Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability and validity of an individually focused food insecurity access scale for assessing inadequate access to food among pregnant Ugandan women of mixed HIV status

Abstract Objective To determine the reliability, validity and correlates of measures of food insecurity (FI) obtained using an individually focused food insecurity access scale (IFIAS) among pregnant women of mixed HIV status in northern Uganda. Design A mixed-methods study involving cognitive interviews nested within a cross-sectional survey. Setting The antenatal care clinic of Gulu Regional Referral Hospital. Subjects Survey respondents included 403 pregnant women, recruited in a ratio of one HIV-infected to two HIV-uninfected respondents, twenty-six (nine of them HIV-infected) of whom were asked to participate in the cognitive interviews. Results Over 80 % of cognitive interview participants reported understanding the respective meanings of six of the nine items (i.e. items 4 to 9) on the IFIAS. Two main factors emerged from rotated exploratory factor analysis of the IFIAS: mild to moderate FI (IFIAS items 1–6) and severe FI (items 7–9). Together, they explained 90·4 % of the FI measure’s variance. The full IFIAS and the two subscales had moderate to high internal consistency (Cronbach’s α ranged from 0.75 to 0.87). Dose–response associations between IFIAS scores, and measures of socio-economic status and women’s diet quality, were observed. Multivariate linear regression revealed significant positive associations between IFIAS scores and HIV infection, maternal age, number of children and a history of internal displacement. IFIAS scores were negatively associated with women’s diet diversity score, asset index and being employed. Conclusions The IFIAS showed strong reliability, validity and contextual relevance among women attending antenatal care in northern Uganda.
Integrating Tensor Factorization with Neighborhood for Item Recommendation in Multidimensional Context

Item recommendation for multidimensional data context is getting increasing attention in recent years. Tensor factorization and neighborhood based collaborative filtering are the major techniques in use, but they address the item recommendation task for multidimensional data in quite different ways and have different strengths. In this paper, we discuss the characteristics of the two techniques, and present an approach for user profiling and neighborhood formation using multidimensional data, and also propose a novel collaborative filtering recommendation model which integrates the tensor factorization based and the neighborhood based collaborative filtering techniques for item recommendation with the Social Tagging Systems (STS) as the application domain. Meanwhile, the proposed recommendation approach is applicable to other application domains where multidimensional data is available. We empirically compare the proposed model against some state-of-the-art collaborative filtering recommendation approaches on two real-world datasets: Bibsonomy and MovieLens. The experimental results show the superiority of the proposed model in terms of recommendation quality.
The effect of multiplexing delay on MMORPG TCP traffic flows

Traffic optimization techniques, based on header compression, multiplexing and tunneling a number of concurrent flows, have been proposed in order to save bandwidth and to reduce the amount of packets per second in certain network segments. These techniques have been standardized for VoIP flows, but they can also be applied to the flows of online games. As a counterpart of the savings, the adding of a multiplexing delay is required in order to get a number of packets to be multiplexed together. The effect of this delay has already been explored for UDP-based games, showing that subjective quality can be maintained while using traffic optimization. In contrast, the present paper explores the issues arising when these optimization techniques are applied to TCP-based games. The interaction of the additional multiplexing delay and the TCP mechanisms is explored, and results are obtained in terms of round trip time increase and traffic overhead modification.
Neural network models for classifying immune cell subsets

The immune system is composed of heterogeneous cell populations and it includes several hundreds of distinct cells types such as neutrohils, eosinophils, basophils, macrophages, dendritic cells, CD4+ and CD8+ T cells, γδ T cells, mast cells, and B cells and each main cell type can be further differentiated into subsets with unique and overlapping functions. For example CD4+ T cells can be differentiated into T helper (Th)1, Th2, Thl7, and regulatory T cell (Treg) subsets. To study molecular mechanisms of cell differentiation, Systems Biology Markup Language (SBML) based Ordinary Differential Equation (ODE) models can be used for representing such processes. These intracellular signaling models often require many equations to accurately represent intracellular pathways and biochemical reactions. Another challenge in studying the immune system and immune responses is the need for integration of complex processes that occur at different time and space scales (i.e., populations, whole organism, tissue level, cellular and molecular) through multi-scale modeling. This study presents two novel neural network models for modeling CD4+ T cell differentiation and immune cell subset classification. The first model reduces the complex ODE intracellular model by focusing on the input and output cytokines and the second model establishes an automated subset classification based on molecular patterns expressed in immune cells. After training, the first model achieves small prediction errors of cytokine concentrations and the second model achieves 98% prediction rate for subset classification. Neural network algorithm and models have been widely used for many data mining tasks such as classification and pattern recognition. However, to the best of our knowledge this study is the first one applying the neural network algorithm for immune cell differentiation and subset classification. In addition, these novel neural network models can be computationally efficiently integrated into multi-scale models with limited computational costs.
Synchronization of a budding yeast cell culture by manipulating inner cell cycle concentrations

