Miracle-dz/UniFAR-FTUs huggingface dataset:
Fingerprint Shell Construction with Prominent Minutiae Points

Fingerprint based authentication is one of the most extensively used authentication technique. Biometric systems relying on fingerprint usually directly uses minutiae points information of a fingerprint and store it as a user template. There are many recent works which show that original fingerprint of a user can be generated from the data of minutiae points. In case of traditional authentication systems based on password there is a liberty to change the password, however biometric data of a user cannot be changed as it is permanently associated with the human body. If any information related to biometric features of a user is stolen or compromised, then in that case we cannot change the compromised information. Therefore, it is essential to make sure that the biometric data is secure. Our motive is to generate a biometric template that will fulfill the necessities of performance, security, revocability and diversity. Moujahdi et al. proposed a technique called Fingerprint shell as a secure representation of fingerprint data using a user key. In this technique, a spiral curve is generated as a secured user template by using the distances between singular point and minutiae points. In this paper, we have proposed a technique in which we have included the quality of minutiae points for the construction of spiral curve. We have used a pair of unique user keys and utilized the information provided by minutiae points to generate a non-invertible user template. In case of compromising of user template by adversary, user has the liberty to generate new template by using different user keys, the new template and the compromised one are non-linkable. We tested our technique on FVC2002 DB1, FVC2002 DB2 and IIT Kanpur fingerprint databases using FVC protocol. Experimental results obtained are encouraging and demonstrate the viability of our technique.
Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks

In recent years, the classification of breast cancer has been the topic of interest in the field of Healthcare informatics, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. With the recent advances in image processing and machine learning, there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis. In this paper, we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes. The first approach is based on the extraction of a set of handcrafted features encoded by two coding models (bag of words and locality constrained linear coding) and trained by support vector machines, while the second approach is based on the design of convolutional neural networks. We have also experimentally tested dataset augmentation techniques to enhance the accuracy of the convolutional neural network as well as “handcrafted features + convolutional neural network” and “ convolutional neural network features + classifier” configurations. The results show convolutional neural networks outperformed the handcrafted feature based classifier, where we achieved accuracy between 96.15% and 98.33% for the binary classification and 83.31% and 88.23% for the multi-class classification.
Neural Dynamics of Variable Grasp-Movement Preparation in the Macaque Frontoparietal Network

Our voluntary grasping actions lie on a continuum between immediate action and waiting for the right moment, depending on the context. Therefore, studying grasping requires an investigation into how preparation time affects this process. Two macaque monkeys (Macaca mulatta; one male, one female) performed a grasping task with a short instruction followed by an immediate or delayed go cue (0–1300 ms) while we recorded in parallel from neurons in the grasp preparation relevant area F5 that is part of the ventral premotor cortex, and the anterior intraparietal area (AIP). Initial population dynamics followed a fixed trajectory in the neural state space unique to each grip type, reflecting unavoidable movement selection, then diverged depending on the delay, reaching unique states not achieved for immediately cued movements. Population activity in the AIP was less dynamic, whereas F5 activity continued to evolve throughout the delay. Interestingly, neuronal populations from both areas allowed for a readout tracking subjective anticipation of the go cue that predicted single-trial reaction time. However, the prediction of reaction time was better from F5 activity. Intriguingly, activity during movement initiation clustered into two trajectory groups, corresponding to movements that were either “as fast as possible” or withheld movements, demonstrating a widespread state shift in the frontoparietal grasping network when movements must be withheld. Our results reveal how dissociation between immediate and delay-specific preparatory activity, as well as differentiation between cortical areas, is possible through population-level analysis. SIGNIFICANCE STATEMENT Sometimes when we move, we consciously plan our movements. At other times, we move instantly, seemingly with no planning at all. Yet, it's unclear how preparation for movements along this spectrum of planned and seemingly unplanned movement differs in the brain. Two macaque monkeys made reach-to-grasp movements after varying amounts of preparation time while we recorded from the premotor and parietal cortex. We found that the initial response to a grasp instruction was specific to the required movement, but not to the preparation time, reflecting required movement selection. However, when more preparation time was given, neural activity achieved unique states that likely related to withholding movements and anticipation of movement, shedding light on the roles of the premotor and parietal cortex in grasp planning.
LIVELIHOOD SECURITY OF FARM HOUSEHOLDS UNDER DIFFERENT FARMING SYSTEMS IN KOLAR DISTRICT OF KARNATAKA – AN ECONOMIC ANALYSIS

