Miracle-dz/UniFAR-FTUs huggingface dataset:
From Visibility to Autonomy: Latinos and Higher Education in the U.S., 1965–2005

In this article, Victoria-Maria MacDonald, John M. Botti, and Lisa Hoffman Clark trace the evolution of higher educational opportunities for Latinos in the United States from the Higher Education Act of 1965 to the designation of Title V in the Act's 1998 reauthorization. The authors argue that this evolution moved through stages, including establishing visibility and legitimacy, self-determination, self-scrutiny, emulation, and, finally, autonomy. The journey toward improving higher educational opportunities for Latinos is juxtaposed with the journey experienced by African Americans in the United States. Because of the enormous historical, social, and political differences between the two groups, the models utilized by and for Blacks were viewed as inadequate for serving Latino needs in higher education. However, the model established by Historically Black Colleges and Universities inspired Latino educators to found Hispanic-Serving Institutions (HSIs). The authors conclude their article by discussing co.
Linearly constrained Bayesian matrix factorization for blind source separation

We present a general Bayesian approach to probabilistic matrix factorization subject to linear constraints. The approach is based on a Gaussian observation model and Gaussian priors with bilinear equality and inequality constraints. We present an efficient Markov chain Monte Carlo inference procedure based on Gibbs sampling. Special cases of the proposed model are Bayesian formulations of non-negative matrix factorization and factor analysis. The method is evaluated on a blind source separation problem. We demonstrate that our algorithm can be used to extract meaningful and interpretable features that are remarkably different from features extracted using existing related matrix factorization techniques.
Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis

Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.
Distributed regression for high-level feature extraction in wireless sensor networks

In sensor networks, energy and communication bandwidth are strongly constrained resources. A key technique to reduce the use of these resources is data aggregation, whereby the data collected by the nodes are combined during the routing. Aggregation is however limited to very few operators. This paper provides a new use of aggregation techniques, by showing that they can be used to extract high-level information by means of regression models. The main idea is to distribute the regression model coefficients in the network, in such a way that the model output is computed by aggregating data along a routing tree. We illustrate the use of the technique for a target tracking task, using the real-world acoustic and seismic data of the SensIT deployment. We show that it provides an effective way to estimate the position of the target while keeping the volume of communication low thanks to aggregation.
Detection of eye movements from fMRI data

Awake humans make eye movements with amplitudes and frequencies that depend on behavioral state and task. This poses two problems for functional magnetic resonance imaging (fMRI) studies that compare brain activity across tasks. First, motion of the eye in the orbit increases the variance of the MR signal in adjacent regions of the orbitofrontal cortex, hampering activation detection. Second, eye movements are associated with activity in a distributed network of brain areas, confounding comparisons of task activation. Direct measurement of eye movements in the scanner bore is possible with expensive and technically demanding equipment. A method is described that detects eye movements directly from MR data without the use of additional equipment. Changes in the MR time series from the vitreous of the eye were observed that correlated with eye movements, as measured directly with an infrared pupil tracking system. In each of 10 subjects, the variance of the MR time series from the eye vitreous was greater when the subject made eye movements than when the subject fixated centrally (average standard deviation (SD) 99.7 vs. 75.6, P = 0.001). The assessment of eye movements directly from fMRI data may be especially useful for retrospective and meta‐analyses. Magn Reson Med 49:376–380, 2003. Published 2003 Wiley‐Liss, Inc.
How to interpret an anonymous bacterial genome: machine learning approach to gene identification.

In this report we address the problem of accurate statistical modeling of DNA sequences, either coding or noncoding, for a bacterial species whose genome (or a large portion) was sequenced but not yet characterized experimentally. Availability of these models is critical for successful solution of the genome annotation task by statistical methods of gene finding. We present the method, GeneMark-Genesis, which learns the parameters of Markov models of protein-coding and noncoding regions from anonymous bacterial genomic sequence. These models are subsequently used in the GeneMark and GeneMark.hmm gene-finding programs. Although there is basically one model of a noncoding region for a given genome, several models of protein-coding region are automatically obtained by GeneMark-Genesis. The diversity of protein-coding models reflects the diversity of oligonucleotide compositions, particularly the diversity of codon usage strategies observed in genes from one and the same genome. In the simplest and the most important case, there are just two gene models-typical and atypical ones. We show that the atypical model allows one to predict genes that escape identification by the typical model. Many genes predicted by the atypical model appear to be horizontally transferred genes. The early versions of GeneMark-Genesis were used for annotating the genomes of Methanoccocus jannaschii and Helicobacter pylori. We report the results of accuracy testing of the full-scale version of GeneMark-Genesis on 10 completely sequenced bacterial genomes. Interestingly, the GeneMark.hmm program that employed the typical and atypical models defined by GeneMark-Genesis was able to predict 683 new atypical genes with 176 of them confirmed by similarity search.
Epidemic Dissemination in Ad Hoc Networks

