Miracle-dz/UniFAR-FTUs huggingface dataset:
The Pore-Forming Haemolysins of Bacillus Cereus: A Review

The Bacillus cereus sensu lato group contains diverse Gram-positive spore-forming bacteria that can cause gastrointestinal diseases and severe eye infections in humans. They have also been incriminated in a multitude of other severe, and frequently fatal, clinical infections, such as osteomyelitis, septicaemia, pneumonia, liver abscess and meningitis, particularly in immuno-compromised patients and preterm neonates. The pathogenic properties of this organism are mediated by the synergistic effects of a number of virulence products that promote intestinal cell destruction and/or resistance to the host immune system. This review focuses on the pore-forming haemolysins produced by B. cereus: haemolysin I (cereolysin O), haemolysin II, haemolysin III and haemolysin IV (CytK). Haemolysin I belongs to the cholesterol-dependent cytolysin (CDC) family whose best known members are listeriolysin O and perfringolysin O, produced by L. monocytogenes and C. perfringens respectively. HlyII and CytK are oligomeric ß-barrel pore-forming toxins related to the α-toxin of S. aureus or the ß-toxin of C. perfringens. The structure of haemolysin III, the least characterized haemolytic toxin from the B. cereus, group has not yet been determined.
Sampling Program Inputs with Mutation Analysis: Going Beyond Combinatorial Interaction Testing

Modern systems tend to be highly configurable. Testing such systems requires selecting test cases from a large input space. Thus, there is a need to systematically sample program inputs in order to reduce the testing effort. In such cases, testing the interactions between program parameters has been identified as an effective way to deal with this problem. In these lines, Combinatorial Interaction Testing (CIT) models the program input interactions and uses this model to select test cases. Going a step further, we apply mutation analysis on the CIT input model to select program test cases. Mutation operates by injecting defects to the program input model and measures the number of defects found by the selected test cases. Experiments performed on four real programs show that measuring the number of model-based defects gives a stronger correlation to code-level faults than measuring the number of the exercised interactions. Therefore, the proposed mutation analysis approach forms a valid and more effective alternative to CIT.
Wearable robotics as a behavioral assist interface like oneness between horse and rider

The Parasitic Humanoid (PH) is a wearable robot for modeling nonverbal human behavior. This anthropomorphic robot senses the behavior of the wearer and has the internal models to learn the process of human sensory motor integration, thereafter it begins to predict the next behavior of the wearer using the learned models. When the reliability of the prediction is sufficient, the PH outputs the errors from the actual behavior as a request for motion to the wearer. Through symbiotic interaction, the internal model and the process of human sensory motor integration approximate each other asymptotically.
Environmental Research and Public Health Vitamin D Status and Its Consequences for Health in South Africa

In this review, reports were retrieved in which vitamin D status, as assessed by serum 25-hydroxyvitamin D [25(OH)D] levels, was measured in South African population groups with varied skin colours and ethnicities. Healthy children and adults were generally vitamin D-sufficient [25(OH)D level >50 nmol/L] but the majority of those aged above 65 years were deficient. A major role for exposure to solar ultraviolet radiation (UVR) in determining 25(OH)D levels was apparent, with the dietary contribution being minor. Limited data exist regarding the impact of recent changes in lifestyles on vitamin D status, such as urbanisation. With regard to disease susceptibility, 11 of 22 relevant publications indicated association between low 25(OH)D levels and disease, with deficiency most notably found in individuals with tuberculosis and HIV-1. Information on the relationship between vitamin D receptor variants and ethnicity, disease or treatment response in the South African population groups demonstrated complex interactions between genetics, epigenetics and the environment. Whether vitamin D plays an important role in protection against the range of diseases that currently constitute a large burden on the health services in South Africa requires further investigation. Only then can accurate advice be given about personal sun exposure or dietary vitamin D supplementation.
A localization algorithm in wireless sensor networks based on MDS with RSSI classified

