Maturation-specific enhancements in lower extremity explosive strength following plyometric training in adolescent soccer players: A systematic review and meta-analysis.

Background: For adolescent soccer players, good sprinting and jumping abilities are crucial for their athletic performance. The application of plyometric training on boosting explosive strength in adolescent soccer players is contingent upon the maturation phase, which can mediate the training-induced adaptations. Purpose: This systematic review and meta-analysis aim to explore the maturation effect of plyometric training on the lower limb explosive power of adolescent soccer players, with vertical countermovement jump (CMJ) and 20-m sprint as the main outcome indicators. Methods: An extensive search of the literature was carried out on various databases including PubMed, Web of Science, Scopus, ProQuest, and the China National Knowledge Infrastructure (CNKI), covering the time period from the establishment of each database to February 6, 2023. The search was conducted using English keywords such as 'Plyometric,' 'Adolescent,' 'football,' and 'Explosive strength.' This study utilized the Cochrane risk of bias assessment tool to conduct a standardized quality evaluation of all the included literature. Additionally, the Review Manager 5.4 software was employed to perform data analysis on all the extracted data. Results: A total of 17 studies involving 681 adolescent soccer players aged 10 to 19 were included. Plyometric training significantly improved CMJ performance across different maturation stages, especially in the post-peak height velocity stage (POST-PHV) [MD = 4.35, 95 % CI (2.11, 6.59), P < 0.01, I2 = 60 %]. The pre-peak height velocity stage (PRE-PHV) showed the next best improvement [MD = 3.00, 95 % CI (1.63, 4.37)], while the middle-peak height velocity stage (MID-PHV) showed the least improvement [MD = 2.79, 95 % CI (1.16, 4.41), P < 0.01, I2 = 49 %]. However, improvements in 20 m sprint ability were only observed in the PRE-PHV [MD = -0.06, 95 % CI (-0.12, 0), P < 0.01, I2 = 0 %] and MID-PHV [MD = -0.18, 95 % CI (-0.27, -0.08), P < 0.01, I2 = 0 %] stages. Conclusion: Plyometric training serves as a potent strategy for boosting the lower limb explosive strength of adolescent soccer players, and the training effect is closely related to the players' biological maturity. Considering biological maturity is a key aspect that this study deems essential for the formulation of effective training programs for these adolescent players.

Optimal Prescription for Superior Outcomes: A Comparative Analysis of Inter-Individual Variability in Adaptations to Small-Sided Games and Short Sprint Interval Training in Young Basketball Players.

This study compared the inter-individual variability in adaptive responses to six weeks of small-sided games (SSG) and short sprint interval training (sSIT) in young basketball players. Thirty well-trained young athletes (age: 16.4 ± 0.6 years; stature: 190 ± 8.4 cm; weight: 84.1 ± 8.2 kg) voluntarily participated and were randomly assigned to SSG (3 sets of 5 min 3v3 on full length (28 m) and half-width (7.5 m) court, with 2 minutes of passive recovery in-between), sSIT (3 sets of 12 × 5 s sprinting with 20 s recovery between efforts and 2 min of rest between sets), or CON (routine basketball-specific technical and tactical drills) groups, each of ten. Before and after the training period, participants underwent a series of laboratory- and field-based measurements to evaluate their maximum oxygen uptake (V̇O2max), first and second ventilatory threshold (VT1 and VT2), oxygen pulse, peak and average power output (PPO and APO), linear speed, change of direction (COD), countermovement jump (CMJ), and vertical jump (VJ). Both SSG and sSIT sufficiently stimulated adaptive mechanisms involved in enhancement of the mentioned variables (p < 0.05). However, sSIT resulted in lower residuals in percent changes in V̇O2max (p = 0.02), O2pulse (p = 0.005), VT1 (p = 0.001), PPO (p = 0.03), and linear speed (p = 0.01) across athletes compared to the SSG. Moreover, sSIT resulted in more responders than SSG in V̇O2max (p = 0.02, φ = 0.500), O2pulse (p = 0.003, φ = 0.655), VT1 (p = 0.003, φ = 0.655), VT2 (p = 0.05, φ = 0.436), and linear speed (p = 0.05, φ = 0.420). Our results indicate that sSIT creates a more consistent level of mechanical and physiological stimulus than SSG, potentially leading to more similar adaptations across team members.

UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation.

Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential. However, there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass. Therefore, this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity. The leaf area index (LAI), soil plant analysis development (SPAD), and fractional vegetation cover were estimated from hyperspectral images, while plant height was estimated by a point cloud model from RGB images. Based on these 4 agronomic parameters, a total of 20 uniformity indices covering multiple growing stages were calculated. The changing trends in the uniformity indices were consistent with the results of visual interpretation. The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass. The results showed that Pielou's index of LAI had the strongest correlation with yield and biomass, with correlation coefficients of -0.760 and -0.801, respectively. The accuracies of the yield (coefficient of determination [R 2] = 0.616, root mean square error [RMSE] = 1.189 Mg/ha) and biomass estimation model (R 2 = 0.798, RMSE = 1.952 Mg/ha) using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters. Therefore, the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.

Predicting nonsmooth chaotic dynamics by reservoir computing.

Reservoir computing (RC) has been widely applied to predict the chaotic dynamics in many systems. Yet much broader areas related to nonsmooth dynamics have seldom been touched by the RC community which have great theoretical and practical importance. The generalization of RC to this kind of system is reported in this paper. The numerical work shows that the conventional RC with a hyperbolic tangent activation function is not able to predict the dynamics of nonsmooth systems very well, especially when reconstructing attractors (long-term prediction). A nonsmooth activation function with a piecewise nature is proposed. A kind of physics-informed RC scheme is established based on this activation function. The feasibility of this scheme has been proven by its successful application to the predictions of the short- and long-term (reconstructing chaotic attractor) dynamics of four nonsmooth systems with different complexity, including the tent map, piecewise linear map with a gap, both noninvertible and discontinuous compound circle maps, and Lozi map. The results show that RC with the new activation function is efficient and easy to run. It can make perfectly both short- and long-term predictions. The precision of reconstructing attractors depends on their complexity. This work reveals that, to make efficient predictions, the activation function of an RC approach should match the smooth or nonsmooth nature of the dynamical systems.

Predicting chaotic dynamics from incomplete input via reservoir computing with (D+1)-dimension input and output.

Predicting future evolution based on incomplete information of the past is still a challenge even though data-driven machine learning approaches have been successfully applied to forecast complex nonlinear dynamics. The widely adopted reservoir computing (RC) can hardly deal with this since it usually requires complete observations of the past. In this paper, a scheme of RC with (D+1)-dimension input and output (I/O) vectors is proposed to solve this problem, i.e., the incomplete input time series or dynamical trajectories of a system, in which certain portion of states are randomly removed. In this scheme, the I/O vectors coupled to the reservoir are changed to (D+1)-dimension, where the first D dimensions store the state vector as in the conventional RC, and the additional dimension is the corresponding time interval. We have successfully applied this approach to predict the future evolution of the logistic map and Lorenz, Rössler, and Kuramoto-Sivashinsky systems, where the inputs are the dynamical trajectories with missing data. The dropoff rate dependence of the valid prediction time (VPT) is analyzed. The results show that it can make forecasting with much longer VPT when the dropoff rate θ is lower. The reason for the failure at high θ is analyzed. The predictability of our RC is determined by the complexity of the dynamical systems involved. The more complex they are, the more difficult they are to predict. Perfect reconstructions of chaotic attractors are observed. This scheme is a pretty good generalization to RC and can treat input time series with regular and irregular time intervals. It is easy to use since it does not change the basic architecture of conventional RC. Furthermore, it can make multistep-ahead prediction just by changing the time interval in the output vector into a desired value, which is superior to conventional RC that can only do one-step-ahead forecasting based on complete regular input data.

