Crowded Environment Research Articles

Effect of electroacupuncture on expression of protein phosphorylation in hippocampus tissues of rats with chronic fatigue syndrome.

To observe the effect of electroacupuncture (EA) on behavior and hippocampal protein phosphorylation in rats with chronic fatigue syndrome (CFS), so as to explore its mechanisms underlying improvement of CFS. Male SD rats were randomly divided into control, model and EA groups (n=12 rats in each group). The CFS model was established by chronic multifactor combined with stress stimulation (treadmill training + restraint stress + sleep disturbance + crowded environment). For rats of the EA group, EA (1 mA, frequency of 10 Hz) was applied to "Shenting" (GV24) (with an acupuncture needle penetrated from GV24 to "Baihui" ［GV20］) and "Dazhui" (GV14) for 15 min, once daily for 28 days. After treatment, the body weight, food intake and water intake of rats in each group were observed. The fatigue degree of rats was evaluated by Semi-quantitative score observation table of the general condition of experimental rats.The open field test (OFT) was used to assess the rats'anxiety severity by detecting the total number of grid-crossing and the times of the central area entered in 5 min, and Morris water maze test was employed to assess the rats' learning-memory ability by detecting the escape latency in 1 min, and the times of the original platform quadrant crossing in 1 min. The hippocampaus was taken for phosphorylated Label-free quantitative proteomics analysis by using Maxquant technology based on full scan mode to calculate the integral of each peptide signal of liquid chromatography-mass spectrometry(LC-MS). The differentially-expressed proteins (>1.5 folds for up-regulation or <0.67 folds for down-regulation) were evaluated by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Compared with the control group, the body weight, food intake, and the times of original-platform quadrant crossing of spatial exploring of Morris water maze test were significantly decreased (P<0.01, P<0.05) , and the score of general conditions, times of grid-crossing and center area-entering of OFT, and the escape latency of navigation task were apparently increased (P<0.01) in rats of the model group. After EA intervention, the decreased original-platform quadrant crossing, and the increased score of general conditions, times of grid-crossing and the escape latency of navigation task were all reversed (P<0.01, P<0.05). Outcomes of proteomics analysis indicated that compared with the model group, there were 297 differentially expressed peptide (48 up-regulated and 249 down-regulated) segments in the control group, and there were 245 differentially expressed peptide (185 up-regulated and 60 down-regulated) segments in the EA group, in which, 25 overlapping peptide segments were reversed after EA treatment, corresponding to 24 proteins, mainly involving cytoskeletal structure. GO function annotation analysis showed that the top three differentially expressed phosphorylated proteins involved in the effect of EA intervention were the actin filament polymerization, protein depolymerization and cytoskeletal tissue in the biological process, the actin binding, structural molecular activity and cytoskeletal protein binding in the molecular function, and the cytoskeleton, dendrites and dendritic trees in the cellular component, respectively. The KEGG pathway annotation analysis for differentially expressed phosphorylated proteins showed that theinsulin secretion, axon guidance, phosphatidylinositol signaling system and lysine biosynthesis, etc. were involved in the effect of EA intervention. EA of GV24-GV20 and GV14 can improve the general state, anxiety and learning-memory ability of CFS model rats, which may be related to its functions in regulating the hippocampal protein phosphorylation level, and repairing the structure and function of synapses in hippocampus.

Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones

In computer vision, navigating multi-object tracking in crowded scenes poses a fundamental challenge with broad applications ranging from surveillance systems to autonomous vehicles. Traditional tracking methods encounter difficulties associating noisy object detections and maintaining consistent labels across frames, particularly in scenarios like video surveillance for crowd control and public safety. This paper introduces ’Improved Space-Time Neighbor-Aware Network (STNNet),’ an advanced framework for online Multi-Object Tracking (MOT) designed to address these challenges. Expanding upon the foundational STNNet architecture, our enhanced model incorporates deep reinforcement learning techniques to refine decision-making. By framing the online MOT problem as a Markov Decision Process (MDP), Improved STNNet learns a sophisticated policy for data association, adeptly handling complexities such as object birth/death and appearance/disappearance as state transitions within the MDP. Through extensive experimentation on benchmark datasets, including the MOT Challenge, our proposed Improved STNNet demonstrates superior performance, surpassing existing methods in demanding, crowded scenarios. This study showcases the effectiveness of our approach and lays the groundwork for advancing real-time video analysis applications, particularly in dynamic, crowded environments. Additionally, we utilize the dataset provided by STNNET for density map estimation, forming the basis for our research.•Develop an advanced framework for online Multi-Object Tracking (MOT) to address crowded scene challenges, particularly improving object association and label consistency across frames.•Explore integrating Deep Reinforcement learning techniques into the MOT framework, framing the problem as an MDP to refine decision-making and handle complexities such as object birth or death and appearance or disappearance transitions.

Behavioral transition of a fish school in a crowded environment.

