Miracle-dz/UniFAR-FTUs huggingface dataset:
Mortality rates in community-dwelling Tanzanians with dementia and mild cognitive impairment: a 4-year follow-up study.

BACKGROUND we have previously conducted a community-based prevalence study of dementia in older adults living in the rural Hai district of Tanzania. The aim of this study was to record mortality rates at 4 years post-diagnosis, of those with dementia, mild cognitive impairment (MCI) and no cognitive impairment. METHODS during Phase I of the prevalence study, 1,198 people aged 70 years and over were screened, and a stratified sample of 296 was assessed for the presence of dementia or MCI in Phase II. Seventy-eight people had dementia and 46 had MCI. Four years after diagnosis, we attempted to follow-up all those seen in Phase II and record all deaths. RESULTS of the 296, follow-up data were available for 287 (97.0%), including 77 with dementia and 45 with MCI. Of the 172 with no cognitive impairment, 165 (95.9%) were followed up and a sample of 89 people selected as representative of the background population. Forty-eight people with dementia (62.3%), 19 with MCI (42.2%) and 11 with no cognitive impairment (12.4%) had died at 4-year follow-up. After adjusting for the effects of age, gender and education, the hazard ratio was 6.33 (95% CI 3.19-12.58) for dementia and 3.57 (95% CI 1.64-7.79) for MCI relative to people with no cognitive impairment. Mortality rates were highest in those with vascular dementia. CONCLUSION dementia and MCI were associated with excess mortality relative to those with no cognitive impairment.
Understanding Solid State Physics

Introduction Crystal Clear: Bonding and Crystal Structures The Rejection of Perfection: Defects, Amorphous Materials, and Polymers Stressed Out: The Mechanical Properties of Solids In, Out, Shake It All About: Diffraction, Phonons, and Thermal Properties of Solids Unable to Resist: Metals, Semiconductors, and Superconductors Chips with Everything: Semiconductor Devices and Dielectrics Living in a Magnetic World: Magnetism and Its Applications Appendix A: Some Useful Maths Appendix B: Vibrations and Waves Appendix C: Revision of Atomic Physics Appendix D: Revision of Quantum Mechanics Appendix E: Revision of Statistical Mechanics Appendix F: Glossary of Terms Index
Anchoring high concentrations of SynGAP at postsynaptic densities via liquid-liquid phase separation

ABSTRACT SynGAP, encoded by SYNGAP1, is a Ras/Rap GTPase activator specifically expressed in the nervous systems. SynGAP is one of the most abundant proteins in the postsynaptic densities (PSDs) of excitatory synapses and acts as a critical synaptic activity brake by tuning down synaptic GTPase activities. Mutations of SYNGAP1 have been frequently linked to brain disorders including intellectual disability, autisms, and seizure. SynGAP has been shown to undergo fast dispersions from synapses in response to stimulations, a strategy that neurons use to control the specific activities of the enzyme within the tiny, semi-open compartments in dendritic spines. However, the mechanism governing the activity-dependent synaptic localization modulations of SynGAP is poorly understood. It has been shown recently that SynGAP α1, via specifically binding to PSD-95, can undergo liquid-liquid phase separation forming membraneless, condensed protein-rich sub-compartments. This phase transition-mediated, PSD-95-dependent synaptic enrichment of SynGAP α1 not only suggests a dynamic anchoring mechanism of the protein within the PSD, but also implies a new model for the PSD formation in living neurons.
SC‐OCR: similarity‐based clustering and optimum cache replacement approach

Big data is a new term used to identify the large scale and complex datasets. Big data is now rapidly expanding in all science and engineering domains, owing to the fast development of networking, data storage, and data collection capacity. Big data mining is the capability of extracting useful information from these large datasets. Nowadays, the integration of cloud computing with the data mining for the big data mining process is a challenging task. In order to process the huge amount of data, it is necessary to concentrate the improvement on the big data computation. Most of the existing approaches use the MapReduce to compute the big data. The increase in the computational cost and memory consumption are the main drawbacks of the existing approaches. To overcome these limitations, this paper proposes a similarity‐based clustering and optimum cache replacement approach for big data computing applications. The job recovery process is initiated by copying the data in the cloud server and forwarding the data copy for further processing. Then, the job is divided into clusters based on the similarity‐based clustering approach. Finally, the cache concept is introduced with the optimum cache replacement algorithm to avoid repeated execution of the jobs by queue management. The proposed approach is compared with the existing Spark and Hadoop approaches. The proposed approach achieves better performance in terms of iteration time, query response time, job completion time, and clustering accuracy. Copyright © 2016 John Wiley & Sons, Ltd.
Ieee Transactions on Pattern Analysis and Machine Intelligence Learning And-or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation

—This paper presents a method for learning an And-Or model to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). Firstly, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on CAD simulations. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Secondly, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets — the KITTI dataset [1], the PASCAL VOC2007 car dataset [2], and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation — the PASCAL VOC2006 car dataset [2], the 3D car dataset [3], and the PASCAL3D+ car dataset [4]. Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation.
Buying Time for Resource-Bounded Planning

To react appropriately in hard real-time environments, a system must either employ fast dynamic planning or recall a pre-built reactive response. For complex problems, real-time, accurate dynamic planning may be impossible, and the complete set of possible reactions may be too large to build and store in advance. In this paper, we propose combining offiine and online planning such that a set of offline plans are built and stored to maximize the amount of time available for any subsequent online planning. We explore this concept in CIRCA, the Cooperative Intelligent Real-time Control Architecture, which has concentrated on guaranteeing failure avoidance via a combination of planning, scheduling, and real-time plan execution. We are in the process of implementing a plan cache which will increase CIRCA’s overall likelihood of success in timeconstrained situations, and we illustrate potential gains from these modifications using a simple example from the fully-automated aircraft domain.
Heart rate controlled music recommendation for low stress air travel

Long distance travel is an unusual activity for humans. The economical cabin environment (low air circulation, limited space, low humidity, etc.) during the long haul flights causes discomfort and even stress for many passengers. In-flight video and music systems are commonly available to improve the comfort level of the passengers. However, current in-flight music systems do not explore how the content can be used to reduce passengers stress. Most of these systems are designed and implemented assuming a homogeneous passenger group that has similar tastes and desires. In this paper, we present a heart rate controlled in-flight music recommendation system for reducing the stress during air travel. The system recommends personalized music playlists to the passengers and attempts to keep their heart rate in a normal range with these playlists. Experiments in a simulated long haul flight cabin environment find that the passengers’ stress can indeed be significantly reduced through listening to the recommended music playlists.
Designing Tree-Based Barrier Synchronization on 2D Mesh Networks

In this paper, we consider a tree-based routing scheme for supporting barrier synchronization on scalable parallel computers with a 2D mesh network. Based on the characteristics of a standard programming interface, the scheme builds a collective synchronization (CS) tree among the participating nodes using a distributed algorithm. When the routers are set up properly with the CS tree information, barrier synchronization can be accomplished very efficiently by passing simple messages. Performance evaluations show that our proposed method performs better than previous path-based approaches and is less sensitive to variations in group size and startup delay. However, our scheme has the extra overhead of building the CS tree. Thus, it is more suitable for parallel iterative computations in which the same barrier is invoked repetitively.
Text Simplification using Typed Dependencies: A Comparision of the Robustness of Different Generation Strategies

We present a framework for text simplification based on applying transformation rules to a typed dependency representation produced by the Stanford parser. We test two approaches to regeneration from typed dependencies: (a) gen-light, where the transformed dependency graphs are linearised using the word order and morphology of the original sentence, with any changes coded into the transformation rules, and (b) gen-heavy, where the Stanford dependencies are reduced to a DSyntS representation and sentences are generating formally using the RealPro surface realiser. The main contribution of this paper is to compare the robustness of these approaches in the presence of parsing errors, using both a single parse and an n-best parse setting in an overgenerate and rank approach. We find that the gen-light approach is robust to parser error, particularly in the n-best parse setting. On the other hand, parsing errors cause the realiser in the gen-heavy approach to order words and phrases in ways that are disliked by our evaluators.
Efficient generation of pre-silicon MOS model parameters for early circuit design

The technology development cycle continues to shrink, which very often requires evaluation of circuit design and technology choices using circuit simulators at the time when no real silicon is available. In this paper, we present an efficient methodology for generating pre-silicon device models for advanced CMOS processes. The methodology allows accurate prediction of the full MOS I-V characteristics for the future technologies combining a constraint backpropagation algorithm based upon a few critical specifications, physical models for the advanced device phenomena, and the empirical data from devices of an existing technology. The methodology has been tested on two CMOS production technologies. Good prediction results are achieved: for nMOS the rms error is 1%-2%, for pMOS it is 2%-4%.
A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data

This study proposes a Deep Belief Network model to classify traffic flow states. The model is capable of processing massive, high-density, and noise-contaminated data sets generated from smartphone sensors. The statistical features of Vehicle acceleration, angular acceleration, and GPS speed data, recorded by smartphone software, are analyzed, and then used as input for traffic flow state classification. Data from a five-day experiment is used to train and test the proposed model. A total of 747,856 sets of data are generated and used for both traffic flow states classification and sensitivity analysis of input variables. The result shows that the proposed Deep Belief Network model is superior to traditional machine learning methods in both classification performance and computational efficiency.
Cascaded classification of gender and facial expression using active appearance models