Insights into the Functional Components in Wheat Grain: Spatial Pattern, Underlying Mechanism and Cultivation Regulation.

Wheat is a staple crop; its production must achieve both high yield and good quality due to worldwide demands for food security and better quality of life. It has been found that the grain qualities vary greatly within the different layers of wheat kernels. In this paper, the spatial distributions of protein and its components, starch, dietary fiber, and microelements are summarized in detail. The underlying mechanisms regarding the formation of protein and starch, as well as spatial distribution, are discussed from the views of substrate supply and the protein and starch synthesis capacity. The regulating effects of cultivation practices on gradients in composition are identified. Finally, breakthrough solutions for exploring the underlying mechanisms of the spatial gradients of functional components are presented. This paper will provide research perspectives for producing wheat that is both high in yield and of good quality.

Preventive Effect Observation of Dapagliflozin on Middle and Later Ventricular Remodeling in Patients with Acute ST Segment Elevation Anterior Wall Myocardial Infarction: A Single-Center, Retrospective Cohort Study.

Objective: This study aimed to observe the effect of dapagliflozin on left ventricular ejection function (LVEF) and left ventricular end-diastolic volume (LVEDV) in patients with acute anesthesia ST segment elevation myocardial infarction (ASTEMI) and explore the effect of prophylactic treatment on ventricular remodeling (VR). Methods. A retrospective cohort design was employed to collect 188 patients with anterior wall STEMI who received emergency percutaneous coronary intervention (PCI). The patients were divided into dapagliflozin group and control group. The baseline data, the results of echocardiography at 6 months and on admission, and the proportion of VR were compared between the two groups. Echocardiography followed up for the two groups for 6 months after PCI and VR (LVEDV increased ≥20%) were considered the main clinical outcomes. Single-factor and multifactor logistic regression was conducted to explore the preventive effect of dapagliflozin on VR in patients with anterior wall STEMI. Results. There were significant differences in gender, history of diabetes, glycosylated hemoglobin (Hb1AC), admission LVEF, Killip grade of heart failure, and brain natriuretic peptide (BNP) between the dapagliflozin group and the control group regarding the baseline data. Compared with the results of echocardiography at admission and 6 months, the decrease in LVEDV and the increase of LVEF at 6 months in the dapagliflozin group were significantly higher than those in the control group. During the follow-up of 6 months, the VR rate in the dapagliflozin group was significantly lower than that in the control group. Multifactor logistic regression analysis suggested that the risk of VR was reduced by taking dapagliflozin after the adjustment of the confounding factors. Additionally, the combined use of dapagliflozin, ACEI/ARB, and ß-block can further reduce the risk. Conclusion. Regular taking of dapagliflozin has a positive effect on the improvement of middle and LVEF and left ventricular volume enlargement in patients with anterior wall STEMI, as well as the prevention of the occurrence of VR.

Information processing and energy efficiency of temperature-sensitive Morris-Lecar neuron.

In biological organisms, the temperature is an important factor, which affects the motion of micro-particles and biochemical reaction. In current work, we investigate the effect of temperature on the capacity of information processing and the energy efficiency of the Hodgkin's three basic classes of neurons. Increasing the temperature, both of the total entropy and information rate would maintain nearly as constant, and then decrease rapidly and fall to zero. Moreover, energy consumption is reduced as the temperature increases. However, the energy consumption of the class 1 neuron is remarkably greater than that of class 2 and 3 neurons with the same temperature. It is also interesting that the class 3 neuron consumes less energy than that of class 1 and 2 neurons, but generates the same value of total entropy and information rate for the same condition.

Emergence of an optimal temperature in action-potential propagation through myelinated axons.

