Miracle-dz/UniFAR-FTUs huggingface dataset:
The functional neuroanatomy of verbal memory in Alzheimer’s disease: [18F]-Fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) correlates of recency and recognition memory

ABSTRACT Introduction: The objective of this study was to distinguish the functional neuroanatomy of verbal learning and recognition in Alzheimer’s disease (AD) using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning task. Method: In 81 Veterans diagnosed with dementia due to AD, we conducted a cluster-based correlation analysis to assess the relationships between recency and recognition memory scores from the CERAD Word Learning Task and cortical metabolic activity measured using [18F]-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET). Results: AD patients (Mini-Mental State Examination, MMSE mean = 20.2) performed significantly better on the recall of recency items during learning trials than of primacy and middle items. Recency memory was associated with cerebral metabolism in the left middle and inferior temporal gyri and left fusiform gyrus (p < .05 at the corrected cluster level). In contrast, recognition memory was correlated with metabolic activity in two clusters: (a) a large cluster that included the left hippocampus, parahippocampal gyrus, entorhinal cortex, anterior temporal lobe, and inferior and middle temporal gyri; (b) the bilateral orbitofrontal cortices (OFC). Conclusions: The present study further informs our understanding of the disparate functional neuroanatomy of recency memory and recognition memory in AD. We anticipated that the recency effect would be relatively preserved and associated with temporoparietal brain regions implicated in short-term verbal memory, while recognition memory would be associated with the medial temporal lobe and possibly the OFC. Consistent with our a priori hypotheses, list learning in our AD sample was characterized by a reduced primacy effect and a relatively spared recency effect; however, recency memory was associated with cerebral metabolism in inferior and lateral temporal regions associated with the semantic memory network, rather than regions associated with short-term verbal memory. The correlates of recognition memory included the medial temporal lobe and OFC, replicating prior studies.
Genetic Diversity and Population History of a Critically Endangered Primate, the Northern Muriqui (Brachyteles hypoxanthus)

Social, ecological, and historical processes affect the genetic structure of primate populations, and therefore have key implications for the conservation of endangered species. The northern muriqui (Brachyteles hypoxanthus) is a critically endangered New World monkey and a flagship species for the conservation of the Atlantic Forest hotspot. Yet, like other neotropical primates, little is known about its population history and the genetic structure of remnant populations. We analyzed the mitochondrial DNA control region of 152 northern muriquis, or 17.6% of the 864 northern muriquis from 8 of the 12 known extant populations and found no evidence of phylogeographic partitions or past population shrinkage/expansion. Bayesian and classic analyses show that this finding may be attributed to the joint contribution of female-biased dispersal, demographic stability, and a relatively large historic population size. Past population stability is consistent with a central Atlantic Forest Pleistocene refuge. In addition, the best scenario supported by an Approximate Bayesian Computation analysis, significant fixation indices (ΦST = 0.49, ΦCT = 0.24), and population-specific haplotypes, coupled with the extirpation of intermediate populations, are indicative of a recent geographic structuring of genetic diversity during the Holocene. Genetic diversity is higher in populations living in larger areas (>2,000 hectares), but it is remarkably low in the species overall (θ = 0.018). Three populations occurring in protected reserves and one fragmented population inhabiting private lands harbor 22 out of 23 haplotypes, most of which are population-exclusive, and therefore represent patchy repositories of the species' genetic diversity. We suggest that these populations be treated as discrete units for conservation management purposes.
Analysis of the inverse kinematics for 5 DOF robot arm using D-H parameters

This paper proposes an algorithm to develop the kinematic model of a 5 DOF robot arm. The formulation of the problem is based on finding the D-H parameters of the arm. Brute Force iterative method is employed to solve the system of non linear equations. The focus of the paper is to obtain the accurate solutions by reducing the root mean square error. The result obtained will be implemented to grip the objects. The trajectories followed by the end effector for the required workspace coordinates are plotted. The methodology used here can be used in solving the problem for any other kinematic chain of up to six DOF.
Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials

Objective. Steady-state visual evoked potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain–computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. Approach. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration. Main results. The Compact-CNN demonstrates across subject mean accuracy of approximately 80%, out-performing current state-of-the-art, hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase- and amplitude-related features associated with the structure of the dataset. Significance. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g. asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.
Analyzing Immunoglobulin Repertoires

