Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Analysis of structural MRI and PET (FDG and 18F-AV-45) for Alzheimer's disease and its prodromal stages

A multivariate analysis method, orthogonal partial least squares to latent structures (OPLS), was used to discriminate Alzheimer's disease (AD), early and late mild cognitive impairment (EMCI and LMCI) from cognitively normal control (CN) using MRI and PET measures. FreeSurfer 5.1 generated 271 MRI features including 49 subcortical volumes, 68 cortical volumes, 68 cortical thicknesses, 70 surface areas and 16 hippocampus subfields. Subjects with all aforementioned MRI measures passing quality control and valid Fludeoxyglucose (18F) (FDG) and Florbetapir (18F) PET scans were selected from ADNI database, resulting in a total of 524 participants (137 CN, 214 EMCI, 103 LMCI and 70 AD) for the study. Altogether 286 features including 15 significant PET uptake features (7 for FDG and 8 for AV-45) were utilized for OPLS analysis. Predictive power was evaluated by Q2(Y), a quantifier of the statistical significance for class separation. The results show that MRI features (Q2(Y) =0.645), and PET features (Q2(Y) = 0.636) has comparable predictive power in separating AD from CN, and MRI features are better predictor of LMCI (Q2(Y) = 0.282) than PET (Q2(Y) = 0.294). Combination of PET and MRI has the most predictive power for LMCI and AD with Q2(Y) of 0.294 and 0.721, respectively. While for EMCI, cortical thickness was found to be the best predictor with a Q2(Y) of 0.108, suggesting cortical thickness may be the first structural change ahead of others and should be prioritized in prediction of very mild cognitive impairment.
Elliptic Curve Cryptography with Efficiently Computable Endomorphisms and Its Hardware Implementations for the Internet of Things

Verification of an ECDSA signature requires a double scalar multiplication on an elliptic curve. In this work, we study the computation of this operation on a twisted Edwards curve with an efficiently computable endomorphism, which allows reducing the number of point doublings by approximately 50 percent compared to a conventional implementation. In particular, we focus on a curve defined over the 207-bit prime field <inline-formula><tex-math notation="LaTeX"> $\mathbb {F}_p$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq1-2623609.gif"/></alternatives> </inline-formula> with <inline-formula><tex-math notation="LaTeX">$p = 2^{207}-5{,}131$</tex-math><alternatives> <inline-graphic xlink:href="liu-ieq2-2623609.gif"/></alternatives></inline-formula>. We develop several optimizations to the operation and we describe two hardware architectures for computing the operation. The first architecture is a small processor implemented in 0.13 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives> <inline-graphic xlink:href="liu-ieq3-2623609.gif"/></alternatives></inline-formula>m CMOS ASIC and is useful in resource-constrained devices for the Internet of Things (IoT) applications. The second architecture is designed for fast signature verifications by using FPGA acceleration and can be used in the server-side of these applications. Our designs offer various trade-offs and optimizations between performance and resource requirements and they are valuable for IoT applications.
Sharing for Health: A Study of Chinese Adolescents’ Experiences and Perspectives on Using Social Network Sites to Share Health Information

Purpose: This exploratory qualitative study examines Chinese adolescents’ health information sharing habits on social network sites. Method: Ten focus group meetings with 76 adolescents, ages 12 to 17 years, were conducted at community-based organizations in Chicago’s Chinatown. The research team transcribed the recording and analyzed the transcripts using ATLAS.ti. Results: Chinese adolescents are using different social network sites for various topics of health information including food, physical activity, and so on. Adolescents would share useful and/or interesting health information. Many adolescents raised credibility concerns regarding health information and suggested evaluating the information based on self-experience or intuition, word-of-mouth, or information online. Conclusion: The findings shed lights on future intervention using social network sites to promote health among Chinese adolescents in the United States. Implications for Practice: Future interventions should provide adolescents with interesting and culturally sensitive health information and educate them to critically evaluate health information on social network sites.
N-pass N-detection Fault Simulation and Its Applications

An n-detection fault simulation process called n-pass n-detection fault simulation is described. n-pass n-detection fault simulation can be implemented such that it has the same computational complexity (and run time) as the conventional n-detection fault simulation process; however, it is more effective for applications where it is necessary to identify tests that detect large numbers of faults. One such application considered in this work is that of ordering a given test set so as to steepen its fault coverage curve. Experimental results are presented to demonstrate that improved test ordering is obtained by using the proposed n-pass n-detection fault simulation process using approximately the same run time as when conventional n-detection fault simulation is used. n-pass n-detection fault simulation is also effective in cases where the value of n is required to change dynamically during the fault simulation process. This is useful in order to accommodate a limit on the run time of n-detection fault simulation, or when it is not possible to specify a value for n in advance.
Living With Type 2 Diabetes in San Jose Tecoh, Yucatan, Mexico: A Phenomenological Study

