Miracle-dz/UniFAR-FTUs huggingface dataset:
Young to Middle-Aged Dogs with High Amyloid-β Levels in Cerebrospinal Fluid are Impaired on Learning in Standard Cognition tests

Understanding differences in Alzheimer’s disease biomarkers before the pathology becomes evident can contribute to an improved understanding of disease pathogenesis and treatment. A decrease in amyloid-β (Aβ)42 in cerebrospinal fluid (CSF) is suggested to be a biomarker for Aβ deposition in brain. However, the relevance of CSF Aβ levels prior to deposition is not entirely known. Dogs are similar to man with respect to amyloid-β protein precursor (AβPP)-processing, age-related amyloid plaque deposition, and cognitive dysfunction. In the current study, we evaluated the relation between CSF Aβ42 levels and cognitive performance in young to middle-aged dogs (1.5–7 years old). Additionally, CSF sAβPPα and sAβPPβ were measured to evaluate AβPP processing, and CSF cytokines were measured to determine the immune status of the brain. We identified two groups of dogs showing consistently low or high CSF Aβ42 levels. Based on prior studies, it was assumed that at this age no cerebral amyloid plaques were likely to be present. The cognitive performance was evaluated in standard cognition tests. Low or high Aβ concentrations coincided with low or high sAβPPα, sAβPPβ, and CXCL-1 levels, respectively. Dogs with high Aβ concentrations showed significant learning impairments on delayed non-match to position (DNMP), object discrimination, and reversal learning compared to dogs with low Aβ concentrations. Our data support the hypothesis that high levels of CSF Aβ in dogs coincide with lower cognitive performance prior to amyloid deposition. Further experiments are needed to investigate this link, as well as the relevance with respect to Alzheimer’s disease pathology progression.
Outrunner generator with optimized cogging torque pattern for an electromechanical energy harvester

In contrast to widely applied permanent magnet (PM) machine optimization for minimization of cogging torque, this paper describes a method for shaping the cogging torque of a PM outrunner machine towards a desired sinusoidal torque pattern. The underlying goal of this approach is to compensate the cogging torque of a kinetic energy harvester (KEH) with the optimized counter cogging torque of a generator connected to the same shaft. Therefore, the total cogging torque is highly reduced, and a self-starting electromechanical energy harvester, comprising KEH and outrunner generator is formed. The key degree of freedom for shaping the cogging torque is the sinusoidal modulation of the machine's air gap. An algorithm based on the multi-dimensional Secant method, which is related to the multi-dimensional Newton-Raphson method, first evaluates the cogging torque of a given generator geometry with two-dimensional finite element method (2-D FEM) simulations and then iterates the geometry of the outrunner machine until the cogging torque target is achieved. Using the presented optimization approach, a generator design with the desired sinusoidal cogging torque pattern is obtained, achieving a total cogging torque reduction of the overall electromechanical energy harvesting system of 90%.
Non-resonant electrostatic energy harvesting from a rolling mass

Energy harvesting devices typically have a resonance frequency at which optimum power transduction is achieved. This is impractical for power sources with low or broad frequency ranges, such as human motion. Previously, a novel, non-resonant electrostatic energy harvesting device was proposed, using an external proof mass in the form of a rolling cylinder. Substrate parasitic capacitance was identified as a key issue for successful device operation. In this paper, a new fabrication method is presented. Parasitic capacitance effects are reduced using glass instead of silicon as a substrate. A scalable fabrication technique for contact-to-dielectric alignment below 100 nm using electrodeposition was developed. Initial devoce characterization shows output power for the first time, with some voltage gain. Further improvements on the fabrication and characterization techniques are proposed which may lead to consistent, non-resonant power transduction from human body motion or other power sources. Implementation concepts are outlined.
Shopper's eye: using location-based filtering for a shopping agent in the physical world