The synchronization of cell populations is an important topic not only in research but also for biotechnological production processes. Synchronization means that all cells in the culture are in the same phase of the cell cycle and that they divide, at best, in unison. A synchronized cell culture is an important topic in molecular biology to analyze cell cycle events and to understand how the cells control growth and division. It has a high potential in biotechnological production processes because many products which are of commercial interest are cell cycle dependent or are synthesized in a cell cycle dependent manner. In this work a strategy for the synchronization of an in silico budding yeast cell culture is computed and analyzed. The in silico culture is based on a single cell cycle model taken from the literature which describes the cell cycle of Saccharomyces cerevisiae by the concentrations and interactions of proteins that participate in the cell cycle. By individually setting the parameters for each cell, the model can be used to model a non-homogenous cell population. The synchronization approach followed here is based on the manipulation of the concentrations of native proteins. The variation of two parameters in the model that can be achieved by different gene expression rates leads to a modified expression of two cyclins that appear in the late G1 phase. Dependent on the concentration of these two cyclins, the G1 phase can be either lengthened or shortened, thus maintaining the cells in the G1 phase or accelerating their transition to the other phases. Due to the non-convexity of the problem, the optimization is done by a memetic algorithm. The synchronization is applied to an exponential growing cell population inside a batch reactor and a chemostat. A detailed analysis of the effect of the expression levels is presented. For the chemostat, a periodic pattern for synchronizing and keeping the culture in synchrony is computed.
Flavobacterium sp. Strain 4221 and Pedobacter sp. Strain 4236 β-1,3-Glucanases That Are Active at Low Temperatures

ABSTRACT Secretion of β-1,3-glucanases by the arctic bacterial isolates 4221 and 4236, related to the genera Flavobacterium and Pedobacter, was discovered. Escherichia coli and Lactococcus lactis expression of β-1,3-glucanases Glc4221-1 and Glc4236-1 from the respective isolates was achieved. The enzymes hydrolyzed fungal cell walls and retained activity at low temperatures.
Schisandrin B Enhances Glutathione Redox Cycling and Protects Against β-amyloid-induced Apoptosis in Human Neuroblastoma SH-SY5Y Cells: A Comparative Study of Various Phytochemicals

In the present study, we aim to define the cytoprotective mechanism of ()schisandrin B (()Sch B) in comparison with other phytochemicals in SH-SY5Y cells. The effects of ()Sch B and curcumin (Cur), resveratrol (Rev) and epigallocatechin gallate (EGCG) on -amyloid (A)-induced apoptosis were investigated in SH-SY5Y cells. Cellular reduced glutathione (GSH) levels and peroxide-induced GSH depletion were measured. Activities of glutathione reductase (GR) and glucose-6-phosphate dehydrogenase (G6DPH) in Aβ-challenged cells were also examined. All tested phytochemicals were investigated for the activation of nuclear factor erythroid-2 related factor 2 (Nrf2) in SH-SY5Y cells, using a luciferase-based assay. Finally, they were examined for the effect on the extent of phosphorylation of Tau in A- challenged cells. The results showed that only ()Sch B and EGCG protected against Aβ-induced apoptosis in SH-SY5Y cells. The cytoprotection afforded by ()Sch B and EGCG were associated with an increase in cellular GSH levels in Aβ- challenged cells and a reduction in peroxide-induced GSH depletion. However, only ()Sch B, but not EGCG, increased G6DPH and GR activities in Aβ-challenged cells and caused the activation of Nrf2 in unchallenged cells. Both ()Sch B and EGCG reduced the extent of Tau phosphorylayion in Aβ-challenged cells. In conclusion, ()Sch B may enhance cellular glutathione redox cycling, presumably by increasing G6PDH and GR activities, via activation of the Nrf2 signaling pathway, whereas EGCG likely acts as a radical scavenger. Both ()Sch B and EGCG suppressed the phosphorylation of Tau in Aβ-challenged cells, suggesting their potential in ameliorating the pathological condition of Alzheimer's disease.
Online collision and occlusion detection based on 3D Cuboid Maps for a daily assistive robot with a tilting LRF

This paper describes about online collision and occlusion detection for a daily assistive robot. It is assumed that 3D map about around environment is constructed by collecting datasets gradually measured using a Laser RangeFinder mounted on a tilting platform, we develop an approach to detect collision and occlusion based on the 3D map while a robot tries to manipulate objects in daily environment. As the map representation, Time-series Composite Cuboid Map is proposed. It copes with temporal sequence explicitly, and map updating is efficiently performed. Several experiments on the assumption of daily assistance by a robot, we show the effectiveness of our approach.
INECO Frontal Screening (IFS): A brief, sensitive, and specific tool to assess executive functions in dementia–CORRECTED VERSION

