessary evidence on the efficacy of CAM therapies makes it necessary for researchers to investigate these treatment options further.
Biomarkers for cervical cancer screening: the role of p16INK4a to highlight transforming HPV infections

Biomarkers indicating the initiation of neoplastic transformation processes in human papillomavirus (HPV)-infected epithelial cells are moving into the focus of cancer prevention research, particularly for anogenital cancer, including cancer of the uterine cervix. Based on the in-depth understanding of the molecular events leading to neoplastic transformation of HPV-infected human cells, the cyclin-dependent kinase inhibitor p16INK4a turned out to be substantially overexpressed in virtually all HPV-transformed cells. This finding opened novel avenues in diagnostic histopathology to substantially improve the diagnostic accuracy of cervical cancer and its precursor lesions. Furthermore, it provides a novel technical platform to substantially improve the accuracy of cytology-based cancer early-detection programs. Here, we review the molecular background and the current evidence for the clinical utility of the p16INK4a biomarker for HPV-related cancers, and cervical cancer prevention in particular.
STATISTICAL AND STRUCTURAL DESCRIPTIONS FOR IMAGE TO MAP REGISTRATION

The registration of imagery to maps is becoming increasingly important for a large number of applications. Today, this task still relies mostly on an operator, manually identifying corresponding points from the image and the GIS dataset for registration. The challenges for automating the process arise mainly from their dissimilar data structures (raster vs. vector), orientation variations, as well as occlusionintroduced extraction errors due to shadows, buildings or objects on the roads. In this paper, we present a robust automated approach that models road networks extracted from the two datasets as graphs, using statistical and structural descriptions of these road networks. The proposed approach starts by statistically analyzing local geometrical and topological properties of road networks such as orientation and number of connections. Such statistical similarity measures can be used to thin the number of potential matches when comparing a target road structure to a spatial database, resulting in computational efficiency. Subsequently, by considering the spatial distribution and structure similarity in a neighbourhood, we formulate a global compatibility function to measure the overall goodness of correspondence. We achieve an optimal matching by finding an optimal morphism that maximizes this compatibility function. The experimental results demonstrate the robustness of our approach.
Quantification of HLA-DM-Dependent Major Histocompatibility Complex of Class II Immunopeptidomes by the Peptide Landscape Antigenic Epitope Alignment Utility

The major histocompatibility complex of class II (MHCII) immunopeptidome represents the repertoire of antigenic peptides with the potential to activate CD4+ T cells. An understanding of how the relative abundance of specific antigenic epitopes affects the outcome of T cell responses is an important aspect of adaptive immunity and offers a venue to more rationally tailor T cell activation in the context of disease. Recent advances in mass spectrometric instrumentation, computational power, labeling strategies, and software analysis have enabled an increasing number of stratified studies on HLA ligandomes, in the context of both basic and translational research. A key challenge in the case of MHCII immunopeptidomes, often determined for different samples at distinct conditions, is to derive quantitative information on consensus epitopes from antigenic peptides of variable lengths. Here, we present the design and benchmarking of a new algorithm [peptide landscape antigenic epitope alignment utility (PLAtEAU)] allowing the identification and label-free quantification (LFQ) of shared consensus epitopes arising from series of nested peptides. The algorithm simplifies the complexity of the dataset while allowing the identification of nested peptides within relatively short segments of protein sequences. Moreover, we apply this algorithm to the comparison of the ligandomes of cell lines with two different expression levels of the peptide-exchange catalyst HLA-DM. Direct comparison of LFQ intensities determined at the peptide level is inconclusive, as most of the peptides are not significantly enriched due to poor sampling. Applying the PLAtEAU algorithm for grouping of the peptides into consensus epitopes shows that more than half of the total number of epitopes is preferentially and significantly enriched for each condition. This simplification and deconvolution of the complex and ambiguous peptide-level dataset highlights the value of the PLAtEAU algorithm in facilitating robust and accessible quantitative analysis of immunopeptidomes across cellular contexts. In silico analysis of the peptides enriched for each HLA-DM expression conditions suggests a higher affinity of the pool of peptides isolated from the high DM expression samples. Interestingly, our analysis reveals that while for certain autoimmune-relevant epitopes their presentation increases upon DM expression others are clearly edited out from the peptidome.
The University of Hong Kong Final Year Project Final Report Stock AR Augmented Reality Stocktaking System with RGB-D based Object Counting