1. ABSTRACT Agents of all types rely on easily computed features that are suggestive of a user’s preferences and goals to define and constrain their tasks. Although a person’s location is usually suggestive of their current activity, agents have not relied upon a user’s location to constrain their task. This is because users have typically used their computers only from home or work, and because location has not been easily computable. However the explosive growth in the use personal digital assistants (PDAs), laptop computers, and global positioning system (GPS) receivers is enabling people to use computers in the most remote of locations, and to have their location accessed by software.
Mechanical Properties of Poly(ether ether ketone) Composites Reinforced by Carbon Nano-Platelet Chains and Nanoalumina

Carbon nanoplatelet and nanoalumina reinforced PEEK nanocomposites were fabricated by twin-screw extrusion followed by injection molding. The effect of the filler loading on mechanical properties, morphology, dielectric strength, and thermal stability of the composites has been analyzed. The mechanical properties were found to increase with nanoplatelet content up to 1% loading (optimum filler content) and after that, due to agglomeration of filler, slight decrease in properties were observed. For alumina-filled systems mechanical properties increased with increasing filler content due to the well-dispersed fillers in the composites. The modulus and toughness of alumina-filled composites were higher than platelet-filled composites.
Cuttlefish: A Lightweight Primitive for Adaptive Query Processing

Modern data processing applications execute increasingly sophisticated analysis that requires operations beyond traditional relational algebra. As a result, operators in query plans grow in diversity and complexity. Designing query optimizer rules and cost models to choose physical operators for all of these novel logical operators is impractical. To address this challenge, we develop Cuttlefish, a new primitive for adaptively processing online query plans that explores candidate physical operator instances during query execution and exploits the fastest ones using multi-armed bandit reinforcement learning techniques. We prototype Cuttlefish in Apache Spark and adaptively choose operators for image convolution, regular expression matching, and relational joins. Our experiments show Cuttlefish-based adaptive convolution and regular expression operators can reach 72-99% of the throughput of an all-knowing oracle that always selects the optimal algorithm, even when individual physical operators are up to 105x slower than the optimal. Additionally, Cuttlefish achieves join throughput improvements of up to 7.5x compared with Spark SQL's query optimizer.
Association between urinary symptoms and quality of life in HTLV-1 infected subjects without myelopathy.

OBJECTIVE To investigate the relationship between urinary symptoms and quality of life of patients infected with HTLV-1. MATERIALS AND METHODS This is a cross-sectional study that enrolled individuals with HTLV-1 positive serology from February 2010 to March 2011. Participants were HTLV-1 infected subjects followed in the HTLV-1 clinic of the University Hospital in Salvador, Bahia, Brazil. Patients with HTLV-1 associated myelopathy / tropical spastic paraparesis (HAM/TSP), who had evidence of other neurological diseases, diabetes mellitus or were pregnant were excluded from the study. The questionnaire SF-36 was used to evaluate quality of life and the questionnaire OAB-V8 was used to evaluate urinary symptoms. RESULTS From the 118 individuals evaluated, 50 (42.4%) complained of urinary symptoms and 68 (57.6%) did not. Most participants were females. There was no difference between the groups regarding demographic variables. The group with symptoms showed significantly lower scores in all domains of the SF-36 questionnaire. The domains with greatest differences were vitality and general health state. CONCLUSIONS Urinary symptoms negatively influence the quality of life of individuals infected with HTLV-1.
A Digital Microfluidics Platform for Loop-Mediated Isothermal Amplification Detection