In biological organisms, an optimal temperature exists at which the system functioning is maximized or is most effective. To obtain a general and quantitative understanding of the emergence of the optimal temperature is a challenging task. We aim to gain insights into this significant problem in biological physics by addressing the problem of propagation of action potential in myelinated axons. In particular, we construct a Hodgkin-Huxley type of cortical, compartmental model to describe the nodes of Ranvier with coupling between a pair of neighboring compartments characterized by internodal conductance and investigate the effect of temperature on the propagation of the action potential. We conduct direct numerical simulations and develop a physical analysis by taking advantage of the spatially continuous approximation. We find that increasing the temperature requires a larger value of the critical internodal conductance for successful propagation. The striking finding is the spontaneous emergence of an optimal temperature in the sense that, for the propagation of a single action potential at a fixed value of the internodal conductance, the minimum average passage time for one node of Ranvier occurs at this temperature value. A remarkable phenomenon is that the value of the optimal temperature is similar to those of living biological systems observed in experiments.

Autapses promote synchronization in neuronal networks.

Neurological disorders such as epileptic seizures are believed to be caused by neuronal synchrony. However, to ascertain the causal role of neuronal synchronization in such diseases through the traditional approach of electrophysiological data analysis remains a controversial, challenging, and outstanding problem. We offer an alternative principle to assess the physiological role of neuronal synchrony based on identifying structural anomalies in the underlying network and studying their impacts on the collective dynamics. In particular, we focus on autapses - time delayed self-feedback links that exist on a small fraction of neurons in the network, and investigate their impacts on network synchronization through a detailed stability analysis. Our main finding is that the proper placement of a small number of autapses in the network can promote synchronization significantly, providing the computational and theoretical bases for hypothesizing a high degree of synchrony in real neuronal networks with autapses. Our result that autapses, the shortest possible links in any network, can effectively modulate the collective dynamics provides also a viable strategy for optimal control of complex network dynamics at minimal cost.

Spike-Threshold Variability Originated from Separatrix-Crossing in Neuronal Dynamics.

The threshold voltage for action potential generation is a key regulator of neuronal signal processing, yet the mechanism of its dynamic variation is still not well described. In this paper, we propose that threshold phenomena can be classified as parameter thresholds and state thresholds. Voltage thresholds which belong to the state threshold are determined by the 'general separatrix' in state space. We demonstrate that the separatrix generally exists in the state space of neuron models. The general form of separatrix was assumed as the function of both states and stimuli and the previously assumed threshold evolving equation versus time is naturally deduced from the separatrix. In terms of neuronal dynamics, the threshold voltage variation, which is affected by different stimuli, is determined by crossing the separatrix at different points in state space. We suggest that the separatrix-crossing mechanism in state space is the intrinsic dynamic mechanism for threshold voltages and post-stimulus threshold phenomena. These proposals are also systematically verified in example models, three of which have analytic separatrices and one is the classic Hodgkin-Huxley model. The separatrix-crossing framework provides an overview of the neuronal threshold and will facilitate understanding of the nature of threshold variability.

Simulation of avascular tumor growth by agent-based game model involving phenotype-phenotype interactions.

All tumors, both benign and metastatic, undergo an avascular growth stage with nutrients supplied by the surrounding tissue. This avascular growth process is much easier to carry out in more qualitative and quantitative experiments starting from tumor spheroids in vitro with reliable reproducibility. Essentially, this tumor progression would be described as a sequence of phenotypes. Using agent-based simulation in a two-dimensional spatial lattice, we constructed a composite growth model in which the phenotypic behavior of tumor cells depends on not only the local nutrient concentration and cell count but also the game among cells. Our simulation results demonstrated that in silico tumors are qualitatively similar to those observed in tumor spheroid experiments. We also found that the payoffs in the game between two living cell phenotypes can influence the growth velocity and surface roughness of tumors at the same time. Finally, this current model is flexible and can be easily extended to discuss other situations, such as environmental heterogeneity and mutation.

