Miracle-dz/UniFAR-FTUs huggingface dataset:
Signaling-Related Mobility Changes in Bacterial Chemotaxis Receptors Revealed by Solid-State NMR

Bacteria employ remarkable membrane-bound nanoarrays to sense their environment and direct their swimming. Arrays consist of chemotaxis receptor trimers of dimers that are bridged at their membrane-distal tips by rings of two cytoplasmic proteins, a kinase CheA and a coupling protein CheW. It is not clear how ligand binding to the periplasmic domain of the receptor deactivates the CheA kinase bound to the cytoplasmic tip ∼300 Å away, but the mechanism is thought to involve changes in dynamics within the cytoplasmic domain. To test these proposals, we applied solid-state NMR mobility-filtered experiments to functional complexes of the receptor cytoplasmic fragment (U–13C,15N-CF), CheA, and CheW. Assembly of these proteins into native-like, homogeneous arrays is mediated by either vesicle binding or molecular crowding agents, and paramagnetic relaxation enhancement is used to overcome sensitivity challenges in these large complexes. INEPT spectra reveal that a significant fraction of the receptor is dynamic on the nanosecond or shorter time scale, and these dynamics change with signaling state. The mobile regions are identified through a combination of biochemical and NMR approaches (protein truncations and unique chemical shifts). The INEPT spectra are consistent with an asymmetric mobility in the methylation region (N-helix mobility ≫ C-helix mobility) and reveal an increase in the mobility of the N-helix in the kinase-off state. This finding identifies functionally relevant dynamics in the receptor, and suggests that this N-helix segment plays a key role in propagating the signal.
Modeling and control of distributed thermal systems

This paper investigates the application of model-based control design techniques to distributed temperature control systems. Multivariable controllers are an essential part of modern-day rapid thermal processing (RTP) systems. We consider all aspects of the control problem beginning with a physics-based model and concluding with implementation of a real-time embedded controller. The thermal system used as an example throughout is an RTP chamber widely used in semiconductor wafer processing. With its exceptionally stringent performance requirements (low nonuniformity of wafer temperature, high temperature ramp rates), RTP temperature control is a challenging distributed temperature control problem. Additionally, it is important in the semiconductor industry because of the progressively smaller "thermal budget" resulting from ever decreasing integrated circuit dimensions. Despite our emphasis on faster cold wall single-wafer processing RTP chambers, the approach described here for solving distributed temperature control problems is equally applicable to slower distributed thermal systems, such as hot-wall batch-processing furnaces. For the physical model, finite volume techniques are used to develop high-fidelity heat transfer models that may be used for both control design and optimal chamber design. Model-order reduction techniques are employed to reduce these models to lower orders for control system design. In particular, principal orthogonal decomposition techniques have been used to derive low order models. Techniques such as linear quadratic Gaussian H/sub 2//H/sub /spl infin// methods are employed for feedback control design. While the methods are illustrated here using a generic RTP system, they have been successfully implemented on commercial RTP chambers.
Long-range hot-carrier transport in hybrid perovskites visualized by ultrafast microscopy

Extraction of photoexcited charge carriers transported up to 600 nanometers in CH3NH3PbI3 could boost solar cell efficiency. Seeing hot carriers break the limit If charge carriers in solar cells can be harvested before they cool on their way to equilibrium, then the Shockley-Queisser limit of 33% for solar cell efficiency can potentially be beaten. Long carrier lifetimes (∼100 ps) have been reported for hybrid organic-inorganic metal halide perovskites. Guo et al. imaged charge transport in CH3NH3PbI3 thin films with transient absorption microscopy. Hot carriers could travel up to 600 nm, which suggests that devices that harvest hot carriers may be feasible. Science, this issue p. 59 The Shockley-Queisser limit for solar cell efficiency can be overcome if hot carriers can be harvested before they thermalize. Recently, carrier cooling time up to 100 picoseconds was observed in hybrid perovskites, but it is unclear whether these long-lived hot carriers can migrate long distance for efficient collection. We report direct visualization of hot-carrier migration in methylammonium lead iodide (CH3NH3PbI3) thin films by ultrafast transient absorption microscopy, demonstrating three distinct transport regimes. Quasiballistic transport was observed to correlate with excess kinetic energy, resulting in up to 230 nanometers transport distance that could overcome grain boundaries. The nonequilibrium transport persisted over tens of picoseconds and ~600 nanometers before reaching the diffusive transport limit. These results suggest potential applications of hot-carrier devices based on hybrid perovskites.
Multi-core for K-means clustering on FPGA

