Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualization and Comparison of Single and Combined Parametric and Nonparametric Discriminant Methods for Leukemia Type Recognition Based on Gene Expression

Abstract A gene expression data set, containing 3051 genes and 38 tumor mRNA training samples, from a leukemia microarray study, was used for differentiation between ALL and AML groups of leukemia. In this paper, single and combined discriminant methods were applied on the basis of the selected few most discriminative variables according to Wilks’ lambda or the leave-one-out error of first nearest neighbor classifier. For the linear, quadratic, regularized, uncorrelated discrimination, kernel, nearest neighbor and naive Bayesian classifiers, two-dimensional graphs of the boundaries and discriminant functions for diagnostics are presented. Cross-validation and leave-one-out errors were used as measures of classifier performance to support diagnosis coming from this genomic data set. A small number of best discriminating genes, from two to ten, was sufficient to build discriminant methods of good performance. Especially useful were nearest neighbor methods. The results presented herein were comparable with outcomes obtained by other authors for larger numbers of applied genes. The linear, quadratic, uncorrelated Bayesian and regularized discrimination methods were subjected to bagging or boosting in order to assess the accuracy of the fusion. A conclusion drawn from the analysis was that resampling ensembles were not beneficial for two-dimensional discrimination.
Cost per DALY averted thresholds for low- and middle-income countries: evidence from cross country data

Low- and middle-income countries (LMICs) face difficult decisions about which health care interventions are worthwhile given existing constraints on health care expenditure. Decisions require some assessment of the health opportunity costs of proposed investments, i.e., a â€˜supply sideâ€™ cost-effectiveness threshold (CET) that represents the likely health effects of changes in health care expenditure. This paper provides a framework for generating country-level CETs using existing published estimates of the mortality effect of health expenditure. Two different estimation strategies are used (Bokhari et al (2007) and Moreno-Serra and Smith (2015)) and, where possible, estimation is extended to include other measures of mortality, survival and disability outcomes, reflecting the demographic and other characteristics of each LMIC. The results suggest that CETs representing likely health opportunity costs tend to be below the lower bound suggested by WHO of 1x GDP per capita. Hence, many previous and existing recommendations about which interventions are cost-effective that are based on the WHO threshold are likely to do more harm than good.
Technical and Economic Impact of PV-BESS Charging Station on Transformer Life: A Case Study

The uncoordinated charging of several plug-in electric vehicles in a parking garage can potentially overload distribution transformer reducing its lifespan. Attempting to mitigate this issue, this paper proposes a smart charging method to minimize electricity consumption costs and avoid transformer overloading by considering a charging station integrated with photovoltaic generation and battery energy storage system. The optimal coordination among all these elements is investigated assessing transformer hottest-spot temperature and loss-of-life, considering a case study with time-of-use rate and meteorological data from Texas, USA. In addition, an economic analysis is developed to evaluate the viability of the project, with a sensitivity study considering different charging fees and variation in the daily number of vehicles parked in the garage. The results show the proposed approach is feasible, yields tangible financial benefits and can preserve distribution transformer life.
Self-Assembled, Adjuvant/Antigen-Based Nanovaccine Mediates Anti-Tumor Immune Response against Melanoma Tumor

Malignant melanoma is a highly aggressive type of cancer that requires radical treatment strategies to inhibit the cancer cell progression and metastasis. In recent years, preclinical research and clinical trials on melanoma treatment have been considerably focused on the adjuvant-based immunotherapy for enhancing the immune response of innate immune cells against cancer cells. However, the clinical outcome of these adjuvant-based treatments is inadequate due to an improper delivery system for these immune activators to reach the target site. Hence, we developed a vaccine formulation containing tumor lysate protein (TL) and poly I:C (PIC) complexed with positively charged poly (sorbitol-co-polyethylenimine (PEI) (PSPEI). The resulting ionic PSPEI-polyplexed antigen/adjuvant (PAA) (PSPEI-PAA) nanocomplexes were stable at the physiological condition, are non-toxic, and have enhanced intracellular uptake of antigen and adjuvant in immature dendritic cells leading to dendritic cell maturation. In the murine B16F10 tumor xenograft model, PSPEI-PAA nanocomplexes significantly suppressed tumor growth and did not exhibit any noticeable sign of toxicity. The level of matured dendritic cells (CD80+/CD86+ cells) in the tumor draining lymph node of PSPEI-PAA treated tumor mice were enhanced and therefore CD8+ T cells infiltration in the tumor were enriched. Additionally, the cytotoxic T lymphocytes (CTLs) assay involving co-culturing of splenocytes isolated from the PSPEI-PAA-treated mice with that of B16F10 cells significantly revealed enhanced cancer killing by the TL-reactivated CTLs compared to untreated control mice bearing tumor. Therefore, we strongly believe that PSPEI-PAA nanocomplexes could be an efficient antigen/adjuvant delivery system and enhance the antitumor immune response against melanoma tumor in the future clinical trials.
A Constrained Deep Neural Network for Ordinal Regression

