Miracle-dz/UniFAR-FTUs huggingface dataset:
HIV-1 subtype influences susceptibility and response to monotherapy with the protease inhibitor lopinavir/ritonavir

Objective PI susceptibility results from a complex interplay between protease and Gag proteins, with Gag showing wide variation across HIV-1 subtypes. We explored the impact of pre-treatment susceptibility on the outcome of lopinavir/ritonavir monotherapy. Methods Treatment-naive individuals who experienced lopinavir/ritonavir monotherapy failure from the MONARK study were matched (by subtype, viral load and baseline CD4 count) with those who achieved virological response (‘successes’). Successes were defined by viral load <400 copies/mL after week 24 and <50 copies/mL from week 48 to week 96. Full-length Gag–protease was amplified from patient samples for in vitro phenotypic susceptibility testing, with susceptibility expressed as fold change (FC) relative to a subtype B reference strain. Results Baseline lopinavir susceptibility was lower in viral failures compared with viral successes, but the differences were not statistically significant (median lopinavir susceptibility: 4.4 versus 8.5, respectively, P = 0.17). Among CRF02_AG/G patients, there was a significant difference in lopinavir susceptibility between the two groups (7.1 versus 10.4, P = 0.047), while in subtype B the difference was not significant (2.7 versus 3.4, P = 0.13). Subtype CRF02_AG/G viruses had a median lopinavir FC of 8.7 compared with 3.1 for subtype B (P = 0.001). Conclusions We report an association between reduced PI susceptibility (using full-length Gag–protease sequences) at baseline and subsequent virological failure on lopinavir/ritonavir monotherapy in antiretroviral-naive patients harbouring subtype CRF02_AG/G viruses. We speculate that this may be important in the context of suboptimal adherence in determining viral failure.
Exploring gaming mechanisms to enhance knowledge acquisition in virtual worlds

3D environments are very popular among gamers and ever more used for simulations and serious games. The educational value of setting an adventure/simulation in a Virtual World (VW) is significant, in particular when training a workforce in performing dangerous/special tasks. However, when it comes to the exploration of a world to learn from it - as it happens in several real-life experiences - there is the risk that a lot of "knowledge in the world" is wasted because of a lack of appropriate guidance and the difficulty of providing more in-depth information about some items without interrupting the flow of the game. This paper discusses a Virtual Reality (VR) environment - aimed at promoting a meaningful interaction with artistic heritage - that has been enhanced with embedded microGames (mGs). mGs challenge the player to discover/investigate historical/artistic details related to a reconstructed area and virtually interact with pieces of the heritage in their context. mGs can be thought of as sort of 1-level links in the "hypertext" represented by the 3D environment where the player lives her/his cultural adventure. Preliminary informal tests have suggested that the approach is valid and also provided some guidelines about how to properly and smoothly integrate mGs in a 3D environment, which is an important requirement in order not to startle/distract the player.
Palm-print recognition based on DCT domain statistical features extracted from enhanced image

In this paper, a feature extraction algorithm for palm-print recognition is proposed based on statistical features of two-dimensional discrete cosine transform (2D-DCT), which efficiently exploits the local spatial variations in a palm-print image. First, adaptive median filtering followed by Top-Hat transform is employed on a given palm-image to obtain palm-line enhancement by reducing the effect of noise and lighting variations. Unlike conventional median filtering, adaptive median filtering operates only on pixels, which are not structurally aligned and can preserve detail while performing overall smoothing operation. The entire enhanced image is segmented into several small spatial modules and 2D-DCT is performed on each module. Instead of considering all DCT coefficients, a set of statistical features are extracted in DCT domain, which drastically reduces the feature dimension and precisely captures the detail variations within the palm-print image. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.
Achieving k-anonymity using Minimum Spanning Tree based Partitioning

Protecting individual‟s privacy has become a major concern among privacy research community. Many frameworks and privacy principles were proposed for protecting the privacy of the data that is being released to the public for mining purpose. k-anonymization was the most popular among the proposed techniques in which the sensitive association between the sensitive attributes and their corresponding identifiers are de-associated. In this paper, we proposed an enhanced k-anonymity technique by using Minimum Spanning Tree (MST) partitioning approach. In this technique we disclose the information of the individuals pertaining to minimum group size i.e., k. We achieve this technique in two phases. During the first phase, MST for the given dataset is partitioned to generate equivalence classes and in the subsequent phase whether the equivalence class size is achieved to that of the minimum group size k is verified. Our approach resulted in achieving the optimal anonymization along with data utility. We showed the efficacy of our proposed technique by running a series of experiments in terms of information loss to show that our technique adheres to the quality of the anonymized data.
Potassium channel activators differentially modulate the effect of sodium channel blockade on cardiac conduction