Somatic assembly of T cell receptor and B cell receptor (BCR) genes produces a vast diversity of lymphocyte antigen recognition capacity. The advent of efficient high-throughput sequencing of lymphocyte antigen receptor genes has recently generated unprecedented opportunities for exploration of adaptive immune responses. With these opportunities have come significant challenges in understanding the analysis techniques that most accurately reflect underlying biological phenomena. In this regard, sample preparation and sequence analysis techniques, which have largely been borrowed and adapted from other fields, continue to evolve. Here, we review current methods and challenges of library preparation, sequencing and statistical analysis of lymphocyte receptor repertoire studies. We discuss the general steps in the process of immune repertoire generation including sample preparation, platforms available for sequencing, processing of sequencing data, measurable features of the immune repertoire, and the statistical tools that can be used for analysis and interpretation of the data. Because BCR analysis harbors additional complexities, such as immunoglobulin (Ig) (i.e., antibody) gene somatic hypermutation and class switch recombination, the emphasis of this review is on Ig/BCR sequence analysis.
Slow Feature Analysis for Mitotic Event Recognition

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.
Smart Toys Design Opportunities for Measuring Children's Fine Motor Skills Development

Smart tangible toys, designed for hand manipulation, can transform fine motor skills assessment into enjoyable activities which are engaging for children to play (partially) unsupervised. Such toys can support school teachers and parents for early detection of deficiencies in motor skills development of children, as well as objectively monitor the progress of skills development over time. To make a game enjoyable for children with different skills level, these smart toys could offer an adaptive game play. In this paper we describe the design and deployment of a digital board game, equipped with sensors, which we use to explore the potential of using smart toys for fine motor skills assessment in children.
Feature Based Approach to Named Entity Recognition and Linking for Tweets

In this paper, we describe our approach for Named Entity rEcognition and Linking Challenge (NEEL) at the #Micro-posts2016. The task is to automatically recognize entities and their types from English microposts, and link them to corresponding DBpedia 2015 entries. If the resources do not exist, we use NIL identiﬁers instead. The task is unique as twitter data is informal in nature with non-conformational spellings, random contractions and various other noises. For this task, we developed our system using a hybrid model. We have used various existing named entity recognition (NER) systems and combined them with our classiﬁer to improve the results.
Feeding Preferences and the Nutritional Value of Tropical Algae for the Abalone Haliotis asinina

Understanding the feeding preferences of abalone (high-value marine herbivores) is integral to new species development in aquaculture because of the expected link between preference and performance. Performance relates directly to the nutritional value of algae – or any feedstock – which in turn is driven by the amino acid content and profile, and specifically the content of the limiting essential amino acids. However, the relationship between feeding preferences, consumption and amino acid content of algae have rarely been simultaneously investigated for abalone, and never for the emerging target species Haliotis asinina. Here we found that the tropical H. asinina had strong and consistent preferences for the red alga Hypnea pannosa and the green alga Ulva flexuosa, but no overarching relationship between protein content (sum of amino acids) and preference existed. For example, preferred Hypnea and Ulva had distinctly different protein contents (12.64 vs. 2.99 g 100 g−1) and the protein-rich Asparagopsis taxiformis (>15 g 100 g−1 of dry weight) was one of the least preferred algae. The limiting amino acid in all algae was methionine, followed by histidine or lysine. Furthermore we demonstrated that preferences can largely be removed using carrageenan as a binder for dried alga, most likely acting as a feeding attractant or stimulant. The apparent decoupling between feeding preference and algal nutritive values may be due to a trade off between nutritive values and grazing deterrence associated with physical and chemical properties.
Classification of mental arithmetic and resting-state based on Ear-EEG

Electroencephalography (EEG) has been mainly utilized for developing brain-computer interface (BCI) systems. In recent, use of Ear-EEG measured around the ears has been proposed to enhance the practicality of conventional EEG-based BCI systems. Most of BCI systems based on Ear-EEG have used exogenous BCI paradigms employing external stimuli. In this study, we investigated the feasibility of using Ear-EEG in developing an endogenous BCI system that uses self-modulated brain signals. EEG data was measured while subjects performed mental arithmetic (MA) and baseline (BL) task. EEG data analysis was performed after dividing the whole brain area into four regions of interest (frontal, central, occipital, and ear area) to compare their EEG characteristics and classification performance. Similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs, and classification performance was insignificant between them, except occipital area (frontal: 72.6 %, central: 76.7 %, occipital: 82.6 % and ear: 75.6 %). From the results, we could confirm the possibility of using Ear-EEG for developing an endogenous BCI system.
Vision platform for mobile intelligent robot based on 81.6 GOPS object recognition processor