Ordinal regression is a supervised learning problem aiming to classify instances into ordinal categories. It is challenging to automatically extract high-level features for representing intraclass information and interclass ordinal relationship simultaneously. This paper proposes a constrained optimization formulation for the ordinal regression problem which minimizes the negative loglikelihood for multiple categories constrained by the order relationship between instances. Mathematically, it is equivalent to an unconstrained formulation with a pairwise regularizer. An implementation based on the CNN framework is proposed to solve the problem such that high-level features can be extracted automatically, and the optimal solution can be learned through the traditional back-propagation method. The proposed pairwise constraints make the algorithm work even on small datasets, and a proposed efficient implementation make it be scalable for large datasets. Experimental results on four real-world benchmarks demonstrate that the proposed algorithm outperforms the traditional deep learning approaches and other state-of-the-art approaches based on hand-crafted features.
Benchmarking Usability of Assistive Robotic Systems : Methodology and Application

Usability can be defined as the degree of a produc t’s fitting to the characteristics of a person or of a group of people. The concept of usability includes aspects of using a product that are closely linked to the user’s degree of satisfac tion and preferences. As a multidisciplinary concept, defini t ons may vary depending upon the specific area on which one focus es. However, common terms can be found throughout literature. Pa rameters such as the difficulty and steepness of the learnin g curve for the end-user, or flexibility and adaptability are commonly evaluated. In the context of Assistive Robots, factors taken i nto account are related to user acceptance, security, precision of task execution, and overall system efficiency. Hence, it is also cl osely linked to the concept of dependability. Boundary conditions relat ed to the environment and to the user must be taken into acco unt. In this paper, the importance of the role of benchmarking t he usability of Assistive Robots is discussed, and a methodology fo r btaining usability data from experiments is proposed. The pr oposed methodology is part of a continuous improvement framework that is based on the System Knowledge Space, which will be described within the text. Then, a general view at results ex tracted from experiments performed with an Assistive Robot and r eal potential system end-users in realistic scenarios is given. T his exemplary usability benchmarking assessment follows the guide lines of the methodology that is proposed. The experiments that are described were developed as part of the ASIBOT program at the Universidad Carlos III de Madrid in collaboration w ith the National Paraplegic Hospital in Toledo (Hospital Nacional de Parapléjicos de Toledo). The last part of our paper deals with results of how these experiences have influenced ac tual and future research efforts and discusses how this should posi tively affect the scientific research and developer community.
A Descriptive Study to Assess the Level of Internet Addiction among Adolescents: A Case Study of High Schools in Mangalore

Adolescents are more vulnerable to internet addiction (IA) as they have less ability to control their enthusiasm for internet activities. In this study, a descriptive approach was used to assess the level of IA and its impact among high school students. A conceptual framework was based on Roy’s Adaptation Model. The tool consisted of a baseline proforma, a modified form of IA test and a structured questionnaire developed during the study. Those tools were employed to assess the level of IA and its impact among adolescents of English medium high schools. Opinions from fourteen experts were used to modify the tools for the validity. The reliability of the tool was determined by using a split half technique. The reliability was measured by Karl Pearson’s coefficient correlation and was found to be reliable. The study revealed that majority (70.5%) of the adolescents were normal users, 23% had a mild addiction, 6% had a moderate addiction and 0.5% had a severe addiction. The majority (73%) of samples had a mild impact, 16.5% had a moderate impact, and 10.5% had no impact. Some of their demographic variables like age, class, and occupation of fathers have significant associations with both IA and impact of IA.
A systems biology approach to bovine fertility and metabolism: Development of a glucose insulin model.

To counteract the antagonistic relationship between milk yield and fertility in dairy cow, a deeper understanding of the underlying biological mechanisms is required. For this purpose, we study physiological networks related to reproduction and metabolism in dairy cows. We interactively develop dynamic, mechanistic models by fitting the models to experimental data and mechanistic knowledge. We have already developed models for potassium balance and hormonal regulation of fertility in the dairy cow, which will briefly be reviewed here. The main focus of this article is a glucose-insulin model currently developed by us. This model links the bovine hormonal cycle and the potassium balance to glucose and thus to energy metabolism. The models can be applied in scientific research, education, experimental planning, drug development and production on farms.
Energy Efficiency Optimization for CoMP-SWIPT Heterogeneous Networks