Expression and functional perspectives of miR-184 in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignant tumors, with its 5-year survival rate lower than 5%. MicroRNAs (miR) have been known as important regulators for the tumorigenesis, progression, invasion and metastasis of various cancers. MiR-184 was found to be abnormally expressed in various cancers including glioma and oral carcinoma. The expression and functional role of miR-184 in PDAC, however, remains unclear. PDAC cell line PANC-1 was transfected with miR-184 inhibitor. Real-time PCR was used to detect the expression of miR-184 in untreated PANC-1, miR-184 inhibitor transfected PANC-1 and controlled normal pancreatic ductal epithelial cell line HPDE6c7. MTT assay was used to detect the effect of miR-184 on the proliferation of PANC-1 cells, while invasion assay and Western blotting were employed to describe the effect on cell invasion ability and expression of caspase-3, respectively. In PANC-1 cells, miR-184 was abundantly expressed. The transfection of inhibitor effectively suppressed the expression of miR-184, and further inhibited both cell proliferation and invasion abilities, in addition to the up-regulation of pro-apoptotic protein caspase 3 expression. The up-regulation of miR-184 in PDAC may facilitate the proliferation and invasion ability, and inhibit apoptosis of tumor cells, thus potentiating the occurrence and development of PDAC. MiR-184, therefore, is a potential molecular target for therapy.

Effect of autaptic activity on the response of a Hodgkin-Huxley neuron.

An autapse is a special synapse that connects a neuron to itself. In this study, we investigated the effect of an autapse on the responses of a Hodgkin-Huxley neuron to different forms of external stimuli. When the neuron was subjected to a DC stimulus, the firing frequencies and the interspike interval distributions of the output spike trains showed periodic behaviors as the autaptic delay time increased. When the input was a synaptic pulse-like train with random interspike intervals, we observed low-pass and band-pass filtering behaviors. Moreover, the region over which the output ISIs are distributed and the mean firing frequency display periodic behaviors with increasing autaptic delay time. When specific autaptic parameters were chosen, most of the input ISIs could be filtered, and the response spike trains were nearly regular, even with a highly random input. The background mechanism of these observed dynamics has been analyzed based on the phase response curve method. We also found that the information entropy of the output spike train could be modified by the autapse. These results also suggest that the autapse can serve as a regulator of information response in the nervous system.

Influence of autapse on mode-locking structure of a Hodgkin-Huxley neuron under sinusoidal stimulus.

We investigated the mode-locking behaviors of a Hodgkin-Huxley neuron with an autapse under sinusoidal stimulus. A neuron without an autapse can exhibit rich p:q mode-locking (i.e. p output action potentials generated by q cycles stimulations) behaviors with periodic stimuli. In the presence of the autaptic connection, the p:q mode-locking behaviors are completely reset. The autapse extends the scope of mode-locking. The autapse can enhance or suppress the status of mode-locking. Even for some specified autaptic parameters, the neuron could be driven into the sub-threshold oscillation. Our results suggested that the autapse can serve as a potential control option for adjusting the mode-locking firing behaviors. We also found that changing the delay time is much more effectively operable to regulate the response behavior than the autaptic intensity.

First-spike latency in Hodgkin's three classes of neurons.

We study the first-spike latency (FSL) in Hodgkin's three classes of neurons with the Morris-Lecar neuron model. It is found that all the three classes of neurons can encode an external stimulus into FSLs. With DC inputs, the FSLs of all of the neurons decrease with input intensity. With input current decreased to the threshold, class 1 neurons show an arbitrary long FSL whereas class 2 and 3 neurons exhibit the short-limit FSLs. When the input current is sinusoidal, the amplitude, frequency and initial phase can be encoded by all the three classes of neurons. The FSLs of all of the neurons decrease with the input amplitude and frequency. When the input frequency is too high, all of the neurons respond with infinite FSLs. When the initial phase increases, the FSL decreases and then jumps to a maximal value and finally decreases linearly. With changes in the input parameters, the FSLs of the class 1 and 2 neurons exhibit similar properties. However, the FSL of the class 3 neurons became slightly longer and only produces responses for a narrow range of initial phase if input frequencies are low. Moreover, our results also show that the FSL and firing rate responses are mutually independent processes and that neurons can encode an external stimulus into different FSLs and firing rates simultaneously. This finding is consistent with the current theory of dual or multiple complementary coding mechanisms.

