Role of proprioception in corrective visually-guided movements: larger movement errors in both arms of a deafferented individual compared to control participants.

Proprioception plays an important role in both feedforward and feedback processes underlying movement control. This has been shown with individuals who suffered a profound proprioceptive loss and use vision to partially compensate for the sensory loss. The purpose of this study was to specifically examine the role of proprioception in feedback motor responses to visual perturbations by examining voluntary arm movements in an individual with a rare case of selective peripheral deafferentation (GL). We compared her left and right hand movements with those of age-matched female control participants (70.0 years ± 0.2 SEM) during a reaching task. Participants were asked to move their unseen hand, represented by a cursor on the screen, quickly and accurately to reach a visual target. A visual perturbation could be pseudorandomly applied, at movement onset, to either the target position (target jump) or the cursor position (cursor jump). Results showed that despite the continuous visual feedback that was provided, GL produced larger errors in final position accuracy compared to control participants, with her left nondominant hand being more erroneous after a cursor jump. We also found that the proprioceptively-deafferented individual produced less spatially efficient movements than the control group. Overall, these results provide evidence of a heavier reliance on proprioceptive feedback for movements of the nondominant hand relative to the dominant hand, supporting the view of a lateralization of the feedback processes underlying motor control.

Stability and synchronization of fractional-order reaction-diffusion inertial time-delayed neural networks with parameters perturbation.

This study is centered around the dynamic behaviors observed in a class of fractional-order generalized reaction-diffusion inertial neural networks (FGRDINNs) with time delays. These networks are characterized by differential equations involving two distinct fractional derivatives of the state. The global uniform stability of FGRDINNs with time delays is explored utilizing Lyapunov comparison principles. Furthermore, global synchronization conditions for FGRDINNs with time delays are derived through the Lyapunov direct method, with consideration given to various feedback control strategies and parameter perturbations. The effectiveness of the theoretical findings is demonstrated through three numerical examples, and the impact of controller parameters on the error system is further investigated.

An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Investigation of adaptive muscle synergy modulated motor responses to grasping perturbations.

This study investigated how muscle synergies adapt in response to unexpected changes in object weight during lifting tasks. The aim was to discover which motor control strategies individuals use to maintain their grasping performance. Muscle synergies were extracted from the muscle activity of fifteen healthy participants who lifted objects of identical appearance but varying weights in a randomized order, which introduced artificial perturbations. Reaching and manipulation phases of object lifting were analyzed using constrained non-negative matrix factorization and k-means clustering. Participants exhibited a perturbation-independent and thus consistent recruitment of spatial synergy components, while significant adaptations in muscle synergy activation occurred in response to unexpected perturbations. Perturbations caused by unexpectedly heavy objects led to delayed and gradual increases in muscle synergy activation until the force required to lift the object was reached. In contrast, perturbations caused by lighter objects led to reductions in excess muscle synergy activation occurring later. Sensorimotor control maintains the modularity of muscle synergies. Even when external mechanical perturbations occur, the grasping performance is preserved, and control is adapted solely through muscle synergy activation. These results suggest that using pure spatial synergy components as control signals for myoelectric arm prostheses may prevent them from malfunctioning due to external perturbations.

Effects of Cognitive Task During Unstable Walking on the Frontal Cerebral Blood Flow in Elderly Retirement Home Residents Leading an Independent Daily Life.