In this paper, a fundamental study of energy efficiency (EE) optimization for coordinated multi-point (CoMP) simultaneous wireless information and power transfer (SWIPT) heterogeneous networks (HetNets) is provided. We aim to optimize the EE while satisfying certain quality-of-service requirements in regard to transmission rate and energy harvesting at both the macro cell and small cells. The corresponding joint beamforming and power allocation in the presence of intra- and inter-cell interference constitutes an EE maximization problem that is non-convex, and hence, very challenging to solve. In order to solve this problem, we propose to separate the beamforming design and power allocation processes. First, we adopt linear zero-forcing (ZF) beamforming to suppress the multi-user interference from both the energy harvesting users (EH-UEs) as well as the information decoding UEs (ID-UEs), thus transforming the HetNet under consideration to a virtual point-to-point system. An efficient power allocation algorithm is then developed to maximize the corresponding EE. On the other hand, the ZF strategy does not utilize the notion that interference benefits the EH-UEs. As a result, we propose a partial ZF approach by differentiating the EH-UEs and ID-UEs in order to further improve the EE. Our findings show that the EE can be significantly improved through the integration of CoMP-SWIPT in HetNets.
Semi-Linearized Proximal Alternating Minimization for a Discrete Mumford–Shah Model

The Mumford–Shah model is a standard model in image segmentation, and due to its difficulty, many approximations have been proposed. The major interest of this functional is to enable joint image restoration and contour detection. In this work, we propose a general formulation of the discrete counterpart of the Mumford–Shah functional, adapted to nonsmooth penalizations, fitting the assumptions required by the Proximal Alternating Linearized Minimization (PALM), with convergence guarantees. A second contribution aims to relax some assumptions on the involved functionals and derive a novel Semi-Linearized Proximal Alternated Minimization (SL-PAM) algorithm, with proved convergence. We compare the performances of the algorithm with several nonsmooth penalizations, for Gaussian and Poisson denoising, image restoration and RGB-color denoising. We compare the results with state-of-the-art convex relaxations of the Mumford–Shah functional, and a discrete version of the Ambrosio–Tortorelli functional. We show that the SL-PAM algorithm is faster than the original PALM algorithm, and leads to competitive denoising, restoration and segmentation results.
A Cross-Sectional Prospective Study of Glycated Hemoglobin ( Hba 1 c ) and Fasting Blood Glucose ( Fbg ) Level In Both Diabetic and Non-Diabetic Patients in Context to Nepalese General Population

Mathews Journal of Diabetes & Obesity A Cross-Sectional Prospective Study of Glycated Hemoglobin (Hba1c) and Fasting Blood Glucose (Fbg) Level In Both Diabetic and Non-Diabetic Patients in Context to Nepalese General Population Prachand Man Singh Rajbhandari, Prabin Gyawali, Ram Vinod Mahato, Dipesh Chaudhary, Shubhash Paudel, Nani Kaji Deshar, Niranjan Koirala* Department of Medical Biochemistry, Faculty of Science and Technology, Nobel College, Pokhara University, Kathmandu, Nepal. Corresponding Author: Niranjan Koirala, Head of the Department, Medical Biochemistry, Nobel College, Pokhara University, Kathmandu, Nepal, Tel: +977(1)4110525; Email: koirala.biochem@gmail.com
The adoption of green initiatives in logistics service providers - a strategic perspective

Logistics service providers (LSPs) have started to transform their operations and strategy to be more effective from a green perspective. One concern that is of particular interest of LSPs is how to create organisational green awareness and translate this into practice in their operations. The purpose of this paper is to investigate the roles of green strategic commitment and organisation in LSPs' adoption of green initiatives. The data for this study were collected through a questionnaire survey, investigating a sample of LSPs operating in the Swedish, Finnish and Italian markets. From the literature analysis, a set of testable hypotheses was developed. The results indicate that there are relationships between the nature, as well as the scope, of functional involvement and the green strategic priority. The results further suggest that the inclusion of environmental consideration in the overall business strategy is positively related to the involvement and coordination of multiple functions as well as existence of a separate environmental function.
Improving Symptoms of Primary Sclerosing Cholangitis in a Male Patient aged 47 by Iranian Traditional Medicine in Tehran , 2016

Citation: Sayyed Mohammadali Soroushzadeh. Improving Symptoms of Primary Sclerosing Cholangitis in a Male Patient aged 47 by Iranian Traditional Medicine in Tehran, 2016. Asian J Clin Case Rep Trad Alt Med, Apr Sep 2017; 1(2-3): 33-38 © 2017 The Author(s). Open Access. This article is distributed under the SINAWEB Publication in http://sinaweb.net. Soroushzadeh. Asian J Clin Case Rep Trad Alt Med. Apr Sep 2017; 1(2-3): 33-38 DOI: 10.22040/AJCRTAM.2017. 27584
Long‐distance colonization of the Western Mediterranean by Cistus ladanifer (Cistaceae) despite the absence of special dispersal mechanisms