Abstract Although several brief sensitive screening tools are available to detect cognitive dysfunction, few have been developed to quickly assess executive functioning (EF) per se. We designed a new brief tool to evaluate EF in neurodegenerative diseases. Patients with an established diagnosis of behavioral variant frontotemporal dementia (bvFTD; n = 22), Alzheimer disease (AD; n = 25), and controls (n = 26) were assessed with a cognitive screening test, the INECO Frontal Screening (IFS), and EF tests. Clinical Dementia Rating Scale (CDR) scores were obtained for all patients. Internal consistency of the IFS was very good (Cronbach’s alpha = .80). IFS total (out of 30 points) was 27.4 (SD = 1.6) for controls, 15.6 (SD = 4.2) for bvFTD, and 20.1 (SD = 4.7) for AD. Using a cutoff of 25 points, sensitivity of the IFS was 96.2%, and specificity 91.5% in differentiating controls from patients with dementia. The IFS correlated significantly with the CDR and executive tasks. The IFS total discriminated controls from demented patients, and bvFTD from AD. IFS is a brief, sensitive, and specific tool for the detection of executive dysfunction associated with neurodegenerative diseases. The IFS may be helpful in the differential diagnosis of FTD and AD. (JINS, 2009, 15, 777–786.)
Specific and azurophilic granules from rabbit polymorphonuclear leukocytes. II. Cell surface localization of granule membrane and content proteins before and after degranulation

The compositional relationship between the cell surface of rabbit polymorphonuclear leukocytes (PMNs) and the membranes of PMN cytoplasmic granules has been investigated. Heterophilic PMNs obtained from peritoneal exudates contained 13 cell surface polypeptides ranging in molecular weight from 220,000 to 12,000 daltons as determined by lactoperoxidase-catalyzed protein iodination and gel electrophoresis. Of these, four polypeptides co-migrated with proteins identified as the major constituents of specific (SpG) and azurophilic (AzG) granule membranes. The most notable of these were cell surface proteins of 145,000 and 96,000 daltons that co-migrated with proteins identified as granule content proteins released from PMNs during exocytosis. Extensive washing did not remove these proteins from the cell surface. Iodination of PMNs after the release of SpG and AzG contents by calcium ionophore- induced exocytosis revealed that there was not a dramatic quantitative change in the proteins on the cell surface. Instead, there were large, quantitative increases in the relative amounts of (125)I that were incorporated into several pre-existing cell surface proteins; all of these cell surface proteins co-migrated as a set with those polypeptides identified as either granule membrane or content proteins. Although nearly all of the major polypeptides of SpG and AzG had counterparts on the cell surface of freshly isolated peritoneal exudates PMNs, there were several polypeptides that were unique to the cell surface. Thus, the PMN has at least three membrane compartments with strikingly different protein compositions.
THE FINITE-STATE HARD CORE MODEL ON A REGULAR TREE

The classical hard core model from statistical physics, with activity λ > 0 and capacity C = 1, on a graph G, concerns a probability measure on the set I(G) of independent sets of G, with the measure of each independent set I ∈ I(G) being proportional to λ|I|. When G is a (b + 1)-regular tree, a generalization of the hard core model that has capacity C larger than one was studied by Mitra et al., as an idealized model of multicasting in communication networks. In this work, we consider this generalization and prove rigorously some of the conjectures made by Mitra et al. In particular, we show that the nature of the phase transition indeed depends on the parity of the capacity parameter C ∈ Z+. In addition, for large b we identify a short interval of values for λ above which the model exhibits phase co-existence and below which there is phase uniqueness. For odd C, this transition occurs in the region of λ = (e/b)1/dC/2e, while for even C, it occurs around λ = (log b/b(C + 2)).
Performance Evaluation of Digital Modulation Techniques in Multi path Communication Channel Interface Air

This paper investigates the performance of different digital modulation techniques in Multipath fading channel interface air. The paper describes characteristics of wireless communication channels with emphasis on fading channel. This study examines the inherent attributes of the digital modulation schemes to overcome the impairment introduced by the channel. The channel was modeled and simulated using 6 rays. The evaluation of the different modulation techniques was carried on the modeled multipath channel. This was carried out to understand the contributions of channel characteristics to effective wireless communication. The BER for simulated modeled channels agreed with the theoretical results. It was also observed that multipath fading channel characteristic limits the data rate in wireless communication. Index Terms—BER, communication channels, modulation and Noise
Pondering personal privacy: a pragmatic approach to the Fourth Amendment protection of privacy in the information age