Diminished repolarization reserve contributes to the arrhythmogenic substrate in many disease states. Pharmacological activation of K+ channels has been suggested as a potential antiarrhythmic therapy in such conditions. Having previously demonstrated that IK1 and IKr can modulate cardiac conduction, we tested here the effects of pharmacological IKATP and IKs activation on cardiac conduction and its dependence on the sodium current (INa).
Automated Inspection of Complex Composite Components in the Production Cycle

Automated Inspection of Complex Composite Components in the Production Cycle Albrecht MAURER , Wolfgang HAASE , Rudolf HENRICH , Roman KOCH , Walter DE ODORICO 1 GE Inspection Technologies Industriegebiet Sued E11, 63755 Alzenau, Germany Tel. : +49 6188 4466 12, Fax: +49 6188 4466 88 E-mail: nutronik@ge.com, www.geinspectiontechnologies. com Airbus Deutschland GmbH, Huenefeldstrasse 1-5, 28199 Bremen, Germany Tel. : +49 421 538 4777, Fax: +49 421 538 4180 E-mail: rudolf.henrich@airbus.com, www.airbus.com
On the space efficiency of one way quantum finite automata

We exhibit a family {L_n} of finite regular languages such that any 1-way quantum finite automaton (QFA) accepting L_n with probability 1/2 + epsilon (for any constant epsilon > 0) must have 2^{Omega(n/log n)} states. On the other hand, each language L_n can be recognized by a deterministic finite automaton (DFA) of size O(n). This is in immediate contrast to the recent result of Ambainis and Freivalds which shows that for some regular languages, 1-way QFAs can be exponentially more space-efficient as compared to DFAs.
Factor Analysis of Critical Thinking among Nursing Students from Private Higher Education Institutions

Background: Nursing Council of Thailand defined the critical thinking as a main competency of registered nurse and midwives. Critical thinking as Facione and Facione’s concept consisted of critical thinking skill and disposition component of critical thinking. This was the second order confirmatory factor analysis that would affect to theoretical and practical instruction management as the guidelines for promoting the critical thinking of nursing students further. Methods: The participants were 549 first year nursing students from private higher education institutions: Huachiew chalermprakiet university, Vongchavalitkul university, Western university, Siam university and College of Asia Scholars. Simple random sampling was used in this research. Research instruments were questionnaires on general characteristics among nursing students and critical thinking. The data were analyzed by using descriptive statistics and confirmatory factor analysis. Results : Critical thinking consisted of two elements: the first element was critical thinking skills which were thinking skills on other experiences such as interpretation, analysis, evaluation, inference, explanation, and meta-cognitive self-regulation. The second element was disposition element of critical thinking. It comprised personal characteristics which supported intellectual development process. It consisted of truth-seeking, open-mind, analyticity, systematicity, critical thinking self-confidence, inquisitiveness and maturity. The results showed that the critical thinking model was consistent with empirical data, chi-square = 54.845, df = 42, p-value = . 088 (p-value higher than .05 is consistency), c 2 /df = 1.306 (acceptable levels is not over 2), GFI = . 985, CFI = . 997 (acceptable level gets close 1), RMR = . 008, RMSEA = . 024. (acceptable level gets close 0). The coefficient of standard factor loading of variables was different from zero with statistical significance at . 05. The variable with the highest level of factor loading was explanation (b sc = .856) followed by inference (b sc = .832). The variable with the lowest level of factor loading was analysis (b sc =.637). Conclusion: Critical thinking consisted of two main elements: 1) critical thinking skills and 2) personal characteristics of critical thinkers. Factor analysis of critical thinking among nursing students from private higher education institutions showed that critical thinking model was consistent with empirical data. In theoretical and practical teaching, nursing academies should promote critical thinking by using the following critical thinking skills and personal characteristics of critical thinkers to improve critical thinking in nursing students.
Holographic Cosmology 3.0