Digital microfluidics (DMF) arises as the next step in the fast-evolving field of operation platforms for molecular diagnostics. Moreover, isothermal schemes, such as loop-mediated isothermal amplification (LAMP), allow for further simplification of amplification protocols. Integrating DMF with LAMP will be at the core of a new generation of detection devices for effective molecular diagnostics at point-of-care (POC), providing simple, fast, and automated nucleic acid amplification with exceptional integration capabilities. Here, we demonstrate for the first time the role of coupling DMF and LAMP, in a dedicated device that allows straightforward mixing of LAMP reagents and target DNA, as well as optimum temperature control (reaction droplets undergo a temperature variation of just 0.3 °C, for 65 °C at the bottom plate). This device is produced using low-temperature and low-cost production processes, adaptable to disposable and flexible substrates. DMF-LAMP is performed with enhanced sensitivity without compromising reaction efficacy or losing reliability and efficiency, by LAMP-amplifying 0.5 ng/µL of target DNA in just 45 min. Moreover, on-chip LAMP was performed in 1.5 µL, a considerably lower volume than standard bench-top reactions.
Recognizing proxemics in personal photos

Proxemics is the study of how people interact. We present a computational formulation of visual proxemics by attempting to label each pair of people in an image with a subset of physically based “touch codes. ” A baseline approach would be to first perform pose estimation and then detect the touch codes based on the estimated joint locations. We found that this sequential approach does not perform well because pose estimation step is too unreliable for images of interacting people, due to difficulties with occlusion and limb ambiguities. Instead, we propose a direct approach where we build an articulated model tuned for each touch code. Each such model contains two people, connected in an appropriate manner for the touch code in question. We fit this model to the image and then base classification on the fitting error. Experiments show that this approach significantly outperforms the sequential baseline as well as other related approches.
Chemical identification of point defects and adsorbates on a metal oxide surface by atomic force microscopy

Atomic force microscopy in the non-contact mode (nc-AFM) can provide atom-resolved images of the surface of, in principle, any material independent of its conductivity. Due to the complex mechanisms involved in the contrast formation in nc-AFM imaging, it is, however, far from trivial to identify individual surface atoms or adsorbates from AFM images. In this work, we successfully demonstrate how to extract detailed information about defects and the chemical identity of adsorbates on a metal oxide surface from nc-AFM images. We make use of the observation that the apex of the AFM tip can be altered to expose either a positive or negative tip termination. The complementary set of images recorded with the two tip terminations unambiguously define the ionic sub-lattices and reveal the exact positions of oxygen vacancies and hydroxyl (OH) defects on a TiO2 surface. Chemical specificity is extracted by comparing the characteristic contrast patterns of the defects with results from comprehensive AFM simulations. Our methodology of analysis is generally applicable and may be pivotal for uncovering surface defects and adsorbates on other transition metal oxides designed for heterogeneous catalysis, photo-electrolysis or biocompatibility.
An Optimized Speculative Execution Strategy Based on Local Data Prediction in a Heterogeneous Hadoop Environment

Hadoop is a famous distributed computing framework that is applied to process large-scale data. "Straggling tasks" have a serious impact on Hadoop performance due to imbalance of slow tasks distribution. Speculative execution (SE) presents a way to deal with Straggling tasks by monitoring the real-time progress of running tasks and replicating potential "Stragglers" on another node to increase the opportunity of completing backup tasks ahead of original. Current proposed SE strategies meet their challenges such as misjudgment of "Straggling tasks", improper selection of backup nodes, etc., which result in inefficient performance of the SE and its Hadoop system. In this paper, we propose an optimized SE strategy based on local data prediction, which collects task execution information in real time and uses Locally Weighted Regression (LWR) to predict remaining time of each running tasks, and selects an appropriate backup task node according to the actual requirements. It also combines a cost-benefit model to maximize the effectiveness of SE. According to the results, the proposed SE strategy implemented in Hadoop-2.6. 0 enhances the accuracy of selecting potential Straggler task candidates, and shows better performance in various situations in a heterogeneous Hadoop environment.
Population Pharmacokinetics and Dose Optimization of Teicoplanin during Venoarterial Extracorporeal Membrane Oxygenation