Aim There are few biogeographical and evolutionary studies that address plant colonization and lineage origins in the Mediterranean. Cistus serves as an excellent model with which to study diaspore dispersal and distribution patterns of plants exhibiting no special long‐distance dispersal mechanisms. Here we analyse the pattern of genetic variation and divergence times to infer whether the African–European disjunction of C. ladanifer L. is the result of long‐distance dispersal or of vicariance events.
Sensor Networks for Landslide Detection and Monitoring

Mobile land slide detector based on sensor network for landslide detection. The whole system was validated by the issuance of a landslide warning in the month of July (last monsoon 3season), which facilitated pre-emptive action by the local government and community to prevent loss of human life. In this system rain sensor, soil moisture sensor, flex sensor, accelerometer sensor these sensors are used for detection of landslide.
Adynamic Brownian bridgemovementmodel to estimate utilization distributions for heterogeneous animalmovement

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal’s movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.
A Validation of Coarse Scale Global Vegetation Height Map for Biomass Estimation in Hemiboreal Forests in Estonia

A public release of global vegetation height map, based on data from spaceborne lidar GLAS, was validated using forest management inventory (FI) data and airborne laser (ALS) data from two 15 × 15 km test sites in Estonia: the first one in Aegviidu and the second one in Laeva. For each global vegetation height (GVH) map pixel located in the test sites we calculated forest height based on the FI data and on ALS data. Linear regression analysis was then used to evaluate the relationships between GVH map values (HGVH), FI forest height (HFI) and ALS-based Lorey’s forest height (HALS). In the second test HGVH and HALS were evaluated for estimating forest biomass using regression analysis. The biomass was calculated for each GVH pixel using FI data and allometric regression models. The correlation between HGVH and HFI or HALS in both test sites was weak – in Aegviidu r < 0.25 and in Laeva r < 0.15; and, the relationship was not statistically significant in Laeva. The airborne lidar based HALS had a strong positive correlation with forest biomass and the determination coefficient of linear regression was R2 > 0.6 (p < 0.01) in both test sites. The relationship between HGVH and biomass was scattered and determination coefficient for linear model was small (R2 < 0.15, p < 0.01). Although in this study only weak correlation between measured forest heights (HFI and HALS) and spaceborne lidar based HGVH was found, the GVH type estimates are essential for the areas, where forest inventory data or airborne lidar data is not available. The obtained results show that forest height estimates from ALS or spaceborne lidar could be used directly for estimating biomass in managed hemiboreal forests at coarse spatial resolution.
Towards a Virtualized Next Generation Internet

A promising solution to overcome the Internet ossification is network virtualization in which Internet Service Providers (ISPs) are decoupled into two tiers: service providers (SPs), and infrastructure providers (InPs). The former maintain and customize virtual network(s) to meet the service requirement of end-users, which is mapped to the physical network infrastructure that is managed and deployed by the latter via the Virtual Network Embedding (VNE) process. VNE consists of two major components: node assignment, and link mapping, which can be shown to be NP-Complete. In the first part of the dissertation, we present a path-based ILP model for the VNE problem. Our solution employs a branch-and-bound framework to resolve the integrity constraints, while embedding the column generation process to effectively obtain the lower bound for branch pruning. Different from existing approaches, the proposed solution can either obtain an optimal solution or a near-optimal solution with guarantee on the solution quality. A common strategy in VNE algorithm design is to decompose the problem into two sequential sub-problems: node assignment (NA) and link mapping (LM). With this approach, it is inexorable to sacrifice the solution quality since the NA is not holistic and not-reversible. In the second part, we are motivated to answer the question: Is it possible to maintain the simplicity of the Divideand-Conquer strategy while still achieving optimality? Our answer is based on a decomposition framework supported by the Primal-Dual analysis of the path-based ILP model. This dissertation also attempts to address issues in two frontiers of network virtualization: survivability, and integration of optical substrate. In the third part, we address the survivable network embedding (SNE) problem from a network flow perspective, considering both splittable and non-splittable flows. In addition, the explosive growth of the Internet traffic calls for the support of a bandwidth-abundant optical substrate, despite the extra dimensions of complexity caused by the heterogeneities of optical resources, and the physical feature of optical transmission. In this fourth part, we present a holistic view of motivation, architecture, and challenges on the way towards a virtualized optical substrate that supports network virtualization. INDEX WORDS: Network Virtualization, Virtual Network Embedding, Network Survivability, Optical Virtualization TOWARDS A VIRTUALIZED NEXT GENERATION INTERNET