We present a new version of holographic cosmology, which is compatible with present observations. A primordial p = ρ phase of the universe is followed by a brief matter dominated era and a brief period of inflation, whose termination heats the universe. The flatness and horizon problems are solved by the p = ρ dynamics. The model is characterized by two parameters, which should be calculated in a more fundamental approach to the theory. For a large range in the phenomenologically allowed parameter space, the observed fluctuations in the cosmic microwave background were generated during the p = ρ era, and are exactly scale invariant. The scale invariant spectrum cuts off sharply at both upper and lower ends, and this may have observational consequences. We argue that the amplitude of fluctuations is small but cannot yet calculate it precisely.
An Approach Toward Fast Gradient-Based Image Segmentation

In this paper, we present and investigate an approach to fast multilabel color image segmentation using convex optimization techniques. The presented model is in some ways related to the well-known Mumford-Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. The objective function should represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive to solve. This paper introduces such a model, the nontrivial transformation of this model into a convex-concave saddle point problem, and the numerical treatment of the problem. We evaluate our approach by applying our algorithm to various images and show that our results are competitive in terms of quality at unprecedentedly low computation times. Our algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images (Fig. 1).
Topic-based Selectivity Estimation for Hybrid Queries over RDF Graphs

The Resource Description Framework (RDF) has become an accepted standard for describing entities on the Web. Many such RDF descriptions are text-rich – besides structured data, they also feature large portions of unstructured text. As a result, RDF data is frequently queried using predicates matching structured data, combined with string predicates for textual constraints: hybrid queries. Evaluating hybrid queries requires accurate means for selectivity estimation. Previous works on selectivity estimation, however, suffer from inherent drawbacks, reflected in efficiency and effective issues. In this paper, we present a general framework for hybrid selectivity estimation. Based on its requirements, we study the applicability of existing approaches. Driven by our findings, we propose a novel estimation approach, TopGuess, exploiting topic models as data synopsis. This enables us to capture correlations between structured and unstructured data in a uniform and scalable manner. We study TopGuess in theorical manner, and show TopGuess to guarantee a linear space complexity w.r. t. text data size, and a selectivity estimation time complexity independent from its synopsis size. In experiments on real-world data, TopGuess allowed for great improvements in estimation accuracy, without sacrificing runtime performance
A study on behavior-based mobile malware analysis system against evasion techniques

With the spread of smartphones, our daily lives become more convenient. However, using smartphone has also made us to be exposed to various security threats. Especially, the number of mobile malware has been increasing with years and mobile malwares are becoming more intelligent and sophisticated. There are a lot of measures to deal with mobile malwares, but techniques evading them have also been developed. In this paper, we describe various analysis evasion techniques used by mobile malwares and proposes behavior-based mobile malware analysis system against evasion techniques.
BIOFILM FORMATION OF Vibrio cholerae ON STAINLESS STEEL USED IN FOOD PROCESSING

Vibrio cholerae represents a significant threat to human health in developing countries. This pathogen forms biofilms which favors its attachment to surfaces and its survival and transmission by water or food. This work evaluated the in vitro biofilm formation of V. cholerae isolated from clinical and environmental sources on stainless steel of the type used in food processing by using the environmental scanning electron microscopy (ESEM). Results showed no cell adhesion at 4 h and scarce surface colonization at 24 h. Biofilms from the environmental strain were observed at 48 h with high cellular aggregations embedded in Vibrio exopolysaccharide (VPS), while less confluence and VPS production with microcolonies of elongated cells were observed in biofilms produced by the clinical strain. At 96 h the biofilms of the environmental strain were released from the surface leaving coccoid cells and residual structures, whereas biofilms of the clinical strain formed highly organized structures such as channels, mushroom-like and pillars. This is the first study that has shown the in vitro ability of V. cholerae to colonize and form biofilms on stainless steel used in food processing.
Using Monte Carlo simulation for improving data availability in P2P network

In this paper we present a replication strategy to improve data availability in P2P Networks. The focus of the paper is to replicate data to nodes which are highly available and complement one another in terms of uptimes. This would decrease the replication management overhead until the number of replicas falls to a certain threshold. Replication to reliable node would improve the cost of replication by avoiding irregular nodes of the network. We run Monte Carlo simulation based on past traces of Kad, OverNet, Bittorrent and PlanetLab, to present how our replication keeps data sustainable in the network. In our evaluation we show that a life pattern along with the availability of nodes improves overall data availability. We perform our evaluation on a Kademlia network, and show that our approach reduces over head compared to existing approaches to data availability.
Energy Donor Effect on the Sensing Performance for a Series of FRET-Based Two-Photon Fluorescent Hg2+ Probes