To enable power-efficient object recognition of mobile intelligent robots, 81.6 GOPS object recognition processor is proposed. Based on analysis of scale invariant feature transform (SIFT) algorithm, architecture of the proposed processor is designed to support both task and data level parallelism. 10 processing elements (PEs) are integrated for task parallelism, and each PE is equipped with SIMD instruction for data parallelism as well. In addition, Visual Image Processing memory replaces complex local maximum pixel search operation with a single read operation for further performance gain. With the proposed processor, we also realized vision platform for real-time SIFT computation of mobile robots. The chip operation is tested up to 200 MHz and consumes 540 mW in the vision platform at 1.8 V supply voltage and 100 MHz operation frequency.
Simultaneous determination of glycol ethers and their acetates in cosmetics by gas chromatography with mass spectrometry.

A gas chromatography with mass spectrometry method was developed for the simultaneous determination of ten kinds of glycol ethers and their acetates in cosmetics. The samples were extracted with methanol/ethyl acetate (80:20, v/v), further treated with vortex and ultrasound, and analyzed by gas chromatography with mass spectrometry. The concentration of each analyte was calibrated by the external standard method. Under the optimal conditions, the analytes showed linear relationship in the range of 0.05-25 mg/L with determination coefficients larger than 0.9987. The limits of detection and quantification were in the range of 0.09-0.59 and 0.31-1.95 mg/kg, respectively. The average recoveries of three spiked levels were 80.2-105.4% with intra- and interday precisions of 1.1-6.3 and 1.9-6.5%, respectively. Method validation from different labs confirmed the satisfactory recoveries and precisions. This method shows advantages of simple, high sensitivity, and high recovery, which can be applied to the detection of glycol ethers and acetates in cosmetics.
Morphological Analyzer for Kokborok

Morphological analysis is concerned with retrieving the syntactic and morphological properties or the meaning of a morphologically complex word. Morphological analysis retrieves the grammatical features and properties of an inflected word. However, this paper introduces the design and implementation of a Morphological Analyzer for Kokborok, a resource constrained and less computerized Indian language. A database driven affix stripping algorithm has been used to design the Morphological Analyzer. It analyzes the Kokborok word forms and produces several grammatical information associated with the words. The Morphological Analyzer for Kokborok has been tested on 56732 Kokborok words; thereby an accuracy of 80% has been obtained on a manual check.
Fragile X Mental Retardation Protein Restricts Small Dye Iontophoresis Entry into Central Neurons

Fragile X mental retardation protein (FMRP) loss causes Fragile X syndrome (FXS), a major disorder characterized by autism, intellectual disability, hyperactivity, and seizures. FMRP is both an RNA- and channel-binding regulator, with critical roles in neural circuit formation and function. However, it remains unclear how these FMRP activities relate to each other and how dysfunction in their absence underlies FXS neurological symptoms. In testing circuit level defects in the Drosophila FXS model, we discovered a completely unexpected and highly robust neuronal dye iontophoresis phenotype in the well mapped giant fiber (GF) circuit. Controlled dye injection into the GF interneuron results in a dramatic increase in dye uptake in neurons lacking FMRP. Transgenic wild-type FMRP reintroduction rescues the mutant defect, demonstrating a specific FMRP requirement. This phenotype affects only small dyes, but is independent of dye charge polarity. Surprisingly, the elevated dye iontophoresis persists in shaking B mutants that eliminate gap junctions and dye coupling among GF circuit neurons. We therefore used a wide range of manipulations to investigate the dye uptake defect, including timed injection series, pharmacology and ion replacement, and optogenetic activity studies. The results show that FMRP strongly limits the rate of dye entry via a cytosolic mechanism. This study reveals an unexpected new phenotype in a physical property of central neurons lacking FMRP that could underlie aspects of FXS disruption of neural function. SIGNIFICANCE STATEMENT FXS is a leading heritable cause of intellectual disability and autism spectrum disorders. Although researchers established the causal link with FMRP loss >;25 years ago, studies continue to reveal diverse FMRP functions. The Drosophila FXS model is key to discovering new FMRP roles, because of its genetic malleability and individually identified neuron maps. Taking advantage of a well characterized Drosophila neural circuit, we discovered that neurons lacking FMRP take up dramatically more current-injected small dye. After examining many neuronal properties, we determined that this dye defect is cytoplasmic and occurs due to a highly elevated dye iontophoresis rate. We also report several new factors affecting neuron dye uptake. Understanding how FMRP regulates iontophoresis should reveal new molecular factors underpinning FXS dysfunction.
A dimensionless dynamic contrast enhanced MRI parameter for intra-prostatic tumour target volume delineation: initial comparison with histology

