Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Intracellular Trafficking Dynamics of Brain-Derived Neurotrophic Factor Complexes in the Neuronal Soma Revealed by Single Quantum Dot Tracking

Accumulating evidence underscores the importance of ligand-receptor dynamics in shaping cellular signaling. In the nervous system, growth factor-activated Trk receptor trafficking serves to convey biochemical signaling that underlies fundamental neural functions. Focus has been placed on axonal trafficking but little is known about growth factor-activated Trk dynamics in the neuronal soma, particularly at the molecular scale, due in large part to technical hurdles in observing individual growth factor-Trk complexes for long periods of time inside live cells. Quantum dots (QDs) are intensely fluorescent nanoparticles that have been used to study the dynamics of ligand-receptor complexes at the plasma membrane but the value of QDs for investigating ligand-receptor intracellular dynamics has not been well exploited. The current study establishes that QD conjugated brain-derived neurotrophic factor (QD-BDNF) binds to TrkB receptors with high specificity, activates TrkB downstream signaling, and allows single QD tracking capability for long recording durations deep within the soma of live neurons. QD-BDNF complexes undergo internalization, recycling, and intracellular trafficking in the neuronal soma. These trafficking events exhibit little time-synchrony and diverse heterogeneity in underlying dynamics that include phases of sustained rapid motor transport without pause as well as immobility of surprisingly long-lasting duration (several minutes). Moreover, the trajectories formed by dynamic individual BDNF complexes show no apparent end destination; BDNF complexes can be found meandering over long distances of several microns throughout the expanse of the neuronal soma in a circuitous fashion. The complex, heterogeneous nature of neuronal soma trafficking dynamics contrasts the reported linear nature of axonal transport data and calls for models that surpass our generally limited notions of nuclear-directed transport in the soma. QD-ligand probes are poised to provide understanding of how the molecular mechanisms underlying intracellular ligand-receptor trafficking shape cell signaling under conditions of both healthy and dysfunctional neurological disease models.
Modelling the human rhesus proteins: implications for structure and function

The mammalian rhesus (Rh) proteins that carry the Rh blood group antigens of red blood cells are related to the ammonium channel (Amt) proteins found in both pro‐ and eukaryotes. However, despite their clinical importance the structure of the Rh antigens is presently unknown. We have constructed homology models of the human Rh proteins, RhD and RhAG using the structure of the Escherichia coli ammonia channel AmtB as a template, together with secondary structure predictions and the extensive available biochemical data for the Rh proteins. These models suggest that RhAG and the homologous non‐erythrocyte Rhesus glycoproteins, RhBG and RhCG, have a very similar channel architecture to AmtB. By comparison, RhD and RhCE have a different arrangement of residues, indicating that if they function as ammonia channels at all, they must do so by a different mechanism. The E. coli AmtB protein is a homotrimer and our models provoke a reassessment of the widely accepted tetrameric model of the organisation of the erythrocyte Rh complex. A critical analysis of previously published data, together with sequencing yield data, lead us to suggest that the erythrocyte Rh complex could indeed also be trimeric.
Brief announcement: concurrent non-commutative boosted transactions

Transactional boosting is a methodology which improves transaction performance by using data-structure commutativity and abstract locks for synchronization. We announce a method for concurrent execution of non-commuting operations from distinct boosted transactions. Abstract locks are passed from one transaction to the next, and dependencies are created, enforcing certain commit orders. We summarize the approach and describe novel techniques for (i) performing recovery lazily and (ii) detecting cyclic dependencies.
Influence of Lithography Distortion due to Metal Fill on CA

