Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Single Molecule Detection Platform Using a Buried ARROW Design

Improved Single Molecule Detection Platform Using a Buried ARROW Design Thomas Allen Wall Department of Electrical and Computer Engineering, BYU Doctor of Philosophy As the microelectronics industry pushes microfabrication processes further, the lab-on-achip field has continued to piggy-back off the industry’s fabrication capabilities with the goal of producing total chemical and biological systems on small chip-size platforms. One important function of such systems is the ability to perform single molecule detection. There are currently many methods being researched for performing single molecule detection, both macro and micro in scale. This dissertation focuses on an optofluidic, lab-on-a-chip platform called the ARROW biosensor, which possesses several advantages over macro-scale single molecule detection platforms. These advantages include an amplification-free detection scheme, cheap parallel fabrication techniques, rapid single molecule detection results, and extremely low volume sample probing, which leads to ultra-sensitive detection. The ARROW biosensor was conceived in the early 2000s; however, since then it has undergone many design changes to improve and add new functionality to the lab-on-a-chip; however, water absorption in the plasma enhanced chemical vapor deposited silicon dioxide has been a problem that has plagued the biosensor platform for some time. Moisture uptake in the oxide layer of the ARROWs leads to loss of waveguiding confinement and drastically decreases the overall sensitivity of the ARROW biosensors. New ARROW designs were investigated to alleviate the negative water absorption effects in the ARROWs. The new waveguide designs were tested for resiliency to water absorption and the buried ARROW (bARROW) design was determined to be the most successful at preventing negative water absorption effects from occurring in the PECVD oxide waveguides. The bARROWs were integrated into the full biosensor platforms and used to demonstrate high sensitivity single molecule detection without any signs of water absorption affecting the bARROWs’ waveguiding capabilities. The bARROW biosensors are not only water resistant, they also proved to be the most sensitive biosensors yet fabricated with average signal-to-noise ratios around 80% higher than any previously fabricated ARROW biosensors.
One- and Two-Stage Arrhenius Models for Pharmaceutical Shelf Life Prediction

One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data.
Analysis of Microbial Communities Associated with Bioremediation Systems for Thiocyanate-Laden Mine Water Effluents

During the processing of refractory gold ores, cyanide (CN-) and residual sulphur species react to form an effluent stream containing thiocyanate (SCN-) and residual CN-. The release of SCN- and CN- containing effluent water to the environment is prohibited, necessitating effective treatment prior to discharge and/or reuse of contaminated plant water. Biologically mediated effluent remediation processes have been developed for commercial use, to remediate SCN- containing effluents, with the aim of enabling recycling of process water and improving the quality of effluent water prior to disposal. Bioremediation processes to treat these effluents rely on a complex consortium of microorganisms to metabolise the SCN- resulting in the production of ammonium that is in turn removed by conversion to nitrite and subsequent denitrification. Increasingly, genomic methods are being used to investigate processes in wastewater treatment to identify key microbial species and, thereby, inform the rationale design and operation of these bioremediation systems. The microbial ecology of laboratory-based SCN- degrading bioprocesses have been investigated, using genome resolved metagenomics, to provide detailed information on the community composition and metabolic profile of abundant microbial community members. Our on-going research is focused on developing a greater understanding of the heterotrophic and autotrophic populations of microorganisms within the SCN- degrading community as well as the role of the component members in SCN- destruction. We are interested in the formation of microbial biofilm and the spatial distribution of key microorganisms within the resulting biofilm communities. This information is being used to inform further rational development of SCN- degradation processes for treatment of contaminated wastewater effluents.
Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique

Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code, domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the distance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doctors or Para-medics to find out the patient’s level in the cardiologic disease, deduce the medicines required in seconds and propose them to the patient. In order to measure the reusability K-means clustering algorithm is used.
An Adaptive Procedure for Multiclass Pattern Classification

Abstract—A more general adaptive procedure for determining linear or piecewise linear discriminant functions for multiclass pattern classification is proposed. The adaptive procedure is a many-pattern or group-pattern adaptation. The training sequence consists of groups of vectors in matrix form instead of single vectors. The convergence proof shows that this procedure terminates in a finite number of adaptions if the solution exists. A necessary and suffcient condition is developed for testing the linear separability of each subset of (d + 1) samples. Furthermore, the proposed procedure can be implemented with the addition of only a little complexity to existing systems. Computer simulations indecate satisfactory results.
Testing Measurement Invariance in Longitudinal Data With Ordered-Categorical Measures