The role of coincidence-detector neurons in the reliability and precision of subthreshold signal detection in noise.

Subthreshold signal detection is an important task for animal survival in complex environments, where noise increases both the external signal response and the spontaneous spiking of neurons. The mechanism by which neurons process the coding of signals is not well understood. Here, we propose that coincidence detection, one of the ways to describe the functionality of a single neural cell, can improve the reliability and the precision of signal detection through detection of presynaptic input synchrony. Using a simplified neuronal network model composed of dozens of integrate-and-fire neurons and a single coincidence-detector neuron, we show how the network reads out the subthreshold noisy signals reliably and precisely. We find suitable pairing parameters, the threshold and the detection time window of the coincidence-detector neuron, that optimize the precision and reliability of the neuron. Furthermore, it is observed that the refractory period induces an oscillation in the spontaneous firing, but the neuron can inhibit this activity and improve the reliability and precision further. In the case of intermediate intrinsic states of the input neuron, the network responds to the input more efficiently. These results present the critical link between spiking synchrony and noisy signal transfer, which is utilized in coincidence detection, resulting in enhancement of temporally sensitive coding scheme.

Role of axonal sodium-channel band in neuronal excitability.

Action potential initiation (API) is a crucial step in neural information integration and communication. A stable site of API is beneficial for neural coding. A specialized band with a high density of sodium channels in the axon initial segment has been proved as the primary site of API. In this paper we investigate the role of a dense sodium-channel band in the threshold and the location of API of a neuron with a multicompartment model. It is shown that the threshold of the injected current for API is enhanced by a farther distance between the stimulated site and the band and lowered by an increase of the sodium-channel density and the bandwidth. There exists an optimal location of the dense sodium-channel band, which enables the lowest threshold for API. In the case of an excitable dendrite, the site of API shifts from the band to the stimulated dendritic site with increasing stimuli strength. A larger bandwidth and higher sodium-channel density elevate the stability of the sodium-channel band as the site of API and an optimal band position enables the highest stability.

Response of Morris-Lecar neurons to various stimuli.

We studied the responses of three classes of Morris-Lecar neurons to sinusoidal inputs and synaptic pulselike stimuli with deterministic and random interspike intervals (ISIs). It was found that the responses of the output frequency of class 1 and 2 neurons showed similar evolution properties by varying input amplitudes and frequencies, whereas class 3 neuron exhibited substantially different properties. Specifically, class 1 and 2 neurons display complicated phase locking (p : q, p > q, denoting output action potentials per input spikes) in low-frequency sinusoidal input area when the input amplitude is above their threshold, but a class 3 neuron does not fire action potentials in this area even if the amplitude is much higher. In the case of the deterministic ISI synaptic injection, all the three classes of neurons oscillate spikes with an arbitrary small frequency. When increasing the input frequency (both sinusoidal and deterministic ISI synaptic inputs), all neurons display 1 : 1 phase locking, whereas the response frequency decreases even fall to zero in the high-frequency input area. When the random ISI synaptic pulselike stimuli are injected into the neurons, one can clearly see the low-pass filter behaviors from the return map. The output ISI distribution depends on the mean ISI of input train as well as the ISI variation. Such different responses of three classes of neurons result from their distinct dynamical mechanisms of action potential initiation. It was suggested that the intrinsic dynamical cellular properties are very important to neuron information processing.