Abstract As technology with surveillance capacities has advanced, the debate over the rights of the citizenry to be free from governmental breaches of personal privacy has intensified. Within the United States, government actions legally challenged as intrusions into personal privacy have been analyzed under the Fourth Amendment, but Supreme Court rulings in such cases lack a clear and consistent rationale. Additionally, while more than a dozen federal privacy statutes have been enacted, each piece of legislation pertains to a specific type of information (e.g. driver’s license information, education records, and financial records). There is no overarching federal legislation which protects the individual’s private affairs from warrantless government inspection. A key issue underlying the scope of the debate and the variation in court decisions and public policies pertinent to invasions of privacy by government agencies is the lack of a clear and cogent definition of ‘privacy. ’ By means of a review of the evolution of legal protections of privacy under the Fourth Amendment and a review of the evolution of technology with surveillance applications, it is suggested that there is a need for a sound operational definition of privacy. As a starting point for an informed and pragmatic dialogue on this matter, an operational definition of privacy built upon extant case and statutory law is provided.
Where does learning take place? The role of intergovernmental cooperation for policy diusion in Switzerland

Dear participants of the comparative politics workshop. This paper is part of my on-going dissertation project on policy diusion in federal states. So far, I have analyzed the diusion of a particular policy, namely health insurance subsidy policies, among the sub-national unites of Switzerland. I am now focusing on factors that enhance (or hinder) policy learning. That is where this paper is situated. I’m grateful for any comments.
Harmonizing Dependability and Real Time in CAN Networks

The particular mechanisms which the Controller Area Network (CAN) protocol defines to guarantee dependable communication with real-time constrains makes this fieldbus very suitable for many small-size distributed embedded systems. Nevertheless, it has been also reported that CAN protocol exhibits some liabilities concerning both dependability and real-time; though solutions to these problems have been already suggested. In this paper, an analysis of the atomic broadcast property in the Time-Triggered CAN (TTCAN) protocol is used to reason about the compatibility between techniques intended to improve CAN dependability and techniques intended to improve CAN real-time behavior. Authors claim that, although a number of problems have been solved individually, further research has to be carried out in order to properly integrate such solutions.
Fine-Grained Micro-Tasks for MapReduce Skew-Handling

Recent work on MapReduce has considered the problems of skew, where a job’s tasks exhibit large variance in size and processing cost, and stragglers, tasks that run slowly due to conditions on particular nodes. In this paper, we discuss an extremely simple approach to mitigating skew and stragglers: break the workload into many small tasks that are dynamically scheduled at runtime. This approach is only effective in systems with high-throughput, low-latency task schedulers and efficient data materialization, so we propose techniques for scaling these components. To demonstrate the efficacy of this technique, we compare micro-tasks to other skew handling techniques using the Spark cluster computing framework.
Framework for detection, assessment and assistance of university students with dyslexia and/or reading difficulties

During the past years, the adoption of Learning Management System (LMS) to support an e-learning process has been continuously growing. Hence, a potential need and meaningful factor to provide a personalized support, within the context of these systems, has been the identification of particular characteristics of students to provide adaptations of the system’s elements to the individual traits. One particular characteristic that has been little studied in a personalized e-learning process are the learning disabilities (LD) of students. Dyslexia is a common LD in Spanish-speaking university students, which is specifically referred to the manifestation of different difficulties in reading. Dyslexia requires special attention by higher educational institutions to detect, assess, and assist affected students during their learning process. Thereby, an open challenge has been identified from this implication: How to include Spanish-speaking university students with dyslexia and/or reading difficulties in an e-learning process?
Robust Design Optimization in industrial virtual product development

In this paper different strategies to search for a robust design are presented and investigated with respect to their efficiency and applicability to time consuming numerical models. After starting with deterministic optimization we introduce different measures to define the robustness of a design. An iterative Robust Design Optimization (RDO) is proposed where deterministic optimization is combined with variance-based robustness analysis and final reliability proof. The iterative procedure is compared to coupled RDO approaches, where the robustness or reliability measures are calculated for each optimization design. For such a procedure often global approximation models are used in order to enable the application for more complex problems.
Points of (Dis)parity: Expectation Disconfirmation from Common Attributes in Consumer Choice

Whereas many theories of decision making predict that presenting or not presenting common features of choice alternatives should not affect choice, in this research, the authors show that common features can be a powerful driver of choice behavior. They conjecture that consumers often hold expectations about the features that choice alternatives have in common, and they demonstrate that presenting (vs. omitting information about) a common feature increases the choice probability of the alternative that would have been expected to perform worse on the common feature, given its performance on differentiating features. This effect occurs because performance on the common feature is judged not at face value but relative to an expectation about which product should perform best on that feature. The effect holds even though performance on the common feature is clearly the same when alternatives are presented side by side. Finally, the authors demonstrate four boundary conditions of this effect.