Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunohistochemical evaluation of oral epithelial dysplasia using cyclin-D1, p27 and p63 expression as predictors of malignant transformation

Objective: To evaluate the degree of expression of cyclin-D1, p27 and p63 in mild, moderate and severe dysplasia using immunohistochemical evaluation in order to illustrate their prognostic value and attempt to propose a molecular grading system for oral epithelial dysplasia. Materials and Methods: The analysis included thirty cases of mild, moderate and severe dysplasia from Department of Oral and Maxillofacial Pathology, Saveetha Dental College, Chennai after a critical review of the Hematoxylin and Eosin (H and E) stained sections. They were subjected to immunohistochemical evaluation using the markers cyclin-D1, p27 and p63. The assessment of the expression based on staining intensity and distribution of immunohistochemical staining of the various markers was analyzed followed by statistical analysis. Results: A highly significant increase in the expression of cyclin-D1 (P < 0.000) and p63 (P < 0.001) and a moderately significant decrease in the expression of p27 (P < 0.012) with the increasing severity of dysplasia was observed in our study. Conclusions: The result of our research affirms the fact that the increase in the expression of markers of cell cycle regulators such as cyclin D1, decrease in the expression of cell cycle inhibitors like p27 and increased expression of p63 in parallel with the increasing severity of dysplasia, emphasizes the use of immunohistochemical markers cyclin D1, p27 and p63 as prognostic markers for better understanding the behaviour of these potentially malignant disorders aiming towards proposing a molecular grading system for oral epithelial dysplasia to enable timely management prior to their possible malignant transformation.
Morphological and Genetic Diversity of Rhizobia Nodulating Cowpea (Vigna unguiculata L.) from Agricultural Soils of Lower Eastern Kenya

Limited nitrogen (N) content in the soil is a major challenge to sustainable and high crop production in many developing countries. The nitrogen fixing symbiosis of legumes with rhizobia plays an important role in supplying sufficient N for legumes and subsequent nonleguminous crops. To identify rhizobia strains which are suitable for bioinoculant production, characterization of rhizobia is a prerequisite. The objective of this study was to assess the morphological and genetic diversity of rhizobia that nodulates cowpea in agricultural soils of lower eastern Kenya. Twenty-eight rhizobia isolates were recovered from soil samples collected from farmers' fields in Machakos, Makueni, and Kitui counties in lower eastern Kenya and characterized based on morphological characteristics. Thirteen representative isolates were selected and characterized using BOX repetitive element PCR fingerprinting. Based on the dendrogram generated from morphological characteristics, the test isolates were distributed into two major clusters at a similarity of 75%. Phylogenetic tree, based on BOX repetitive element PCR, grouped the isolates into two clusters at 90% similarity level. The clustering of the isolates did not show a relationship to the origin of soil samples, although the isolates were genetically diverse. This study is a prerequisite to the selection of suitable cowpea rhizobia to develop bioinoculants for sustainable crop production in Kenya.
Community detection by popularity based models for authored networked data

Community detection has emerged as an attractive topic due to the increasing need to understand and manage the networked data of tremendous magnitude. Networked data usually consists of links between the entities and the attributes for describing the entities. Various approaches have been proposed for detecting communities by utilizing the link information and/or attribute information. In this work, we study the problem of community detection for networked data with additional authorship information. By authorship, each entity in the network is authored by another type of entities (e.g., wiki pages are edited by users, products are purchased by customers), to which we refer as authors. Communities of entities are affected by their authors, e.g., two entities that are associated with the same author tend to belong to the same community. Therefore leveraging the authorship information would help us better detect the communities in the networked data. However, it also brings new challenges to community detection. The foremost question is how to model the correlation between communities and authorships. In this work, we address this question by proposing probabilistic models based on the popularity link model [1], which is demonstrated to yield encouraging results for community detection. We employ two methods for modeling the authorships: (i) the first one generates the authorships independently from links by community memberships and popularities of authors by analogy of the popularity link model; (ii) the second one models the links between entities based on authorships together with community memberships and popularities of nodes, which is an analog of previous author-topic model. Upon the basic models, we explore several extensions including (i) we model the community memberships of authors by that of their authored entities to reduce the number of redundant parameters; and (ii) we model the communities memberships of entities and/or authors by their attributes using a discriminative approach. We demonstrate the effectiveness of the proposed models by empirical studies.
Robust H2 filter design for bilinear systems using T-S fuzzy approach

