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We studied the dynamics of a neural network that has both recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity was sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient signals was represented by the sequence of activity patterns. We then applied this model to represent the trace eyeblink conditioning, which is mediated by the hippocampus. We assumed this model as CA3 of the hippocampus and considered an output neuron corresponding to a neuron in CA1. The activity pattern of the output neuron was similar to that of CA1 neurons during trace eyeblink conditioning, which was experimentally observed.

Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.

There is a growing body of work on the ex vivo tensile testing of tendon repairs, an appreciable amount of which are performed on cadaveric porcine flexor tendons. However, there is little information in the literature on exactly how to perform the dissections necessary to obtain flexor tendons from porcine trotters. We present a simple method to rapidly harvest tendons from the porcine foot, allowing large amounts of material to be harvested in little time for the purpose of tensile testing of tendon repairs.

Amplified detection of nucleic acid by G-quadruplex based hybridization chain reaction.

Dow opens Photovoltaics Films Application Lab in Shanghai.

Researchers discover molecular mechanisms of left-right asymmetric control in the sea urchin.

China mulls new rule on human genetic research.

China to phase out organ donation from executed criminals.

Charles River Laboratories to expand research models business in China.

Chinese Science Academy Chief urges seizing on new technological revolution.

BGI contributes genome sequencing and bioinformatics expertise.

Taiwan government to encourage formation of smaller biotech funds.

A preliminary model is presented for estimating floor reaction forces during human walking based only on kinematic data. Such a model would be useful for supplementing purely qualitative gait analysis performed in clinics where force plates would be an unaffordable luxury, but not for situations in which quantitative data would be used in making such decisions as how to perform an orthopedic surgery. In this model, the vertical components of floor reaction forces are determined by conventional double differentiation of kinematic data, but the horizontal (fore-aft) components are based instead on constraints in which the floor reaction forces are characterized as acting through the center of mass of the upper body. To assess the accuracy of our calculations, we gathered data of gait by a healthy 22-year-old woman using a motion analysis system with force plates. Pathological gait data were also examined. Joint moments were computed from both force plate data and from our estimates of floor reaction forces. Prediction of vertical force showed higher reliability than prediction of fore-aft force. Joint moments from kinematics were successfully calculated in normal gait, but not in pathological gait, especially at the hip joint. The proposed approach may have some merit for performing a gait analysis even when no force plate is present, but the inaccuracy increases in the case of a subject whose upper body sways during gait.

Quick-release experiments often produce noticeable oscillations on the measured force and length data in the first few milliseconds after the force release. We measured oscillations in experiments with several species (Rattus norvegicus, Galea musteloides, Rana pipiens) and different experimental setups. These oscillations are generally ignored as artifacts.

This study investigates the cause of the oscillations. A biomechanical model of the experimental setup was developed consisting of a geometric model describing the setup and a Hill-type muscle-tendon model including the force-length-velocity relation and a linear spring in series. Muscle properties of each muscle were determined by the ISOFIT method. Model calculations and forward simulations of quick-release experiments based on experimentally determined muscle properties reveal that the observed oscillations are not artifacts (instrument and control), but the result of interactions of muscle-tendon properties with the inertia of muscles, bones and lever system.

Diagnosis of diabetes is usually achieved by obtaining a single reading of blood-glucose concentration value from the Oral Glucose Tolerance Test (OGTT). However, the result itself is inadequate in providing insight into the glucose-regulatory etiology of diabetes disease disorder, which is important for treatment purposes. The objective of this project was to conduct clinical simulation and parametric identification of OGTT model for diagnosis of diabetic patient, so as to classify diabetic or at-risk patients into different categories, depending on the nature of their blood-glucose tolerance response to oral injestion of a bolus of glucose. In other words, the patient classification depends on how the blood-glucose concentration varies with time; i.e. how much does it peak, how long does it takes to reach its peak value, how fast does it return to the fasting value, etc. during the oral glucose tolerance test.

To represent this blood-glucose concentration [y(t)] regulatory dynamics, the model selected is a second-order differential equation, of blood-glucose concentration response to a bolus of ingested glucose Gδ(t). This model was then applied to the test subjects by making the model solution-expression for y(t) match the monitored clinical data of blood-glucose concentration at different time intervals, through clinical simulation and parametric identification. The solutions obtained from the model to fit the clinical data were different for normal and diabetic test subjects. The clinical data of "normal" subjects could be simulated by means of an under-damped solution of the model, as: y(t) = (G/ω)e^-ωtsin ωt. The data of "diabetic" patients needed to be simulated by means of an over-damped solution of the model, as: y(t) = (G/ω)e^-Atsin hωt, where G represents the magnitude of the impulse input Gδ(t) to the model (in gms of glucose per litre of blood pool volume), ω is the damped oscillatory frequency of the model, w_n is the natural frequency of the system and .

In order to facilitate differential diagnosis, we developed a non-dimensional diabetic index (DI) expressed as: [Ay_maxT_d/GT_max]. This index can be used to facilitate the diagnosis of diabetes as well as for assessing the risk to becoming diabetic.

Both sensory information and mechanical properties of the musculoskeletal system are necessary for fast and appropriate reactions of humans and animals to environmental perturbations. In this paper, we focus on the musculoskeletal system and study the stability of a human elbow in an equilibrium state. We derive a biomechanical model of the human elbow, including an antagonistic pair of muscles, and investigate the stability analytically based on the theory of Ljapunov. Depending on the elbow angle and the level of coactivation, we obtain the following three qualitatively different behaviors: unstable, stable with real eigenvalues, and stable with complex eigenvalues. If the eigenvalues are real, then the system is critically damped; for complex eigenvalues, solutions near the equilibrium are oscillating. Based on experimental data, we found that in principle real and complex behaviors may occur in human arm movements. The experiments support the analytical predictions. Furthermore, in agreement with the simulations, we found differences in the experimental results among the subjects. The results of this study support the assumption that arm movements around an equilibrium point may be self-stabilized without sensory feedback or motor control, based only on mechanical properties of musculoskeletal systems.

The aim of this study was to investigate how the forces required to stabilize the lumbar spine in the standing posture may be affected by variation in its shape. A two-dimensional model of the lumbar spine in the sagittal plane was developed that included a simplified representation of the lumbar extensor muscles. The shape of the model was varied by changing both the magnitude and distribution of the lumbar curvature. The forces required to produce a resultant load traveling along a path as close to the vertebral body centroids as possible (a follower load) were determined. In general,the forces required to produce a follower load increased as the curvature became larger and more evenly distributed. The results suggest that the requirements of the lumbar muscles to maintain spinal stability in vivo will vary between individuals. This has implications for understanding the role of spinal curvature and muscle atrophy in back pain.

This chapter gives an overview of various ventricular assist devices with a particular focus on axial flow pumps. Using the Hemopump as an example, we developed a model of a canine circulatory system assisted by an axial flow pump, and used computer simulation to predict the effects of the assist device under various hemodynamic conditions. In general, the results from the simulation are in good agreement with that observed in clinical and animal experiments. The same model is further used to explore the techniques and strategy for optimum control of the assist device by introducing an objective function, and choosing suitable membership functions with associated weighting factors, the model offers great flexibility in choosing the targeted hemodynamic variables, in specifying the particular way that each of these variables is to be optimized, and in assigning the relative importance of each targeted variable. The methods used and the results obtained in this study can be incorporated into the design of an advanced physiological controller for a long-term operation of the axial flow pump-based assist device as well as other types of continuous-flow LVAD.