OBJECTIVES: This study aimed to examine the effects of cognitive tasks during walking with perturbation on the cerebral blood flow. METHODS: The subjects were a total of 20 persons, consisting of 12 healthy adults aged 21-47 years (adult group) and 8 retirement home residents aged 67-85 years who led an independent daily life and could walk independently (elderly group). Oxyhemoglobin was measured using wireless functional near-infrared spectroscopy (fNIRS). An analysis was conducted using the Wilcoxon rank sum test to compare the variation of oxyhemoglobin between walking with perturbation (WP) and walking with perturbation and cognitive tasks (WPC) in each group. In addition, we compared the variation of oxyhemoglobin between groups by analysis of covariance adjusting for the value of WP. RESULTS: In the adult group, the left and right oxyhemoglobin significantly increased under WPC (p=0.0122, 0.0015, respectively). On the other hand, in the elderly group, the right and left oxyhemoglobin did not significantly change under WPC. CONCLUSIONS: These results suggest that the effect of a cognitive task during unstable walking conditions differs between healthy adults and elderly persons, and that this may be important when considering postural control strategies, especially in the elderly.

Computational discovery of tripeptide inhibitors targeting monkeypox virus A42R profilin-like protein.

Monkeypox is an infectious disease caused by the monkeypox virus (MPXV), a member of the Orthopoxvirus genus closely related to smallpox. The structure of the A42R profilin-like protein is the first and only available structure among MPXV proteins. Biochemical studies of A42R were conducted in the 1990s and later work also analyzed the protein's function in viral replication in cells. This study aims to screen tripeptides for their potential inhibition of the A42R profilin-like protein using computational methods, with implications for MPXV therapy. A total of 8000 tripeptides underwent molecular docking simulations, resulting in the identification of 20 compounds exhibiting strong binding affinity to A42R. To validate the docking results, molecular dynamics simulations and free energy perturbation calculations were performed. These analyses revealed two tripeptides with sequences TRP-THR-TRP and TRP-TRP-TRP, which displayed robust binding affinity to A42R. Markedly, electrostatic interactions predominated over van der Waals interactions in the binding process between tripeptides and A42R. Three A42R residues, namely Glu9, Ser12, and Arg38, appear to be pivotal in mediating the interaction between A42R and the tripeptide ligands. Notably, tripeptides containing two or three tryptophan residues demonstrate a pronounced binding affinity, with the tripeptide comprising three tryptophan amino acids showing the highest level of affinity. These findings offer valuable insights for the selection of compounds sharing a similar structure and possessing a high affinity for A42R, potentially capable of inhibiting its enzyme activity. The study highlights a structural advantage and paves the way for the development of targeted therapies against MPXV infections.

Reciprocal Inhibition and Coactivation of Ankle Muscles in Low- and High-Velocity Forward and Backward Perturbations.

Reciprocal inhibition and coactivation are strategies of the central nervous system used to perform various daily tasks. In automatic postural responses (APR), coactivation is widely investigated in the ankle joint muscles, however reciprocal inhibition, although clear in manipulative motor actions, has not been investigated in the context of APRs. The aim was to identify whether reciprocal inhibition can be observed as a strategy in the recruitment of gastrocnemius Medialis (GM), Soleus (So) and Tibialis Anterior (TA) muscles in low- and high-velocity forward and backward perturbations. We applied two balance perturbations with a low and a high velocity of displacement of the movable platform in forward and backward conditions and we evaluated the magnitude and latency time of TA, GM and So activation latency, measured by electromyography (EMG). In forward perturbations, coactivation of the three muscles was observed, with greater activation amplitude of the GM and lesser amplitude of the So and TA muscles. For backward, the pattern of response observed was activation of the TA muscle, a decrease in the EMG signal, which characterizes reciprocal inhibition of the GM muscle and maintenance of the basal state of the So muscle. This result indicates that backward perturbations are more challenging.

Nanohybrid-Based Redox Homeostasis Perturbators Escaped from Early Lysosomes toward Amplified Sensitization of Tumor Cells and Photothermally Maneuvered Pyroptosis Therapy.