In this paper, the H2 filtering of bilinear systems is considered using T-S fuzzy approach. For the bounded input, the bilinear system can be transformed to the Takagi-Sugeno (T-S) fuzzy dynamic system by putting the control input as the premise variable. Then the robust H2 filter for T-S fuzzy system is designed upon existing results using the linear matrix inequality(LMI). The feasibility and effectiveness of the designed filter is shown in the numerical example section.
Extended range tuning elements for a microelectromechanical systems external cavity laser

A modified tuning element for a Littrow external cavity laser is demonstrated using microelectromechanical systems technology. The device combines a vertically etched blazed grating, a compound flexure suspension and drive elements for translating and rotating the grating. Tuning is linearized by linking the grating to a remote actuator by a flexible linkage. A simple theoretical model of the linkage is presented. Devices with electrostatic and electrothermal drives are both demonstrated by deep reactive etching of bonded silicon on insulator, and used to construct electrically tunable lasers. Prototypes show improved electromechanical characteristics and tuning over a range of 8 nm is demonstrated.
Coevolving Systems and the Organization of Agile Software Development

Despite the popularity of agile methods in software development and increasing adoption by organizations there is debate about what agility is and how it is achieved. The debate suffers from a lack of understanding of agile concepts and how agile software development is practiced. This paper develops a framework for the organization of agile software development that identifies enablers and inhibitors of agility and the emergent capabilities of agile teams. The work is grounded in complex adaptive systems (CAS) and draws on three principles of coevolving systems: match coevolutionary change rate, maximize self-organizing, and synchronize exploitation and exploration. These principles are used to study the processes of two software development teams, one a team using eXtreme Programming (XP) and the other a team using a more traditional, waterfall-based development cycle. From the cases a framework for the organization of agile software development is developed. Time pacing, self-management with discipline and routinization of exploration are among the agile enablers found in the cases studies while event pacing, centralized management, and lack of resources allocated to exploration are found to be inhibitors to agility. Emergent capabilities of agile teams that are identified from the research include coevolution of business value, sustainable working with rhythm, sharing and team learning, and collective mindfulness.
Composite coatings and their performance in corrosive environment

AbstractPretreated steel specimens coated with epoxy resin, either pure or containing iron powder, were exposed to a corrosive environment (aqueous 3·5 wt-%NaCl solution). Data concerning the modification of their properties were recorded via electrochemical impedance spectroscopy measurements at medium and high frequencies. Analysis of time dependent elements of proposed models has enabled the individual contributions to the impedance of the phases created during immersion to be distinguished and changes in the behaviour of the coatings due to water uptake to be monitored. These results, together with water permeability and hardness measurements and visual analysis, have revealed minor differences in the performance of the metal filled coatings compared with those of the pure epoxy.
Spee-Navi: An Indoor Navigation System via Speech Crowdsourcing

We present a speech crowdsourcing-based navigation system, Spee-Navi, which is able to guide users to the target in ambiguous indoor environments. The proposed approach leverages the microphones in the commercial off-the-shelf devices to extract the semantic feature for space partition via Automatic Speech Recognition (ASR). Moreover, the reversed Dead Reckoning from the target area to the user is implemented to provide a shortest path without comprehensive indoor localization or even the floor plan. Spee-Navi collects a rich set of sensor readings during the course of a guider's walk, analyzes the location semantics of purchase areas using ASR when the guider stops, and then packs them into a navigation trajectory at the entrance. The followers, who have been provided an appropriate path on the basis of semantics, track the navigation trajectory and get prompt visual instructions. We detect both course-grained and fine-grained semantics, and conduct extensive experiments. The evaluation results show that Spee-Navi can navigate a user to the target area within 9 steps error.
Profile face detection using flipping scheme with genetic algorithm

This paper adopts the genetic algorithm optimized flipping scheme to transform the frontal face detector to be profile face detectors for various profile views, rather than the normally building different detectors for different views, or small view ranges. The flipping scheme takes advantage of the over-representation characteristic of the frontal face detector trained with the SURF cascade framework. However, the previous flipping scheme utilizes the classic sliding window method to search for the the hypothetical face axis of symmetry, which is time-consuming. This paper treats this searching as an optimization problem and use the genetic algorithm to optimize the coordinates, the scale and rotation angle of the profile face. We implemented the real time detection of profile faces from different views with genetic algorithm optimized flipping scheme.
Hit Expansion from Screening Data Based upon Conditional Probabilities of Activity Derived from SAR Matrices