Stocktaking plays a very important role in business decision making. However, even in this digital age, some works are still done manually and require many extra tools. With the new technologies like Augmented Reality (AR) and depth sensing, computers and even smartphones can locate and recognize items easily. Therefore, this report proposes a new stocktaking system that minimize the amount of human involvement as well as the number of tools that they need by using the latest technologies. The new system can recognize product ID on a price tag and count the items automatically which can improve the stocktaking speed and hence shorten the store downtime. In addition, the new system also uses an AR interface in order to immediately provide workers with information, such as their current progress, that are not provided by the traditional solution. As a result, the workers do not have to carry other tools to assist them. This report reviews existing AR platforms and algorithms by evaluating their strengths, weaknesses and usability in this project. It also describes the detail implementation of the new system, from the final choice of platform to the improved design of the counting algorithm. Besides, this report also details the current status of this project, such as the implementation of the algorithm as well as some test results using the improved algorithm. Moreover, it also describes the problems encountered during this project, and the future plan.
Spontaneous facial expressions of happy bowlers and soccer fans

Kraut and Johnston (1979) found surprisingly few smiles in large samples of bowlers and hockey fans during happy events--unless they were simultaneously engaged in social interaction. A limitation of their studies is that there was no direct test of subjects' actual emotional experience at the moments in which they were observed. This article reports two field studies in which emotions were reported by bowlers and by soccer fans. Analysing facial behaviour of those who reported happiness, we found a low probability of smiling in the absence of social interaction (.09 for bowlers and .07 for soccer fans) and a high probability of smiling during social interaction (.78 and .70). These findings question the common assumption that smiles are an indicator of happiness per se, and support the alternative hypothesis of a more complex and indirect relationship between smiling and happiness.
Improving Breast Cancer Preventive Behavior among Female Medical Staff: The Use of Educational Intervention based on Health Belief Model.

BACKGROUND Breast cancer is one of the most common types of cancer prevalent among women worldwide. Preventive behaviors such as early diagnosis through screening tests play an important role in prevention and control of the disease. This study aimed to determine the effects of educational intervention using a health belief model on breast cancer preventive behaviors. METHODS This interventional study was conducted on 130 female employees of Zahedan University of Medical Sciences who were randomly divided into intervention and control groups. A questionnaire, made and validated by the researcher, was completed before and one month after training by the study subjects. Data were analysed using regression analysis, independent sample T-test, chi-square and Pearson's correlation coefficient using the SPSS software 18. RESULTS There were significant changes in the training group, following educational intervention in the awareness construct and in some constructs of the model including perceived susceptibility, perceived benefits, and perceived barriers, as well as in practice compared to the control group (P < 0.05). Nevertheless, perceived barriers remained as the only predictor in the model, such that for every unit increase in this variable, the behavior score increased by 18%. CONCLUSION The use of educational intervention based on Health Belief Model had positive effect on knowledge of breast cancer preventive behaviors among participants.
The Impact of Mispronunciations on Toddler Word Recognition: Evidence for Cascaded Activation of Semantically Related Words from Mispronunciations of Familiar Words

While the specificity of infants' early lexical representations has been studied extensively, researchers have only recently begun to investigate how words are organized in the developing lexicon and what mental representations are activated during processing of a word. Integrating these two lines of research, the current study asks how specific the phonological match between a perceived word and its stored form has to be in order to lead to (cascaded) lexical activation of related words during infant lexical processing. We presented German 24-month-olds with a cross-modal semantic priming task where the prime word was either correctly or incorrectly pronounced. Results indicate that correct pronunciations and mispronunciations both elicit similar semantic priming effects, suggesting that the infant word recognition system is flexible enough to handle deviations from the correct form. This might be an important prerequisite to children's ability to cope with imperfect input and to recognize words under more challenging circumstances.
Lifespan analysis of brain development, gene expression and behavioral phenotypes in the Ts1Cje, Ts65Dn and Dp(16)1/Yey mouse models of Down syndrome

