Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti‐ganglioside GM1 antibodies in guillain‐barré syndrome and their relationship to Campylobacter jejuni infection

To clarify the association between Campylobacter jejunei (Cj) infection and antibodies to ganglioside GM1 (anti‐GM1) in Guillain‐Barré syndrome (GBS), we have carried out a prospective case‐control study of 96 patients with GBS. Cj infection occurred in 25 (26%) patients. IgG and/or IgM anti‐GM1 were identified in 24 (25%) patients and in 1 of 71 (1.4%) household controls (p < 0.001). Thirteen of the 25 (52%) Cj‐postive patients had anti‐GM1 compared with 11 of the 71 (15%) Cj‐negative patients (p < 0.001). Neither the peak overall disability nor the 1‐year disability differed between the anti‐GM1‐positive and anti‐GM1‐negative patients. However, patients with the combimation of Cj infection and anti‐GM1 positivity recovered more slowly than Cj/anti‐GM1‐negative patients (p = 0.05), were more likely to have axonal degeneration, and were significantly more disabled at the end of 1 year (p = 0.02). The presence of Cj infection is more important than anti‐GM1 postivity in determining the extent of axonal involvement and, hence, prognosis. Since the presence of anti‐GM1 is not a significant poor‐prognostic factor, a search should be made for other properties of Cj infection that would account for its relationship to axonal degeneration.
Quantification of CDR1 Gene Expression in Fluconazole Resistant Candida Glabrata Strains Using Real-time PCR

Background: The opportunistic fungi, particularly Candida glabrata has been known as main etiologic agents of life-threating infections in some patients. Although fluconazole is the most effective antifungal agent against candidiasis, C. glabrata, fluconazole-resistant strains have been increased recently overexpression or mutations of ATP-binding cassette (ABC) transporter family membrane proteins such as; Cg CDR1, Cg CDR2 are responsible for fluconazole resistance in a large proportion of candidiasis cases. The aim of this study was to evaluate CDR1 gene expression level as one of main mechanism involved in this resistance using. Methods: Candida glabrata strains were collected from various clinical samples in hospitals of Tehran in 2015 . After validation of all isolates by conventional and molecular methods, the susceptibility analysis to fluconazole of all isolates was performed using CLSI broth microdilution M27-A3 and M27-S4 protocols. Two isolates have been selected based on difference in susceptibility and CDR1-mRNA expression level of isolates was measured by Real-time PCR method. Results: Susceptibility results revealed that 32%, 64% and 4% of strains were susceptible, dose-dependent (DD) and resistant to fluconazole respectively. Furthermore, resistance strain of C. glabrata (MIC≥64 μg/ml) showed overexpression of CDR1 compared with sensitive strain in Real-time PCR analysis. Conclusion: Thus, it is necessary to investigate the functions of CgCDR1 genes as a transporter-related gene.
Mixed Mode Cascaded Classification Models

We present here Mixed Mode Cascaded Classification Models, an algorithmic framework that seeks to effectively solve a wide range of machine learning tasks in a “plug and play” manner. It does this by sharing predictions between machine learning tasks, thus giving each task additional high-level information that can be used to solve its specific problem. We consider here a specific implementation of this framework in which we combine the machine vision tasks of scene categorization and depth estimation. In addition, we consider the use of a Poisson distribution for depth estimation. Experimental results are provided.
Coordinated decentralized adaptive control with sliding mode of composite plants with time-varying delays in nonlinear interconnections

In this paper, a coordinated decentralized state-feedback model reference adaptive control with a sliding mode is developed for a class of large-scale systems with time-varying delays in the nonlinear interconnections. The design procedure is based on the concept of reference model coordination. A novel decentralized controller parametrization that is robust to unknown information exchange delays and to external disturbances with unknown bounds is proposed.
Characterization of lesions in dense breasts: Does tomosynthesis help?

