Miracle-dz/UniFAR-FTUs huggingface dataset:
Long non-coding RNA TUG1 promotes cervical cancer progression by regulating the miR-138-5p-SIRT1 axis

Increasing evidences showed that long non-coding RNAs (lncRNAs) play vital roles in tumor progression. Recent studies indicated that lncRNA TUG1 was upregulated and promoted tumor processes in several cancers. However, the expression and underlying mechanism of TUG1 in cervical cancer remain unclear. In the present study, we found that TUG1 expression was upregulated in cervical cancer tissues and correlated with advanced clinical features and poor overall survival. TUG1 knockdown suppressed cervical cancer cell growth and metastasis in vitro and tumor growth in vivo. In addition, our results indicated that TUG1 could act as an endogenous sponge by directly binding to miR-138-5p and suppressed miR-138-5p expression. Furthermore, we found that TUG1 could reverse the inhibitory effect of miR-138-5p on cervical cancer cells processes, which might be involved in the activation of SIRT1, a target gene of miR-138-5p, and activation of Wnt/β-catenin signaling pathway. Taken together, we elucidated that TUG1 might promote cervical cancer malignant progression via miR-138-5p-SIRT1-Wnt/β-catenin signaling pathway axis.
GOLOIS wins Phantom-Go Tournament

Phantom Go is a two-player game often played as an entertaining variation by regular Go players. It is the equivalent of Kriegspiel for Chess. Phantom Go is a variant of classic Go in which the players do not see the opponent’s moves. Two players each have their own board, on which only their own stones are visible. A referee is needed. He has a reference board on which the actual state of the game is maintained. The players communicate their move decisions to the referee. There are a few variations to the rules of Phantom Go, in the rules used for the tournament, the referee has the following replies to a suggested move:
Improving Laboratories Efficiency through Website Using Text Mining

Text mining is an emerging technology that can be used to augment existing data in corporate databases by making unstructured text data available for analysis. This research aim to present a proposed text mining system customized to improve laboratories efficiency. This is done by taking the electronic comments and e-mails produced to the organization web as inputs for that proposed mining system. The proposed text miner is customized for emails and comments written to the organization. For that the basic text mining algorithm will almost be modified by adding new steps, modify some steps by customizing Natural Language Processing (NLP), data mining techniques and building the document database. The proposal applied on the Computer Sciences Department Web and the results obtained are suitable to be published.
Recognizing Bedside Events Using Thermal and Ultrasonic Readings

Falls in homes of the elderly, in residential care facilities and in hospitals commonly occur in close proximity to the bed. Most approaches for recognizing falls use cameras, which challenge privacy, or sensor devices attached to the bed or the body to recognize bedside events and bedside falls. We use data collected from a ceiling mounted 80 × 60 thermal array combined with an ultrasonic sensor device. This approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner. We evaluate three different approaches towards recognizing location and posture of an individual. Bedside events are recognized using a 10-second floating image rule/filter-based approach, recognizing bedside falls with 98.62% accuracy. Bed-entry and exit events are recognized with 98.66% and 96.73% accuracy, respectively.
Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices and its application in brain–computer interfaces

Objective. In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner. Approach. The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples. Main results. We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique—leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data. Significance. The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain–computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.
The impact of a livelihood program on depressive symptoms among people living with HIV in Cambodia.

Background Psychological and social problems are major concerns in this era of successful antiretroviral therapy. Although livelihood programs have been implemented extensively to improve the daily living conditions of people living with HIV in Cambodia, no studies have yet investigated the impacts of these programs on the mental health of this vulnerable population. Therefore, we examined the impact of a livelihood program on depressive symptoms and associated factors among people living with HIV in Cambodia. Design A quasi-experimental, nonequivalent comparison group study was conducted in six provinces of Cambodia in 2014. Data were collected from an intervention group comprising 357 people living with HIV who had participated in the livelihood program and a comparison group comprising 328 people living with HIV who had not participated in this program. Multiple logistic regression analysis was carried out to examine the association between livelihood-program participation and depressive symptoms as measured by the depressive symptoms subscale of the 25-item Cambodian version of the Hopkins Symptom Checklist. A propensity score matching was used to examine the effect of the livelihood program on depressive symptoms while controlling for selection bias. Results Overall, 56.0% and 62.7% of the participants in the intervention and comparison groups, respectively, met the Hopkins Symptom Checklist threshold for depressive symptoms. The multiple logistic regression analysis showed that the participants in the intervention group had significantly lower odds of having depressive symptoms (adjusted odds ratio 0.68, 95% confidence interval 0.52-0.88). The analysis from propensity score matching indicated that the livelihood program helped mitigate depressive symptoms among the participants in the intervention group (T=-1.99). Conclusions The livelihood program appeared to help mitigate the burden of depressive symptoms among people living with HIV in Cambodia. Thus, this program should be scaled up and modified to better improve participants' mental health.
Grasping the experience of the other from an interview: Self-transposition in use.