This paper presents an approach to recognising the gender and expression of face images by means of active appearance models (AAM). Features extracted by a trained AAM are used to construct support vector machine (SVM) classifiers for 4 elementary emotional states (happy, angry, sad, neutral). These classifiers are arranged into a cascade structure in order to optimise overall recognition performance. Furthermore, it is shown how performance can be further improved by first classifying the gender of the face images using an SVM trained in a similar manner. Both gender-specific expression classification and expression-specific gender classification cascades are considered, with the former yielding better recognition performance. We conclude that there are gender-specific differences in the appearance of facial expressions that can be exploited for automated recognition, and that cascades are an efficient and effective way of performing multi-class recognition of facial expressions
A Novel Low-Profile High-Gain UHF Antenna Using High-Impedance Surfaces

A novel wideband low-profile planar antenna design based on the use of a high-impedance surface (HIS) is presented for digital television (DTV) reception in the UHF band. The proposed design is based on a wideband monopole antenna in close proximity to an artificial ground plane, composed of an array of square metal loops at the top, an air gap and a ground plane at the bottom, without grounded vias. Low-cost substrates are employed in the design: rigid PVC for the antenna and foamed PVC for the artificial ground plane. The advantages of the proposed design for DTV applications are high gain, low profile, light weight, low cost and large bandwidth. Details of the proposed antenna design are described, and measurements together with a comparison to a conventional antenna with PEC ground plane are presented.
Maps of Visual Space in Human Occipital Cortex Are Retinotopic, Not Spatiotopic

We experience the visual world as phenomenally invariant to eye position, but almost all cortical maps of visual space in monkeys use a retinotopic reference frame, that is, the cortical representation of a point in the visual world is different across eye positions. It was recently reported that human cortical area MT (unlike monkey MT) represents stimuli in a reference frame linked to the position of stimuli in space, a “spatiotopic” reference frame. We used visuotopic mapping with blood oxygen level-dependent functional magnetic resonance imaging signals to define 12 human visual cortical areas, and then determined whether the reference frame in each area was spatiotopic or retinotopic. We found that all 12 areas, including MT, represented stimuli in a retinotopic reference frame. Although there were patches of cortex in and around these visual areas that were ostensibly spatiotopic, none of these patches exhibited reliable stimulus-evoked responses. We conclude that the early, visuotopically organized visual cortical areas in the human brain (like their counterparts in the monkey brain) represent stimuli in a retinotopic reference frame.
Phase Interval Value Analysis for the Motor Imagery Task in BCI

In neuroscience, phase is assumed to contain more important information about the neural activity than amplitude. However, the most exploited feature in electroencephalogram (EEG) based brain computer interface (BCI) is the amplitude change, phase has been largely ignored, and only phase locking values (PLV) has been introduced in EEG classification recently. In this paper, we define phase interval value (PIV) to explore the phase information of EEG from a new perspective and propose a computational model based on the ordered Parallel Factors (PARAFAC) algorithm to extract feature from multi-way PIV data for single trial EEG classification. Application to the motor imagery task demonstrates that PIV is quite effective for EEG classification, providing significant and discriminative features in spatial and spectral dimension. PIV might become an important new tool in the analysis of EEG phase characteristic, and has the great potential use in BCI.
The roles of ERAS during cell lineage specification of mouse early embryonic development

Eras encodes a Ras-like GTPase protein that was originally identified as an embryonic stem cell-specific Ras. ERAS has been known to be required for the growth of embryonic stem cells and stimulates somatic cell reprogramming, suggesting its roles on mouse early embryonic development. We now report a dynamic expression pattern of Eras during mouse peri-implantation development: its expression increases at the blastocyst stage, and specifically decreases in E7.5 mesoderm. In accordance with its expression pattern, the increased expression of Eras promotes cell proliferation through controlling AKT activation and the commitment from ground to primed state through ERK activation in mouse embryonic stem cells; and the reduced expression of Eras facilitates primitive streak and mesoderm formation through AKT inhibition during gastrulation. The expression of Eras is finely regulated to match its roles in mouse early embryonic development during which Eras expression is negatively regulated by the β-catenin pathway. Thus, beyond its well-known role on cell proliferation, ERAS may also play important roles in cell lineage specification during mouse early embryonic development.
Three-dimensional Mapping of Unitary Synaptic Connections by Two-photon Macro Photolysis of Caged Glutamate Slice Preparation