In the process of integrated circuit design and manufacturing, dummy metal fill can improve the planarity of layout after Chemical Mechanical Polishing (CMP). However, it will also cause lithography distortion and Critical Area (CA) variation. This paper compares and analyzes the influences of lithography distortion due to metal fill on CA from the perspectives of different defect particles based on 45nm technology node. The results indicate that dummy metal fill can increase open CA after lithography and the defect particle with the diameter of 0.066um leads to the largest increment percentage of open CA, which will take up almost 10%. This paper is instructive in researching dummy metal fill and CA or related fields in the future.
A Zeroth-Order Resonance Patch Antenna for Dual Band Operations with Broad E-plane Beam Widths

this paper, design of probe fed zeroth order resonance (ZOR) antenna with dual frequency operation is presented. The mushroom structure is placed at the center of the geometry and the grounded via to have the desired ZOR response. The feed is connected between the inner ring and the mushroom structure. There are several ZOR antennas are available in literature with only one resonant frequency. However, in this work we presented ZOR antenna which excites dual resonances. The second resonance is obtained by placing two stubs and an outer ring. Also, it may be noted that we get nearly broader E-plane beamwidths (112 and 90 degrees) at both the resonance frequencies. The bandwidth of 7.8% at second resonant frequency was obtained as compared to the conventional patch antennas (2~3%). At first resonance only narrow band performance was observed. Measured results fairly agree with the simulated data.
Declarative Data Merging with Conflict Resolution

Database integration is a growing and increasingly important field in both research and industry. Integration requires many steps from initial schema integration and schema mapping, to data scrubbing and cleansing, and finally to data merging. While much research has concentrated on the first steps performed at schema level, there are only few publications about actual, practical merging of the data in an integrated database or in a query against multiple databases. When merging data, especially data from autonomous sources, there is a large potential for decreasing the quality of the merged data, even below the level of the original sources. The main reasons for decreased quality are data conflicts among the sources. To address this problem, we define resolution functions merging conflicting data. We present several alternatives of merging relational data sources with common queries through grouping & aggregating and through partitioning & joining. The resulting queries use resolution functions and can be used to migrate data from multiple sources to a target database, or to define an integrating view on multiple sources. We describe and analyze the advantages of the different approaches, and describe our practical solution in the framework of a schema mapping and data transformation tool.
Attention-deficit hyperactivity disorder in adults

Attention-deficit hyperactivity disorder (ADHD) is an established diagnosis in children but there is a lack of agreement about its validity as a distinct entity in adults. Literature suggests that between one-third and two-thirds of children diagnosed with ADHD continue to manifest symptoms into adulthood. An adult diagnosis should be done on the basis of a thorough assessment, structured and semi-structured clinical interview, and with a complete understanding of the symptoms that manifest in adults. This may be supplemented by the use of rating scales. We present a review of the literature covering aetiology, clinical presentations, diagnostic evaluation and management of ADHD in adults.
Hyperpolarized lithium‐6 as a sensor of nanomolar contrast agents

Lithium is widely used in psychotherapy. The 6Li isotope has a long intrinsic longitudinal relaxation time T1 on the order of minutes, making it an ideal candidate for hyperpolarization experiments. In the present study we demonstrated that lithium‐6 can be readily hyperpolarized within 30 min, while retaining a long polarization decay time on the order of a minute. We used the intrinsically long relaxation time for the detection of 500 nM contrast agent in vitro. Hyperpolarized lithium‐6 was administered to the rat and its signal retained a decay time on the order of 70 sec in vivo. Localization experiments imply that the lithium signal originated from within the brain and that it was detectable up to 5 min after administration. We conclude that the detection of submicromolar contrast agents using hyperpolarized NMR nuclei such as 6Li may provide a novel avenue for molecular imaging. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.
NMR-based metabolomics and breath studies show lipid and protein catabolism during low dose chronic T1AM treatment

3‐Iodothyronamine (T1AM), an analog of thyroid hormone, is a recently discovered fast‐acting endogenous metabolite. Single high‐dose treatments of T1AM have produced rapid short‐term effects, including a reduction of body temperature, bradycardia, and hyperglycemia in mice.
Protecting consumer privacy from electric load monitoring