In this paper, we proposed a localization algorithm in wireless sensor networks based on MDS combined with weight. The way of determining weight is according to RSSI which is classified by an experiential method. In the new method, the shortest path matrix could be modified in order to obtain better accuracy. Although without range measuring, it is proved that a better accuracy could be obtained in the new algorithm.
Phone-based motion control in VR: analysis of degrees of freedom

We introduce a one-handed travel technique for virtual environments (VE), we call Phone-Based Motion Control. The travel technique uses a mobile phone with integrated sensors as a 3D spatial input device. We benefit from the touch capability to change the viewpoint translation in the VE, while the orientation of the viewpoint in the VE is controlled by the built-in sensors. The travel interaction clearly distinguishes between translation (touch based translation control) and rotation (steer based rotation control), putting each set of degrees of freedom to a separate interaction technique. This work examines how many degrees of freedom are needed to perform the travel task as easy as possible. It also investigates different mapping functions between the user's actions and the viewpoint reactions in the VR. For that purpose, four metaphors are developed for the steer based rotation control technique. The results of the user study indicate the trend that 4 DOF metaphors perform best, and that the usage of a mobile roll to control the viewpoint is the desired mapping.
Neural Network Approach to variable vehicle speed limitation upon weather conditions

The target of this research is to adapt a Neural Network Approach that could be used in determining proper vehicle speed upon relevant conditions data. These include weather condition such as: Precipitation intensity, speed of wind, and degree of visibility, and road surface condition. Data are input to neural network. Output driven is a decision about allowed (advised) speed of a vehicle along highway which is appropriate to the atmospheric and road surface conditions at locations of interest. Such application could reduce the possibility of accidents resulting from excess speed when bad weather condition.
The Effect of Word of Mouth on Sales : New Answers from the Consumer Journey Data with Deep Learning

Online Word-of-Mouth has great impact on product sales. Although aggregate data suggests that customers read review text rather than relying only on summary statistics, little is known about consumers’ review reading behavior and its impact on conversion at the granular level. To fill this research gap, we analyze a comprehensive dataset that tracks individual-level search, review reading, as well as purchase behaviors and achieve two objectives. First, we describe consumers’ review reading behaviors. In contrast to what has been found with aggregate data, individual level consumer journey data shows that around 70% of the time, consumers do not read reviews in their online journeys; they are less likely to read reviews for products that are inexpensive and have many reviews. Second, we quantify the causal impact of quantity and content information of reviews read on sales. The identification relies on the variation in the reviews seen by consumers due to newly added reviews. To extract content information, we apply Deep Learning natural language processing models and identify six dimensions of content in the reviews. We find that aesthetics and price content in the reviews significantly affect conversion. Counterfactual simulation suggests that re-ordering review content can have the same effect as a 1.6% price cut for boosting conversion.
Attractor metadynamics in a recurrent neural network: adiabatic vs. symmetry protected flow

In dynamical systems with distinct time scales the time evolution in phase space may be influenced strongly by slow manifolds. Orbits then typically follow the slow manifold, which hence act as a transient attractor, performing in addition rapid transitions between distinct branches of the slow manifold on the time scales of the fast variables. These intermittent transitions correspond to state switching within transient state dynamics. A full characterization of slow manifolds is often difficult, e. g. in neural networks with a large number of dynamical variables, due to the generically complex shape. We therefore introduce here the concept of locally attracting points, the target points. The set of target points is, by definition, the subsets of the slow manifold guiding the time evolution of a given trajectory. We consider here systems, in which the overall dynamics settles in the limit of long times either in a limit cycle switching between transient states, or in a chaotic attractor. The set of target points then decomposes into one-dimensional (or fractal) branches, which can be analyzed directly. Here we examine the role of target points as transiently stable attractors in an autonomously active recurrent neural network. We quantify their influence on the transient states by measuring the effective distance between trajectories and the corresponding target points in phase space. We also present an example of chaotic dynamics, discussing how the chaotic motion is related to the set of transient attractors. The network considered contains, for certain parameters settings, symmetry protected solutions in the form of travelling waves. We find, that the slow manifold does not guide the flow in this regime, which we denote as non-adiabatic, even though there are up to four orders of magnitude difference between the slow and the fast time scales.
Gangguan Cemas Menyeluruh