The study was undertaken in Kolar district of Karnataka state to assess the outcomes of livelihood security like Economic security, Food security, Educational security, Health security, Habitat security and Social network security. Four farming systems viz. Crop+Dairy, Crop+Sericulture, Crop+Dairy+Sericulture and Crop+Sheep were identified as major farming systems based on preliminary survey and previous studies in the area. The study based on the primary data from 120 farmers covering equal samples under major farming systems was elicited through survey method for the period 2010–11. The data was analyzed using Garrett ranking technique, Gini co-efficient and Conventional economic and simple statistical tools like ratios, percentage and indices etc. The result revealed that net annual income realized by a household was highest in C+D+S ( 3, 58, 880/-) farming system of which 43.78 per cent was from sericulture enterprise alone and least in C+Sh ( 46, 281/-) farming system of which 59.95 per cent was from non-farm activities. When compared to Farm households of rainfed based farming systems (Crop+Sheep) irrigation based farming system (Crop+Dairy, Crop+Sericulture, Crop+Dairy+Sericulture) households are on par with Indian Council of Medical Research (ICMR) norms in cereal consumption. Public Distribution System (PDS) playing an important role in food security of farm households of rainfed farming systems through supplying food grains at cheaper prices. The overall livelihood security is high in case of C+D+S farm households.
Depsidomycins B and C: New Cyclic Peptides from a Ginseng Farm Soil-Derived Actinomycete

LC/MS-based chemical profiling of a ginseng farm soil-derived actinomycete strain, Streptomyces sp. BYK1371, enabled the discovery of two new cyclic heptapeptides, depsidomycins B and C (1 and 2), each containing two piperazic acid units and a formyl group at their N-terminus. The structures of 1 and 2 were elucidated by a combination of spectroscopic and chemical analyses. These new compounds were determined to possess d-leucine, d-threonine, d-valine, and S-piperazic acid based on the advanced Marfey’s method and a GITC (2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl isothiocyanate) derivatization of their hydrolysates, followed by LC/MS analysis. Depsidomycins B and C displayed significant antimetastatic activities against metastatic breast cancer cells (MDA-MB-231).
Relevance of MTHFR polymorphisms with response to fluoropyrimidine-based chemotherapy in oesophagogastric cancer: a meta-analysis

Objective To evaluate the association between methylenetetrahydrofolate reductase (MTHFR) polymorphisms and the response to fluoropyrimidine-based chemotherapy in oesophagogastric cancer. Design Meta-analysis. Methods We searched PubMed, Embase and Web of Science databases from inception up to October 2017 for relevant studies. The statistical analysis was performed using STATA V.12. 0 software. The pooled ORs and 95% CIs were used to assess the strength of the association under the allele, dominant and recessive models. We also conducted subgroup analysis stratified by cancer type, ethnicity and study design. Additionally, the sensitivity analysis was performed by sequential omission of individual studies, and the publication bias was detected using both Begg’s test and Egger’s test. Results A total of 2020 patients from 12 studies were included in this meta-analysis. The results showed that there was no significant association between MTHFR C677T (rs1801133) and A1298C (rs1801131) polymorphisms and the clinical response to fluoropyrimidine-based chemotherapy under all of the three genetic models (T vs C: OR 0.93, 95% CI 0.76 to 1.15; C vs A: OR 0.88, 95% CI 0.56 to 1.40. CT+TT vs CC: OR 0.94, 95% CI 0.72 to 1.23; AC+CC vs AA: OR 0.80, 95% CI 0.47 to 1.35. TT vs CC+CT: OR 1.02, 95% CI 0.74 to 1.39; CC vs AA+AC: OR 1.15, 95% CI 0.50 to 2.67). When stratified by cancer type, ethnicity or study design, the association was still not significant in all subgroups. Conclusions This meta-analysis suggested that MTHFR polymorphisms could not be considered as reliable factors for predicting the response to fluoropyrimidine-based chemotherapy in oesophagogastric cancer.
Words Are Important