In this paper, a configurable many-core hardware/software architecture is proposed to efficiently execute the widely known and commonly used K-means clustering algorithm. A prototype was designed and implemented on a Xilinx Zynq-7000 All Programmable SoC. A single core configured with the slowest configuration achieves a 10× speed-up compared to the software only solution. The system is fully scalable and capable of achieving much higher speed-ups by increasing its parallelism.
Gain Scheduled $H_{\infty}$  Control for Air Path Systems of Diesel Engines Using LPV Techniques

This paper addresses the modeling and control of the air path system of diesel engines. The underlying issues are critical for the control of the transient exhaust gas fraction pumped into the cylinders, which is known to be a dominant factor to reduce the nitrogen oxides (NO x) emissions. In this paper, we propose a new approach, based on a data-based grey-box linear parameter varying (LPV) model as well as on the gain scheduled Hinfin technique for the controller design. The modeling step is shown to lead naturally to a so-called quasi-LPV structure, which also delivers the scheduling variables to be accounted for. Using this information, gain scheduled Hinfin techniques allow to design a controller which enforces a much better tracking performance than the standard production electronic control unit, while not requiring any calibration work. The performance of the proposed approach is demonstrated by experimental results
Do patients know their diagnosis of cancer?

Abstract Aims: Patients' lack of knowledge about their diagnosis still exists. We aimed to examine whether, in our population, the patients' age, marital status, level of education, occupation, gender and diagnosis influence their knowledge of their diagnosis. Additionally we aimed to see if knowing their diagnosis affected their QoL. Patients and Methods: We used a sociodemographic questionnaire and the EORTC (European Organization for Research and Treatment of Cancer, Study Group on Quality of Life) QLQ-C30 (Quality of Life Questionnaire of cancer) to obtain information about the total QoL Score. Results: We found that in a total of 238 cancer patients, 163 patients knew that they had cancer or a tumour, and 75 did not. We observed statistically significant differences between the ages, the education level, the occupation (employed/unemployed), and the diagnosis of those patients that knew their diagnosis from those that did not. For the variables total of QoL Score, marital status and gender we did not observe statistically significant differences between the patients that knew their diagnosis and those that did not. The association between knowledge of diagnosis and occupation was very small as was the association between knowledge of diagnosis and diagnosis. The association between knowledge and age was moderate and between knowledge and education was small. Conclusions: In our sample, knowledge was influenced by age (older, less knowledge), education level (less years, less knowledge), diagnosis (prostate cancer had more patients that did not know their diagnosis) and occupation. Knowing their diagnosis did not diminish patients' QoL.
‘Unplugged’: A new European school programme against substance abuse

This paper presents the rationale, development and application of ‘Unplugged’, a new school programme for the prevention of substance abuse, which is based on the comprehensive social influence approach (CSI). The programme was developed, implemented and evaluated by a cross-disciplinary group of experts in the frame of a multi-centre study in seven European countries. The basic curriculum consists of 12 units, which are delivered by class teachers to a target population of 12–14 year-old students. Additional programme components were workshops for the students’ parents and debriefing sessions led by class peers. The programme's application was monitored by means of structured report forms. This process monitoring allowed for the analysis of problems in implementation, as well as of participatory aspects. ‘Unplugged’ is the first comprehensive social influence school curriculum in Europe of which the efficacy has been evaluated in a field trial, and it also constitutes an example of a theory- and evidence-based programme against youths’ substance use. This report can provide public health and school decision makers with tools to make an informed choice on these kinds of programmes and to survey their application.
Cerebral Asymmetry in Schizophrenia

