Miracle-dz/UniFAR-FTUs huggingface dataset:
High‐intensity resistance training amplifies muscle hypertrophy and functional gains in persons with Parkinson's disease

Strength deficits in persons with Parkinson's disease (PD) have been identified as a contributor to bradykinesia. However, there is little research that examines the effect of resistance training on muscle size, muscle force production, and mobility in persons with PD. The purpose of this exploratory study was to examine, in persons with PD, the changes in quadriceps muscle volume, muscle force production, and mobility as a result of a 12‐week high‐force eccentric resistance training program and to compare the effects to a standard‐care control. Nineteen individuals with idiopathic PD were recruited and consented to participate. Matched assignment for age and disease severity resulted in 10 participants in the eccentric group and 9 participants in the control group. All participants were tested prior to and following a 12‐week intervention period with testing and training conducted at standardized times in their medication cycle. The eccentric group performed high‐force quadriceps contractions on an eccentric ergometer 3 days a week for 12 weeks. The standard‐care group exercise program encompassed standard exercise management of PD. The outcome variables were quadriceps muscle volume, muscle force, and mobility measures (6‐minute walk, stair ascent/descent time). Each outcome variable was tested using separate one‐way analyses of covariance on the difference scores. Muscle volume, muscle force, and functional status improvements occurred in persons with PD as a result of high‐force eccentric resistance training. The eccentric group demonstrated significantly greater difference scores for muscle structure, stair descent, and 6‐minute walk (P < 0.05). Magnitude of effect size estimators for the eccentric group consistently exceeded those in the standard‐care group for all variables. To our knowledge, this is the first clinical trial to investigate and demonstrate the effects of eccentric resistance training on muscle hypertrophy, strength, and mobility in persons with PD. Additional research is needed to determine the anatomical and neurological mechanisms of the observed strength gains and mobility improvements. © 2006 Movement Disorder Society
Lipid-based colloidal carriers for peptide and protein delivery – liposomes versus lipid nanoparticles

This paper highlights the importance of lipid-based colloidal carriers and their pharmaceutical implications in the delivery of peptides and proteins for oral and parenteral administration. There are several examples of biomacromolecules used nowadays in the therapeutics, which are promising candidates to be delivered by means of liposomes and lipid nanoparticles, such as solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC). Several production procedures can be applied to achieve a high association efficiency between the bioactives and the carrier, depending on the physicochemical properties of both, as well as on the production procedure applied. Generally, this can lead to improved bioavailability, or in case of oral administration a more consistent temporal profile of absorption from the gastrointestinal tract. Advantages and drawbacks of such colloidal carriers are also pointed out. This article describes strategies used for formulation of peptides and proteins, methods used for assessment of association efficiency and practical considerations regarding the toxicological concerns.
Analysis of Electric Vehicles as Mobile Energy Storage in commercial buildings: Economic and environmental impacts

This paper investigates the application of Electric Vehicles (EVs) as Mobile Energy Storage (MES) in commercial buildings. Thus, energy systems of a commercial building including its grid connection, Distributed Energy Resources (DERs), Energy Storage (ES), and demand profile are modeled. Based on the developed models, a Mixed Integer Linear Programming (MILP) problem is formulated to optimize the operation of a commercial building microgrid (μθ) minimizing its daily energy costs and greenhouse gas emissions (GHG). Technical and operational constraints of DERs and ESS such as minimum up time and down time, load sharing characteristics of diesel generators, and charging and discharging constraints of ES devices are formulated to appropriately model the operation of a grid connected commercial μΘ. A realistic example with solar Photo-Voltaic (PV), Fuel Cell (FC), Micro-Turbine (MT), Stationary Energy Storage (SES), and MES is used to study effects of integrating EVs as MES on energy costs and GHG emissions of a commercial building. Simulation results that reveals potential economical and environmental impacts of EVs are presented and discussed.
Accurate modeling and positioning of a magnetically controlled catheter tip.