We have worked on an epidemic dissemination protocol to maintain soft-state in a decentralized, peer-to-peer fashion, in ad hoc networks. This protocol is an enhancement of Passive Distributed In- dexing (PDI) method proposed by Lindemann and Waldhorst. We have enhanced PDI in order to reduce the number of broadcast messages when the search for an item may span several hops. Three enhancements are proposed: (i) Lazy query propagation to delay the propagation of query messages such that local responses can inhibit unnecessary search. (ii) Quench waves to stop an already initiated query propagation when still possible. Decision rules based solely on local information determine whether to start a quench wave or not. (iii) The use of Multi-Point Relay (MPR) or similar protocol and algorithm, to reduce redundant broadcast messages.
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection

Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data. In particular, we leverage novel StochasticNet radiomic sequencers for extracting custom radiomic features tailored for characterizing unique cancer tissue phenotype. Using StochasticNet radiomic sequencers discovered using a wealth of lung CT data, we perform binary classification on 42,340 lung lesions obtained from the CT scans of 93 patients in the LIDC-IDRI dataset. Preliminary results show significant improvement over previous state-of-the-art methods, indicating the potential of the proposed discovery radiomics framework for improving cancer screening and diagnosis.
Sparse matrix storage revisited

In this paper, we consider alternate ways of storing a sparse matrix and their effect on computational speed. They involve keeping both the indices and the non-zero elements in the sparse matrix in a single data structure. These schemes thus help reduce memory system misses that occur when the usual indexing based storage schemes are used to store sparse matrices and give promising performance improvements.
Proactive adaptations in sensor network query processing

Wireless sensor networks (WSN) are used by many applications for event and environmental monitoring. Due to the resource-limited nodes in WSNs, there has been much research into extending the functional lifetime of the network through energy-saving techniques. Sensor Network Query Processing (SNQP) is one such technique. SNQP uses information about a query and the WSN over which it is to be run, to generate an energy-efficient Query Execution Plan (QEP) that distributes processing in the form of QEP fragments to the nodes in the WSN. However, any QEP is likely to drain the batteries of the nodes unevenly, and, as a result, nodes used in a QEP may run out of energy when there are significant energy stocks still available in the WSN. An adaptive query processor could react to energy depletion, for example, by generating a revised plan that refrains from using the drained nodes. However, adapting only when a node has been depleted may provide few opportunities for the creation of effective new QEPs. In this paper, we introduce an approach that determines, at query compilation time, a sequence of QEPs with switch times for transitioning between successive plans with a view to extending the overall lifetime of the query. We describe how this approach has been implemented as an extension to an existing SNQP and present experimental results indicating that it can significantly increase QEP lifetimes.
The problem of non-response in population surveys on the topic of HIV and sexuality: a comparative study.

BACKGROUND Throughout the Western world, response rates are falling in population surveys. The aim of the study was to explore for the presence of non-response bias in two Swedish research projects on HIV ['HIV in Sweden' (HIS11)] and sexuality ['Sex and health' (UngKAB09)]. METHODS The study used four data sets (two from each project), each of which had been generated using different methods. By comparing means and percentages on 15 items across the data sets, we explore the potential presence of non-response bias due to interest in the survey topic (topic salience), and discuss the suitability of two increasingly common methods for recruiting respondents: the pre-recruited probability web panel and the self-selected web survey. RESULTS While a higher proportion of the respondents in the HIS11 substudies had been tested for HIV and were inclined to perceive themselves as being at high risk of HIV infection, the respondents in the UngKAB09 substudies were on average more sexually active. Further, while there was little variation in the results between the HIS11 substudies (postal/web survey and pre-recruited web panel), there were some fairly large differences in the results between the UngKAB09 substudies (web surveys, one of which was based on a self-selected sample). CONCLUSION The study concludes that (i) there are signs of non-response bias that may be due to topic salience, (ii) while care must be taken when using self-selection sampling methods, the pre-recruited probability web panel might provide a cost- and time-effective alternative for recruiting respondents in future population surveys.
Visualizing Causal Semantics Using Animations

Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. We extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent on a set of structural and temporal rules. We designed animated representations, based on Michotte's rules, for showing complex causal relationships or causal semantics. In this paper we describe a set of animations for showing semantics such as causal amplification, causal strength, causal dampening, and causal multiplicity. In a two part study we compared the effectiveness of both the static and animated representations. The first study (N=44) asked participants to recall passages that were previously displayed using both types of representations. Participants were 8% more accurate in recalling causal semantics when they were presented using animations instead of static graphs. In the second study (N=112) we evaluated the intuitiveness of the representations. Our results showed that while users were as accurate with the static graphs as with the animations, they were 9% faster in matching the correct causal statements in the animated condition. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations.
Lymphokine-activated killer and dendritic cell carriage enhances oncolytic reovirus therapy for ovarian cancer by overcoming antibody neutralization in ascites

Reovirus is an oncolytic virus (OV), which acts by both direct tumor cell killing and priming of antitumor immunity. A major obstacle for effective oncolytic virotherapy is effective delivery of OV to tumor cells. Ovarian cancer is often confined to the peritoneal cavity and therefore i.p. delivery of reovirus may provide the ideal locoregional delivery, avoiding systemic dissemination. However, ovarian cancer is associated with an accumulation of ascitic fluid, which may interfere with oncolytic viral therapy. Here, we investigated the effect of ascites on reovirus‐induced oncolysis against primary ovarian cancer cells and ovarian cancer cell lines. In the absence of ascites, reovirus was cytotoxic against ovarian cancer cells; however, cytotoxicity was abrogated in the presence of ascitic fluid. Neutralizing antibodies (NAb) were identified as the cause of this inhibition. Loading OV onto cell carriers may facilitate virus delivery in the presence of NAb and immune cells which have their own antitumor effector activity are particularly appealing. Immature dendritic cells (iDC), Lymphokine‐activated killer (LAK) cells and LAKDC cocultures were tested as potential carriers for reovirus for tumor cell killing and immune cell priming. Reovirus‐loaded LAKDC, and to a lesser degree iDC, were able to: (i) protect from NAb and hand‐off reovirus for tumor cell killing; (ii) induce a proinflammatory cytokine milieu (IFNɣ, IL‐12, IFNα and TNFα) and (iii) generate an innate and specific antitumor adaptive immune response. Hence, LAKDC pulsed with reovirus represent a novel, clinically practical treatment for ovarian cancer to maximise both direct and innate/adaptive immune‐mediated tumor cell killing.
Furanocoumarin Derivatives in Kampo Extract Medicines Inhibit Cytochrome P450 3A4 and P-Glycoprotein

Furanocoumarins in grapefruit are known to show inhibitory effects against P-glycoprotein (P-gp) and CYP3A4 in intestinal epithelial cells; however, furanocoumarin derivatives are widely contained in the plants of Rutaceae and Umbelliferae families, which are used as components of Kampo extract medicines. In this study, we investigated the inhibitory effects of 12 furanocoumarins extracted from plants in the Umbelliferae family against P-gp and CYP3A4 activity. Furthermore, we studied their inhibitory effect on P-gp when furanocoumarins are used as Kampo extract medicine rather than as an isolated single compound. From screening of the CYP3A4 inhibitory effect, notopterol and rivulobirin A, the only dimer types of furanocoumarin, were found to be potent inhibitors of CYP3A4. On the other hand, byakangelicol and rivulobirin A showed strong P-gp inhibition from the screening of P-gp inhibitor evaluated by quinidine permeation through the Caco-2 monolayer; however, the chemical structural relationship of furanocoumarins between P-gp and CYP3A4 inhibitory effects could not be obtained. We also investigated the effect of these furanocoumarins on the transport of digoxin through the Caco-2 monolayer. The inhibitory effect of rivulobirin A was more potent than that of byakangelicol. Application of either Senkyu-cha-cho-san or Sokei-kakketsu-to, which are composed of herbal remedies in the Umbelliferae group, significantly decreased the efflux ratio of digoxin. In conclusion, it was found that some furanocoumarins extracted from the plants in the Umbelliferae family strongly inhibited P-gp and CYP3A4. Kampo extract medicines containing herbal remedies belonging to the Umbelliferae family may cause a drug-drug interaction with P-gp or a CYP3A4 substrate drug.
Endocytosis Is Crucial for Cell Polarity and Apical Membrane Recycling in the Filamentous Fungus Aspergillus oryzae