This article describes a part of the interview process that is never usually reported. Listening to what people say is the key to increasing our knowledge of human existences. Procuring knowledge about human experience is much more challenging. Although good sources on how to prepare and conduct an interview exist, the process of the interviewer's perception of the interviewee's message and meaning is less examined. Beyond the role of eliciting the data, the researcher endeavours to reproduce the interviewee's narration and not the voice of the researcher. By illustrating the process during the interview, further transparency and thereby validity may be achieved. To exemplify this, the perception of the interviewer is explored, and here Heidegger's work on self-transposition has proved to be helpful.
Kinect-Based Virtual Game for the Elderly that Detects Incorrect Body Postures in Real Time

Poor posture can result in loss of physical function, which is necessary to preserving independence in later life. Its decline is often the determining factor for loss of independence in the elderly. To avoid this, a system to correct poor posture in the elderly, designed for Kinect-based indoor applications, is proposed in this paper. Due to the importance of maintaining a healthy life style in senior citizens, the system has been integrated into a game which focuses on their physical stimulation. The game encourages users to perform physical activities while the posture correction system helps them to adopt proper posture. The system captures limb node data received from the Kinect sensor in order to detect posture variations in real time. The DTW algorithm compares the original posture with the current one to detect any deviation from the original correct position. The system was tested and achieved a successful detection percentage of 95.20%. Experimental tests performed in a nursing home with different users show the effectiveness of the proposed solution.
Optimization of Hierarchical Menus by Genetic Algorithm

Hierarchical menus are widely used as a standard user interface in modern applications that use GUI. The performance of the menu depends on many factors; structure, layout, colors and so on. There have been many researches that proposed novel menus, but there have been little work that aimed to improve the performance of menu by optimizing its structure. This paper proposes an algorithm based on genetic algorithm for optimizing the performance of menu. The algorithm aims to a minimize the average selection time of menu items considering movement and decision time. We will show preliminary results of a static hierarchical menu of a cellular phone as the first step example where a small screen and limited input device is e assumed.
Taint Analysis in Practice Automated Counter-measures on the World Wide Web

In this paper I will look into various software and hardware based attack-detection and repair mechanisms. These mechanisms try to protect software systems against exploits by, for example, detecting a control-flow modification and stopping the process. The emphasis will lay on techniques using taint analysis and the various aspects of this kind of systems. Generation of signature for filtering malicious network data and automatic patch generation based on signatures of these attacks are the next steps to protect the software against future attacks.
Parallel implementation of time delay neural networks for phoneme recognition

Large training problems using the backpropagation (BP) as a training algorithm are very computationally demanding when used for multilayer perceptrons (MLPs). In speech recognition applications, this is especially true. In order to overcome these problems, implementation issues are addressed. The algorithm is parallelized by splitting the training database across many processors and updating the network weights using an arithmetic average of the weight updates for each transputer microprocessor. The parallel implementation achieves a speedup factor of 8.8 over a VAX 3600 and scales well up to 16 transputers in a hypercube configuration. Phoneme recognition accuracy for Afrikaans fricatives, nasals, stop consonants and glides is given. <<ETX>>
TOWARDS COMPUTER-GENERATED MNEMONIC PHRASES: EXPERIMENTS WITH GENETIC ALGORITHMS AND N-GRAMS

Mnemonic phrases have the potential to help people commit information to memory and may be a valuable aid to education. However, their widespread application is currently limited by the effort and creativity required to generate them manually. This paper describes a method for the automatic generation of effective mnemonics by computer, framing the task as an optimisation problem to be solved by Genetic Algorithms using parser output and n-gram frequencies to evaluate fitness. Grammatical constraints and lexical familiarity are parameters tested for their ability to produce more memorable sentences. The method has been implemented using custom code and existing libraries, and tested, showing promising results on list data of increasing difficulty.
Lung and liver SBRT using helical tomotherapy - a dosimetric comparison of fixed jaw and dynamic jaw delivery.