ABSTRACT Down syndrome (DS) results from triplication of human chromosome 21. Neuropathological hallmarks of DS include atypical central nervous system development that manifests prenatally and extends throughout life. As a result, individuals with DS exhibit cognitive and motor deficits, and have delays in achieving developmental milestones. To determine whether different mouse models of DS recapitulate the human prenatal and postnatal phenotypes, here, we directly compared brain histogenesis, gene expression and behavior over the lifespan of three cytogenetically distinct mouse models of DS: Ts1Cje, Ts65Dn and Dp(16)1/Yey. Histological data indicated that Ts65Dn mice were the most consistently affected with respect to somatic growth, neurogenesis and brain morphogenesis. Embryonic and adult gene expression results showed that Ts1Cje and Ts65Dn brains had considerably more differentially expressed (DEX) genes compared with Dp(16)1/Yey mice, despite the larger number of triplicated genes in the latter model. In addition, DEX genes showed little overlap in identity and chromosomal distribution in the three models, leading to dissimilarities in affected functional pathways. Perinatal and adult behavioral testing also highlighted differences among the models in their abilities to achieve various developmental milestones and perform hippocampal- and motor-based tasks. Interestingly, Dp(16)1/Yey mice showed no abnormalities in prenatal brain phenotypes, yet they manifested behavioral deficits starting at postnatal day 15 that continued through adulthood. In contrast, Ts1Cje mice showed mildly abnormal embryonic brain phenotypes, but only select behavioral deficits as neonates and adults. Altogether, our data showed widespread and unexpected fundamental differences in behavioral, gene expression and brain development phenotypes between these three mouse models. Our findings illustrate unique limitations of each model when studying aspects of brain development and function in DS. This work helps to inform model selection in future studies investigating how observed neurodevelopmental abnormalities arise, how they contribute to cognitive impairment, and when testing therapeutic molecules to ameliorate the intellectual disability associated with DS. This article has an associated First Person interview with the first author of the paper. Editor's choice: Comparison of corticogenesis, gene expression and behavior in three mouse models of Down syndrome revealed major differences between the models as well as significant limitations in each strain for understanding neurobiological changes in the human phenotype.
Social Network Fusion and Mining: A Survey

Looking from a global perspective, the landscape of online social networks is highly fragmented. A large number of online social networks have appeared, which can provide users with various types of services. Generally, the information available in these online social networks is of diverse categories, which can be represented as heterogeneous social networks (HSN) formally. Meanwhile, in such an age of online social media, users usually participate in multiple online social networks simultaneously to enjoy more social networks services, who can act as bridges connecting different networks together. So multiple HSNs not only represent information in single network, but also fuse information from multiple networks. Formally, the online social networks sharing common users are named as the aligned social networks, and these shared users who act like anchors aligning the networks are called the anchor users. The heterogeneous information generated by users' social activities in the multiple aligned social networks provides social network practitioners and researchers with the opportunities to study individual user's social behaviors across multiple social platforms simultaneously. This paper presents a comprehensive survey about the latest research works on multiple aligned HSNs studies based on the broad learning setting, which covers 5 major research tasks, i.e., network alignment, link prediction, community detection, information diffusion and network embedding respectively.
CHALLENGES TO ENERGY EFFICIENT VIRTUAL MACHINE ALLOCATION IN CLOUDS: A REVIEW

Virtual Machine (VM) Consolidation contributed a lot in energy efficient computing in the cloud data centers. Virtualization played a great role in virtual machine consolidation by facilitating the live VMs to be migrated resulting in packing the maximum number of VMs in minimum possible physical hosts . Energy efficient computing is not only good for reducing the overall operational cost of the data centers but also provides environmental benefits. Various algorithms have been proposed for VM consolidation which utilimately leads to energy efficient computing. But only high VM consolidation ratio is not a key to energy efficient data centers. A tradeoff between the consolidation ratio and the QoS (Quality of Service) is required preventing the SLA (Service Level Agreement) violation. In this paper we have presented various challenges to energy efficient VM allocation in clouds.
Emergency Management and Tourism Stakeholder Responses to Crises

