Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of fragmentation on the genetic structure of Theobroma speciosum (Malvaceae) populations

Native Theobroma species, such as cacauhy, are losing their habitat due to the intense forest fragmentation in the Amazon region, and preserving their genetic diversity has been the main aim of many conservation programs. The aim of the present study is to assess whether fragmentation and habitat reduction affect the genetic structure and lead to genetic diversity losses in natural Theobroma speciosum populations. The study was conducted in two Mato Grosso State (Brazil) locations, namely: Apiacas and Alta Floresta counties. Juruena National Park (JNP) in Apiacas County holds a natural T. speciosum population that has not underwent anthropic influences. A population composed of individuals from three anthropized urban forest parks (UF) in Alta Floresta County was analyzed. The leaves of 75 T. speciosum individuals distributed in the urban forest fragments and of 100 individuals found in the Juruena National Park were sampled. All nine microsatellite loci showed high polymorphism levels between categories (adults and sub-adults), in both populations. The sub-adult individuals of the population (UF) in the fragmented area showed higher value (0.71), whereas the preserved population (JNP) category presented the same value (0.69). The increasing trend of estimating the fixation index towards the fragmented population was observed. The analysis of molecular variance showed 83% genetic diversity within categories; 16%, between populations; and only 1%, between categories. Although the effects were small, a persistent fragmentation process can increase the inbreeding levels and facilitate the genetic drift action. These effects may lead the T. speciosum populations to inbreeding depression, diversity loss and genetic structure change in the course of several generations.
Interaction Effects of Gender and Motivational Beliefs on Self-Regulated Learning: A Study at ICT- Integrated Schools.

Purpose – This study aimed to examine the interaction effects of gender and motivational beliefs on students’ self-regulated learning. Specifically, three types of motivational beliefs under the Expectancy-Value Model were examined, namely self-efficacy, control beliefs and anxiety. Methodology – A quantitative correlational research design was used to achieve the research objectives. Data were collected through the questionnaire survey method from 322 secondary school students (166 males; 156 females). The samples were taken from two ICTintegrated schools located in Peninsular Malaysia. The learning environment in these schools was conducive for self-regulated learning. The Learning Strategies Scale and the Motivation Scale, taken from the Motivated Strategies for Learning Questionnaire (MSLQ) were used to measure the variables of the study. Findings – The findings showed that self-efficacy and control beliefs were posistively related to students’self-regulated learning. Anxiety, however, was found to be negatively related to selfregulated learning. The interactions between gender and levels of motivational beliefs on self-regulated learning were also explored in this study. The relationships between self-efficacy and selfregulated learning differed according to gender. However, there were no significant interaction effects between gender and internal ht tp :// m jli . u um . e du . m y 26 Malaysian Journal of Learning and Instruction: Vol. 13 (2016): 25-41 control beliefs on self-regulated learning. This implies that gender differences in self-regulated learning were not due to the differences in control beliefs and anxiety. Significance – This study offers insights on the interaction effects between motivational beliefs, and gender and self-regulated learning. It may helps to develop effective intructional strategies to enhance students' self-regulated learning skill in ICT-related learning environments.
Feedback attribution of the landsea warming contrast in a global warming simulation of the NCAR CCSM 4

One of the salient features in both observations and climate simulations is a stronger land warming than sea. This paper provides a quantitative understanding of the main processes that contribute to the land-sea warming asymmetry in a global warming simulation of the NCAR CCSM4. The CO2 forcing alone warms the surface nearly the same for both land and sea, suggesting that feedbacks are responsible for the warming contrast. Our analysis on one hand confirms that the principal contributor to the above-unity land-to-sea warming ratio is the evaporation feedback; on the other hand the results indicate that the sensible heat flux feedback has the largest land-sea warming difference that favors a greater ocean than land warming. Therefore, the results uniquely highlight the importance of other feedbacks in establishing the above-unity land-to-sea warming ratio. Particularly, the SW cloud feedback and the ocean heat storage in the transient response are key contributors to the greater warming over land than sea. S Online supplementary data available from stacks.iop. org/ERL/9/124005/mmedia
Analysis and Design of Biological Systems Using Finite State Abstractions

