Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetics of antifungal activity of home-generated ozonated water on Candida albicans

Background and Purpose: Candida-associated denture stomatitis is one of the most common forms of oral candidiasis among denture wearers. Regarding this, the aim of the present study was to evaluate the antifungal effects of home-generated ozonated water on the adhesion of the C. albicans attached to the surface of the denture base acrylic resins. Materials and Methods: For the purpose of the study, different concentrations of C. albicans were added to the tubes containing acrylic resin blocks, and then incubated for 2 h at 35°C. The samples were assigned into three groups, each of which contained 42 samples, including normal saline (NS) solution as the negative control, nystatin (N) solution as the positive control, and ozonated water as the test group. The samples were washed and placed in an ultrasonic bath. Subsequently, the saline solution was cultured on Sabouraud dextrose agar. The concentrations of Candida were evaluated during the contact times. Results: The test group (i.e., ozonated water) with 114 colony-forming units (CFU) showed a significant reduction of Candida colonies, compared to the NS group with 2,172 CFU. The 120- and 1-minute incubation with ozonated water showed the highest and lowest effects on the viability of Candida adhered to the acrylic resin, respectively. Conclusion: Based on the findings, home-generated ozonated water can be applied to remove the Candida attached to the surface of the denture plates.
Dexamethasone for unresponsive diabetic macular oedema: optical coherence tomography biomarkers

To analyse the effects of intravitreal dexamethasone implant (DEX) in patients with diabetic macular oedema (DME) unresponsive to ranibizumab treatment, in relation to the inflammatory optical coherence tomography (OCT) retinal features, subfoveal neuroretinal detachment (SND) and hyperreflective retinal spots (HRS).
Mangrove Ecosystem Service Values and Methodological Approaches to Valuation: Where Do We Stand?

Mangroves, seagrass meadows, and salt marshes, collectively termed "Blue Forests," are counted among the most valuable and productive coastal ecosystems on the planet. A recent literature review of the Blue Forest valuation research identified mangroves as the most frequently analyzed of these ecosystems, yet the literature demonstrates several deficits in terms of geographic location of studies, methods used to value the services, and most notably, a lack of valuation for cultural services. To better understand this, we analyzed the studies dealing specifically with mangroves from the original literature review to quantify what has been valued, where, by which methods, and the variation in the published values. We then use this information to synthesize our current level of knowledge on the type and value of services provided by mangroves, discuss data gaps, and address specifically the collection of data relevant to cultural ecosystem services (CES). Our results shed light on two principle issues affecting the mangrove valuation literature: overuse of benefit transfer in valuing mangrove ecosystem services and a lack of attention paid to the CES that mangroves provide. The mangrove valuation literature is not yet robust, lacking estimates of many ecosystem services, including CES, such as spiritual and aesthetic value. Most published studies focus on a small selection of ecosystem services based on the availability of benefit transfer values and the ability to easily measure values with market prices. Thus, many ecosystem services that cannot be valued monetarily, but that are often equally important to local communities, are ignored. Given the wide range of ecosystem services mangroves provide and the variety of valuation methods that need to be collectively employed, we argue that doing valuation studies well requires a multi-disciplinary approach, bringing together anthropologists, social scientists, ecologists and economists. Thoughtfully and thoroughly including the local stakeholders in valuation studies and the resultant policy discussions leads to a more holistic understanding of the services mangroves provide, and viable solutions with an increase in local willingness to act in accordance with those solutions.
Septic Cavernous Sinus Thrombosis Associated With Orbital Cellulitis: A Report of 6 Cases and Review of Literature.

PURPOSE To describe risk factors, clinical parameters, treatment, and prognosis for patients with septic cavernous sinus thrombosis presenting with orbital cellulitis. METHODS Retrospective case series of 6 patients identified with septic cavernous sinus thrombosis and orbital cellulitis confirmed by magnetic resonance imaging at a tertiary care center from January 1980 to December 2016. Medical records were reviewed for demographics, risk factors, symptoms, etiology, radiographic diagnosis, complications, treatments, and outcomes. In addition, a literature review was performed from 2005 to 2018, and 119 cases of septic cavernous sinus thrombosis confirmed by imaging were included for aggregate comparison. This study adheres to the tenets of the Declaration of Helsinki, and institutional review board approval was obtained. RESULTS All 6 cases presented with headache, fever, ocular motility deficit, periorbital edema, and proptosis. The primary source of infection included sinusitis (n = 4) and bacteremia (n = 2). Identified microorganisms included methicillin resistant Staphylococcus aureus (n = 3) and Streptococcus anginosus (n = 1). All cases were treated with broad-spectrum intravenous antibiotics and anticoagulation, and one case underwent endoscopic sinus surgery. The mean time between initial presentation to diagnosis of cavernous sinus thrombosis was 2.8 days, and the average length of hospital admission was 21 days. The mortality rate was 0%, but 4 cases were discharged with neurological deficits including vision loss (n = 1) and ocular motility disturbance (n = 3). Literature review produced an additional 119 cases. CONCLUSIONS Early diagnostic imaging with contrast-enhanced CT or MRI should be initiated in patients with risk factors and ocular symptoms concerning for cavernous sinus thrombosis. Treatment entails early administration of broad-spectrum intravenous antibiotics, anticoagulation, and surgical drainage when applicable.
Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment Systems