A goal of developmental research is to examine individual changes in constructs over time. The accuracy of the models answering such research questions hinges on the assumption of longitudinal measurement invariance: The repeatedly measured variables need to represent the same construct in the same metric over time. Measurement invariance can be studied through factor models examining the relations between the observed indicators and the latent constructs. In longitudinal research, ordered-categorical indicators such as self- or observer-report Likert scales are commonly used, and these measures often do not approximate continuous normal distributions. The present didactic article extends previous work on measurement invariance to the longitudinal case for ordered-categorical indicators. We address a number of problems that commonly arise in testing measurement invariance with longitudinal data, including model identification and interpretation, sparse data, missing data, and estimation issues. We also develop a procedure and associated R program for gauging the practical significance of the violations of invariance. We illustrate these issues with an empirical example using a subscale from the Mexican American Cultural Values scale. Finally, we provide comparisons of the current capabilities of 3 major latent variable programs (lavaan, Mplus, OpenMx) and computer scripts for addressing longitudinal measurement invariance.
Structural basis of a Ni acquisition cycle for [NiFe] hydrogenase by Ni-metallochaperone HypA and its enhancer

Significance The metal ions in proteins are correctly incorporated by specific metallochaperones. However, it remains unclear how metallochaperones regulate their metal binding affinity during acquisition of correct metal ions and deliver them to target proteins. In this study, we have determined the crystal structures of a transient complex between a Ni metallochaperone HypA and its partner ATPase protein HypBAT, which incorporate a Ni ion into [NiFe] hydrogenase. The structures reveal that HypBAT induces conformational change of HypA through complex formation, leading to formation of a Ni binding site. Consequently, the Ni-binding affinity of HypA is increased from micromolar to nanomolar range (by ∼600-fold). These results indicate that HypBAT functions as a metallochaperone enhancer, which regulates metal binding affinity of metallochaperones. The Ni atom at the catalytic center of [NiFe] hydrogenases is incorporated by a Ni-metallochaperone, HypA, and a GTPase/ATPase, HypB. We report the crystal structures of the transient complex formed between HypA and ATPase-type HypB (HypBAT) with Ni ions. Transient association between HypA and HypBAT is controlled by the ATP hydrolysis cycle of HypBAT, which is accelerated by HypA. Only the ATP-bound form of HypBAT can interact with HypA and induces drastic conformational changes of HypA. Consequently, upon complex formation, a conserved His residue of HypA comes close to the N-terminal conserved motif of HypA and forms a Ni-binding site, to which a Ni ion is bound with a nearly square-planar geometry. The Ni binding site in the HypABAT complex has a nanomolar affinity (Kd = 7 nM), which is in contrast to the micromolar affinity (Kd = 4 µM) observed with the isolated HypA. The ATP hydrolysis and Ni binding cause conformational changes of HypBAT, affecting its association with HypA. These findings indicate that HypA and HypBAT constitute an ATP-dependent Ni acquisition cycle for [NiFe]-hydrogenase maturation, wherein HypBAT functions as a metallochaperone enhancer and considerably increases the Ni-binding affinity of HypA.
Enhancing Web Portals with Ontology-Based Data Access: The Case Study of South Africa's Accessibility Portal for People with Disabilities

Web portal software is relatively easy to set up and populate from the perspective of the end-user, but it leaves the back-end database devoid of subject domain semantics due to the requirement for a generic implementation. This approach seriously hampers effective search capabilities to retrieve relevant information. Ontology-based data access (OBDA) could, in theory, solve this problem through adding a semantic ‘layer’ over such web portal implementations. To this end, we provide and demonstrate the proof-of-concept methodology by enhancing the operational National Accessibility Portal of South Africa. We developed the adolena ontology, which is based on both the semantics in the database and augmented with notions from foundational and related domain ontologies. adolena was then made compliant with the OWL2 profile DL-LiteA and mapped to the relational database using the OBDA Plugin for Protege. Experimentation with OBDA queries unequivocally demonstrates its advantages compared to the portal’s keyword-based
Unsupervised Residential Power Usage Monitoring Using a Wireless Sensor Network