The hemispheres of the human brain are anatomically and functionally asymmetric, and many cognitive and motor functions such as language and handedness are lateralized. This review examines anatomical, psychological, and physiological approaches to the understanding of separate hemispheric functions and their integration. The concept of hemispheric laterality plays a central role in current neuropsychological and pathophysiological models of schizophrenia. Reduced hemispheric asymmetry has also been reported for other mental disorders, for example, bipolar disorder. Recent research reflects an increasing interest in the molecular and population genetics of laterality and its potential link with animal models of schizophrenia. The authors review the principles of laterality and brain asymmetry and discuss the evidence for changes in asymmetry in schizophrenia and other mental disorders.
Application of Predictive Sliding Mode Controllers to a Solar Plant

This brief presents the application of different predictive sliding mode controllers to the control of the outlet oil temperature of a field of distributed solar collectors. These controllers have been tuned based on a first-order plus dead time model that can be easily obtained with the reaction curve method. Satisfactory results have been obtained and several experimental tests showing the tracking and disturbance rejection capabilities are shown and discussed.
Computer modeling of epilepsy

This chapter reviews current computational models and proposes future directions for computational modeling in the field of epilepsy. The models include single cells with mutated ion channels; small‐ and large‐scale networks of detailed cells; and macroscopic, mean‐field models of network dynamics. In addition, we consider the potential therapeutic applications of modeling. For an expanded treatment of this topic see Jasper’s Basic Mechanisms of the Epilepsies, Fourth Edition (Noebels JL, Avoli M, Rogawski MA, Olsen RW, Delgado‐Escueta AC, eds) published by Oxford University Press (available on the National Library of Medicine Bookshelf [NCBI] at http://www.ncbi.nlm.nih.gov/books).
A novel FPGA-based SVM classifier

Support Vector Machines (SVMs) are a powerful supervised learning tool, providing state-of-the-art accuracy at a cost of high computational complexity. The SVM classification suffers from linear dependencies on the number of the Support Vectors and the problem's dimensionality. In this work, we propose a scalable FPGA architecture for the acceleration of SVM classification, which exploits the device heterogeneity and the dynamic range diversities among the dataset attributes. Furthermore, this work introduces the first FPGA-oriented cascade SVM classifier scheme, which intensifies the custom-arithmetic properties of the heterogeneous architecture and boosts the classification performance even more. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 2–3 orders of magnitude, compared to the CPU implementation, while outperforming other proposed FPGA and GPU approaches by more than 7 times.
Resting-State Functional Brain Connectivity

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain’s low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.
Intentions in the Brain

Is it possible to understand the intentions of others by merely observing their movements? Current debate has been mainly focused on the role that mirror neurons and motor simulation may play in this process, with surprisingly little attention being devoted to how intentions are actually translated into movements. Here, we delineate an alternative approach to the problem of intention-from-movement understanding, which takes “action execution” rather than “action observation” as a starting point. We first consider whether and to what extent, during action execution, intentions shape movement kinematics. We then examine whether observers are sensitive to intention information conveyed by visual kinematics and can use this information to discriminate between different intentions. Finally, we consider the neural mechanisms that may contribute to intention-from-movement understanding. We argue that by reframing the relationship between intention and movement, this evidence opens new perspectives into the neurobiology of how we know other minds and predict others’ behavior.
A Motion Planning-Based Adaptive Control Method for an Underactuated Crane System

This brief proposes a motion planning-based adaptive control strategy for an underactuated overhead crane system. To improve the transportation efficiency and enhance the safety of the crane system, the trolley is required to reach the desired position fast enough, while the swing of the payload needs to be within an acceptable domain. To achieve these objectives, a novel two-step design strategy consisting of a motion planning stage and an adaptive tracking control design stage, is proposed to control such an underactuated system as an overhead crane. Specifically, a novel desired trajectory, which satisfies physical constraints of an overhead crane, is proposed for the trolley by fusing theoretical analysis results with the conventional empirical trajectory planning methods. An adaptive control law is then constructed in the second step to make the trolley track the planned trajectory, where some online update mechanism is introduced to ensure that the controller works well with different working conditions. As shown by Lyapunov techniques, the proposed adaptive controller guarantees asymptotic tracking result even in the presence of uncertainties including system parameters and various disturbance. Some experiment results demonstrate that the proposed control method achieves superior performance for the underactuated cranes.
Deriving genomic diagnoses without revealing patient genomes