Lexicon-based sentiment analysis (SA) aims to address the problem of extracting people’s opinions from their comments on the Web using a predefined lexicon of opinionated words. In contrast to the machine learning (ML) approach, lexicon-based methods are domain-independent methods that do not need a large annotated training corpus and hence are faster. This makes the lexicon-based approach prevalent in the SA community. However, the story is different for the Persian language. In contrast to English, using the lexicon-based method in Persian is a new discipline. There are rather limited resources available for SA in Persian, making the accuracy of the existing lexicon-based methods lower than other languages. In the current study, first an exhaustive investigation of the lexicon-based method is performed. Then two new resources are introduced to address the problem of resource scarcity for SA in Persian: a carefully labeled lexicon of sentiment words, PerLex, and a new handmade dataset of about 16,000 rated documents, PerView. Moreover, a new hybrid method using both ML and the lexicon-based approach is presented in which PerLex words are used to train the ML algorithm. Experiments are carried out on our new PerView dataset. Results indicate that the accuracy of PerLex is higher than the existing CNRC, Adjectives, SentiStrength, PerSent, and LexiPers lexicons. In addition, the results show that using PerLex significantly decreases the execution time of the proposed system in comparison to the above-mentioned lexicons. Moreover, the results demonstrate the excellence of using opinionated lexicon terms followed by bigrams as the features employed in the ML method.
Learning Factorized Representations for Open-set Domain Adaptation

Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach significantly outperforms the state-of-the-art in open-set domain adaptation.
Training LSTM Networks With Resistive Cross-Point Devices

In our previous work we have shown that resistive cross point devices, so called resistive processing unit (RPU) devices, can provide significant power and speed benefits when training deep fully connected networks as well as convolutional neural networks. In this work, we further extend the RPU concept for training recurrent neural networks (RNNs) namely LSTMs. We show that the mapping of recurrent layers is very similar to the mapping of fully connected layers and therefore the RPU concept can potentially provide large acceleration factors for RNNs as well. In addition, we study the effect of various device imperfections and system parameters on training performance. Symmetry of updates becomes even more crucial for RNNs; already a few percent asymmetry results in an increase in the test error compared to the ideal case trained with floating point numbers. Furthermore, the input signal resolution to the device arrays needs to be at least 7 bits for successful training. However, we show that a stochastic rounding scheme can reduce the input signal resolution back to 5 bits. Further, we find that RPU device variations and hardware noise are enough to mitigate overfitting, so that there is less need for using dropout. Here we attempt to study the validity of the RPU approach by simulating large scale networks. For instance, the models studied here are roughly 1500 times larger than the more often studied multilayer perceptron models trained on the MNIST dataset in terms of the total number of multiplication and summation operations performed per epoch.
Learning distributed event representations with a multi-task approach

Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.
PHYSICS CANDIDATE TEACHERS’ REASONS FOR CHOOSING ACADEMIC DEPARTMENTS, ORDER OF THEIR PREFERENCES AND IDEAS ABOUT FUTURE

The aim of this study is to determine recently enrolled prospective teachers’ reasons for choosing physics education department, whether they think of working as a physics teacher and reasons for working or not working as a physics teacher. In addition, it is aimed to reveal prospective teachers’ sex, age, type of graduated high school, undergraduate placement (LYS) scores, made preferences and list of their preferences. In order to achieve this aim, 21 students, who deserved to be enrolled to physics education program at Balikesir University, Necatibey Faculty of Education in the academic term of 2011-2012, were asked to fill in “Reasons for Preferences about Teacher Education Department Questionnaire”. Some data obtained from the questionnaire, which consists of 10 questions, have been presented with tables of frequency analysis while the other data related to open ended questions have been examined by using content analysis. Analysis results show that majority of students (66.67%) are graduated from general high schools and have a wide range of scores (differentiates between 233 and 246) obtained from LYS exam. All prospective teachers were found to choose different number of physics education programs ranging from zero to five. Additionally, most of the prospective teachers (85.71%) indicated that they would work as a physics teacher. Reasons for preferences and doing teaching job are presented in tables by categorising in distinctive thematic headings. Key words: Physics candidate teacher.
Substantial Heterogeneity in Progress Toward Reaching the 90-90-90 HIV Target in the WHO European Region