A new methodology for activity prediction of compounds from SAR matrices is introduced that is based upon conditional probabilities of activity. The approach has low computational complexity, is primarily designed for hit expansion from biological screening data, and accurately predicts both active and inactive compounds. Its performance is comparable to state‐of‐the‐art machine learning methods such as support vector machines or Bayesian classification. Matrix‐based activity prediction of virtual compounds further extends the spectrum of computational methods for compound design.
How a contextualized curriculum work in practice

In developing countries higher learning institutions have been at the forefront of acquisition developing and use of technology. Most of institutions influence the dissemination and therefore accessibility of technology in their respective regions. Moreover the higher learning institutions have been at the forefront in producing the ICT skills in need within the respective communities and hence influence the knowledge and its application within the society. In developing countries such ICT skills being produced mostly focus on satisfying the various industrial ICT demands. In this study we try to analyze the role of the higher learning institutions in the provision of ICT education and therefore assess the application of such educational curriculum to stimulate development and information access to information divisive communities in the developing countries.
Product-Based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and contains multiple fields, a typicalrepresentation is to transform it into a high-dimensional sparsebinary feature representation via one-hot encoding. Facing withthe extreme sparsity, traditional models may limit their capacityof mining shallow patterns from the data, i.e. low-order featurecombinations. Deep models like deep neural networks, on theother hand, cannot be directly applied for the high-dimensionalinput because of the huge feature space. In this paper, we proposea Product-based Neural Networks (PNN) with an embeddinglayer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfieldcategories, and further fully connected layers to explorehigh-order feature interactions. Our experimental results on twolarge-scale real-world ad click datasets demonstrate that PNNsconsistently outperform the state-of-the-art models on various metrics.
Hand gesture classification using inertial based sensors via a neural network

In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor.
Cyber-Attack on Overloading Multiple Lines: A Bilevel Mixed-Integer Linear Programming Model

In this letter, we propose a bilevel mixed-integer linear programming (BMILP) model for cyber-attack on multiple transmission lines in economic dispatch by injecting false load data. In contrast to the state of art research, the proposed model is able to overload a set of lines that are selected by the attacker in the framework of BMILP. Therefore, the proposed model is computationally efficient due to its bilevel linear programming property. The effectiveness of the proposed bilevel linear approach has been successfully validated on an IEEE 118-bus system.
Evidence-Based Guidelines for the Pharmacologic Management of Methamphetamine Dependence, Relapse Prevention, Chronic Methamphetamine-Related, and Comorbid Psychiatric Disorders in Post-Acute Settings

Abstract The increasing abuse of the street drug crystal meth (methamphetamine) in many countries worldwide has resulted in a growing demand to treat patients who have acquired a methamphetamine-related disorder. The results of a systematic literature search which led to the consensus-based recommendations by the Working Group of the German Agency for Quality in Medicine (Ärztliches Zentrum für Qualität in der Medizin - ÄZQ) are presented. Pharmacological treatments were reviewed in 58 out of the 103 publications included. They were mainly randomized controlled trials (RCT). Despite increased research activities, none of the medications studied demonstrated a convincing and consistent effect on abstinence rates, despite some having an impact on craving and retention rates or symptom control. In addition, as yet there is no sufficient evidence available for dopamine analogue treatment (“substitution”) after the initial withdrawal-period. Methamphetamine-related, post-acute persistent or comorbid syndromes such as methamphetamine-associated psychosis (MAP), depressive syndromes, anxiety, and sleep disorders are usually treated in a symptom-oriented manner. Risks of interactions with methamphetamine have to be taken in account when prescribing medications with doubtful efficacy. Further research is warranted.
Hemolytic anemia in alcoholic liver disease: Zieve syndrome