ABSTRACT Establishing the occurrence of endocytosis in filamentous fungi was elusive in the past mainly due to the lack of reliable indicators of endocytosis. Recently, however, it was shown that the fluorescent dye N-(3-triethylammoniumpropyl)-4-(p-diethyl-aminophenyl-hexatrienyl)pyridinium dibromide (FM4-64) and the plasma membrane protein AoUapC (Aspergillus oryzae UapC) fused to enhanced green fluorescent protein (EGFP) were internalized from the plasma membrane by endocytosis. Although the occurrence of endocytosis was clearly demonstrated, its physiological importance in filamentous fungi still remains largely unaddressed. We generated a strain in which A. oryzae end4 (Aoend4), the A. oryzae homolog of Saccharomyces cerevisiae END4/SLA2, was expressed from the Aoend4 locus under the control of a regulatable thiA promoter. The growth of this strain was severely impaired, and its hyphal morphology was altered in the Aoend4-repressed condition. Moreover, in the Aoend4-repressed condition, neither FM4-64 nor AoUapC-EGFP was internalized, indicating defective endocytosis. Furthermore, the localization of a secretory soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) was abnormal in the Aoend4-repressed condition. Aberrant accumulation of cell wall components was also observed by calcofluor white staining and transmission electron microscopy analysis, and several genes that encode cell wall-building enzymes were upregulated, indicating that the regulation of cell wall synthesis is abnormal in the Aoend4-repressed condition, whereas Aopil1 disruptants do not display the phenotype exhibited in the Aoend4-repressed condition. Our results strongly suggest that endocytosis is crucial for the hyphal tip growth in filamentous fungi.
Robust synchronisation of heterogeneous networks via integral quadratic constraints

A general framework for the study of robust synchronisation in large-scale networks is provided. Agents are represented as a common nominal linear time-invariant (LTI) single-input-single-output (SISO) system with simple poles on the imaginary axis, subject to LTI SISO stable multiplicative perturbations. The agents exchange information in order to achieve output-synchronisation, namely steer their outputs to the same, possibly time-varying, signal. Such an information exchange is modeled through a sparse dynamical operator that maps the outputs of the agents into their inputs. The theory of integral quadratic constraints is used to capture the structural uncertainties of the perturbations, and to give certificates for robust synchronisation of the systems. Since the IQC theory is nominally applied to open-loop stable systems, the main idea is to introduce a new space of signals with respect to which the notion of feedback stability implies that of synchronisation under appropriate assumptions on the interconnection operator. The proposed criterion unifies and extends several results in the literature.
An adaptive p-persistent CSMA scheme for LTE in unlicensed bands

With the exponential growth of wireless data demand and the increasing scarcity of licensed spectrum, mobile network operators are considering extending the use of Long Term Evolution (LTE) into the unlicensed spectrum to offload their traffic. In the future, it is very likely that LTE will coexist with WiFi on the same unlicensed bands, such as the Industrial, Scientific and Medical (ISM) bands. However, coexistence issues arise when the two technologies overlap with each other. Existing simulation results show that LTE is slightly impacted by such coexistence while WiFi performance is severely degraded. In order to improve LTE/WiFi coexistence, we propose an adaptive p-persistent Carrier Sense Multiple Access (CSMA) scheme for LTE in unlicensed bands. With the introduction of an additional factor to the conventional p-persistent CSMA scheme, the probability of LTE accessing the unlicensed channels is dynamically varied according to the interference measurements. Simulation results show that the proposed LTE adaptive p-persistent CSMA scheme can provide a flexible tradeoff between LTE and WiFi when they coexist with each other.
Synthesis And Optimization of Streptomycin Loaded Chitosan-Alginate Nanoparticles

Nanoformulation consisting of Streptomycin loaded chitosan-alginate nanoparticles were prepared using ionotropic-pregelation method and optimization was done in terms of polymer concentration, crosslinker concentration and stirring time. The optimal parameters were found to be Chitosan 0.75mg/ml, calcium chloride 1% (w/v) and stirring time 90 min. Polymer (chitosan and sodium-alginate) and crosslinker (calcium-chloride) at these concentrations had significant synergistic effect on particle size and % encapsulation efficiency. Increase in polymer and crosslinker concentration resulted in an increase in particle size. Encapsulation efficiency, first showed an increase followed by a decrease, on increasing the polymer concentration whereas it increased with an increase in cross linker concentration. The nanoformulation so formed showed particle size 328.4 nm & drug encapsulation 93.32%.