Abstract Context: Mammography in dense breasts is challenging due to lesion obscuration by tissue overlap. Does tomosynthesis offers a solution? Aims: To study the impact of digital breast tomosynthesis (DBT) in characterizing lesions in breasts of different mammographic densities. Settings and Design: Prospective blinded study comparing mammography in two views with Mammography + Tomosynthesis. Methods and Material: Tomosynthesis was performed in 199 patients who were assigned Breast imaging reporting and data system (BIRADS) categories 0, 3, 4, or 5 on two-dimensional (2D) mammogram. Mammograms were first categorized into one of 4 mammographic breast densities in accordance with the American College of Radiology (ACR). Three radiologists independently analyzed these images and assigned a BIRADS category first based on 2D mammogram alone, and then assigned a fresh BIRADS category after taking mammography and tomosynthesis into consideration. A composite gold-standard was used in the study (histopathology, ultrasound, follow-up mammogram, magnetic resonance imaging). Each lesion was categorized into 3 groups—superior categorization with DBT, no change in BIRADS, or inferior BIRADS category based on comparison with the gold-standard. The percentage of lesions in each group was calculated for different breast densities. Results: There were 260 lesions (ages 28–85). Overall, superior categorization was seen in 21.2% of our readings on addition of DBT to mammography. DBT was most useful in ACR Densities 3 and 4 breasts where it led to more appropriate categorization in 27 and 42% of lesions, respectively. DBT also increased diagnostic confidence in 54.5 and 63.6% of lesions in ACR Densities 3 and 4, respectively. Conclusions: In a diagnostic setting, the utility of tomosynthesis increases with increasing breast density. This helps in identifying the sub category of patients where DBT can actually change management.
Scaling-up of an up-flow anaerobic sludge blanket incorporated with a bio-filter liner system for treating kitchen wastes

A bottom biofilter liner incorporated to an Up-flow Anaerobic Sludge Blanket Reactor (UASBR) was designed, developed and evaluated for food wastes for 47 days as a semi-batch set-up with intermittent up-flow circulation (very infrequent) to simulate the start-up. This study was conducted to scale-up already developed UASBR. In the mineralization process, the surface area at the bottom of the reactor is the most critical parameter. Thus, it has been found that total dissolved solids (TDS) of bottom, middle and top strata seem to follow first order rate reactions. The TDS was divided by the feeding that took place at different times to give a unit production of TDS at time. The average unit value of the bottom strata was then divided by the lowest experimental TDS value, thus yielding a ratio equating the two areas of desired to that of the experimental value. Therefore, a diameter (or area) could be found for any loading rate. The height of the reactor was found by considering ratios of the volumes to that of the predicted maximum TDS concentration obtained from the learning curve of the first order reactions and the maximum experimental value. The total height of reactor is 3.4 m, which is optimum for kitchen wastes, would remain constant for different loading rates. A prototype would be constructed and performance would be evaluated to reconfirm the hypothesis that a biofilter liner system is effective in reducing inhibitions often encountered in UASB reactors.
3D collision avoidance algorithm for Unmanned Aerial Vehicles with limited field of view constraints

Unmanned Aerial Vehicles (UAVs) are becoming a significant field of research with numerous applications, ranging from mapping to surveillance. New applications, such as aerial delivery of goods, are expected to appear in the next years and will require more and more autonomy from UAVs. One challenge preventing UAVs from being fully autonomous is their current limitations in handling potential collisions among multiple vehicles. This paper presents a collision avoidance algorithm for fixed-wing UAVs navigating in a three dimensional space. It satisfies limited field of view constraints that stem from the use of a single camera system as sensing device. The proposed algorithm uses potential fields to both navigate and avoid obstacles. To guarantee collision avoidance, the algorithm is enhanced with a turning behavior that allows for ensuring the safety of the method. Simulations are performed to show the effectiveness of the proposed algorithm.
Outcomes of tongue-pressure strength and accuracy training for dysphagia following acquired brain injury

Abstract The purpose of this study was to measure treatment outcomes in a group of six adults with chronic dysphagia following acquired brain injury, who each completed 24 sessions of tongue-pressure resistance training, over a total of 11–12 weeks. The treatment protocol emphasized both strength and accuracy. Biofeedback was provided using the Iowa Oral Performance Instrument. Amplitude accuracy targets were set between 20–90% of the patient's maximum isometric pressure capacity. Single subject methods were used to track changes in tongue strength (maximum isometric pressures), with functional swallowing outcomes measured using blinded ratings of a standard pre- and post-treatment videofluoroscopy protocol. Improvements were seen in post-treatment measures of tongue pressure and penetration–aspiration. No improvements were seen in pharyngeal residues, indeed worsening residue was seen in some patients.
Detection of Anaplasma phagocytophilum, Babesia microti, Borrelia burgdorferi, Borrelia miyamotoi, and Powassan Virus in Ticks by a Multiplex Real-Time Reverse Transcription-PCR Assay

The understanding of pathogen prevalence is an important factor in the determination of human risks for tick-borne diseases and can help guide diagnosis and treatment. The implementation of our assay addresses a critical need in surveillance of tick-borne diseases, through generation of a comprehensive assessment of pathogen prevalence in I. scapularis. Our finding of a high frequency of ticks infected with Babesia microti in Suffolk County, NY, implicates this agent as a probable frequent cause of non-Lyme tick-borne disease in this area. ABSTRACT Ixodes scapularis ticks are implicated in transmission of Anaplasma phagocytophilum, Borrelia burgdorferi, Borrelia miyamotoi, Babesia microti, and Powassan virus. We describe the establishment and implementation of the first multiplex real-time PCR assay with the capability to simultaneously detect and differentiate all five pathogens in a single reaction. The application of this assay for analysis of ticks at sites in New York and Connecticut revealed a high prevalence of B. microti in ticks from Suffolk County, NY. These findings are consistent with reports of a higher incidence of babesiosis from clinicians managing the care of patients with tick-borne diseases in this region. IMPORTANCE The understanding of pathogen prevalence is an important factor in the determination of human risks for tick-borne diseases and can help guide diagnosis and treatment. The implementation of our assay addresses a critical need in surveillance of tick-borne diseases, through generation of a comprehensive assessment of pathogen prevalence in I. scapularis. Our finding of a high frequency of ticks infected with Babesia microti in Suffolk County, NY, implicates this agent as a probable frequent cause of non-Lyme tick-borne disease in this area.
Scorpion Venom Heat-Resistant Peptide Protects Transgenic Caenorhabditis elegans from β-Amyloid Toxicity

Scorpion venom heat-resistant peptide (SVHRP) is a component purified from Buthus martensii Karsch scorpion venom. Our previous studies found SVHRP could enhance neurogenesis and inhibit microglia-mediated neuroinflammation in vivo. Here, we use the transgenic CL4176, CL2006, and CL2355 strains of Caenorhabditis elegans which express the human Aβ1-42 to investigate the effects and the possible mechanisms of SVHRP mediated protection against Aβ toxicity in vivo. The results showed that SVHRP-fed worms displayed remarkably decreased paralysis, less abundant toxic Aβ oligomers, reduced Aβ plaque deposition with respect to untreated animals. SVHRP also suppressed neuronal Aβ expression-induced defects in chemotaxis behavior and attenuated levels of ROS in the transgenic C. elegans. Taken together, these results suggest SVHRP could protect against Aβ-induced toxicity in C. elegans. Further studies need to be conducted in murine models and humans to analyze the effectiveness of the peptide.
A survey on wireless mesh networks

Wireless mesh networks (WMNs) have emerged as a key technology for next-generation wireless networking. Because of their advantages over other wireless networks, WMNs are undergoing rapid progress and inspiring numerous applications. However, many technical issues still exist in this field. In order to provide a better understanding of the research challenges of WMNs, this article presents a detailed investigation of current state-of-the-art protocols and algorithms for WMNs. Open research issues in all protocol layers are also discussed, with an objective to spark new research interests in this field.
Evolution of Perceptions and Play

An agent with a misperception may have an evolutionary advantage when others recognize his misperception and its behavioral implications. In such situations evolutionary forces can lead to misperceptions, yielding "irrational behavior," such as the play of strictly dominated strategies. We point out that this reasoning relies on the assumption of subjective rationality-agents are assumed to choose the behavior that maximizes their perceived payoffs. However, subjective rationality does not have solid evolutionary foundations: in the presence of misperceptions, agents who do not maximize their perceived payoffs may have greater fitness than those who do. We show that relaxing the subjective rationality requirement, somewhat paradoxically, leads to effectively rational behavior: although agents may have systematic misperceptions, they will develop other biases to undo these misperceptions, and will act as if they are rational. As a result, systematic biases in experimental settings may not necessarily translate into irrational behavior. We also demonstrate that the same evolutionary forces, in the long run, lead agents to play as if they have a common prior, even though each agent will have different and possibly incorrect perceptions of payoffs and the rules of the game.
Iterative translation disambiguation for cross-language information retrieval

Finding a proper distribution of translation probabilities is one of the most important factors impacting the effectiveness of a cross-language information retrieval system. In this paper we present a new approach that computes translation probabilities for a given query by using only a bilingual dictionary and a monolingual corpus in the target language. The algorithm combines term association measures with an iterative machine learning approach based on expectation maximization. Our approach considers only pairs of translation candidates and is therefore less sensitive to data-sparseness issues than approaches using higher n-grams. The learned translation probabilities are used as query term weights and integrated into a vector-space retrieval system. Results for English-German cross-lingual retrieval show substantial improvements over a baseline using dictionary lookup without term weighting.
Enabling Community Through Social Media

Background Social network analysis provides a perspective and method for inquiring into the structures that comprise online groups and communities. Traces from interaction via social media provide the opportunity for understanding how a community is formed and maintained online. Objective The paper aims to demonstrate how social network analysis provides a vocabulary and set of techniques for examining interaction patterns via social media. Using the case of the #hcsmca online discussion forum, this paper highlights what has been and can be gained by approaching online community from a social network perspective, as well as providing an inside look at the structure of the #hcsmca community. Methods Social network analysis was used to examine structures in a 1-month sample of Twitter messages with the hashtag #hcsmca (3871 tweets, 486 unique posters), which is the tag associated with the social media–supported group Health Care Social Media Canada. Network connections were considered present if the individual was mentioned, replied to, or had a post retweeted. Results Network analyses revealed patterns of interaction that characterized the community as comprising one component, with a set of core participants prominent in the network due to their connections with others. Analysis showed the social media health content providers were the most influential group based on in-degree centrality. However, there was no preferential attachment among people in the same professional group, indicating that the formation of connections among community members was not constrained by professional status. Conclusions Network analysis and visualizations provide techniques and a vocabulary for understanding online interaction, as well as insights that can help in understanding what, and who, comprises and sustains a network, and whether community emerges from a network of online interactions.
Reinforcement Learning and Function Approximation

Relational reinforcement learning combines traditional reinforcement learning with a strong emphasis on a relational (rather than attribute-value) representation. Earlier work used relational reinforcement learning on a learning version of the classic Blocks World planning problem (a version where the learner does not know what the result of taking an action will be). “Structural” learning results have been obtained, such as learning in a mixed 3–5 block environment and being able to perform in a 3 or 10 block environment. Here we instead take a function approximation approach to reinforcement learning for this same problem. We obtain similar learning accuracies, with much better running times, allowing us to consider much larger problem sizes. For instance, we can train on a mix of 3–7 blocks and then perform well on worlds with 100–800 blocks—using less running time than the relational method required for 3–10 blocks.
RATE-ADJUSTMENT ALGORITHM FOR AGGREGATE TCP CONGESTION CONTROL

The TCP congestion-control mechanism is an algorithm designed to probe the available bandwidth of the network path that TCP packets traverse. However, it is well-known that the TCP congestion-control mechanism does not perform well on networks with a large bandwidth-delay product due to the slow dynamics in adapting its congestion window, especially for short-lived flows. One promising solution to the problem is to aggregate and share the path information among TCP connections that traverse the same bottleneck path, i.e., Aggregate TCP. However, this paper shows via a queueing analysis of a generalized processor-sharing (GPS) queue with regularly-varying service time that a simple aggregation of local TCP connections together into a single aggregate TCP connection can result in a severe performance degradation. To prevent such a degradation, we introduce a rate-adjustment algorithm. Our simulation confirms that by utilizing our rate-adjustment algorithm on aggregate TCP, connections which would normally receive poor service achieve significant performance improvements without penalizing connections which already receive good service.
The Effect on Throughput of Multiprocessing in a Multiprogramming Environment