Kecemasan adalah perasaan takut yang tidak jelas dan tidak didukung oleh situasi. Ketika merasa cemas, individu merasa tidak nyaman atau takut atau mungkin memiliki firasat akan ditimpa malapetaka padahal ia tidak mengerti mengapa emosi yang mengancam tersebut terjadi. Neurotransmiter utama terhadap gangguan kecemasan adalah peningkatan norepinefrin, serotonin, dan gamma aminobutyric acid (GABA). Perkiraan yang diterima untuk prevalensi gangguan kecemasan umum dalam satu tahun terentang dari 3 sampai 8 persen. Rasio wanita dan laki-laki adalah kira-kira 2:1, usia onset sukar untuk ditentukan, karena sebagian besar pasien melaporkan bahwa mereka mengalami kecemasan selama yang dapat mereka ingat. Ny. W, 56 tahun dengan keluhan merasa cemas, dadanya berdebar, keringat dingin, dan sulit tidur. Perasaan cemas ini berlangsung beberapa saat dan hilang timbul. Keluhan muncul sejak ± 1 tahun sebelum masuk rumah sakit, diawali oleh perubahan dalam pekerjaan atau kegiatan pasien yang semakin berat. Pasien didiagnosa mengalami gangguan cemas menyeluruh. Pasien diterapi dengan psikofarmakologi berupa alprazolam 2 x 0,25 mg dan dilakukan intervensi psikososial kepada keluarga dan pasiennya. Tatalaksana gangguan cemas menyeluruh dapat dilakukan dengan psikoterapi dan pemberian obat golongan Benzodiazepine. Kata kunci: benzodiazepine, GABA, gangguan cemas menyeluruh, kecemasan
Inapproximability of Combinatorial Problems via Small LPs and SDPs

Motivated by [12], we provide a framework for studying the size of linear programming formulations as well as semidefinite programming formulations of combinatorial optimization problems without encoding them first as linear programs. This is done via a factorization theorem for the optimization problem itself (and not a specific encoding of such). As a result we define a consistent reduction mechanism that degrades approximation factors in a controlled fashion and which, at the same time, is compatible with approximate linear and semidefinite programming formulations. Moreover, our reduction mechanism is a minor restriction of classical reductions establishing inapproximability in the context of PCP theorems. As a consequence we establish strong linear programming inapproximability (for LPs with a polynomial number of constraints) for several problems that are not 0/1-CSPs: we obtain a 3/2-epsilon inapproximability for Vertex Cover (which is not of the CSP type) answering an open question in [12], we answer a weak version of our sparse graph conjecture posed in [6] showing an inapproximability factor of 1/2+ε for bounded degree IndependentSet, and we establish inapproximability of MaxMULTICUT (a non-binary CSP). In the case of SDPs, we obtain relative inapproximability results for these problems.
GIPSCo: A Method for Comparison of Protein Structures Based on Geometric Invariants

Protein structure comparison is important for elucidation of evolutionary relationships, function and f unctionally important amino acid residues. We propose Geometric Invariant based Protein Structure Comparison (GIPSCo) , an approach that compares a protein pair by using geometric invariants of local geometry of the backbone structures. Th e geometric invariants capture enough structural informati on so that the method has a relatively low false positive rate. The method first generates a list of aligned fragment pairs (AFPs) using the geometric invariants of the local geometry . These structurally similar AFPs are assembled using a graph theoretic approach to maximum weighted clique to obtain global structural alignment between two proteins. The proposed method is purely geometric in nature and is independent of sequence alignment and secondary structure assignment. Results for sample protein pairs were compared with well established methods of protein structure alignme nt. Further, our method could detect some structural similarit ies that were missed by other algorithms.
Nonparametric bayesian upstream supervised multi-modal topic models