The smart grid introduces concerns for the loss of consumer privacy; recently deployed smart meters retain and distribute highly accurate profiles of home energy use. These profiles can be mined by Non Intrusive Load Monitors (NILMs) to expose much of the human activity within the served site. This paper introduces a new class of algorithms and systems, called Non Intrusive Load Leveling (NILL) to combat potential invasions of privacy. NILL uses an in-residence battery to mask variance in load on the grid, thus eliminating exposure of the appliance-driven information used to compromise consumer privacy. We use real residential energy use profiles to drive four simulated deployments of NILL. The simulations show that NILL exposes only 1.1 to 5.9 useful energy events per day hidden amongst hundreds or thousands of similar battery-suppressed events. Thus, the energy profiles exhibited by NILL are largely useless for current NILM algorithms. Surprisingly, such privacy gains can be achieved using battery systems whose storage capacity is far lower than the residence's aggregate load average. We conclude by discussing how the costs of NILL can be offset by energy savings under tiered energy schedules.
Detection of Multivariate Cyclostationarity

This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loève spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.
Structural and Resting State Functional Connectivity of the Subthalamic Nucleus: Identification of Motor STN Parts and the Hyperdirect Pathway

Deep brain stimulation (DBS) for Parkinson’s disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN’s connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called “hyperdirect” pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.
A Novel Voltage Stabilization Control Scheme for Stand-alone Wind Energy Conversion Systems

This paper proposes a novel low cost FACTS based voltage stabilization scheme for a stand-alone wind energy conversion systems using self excited squirrel-cage induction generator (SEIG) driven by a wind turbine and interfaced to electric load. The new control scheme is designed not only to ensure bus voltage stabilization, but also to improve energy utilization using a low-cost PWM-switched modulated power filter compensator (MPFC) driven by a tri-loop dynamic error driven error scaled regulator. Numerous validation cases were simulated under different load types and excursions including linearized, motorized and nonlinear type loads. The proposed FACTS scheme is simulated and satisfactory results are obtained showing the applicability of the scheme for supplying electricity to remote areas such as isolated villages, heating, water pumping, ventilation and air conditioning.
Research on automatic spraying of single-walled carbon nanotubes and detection of spraying effects

Single-walled carbon nanotubes (SWNTs) have been introduced as compliant electrodes for dielectric elastomers (DEs) due to fault tolerance. To acquire a better electrostrictive strain and longer lifetime, it is essential to obtain a certain and uniform width of the SWNT electrode. To ensure uniform width manually, a small flux and longer time are necessary. Moreover, it is difficult to control the width of the electrode for the randomness of manual spraying. Therefore, a new type of automatic spraying process is presented in this paper. The width and homogeneity of the electrode can be easily controlled by certain parameters of the process. Two methods for detecting the homogeneity of the electrode are introduced in this paper: Measurement of surface resistance and luminosity. The coefficient of variation (CV) values detected by the two methods are virtually equal and less than 8%, which shows the feasibility of the detection method and homogeneity of automatic spraying. The speed of automatic spraying is 102 mm2/s, which is higher than that of manual spraying. The spraying process and the method used to detect homogeneity in this paper provide a reference for the relevant processes.
The type III secretion system apparatus determines the intracellular niche of bacterial pathogens

Significance Bacterial pathogens use a variety of strategies to evade host cell innate immune responses. For some, this includes the establishment of an intracellular replicative niche. Although many intracellular pathogens remodel phagosomes to support bacterial replication, others lyse their internalization vacuole to reside within the host cell cytosol. Little is currently known regarding how bacteria, particularly Gram-negative pathogens, mediate phagosomal escape. Using complementary reductionist and functional interchangeability experimental approaches, we demonstrate that the type III secretion system machinery itself directly modulates the extent to which bacteria escape from phagosomes. Given the high prevalence of type III secretion systems among intracellular bacterial pathogens, these studies have identified a potential means by which the intracellular niche of Gram-negative bacteria is defined. Upon entry into host cells, intracellular bacterial pathogens establish a variety of replicative niches. Although some remodel phagosomes, others rapidly escape into the cytosol of infected cells. Little is currently known regarding how professional intracytoplasmic pathogens, including Shigella, mediate phagosomal escape. Shigella, like many other Gram-negative bacterial pathogens, uses a type III secretion system to deliver multiple proteins, referred to as effectors, into host cells. Here, using an innovative reductionist-based approach, we demonstrate that the introduction of a functional Shigella type III secretion system, but none of its effectors, into a laboratory strain of Escherichia coli is sufficient to promote the efficient vacuole lysis and escape of the modified bacteria into the cytosol of epithelial cells. This establishes for the first time, to our knowledge, a direct physiologic role for the Shigella type III secretion apparatus (T3SA) in mediating phagosomal escape. Furthermore, although protein components of the T3SA share a moderate degree of structural and functional conservation across bacterial species, we show that vacuole lysis is not a common feature of T3SA, as an effectorless strain of Yersinia remains confined to phagosomes. Additionally, by exploiting the functional interchangeability of the translocator components of the T3SA of Shigella, Salmonella, and Chromobacterium, we demonstrate that a single protein component of the T3SA translocon—Shigella IpaC, Salmonella SipC, or Chromobacterium CipC—determines the fate of intracellular pathogens within both epithelial cells and macrophages. Thus, these findings have identified a likely paradigm by which the replicative niche of many intracellular bacterial pathogens is established.
Recall and recognition of verbal paired associates in early Alzheimer's disease