The purpose of the study was to evaluate the time effectiveness and dose distribution details of dynamic jaw delivery compared to the regular helical tomotherapy delivery mode in stereotactic body radiation therapy (SBRT) of liver and lung tumors. Ten patients with liver and ten patients with lung tumors were chosen to analyze the dose profiles and treatment times of regular helical tomotherapy delivery (2.5 cm field width) and new helical tomotherapy mode using dynamic jaw delivery with 5 cm field width. A median dose between 24 and 30 Gy was delivered in a single fraction. Regular helical tomotherapy took an average of 31.9 ± 6.7 min (lung SBRT) and 41.7 ± 15.0 min (liver SBRT). A reduction in delivery duration of 38.8% to 19.5 ± 2.9 min could be accomplished for lung irradiation (p < 0.05) and by 50.8% to 20.5 ± 6.0 min for liver SBRT (p < 0.05). Target coverage, as well as conformity and uniformity indices, showed no significant differences. No significant increase in organs-at-risk exposure could be detected either for lung or liver tumors. Therefore, use of new delivery mode with dynamic jaws improves treatment efficiency by reducing beam-on time, while maintaining excellent plan quality. PACS numbers: 87.55. D-, 87.53. Ly, 87.55. N.
Linguistic Strategies for Improving Informed Consent in Clinical Trials Among Low Health Literacy Patients.

Background Improving informed consent to participate in randomized clinical trials (RCTs) is a key challenge in cancer communication. The current study examines strategies for enhancing randomization comprehension among patients with diverse levels of health literacy and identifies cognitive and affective predictors of intentions to participate in cancer RCTs. Methods Using a post-test-only experimental design, cancer patients (n = 500) were randomly assigned to receive one of three message conditions for explaining randomization (ie, plain language condition, gambling metaphor, benign metaphor) or a control message. All statistical tests were two-sided. Results Health literacy was a statistically significant moderator of randomization comprehension ( P = .03). Among participants with the lowest levels of health literacy, the benign metaphor resulted in greater comprehension of randomization as compared with plain language ( P = .04) and control ( P = .004) messages. Among participants with the highest levels of health literacy, the gambling metaphor resulted in greater randomization comprehension as compared with the benign metaphor ( P = .04). A serial mediation model showed a statistically significant negative indirect effect of comprehension on behavioral intention through personal relevance of RCTs and anxiety associated with participation in RCTs ( P < .001). Conclusions The effectiveness of metaphors for explaining randomization depends on health literacy, with a benign metaphor being particularly effective for patients at the lower end of the health literacy spectrum. The theoretical model demonstrates the cognitive and affective predictors of behavioral intention to participate in cancer RCTs and offers guidance on how future research should employ communication strategies to improve the informed consent processes.
Architecture-driven synthesis techniques for VLSI implementation of DSP algorithms

The state of the art of compiling digital signal processing (DSP) algorithms into silicon is discussed. It is indicated how digital signal processing differs from numerical data processing, including the consequences for the synthesis tools. On the basis of a broad range of DSP applications, four classes of architectures are then distinguished to serve as templates for four different synthesis systems. Although each of these four silicon compilers is tuned to a specific class of applications in order to generate area-efficient chips, they all accept as input the same behavioral DSP specification. The four selected architectural styles are best characterized by hard-wired bit-serial data-paths, microcoded multiprocessors, cooperating bit-parallel data-paths, and regular arrays. The characteristics of the first three architectures are treated in more detail in a discussion of three different Cathedral synthesis environments for their respective design. A fourth Cathedral environment, aiming at the synthesis of regular arrays, is still in an early stage of development and is not discussed. The claims for the compilers are substantiated by typical designs. >
Algorithm-Oriented Generic Software Library Development

generic algorithms, algorithmic abstraction, software libraries, abstract datatypes, C++ We outline an approach to construction of software libraries in which generic algorithms (algorithmic abstractions) playa more central role than in conventional software library technology or in the objectoriented programming paradigm. Our approach is to consider algorithms first, decide what types and access operations they need for efficient execution and regard the types and operations as formal parameters that can be instantiated in many different ways, as long as, the actual parameters satisfy the assumptions on which the correctness and efficiency of the algorithms are based. The means by which instantiation is carried out is language dependent; in the C++ examples in this paper we instantiate generic algorithms by constructing classes that define the needed types and access operations. By use of such compile-time techniques and careful attention to algorithmic issues, it is possible to construct software components of broad utility with no sacrifice of efficiency.
Expressing surprise by particles

