Roles of Transcription Factors in the Development and Reprogramming of the Dopaminergic Neurons

The meso-diencephalic dopaminergic (mdDA) neurons regulate various critical processes in the mammalian nervous system, including voluntary movement and a wide range of behaviors such as mood, reward, addiction, and stress. mdDA neuronal loss is linked with one of the most prominent human movement neurological disorders, Parkinson’s disease (PD). How these cells die and regenerate are two of the most hotly debated PD research topics. As for the latter, it has been long known that a series of transcription factors (TFs) involves the development of mdDA neurons, specifying cell types and controlling developmental patterns. In vitro and in vivo, TFs regulate the expression of tyrosine hydroxylase, a dopamine transporter, vesicular monoamine transporter 2, and L-aromatic amino acid decarboxylase, all of which are critical for dopamine synthesis and transport in dopaminergic neurons (DA neurons). In this review, we encapsulate the molecular mechanism of TFs underlying embryonic growth and maturation of mdDA neurons and update achievements on dopaminergic cell therapy dependent on knowledge of TFs in mdDA neuronal development. We believe that a deeper understanding of the extrinsic and intrinsic factors that influence DA neurons’ fate and development in the midbrain could lead to a better strategy for PD cell therapy.

Public Transit Development Predicts Spatial Distribution of Dengue Virus Incidence in Medellín, Colombia

Dengue is a growing global threat in some of the world’s most rapidly growing landscapes. Urbanization and human movement affect the spatial dynamics and magnitude of dengue outbreaks; however, precise effects of urban growth on dengue is not well understood because of a lack of sufficiently fine-scaled data. We analyzed nine years of address-level dengue case data in Medellin, Colombia during a period of public transit expansion. We correlate changes in the spread and magnitude of localized outbreaks to changes in accessibility and usage of public transit. Locations closer to and with a greater utilization of public transit had greater dengue incidence. This relationship was modulated by socioeconomic status; lower socioeconomic status locations experienced stronger effects of public transit accessibility and usage on dengue incidence. Public transit is a vital urban resource, particularly among low socioeconomic populations; these results highlight the importance of public health services concurrent with urban growth.

A Stretchable Strain Sensor Based on CNTs/GR for Human Motion Monitoring

Flexible/stretchable strain sensors have attracted much attention due to their advantages for human-computer interaction, smart wearable and human monitoring. However, there are still great challenges on gaining super durability, quick response, and wide sensing range. This paper provides a simple process to obtain a sensor which is based on graphene (GR)/carbon nanotubes (CNTs) and Ecoflex hybrid, which demonstrates superb endurance (over 1000 cycles at 100% strain), remarkable sensitivity (strain over 125% sensitivity up to 20) and wide sensing range (175%). All results indicate that it is capable for human movement monitoring, such as finger and knee bending and pulse beat. Most importantly, it can be used as a warning function for the night cyclist’s ride. This research provides the feasibility of using this sensor for practical applications.

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution

Background Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory environment. Previous studies have used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment. Purpose We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. Methods Nineteen subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects. Results The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accuracy of quantifying and monitoring external forces experienced by the body when running.

Neural model of human gait and its implementation in MATLAB and Simulink Environment using Deep Learning Toolbox

The article presents selected results of research on the modeling of humanoid robots, including the results of neural modeling of human gait and its implementation in the environment MATLAB and Simulink with the use of Deep Learning Toolbox. The subject of the research was placed within the scope of the available literature on the subject. Then, appropriate research experiments on human movement along a given trajectory were developed. First, the method of measuring the parameters present in the experiment was established, i.e. input quantities (displacement of the left heel, displacement of the right heel) and output quantities (displacement of the measurement point of the human body in space). Then, research experiments were carried out, as a result of which numerical data were measured in order to use them for teaching and testing the Artificial Neural Network. The Perceptron Artificial Neural Network architecture was used to build a model of a neural human walk along a given trajectory. The obtained results were discussed and interpreted, drawing a number of important conclusions.

Analysis and Research on Human Movement in Sports Scene

As a whole-body sport, skipping rope plays an increasingly important role in daily life. In rope-skipping education, due to the lack of professional teachers, the training efficiency of students is low. The rope-skipping monitoring device is heavy and expensive, and the cost of labor statistics and energy consumption are high. In order to quickly analyze the movement process of students and provide correct guidance, this article implements the movement analysis method of the human body movement process. The problem of limb posture analysis in rope skipping is transformed into a multilabel classification problem, a real-time human motion analysis method based on mobile vision is proposed, and the algorithm model is verified in the rope-skipping scene. The experimental results prove that this paper proposes the improved algorithm, which achieved the expected effect. In the analysis of rope-skipping action, the choice of hyperparameters during the experiment is introduced, and it is verified that the proposed ALSTM-LSTM can solve the problem of multilabel classification in the rope-skipping process. The accuracy rate reaches 95.1%, and it can provide the best in all indicators and good performance. It is of great significance for movement analysis and movement quality evaluation during exercise.

Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements

Clues from human movement disorders have long suggested that the neurotransmitter dopamine plays a role in motor control, but how the endogenous dopaminergic system influences movement is unknown. Here we examined the relationship between dopaminergic signaling and the timing of reward-related movements in mice. Animals were trained to initiate licking after a self-timed interval following a start-timing cue; reward was delivered in response to movements initiated after a criterion time. The movement time was variable from trial-to-trial, as expected from previous studies. Surprisingly, dopaminergic signals ramped-up over seconds between the start-timing cue and the self-timed movement, with variable dynamics that predicted the movement/reward time on single trials. Steeply rising signals preceded early lick-initiation, whereas slowly rising signals preceded later initiation. Higher baseline signals also predicted earlier self-timed movements. Optogenetic activation of dopamine neurons during self-timing did not trigger immediate movements, but rather caused systematic early-shifting of movement initiation, whereas inhibition caused late-shifting, as if modulating the probability of movement. Consistent with this view, the dynamics of the endogenous dopaminergic signals quantitatively predicted the moment-by-moment probability of movement initiation on single trials. We propose that ramping dopaminergic signals, likely encoding dynamic reward expectation, can modulate the decision of when to move.