Introduction: Understanding the psychosocial aspects that accompany and influence type 2 diabetes mellitus (T2DM) has become increasingly important for culturally appropriate health care in Mexico. The purpose of this study was to understand the lived experience of individuals’ with T2DM in Merida, Yucatan. Method: A phenomenological method guided data collection and analysis. Patients in San Jose Tecoh, diagnosed with T2DM for at least 5 years, were interviewed. Results: Eight (six female, two male) patients described their lived experience. Individuals discussed knowledge of the disease (e.g., care, beliefs about origin, consequences, type, symptoms) and changes arising from diagnosis (e.g., emotional, dietary, physiological, lifestyle and self-care, economic). Discussion: The Mayan–Yucatecan cultural perceptions of factors that influence physical health are unique to this region. Nurses can facilitate improvement in T2DM health outcomes through providing information and encouraging self-care. Our results emphasize the importance of considering T2DM in terms of differences between and within culture.
Efficient PET-CT image retrieval using graphs embedded into a vector space

Combined positron emission tomography and computed tomography (PET-CT) produces functional data (from PET) in relation to anatomical context (from CT) and it has made a major contribution to improved cancer diagnosis, tumour localisation, and staging. The ability to retrieve PET-CT images from large archives has potential applications in diagnosis, education, and research. PET-CT image retrieval requires the consideration of modality-specific 3D image features and spatial contextual relationships between features in both modalities. Graph-based retrieval methods have recently been applied to represent contextual relationships during PET-CT image retrieval. However, accurate methods are computationally complex, often requiring offline processing, and are unable to retrieve images at interactive rates. In this paper, we propose a method for PET-CT image retrieval using a vector space embedding of graph descriptors. Our method defines the vector space in terms of the distance between a graph representing a PET-CT image and a set of fixed-sized prototype graphs; each vector component measures the dissimilarity of the graph and a prototype. Our evaluation shows that our method is significantly faster (≈800× speedup, p <; 0.05) than retrieval using the graph-edit distance while maintaining comparable precision (5% difference, p > 0.05).
Modern floorplanning based on B/sup */-tree and fast simulated annealing

Unlike classical floorplanning that usually handles only block packing to minimize silicon area, modern very large scale integration (VLSI) floorplanning typically needs to pack blocks within a fixed die (outline), and additionally considers the packing with block positions and interconnect constraints. Floorplanning with bus planning is one of the most challenging modern floorplanning problems because it needs to consider the constraints with interconnect and block positions simultaneously. In this paper, the authors study two types of modern floorplanning problems: 1) fixed-outline floorplanning and 2) bus-driven floorplanning (BDF). This floorplanner uses B/sup */-tree floorplan representation based on fast three-stage simulated annealing (SA) scheme called Fast-SA. For fixed-outline floorplanning, the authors present an adaptive Fast-SA that can dynamically change the weights in the cost function to optimize the wirelength under the outline constraint. Experimental results show that this floorplanner can achieve 100% success rates efficiently for fixed-outline floorplanning with various aspect ratios. For the BDF, the authors explore the feasibility conditions of the B/sup */-tree with the bus constraints, and develop a BDF algorithm based on the conditions and Fast-SA. Experimental results show that this floorplanner obtains much smaller dead space for the floorplanning with hard/soft macro blocks, compared with the most recent work. In particular, this floorplanner is more efficient than the previous works.
High-quality transition fault atpg for small delay defects

A new framework is proposed to generate compact quality tests to detect small delay defects by activating and propagating transition faults only along implicitly kept sensitizable critical paths. It is shown how to implicitly generate functions to derive tests for the proposed framework. The novelty of the method relies on a multivalued algebra that is used to generate the test functions with a single circuit traversal, independent of the number of critical paths. Experimental results demonstrate the effectiveness of the method.
Remote measurement of cognitive stress via heart rate variability

