Morphology and function of the forelimb in arboreal frogs: specializations for grasping ability.

Frogs are characterized by a unique morphology associated with their saltatory lifestyle. Although variation in the form and function of the pelvic girdle and associated appendicular system related to specialized locomotor modes such as swimming or burrowing has been documented, the forelimbs have typically been viewed as relatively unspecialized. Yet, previous authors have noted versatility in forelimb function among arboreal frogs associated with feeding. Here we study the morphology and function of the forelimb and hand during locomotion in two species of arboreal frogs (Litoria caerulea and Phyllomedusa bicolor). Our data show a complex arrangement of the distal forelimb and hand musculature with some notable differences between species. Analyses of high‐speed video and video fluoroscopy recordings show that forelimbs are used in alternating fashion in a diagonal sequence footfall pattern and that the position of the hand is adjusted when walking on substrates of different diameters. Electromyographic recordings show that the flexors of the hand are active during substrate contact, suggesting the use of gripping to generate a stabilizing torque. Measurements of grasping forces in vivo and during stimulation experiments show that both species, are capable of executing a so‐called power grip but also indicates marked differences between species, in the magnitude of forces generated. Stimulation experiments showed an increased control of digit flexion in the more specialized of the two species, allowing it to execute a precision grip paralleled only by that seen in primates.Fil: Manzano, Adriana Silvina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Abdala, Virginia Sara Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Biodiversidad Neotropical. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Biodiversidad Neotropical. Instituto de Biodiversidad Neotropical; ArgentinaFil: Herrel, Anthony. University of Antwerp; Bélgic

Reducing progression of knee OA features assessed by MRI in overweight and obese women: Secondary outcomes of a preventive RCT

Objective: To evaluate the preventive effects of a randomized controlled trial on progression of Magnetic Resonance Imaging (MRI) features of knee osteoarthritis (OA) in overweight and obese women. Design: In a 2 × 2 factorial design, 2.5 years effects of a diet and exercise program and of glucosamine sulphate (double-blind, placebo-controlled) were evaluated in 407 middle-aged women with body mass index (BMI) ≥ 27 kg/m2 without clinical signs of knee OA at baseline (ISRCTN 42823086). MRIs were scored with the MRI Osteoarthritis Knee Score (MOAKS). Progression was defined for bone marrow lesions (BMLs), cartilage defects, osteophytes, meniscal abnormalities and meniscal extrusion. Analyses on knee level were performed over the four intervention groups using adjusted Generalized Estimating Equations (GEE). Results: 687 knees of 347 women with mean age 55.7 years (±3.2 SD) and mean BMI 32.3 kg/m2 (±4.2 SD) were analyzed. Baseline prevalence was 64% for BMLs, 70% for cartilage defects, 24% for osteophytes, 66% for meniscal abnormalities and 52% for meniscal extrusions. The diet and exercise program + placebo intervention showed significantly less progression of meniscal extrusion compared to placebo only (12% vs 22%, OR 0.50, 95% CI [0.27-0.92]). The interventions did not result in significant differences on other OA MRI features. Conclusions: In subjects at high risk for future knee OA development, a diet and exercise program, glucosamine sulphate and their combination showed small and mainly non-significant effects on the progression of OA MRI features. Only progression of meniscal extrusion was significantly diminished by the diet and exercise program

HUMA:Heterogeneous, Ultra Low-Latency Model Accelerator for The Virtual Brain on a Versal Adaptive SoC

Brain modeling can occur at different levels of abstraction, each aimed at a different purpose. The Virtual Brain (TVB) is an open-source platform for constructing and simulating personalized brain-network models, favoring whole-brain macro-scales while reducing micro-level detail. Among other purposes, TVB is used to build patient-specific, digital, brain twins that can be used in different clinical settings, such as the study and treatment of epilepsy. However, fitting patient-specific TVB models requires a large number of successive and time-consuming simulations. By studying the internal structure of TVB, we observed heterogeneous computation needs in its models which could be leveraged to accelerate simulations. In this work, we designed and implemented HUMA, a heterogeneous, ultra low-latency, dataflow architecture on an AMD Versal Adaptive SoC to accelerate TVB fitting to different patient-brain makeups. Our heterogeneous solution runs about 27× faster compared to a modern-day, server-class, 32-core CPU while consuming a fraction of its power. Additionally, it delivers on average about 14× lower latency, 1.7× better power efficiency and an order-of-magnitude lower energy consumption when compared against the high-performance GPU version of TVB. The achieved latency savings reveal a significant potential in model-fitting for individual patients as well as in closed-loop biohybrid experiments.</p

Tricking AI chips into simulating the human brain:A detailed performance analysis