This project is concerned with the study of finite state abstractions for biological systems. The goal is to represent a complex biological system through a finite state machine, without losing the underlying dynamics of the system. Such simplifications are very important in the biological context; synthetic biology for example aims to modify or mimic biological systems, a task that requires numerous simulations before performing expensive and time consuming real-life experiments in vitro or in vivo. To assist this task, we try to exploit the intrinsic structure of biological systems, and more specifically the notion of monotonicity or mixed monotonicity, which is a natural property of biological systems. An algorithm to produce a finite abstraction of a mixed monotone system is explored and applied to two different casestudies: a discrete time dynamical system representing the dynamics of the Tribolium castaneum flour beetle, and a continuous time dynamical system representing the metabolism of lactose in E. Coli. We verify that our abstracted models satisfy certain behaviours of the original systems, which we express as properties defined in linear temporal logic (LTL).
Severity Classification of Fundus Images for Diabetic Retinopathy

Problem Introduction According to the 2014 National Diabetes Statistics report, over 29.1 million Americans or 9.3% of the population had diabetes. As the disease progresses, the growth and proliferation of small, weak blood vessels prone to leakage leads to organ damage. Three parts of the body are affected: the kidneys, nervous system, and eyes. The eye disease, diabetic retinopathy (DR), is the leading cause of blindness in the working-age population. However if DR can be detected early, proper treatment (such as with pan-retinal photocoagulation or PRP) may slow or forestall symptoms. The present work will first describe the data set, the grading criteria, and previous literature on automatic diagnosis of diabetic retinopathy. Beyond the background description, hand-designed feature segmentation algorithms to identify parts of the image related to normal anatomy or disease are described. We then discuss our application of modern machine learning methods like SVM and convolutional neural networks to solve the problem.
Association between leptin and leptin receptor gene polymorphisms and breast cancer risk in premenopausal and postmenopausal Mexican women

Purpose: This study aims to evaluate the association between leptin (-2548 G>A) and leptin receptor (p.K109R, p.Q223R, p.K656N) polymorphisms with breast cancer (BC) risk in premenopausal and postmenopausal Mexican women. Material and methods: 274 women were included, 76 with BC in premenopause, 66 with BC in postmenopause, 108 without BC in premenopause, and 22 without BC in postmenopause. Four polymorphisms were genotyped by PCR, High Resolution Melting, and DNA direct sequencing. χ, logistic regression analysis was performed with SPSS and XLSTAT software. Results: There was statistically significant risk of BC in postmenopausal women with LEPR p.Q223R+p.R223R (OR: 8.4, 95% CI: 2.7-25.6, P <0.001), and in premenopausal women with LEP-2548G>A+ -2548A>A genotype (OR: 1.7, 95% CI: 1.0-3.0, P = 0.04). Discussion and Conclusions: This is the first study from Mexico that includes four polymorphisms on LEP and LEPR genes. Our data indicate that both LEPR p.Q223R in postmenopausal women and LEP-2548G>A in premenopausal women have a strong association with BC, which suggests that these polymorphisms play a role in the development of BC in Mexican women. We detected coincidences and differences with respect to other populations. This highlights the importance of conducting genetic epidemiologic studies to identify the risk of gene polymorphisms and BC for each population.
Building a Knowledge-Based Tool for Auto -

The prevention of cardiovascular diseases needs first to quantify the cardiovascular risk. To estimate this risk, French national health authorities provided clinical practice guidelines extending the existing European SCORE, which doesn't include all the cardiovascular risk factors (e.g. diabetes). Hence, French national clinical practice guidelines to quantify the cardiovascular risk is able to deal with more clinical situations than the SCORE. The goal of this paper is to formalize knowledge extracted from these guidelines and implement the rules so that they can be used into an auto-assessing tool of cardiovascular risk. Formalization followed five steps and was conducted under the guidance of medical experts. It resulted into a decision tree fed by eight decision variables. Evaluation of the accuracy of the decision tree showed 80% of agreement with an expert in medical informatics in predicting the cardiovascular risk level for 15 different clinical situations. Discrepancies correspond to the knowledge gaps within Clinical Practice Guidelines. We intend to extend the implementation of the decision tree to a complete tool, for allowing patient to auto-assess their cardiovascular risk. This tool will be integrated into a platform providing recommendations adapted to the calculated level of cardiovascular risk.
MMAS and ACS for GPS surveying problem

Ant Colony Optimization(ACO) have been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem. In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed. Minimizing the cost of this schedule is the goal of this work.
COMPLEXITY OF LINEAR IDEMPOTENT MAL’CEV CONDITIONS RESULTS ON THE COMPUTATIONAL COMPLEXITY OF LINEAR IDEMPOTENT MAL’CEV CONDITIONS By JONAH HOROWITZ, B.MATH, M.SC

In this thesis we examine the computational complexity of determining the satisfaction of various Mal’cev conditions. First we present a novel classification of linear idempotent Mal’cev conditions based on the form of the equations with which they are represented. Using this classification we present a class of conditions which can be detected in polynomial time when examining idempotent algebras. Next we generalize an existing result of Freese and Valeriote by presenting another class of conditions whose satisfaction is exponential time hard to detect in the general case, and en route we prove that it is equally hard to detect local constant terms. The final new contribution is an extension of a recent result of Maróti to a subclass class of weak Mal’cev conditions, proving that their detection is decidable and providing a rough upperbound for the complexity of the provided algorithm for said detection. We close the thesis by reviewing the current state of knowledge with respect to determining satisfaction of linear idempotent Mal’cev conditions.
Sensitivity of the Mediterranean sea level to atmospheric pressure and free surface elevation numerical formulation in NEMO

Abstract. The sensitivity of the dynamics of the Mediterranean Sea to atmospheric pressure and free surface elevation formulation using NEMO (Nucleus for European Modelling of the Ocean) was evaluated. Four different experiments were carried out in the Mediterranean Sea using filtered or explicit free surface numerical schemes and accounting for the effect of atmospheric pressure in addition to wind and buoyancy fluxes. Model results were evaluated by coherency and power spectrum analysis with tide gauge data. We found that atmospheric pressure plays an important role for periods shorter than 100 days. The free surface formulation is important to obtain the correct ocean response for periods shorter than 30 days. At frequencies higher than 15 days−1 the Mediterranean basin's response to atmospheric pressure was not coherent and the performance of the model strongly depended on the specific area considered. A large-amplitude seasonal oscillation observed in the experiments using a filtered free surface was not evident in the corresponding explicit free surface formulation case, which was due to a phase shift between mass fluxes in the Gibraltar Strait and at the surface. The configuration with time splitting and atmospheric pressure always performed best; the differences were enhanced at very high frequencies.
Sparse Coding of Pitch Contours with Deep Auto-Encoders

This paper presents a sparse coding algorithm based on deep auto-encoders for the stylization and the clustering of pitch contours. The main objective of the proposed algorithm is to learn a set of pitch templates that can be easily interpreted by humans and whose combination can approximate efﬁciently the observed pitch contours. The proposed learning architecture is based on deep auto-encoders, commonly used to learn non-linear and low-dimensional latent representations that approximate the observed data. The proposed deep architecture is based on stacked auto-encoders and the sparsity of the network is investigated in order to learn a more robust and general representation of the pitch contours (dropout, denoising auto-encoder, sparsity regularization). The deep auto-encoding of the pitch contours is illustrated and discussed on the TIMIT American-English speech database † with comparison of other existing stylization and clustering algorithms.
Patients with Oral Tumors

K. W. Grätz, Zürich C. Hämmerle, Zürich S. Hänni, Bern E. Hellwig, Freiburg C. Katsaros, Bern N. Kellerhoff, Bern J. T. Lambrecht, Basel K. Lädrach, Bern H. T. Lübbers, Zürich R. Männchen, Winterthur C. Marinello, Basel G. Menghini, Zürich R. Mericske-Stern, Bern A. Mombelli, Genève F. Müller, Genève K. Neuhaus, Bern I. Nitschke, Zürich C. Ramseier, Bern S. Ruf, Giessen G. Salvi, Bern M. Schätzle, Luzern S. Scherrer, Genève P. R. Schmidlin, Zürich A. Sculean, Bern R. Seemann, Bern P. Sequeira, Bern U. Thüer, Meikirch H. van Waes, Zürich T. von Arx, Bern C. Walter, Basel T. Waltimo, Basel R. Weiger, Basel M. Zehnder, Zürich B. Zimmerli, Bern N. U. Zitzmann, Basel
A Framework for Community Detection from Social Media

The past decade has witnessed the emergence of participatory Web and social media, bringing together people in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social Media refers to interaction among people in which they create, share and exchange information and ideas in virtual communities and networks. Social Media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalled scale. In the study of complex networks, a network is said to have community structure if the nodes can be easily grouped into sets of nodes (even overlapping) such that each set of nodes is densely connected internally. Community structure are quite common in real networks. Social Networks often include community groups based on common location, interests, occupation etc. Metabolic Networks have communities based on functional groupings. Citation Networks form communities by research topic. Being able to identify these sub-structures within a network can provide insight into how network function and topology affect each other. In this thesis, we design an end-to-end framework for identifying communities from raw, noisy social media data. The framework is composed of two important phases. First, we introduce a new method of converting the raw, noisy social media data into a weighted entity-entity co-occurrence based consistency network. This includes a simple iterative noise removal procedure for cleaning the entity consistency network by removing noisy entity pairs. Secondly, we propose an approach for identifying coherent communities from the weighted entity network, by introducing novel notions of communityness and community, based on eigenvector centrality. We use this framework to solve three different problems from two distinct domains. The first problem involves detecting communities from raw social media data and showing the application of the communities discovered in a recommendation engine setting. We use the framework for converting the raw data into a clean network and propose a highly parallelizable seed based greedy algorithm to detect as many communities as possible from the weighted entity consistency network. Our framework for community detection is unsupervised, domain agnostic, noise robust, computationally efficient and can be used in different Web Mining applications like Recommendation Systems, Topic Detection, User Profiling etc. We also design an recommendation system to evaluate our framework with existing state-of-art
New Mass‐Conserving Bedrock Topography for Pine Island Glacier Impacts Simulated Decadal Rates of Mass Loss