ABSTRACT The pharmacokinetics (PK) of drugs are known to be significantly altered in patients receiving extracorporeal membrane oxygenation (ECMO). However, clinical studies of the PK of drugs administered during ECMO are scarce, and the proper dosing adjustment has yet to be established. We developed a population PK model for teicoplanin, investigated covariates influencing teicoplanin exposure, and suggested an optimal dosing regimen for ECMO patients. Samples for PK analysis were collected from 10 adult patients, and a population PK analysis and simulations were performed to identify an optimal teicoplanin dose needed to provide a >50% probability of target attainment at 72 h using a trough concentration target of >10 μg/ml for mild to moderate infections and a trough concentration target of >15 μg/ml for severe infections. Teicoplanin was well described by a two-compartment PK model with first-order elimination. The presence of ECMO was associated with a lower central volume of distribution, and continuous renal replacement therapy (CRRT) was associated with a higher peripheral volume of distribution. For mild to moderate infections, an optimal dose was a loading dose (LD) of 600 mg and a maintenance dose (MD) of 400 mg for ECMO patients not receiving CRRT and an LD of 800 mg and an MD of 600 mg for those receiving CRRT. For severe infections, an optimal dose was an LD of 1,000 mg and an MD of 800 mg for ECMO patients not receiving CRRT and an LD of 1,200 mg and an MD of 1,000 mg for those receiving CRRT. In conclusion, doses higher than the standard doses are needed to achieve fast and appropriate teicoplanin exposure during ECMO. (This study has been registered at ClinicalTrials.gov under identifier NCT02581280.)
Implementation of the Fit Body and Soul, a Church-Based Life Style Program for Diabetes Prevention in High-Risk African Americans

Purpose The purpose of this study was to determine the effects of a behavioral faith-based diabetes prevention program called the Fit Body and Soul program in a semi-urban African-American church using a community-based participatory approach. Methods The 12-session Fit Body and Soul program was modified from the group lifestyle balance intervention that was modified from the successful National Institute of Health (NIH) funded Diabetes Prevention Program. The Fit Body and Soul program was implemented in a semi-urban African-American church community. Based on the results of physical examinations and increased body mass index (BMI ≥ 25), 40 adult members of the church were identified as being at high risk for diabetes. Four church ministers, after receiving Fit Body and Soul program training for 2 days, served as study interventionists. The primary objective was weight loss of at least 5% by the end of the 12-session Fit Body and Soul intervention. Results Screening of church participants was conducted at the Gospel Water Branch Baptist Church in Augusta, Georgia. A total of 40 individuals having a BMI ≥ 25 were selected. Of the 40, a total of 35 (87.5%) attended at least 10 sessions and provided information required for the study. Of the 35, a total of 48% lost at least 5% of baseline weight, 26% lost 7% or more, and 14% lost >10% of baseline weight. Conclusions This pilot trial suggests that carrying out a larger Fit Body and Soul study in a faith-based setting, using behavioral lifestyle interventions, in the context of a diabetes prevention program for African American communities is feasible, as is the possibility that subjects in that larger study will achieve a clinically significant degree of weight loss.
Morphology and Crystallographic Characterization of Nickel Nanowires—Influence of Magnetic Field and Current Density During Synthesis

This paper presents results and discussion from a comprehensive morphological and crystallographic characterization of nickel nanowires synthesized by template-based electrodeposition method. In particular, the influence of magnetic and electric field (current density) conditions during the synthesis of nickel nanowires was studied. The structure and morphology of the synthesized nanowires were studied using Helium ion microscopy (HIM) and scanning electron microscopy (SEM) methods. The HIM provided higher quality data and resolution compared to conventional SEM imaging. The crystallographic properties of the grown nanowires were also studied using X-ray diffraction (XRD). The results clearly indicated that the morphological and crystallographic properties of synthesized nickel nanowires were strongly influenced by the applied magnetic field and current density intensity during the synthesis process. [DOI: 10.1115/1.4028026]
Dosimetric evaluation of magnetic resonance-generated synthetic CT for radiation treatment of rectal cancer