The primary impairment in early Alzheimer's disease (AD) is encoding/consolidation, resulting from medial temporal lobe (MTL) pathology. AD patients perform poorly on cued-recall paired associate learning (PAL) tasks, which assess the ability of the MTLs to encode relational memory. Since encoding and retrieval processes are confounded within performance indexes on cued-recall PAL, its specificity for AD is limited. Recognition paradigms tend to show good specificity for AD, and are well tolerated, but are typically less sensitive than recall tasks. Associate-recognition is a novel PAL task requiring a combination of recall and recognition processes. We administered a verbal associate-recognition test and cued-recall analogue to 22 early AD patients and 55 elderly controls to compare their ability to discriminate these groups. Both paradigms used eight arbitrarily related word pairs (e.g., pool-teeth) with varying degrees of imageability. Associate-recognition was equally effective as the cued-recall analogue in discriminating the groups, and logistic regression demonstrated classification rates by both tasks were equivalent. These preliminary findings provide support for the clinical value of this recognition tool. Conceptually it has potential for greater specificity in informing neuropsychological diagnosis of AD in clinical samples but this requires further empirical support. (JINS, 2008, 14, 591–600.)
Energy Efficient Clustering using a Wake-up Receiver

We present a new energy-saving method for creating node clusters with similar sensor readings. The idea is to wake up a node/main transceiver only when data correlated to the node’s own data is transmitted. This is achieved by encoding relevant parts of local data in a wake-up receiver’s data pattern. Through this new protocol the building of clusters becomes highly energy-efficient, even if re-clustering is required, the energy consumed for clustering is still low. For environments where re-clustering is often necessary a clustering scheme can be used to further reduce the energy consumption. This further reduction can be achieved efficiently through the transmission of the sensor readings from only one node of the cluster to the data sink. One reading is therefore sufficient to approximate the readings of the whole cluster because all nodes within this cluster have similar sensor readings.
Mobility-Assisted Spatiotemporal Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability of physical reality. This paper explores efficient use of mobile sensors to address the limitations of static WSNs in target detection. We propose a data fusion model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.
Learning to Detect Concepts from Webly-Labeled Video Data

Learning detectors that can recognize concepts, such as people actions, objects, etc., in video content is an interesting but challenging problem. In this paper, we study the problem of automatically learning detectors from the big video data on the web without any additional manual annotations. The contextual information available on the web provides noisy labels to the video content. To leverage the noisy web labels, we propose a novel method called WEbly-Labeled Learning (WELL). It is established on two theories called curriculum learning and self-paced learning and exhibits useful properties that can be theoretically verified. We provide compelling insights on the latent non-convex robust loss that is being minimized on the noisy data. In addition, we propose two novel techniques that not only enable WELL to be applied to big data but also lead to more accurate results. The efficacy and the scalability of WELL have been extensively demonstrated on two public benchmarks, including the largest multimedia dataset and the largest manually-labeled video set. Experimental results show that WELL significantly outperforms the state-of-the-art methods. To the best of our knowledge, WELL achieves by far the best reported performance on these two webly-labeled big video datasets.