Learning with multi-modal data is at the core of many multimedia applications, such as cross-modal retrieval and image annotation. In this paper, we present a nonparametric Bayesian approach to learning upstream supervised topic models for analyzing multi-modal data. Our model develops a compound nonparametric Bayesian multi-modal prior to describe the correlation structure of data both within each individual modality and between different modalities. It extends the hierarchical Dirichlet process (HDP) through incorporating upstream supervised response variables and values of latent functions under Gaussian process (GP). Upstream responses shared by data from multiple modalities are beneficial for discriminatively training and GP allows flexible structure learning of correlations. Hence, our model inherits the automatic determination of the number of topics from HDP, structure learning from GP and enhanced predictive capacity from upstream supervision. We also provide efficient variational inference and prediction algorithms. Empirical studies demonstrate superior performances on several benchmark datasets compared with previous competitors.
STIM1 plays an important role in TGF-β-induced suppression of breast cancer cell proliferation

Store-operated calcium entry (SOCE) signaling is involved in cancer progression. Stromal interaction molecule 1 (STIM1) triggers store-operated calcium channels to induce SOCE. Transforming growth factor-β (TGF-β) influences a wide range of cellular behaviors, including cell proliferation. However, little is known about the relationship between calcium signaling and TGF-β signaling in cancer cell proliferation. Here, we found that TGF-β induced cell cycle arrest at the G0/G1 phase and suppressed cell proliferation in MDA-MB-231 and MCF-7 breast cancer cells. These effects were impaired by extracellular Ca2+ chelator EGTA or SOCE specific inhibitor SKF96365 in MDA-MB-231 cells. Treating MDA-MB-231 cells with TGF-β for 24 and 48 h markedly decreased STIM1 expression and thapsigargin-induced SOCE. A transcriptional inhibitor of STIM1, Wilm's tumor suppressor 1 (WT1), was upregulated in TGF-β-treated MDA-MB-231 cells, and knockdown of WT1 expression partially restored the TGF-β-induced downregulation of STIM1. Stably overexpressing STIM1 in MDA-MB-231 cells restored the TGF-β-induced effects. The p21 mRNA level increased in SKF96365- or TGF-β-treated MDA-MB-231 cells, whereas that for cyclin E1 decreased. Our findings demonstrate for the first time that STIM1 and SOCE are involved in the TGF-β-induced suppression of cell proliferation. Furthermore, our studies also provide a new approach to inhibit breast cancer cell proliferation with small molecules targeting STIM1 and SOCE.
Portrait of an odd-eyed cat: cultural crossing as a trademark for a dutch-thai strategic alliance

This chapter attempts a step forward in seeking a richer understanding of the dynamics of strategic alliances, specifically when viewed from a cross-cultural perspective. We report selected materials from a study designed to build a theory of culture and learning in organizations based on observations of and open-ended interviews with Dutch and Thai employees working for four selected Dutch firms in Thailand. Here we present one of those cases, a Dutch-Thai joint venture that thrived by weaving together the many intricate cultural webs to achieve a unique pattern of partnership which, metaphorically speaking, became its indispensible trademark. The case illustrates how the three levels of culture – national, organizational, and professional cultures – could all interlace in a real world setting and serve as an instrumental force of success amidst tension in one particular cross-border strategic alliance. First, we adopt a thick descriptive style of case narration to present the case of a Dutch- Thai joint venture, Chuchawal-De Weger Internationaal (CDW), painting a portrait of its origin, evolution and characteristics. Next, we turn to elaborate on the particular issue of cultural crossing, its exact theorized properties, dimensions and implications. Finally, we relate the case of CDW to the proposed theory and conclude with a reflection on how this case and our interpretation of it illuminate the complex role culture can play in the dynamics of strategic alliances.
An interaction framework for level-of-abstraction visualization of 3D geovirtual environments

3D geovirtual environments constitute effective media for the analysis and communication of complex geospatial data. Today, these environments are often visualized using static graphical variants (e.g., 2D maps, 3D photorealistic) from which a user is able to choose from. To serve the different interests of users in specific information, however, the spatial and thematic granularity at which model contents are represented (i.e., level of abstraction) should be dynamically adapted to the user's context, which requires specialized interaction techniques for parameterization. In this work, we present a framework that enables interaction interfaces to parameterize the level-of-abstraction visualization according to spatial, semantic, and thematic data. The framework is implemented in a visualization system that provides image-based rendering techniques for context-aware abstraction and highlighting. Using touch and natural language interfaces, we demonstrate its versatile application to geospatial tasks, including exploration, navigation, and orientation.
Low-Dose Versus Standard Dose of Bacillus Calmette-Guerin in the Treatment of Nonmuscle Invasive Bladder Cancer