Background: Achieving the UNAIDS 90-90-90 target by 2020 is expected to end the HIV epidemic by 2030. We report on progress in the WHO European Region in meeting this target. Methods: The European Centre for Disease Prevention and Control (ECDC) sent questionnaires to 55 countries in 2016. We report estimates for 4 stages of the continuum of HIV care (living with HIV, diagnosed, treated, and virally suppressed), corresponding to the Joint United Nations Programme on HIV and AIDS (UNAIDS) target and explore differences by subregion and challenges with reporting data. Findings: Forty-four countries provided data for ≥1 stage, and 29 for all 4 stages. Estimated HIV prevalence was 0.19% (range 0.02%–0.84%, n = 37 countries providing stage 1 data). The proportion diagnosed of people living with HIV ranged from 38% to 98% (n = 37 reporting number of people living with HIV and diagnosed). The proportion on ART of those diagnosed ranged from 27% to 96% (n = 40 reporting numbers diagnosed and treated), and viral suppression rates ranged from 32% to 97% (n = 31 providing numbers treated and virally suppressed). The overall continuum of care estimate for 29 countries with complete data was 81-84-88, which differed by subregion: 84-88-90, 84-69-62, and 57-45-57 for the western, central, and eastern subregions, respectively. Challenges in reporting data included absence of a single data source for all stages, shortage of expertise, and lack of financial and human resources. Conclusions: There is an urgent need to strengthen HIV testing programs throughout Europe, particularly in the eastern subregion, and to remove constraints hampering access to testing and care. Recent changes to treatment guidelines should help reduce the numbers diagnosed not treated.
Body Mass Index, Quality of Life and Migraine in Students

Introduction The main primary headache among children and adolescents is migraine, a neurological disease characterized by a high prevalence of recurrent, moderate to severe pain [1] leading to decreased quality of life (QL) [2]. Migraine is reported globally with a higher prevalence in students. The present study aims to evaluate the association between nutritional status, quality of life (QL) and characteristics of migraine. Citation: Castro K, Rockett FC, Klein, Parizotti, Perla AS, et al. (2014) Body Mass Index, Quality of Life and Migraine in Students. J Nutr Health Sci 1(2): 205. doi: 10.15744/2393-9060.1. 205
The gut microbiome of nonhuman primates: Lessons in ecology and evolution

The mammalian gastrointestinal (GI) tract is home to trillions of bacteria that play a substantial role in host metabolism and immunity. While progress has been made in understanding the role that microbial communities play in human health and disease, much less attention has been given to host‐associated microbiomes in nonhuman primates (NHPs). Here we review past and current research exploring the gut microbiome of NHPs. First, we summarize methods for characterization of the NHP gut microbiome. Then we discuss variation in gut microbiome composition and function across different NHP taxa. Finally, we highlight how studying the gut microbiome offers new insights into primate nutrition, physiology, and immune system function, as well as enhances our understanding of primate ecology and evolution. Microbiome approaches are useful tools for studying relevant issues in primate ecology. Further study of the gut microbiome of NHPs will offer new insight into primate ecology and evolution as well as human health.
Fluorocholine Transport Mediated by the Organic Cation Transporter 2 (OCT2, SLC22A2): Implication for Imaging of Kidney Tumors

[18F]fluorocholine is the fluorinated analog of [11C]choline and is used in positron emission tomography to monitor tumor metabolic activity. Although important to optimize its use and expand the clinical indications, the molecular determinants of fluorocholine cellular uptake are poorly characterized. In this work, we described the influx kinetics of fluorocholine mediated by the organic cation transporter 2 (OCT2, SLC22A2) and compared with that of choline. Then we characterized the expression pattern of OCT2 in renal cell carcinoma (RCC). In HEK293 cells stably transfected with OCT2 fluorocholine influx, kinetics was biphasic, suggesting two independent binding sites: a high-affinity (Km = 14 ± 8 µM, Vmax = 1.3 ± 0.5 nmol mg−1 min−1) and a low-affinity component (Km = 1.8 ± 0.3 mM, Vmax = 104 ± 4.5 nmol mg−1 min−1). Notably, choline was found to be transported with sigmoidal kinetics typical of homotropic positive cooperativity (h = 1.2, 95% confidence interval 1.1–1.3). OCT2 mRNA expression level was found significantly decreased in primary but not in metastatic RCC. Tissue microarray immunostaining of 216 RCC biopsies confirmed that the OCT2 protein level was consistent with that of the mRNA. The kinetic properties described in this work suggest that OCT2 is likely to play a dominant role in [18F]fluorocholine uptake in vivo. OCT2-altered expression in primary and metastatic cancer cells, as compared with the surrounding tissues, could be exploited in RCC imaging, especially to increase the detection sensitivity for small metastatic lesions, a major clinical challenge during the initial staging of RCC.