In recent years, significant strides in Artificial Intelligence (AI) have led to various practical applications, primarily centered around training and deployment of deep neural networks (DNNs). These applications, however, require considerable computational resources, predominantly reliant on modern Graphics-Processing Units (GPUs). Yet, the quest for larger and faster DNNs has spurred the creation of specialized AI chips and efficient Machine-Learning (ML) software tools like TensorFlow and PyTorch have been developed for striking a balance between usability and performance. Simultaneously, the field of computational neuroscience shares a similar quest for increased computational power to simulate more extensive and detailed brain models, while also keeping usability high. Although GPUs have also entered this field, programming complexity remains high, resulting in cumbersome simulations. Inspired by AI progress, we introduce a workflow for easily accelerating brain simulations using TensorFlow and evaluate the performance of various, cutting-edge AI chips – including the Graphcore Intelligence-Processing Unit (IPU), GroqChip, Nvidia GPU with Tensor Cores, and Google Tensor-Processing Unit (TPU) – when simulating a biologically detailed as well as simpler brain models. Our model simulations explore the architectural tradeoffs of a modern-day CPU and these four AI platforms by varying computational density, memory requirements and floating-point numerical accuracy. Results show that the GroqChip achieves the best performance for small networks, yet is unable to simulate large-scale networks. At the scale of mammalian brains, the GPU, IPU and TPU achieve speedups ranging from 29x to 1,208x times over CPU runtimes. Remarkably, the TPU sets a new record for the largest, real-time simulation of the inferior-olivary nucleus in the brain. Reduced-accuracy floating-point implementations make some simulation results unreliable for brain research, notably for the GroqChip. Consequently, this work underscores the potential of ML libraries for accelerating brain simulations as well as the critical role of AI-chip numerical accuracy for biophysically realistic brain models.</p

Efficient and Realistic Brain Simulation:A Review and Design Guide for Memristor-Based Approaches

Computational-neuroscience research is increasingly in need of larger, biophysically realistic brain models. These analog-in-nature models build upon the Hodgkin-Huxley (HH) formalism and are run on digital, high-performance computing systems making simulation very computationally expensive. In circuit form, these models are theoretically suitable for efficient analog implementation. However, the ion-channel components –predominantly, sodium and potassium– are nonlinear, time-varying resistors, lacking an efficient implementation. Chua et al. proved that these ion-channel models are in fact memristors –devices with a conductance as a function of applied-voltage history– claiming that “memristors are the right stuff for building brains”. However, the kind of actual memristor implementation that is the right one for building brains is not defined. In this article, the device class and characteristics of such memristors are defined and existing memristive implementations of HH-like designs are then reviewed. Surprisingly, although often misclassified as such, no physical implementation currently exists that replicates the original HH equations faithfully or efficiently. Having put forward the desired memristor properties, a design guide for screening suitable memristor designs is then proposed. Screening the existing literature reveals that suitable devices likely already exist for potassium ion-channel emulation, while none exists for sodium; this calls for further investigation of higher-order, voltage-controlled and volatile memristors.</p

Role of the finger flexors in rheumatoid deformities of the metacarpophalangeal joints

It is proposed that rheumatoid deformity of the central three metacarpophalangeal joints is caused primarily by the flexor tendons acting on diseased joint restraints. During pinch and grasp the tendons bend volarly and ulnarly at the tunnel mouth; the resultant pulley forces damage the supporting collateral ligaments, especially on the radial side. The flexor tendons and proximal phalanges can then displace volarly and ulnarly, and the fingers deviate ulnarly. In the fifth digit, ulnar deviation apparently results from the incompletely opposed pull of the abductor digiti quinti.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/37698/1/1780070503_ftp.pd

A Lightweight Architecture for Real-Time Neuronal-Spike Classification

Electrophysiological recordings of neural activity in a mouse's brain are very popular among neuroscientists for understanding brain function. One particular area of interest is acquiring recordings from the Purkinje cells in the cerebellum in order to understand brain injuries and the loss of motor functions. However, current setups for such experiments do not allow the mouse to move freely and, thus, do not capture its natural behaviour since they have a wired connection between the animal's head stage and an acquisition device. In this work, we propose a lightweight neuronal-spike detection and classification architecture that leverages on the unique characteristics of the Purkinje cells to discard unneeded information from the sparse neural data in real time. This allows the (condensed) data to be easily stored on a removable storage device on the head stage, alleviating the need for wires. Synthesis results reveal a >95% overall classification accuracy while still resulting in a small-form-factor design, which allows for the free movement of mice during experiments. Moreover, the power-efficient nature of the design and the usage of STT-RAM (Spin Transfer Torque Magnetic Random Access Memory) as the removable storage allows the head stage to easily operate on a tiny battery for up to approximately 4 days

Recognition and localization of relevant human behavior in videos, SPIE,

ABSTRACT Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind&apos;s Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system

Intersecting single-cell transcriptomics and genome-wide association studies identifies crucial cell populations and candidate genes for atherosclerosis

Genome-wide association studies (GWASs) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to the identification of candidate genes. However, previously studied tissues are often non-diseased and heterogeneous in cell composition, hindering accurate candidate prioritization. Therefore, we analysed single-cell transcriptomics from atherosclerotic plaques for cell-type-specific expression to identify atherosclerosis-associated candidate gene-cell pairs.\nWe applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic and cardiovascular disease, risk factors, and other traits. We then intersected these candidates with single-cell RNA sequencing (scRNA-seq) data to identify genes specific for individual cell (sub)populations in atherosclerotic plaques. The coronary artery disease (CAD) loci demonstrated a prominent signal in plaque smooth muscle cells (SMCs) (SKI, KANK2, and SORT1) P-adj. = 0.0012, and endothelial cells (ECs) (SLC44A1, ATP2B1) P-adj. = 0.0011. Finally, we used liver-derived scRNA-seq data and showed hepatocyte-specific enrichment of genes involved in serum lipid levels.\nWe discovered novel and known gene-cell pairs pointing to new biological mechanisms of atherosclerotic disease. We highlight that loci associated with CAD reveal prominent association levels in mainly plaque SMC and EC populations. We present an intuitive single-cell transcriptomics-driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits. Collectively, our workflow allows for the identification of cell-specific targets relevant for atherosclerosis and can be universally applied to other complex genetic diseases and traits.Biopharmaceutic