AbstractWhether low-dose Bacillus Calmette-Guerin (BCG) treatment can reduce the side effects while maintaining efficacy for patients with nonmuscle invasive bladder cancer (NMIBC) is controversial.To investigate whether low-dose BCG treatment can reduce the side effects while maintaining efficacy for patients with NMIBC when compared with standard-dose BCG treatment.A comprehensive literature search of PubMed, EMBASE, CINAHL, LILACS, and CENTRAL databases was conducted to identify relevant randomized controlled trials (RCT) or quasi-randomized controlled trials (qRCT) that have assessed the efficacy of low- and standard-dose BCG therapy for patients with NMIBC. Systematic review and meta-analysis were performed according to Preferred Reporting Items for Systematic Reviews and Meta-analysis Criteria.Six RCTs and 2 qRCTs were eligible for meta-analysis. Low-dose BCG instillation was not inferior to reduce the risk of bladder tumor recurrence (hazard ratio [HR], 1.15; 95% confidence interval [CI], 1.00–1.31; P = 0.05), meanwhile no difference was found regarding tumor progression (HR = 1.08; 95%CI, 0.83–1.42; P = 0.57). However, low-dose BCG provided a significantly lower incidence of overall side effects (RR = 0.75; 95%CI, 0.60–0.94; P = 0.01), systemic side effects (RR = 0.57; 95%CI, 0.34–0.97; P = 0.04), severe side effects (RR = 0.52; 95%CI, 0.36–0.74; P = 0.0003), and withdrawal due to BCG toxicity (RR = 0.49; 95%CI, 0.26–0.91; P = 0.02). In contrast, local side effects were comparable between low- and standard-dose arms (RR = 0.89; 95%CI, 0.73–1.08; P = 0.24). Low-dose BCG instillation significantly reduces the incidence of overall side effects, especially severe and systemic symptoms in patients with NMIBC, while the oncological control efficacy of low-dose BCG is not inferior to standard-dose BCG. Further studies with stratification using different risk factors at randomization are required to assess whether the efficacy of low-dose BCG is comparable to standard dose BCG for different risk of patients.PROSPERO registration No CRD42014014871 (www.crd.york.ac.uk/prospero/).
Linear fuzzy clustering techniques with missing values and their application to local principal component analysis

In this paper, we propose two methods for partitioning an incomplete data set with missing values into several linear fuzzy clusters by extracting local principal components. One is an extension of fuzzy c-varieties clustering that can be regarded as the algorithm for the local principal component analysis of fuzzy covariance matrices. The other is a simultaneous application of fuzzy clustering and principal component analysis of fuzzy correlation matrices. Both methods estimate prototypes ignoring only missing values and they need no preprocessing of data such as the elimination of samples with missing values or the imputation of missing elements. Numerical examples show that the methods provide useful tools for interpretation of the local structures of a database.
Game-Based Remedial Instruction in Mastery Learning for Upper-Primary School Students

The study examines the effectiveness of using computer games for after-school remedial mastery learning. We incorporated instructional materials related to "area of a circle" into the popular Monopoly game to enhance the performance of sixth-grade students learning mathematics. The program requires that students enter the answers to avoid the shortcomings of multiple-choice questions. Throughout the game, whenever students are unable to answer questions correctly they receive immediate remedial instruction specifically for that question. This study sought to compare the effectiveness of game-based and video-based remedial instruction incorporated with elements of mastery learning. The results demonstrate that (a) both instructional videos and the proposed Monopoly game enhance the learning of mathematical concepts; and (b) the Monopoly game is more effective than instructional videos at leveraging the benefits of mastery learning. The goal of the research was to integrate games and mastery learning into after-school remedial instruction and design a game to practice the steps of mastery learning.