Three-dimensional mapping of unitary synaptic connections by two-photon macro photolysis of caged glutamate.stand the precise microarchitecture of the cortical circuitry, it is crucial to know the distribution of synaptic connections and their synaptic strengths at the level of a single cell, rather than a group of cells. Here, we describe a new application of two-photon photolysis of caged glutamate that enabled us to induce an action potential in only a small number (about five) of pyramidal neurons by increasing the volume of two-photon excitation by reducing the effective numerical aperture of the objective. We performed whole cell patch-clamp recordings from layer 2/3 pyramidal neurons in the rat visual cortex and stimulated many neurons in a large three-dimensional space (ϳ600 ϫ 600 ϫ 100 ␮m) including neurons in layers 2/3 and 4 using this new technique. We mapped the density and amplitude of unitary excitatory postsynaptic currents and found that the basic microarchi-tecture of excitatory synaptic connections consists of two regions: a columnar, dense core region with a radius of 150 ␮m and an outer, sparse region. The dense core region includes the majority of strong synaptic connections in layer 2/3. Our results reveal the columnar organization of synaptic connectivity in the rat visual cortex, where functional columns have not been clearly demonstrated. Thus this technique will be a uniquely powerful tool for quantifying synaptic connectivity and manipulating neural activity at the single-cell level. The functional specificity of neurons is similar within the columnar organization spanning from the pia to the white matter (Hubel and Wiesel 1962; Mountcastle 1957). However, each neuron is not a homogeneous constituent of the cluster of neighboring neurons. In the cat visual cortex, each excitatory neuron connects to a small fraction of the neighboring neurons (Thomson et al. 2002) and neighboring neurons with the same orientation preference seldom have similar selectivities in other features (DeAngelis et al. 1999). In addition, since the border between columns with opposite direction preferences is extraordinarily sharp, neighboring neurons on each side of the border have opposite direction selectivity (Ohki et al. 2005). In the rat visual cortex, orientation preference often differs between adjacent neurons and the distance between cells has no correlation with their relative orientation or direction preferences (Girman et al. 1999; Ohki et al. 2005). Thus to reveal the synaptic connectivity patterns that generate the functional specificity of a neuron, it is necessary to quantify a wide distribution …
Anogenital Distance, a Biomarker of Prenatal Androgen Exposure Is Associated With Prostate Cancer Severity

Anogenital distance (AGD), the distance from the centre of the anus to the genitals, is a sexually dimorphic phenotype in mammals. Experimental studies have shown that AGD is a biomarker of prenatal androgen exposure during the masculinisation period of development. The aim of this study is to assess the relationship between anogenital distance (AGD), as an indirect marker of prenatal hormonal environment, and prostate cancer (PCa) severity.
Survival transcriptome in the coenzyme Q10 deficiency syndrome is acquired by epigenetic modifications: a modelling study for human coenzyme Q10 deficiencies

Objectives Coenzyme Q10 (CoQ10) deficiency syndrome is a rare condition that causes mitochondrial dysfunction and includes a variety of clinical presentations as encephalomyopathy, ataxia and renal failure. First, we sought to set up what all have in common, and then investigate why CoQ10 supplementation reverses the bioenergetics alterations in cultured cells but not all the cellular phenotypes. Design Modelling study This work models the transcriptome of human CoQ10 deficiency syndrome in primary fibroblast from patients and study the genetic response to CoQ10 treatment in these cells. Setting Four hospitals and medical centres from Spain, Italy and the USA, and two research laboratories from Spain and the USA. Participants Primary cells were collected from patients in the above centres. Measurements We characterised by microarray analysis the expression profile of fibroblasts from seven CoQ10-deficient patients (three had primary deficiency and four had a secondary form) and aged-matched controls, before and after CoQ10 supplementation. Results were validated by Q-RT-PCR. The profile of DNA (CpG) methylation was evaluated for a subset of gene with displayed altered expression. Results CoQ10-deficient fibroblasts (independently from the aetiology) showed a common transcriptomic profile that promotes cell survival by activating cell cycle and growth, cell stress responses and inhibiting cell death and immune responses. Energy production was supported mainly by glycolysis while CoQ10 supplementation restored oxidative phosphorylation. Expression of genes involved in cell death pathways was partially restored by treatment, while genes involved in differentiation, cell cycle and growth were not affected. Stably demethylated genes were unaffected by treatment whereas we observed restored gene expression in either non-methylated genes or those with an unchanged methylation pattern. Conclusions CoQ10 deficiency induces a specific transcriptomic profile that promotes cell survival, which is only partially rescued by CoQ10 supplementation.