PURPOSE This paper represents the initial phase of a proposed operator-friendly semiautomatic method for positioning and directing an intravascular three-magnet tip catheter in the human heart using an electromagnetic system. METHODS A predictive computer algorithm based on a comprehensive mathematical model is developed, which accurately calculates the magnetic field generated by the electromagnet system as well as the magnetic torques and forces exerted on a three-magnet tip catheter, and generates the necessary electromagnet currents for arbitrary displacement and deflection of the catheter tip within a workspace of 128 × 128 × 128 mm. RESULTS We demonstrate the ability of the developed mathematical model to accurately position a three-magnet tip catheter within the 128 × 128 × 128 mm workspace of a 3D eight-electromagnet system. CONCLUSIONS The ability of the developed mathematical model in predicting the displacement, direction, and deflection of the catheter tip as a function of the electromagnet current values has been verified through experimental results.
Surface interactions and tribochemistry in boundary lubrication of hypereutectic aluminium—silicon alloys

Abstract The tribofilms formed on a hypereutectic Al—Si alloy after sliding against AISI 52100 steel pins in a reciprocating pin-on-plate tribometer with polyalpha olefin (PAO) base oil containing zinc dialkyldithiophosphate (ZDDP) and molybdenum dithiocarbamate (MoDTC) additives have been studied. Surface-sensitive analytical techniques (X-ray photoelectron spectroscopy (XPS), secondary ion mass spectroscopy (SIMS), atomic force microscopy (AFM), Raman spectroscopy, and nanoindentation) have been used for tribofilm chemical and mechanical characterization. The thickness of the tribofilms has been evaluated using depth profiling and focus ion beam (FIB) milling. This is the only reported work to analyse this kind of tribofilm on Al—Si alloys in this detail. From the analysis obtained from miniSIMS, differences in the composition of the top layer of the tribofilms formed from ZDDP and ZDDP+MoDTC lubricants have been observed. When using only ZDDP, this top layer is rich in Zn and Fe; with the addition of MoDTC the top layer contains phosphates, probably from the decomposition of the ZDDP, and MoS2. The effect of adding MoDTC can also be observed in the overall tribofilm thickness and coverage of the samples.
General-Purpose Computation Using Graphics Hardware for Fast HDR Image Processing

This paper presents a new approach to hardware aided image processing and analysis, primarily focused on HDR imaging. In order to achieve interactive frame rates and great processing speeds we propose a library model which is able to utilize efficiently most powerful of the underlying vector hardware. The resulting library architecture was implemented and tested on GPUs and SIMD capable multicore CPUs.
Distance-informed metric learning for Alzheimer's disease staging

Identifying intermediate biomarkers of Alzheimer's disease (AD) is of great importance for diagnosis and prognosis of the disease. In this study, we develop a new AD staging method to classify patients into Normal Controls (NC), Mild Cognitive Impairment (MCI), and AD groups. Our solution employs a novel metric learning technique that improves classification rates through the guidance of some weak supervisory information in AD progression. More specifically, those information are in the form of pairwise constraints that specify the relative Mini Mental State Examination (MMSE) score disparity of two subjects, depending on whether they are in the same group or not. With the imposed constraints, the common knowledge that MCI generally sits in between of NC and AD can be integrated into the classification distance metric. Subjects from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; 56 AD, 104 MCI, 161 controls) were used to demonstrate the improvements made comparing with two state-of-the-art metric learning solutions: large margin nearest neighbors (LMNN) and relevant component analysis (RCA).
New Technology Management Using Time Series Regression and Clustering

Technology can be defined diversely according to the ways of its usage. In this paper, we define technology as a tool making new product and service using the developed results of science and engineering for improving the quality of human life. Technology management (TM) is important factor in the business planning of a company. Many companies have performed TM for developing new products and protecting their intellectual properties (IP). Patent is a typical IP, so we propose a TM approach using new patent analysis method. In this paper, we combine time series regression and clustering techniques. To assess the performance of our research, we will make experiment using the biotechnology patent data from the United State Patent and Trademark Office.
GUILLAIN-BARRÉ SYNDROME COMPLICATING A CHIKUNGUNYA VIRUS INFECTION

In 2006, a major outbreak of Chikungunya fever occurred on Reunion Island involving 297,000 cases (38% of the population). Chikungunya is an arbovirus very rarely associated with neurologic complications. We report three patients presenting with Guillain-Barre syndrome (GBS) after having been infected by the Chikungunya virus. ### Case reports. #### Patient 1. A 51-year-old woman developed facial diplegia followed within 48 hours by tingling sensations and weakness in both hands and feet. She had consulted with her physician 2 weeks before admission, for a viral syndrome including fever, skin rash, and arthralgia. The neurologic examination revealed bilateral facial palsy, quadriparesis more pronounced distally (Medical Research Council [MRC] 3/5) than proximally (MRC 4/5), and global areflexia. A lumbar puncture performed on admission revealed albuminocytologic dissociation. Nerve conduction studies (table) fulfilled criteria1 for GBS. Serologic screening for Campylobacter jejuni , Cytomegalovirus, Mycoplasma pneumoniae , Epstein-Barr virus, Dengue virus, and HIV was negative. View this table: Table …
Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering

Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two types of heterogeneous data (denoted by pair-wise co-clustering), such as documents and terms, have been well studied in the literature, the work on more types of heterogeneous data (denoted by high-order co-clustering) is still very limited. As an attempt in this direction, in this paper, we worked on a specific case of high-order co-clustering in which there is a central type of objects that connects the other types so as to form a star structure of the inter-relationships. Actually, this case could be a very good abstract for many real-world applications, such as the co-clustering of categories, documents and terms in text mining. In our philosophy, we treated such kind of problems as the fusion of multiple pair-wise co-clustering sub-problems with the constraint of the star structure. Accordingly, we proposed the concept of consistent bipartite graph co-partitioning, and developed an algorithm based on semi-definite programming (SDP) for efficient computation of the clustering results. Experiments on toy problems and real data both verified the effectiveness of our proposed method.
A New Generation of Materials for the Calendering Industry

While polyvinyl chloride (PVC) has been employed in the calendering industry for many years, other polymers have not been as widely used. Ethylene styrene interpolymers (ESI) demonstrate the requisite rheological properties and thermal stability to be successfully used in the calendering process. Commercial scale validations were performed on ESI based formulations. Examples of the applicability of ESI to calendering are discussed.
Indoor evacuation routes planning with a grid graph-based model

At present, application of GIS is in a process of transition from macro space to micro space, such as indoor space, a kind of micro environment that has a smaller scale than outdoor space. There have been some applications for indoor space, covering issues like path finding, emergency planning, object tracking, etc. Behind these applications, indoor spatial models are needed to illustrate how built environments are spatially represented. Although some modeling approaches have been proposed, most of them focus only on either structural or topological properties. In view of this problem, recently a grid graph-based modeling approach considering a built environment as a continuous framework is presented, which is able to combine both geometrical and structural properties. In this paper, we employ this approach to implement route analysis based on a hotel floor plan. The result might be applied to the planning for evacuation routes.
Simultaneous evolution of feature subset and neural classifier on high-dimensional data

This paper describes a novel feature selection algorithm which utilises a genetic algorithm to simultaneously optimise a feature subset and the weights for a three-layer feedforward neural network classifier. On the “sonar” data set from UC Irvine, this approach produces results comparable to those reported for other algorithms on the same data, but using fewer input features and a simpler neural network architecture. These results indicate that tailoring a neural network classifier to a specific subset of features has the potential to produce a classifier with low classification error, good generalisability, and relatively low computational overhead.
Design of Office Chair: A Quality Function Deployment Approach

This paper employs a quality function deployment (QFD) methodology to translate customer requirements into design characteristics to improve the design of an office chair. A factor analysis has been carried out on the responses obtained from a cross sectional survey directed at users through a set of questionnaires. It has been obtained that three factors with twenty two items are loaded with a threshold value above 0.7. Finally, quality function deployment is used to extract important design characteristics satisfying the customer requirements
A regulated body-driven CMOS current mirror for low-voltage applications

A CMOS current mirror (CM) based on the body-driven technique and active feedback scheme is presented. The proposed CM is immune to the threshold voltage limitation and offers much higher accuracy over wider current operating range than other body-driven CMs. The complete analysis of the input-output characteristics, system dc current transfer error, frequency, and noise performance is provided. By using a 1.5V/1V single power supply and 0.18-/spl mu/m n-well process, SPECTRE simulation results validate the analytical results and the overall good performance in terms of wider input-output voltage swing, lower input resistance, and larger output resistance compared with the conventional high-swing cascode CM.
Discrimination among Rhinovirus Serotypes for a Variant ICAM-1 Receptor Molecule

ABSTRACT Intercellular adhesion molecule 1 (ICAM-1) is the cellular receptor for the major group of human rhinovirus serotypes, including human rhinovirus 14 (HRV14) and HRV16. A naturally occurring variant of ICAM-1, ICAM-1Kilifi, has altered binding characteristics with respect to different HRV serotypes. HRV14 binds to ICAM-1 only transiently at physiological temperatures but forms a stable complex with ICAM-1Kilifi. Conversely, HRV16 forms a stable complex with ICAM-1 but does not bind to ICAM-1Kilifi. The three-dimensional structures of HRV14 and HRV16, complexed with ICAM-1, and the structure of HRV14, complexed with ICAM-1Kilifi, have been determined by cryoelectron microscopy (cryoEM) image reconstruction to a resolution of approximately 10 Å. Structures determined by X-ray crystallography of both viruses and of ICAM-1 were fitted into the cryoEM density maps. The interfaces between the viruses and receptors contain extensive ionic networks. However, the interactions between the viruses and ICAM-1Kilifi contain one less salt bridge than between the viruses and ICAM-1. As HRV16 has fewer overall interactions with ICAM-1 than HRV14, the absence of this charge interaction has a greater impact on the binding of ICAM-1Kilifi to HRV16 than to HRV14.
Leakage Reduction of Power-Gating Sequential Circuits Based on Complementary Pass-Transistor Adiabatic Logic Circuits