Purpose: T2 weighted and diffusion weighted magnetic resonance imaging (MRI) show promise in isolating prostate tumours. Dynamic contrast enhanced (DCE)-MRI has also been employed as a component in multi-parametric tumour detection schemes. Model-based parameters such as Ktrans are conventionally used to characterize DCE images and require arterial contrast agent (CR) concentration. A robust parameter map that does not depend on arterial input may be more useful for target volume delineation. We present a dimensionless parameter (Wio) that characterizes CR wash-in and washout rates without requiring arterial CR concentration. Wio is compared to Ktrans in terms of ability to discriminate cancer in the prostate, as demonstrated via comparison with histology. Methods: Three subjects underwent DCE-MRI using gadolinium contrast and 7 s imaging temporal resolution. A pathologist identified cancer on whole-mount histology specimens, and slides were deformably registered to MR images. The ability of Wio maps to discriminate cancer was determined through receiver operating characteristic curve (ROC) analysis. Results: There is a trend that Wio shows greater area under the ROC curve (AUC) than Ktrans with median AUC values of 0.74 and 0.69 respectively, but the difference was not statistically significant based on a Wilcoxon signed-rank test (p = 0.13). Conclusions: Preliminary results indicate that Wio shows potential as a tool for Ktrans QA, showing similar ability to discriminate cancer in the prostate as Ktrans without requiring arterial CR concentration.
An Efficient Volumetric Mesh Representation for Real-Time Scene Reconstruction Using Spatial Hashing

Mesh plays an indispensable role in dense realtime reconstruction essential in robotics. Efforts have been made to maintain flexible data structures for 3D data fusion, yet an efficient incremental framework specifically designed for online mesh storage and manipulation is missing. We propose a novel framework to compactly generate, update, and refine mesh for scene reconstruction upon a volumetric representation. Maintaining a spatial-hashed field of cubes, we distribute vertices with continuous value on discrete edges that support O(1) vertex accessing and forbid memory redundancy. By introducing Hamming distance in mesh refinement, we further improve the mesh quality regarding the triangle type consistency with a low cost. Lock-based and lock-free operations were applied to avoid thread conflicts in GPU parallel computation. Experiments demonstrate that the mesh memory consumption is significantly reduced while the running speed is kept in the online reconstruction process.
Thermal Variability Increases the Impact of Autumnal Warming and Drives Metabolic Depression in an Overwintering Butterfly

Increases in thermal variability elevate metabolic rate due to Jensen's inequality, and increased metabolic rate decreases the fitness of dormant ectotherms by increasing consumption of stored energy reserves. Theory predicts that ectotherms should respond to increased thermal variability by lowering the thermal sensitivity of metabolism, which will reduce the impact of the warm portion of thermal variability. We examined the thermal sensitivity of metabolic rate of overwintering Erynnis propertius (Lepidoptera: Hesperiidae) larvae from a stable or variable environment reared in the laboratory in a reciprocal common garden design, and used these data to model energy use during the winters of 1973–2010 using meteorological data to predict the energetic outcomes of metabolic compensation and phenological shifts. Larvae that experienced variable temperatures had decreased thermal sensitivity of metabolic rate, and were larger than those reared at stable temperatures, which could partially compensate for the increased energetic demands. Even with depressed thermal sensitivity, the variable environment was more energy-demanding than the stable, with the majority of this demand occurring in autumn. Autumn phenology changes thus had disproportionate influence on energy consumption in variable environments, and variable-reared larvae were most susceptible to overwinter energy drain. Therefore the energetic impacts of the timing of entry into winter dormancy will strongly influence ectotherm fitness in northern temperate environments. We conclude that thermal variability drives the expression of metabolic suppression in this species; that phenological shifts will have a greater impact on ectotherms in variable thermal environments; and that E. propertius will be more sensitive to shifts in phenology in autumn than in spring. This suggests that increases in overwinter thermal variability and/or extended, warm autumns, will negatively impact all non-feeding dormant ectotherms which lack the ability to suppress their overwinter metabolic thermal sensitivity.