Rationale: Zieve syndrome, a rarely reported disease resulting from alcohol abuse, consists of a triad of symptoms: hemolytic anemia, cholestatic jaundice, and transient hyperlipidemia. It is largely under-recognized and under-reported, possibly because of unawareness of the condition by physicians. Here, we report a case of Zieve syndrome managed at the Jilin University First Bethune Hospital. Patient concerns: A 30-year-old Chinese woman presented with a 4-month history of fatigue, yellowish discoloration of the eyes, and tea-colored urine. She had been a heavy drinker for 2 years prior to onset of the disease with an average daily alcohol intake of 60 g/d and more than 80 g/d for the previous 6 months. Diagnosis: The diagnosis of Zieve syndrome was confirmed based on hemolysis and cholestatic jaundice secondary to alcoholic liver disease and heavy drinking. Bone marrow biopsy and liver biopsy both supported the diagnosis. Interventions: We treated her with abstinence from alcohol and supportive therapy. Outcomes: The patient was discharged 14 days after admission with an improvement in symptoms, which continued to subside during the 2-month follow-up period. Lessons: Doctors confronted with hemolysis in a patient with alcoholic liver disease should be aware of the under-reported Zieve syndrome. Recognition of this syndrome could help doctors avoid unnecessary invasive procedures and emphasize the importance of alcohol abstinence as the mainstay of management. Glucocorticoids may not be useful in treating hemolytic anemia in Zieve syndrome.
Incremental Hashing for Semantic Image Retrieval in Nonstationary Environments

A very large volume of images is uploaded to the Internet daily. However, current hashing methods for image retrieval are designed for static databases only. They fail to consider the fact that the distribution of images can change when new images are added to the database over time. The changes in the distribution of images include both discovery of a new class and a distribution of images within a class owing to concept drift. Retraining of hash tables using all images in the database requires a large computation effort. This is also biased to old data owing to the huge volume of old images which leads to a poor retrieval performance over time. In this paper, we propose the incremental hashing (ICH) method to deal with the two aforementioned types of changes in the data distribution. The ICH uses a multihashing to retain knowledge coming from images arriving over time and a weight-based ranking to make the retrieval results adaptive to the new data environment. Experimental results show that the proposed method is effective in dealing with changes in the database.
Treatment of restless legs syndrome with pregabalin

Objectives: To assess the therapeutic efficacy, required dose, and tolerability of pregabalin in patients with idiopathic restless legs syndrome (RLS). Methods: This was a double-blind, placebo-controlled trial with polysomnographic control, providing Class II evidence. Ninety-eight patients underwent a 2-week single-blind period with placebo; 58 were randomized to receive pregabalin or placebo for 12 weeks under a flexible-dose schedule. Endpoints were mean change from baseline in the International Restless Legs Scale (IRLS) total score, Clinical Global Impression (CGI), and RLS-6 scales, as well as changes in periodic limb movements (PLMs) and sleep architecture. Results: Patients under treatment with pregabalin had a greater improvement in IRLS score than under placebo (63% vs 38.2%; p < 0.05). The mean effective dose of pregabalin at the end of treatment was 322.50 mg/day (±98.77), although therapeutic effects were already seen at a mean dose of 139 mg/day. Similarly, improvements were observed on the CGI, RLS-6 scale, and the Medical Outcomes Study sleep scale (all p < 0.01) when compared to placebo. Treatment with pregabalin also resulted in a reduction of the mean (±SD) PLM index (p < 0.001). Furthermore, there was a marked improvement in sleep architecture with an increase in slow wave sleep (p < 0.01), and decreases in wake after sleep onset and stages 1 and 2 (p < 0.05). Pregabalin was generally well-tolerated. Adverse events were mild but common, and included unsteadiness, daytime sleepiness, and headache. Conclusions: This study shows significant therapeutic effects of pregabalin on both sensorial and motor symptoms in restless legs syndrome. Treatment with pregabalin was associated with an improvement of sleep architecture and periodic limb movements. Adverse events included unsteadiness and sleepiness and should be screened carefully in the working population, particularly when pregabalin is administered in the afternoon. Classification of evidence: This study provides Class II evidence that pregabalin is effective for the treatment of restless legs syndrome and improves sleep architecture and periodic limb movements in placebo-unresponsive patients.
PriGuarder: A Privacy-Aware Access Control Approach Based on Attribute Fuzzy Grouping in Cloud Environments

Data privacy protection is crucial to cloud computing since privacy leakage may prevent users from using cloud services. To ensure data privacy, we propose PriGuarder, a novel privacy-aware access control method. This method spans the three stages of a cloud service, i.e., user registration, data creation, and data access. At each stage, users can choose two modes to interact with the cloud service provider, i.e., direct or indirect. With the indirect mode, an attribute fuzzy grouping scheme is introduced to ensure user identity privacy and attribute privacy in all the three stages. Furthermore, exploiting data encryption and timestamp techniques, new access control protocols are proposed to regulate interactions between users and the cloud service provider. We illustrate the use of our method in the context of Amazon S3. Theoretical analysis and comprehensive simulation experiments have been conducted, which demonstrate the efficacy of PriGuarder.