Leakage dissipation is more and more dominant. Limiting leakage power consumption becomes an important factor in IC designs. This paper presents a reduction technique of leakage consumption for adiabatic sequential circuits based on two-phase CPAL (complementary pass-transistor adiabatic logic) using power-gating scheme. A practical sequential circuit with a mode-5X5X5 counter using two-phase CPAL is demonstrated. All circuits are verified using HSPICE, and BSIM4 model is adopted to reflect the characteristics of the leakage currents. With 45nm and 90nm CMOS process, simulation results show that the leakage losses of the adiabatic sequential circuits with the power-gating scheme are greatly reduced compared with static CMOS one.
Input-Adaptive Models Based Multiple-Model Algorithm for Maneuvering Target Tracking

Abstract The dynamic models with multilevel inputs are adopted in a kind of multiple model estimator for highly maneuvering target tracking. While the target maneuvers with the continuous time-varying accelerations, the estimator increases the levels to improve the percentage of coverage, which induces two problems: the increase of calculation burden and the decrease of the estimation precision due to the competition between the models. A multilevel input-adaptive multiple model (IAMM) algorithm is proposed, in which the inputs are adjusted according to the prior value and the on-line estimated maneuver parameters by introducing a dualistic distribution. The adaptabilities of the inputs can depict the actual maneuver process better compared with the static multilevel inputs. The simulation proves the effectiveness of IAMM algorithm compared with the IMM (Interacting Multiple Model) estimator with models containing multilevel static inputs.
Effect of irradiation on quality of spices

Irradiation has been shown to be an effective method for food decontamination. Spices irradiation is a process of exposing spices to ionizing radiation such as gamma rays emitted from the radioisotopes 60Co and 137Cs, or high energy electrons and X-rays produced by machine sources. The use of ionizing radiation to destroy harmful biological organisms in food is considered a safe, well proven process that has found many applications. Depending on the absorbed dose of radiation, various effects can be achieved resulting in reduced storage losses, extended shelf life and/or improved microbiological and parasitological safety of foods. The most common irradiated commercial products are spices and vegetable seasonings. Spice irradiation is increasingly recognized as a method that reduces post-harvest losses, ensures hygienic quality, and facilitates trade with food products. This article reviews activities focusing on the irradiation of spices from the food safety aspect.
Laparoscopic radical hysterectomy with the use of a modified uterine manipulator for the management of stage IB1 cervix cancer

We prospectively collected data on all patients with stage IB1 cervical cancer, who underwent total laparoscopic radical hysterectomy with the use of a modified uterine manipulator. From January 2000 to December 2005, 54 patients met the study criteria. The mean age was 41.8 ± 7.47 years. Average BMI (kg/m2) was 27.38 ± 3.13. Squamous carcinoma and adenocarcinoma were found in 88.88% and 11.11% of the cases, respectively. The average surgical time was 265 ± 70.8 min. The mean estimated blood loss was 276.11 ± 123.03 ml. The average patient lymph node count was 19.64 ± 5.08. Positive malignant lymph nodes were identified in 11.11% of the cases. Surgical margins were free of disease in all patients. The mean hospital stay was 1.5 ± 1 days. There was no mortality. Total laparoscopic radical hysterectomy can be considered a safe alternative to laparotomy. The use of a uterine manipulator does not pose an increased surgical risk and allows for a simpler and more feasible procedure.
Where the Cognitive and the Social Merge

Among the many contested boundaries in science studies is that between the cognitive and the social. Here, we are concerned to question this boundary from a perspective within the cognitive sciences based on the notion of distributed cognition. We first present two of many contemporary sources of the notion of distributed cognition, one from the study of artificial neural networks and one from cognitive anthropology. We then proceed to reinterpret two well-known essays by Bruno Latour, 'Visualization and Cognition: Thinking with Eyes and Hands' and 'Circulating Reference: Sampling the Soil in the Amazon Forest'. In both cases we find the cognitive and the social merged in a system of distributed cognition without any appeal to agonistic encounters. For us, results do not come to be regarded as veridical because they are widely accepted; they come to be widely accepted because, in the context of an appropriate distributed cognitive system, their apparent veracity can be made evident to anyone with the capacity to understand the workings of the system.
Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation

Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model. Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously.