Miracle-dz/UniFAR-FTUs huggingface dataset:
The Structure of the Complex between Yeast Frataxin and Ferrochelatase

Frataxin is a mitochondrial iron-binding protein involved in iron storage, detoxification, and delivery for iron sulfur-cluster assembly and heme biosynthesis. The ability of frataxin from different organisms to populate multiple oligomeric states in the presence of metal ions, e.g. Fe2+ and Co2+, led to the suggestion that different oligomers contribute to the functions of frataxin. Here we report on the complex between yeast frataxin and ferrochelatase, the terminal enzyme of heme biosynthesis. Protein-protein docking and cross-linking in combination with mass spectroscopic analysis and single-particle reconstruction from negatively stained electron microscopic images were used to verify the Yfh1-ferrochelatase interactions. The model of the complex indicates that at the 2:1 Fe2+-to-protein ratio, when Yfh1 populates a trimeric state, there are two interaction interfaces between frataxin and the ferrochelatase dimer. Each interaction site involves one ferrochelatase monomer and one frataxin trimer, with conserved polar and charged amino acids of the two proteins positioned at hydrogen-bonding distances from each other. One of the subunits of the Yfh1 trimer interacts extensively with one subunit of the ferrochelatase dimer, contributing to the stability of the complex, whereas another trimer subunit is positioned for Fe2+ delivery. Single-turnover stopped-flow kinetics experiments demonstrate that increased rates of heme production result from monomers, dimers, and trimers, indicating that these forms are most efficient in delivering Fe2+ to ferrochelatase and sustaining porphyrin metalation. Furthermore, they support the proposal that frataxin-mediated delivery of this potentially toxic substrate overcomes formation of reactive oxygen species.
The quality of physician-patient relationships. Patients' experiences 1996-1999.

BACKGROUND Our objective was to examine how patients of primary care physicians are responding to a changing health care environment. The quality of their relationship with their primary care physicians and their experience with organizational features of care were monitored over a 3-year period. METHODS This was a longitudinal observational study (1996-1999). Participants completed a self-administered questionnaire at baseline and at follow-up. The questionnaires included measures of primary care quality from the Primary Care Assessment Survey (PCAS). We included insured adults employed by the Commonwealth of Massachusetts who remained with one primary care physician throughout the study period (n=2383). The outcomes were unadjusted mean scale score changes in each of the 8 PCAS over the 3 years and associated standardized difference scores (effect sizes). The 8 PCAS scales measured relationship quality (4 scales: communication, interpersonal treatment, physician's knowledge of the patient, patient trust) and organizational features of care (4 scales: financial access, organizational access, visit-based continuity, integration of care). RESULTS There were significant declines in 3 of the 4 relationship scales: communication (effect size [ES] = -0.095), interpersonal treatment (ES = -0.115), and trust (ES = -0.046). Improvement was observed in physician's knowledge of the patient (ES = 0.051). There was a significant decline in organizational access (ES = -0.165) and an increase in visit-based continuity (ES = 0.060). There were no significant changes in financial access and integration of care indexes. CONCLUSIONS The declines in access and 3 of the 4 indexes of physician-patient relationship quality are of concern, especially if they signify a trend.
On‐the‐path random walk sampling for efficient optimization of minimum free‐energy path

A sampling method is proposed for the efficient acquisition of minimum free‐energy path (MFEP). Here, the MFEP optimization is realized based on the sampling via single on‐the‐path random walk simulation. The present strategy naturally ensures the on‐the‐path structural continuity so that the MFEP optimization can be simplified and its efficiency can be improved as well. © 2009 Wiley Periodicals, Inc. J Comput Chem 2009
GNURadio as a digital signal processing environment: Application to acoustic wireless sensor measurement and time & frequency analysis of periodic signals

The flexibility, reconfigurability and stability of software defined radio yield an attractive alternative to the analog strategy of probing acoustic transducers acting as passive sensors probed through a wireless link or to phase noise characterization of oscillators. However, developing processing blocks is a time consuming activity, yet metrology applications require a dedicated understanding of each processing step. We consider GNURadio as a means of exploiting opensource software as an optimum tradeoff between software re-usability yet compatible with an audit for assessing performance. This signal processing environment is demonstrated on two practical examples, FMCW probing of acoustic delay lines acting as sensors, and quartz tuning fork characterization. Both examples are considered as introductory setups for training and teaching yet a suitable environment for research activities.
Caregiving and Cognitive Function in Older Women

Objectives: Recent findings of better health outcomes in older caregivers than noncaregivers suggest a healthy caregiver hypothesis (HCH) model may be more appropriate than the stress process model for evaluating the health effects of caregiving. In a cross-sectional study, we tested the HCH on two cognitive domains: verbal memory and processing speed. Method: Participants from the Caregiver Study of Osteoporotic Fractures who had a 2-year follow-up interview were categorized as continuous caregivers (n = 194), former caregivers (n = 148), or continuous noncaregivers (n = 574). The Hopkins Verbal Learning Test (HVLT; memory) and Digit Symbol Substitution Task (DSST; processing speed) were administered at the follow-up interview. Results: Continuous caregivers had better memory performance and processing speed than continuous noncaregivers: adjusted mean scores for HVLT were 18.38 versus 15.80 (p < .0001), and for DSST were 35.91 versus 34.38 (p = .09). Discussion: Results support the HCH model for cognitive outcomes in older women caregivers; however, the relationship may be domain specific.
Development of smart ECG machine using LabVIEW for biomedical engineering students

Electrocardiography machine is a useful device to determine the rate and regularity of heart beats. A real ECG machines with complex and multiple functions make a biomedical engineering students difficult to discover. This work has been demonstrated to enable students and end-users to learn the ECG basics principle. The ECG with a less complex circuits performing multiple data and will enable the signal processing analysis. LabVIEW is used to validate and compiling data analysis.
A fuzzy aggregate production planning model for make-to-stock environments

This paper presents an aggregate production planning model for make-to-stock (MTS) environments. Since multiple sources of imprecision and complex inter-relationships at various levels between diverse entities exist in the supply chain (SC), we can use stochastic or fuzzy control models to describe the complex SC processes, associated management, and control tasks. We consider the fuzzy sets theory as a solution to tackle the uncertainties in different terms of the model. Finally, the effectiveness of the proposed model is demonstrated by a numerical example.
Frequency Splitting Phenomena of Magnetic Resonant Coupling Wireless Power Transfer

In magnetic resonant coupling wireless power transfer (WPT) system, the frequency splitting phenomena have a vital influence on the output power. This paper mainly focuses on the frequency splitting of two-coil and three-coil WPT. First, the frequency characteristics of transfer power and system efficiency of the two-coil WPT are analyzed by circuit theory. In addition, the magnetic field distribution of the two splitting frequencies is simulated to analyze which frequency is better for the operating frequency. Second, considering the reference resistance of RF power, the frequency splitting of two-coil and three-coil WPT is analyzed. Finally, the critical coupling, which determines the over coupled region, of two-coil and three-coil WPT is derived. Meanwhile, we propose a tuned frequency method according to the numerical results. In addition, the transfer power can be kept at a relatively constant level in the over coupled region.
Novel Tri-Band Bandpass Filter Using Stub-Loaded Short-Ended Resonator

This paper presents a tri-band bandpass filter (BPF) using a novel stub-loaded resonator. Different from other stub-loaded resonator, this resonator utilized a short-ended main transmission line and a centerly-loaded open stub. The resonator is theoretically analyzed. The first three resonant frequencies can be individually adjusted and this enables the convenient designs of tri-band BPFs. For demonstration, a tri-band BPF is implemented. Transmission zeros are created close to each passband edge, resulting in high skirt selectivity. Comparisons of the measured and simulated results are presented to verify the theoretical predications.
Incorporation of Value-Driven Design in Multidisciplinary Design Optimization

The design of modern day large-scale systems is a highly complex task, involving thousands of individuals from multiple disciplines and various locations in vast organizations. Multidisciplinary Design Optimization (MDO) is a collection of frameworks that are used by design organizations to enable efficient determination of optimal designs of large-scale systems. Common usage of MDO frameworks involves establishing an objective function and requirements. The objective function, requirements and other parameters are then disseminated throughout the organization to various subsystem groups through the MDO frameworks. This paper will explore the use of value-driven design to establish requirement-free utility functions and how those utility functions can be successfully incorporated into existing MDO frameworks.
Stochastic Methods for Parameter Estimation of Multiphysics Models of Fuel Cells

The accurate modeling of complex multiphysical devices and systems is a crucial problem in engineering. Such models are usually characterized by highly nonlinear equations and depend on a high number of parameters, which often cannot be directly measured. In this paper, two stochastic optimization techniques are applied to the solution of such challenging problems in the case of a fuel cell. The algorithms provide satisfactory results, and in particular differential evolution, seldom used in parameter identification for systems of this type, is shown to be powerful and robust.
A QEEG index of level of functional dependence for people sustaining acquired brain injury: The Seville Independence Index (SINDI)

Primary objective: To find an easy-to-use, valid and reliable tool for evaluating the level of functional dependence of an individual with brain damage who seeks a diagnosis of his/her functional dependence in daily activities. Methods: Eighty-one patients with acquired brain injury (ABI) in post-acute phase, 40 traumatic brain injury (TBI) and 41 cerebral vascular accident (CVA), were assessed using quantitative electroencephalography (QEEG) and grouped according to the FIM + FAM scale. Discriminant analysis was performed on QEEG variables to obtain a discriminant function with the best discriminative capacity between functionality groups. Results: Discriminant analysis showed classification accuracy of 100% in the training set sample and 75% in an external cross-validation sample; 100% sensitivity and 100% specificity were reached. Coherence measures were the most numerous variables in the function. Conclusions: These results point out that the discriminant function may be a useful tool in objective evaluations of patients seeking a diagnosis of their level of dependence and that it could be included in current functionality assessment protocols.
Effect of Transcranial Magnetic Stimulation of Acupuncture Point on Brain Network

Transcranial magnetic stimulation (TMS), as a new therapy, has been proven to be efficient in inducing electric fields in the neuronal tissues and modulating brain activity in the applications to stimulate different positions of human body such as the brain, peripheral nerves, and acupuncture points (acupoint) in the last decade. Acupuncture, an ancient therapeutic technique, is gaining popularity in Western medicine, but the underlying regulation mechanism of acupuncture has not been well understood and still needs to be further investigated. By using electroencephalogram, signals before and following stimulation of Shenmen acupoint (HT7) were acquired noninvasively. Then, we adopted a network model analysis from graph theory to construct a network and evaluate network measures of brain connectivity among multiple brain regions before and following stimulation. An analysis of the resulting brain functional network found that the parietal lobe and parietal-occipital lobe shared lower degree and strength following stimulation than before and the frontal lobe shared higher betweenness centrality than before. The network measures with significant differences were distributed over the brain areas contralateral to the stimulated hand, which has probable association with the regulation mechanism of TMS of HT7 acupoint.
Neural network control for large scale systems with faults and perturbations

This paper provides an adaptation algorithm for the control of complex system via recurrent neural networks. The proposed method is derived from RTRL algorithm. Neural emulator and neural controller parameters are one-line updated independently. To illustrate the tracking and the disturbance rejection capabilities of the real time control algorithm and the efficiency of the networks parameters relaxation, an application to the large scale process: Tennessee Eastman Challenge Process (TECP) is presented.
The virtual cluster: a dynamic network environment for exploitation of idle resources

Standard environments for exploiting idle time of workstations are based on some kind of spying process that detects low CPU usage and informs to a scheduler so that work can be dispatched. This approach generates local interference and, since the same local environment is used, could lead to security problems. We are investigating the exploitation of idle times in network resources based on a complete mode change in a candidate node. After the detection that some node is idle, a mode-switcher boots a new operating system that will work over a separate disk partition. After the boot phase the node is linked to a logical network topology and is available to receive jobs. Users can allocate nodes from this virtual cluster through a standard frontend as they would do in a "conventional" cluster. Because nodes may leave and join this virtual machine we use a distributed processor management to allow user applications to cope with this dynamic resource behavior. In this paper we describe the architecture of the virtual cluster and present the results obtained with a mode-switcher and a prototype application under real use conditions.
Immunofluorescent staining of Drosophila larval brain tissue.

INTRODUCTION The Drosophila larval brain is a well-established model for investigating the role of stem cells in development. Neuroblasts (neural stem cells) must be competent to generate many thousands of differentiated neurons through asymmetric divisions during normal development. Studies in fly neuroblasts have been instrumental in identifying how the establishment and maintenance of cell polarity influence cell fate, and they have produced a wide array of molecular cell-polarity markers. Moreover, neuroblasts and their progeny can be positively identified using a variety of cell-fate markers. This article describes procedures for the collection and processing of Drosophila larval brains for examination by immunolocalization of cell-fate and cell-polarity markers. The protocol can be used for dissecting, fixing, and staining brains from larvae at any developmental stage. The number of brains processed using this method is limited only by how many brains can be dissected in 20 min, which is the maximum amount of time dissected tissues should remain in buffer before fixation. This protocol can be used for simultaneous costaining of multiple proteins.
Aggregator-Controlled EV Charging in Pay-as-Bid Reserve Markets With Strict Delivery Constraints

Electric vehicles (EVs), similar to other types of flexible loads, can be controlled to provide additional flexibility to power system operators, which is needed to transition from primarily fossil fuel based electricity generation to more renewable generation. Existing electricity markets already provide economic incentives to offer flexibility. So-called aggregators in charge of controlling EV charging could take advantage of these incentives. However, random driving behavior and area-specific market rules complicate the market participation of EV aggregators in energy and reserve markets. The goal of this paper is to investigate the design and performance of a system that would enable EV aggregators to participate in wholesale electricity markets. We consider a certain type of market environment, which includes an intraday energy market and pay-as-bid reserve markets that require exact reserve delivery and have long operating intervals. We therefore propose a novel approach for concurrent market participation that can deal with these constraints, even when faced with highly uncertain reserve activation and EV behavior.
3-Form Cosmology: Phantom Behaviour, Singularities and Interactions

The latest cosmological observations by the Planck collaboration (and combined with others) are compatible with a phantom-like behaviour ( w < − 1 ) for the dark energy equation of state that drives the current acceleration of the Universe. With this mindset, we look into models where dark energy is described by a 3-form field minimally coupled to gravity. When compared to a scalar field, these models have the advantage of more naturally accommodating a cosmological-constant and phantom-like behaviours. We show how the latter happens for a fairly general class of positive-valued potentials, and through a dynamical system approach, we find that in such cases the 3-form field leads the Universe into a Little Sibling of the Big Rip singular event into the future. In this work, we explore the possibility of avoiding such singularity via an interaction in the dark sector between cold dark matter and the 3-form field. For the kind of interactions considered, we deduce a condition for replacing the LSBR by a late time de Sitter phase. For specific examples of interactions that meet this condition, we look for distinctive imprints in the statefinder hierarchy { S 3 ( 1 ) ; S 4 ( 1 ) } , { S 3 ( 1 ) ; S 5 ( 1 ) } , and in the growth rate of matter, ϵ ( z ) , through the composite null diagnostic (CND).
Measuring Knowledge Delivery Quantity of Associated Knowledge Flow

Associated knowledge flow (AKF) is a sequential link between associated topics, which can be applied to intelligent browsing and personalized recommendation. One key problem is how to measure the knowledge delivery quantity (KDQ) on an AKF. In this paper, a computational method of knowledge delivery quantity on an AKF is proposed. Firstly, considering the keywords and associated relations between two nodes, four key factors for knowledge delivery quantity between two nodes are investigated. Secondly, based on the four factors, an algorithm is proposed to calculate the knowledge delivery quantity between two nodes. Thirdly, the knowledge delivery quantity of a node with adjacent nodes is calculated for the measurement of local knowledge delivery on an AKF. Lastly, according to the local knowledge delivery, the average knowledge delivery quantity is proposed to measure an AKF. Experimental results show that the proposed measurement method is accurate and effective.
Detailed Functional and Proteomic Characterization of Fludarabine Resistance in Mantle Cell Lymphoma Cells

Mantle cell lymphoma (MCL) is a chronically relapsing aggressive type of B-cell non-Hodgkin lymphoma considered incurable by currently used treatment approaches. Fludarabine is a purine analog clinically still widely used in the therapy of relapsed MCL. Molecular mechanisms of fludarabine resistance have not, however, been studied in the setting of MCL so far. We therefore derived fludarabine-resistant MCL cells (Mino/FR) and performed their detailed functional and proteomic characterization compared to the original fludarabine sensitive cells (Mino). We demonstrated that Mino/FR were highly cross-resistant to other antinucleosides (cytarabine, cladribine, gemcitabine) and to an inhibitor of Bruton tyrosine kinase (BTK) ibrutinib. Sensitivity to other types of anti-lymphoma agents was altered only mildly (methotrexate, doxorubicin, bortezomib) or remained unaffacted (cisplatin, bendamustine). The detailed proteomic analysis of Mino/FR compared to Mino cells unveiled over 300 differentially expressed proteins. Mino/FR were characterized by the marked downregulation of deoxycytidine kinase (dCK) and BTK (thus explaining the observed crossresistance to antinucleosides and ibrutinib), but also by the upregulation of several enzymes of de novo nucleotide synthesis, as well as the up-regulation of the numerous proteins of DNA repair and replication. The significant upregulation of the key antiapoptotic protein Bcl-2 in Mino/FR cells was associated with the markedly increased sensitivity of the fludarabine-resistant MCL cells to Bcl-2-specific inhibitor ABT199 compared to fludarabine-sensitive cells. Our data thus demonstrate that a detailed molecular analysis of drug-resistant tumor cells can indeed open a way to personalized therapy of resistant malignancies.
Addressing the cold user problem for model-based recommender systems

Customers of a webshop are often presented large assortments, which can lead to customers struggling finding their desired product(s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, recommender systems using matrix factorization are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopGini, an active learning strategy which combines the popularity of an item with its Gini impurity score.