Remote detection of cognitive load has many powerful applications, such as measuring stress in the workplace. Cognitive tasks have an impact on breathing and heart rate variability (HRV). We show that changes in physiological parameters during cognitive stress can be captured remotely (at a distance of 3m) using a digital camera. A study (n=10) was conducted with participants at rest and under cognitive stress. A novel five band digital camera was used to capture videos of the face of the participant. Significantly higher normalized low frequency HRV components and breathing rates were measured in the stress condition when compared to the rest condition. Heart rates were not significantly different between the two conditions. We built a person-independent classifier to predict cognitive stress based on the remotely detected physiological parameters (heart rate, breathing rate and heart rate variability). The accuracy of the model was 85% (35% greater than chance).
Mining Requirements From Closed-Loop Control Models

Formal verification of a control system can be performed by checking if a model of its dynamical behavior conforms to temporal requirements. Unfortunately, adoption of formal verification in an industrial setting is a formidable challenge as design requirements are often vague, nonmodular, evolving, or sometimes simply unknown. We propose a framework to mine requirements from a closed-loop model of an industrial-scale control system, such as one specified in Simulink. The input to our algorithm is a requirement template expressed in parametric signal temporal logic: a logical formula in which concrete signal or time values are replaced with parameters. Given a set of simulation traces of the model, our method infers values for the template parameters to obtain the strongest candidate requirement satisfied by the traces. It then tries to falsify the candidate requirement using a falsification tool. If a counterexample is found, it is added to the existing set of traces and these steps are repeated; otherwise, it terminates with the synthesized requirement. Requirement mining has several usage scenarios: mined requirements can be used to formally validate future modifications of the model, they can be used to gain better understanding of legacy models or code, and can also help enhancing the process of bug finding through simulations. We demonstrate the scalability and utility of our technique on three complex case studies in the domain of automotive powertrain systems: a simple automatic transmission controller, an air-fuel controller with a mean-value model of the engine dynamics, and an industrial-size prototype airpath controller for a diesel engine. We include results on a bug found in the prototype controller by our method.
TheSPoT: Thermal Stress-Aware Power and Temperature Management for Multiprocessor Systems-on-Chip

Thermal stress including temperature gradients in time and space, as well as thermal cycling, influences lifetime reliability and performance of modern multiprocessor systems-on-chip (MPSoCs). Conventional power and temperature management techniques considering the peak temperature/power consumption do not provide a comprehensive solution to avoid high spatial and temporal thermal variations. This work presents TheSPoT, a novel multilevel thermal stress-aware power and thermal management approach for MPSoCs. At the top level, core consolidation and deconsolidation is performed based on peak temperature, thermal stress, and power consumption constraints. These constraints are also used at the next level, where operating frequencies are determined. At this level, we obtain optimal core frequencies by solving a convex optimization problem. However, thereafter, to reduce the runtime overhead in large MPSoCs, we alternatively propose to use a fast heuristic algorithm. The efficacy of the proposed approaches in reducing the thermal cycles and temporal/spatial temperature gradients is evaluated by comparing the results with the state-of-the-art methods. The evaluation performed on 4-core, 8-core, and 16-core MPSoCs, using PARSEC benchmarks, reveals a considerable reduction in thermal stress. For the 8-core MPSoC case study, on average, for the proposed heuristic(optimal) approach, the mean time to failure improved by 47(35)% compared to the state-of-the-art techniques with only 6(4)% performance degradation. Also, our simulations show that TheSPoT is more efficient in thermal stress reduction when more heterogeneous workloads are used.
Ieee/acm Transactions on Computational Biology and Bioinformatics 1 Biclustering Algorithms for Biological Data Analysis: a Survey

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results of the application of standard clustering methods to genes are limited. These limited results are imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the gene expression matrix has been proposed to date. This simultaneous clustering, usually designated by biclustering, seeks to find sub-matrices, that is subgroups of genes and subgroups of columns, where the genes exhibit highly correlated activities for every condition. This type of algorithms has also been proposed and used in other fields, such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search and the target applications.
Contributions of a Retreat Weekend to the Healing and Coping of Cancer Patients’ Relatives

Relatives play a key role in the daily support and care of cancer patients. This role, however, can negatively affect relatives physically, psychologically, emotionally, and spiritually, challenging their health and well-being. Consequently, this could inhibit relatives’ abilities to continue in their roles as caregivers. Although various studies have examined different interventions for the relatives of cancer patients, no formal research has been published on the role that retreat weekends play in their cancer journeys. In this qualitative study we used semistructured interviews to explore the experiences of 8 relatives who attended a “Skills for Healing Retreat Weekend” in Ontario, Canada. The findings indicate that the retreat, in bringing people together to partake in discussions and activities, fostered a sense of community among the participants. The retreat also had enduring effects, contributing to relatives’ ongoing processes of healing as well as providing them with strategies for coping in their roles as caregivers.
AIDS: An understanding in rural women of South-India

The study aims to find out the knowledge and understanding of acquired immunodeficiency syndrome (AIDS) in 1,200 randomly selected women in the age group of 15-45 years, of 16 villages belonging to Villupuram Health Unit District, Tamil Nadu, India, using a two-stage sampling design. Data collection was done in the period from August to November 2001, using both qualitative and quantitative methods. In all, 28% of the women had not heard of AIDS at all. More than one-third of the women who had heard of AIDS considered AIDS as a serious illness; and among them, 72% ranked AIDS as the number one killer disease. The main findings showed that the rural women's knowledge was poor in areas like cause, symptoms and prevention. Level of literacy of the women was significantly associated with their knowledge of HIV/AIDS ( P <0.05), showing that literates had better knowledge than illiterates. Also, there were misconceptions and false beliefs about cause and spread of the infection, which were found to be more prevalent among illiterates. Television was found to be the major source of knowledge on AIDS among both literates and illiterates. This study suggests a need for innovative, group-based repeated education on AIDS, particularly for rural women, in order to impart better knowledge and understanding on AIDS.
Swarm Robots Search for Multiple Targets Based on an Improved Grouping Strategy

Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally. Obstacle avoidance is considered during the search process. Simulation compared with previous method demonstrates the adaptability, accuracy, and efficiency of the proposed strategy in multiple targets searching.
Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference

Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing effective computational methods to infer lncRNA-disease associations is becoming increasingly important. Current network-based algorithms typically use a network representation to identify novel associations between lncRNAs and diseases. But these methods are concentrated on specific entities of interest (lncRNAs and diseases) and they do not allow to consider networks with more than two types of entities. Considering the limitations in previous computational methods, we develop a new global network-based framework, LncRDNetFlow, to prioritize disease-related lncRNAs. LncRDNetFlow utilizes a flow propagation algorithm to integrate multiple networks based on a variety of biological information including lncRNA similarity, protein-protein interactions, disease similarity, and the associations between them to infer lncRNA-disease associations. We show that LncRDNetFlow performs significantly better than the existing state-of-the-art approaches in cross-validation. To further validate the reproducibility of the performance, we use the proposed method to identify the related lncRNAs for ovarian cancer, glioma, and cervical cancer. The results are encouraging. Many predicted lncRNAs in the top list have been verified by the biological studies.
Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks

Essential proteins are critical to the development and survival of cells. Identification of essential proteins is helpful for understanding the minimal set of required genes in a living cell and for designing new drugs. To detect essential proteins, various computational methods have been proposed based on protein-protein interaction (PPI) networks. However, protein interaction data obtained by high-throughput experiments usually contain high false positives, which negatively impacts the accuracy of essential protein detection. Moreover, most existing studies focused on the local information of proteins in PPI networks, while ignoring the influence of indirect protein interactions on essentiality. In this paper, we propose a novel method, called Essentiality Ranking (EssRank in short), to boost the accuracy of essential protein detection. To deal with the inaccuracy of PPI data, confidence scores of interactions are evaluated by integrating various biological information. Weighted edge clustering coefficient (WECC), considering both interaction confidence scores and network topology, is proposed to calculate edge weights in PPI networks. The weight of each node is evaluated by the sum of WECC values of its linking edges. A random walk method, making use of both direct and indirect protein interactions, is then employed to calculate protein essentiality iteratively. Experimental results on the yeast PPI network show that EssRank outperforms most existing methods, including the most commonly-used centrality measures (SC, DC, BC, CC, IC, and EC), topology based methods (DMNC and NC) and the data integrating method IEW.
Development of Topical Niosomal Gel of Benzoyl Peroxide

Benzoyl peroxide is macrolide antibiotic used commonly for the treatment of acne either alone or in combination. But it suffers from side effects like skin redness, irritation, itching, and edema. Niosomes, a nonionic surfactant vesicular formulation, have been explored extensively for topical application to enhance skin penetration as well as to improve skin retention of drugs. In the present study, Benzoyl peroxide was entrapped into niosomes by thin film hydration technique, and various process parameters were optimized by partial factorial design. The optimized niosomal formulation was incorporated into HPMC K15 gel and extensively characterized for percentage drug entrapment (PDE) and in vitro release performance. The stability of above formulation was studied at different temperatures. The present study demonstrated prolongation of drug release, increased drug retention into skin, and improved permeation across the skin after encapsulation of benzoyl peroxide into niosomal topical gel.