Appliance-level power usage monitoring may help conserve electricity in homes. Several existing systems achieve this goal by exploiting appliances’ power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in a high installation cost. This article presents Supero—the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, nonprofessional users can quickly deploy Supero with considerable flexibility.
Failure-Aware Construction and Reconfiguration of Distributed Virtual Machines for High Availability Computing

In large-scale clusters and computational grids, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs have become an increasingly important concern to system designers and administrators. In this paper, we study how to efficiently utilize system resources for high-availability clusters with the support of the virtual machine (VM) technology. We design a reconfigurable distributed virtual machine (RDVM) infrastructure for clusters computing. We propose failure-aware node selection strategies for the construction and reconfiguration of RDVMs. We leverage the proactive failure management techniques in calculating nodes' reliability status. We consider both the performance and reliability status of compute nodes in making selection decisions. We define a capacity-reliability metric to combine the effects of both factors in node selection, and propose Best-fit algorithms to find the best qualified nodes on which to instantiate VMs to run parallel jobs. We have conducted experiments using failure traces from production clusters and the NAS Parallel Benchmark programs on a real cluster. The results show the enhancement of system productivity and dependability by using the proposed strategies. With the Best-fit strategies, the job completion rate is increased by 17.6% compared with that achieved in the current LANL HPC cluster, and the task completion rate reaches 91.7%.
Magnesium-Ion Storage Capability of MXenes

Rechargeable magnesium-ion batteries (MIBs) with Mg metal anodes have been attracting attention due to their potential safety, low cost, and high theoretical energy densities. Nevertheless, developing a high-energy-density MIB with long cycle life and reasonable rate capability is still a huge challenge due to the lack of high-performance cathodes beyond the Chevrel phases. Here, we investigate the mechanism of Mg-ion uptake and storage by MXenes, that have been theoretically predicted to be promising candidates for MIB cathodes. Flexible and conductive 3D macroporous Ti3C2Tx MXene films were fabricated and tested as MIB cathodes after the incorporation of Mg ions from a Mg2+-containing electrolyte. The 3D MXene cathode exhibited promising cycling stability, accompanied by good rate performance. A 3D Mg0.21Ti3C2Tx MXene electrode delivered, at 0.5, 1, and 5 C, capacities of ∼210, ∼140, and ∼55 mA h g–1, respectively. A reversible intercalation charge-storage mechanism was demonstrated and a possible redox.
Bilateral DMPFC-rTMS Leads to Sustained Remission in Geriatric Treatment-Resistant Depression: A Case Report.

BILATERAL DMPFC-RTMS LEADS TO SUSTAINED REMISSION IN GERIATRIC TREATMENT-RESISTANT DEPRESSION: A CASE REPORT David Sender, Bruno Palazzo Nazar, Tathiana Baczynski, Flávia Paes, Peter Fettes, Jonathan Downar, Carlos Campos, Antonio Egidio Nardi & Sergio Machado Laboratory of Panic & Respiration (LABPR), Institute of Psychiatry (IPUB), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil Psychological Medicine, Institute of Psychiatry – King’s College London – London, United Kingdom Institute of Psychiatry (IPUB), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil Institute of Medical Science, University of Toronto, Canada MRI-Guided rTMS Clinic, University Health Network, Toronto, Canada Polytechnic Institute of Porto, Health School, Porto, Portugal Physical Activity Neuroscience, Physical Activity Sciences Postgraduate Program Salgado de Oliveira University, Niterói, Brazil
THERMO-ACOUSTIC CHARACTERIZATION OF CAN-CAN INTERACTION OF A CAN-ANNULAR COMBUSTION SYSTEM BASED ON UNSTEADY CFD LES SIMULATION

Many modern gas turbines are using can-annular combustors. One important feature of can combustors is that the flame reaction zones are separated by walls. Based on this fact it is often assumed that the acoustic coupling between adjacent cans is negligible. Experimental measurements in can-annular combustion systems however indicate, that a non-negligible coupling between the cans exist and it needs to be taken into account when modeling instability modes. Such can-to-can modes are not easily reproducible in a common single can test rig and are an issue for establishing a reliable acoustic rig-engine correlation. In the presented work the combustor can coupling at the turbine side is investigated by means of numerical simulations. The possible modes are computed using eigenmode analysis. The can cross-talk is quantified using a forced response analysis. For this study a generic simplified geometry with 8 cans is used.
A bivariate autoregressive linear mixed effects model for the analysis of longitudinal data