In a robotic mobile fulfillment system, robots bring shelves, called pods, with storage items from the storage area to pick stations. At every pick station there is a person -- the picker -- who takes parts from the pod and packs them into boxes according to orders. Usually there are multiple shelves at the pick station. In this case, they build a queue with the picker at its head. When the picker does not need the pod any more, a robot transports the pod back to the storage area. At that time, we need to answer a question: "Where is the optimal place in the inventory to put this pod back? ". It is a tough question, because there are many uncertainties to consider before answering it. Moreover, each decision made to answer the question influences the subsequent ones. The goal of this paper is to answer the question properly. We call this problem the Pod Repositioning Problem and formulate a deterministic model. This model is tested with different algorithms, including binary integer programming, cheapest place, fixed place, random place, genetic algorithms, and a novel algorithm called tetris.
Variational Bayesian Monte Carlo

Many probabilistic models of interest in scientific computing and machine learning have expensive, black-box likelihoods that prevent the application of standard techniques for Bayesian inference, such as MCMC, which would require access to the gradient or a large number of likelihood evaluations. We introduce here a novel sample-efficient inference framework, Variational Bayesian Monte Carlo (VBMC). VBMC combines variational inference with Gaussian-process based, active-sampling Bayesian quadrature, using the latter to efficiently approximate the intractable integral in the variational objective. Our method produces both a nonparametric approximation of the posterior distribution and an approximate lower bound of the model evidence, useful for model selection. We demonstrate VBMC both on several synthetic likelihoods and on a neuronal model with data from real neurons. Across all tested problems and dimensions (up to D = 10), VBMC performs consistently well in reconstructing the posterior and the model evidence with a limited budget of likelihood evaluations, unlike other methods that work only in very low dimensions. Our framework shows great promise as a novel tool for posterior and model inference with expensive, black-box likelihoods.
Development of a hindi named entity recognition system without using manually annotated training corpus

: Machine learning based approach for Named Entity Recognition (NER) requires sufficient annotated corpus to train the classifier. Other NER resources like gazetteers are also required to make the classifier more accurate. But in many languages and domains relevant NER resources are still not available. Creation of adequate and relevant resources is costly and time consuming. However a large amount of resources and several NER systems are available in resource-rich languages, like English. Suitable language adaptation techniques, NER resources of a resource-rich language and minimally supervised learning might help to overcome such scenarios. In this paper we have studied a few such techniques in order to develop a Hindi NER system. Without using any Hindi NE annotated corpus we have achieved a reasonable accuracy of F-Measure 73.87 in the developed system.
Mode Normalization

Normalization methods are a central building block in the deep learning toolbox. They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules. When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced. As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features. We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets.
Prioritization of Therapeutic Targets and Trial Design in Cirrhotic Portal Hypertension

Portal hypertension (PH) is the main driver of cirrhosis decompensation, the main determinant of death in patients with cirrhosis. PH results initially from increased intrahepatic vascular resistance. Subsequently, increased inflow from splanchnic vasodilation and increased cardiac output lead to a further increase in portal pressure (PP). Reducing PP in cirrhosis results in better outcomes. Removing the cause of cirrhosis might improve PP. However, this is a slow process and patients may continue to be at risk of decompensation. Additionally, for some chronic liver diseases, such as nonalcoholic fatty liver disease (NAFLD), etiological treatments are not yet available. Therefore, there is a need to develop better therapies specifically aimed at reducing PP. For over 35 years, the mainstay of such therapy has been the use of nonselective beta‐blockers (NSBBs) that act by reducing portal venous inflow. Recently, many drugs (mainly targeting intrahepatic mechanisms) have shown promise in preclinical and early clinical studies and may act alone or synergistically with NSBBs in reducing PP in cirrhosis. The objective of this position paper is to propose a novel framework for the design of clinical trials (phase 1, 2, and 3) in patients with cirrhosis and PH and to prioritize targets and pharmacological therapies in this setting. We have focused the discussion on patients with compensated cirrhosis. The paper summarizes discussions held at The American Association for the Study of Liver Diseases (AASLD) Industry Colloquium in January 2018, with the participation of clinical and translational investigators, regulatory professionals, and industry partners.
Testing Trait-State Isomorphism in a New Domain: An Exploratory Manipulation of Openness to Experience

The trait-state isomorphism hypothesis holds that personality traits and states (i.e., trait-related behavior) are characterized by similar outcomes (Fleeson, 2001). Openness is associated with creative thinking, personal growth, and positive affect. Engaging in behavior associated with openness has also been found to covary with feelings of authenticity. In the present experiment, participants (N = 210) completed a pre-test assessment, five daily exercises designed to either be inert (control condition) or engage the behaviors and cognitions associated with openness (experimental condition), a post-test assessment, and a 2 week follow up assessment. Results supported the isomorphism hypothesis for positive affect but not creative thinking ability or personal growth. Furthermore, open behavior was only associated with authenticity for individuals high on trait openness.
Localizing the Source of an Epidemic Using Few Observations

Localizing the source of an epidemic is a crucial task in many contexts, including the detection of malicious users in social networks and the identification of “patient zeros” of disease outbreaks. The difficulty of this task lies in the strict limitations on the data available: In most cases, when an epidemic spreads, only few individuals, who we will call sensors, provide information about their state. Furthermore, as the spread of an epidemic usually depends on a large number of variables, accounting for all the possible spreading patterns that could explain the available data can easily result in prohibitive computational costs. Therefore, in the field of source localization, there are two central research directions: The design of practical and reliable algorithms for localizing the source despite the limited data, and the optimization of data collection, i.e., the identification of the most informative sensors. In this dissertation we contribute to both these directions. We consider network epidemics starting from an unknown source. The only information available is provided by a set of sensor nodes that reveal if and when they become infected. We study how many sensors are needed to guarantee the identification of the source. A set of sensors that guarantees the identification of the source is called a double resolving set (DRS); the minimum size of a DRS is called the double metric dimension (DMD). Computing the DMD is, in general, hard, hence estimating it with bounds is desirable. We focus on G(N, p) random networks for which we derive tight bounds for the DMD. We show that the DMD is a non-monotonic function of the parameter p, hence there are critical parameter ranges in which source localization is particularly difficult. Again building on the relationship between source localization and DRSs, we move to optimizing the choice of a fixed number K of sensors. First, we look at the case of trees where the uniqueness of paths makes the problem simpler. For this case, we design polynomial time algorithms for selecting K sensors that optimize certain metrics of interest. Next, turning to general networks, we show that the optimal sensor set depends on the distribution of the time it takes for an infected node u to infect a non-infected neighbor v, which we call the transmission delay from u to v. We consider both a lowand a high-variance regime for the transmission delays. We design algorithms for sensor placement in both cases, and we show that they yield an improvement of up to 50% over state-of-the-art methods.
EA15, MIR22, LINC00472 as diagnostic markers for diabetic kidney disease

This study aimed to investigate the molecular mechanisms of diabetic kidney disease (DKD) and to explore new potential therapeutic strategies and biomarkers for DKD. First we analyzed the differentially expressed changes between patients with DKD and the control group using the chip data in Gene Expression Omnibus (GEO) database. Then the gene chip was subjected to be annotated again, so as to screen long noncoding RNAs (lncRNAs) and study expression differences of these lncRNAs in DKD and controlled samples. At last, the function of the differential lncRNAs was analyzed. A total of 252 lncRNAs were identified, and 14 were differentially expressed. In addition, there were 1,629 differentially expressed messenger RNAs (mRNAs) genes, and proliferation and apoptosis adapter protein 15 (PEA15), MIR22, and long intergenic nonprotein coding RNA 472 ( LINC00472) were significantly differentially expressed in DKD samples. Through functional analysis of the encoding genes coexpressed by the three lncRNAs, we found these genes were mainly enriched in type 1 diabetes and autoimmune thyroid disease pathways, whereas in Gene Ontology (GO) function classification, they were also mainly enriched in the immune response, type I interferon signaling pathways, interferon‐γ mediated signaling pathways, and so forth. To summary, we identified EA15, MIR22, and LINC00472 may serve as the potential diagnostic markers of DKD.
MDGAN: Boosting Anomaly Detection Using Multi-Discriminator Generative Adversarial Networks

Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data. In recent years, autoencoders have been shown to be effective anomaly detectors that train only on "normal" data. Generative adversarial networks (GANs) have been used to generate additional training samples for classifiers, thus making them more accurate and robust. However, in anomaly detection GANs are only used to reconstruct existing samples rather than to generate additional ones. This stems both from the small amount and lack of diversity of anomalous data in most domains. In this study we propose MDGAN, a novel GAN architecture for improving anomaly detection through the generation of additional samples. Our approach uses two discriminators: a dense network for determining whether the generated samples are of sufficient quality (i.e., valid) and an autoencoder that serves as an anomaly detector. MDGAN enables us to reconcile two conflicting goals: 1) generate high-quality samples that can fool the first discriminator, and 2) generate samples that can eventually be effectively reconstructed by the second discriminator, thus improving its performance. Empirical evaluation on a diverse set of datasets demonstrates the merits of our approach.
Montage: A Video Prototyping System to Reduce Re-Shooting and Increase Re-Usability

Video prototypes help capture and communicate interaction with paper prototypes in the early stages of design. However, designers sometimes find it tedious to create stop-motion videos for continuous interactions and to re-shoot clips as the design evolves. We introduce Montage, a proof-of-concept implementation of a computer-assisted process for video prototyping. Montage lets designers progressively augment video prototypes with digital sketches, facilitating the creation, reuse and exploration of dynamic interactions. Montage uses chroma keying to decouple the prototyped interface from its context of use, letting designers reuse or change them independently. We describe how Montage enhances video prototyping by combining video with digital animated sketches, encourages the exploration of different contexts of use, and supports prototyping of different interaction styles.
Combining the benefits of tele-rehabilitation and virtual reality-based balance training: a systematic review on feasibility and effectiveness

Abstract Purpose: A motivational surrounding is desirable in stroke rehabilitation considering the need to train repetitively to improve balance, even after discharge from rehabilitation facilities. This review aims to investigate whether it is feasible to combine virtual reality (VR) which allows exercising in game-like environments with tele-rehabilitation in a community-dwelling stroke population. Methods: Literature searches were conducted in five databases, for example, PubMed and the Cochrane Library. Randomized controlled trial (RCT) and non-RCT investigating feasibility and effectiveness of VR-based tele-rehabilitation were included. Based on the risk of bias and study design, methodological quality is ranked according to the GRADE guidelines. Results: Seven studies (n = 120) were included, of which four are RCTs. Evidence regarding therapy adherence and perceived enjoyment of VR, as well as a cost–benefit of tele-rehabilitation emphasizes feasibility. Equal effects are reported comparing this approach to a therapist-supervised intervention in the clinical setting on balance and functional mobility. Conclusions: Tele-rehabilitation could be a promising tool to overcome burdens that restrict accessibility to rehabilitation in the future. VR can increase motivation allowing longer and more training sessions in community-dwelling stroke survivors. Therefore, combining the benefits of both approaches seems convenient. Although evidence is still sparse, functional improvements seem to be equal compared to a similar intervention with therapist-supervision in the clinic, suggesting that for cost-efficient rehabilitation parts of therapy can be transferred to the homes. Implications for rehabilitation The use of tele-rehabilitation could be a promising tool to overcome burdens that restrict the access of stroke survivors to long-term rehabilitative care. VR-based interventions are game-like and therefore seem to provide a motivational environment which allows longer exercise sessions and greater adherence to therapy.
RbWL: Recency-Based Static Wear Leveling for Lifetime Extension and Overhead Reduction in NAND Flash Memory Systems

SUMMARY In this letter, we propose a static wear leveling technique, called Recency-based Wear Leveling (RbWL). The basic idea of RbWL is to execute static wear leveling at minimum levels, because the frequent migrations of cold data by static wear leveling cause signiﬁcant overhead in a NAND ﬂash memory system. RbWL adjusts the execution frequency according to a threshold value that reﬂects the lifetime di ﬀ erence of the hot / cold blocks and the total lifetime of the NAND ﬂash memory system. The evaluation results show that RbWL improves the lifetime of NAND ﬂash memory systems by 52%, and it also reduces the overhead of wear leveling from 8% to 42% and from 13% to 51%, in terms of the number of erase operations and the number of page migrations of valid pages, respectively, compared with other algorithms.