Reactive oxygen species (ROS) hold great potential in tumor pyroptosis therapy, yet they are still limited by short species lifespan and limited diffusion distance. Inducing cells into a metastable state and then applying external energy can effectively trigger pyroptosis, but systemic sensitization still faces challenges, such as limited ROS content, rapid decay, and short treatment windows. Herein, a nanohybrid-based redox homeostasis-perturbator system was designed that synergistically induce early lysosomal escape, autophagy inhibition, and redox perturbation functions to effectively sensitize cells to address these challenges. Specifically, weakly alkaline layered double hydroxide nanosheets (LDH NSs) with pH-responsive degradation properties enabled early lysosomal escape within 4 h, releasing poly(L-dopa) nanoparticles for inducing catechol-quinone redox cycling in the cytoplasm. The intracellular ROS levels were systematically rebounded by 3-4 times in tumor cells and lasted for over 4 h. Subsequently induced lysosomal stress and Ca2+ signaling activation resulted in severe mitochondrial dysfunction, as well as a perilous metastable state. Thereby, sequential near-infrared light was applied to trigger amplified stress through a local photothermal conversion. This led to sufficiently high levels of cleaved caspase-1 and GSDMD activation (2.5-2.8-fold increment) and subsequent pyroptosis response. In addition, OH- released by LDH elevated pH to alleviate the limitation of glutathione depletion by quinones at acidic pH and inhibit protective autophagy. Largely secreted inflammatory factors (2.5-5.6-fold increment), efficient maturation of dendritic cells, and further immune stimulation were boosted for tumor inhibition as a consequence. This study offers a new paradigm and insights into the synergy of internal systematic cellular sensitization and sequential external energy treatment to achieve tumor suppression through pyroptosis.

A comparison of the TempO-Seq and Affymetrix microarray platform using RTqPCR validation.

Next-generation risk assessment relies on mechanistic data from new approach methods, including transcriptome data. Various technologies, such as high-throughput targeted sequencing methods and microarray technologies based on hybridization with complementary probes, are used to determine differentially expressed genes (DEGs). The integration of data from different technologies requires a good understanding of the differences arising from the use of various technologies.To better understand the differences between the TempO-Seq platform and Affymetrix chip technology, whole-genome data for the volatile compound dimethylamine were compared. Selected DEGs were also confirmed using RTqPCR validation. Although the overlap of DEGs between TempO-Seq and Affymetrix was no higher than 37%, a comparison of the gene regulation in terms of log2fold changes revealed a very high concordance. RTqPCR confirmed the majority of DEGs from either platform in the examined dataset. Only a few conflicts were found (11%), while 22% were not confirmed, and 3% were not detected.Despite the observed differences between the two platforms, both can be validated using RTqPCR. Here we highlight some of the differences between the two platforms and discuss their applications in toxicology.

Instabilities and self-organization in spatiotemporal epidemic dynamics driven by nonlinearity and noise.

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID-19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread-anchored in physical considerations-lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing-type instabilities, and the concomitant emergence of steady-state patterns under the interplay between three critical model parameters-the saturation parameter, the noise intensity, and the transmission rate. Employing a second-order perturbation analysis to investigate stability, we uncover both diffusion-driven and noise-induced instabilities and corresponding self-organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

Supragingival microbiota, cytokines, and proteins in individuals with different trajectories in experimental gingivitis.

Background: Gingivitis in response to biofilm formation may exhibit different trajectories. The purposes of the present study were to characterize the composition of the supragingival microbiota and salivary cytokine and protein levels in healthy individuals with different gingivitis patterns, to test the hypothesis that manifestations of gingivitis associate with specific profiles in terms of supragingival microbiota, salivary cytokines, and proteins. Methods: Forty orally and systemically healthy individuals refrained from all oral hygiene procedures for a period of 14 days, followed by a resolution period of 14 days with regular oral care. Supragingival plaque level and bleeding on probing (BOP) were recorded, and supragingival plaque as well as saliva samples were collected at baseline, day 14, and day 28. Based on change in BOP% from baseline to day 14, rapid (n = 15), moderate (n = 10), and slow (n = 15) responders were identified. Supragingival microbiota composition, salivary cytokine, and protein levels were compared between groups at baseline, day 14, and day 28. Results: A significantly higher baseline abundance of Capnocytophaga, Eikenella, and Campylobacter species were recorded in rapid responders, whereas a significantly higher baseline abundance of Streptococcus species were detected in slow responders. Slow responders expressed a high degree of resilience, with minimal difference in microbial composition at baseline and after 14 days of resolution (day 28). On the contrary, significant differences in relative abundance of members of the core microbiota, Streptococcus, Actinomyces, and Rothia species, was noted in baseline samples versus day 28 samples in rapid responders. Comparable baseline cytokine and protein levels were recorded in all groups. Conclusion: Supragingival microbiota composition, but not saliva cytokine and protein profiles, seems to influence the extent of the inflammatory response during development of gingivitis in systemically healthy individuals.