The aim of this paper is to investigate some grammaticalised expressions of surprise, four mirative particles of English: even , only , already and still . It shows where and how the proposed account of mirativity improves the semantic/pragmatic analysis of the four particles. The paper tries to deal with the question whether the analysis motivated does not lead to too much surprise in natural language. It turns out to be possible in all four cases to maintain the line that the particle means one thing and the host another (the host influences the meaning of the particle by supplying antecedents, the particle may disambiguate the host) and that the two meanings are just merged, as if they were separate utterances.The analyses of particles in this paper illustrates the general point that language interpretation is rather holistic and that formal models of interpretation should reflect this, as in the updated semantics. Keywords:expressions of surprise; language interpretation; mirative particles; natural language; semantics
Impact of temporal probability in 4D dose calculation for lung tumors.

The purpose of this study was to evaluate the dosimetric uncertainty in 4D dose calculation using three temporal probability distributions: uniform distribution, sinusoidal distribution, and patient-specific distribution derived from the patient respiratory trace. Temporal probability, defined as the fraction of time a patient spends in each respiratory amplitude, was evaluated in nine lung cancer patients. Four-dimensional computed tomography (4D CT), along with deformable image registration, was used to compute 4D dose incorporating the patient's respiratory motion. First, the dose of each of 10 phase CTs was computed using the same planning parameters as those used in 3D treatment planning based on the breath-hold CT. Next, deformable image registration was used to deform the dose of each phase CT to the breath-hold CT using the deformation map between the phase CT and the breath-hold CT. Finally, the 4D dose was computed by summing the deformed phase doses using their corresponding temporal probabilities. In this study, 4D dose calculated from the patient-specific temporal probability distribution was used as the ground truth. The dosimetric evaluation matrix included: 1) 3D gamma analysis, 2) mean tumor dose (MTD), 3) mean lung dose (MLD), and 4) lung V20. For seven out of nine patients, both uniform and sinusoidal temporal probability dose distributions were found to have an average gamma passing rate >95% for both the lung and PTV regions. Compared with 4D dose calculated using the patient respiratory trace, doses using uniform and sinusoidal distribution showed a percentage difference on average of -0.1%±0.6% and -0.2%±0.4% in MTD, -0.2%±1.9% and -0.2%±1.3% in MLD, 0.09%±2.8% and -0.07%±1.8% in lung V20, -0.1%±2.0% and 0.08%±1.34% in lung V10, 0.47%±1.8% and 0.19%±1.3% in lung V5, respectively. We concluded that four-dimensional dose computed using either a uniform or sinusoidal temporal probability distribution can approximate four-dimensional dose computed using the patient-specific respiratory trace. PACS number: 87.55. D.
Defocus Blur-Invariant Scale-Space Feature Extractions.

We propose modifications to scale-space feature extraction techniques (Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) that make the feature detection and description invariant to defocus blur. Specifically, scale-space blob detection relies on the second derivative responses of images. Our analysis of circular defocus blur (which sufficiently approximates a real camera blur kernel) and its effect on scale-space blob detection suggests that fourth derivative- and not the usual second derivative-is optimal for detecting the blurred blobs while multi-scale descriptors of blurred blobs are effective at establishing correspondences between blurred images. The proposed defocus blur-invariant (DBI) scale-space feature extraction techniques-which we refer to as DBI-SIFT and DBI-SURF-do not require image deblurring nor blur kernel estimation, meaning that their accuracy does not depend on the quality of image deblurring. We offer empirical evidence of blur invariance by establishing interest point correspondences between sharp or blurred reference images and blurred target images.
Towards Pervasive Gait Analysis for Medicine with Wearable Sensors: A Systematic Review for Clinicians and Medical Researchers.

After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics - hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating the existing, as well as emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.