In clinical studies, dependent bivariate continuous responses may approach equilibrium over time. We propose an autoregressive linear mixed effects model for bivariate longitudinal data in which the current responses are regressed on the previous responses of both variables, fixed effects, and random effects. The equilibria are modeled using fixed and random effects. This model is a bivariate extension of the model for univariate longitudinal data given by Funatogawa et al. (Statist. Med. 2007; 26:2113–2130). As an illustration of the approach we analyze parathyroid hormone and serum calcium measurements in the treatment of secondary hyperparathyroidism in chronic hemodialysis patients. Copyright © 2008 John Wiley & Sons, Ltd.
Design and Generation of Highly Diverse Fluorinated Fragment Libraries and their Efficient Screening with Improved 19F NMR Methodology

Fragment screening performed with 19F NMR spectroscopy is becoming increasingly popular in drug discovery projects. With this approach, libraries of fluorinated fragments are first screened using the direct‐mode format of the assay. The choice of fluorinated motifs present in the library is fundamental in order to ensure a large coverage of chemical space and local environment of fluorine (LEF). Mono‐ and poly‐fluorinated fragments to be included in the libraries for screening are selected from both in‐house and commercial collections, and those that are ad hoc designed and synthesized. Additional fluorinated motifs to be included in the libraries derive from the fragmentation of compounds in development and launched on the market, and compounds contained in other databases (such as Integrity, PDB and ChEMBL). Complex mixtures of highly diverse fluorine motifs can be rapidly screened and deconvoluted in the same NMR tube with a novel on the fly combined procedure for the identification of the active molecule(s). Issues and problems encountered in the design, generation and screening of diverse fragment libraries of fluorinated compounds with 19F NMR spectroscopy are analyzed and technical solutions are provided to overcome them. The versatile screening methodology described here can be efficiently applied in laboratories with limited NMR setup and could potentially lead to the increasing role of 19F NMR in the hit identification and lead optimization phases of drug discovery projects.
Hepatitis C virus targets the T cell secretory machinery as a mechanism of immune evasion

T cell activation and the resultant production of interleukin (IL‐2) is a central response of the adaptive immune system to pathogens, such as hepatitis C virus (HCV). HCV uses several mechanisms to evade both the innate and adaptive arms of the immune response. Here we demonstrate that liver biopsy specimens from individuals infected with HCV had significantly lower levels of IL‐2 compared with those with other inflammatory liver diseases. Cell culture–grown HCV particles inhibited the production of IL‐2 by normal peripheral blood mononuclear cells, as did serum from HCV‐infected patients. This process was mediated by the interaction of HCV envelope protein E2 with tetraspanin CD81 coreceptor. HCV E2 attenuated IL‐2 production at the level of secretion and not transcription by targeting the translocation of protein kinase C beta (PKCβ), which is essential for IL‐2 secretion, to lipid raft microdomains. The lipid raft disruptor methyl‐β‐cyclodextrin reversed HCV E2‐mediated inhibition of IL‐2 secretion, but not in the presence of a PKCβ‐selective inhibitor. HCV E2 further inhibited the secretion of other cytokines, including interferon‐γ. Conclusion: These data suggest that HCV E2–mediated disruption of the association of PKCβ with the cellular secretory machinery represents a novel mechanism for HCV to evade the human immune response and to establish persistent infection. (HEPATOLOGY 2011;)
2-Path Cuts for the Vehicle Routing Problem with Time Windows

This paper introduces a strong valid inequality, the 2-path cut, to produce better lower bounds for the vehicle routing problem with time windows. It also develops an effective separation algorithm to find such inequalities. We next incorporate them as needed in the master problem of a Dantzig-Wolfe decomposition approach. In this enhanced optimization algorithm, the coupling constraints require that each customer be serviced. The subproblem is a shortest path problem with time window and capacity constraints. We apply branch and bound to obtain integer solutions. We first branch on the number of vehicles if this is fractional, and then on the flow variables. The algorithm has been implemented and tested on problems of up to 100 customers from the Solomon datasets. It has succeeded in solving to optimality several previously unsolved problems and a new 150-customer problem. In addition, the algorithm proved faster than algorithms previously considered in the literature. These computational results indicate the effectiveness of the valid inequalities we have developed.