Baseline composition of the supragingival microbiota might predict different gingivitis trajectories.Microbial resilience after gingivitis might augment oral homeostasis in individuals with a slow gingivitis trajectory.

Test-Retest Reliability and Visual Perturbation Performance Costs During 2 Reactive Agility Tasks.

CONTEXT: High secondary injury rates after orthopedic surgeries have motivated concern toward the construct validity of return-to-sport test batteries, as it is evident that common strength and functional assessments fail to elicit pertinent behaviors like visual search and reactive decision making. This study aimed to establish the test-retest reliability of 2 reactive agility tasks and evaluate the impact of visual perturbation on physical performance. METHODS: Fourteen physically active individuals completed 2 agility tasks with reaction time (ie, 4 corner agility), working memory, and pathfinding (ie, color recall) components. Participants completed both tasks 4 times in 2 sessions scheduled 7 days apart. Outcomes included performance metrics of reaction time, time to target, number of targets, and total time assessed with reactive training timing gates. To assess test-retest reliability, we used intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and minimal detectable change (MDC). Stroboscopic goggles induced visual perturbation during the fourth trial of each task. To assess the effect of visual perturbation, we used paired t tests and calculated performance costs. RESULTS: The 4-corner agility task demonstrated excellent reliability with respect to reaction time (ICC3,1 = .907, SEM = 0.13, MDC = 0.35 s); time to light (ICC3,1 = .935, SEM = 0.07, MDC = 0.18 s); and number of lights (ICC3,1 = .800, SEM = 0.24, MDC = 0.66 lights). The color recall task demonstrated good-to-excellent test-retest reliability for time to lights (ICC3,1 = .818-.953, SEM = 0.07-0.27, MDC = 0.19-0.74 s); test time (ICC3,1 = .969, SEM = 5.43, MDC = 15.04 s); and errors (ICC3,1 = .882, SEM = 0.19, MDC = 0.53 errors). Visual perturbation resulted in increased time to target (P = .022-.011), number of targets (P = .039), and total test time (P = .013) representing moderate magnitude degradation of performance (d = 0.55-0.87, performance costs = 5%-12%). CONCLUSIONS: Both tasks demonstrated acceptable test-retest reliability. Performance degraded on both tasks with the presence of visual perturbation. These results suggest standardized reactive agility tasks are reliable and could be developed as components of dynamic RTS testing.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

Unveiling the Mechanism of Phenamacril Resistance in F. graminearum: Computational and Experimental Insights into the C423A Mutation in FgMyoI.