High‐resolution ice flow modeling requires bedrock elevation and ice thickness data, consistent with one another and with modeled physics. Previous studies have shown that gridded ice thickness products that rely on standard interpolation techniques (such as Bedmap2) can be inconsistent with the conservation of mass, given observed velocity, surface elevation change, and surface mass balance, for example, near the grounding line of Pine Island Glacier, West Antarctica. Using the BISICLES ice flow model, we compare results of simulations using both Bedmap2 bedrock and thickness data, and a new interpolation method that respects mass conservation. We find that simulations using the new geometry result in higher sea level contribution than Bedmap2 and reveal decadal‐scale trends in the ice stream dynamics. We test the impact of several sliding laws and find that it is at least as important to accurately represent the bedrock and initial ice thickness as the choice of sliding law.
Alleviation of sea water stress on tomato plants by foliar application of aspartic acid and glutathione

Tomato plants ( Lycopersicon esculentum Mill.) treated with 8 and 16% of sea water reduced growth parameters and chemical constituents. Both aspartic acid and glutathione increased plant growth, anthocyanin, ? -tocopherol, ascorbic acid and enzymatic activities. Increased endogenous amino acids led to positive changes in protein electrophoresis and caused obvious changes in anatomical features of the stems. The effect of aspartic acid was superior to that of glutathione in increasing plant growth. Under low saline conditions, maximum plant growth was obtained from plants treated with aspartic acid and 8% of sea water, followed by 4%. Data revealed that antioxidants could partially alleviate the harmful effects of salinity. DOI: http://dx.doi. org/10.3329/bjb.v42i1. 15822 Bangladesh J. Bot. 42 (1): 31-43, 2013 (June)
Paradoxical effect of coating on natural guar gum blended carbomer matrix systems for the neurological depressive disorders

Oral extended release products offer potential advantages in patient compliance and therapeutic outcomes like sustained blood levels with attenuation of adverse effects. In neuropsychiatric disorders like depression, most of the formulations serve a marketing objective rather than a clinical objective. The present investigation was aimed to develop a once daily sustained release formulation for delivery of an acid-labile, water soluble antidepressant, duloxetine HCl. The formulation was pragmatically designed using blend of natural and synthetic polymeric biomaterials that it releases the drug at alkaline pH in a sustained manner. The basic intention was to develop a tablet formulation with hydrophilic matrix core, using blend of release retarding natural biodegradable polymers such as guar gum, carbopol 71G-NF (a synthetic carbomer) and C-Pharm® gel. Barrier coating using HPMC-E5 was given to retard the initial release followed by enteric coating with HPMC-AS to prevent exposure of drug in acidic mileau of the stomach. The formulation exhibited desired release pattern and was described best-fit by Hixon-Crowell model. Stability analysis under stress conditions up to one month displayed good reproducibility. The matrix tablets successfully decreased the symptoms of depression (significant decrease in immobility time) in a rat forced swimming model. Pharmacokinetic data of the formulation revealed (tmax ~ 6 h, Cmax ~ 1157.58 ng/ml, mean AUCt~11145.04 ng*h/ml, and Ka~1.07h-1) good correlation in all animals. Keywords: Matrix tablets; Duloxetine HCl; Sustained release; Enteric coating; Hixon-crowell model
Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4‐year period. Such multiple‐pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6‐day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.
Hybrid Block-based Motion Estimation Considering Regional Motion Characteristics

In this paper, we propose a hybrid block-based motion estimation algorithm that considers regional motion characteristics. The proposed method combines multiple search algorithms according to regional motion types to produce more accurate motion vector fields while preventing the increase of the computational cost. For these reasons, the proposed method first divides an entire image into three regions with different motion types. Experimental results show that the proposed motion estimation method improves the accuracy of the estimated motion vector fields over the existing algorithms.