Nonlinear optical properties of a series of newly-synthesized molecular fluorescent probes for Hg2+ containing the same acceptor (rhodamine group) are analyzed by using time-dependent density functional theory in combination with analytical response theory. Special emphasis is placed on evolution of the probes’ optical properties in the absence and presence of Hg2+. These compounds show drastic changes in their photoabsorption and photoemission properties when they react with Hg2+, indicating that they are excellent candidates for ratiometric and colorimetric fluorescent chemosensors. Most importantly, the energy donor moiety is found to play a dominant role in sensing performance of these probes. Two-photon absorption cross sections of the compounds are increased with the presence of Hg2+, which theoretically suggests the possibility of the probes to be two-photon fluorescent Hg2+ sensors. Moreover, analysis of molecular orbitals is presented to explore responsive mechanism of the probes, where the fluorescence resonant energy transfer process is theoretically demonstrated. Our results elucidate the available experimental measurements. This work provides guidance for designing efficient two-photon fluorescent probes that are geared towards biological and chemical applications.
Image registration by integrating similarity and epipolor constraints

Accurate image registration plays an important role in computer vision, three-dimensional information acquisition and remote sensing processing. Recent researches on image registration are mostly focused on those by using either epipolar geometry or feature extraction, restricting the performance of image registration. A novel algorithm integrating similarity constraint and epipolar constraint is proposed in this paper. This algorithm firstly realizes the similarity constraint utilizing the Harris-Laplace detector combined with the SIFT (Scale Invariant Feature Transform) descriptor. Then, new approach of two-way epipolar constraint is proposed and used in the following processing, which satisfies the epipolar constraint and uniqueness constraint simultaneously. Thirdly, the iteration process is conducted until all constraints are satisfied. Additionally, a generalized algorithm for the situation without localization information of the image acquisition is given. The algorithm is examined through extensive experiments on various images from video sequence to digital camera images and the results show that the proposed algorithm exceeds SIFT in the aspect of match accuracy at the cost of a little more time consume.
Holly Chiang , Yifan Ge Mobile Haze Removal

In this paper, we implement a dehazing algorithm using dark pixel detection in MatLab and C++/OpenCV. The algorithm is originally proposed in Yu et al.’s paper. The simplicity and effectiveness of the algorithm make it possible for us to run the C++/OpenCV implementation on Android phones through android NDK with reasonable speed. Keywords—dehaze; image processing; mobile application; computer vision
“The One Who Chases You Away Does Not Tell You Go”: Silent Refusals and Complex Power Relations in Research Consent Processes in Coastal Kenya

Consent processes have attracted significant research attention over the last decade, including in the global south. Although relevant studies suggest consent is a complex negotiated process involving multiple actors, most guidelines assume consent is a one-off encounter with a clear ‘yes’ or ‘no’ decision. In this paper we explore the concept of ‘silent refusals’, a situation where it is not clear whether potential participants want to join studies or those in studies want to withdraw from research, as they were not actively saying no. We draw on participant observation, in-depth interviews and group discussions conducted with a range of stakeholders in two large community based studies conducted by the KEMRI Wellcome Trust programme in coastal Kenya. We identified three broad inter-related rationales for silent refusals: 1) a strategy to avoid conflicts and safeguard relations within households, - for young women in particular—to appear to conform to the wishes of elders; 2) an approach to maintain friendly, appreciative and reciprocal relationships with fieldworkers, and the broader research programme; and 3) an effort to retain study benefits, either for individuals, whole households or wider communities. That refusals and underlying rationales were silent posed multiple dilemmas for fieldworkers, who are increasingly recognised to play a key interface role between researchers and communities in many settings. Silent refusals reflect and reinforce complex power relations embedded in decisions about research participation, with important implications for consent processes and broader research ethics practice. Fieldworkers need support to reflect upon and respond to the ethically charged environment they work in.
Privacy preserving crowd estimation for safer cities

As sensors are getting cheaper and being widely deployed in cities, the Internet of Things (IoT) is providing us great potential to make cities not only smart but also safer. However, how to utilize the information sensed from physical environments to improve civil safety is still a challenging issue. In this paper we present a methodology to estimate the crowd level for indoor scenarios based on data collected from inexpensive and privacy conscious sensors. Our method can be widely applied into many applications for Safer Cities, ranging from facility monitoring to emergency handling. Different from camera-based approaches, our solution can preserve user privacy, scaling better in terms of costs than existing solutions. Using the data collected from real environments, we examine different supervised learning algorithms and identify that Random Forest is the best model. Our solution has been deployed and tested in a Singapore shopping mall, showing that 95% of the overcrowded situations can be detected.