Phenamacril (PHA) is a highly selective fungicide for controlling fusarium head blight (FHB) mainly caused by F. graminearum and F. asiaticum. However, the C423A mutation in myosin I of F. graminearum (FgMyoI) leads to natural resistance to PHA. Here, based on the computational approaches and biochemical validation, we elucidate the atomic-level mechanism behind the natural resistance of F. graminearum to the fungicide PHA due to the C423A mutation in FgMyoI. The mutation leads to a rearrangement of pocket residues, resulting in increased size and flexibility of the binding pocket, which impairs the stable binding of PHA. MST experiments confirm that the mutant protein FgMyoIC423A exhibits significantly reduced affinity for PHA compared to wild-type FgMyoI and the nonresistant C423K mutant. This decreased binding affinity likely underlies the development of PHA resistance in F. graminearum. Conversely, the nonresistant C423K mutant retains sensitivity to PHA due to the introduction of a strong hydrogen bond donor, which facilitates stable binding of PHA in the pocket. These findings shed light on the molecular basis of PHA resistance and provide new directions for the creation of new myosin inhibitors.

Memory effects of prior subculture may impact the quality of multiomic perturbation profiles.

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

MetaV: A Pioneer in feature Augmented Meta-Learning Based Vision Transformer for Medical Image Classification.

Image classification, a fundamental task in computer vision, faces challenges concerning limited data handling, interpretability, improved feature representation, efficiency across diverse image types, and processing noisy data. Conventional architectural approaches have made insufficient progress in addressing these challenges, necessitating architectures capable of fine-grained classification, enhanced accuracy, and superior generalization. Among these, the vision transformer emerges as a noteworthy computer vision architecture. However, its reliance on substantial data for training poses a drawback due to its complexity and high data requirements. To surmount these challenges, this paper proposes an innovative approach, MetaV, integrating meta-learning into a vision transformer for medical image classification. N-way K-shot learning is employed to train the model, drawing inspiration from human learning mechanisms utilizing past knowledge. Additionally, deformational convolution and patch merging techniques are incorporated into the vision transformer model to mitigate complexity and overfitting while enhancing feature representation. Augmentation methods such as perturbation and Grid Mask are introduced to address the scarcity and noise in medical images, particularly for rare diseases. The proposed model is evaluated using diverse datasets including Break His, ISIC 2019, SIPaKMed, and STARE. The achieved performance accuracies of 89.89%, 87.33%, 94.55%, and 80.22% for Break His, ISIC 2019, SIPaKMed, and STARE, respectively, present evidence validating the superior performance of the proposed model in comparison to conventional models, setting a new benchmark for meta-vision image classification models.

TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.

BACKGROUND: Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data. RESULTS: We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions. CONCLUSION: These results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.

Gradient-free training of recurrent neural networks using random perturbations.

Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle to propagate gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. We subsequently conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic computing applications.

Biomechanical strategies for mitigating unexpected slips: A review.

Slips are the leading cause of falls, and understanding slip biomechanics is crucial for preventing falls and mitigating their negative consequences. This study analyses human biomechanical responses to slips, including kinetic, kinematic, spatiotemporal, and EMG variables. We reviewed 41 studies investigating slip-induced falls in lab settings, computational models, and training approaches. Our analysis focused on reactions and effects of factors like age, fatigue, strength, perturbation intensity, and gait speed. Trailing limbs' hip extension and knee flexion interrupt the swing phase earlier, increasing the support base. The slipping leg responds with two phases: hip extension and knee flexion, then hip flexion and knee extension. Furthermore, our analysis revealed that the medial hamstring muscles play an active role in slip recoveries. Their activation in the slipping limb allows for hip extension and knee flexion, while in the trailing limb, their activation results in the foot touching down. Additionally, successful slip recoveries were associated with co-contraction of the Tibialis Anterior (TA) and Medial Gastrocnemius (MG), which increases ankle joint stability and facilitates foot contact with the ground. Our review identifies various factors that influence biomechanical and muscular responses to slips, including age, perturbation intensity, gait speed, muscular fatigue, and muscular strength. These findings have important implications for designing interventions to prevent slip-related falls, including cutting-edge technology devices based on a deeper understanding of slip recoveries. Future research should explore the complex interplay between biomechanics, muscle activation patterns, and environmental factors to improve slip-fall prevention strategies.

Caught between a ROCK and a hard place: current challenges in structure-based drug design.

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.