Sharing data, protecting privacy Although data-sharing is crucial for making the best use of genetic data in diagnosing disease, many individuals who might donate data are concerned about privacy. Jagadeesh et al. describe a solution that combines a protocol from modern cryptography with frequency-based clinical genetics used to diagnose causal disease mutations in patients with monogenic disorders. This framework correctly identified the causal gene in cases involving actual patients, while protecting more than 99% of individual participants' most private variants. Science, this issue p. 692 Patient diagnosis by secure multiparty computation can enable data sharing while protecting privacy. Patient genomes are interpretable only in the context of other genomes; however, genome sharing enables discrimination. Thousands of monogenic diseases have yielded definitive genomic diagnoses and potential gene therapy targets. Here we show how to provide such diagnoses while preserving participant privacy through the use of secure multiparty computation. In multiple real scenarios (small patient cohorts, trio analysis, two-hospital collaboration), we used our methods to identify the causal variant and discover previously unrecognized disease genes and variants while keeping up to 99.7% of all participants’ most sensitive genomic information private.
Moving in the Anthropocene: Global reductions in terrestrial mammalian movements

Restrictions on roaming Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science, this issue p. 466 Human alterations of the landscape shorten the distances traveled by individual animals. Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
CLINICAL IMPORTANCE OF ASYMMETRYAND HANDEDNESS DIFFERENCES IN COMMON CAROTID ARTERY INTIMA-MEDIA THICKNESS

Earlier publications have reported that common carotid artery intima-media thickness (CCA-IMT) was lower on the right side than on the left side and that left-handed patients have a lower risk of sudden death of brain infarction. This study aimed to determine whether there is an asymmetry and the handedness related difference in CCA-IMT. Ultrasonographic examinations were performed with the use of Aplio US system. In total sample, male and female left-handers, CCA-IMT was greater on left side than on right side. In men, both right and left CCA-IMTs, but in women only right IMT, were lower in the left-handers than in the right-handers. Both right and left CCA-IMTs was positively correlated with age in men and in women. Handedness was a significant factor influencing CCA-IMT with adjustment for sex and age. It was greater on left side than on right side, especially in the left-handers. CCA-IMTs were lower in the left-handers than in the right-handers. These results suggest that hemodynamic stress and intimal damage was larger in the left carotid artery, which was larger in the right-handers compared to the left-handers.
Decentralized Navigation and Conflict Avoidance for Aircraft in 3-D Space

We present an algorithm for the distributed navigation and conflict avoidance of nonholonomic aircraft-like agents in 3-D space. The proposed feedback control scheme offers improved applicability to aircraft navigation and compatibility with Air Traffic Management practice with respect to previous work. Our approach aims to maintain a desired horizontal velocity for each aircraft, while limiting the climb or descent angle within bounds according to aircraft performance characteristics. Moreover, the algorithm is designed to favor straight and level flight, resulting in more sensible manoeuvres that require reduced steering effort. The proposed control scheme is based on the Navigation Functions methodology and offers formally guaranteed conflict avoidance and convergence properties. The performance characteristics of our method are demonstrated through simulation results.
Entanglement distillation between solid-state quantum network nodes

A distillation protocol is developed that enhances entanglement between distant nodes of a quantum network. Entangle, swap, purify, repeat The key to a successful quantum internet will be the ability to generate robust entanglement between distant quantum memories. Unavoidable interactions with the environment, however, generally result in the loss of entanglement. Kalb et al. describe an entanglement distillation protocol that could be used to enhance the purity and robustness of entanglement between quantum nodes of a primitive quantum network. Science, this issue p. 928 The impact of future quantum networks hinges on high-quality quantum entanglement shared between network nodes. Unavoidable imperfections necessitate a means to improve remote entanglement by local quantum operations. We realize entanglement distillation on a quantum network primitive of distant electron-nuclear two-qubit nodes. The heralded generation of two copies of a remote entangled state is demonstrated through single-photon–mediated entangling of the electrons and robust storage in the nuclear spins. After applying local two-qubit gates, single-shot measurements herald the distillation of an entangled state with increased fidelity that is available for further use. The key combination of generating, storing, and processing entangled states should enable the exploration of multiparticle entanglement on an extended quantum network.