Purpose The purpose of this study was to assess the dosimetric equivalence of magnetic resonance (MR)-generated synthetic CT (synCT) and simulation CT for treatment planning in radiotherapy of rectal cancer. Methods This study was conducted on eleven patients who underwent whole-body PET/MR and PET/CT examination in a prospective IRB-approved study. For each patient synCT was generated from Dixon MR using a model-based method. Standard treatment planning directives were used to create a four-field box (4F), an oblique four-field (O4F) and a volumetric modulated arc therapy (VMAT) plan on synCT for treatment of rectal cancer. The plans were recalculated on CT with the same monitor units (MUs) as that of synCT. Dose-volume metrics of planning target volume (PTV) and organs at risk (OARs) as well as gamma analysis of dose distributions were evaluated to quantify the difference between synCT and CT plans. All plans were calculated using the analytical anisotropic algorithm (AAA). The VMAT plans on synCT and CT were also calculated using the Acuros XB algorithm for comparison with the AAA calculation. Results Medians of absolute differences in PTV metrics between synCT and CT plans were 0.2%, 0.2% and 0.3% for 4F, O4F and VMAT respectively. No significant differences were observed in OAR dose metrics including bladder V40Gy, mean dose in bladder, bowel V45Gy and femoral head V30Gy in any techniques. Gamma analysis with 2%/2mm dose difference/distance to agreement criteria showed median passing rates of 99.8% (range: 98.5 to 100%), 99.9% (97.2 to 100%), and 99.9% (99.4 to 100%) for 4F, O4F and VMAT, respectively. Using Acuros XB dose calculation, 2%/2mm gamma analysis generated a passing rate of 99.2% (97.7 to 99.9%) for VMAT plans. Conclusion SynCT enabled dose calculation equivalent to conventional CT for treatment planning of 3D conformal treatment as well as VMAT of rectal cancer. The dosimetric agreement between synCT and CT calculated doses demonstrated the potential of MR-only treatment planning for rectal cancer using MR generated synCT.
RStore: A Distributed Multi-Version Document Store

We address the problem of compactly storing a large number of versions (snapshots) of a collection of keyed documents or records in a distributed environment, while efficiently answering a variety of retrieval queries over those, including retrieving full or partial versions, and evolution histories for specific keys. We motivate the increasing need for such a system in a variety of application domains, carefully explore the design space for building such a system and the various storage-computation-retrieval trade-offs, and discuss how different storage layouts influence those trade-offs. We propose a novel system architecture that satisfies the key desiderata for such a system, and offers simple tuning knobs that allow adapting to a specific data and query workload. Our system is intended to act as a layer on top of a distributed key-value store that houses the raw data as well as any indexes. We design novel off-line storage layout algorithms for efficiently partitioning the data to minimize the storage costs while keeping the retrieval costs low. We also present an online algorithm to handle new versions being added to system. Using extensive experiments on large datasets, we demonstrate that our system operates at the scale required in most practical scenarios and often outperforms standard baselines, including a delta-based storage engine, by orders-of-magnitude.
Arousal and valence prediction in spontaneous emotional speech: felt versus perceived emotion

In this paper, we describe emotion recognition experiments carried out for spontaneous aﬀective speech with the aim to compare the added value of annotation of felt emotion versus annotation of perceived emotion. Using speech material available in the TNO-GAMING corpus (a corpus containing audiovisual recordings of people playing videogames), speech-based aﬀect recognizers were developed that can predict Arousal and Valence scalar values. Two types of recognizers were developed in parallel: one trained with felt emotion annotations (generated by the gamers themselves) and one trained with perceived/observed emotion annotations (generated by a group of observers). The experiments showed that, in speech, with the methods and features currently used, observed emotions are easier to predict than felt emotions. The results suggest that recognition performance strongly depends on how and by whom the emotion annotations are carried out.