This paper investigates some of the effects of varying the number of central processing units (CPU's) available to a multiprogramming system both when parallel processing of a single task (multitasking) is allowed and is not allowed. The variables investigated are those that would be expected to control the CPU queue length distribution and include the degree of multiprogramming, CPU service discipline, CPU service distribution, the degree of cooperation between processors engaged in multitasking, and the ratio of CPU to I/O service capacity. The computer systems are modeled by queueing networks. Analytic results are displayed in many cases with supplementation by numeric and simulation solutions when convenient or necessary. It is demonstrated that under properly defined conditions doubling the number of CPU's in a system can more than double throughput. The effectiveness of multitasking in improving throughput is found to be fairly small if the degree of multiprogramming ranges from modest to high.
Wear of the polyethylene acetabular cup: The effect of head material, head diameter, and cup thickness studied with a hip simulator

Ultra-high molecular weight polyethylene acetabular cups backed by Ti-6Al-4V acetabular shells were articulated against Co-Cr-Mo alloy, ion-implanted Co-Cr-Mo alloy, and zirconia ceramic femoral heads in a hip joint simulator. Three tests of three million walking cycles each were run with five different head-cup combinations. The wear of the cups was measured gravimetrically at half a million cycle intervals. When the thickness of the cup was 10.9 mm, the mean wear rate was 0.14 mm/year against 28 mm dia. Co-Cr-Mo heads and 0.09 mm/year against ion-implanted 28 mm Co-Cr-Mo heads. When the thickness of the cup was 7.0 mm, the mean wear rate was 0.04 mm/year against 28 mm zirconia heads, but when the thickness was 10.9 mm, no wear occurred against 28 and 32 mm zirconia heads. The results indicate that a significant reduction in the wear of the polyethylene cup can be expected if zirconia is used as the head material, instead of Co-Cr-Mo alloy or ion-implanted Co-Cr-Mo alloy. The diameter of the zirconia head, 28 vs. 32 mm, seems unimportant, but a cup thickness much below 10 mm may be disadvantageous.
Tracking Cognitive Decline in Amnestic Mild Cognitive Impairment and Early-Stage Alzheimer Dementia: Mini-Mental State Examination versus Neuropsychological Battery

Background/Aims: Although the Mini-Mental State Examination (MMSE), Clinical Dementia Rating-Sum of Boxes (CDR-SOB), and neuropsychological batteries are widely used for evaluating cognitive function, it remains elusive which instrument best reflects the longitudinal disease progression in amnestic mild cognitive impairment (aMCI) and probable Alzheimer disease (AD). We investigated whether changes in these three instruments over time correlate with loss of cortical gray matter volume (cGMV). Methods: We retrospectively investigated 204 patients (aMCI, n = 114; AD, n = 90) who had undergone MMSE, CDR-SOB, the dementia version of the Seoul Neuropsychological Screening Battery (SNSB-D), and 3-dimensional T1-weighted magnetic resonance images at least twice. We investigated the partial correlation between annual decline in test scores and percent change of cGMV. Results: In aMCI patients, changes in the SNSB-D total score (r = 0.340, p < 0.001) and CDR-SOB (r = 0.222, p = 0.020), but not MMSE, showed a correlation with cGMV loss, with the SNSB-D total score showing the strongest correlation. In AD patients, decline in all three test scores correlated significantly with cGMV loss, with MMSE exhibiting the strongest correlation (r = 0.464, p < 0.001). Conclusion: In aMCI patients, neuropsychological battery, though time-consuming, was the most adequate tool in tracking disease progression. In AD patients, however, MMSE may be the most effective longitudinal monitoring tool when considering cost-effectiveness.