This article examines the contested area of the responsibility for destinations and tourists, within emergency settings. It incorporates a Delphi-Scenario technique to facilitate a structured discussion of emergency management for different destination stakeholders. The Delphi exercise engaged 123 senior international stakeholders, from 9 different industry sectors, across 34 countries to provide a global perspective. The study’s principal focus is on the notion of emergency management, to identify the challenges that stakeholders would face within a disaster scenario. The exercise asked stakeholders to identify with whom the responsibility rests for 18 distinct disaster-related activities. The study proposes a responsibility allocation building-block framework that could help speed up the emergency management responses by “knowing who is going to do what” with a particular focus on dealing with international tourists as a community in a disaster zone.
Learning for Video Compression

One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper, we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis and binarization. The experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding.
Achieving high value care for all and the perverse incentives of 340B price agreements

Section 340B of the Public Health Service Act requires drug manufacturers to enter into price agreements with the Department of Health and Human Services. These agreements result in variation in the price paid to acquire a drug by sector, which complicates the price used in cost-effectiveness analyses. We describe the transactions and sectors in a 340B agreement using a multiple sclerosis drug. Cost-effectiveness estimates were calculated for the drug using drug prices from the manufacturer and payer perspective. We found the amount paid to the manufacturer (340B price) was a good value ($118,256 per quality-adjusted life-year); however, from the payer drug cost perspective, good value ($196,683 per quality-adjusted life-year) was not achieved. Given that emerging value frameworks incorporate cost-effectiveness, these price variations may have downstream negative consequences, including inaccurate coverage and reimbursement policy recommendations. Upcoming policy changes to the 340B program should incentivize pricing schemes hinged on transparency and value.
A simulation approach for power calculation in large cohort studies based on multistate models

Realistic power calculations for large cohort studies and nested case control studies are essential for successfully answering important and complex research questions in epidemiology and clinical medicine. For this, we provide a methodical framework for general realistic power calculations via simulations that we put into practice by means of an R‐based template. We consider staggered recruitment and individual hazard rates, competing risks, interaction effects, and the misclassification of covariates. The study cohort is assembled with respect to given age‐, gender‐, and community distributions. Nested case‐control analyses with a varying number of controls enable comparisons of power with a full cohort analysis. Time‐to‐event generation under competing risks, including delayed study‐entry times, is realized on the basis of a six‐state Markov model. Incidence rates, prevalence of risk factors and prefixed hazard ratios allow for the assignment of age‐dependent transition rates given in the form of Cox models. These provide the basis for a central simulation‐algorithm, which is used for the generation of sample paths of the underlying time‐inhomogeneous Markov processes. With the inclusion of frailty terms into the Cox models the Markov property is specifically biased. An “individual Markov process given frailty” creates some unobserved heterogeneity between individuals. Different left‐truncation‐ and right‐censoring patterns call for the use of Cox models for data analysis. p‐values are recorded over repeated simulation runs to allow for the desired power calculations. For illustration, we consider scenarios with a “testing” character as well as realistic scenarios. This enables the validation of a correct implementation of theoretical concepts and concrete sample size recommendations against an actual epidemiological background, here given with possible substudy designs within the German National Cohort.
Event Forecasting with Pattern Markov Chains

We present a system for online probabilistic event forecasting. We assume that a user is interested in detecting and forecasting event patterns, given in the form of regular expressions. Our system can consume streams of events and forecast when the pattern is expected to be fully matched. As more events are consumed, the system revises its forecasts to reflect possible changes in the state of the pattern. The framework of Pattern Markov Chains is used in order to learn a probabilistic model for the pattern, with which forecasts with guaranteed precision may be produced, in the form of intervals within which a full match is expected. Experimental results from real-world datasets are shown and the quality of the produced forecasts is explored, using both precision scores and two other metrics: spread, which refers to the "focusing resolution" of a forecast (interval length), and distance, which captures how early a forecast is reported.
Shade: Information-Based Regularization for Deep Learning

Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The originality of the approach is to define a prior based on conditional entropy, which explicitly decouples the learning of invariant representations in the regularizer and the learning of correlations between inputs and labels in the data fitting term. Our second contribution is to derive a stochastic version of the regularizer compatible with deep learning, resulting in a tractable training scheme. We empirically validate the efficiency of our approach to improve classification performances compared to standard regularization schemes on several standard architectures.