In open water, social fish gather to form schools, in which fish generally align with each other. In this work, we study how this social behavior evolves when perturbed by artificial obstacles. We measure the behavior of a group of zebrafish in the presence of a periodic array of pillars. When the pillar density is low, the fish regroup with a typical interdistance and a well-polarized state with parallel orientations, similarly to their behavior in open-water conditions. Above a critical density of pillars, their social interactions, which are mostly based on vision, are screened and the fish spread randomly through the aquarium, orienting themselves along the free axes of the pillar lattice. The abrupt transition from natural to artificial orientation happens when the pillar interdistance is comparable to the social distance of the fish, i.e., their most probable interdistance. We develop a stochastic model of the relative orientation between fish pairs, taking into account alignment, antialignment, and tumbling, from a distribution biased by the environment. This model provides a good description of the experimental probability distribution of the relative orientation between the fish and captures the behavioral transition. Using the model to fit the experimental data provides qualitative information on the evolution of cognitive parameters, such as the alignment or the tumbling rates, as the pillar density increases. At high pillar density, we find that the artificial environment imposes its geometrical constraints to the fish school, drastically increasing the tumbling rate.

Surveillance System for Real Time High Precision Recognition of Criminal

In an era marked by escalating concerns over public safety and crime prevention, the demand for sophisticated surveillance technologies has surged. This paper presents a groundbreaking solution: a Surveillance System for Real-Time High-Precision Recognition of Criminals. Leveraging advancements in deep learning and facial recognition, our system transcends the limitations of traditional surveillance setups by autonomously identifying individuals of interest in real-time. By employing a meticulously designed deep neural network framework, our system detects and tracks criminal faces amidst dynamic, crowded environments, effectively enhancing public safety measures. Through a seamless integration of cutting-edge technology and meticulous methodology, our innovative system heralds a new era in proactive crime prevention, offering unparalleled precision and efficiency in identifying and apprehending high-risk individuals. Keywords — Real-time surveillance, High-precision facial recognition, Crime prevention and Deep learning-based system

Membrane localization of actin filaments stabilizes giant unilamellar vesicles against external deforming forces

Actin organization is crucial for establishing cell polarity, which influences processes such as directed cell motility and division. Despite its critical role in living organisms, achieving similar polarity in synthetic cells remains challenging. In this study, we employ a bottom-up approach to investigate how molecular crowders facilitate the formation of cortex-like actin networks and how these networks localize and organize based on membrane shape. Using giant unilamellar vesicles (GUVs) as models for cell membranes, we show that actin filaments can arrange along the membrane to form cortex-like structures. Notably, this organization is achieved using only actin and crowders as a minimal set of components. We utilize surface micropatterning to examine actin filament organization in deformed GUVs adhered to various pattern shapes. Our findings indicate that at the periphery of spherical GUVs, actin bundles align along the membrane. However, in highly curved regions of adhered GUVs, actin bundles avoid crossing the highly curved edges perpendicular to the adhesion site and instead remain in the lower curved regions by aligning parallel to the micropatterned surface. Furthermore, the actin bundles increase the stiffness of the GUVs, effectively counteracting strong deformations when GUVs adhere to micropatterns. This finding is corroborated by real-time deformability cytometry on GUVs with synthetic actin cortices. By precisely manipulating the shape of GUVs, our study provides a minimal system to investigate the interplay between actin structures and the membrane. Our findings provide insights into the spatial organization of actin structures within crowded environments, specifically inside GUVs that resemble the size and shape of cells. This study advances our understanding of actin network organization and functionality within cell-sized compartments.

Macromolecular Crowding Effect on Chitosan-Hyaluronic Acid Complexation and the Activity of Encapsulated Catalase.

The associative phase separation of charged biomacromolecules plays a key role in many biophysical events that take place in crowded intracellular environments. Such natural polyelectrolyte complexation and phase separation often occur at nonstoichiometric charge ratios with the incorporation of bioactive proteins, which is not studied as extensively as those complexations at stoichiometric ratios. In this work, we investigated how the addition of a crowding agent (polyethylene glycol, PEG) affected the complexation between chitosan (CS) and hyaluronic acid (HA), especially at nonstoichiometric ratios, and the encapsulation of enzyme (catalase, CAT) by the colloidal complexes. The crowded environment promoted colloidal phase separation at low charge ratios, forming complexes with increased colloidal and dissolution stability, which resulted in a smaller size and polydispersity (PDI). The binding isotherms revealed that the addition of PEG greatly enhanced the ion-pairing strength (with increased ion-pairing equilibrium constant Ka from 4.92 × 104 without PEG to 1.08 × 106 with 200 g/L PEG) and switched the coacervation from endothermic to exothermic, which explained the promoted complexation and phase separation. At the stoichiometric charge ratio, the enhanced CS-HA interaction in crowded media generated a more solid-like coacervate phase with a denser network, slower chain relaxation, and higher modulus. Moreover, both crowding and complex encapsulation enhanced the activity and catalytic efficiency of CAT, represented by a 2-fold increase in catalytic efficiency (Kcat/Km) under 100 g/L PEG crowding and CS-HA complex encapsulation. This is likely due to the lower polarity in the microenvironment surrounding the enzyme molecules. By a systematic investigation of both nonstoichiometric and stoichiometric charge ratios under macromolecular crowding, this work provided new insights into the complexation between natural polyelectrolytes in a scenario closer to an intracellular environment.

Maximizing profitability through cloud-enabled Reinforcement Learning for UAV coverage in real-time e-business applications

Real-time e-business applications are vital for operational efficiency, but connectivity challenges persist, particularly in remote or crowded areas. Drone Base Station (DBS) architecture, proposed for Beyond fifth Generation (B5G) and Sixth Generation (6G) multi-cell networks, offers on-demand hotspot coverage, addressing connectivity gaps in remote or crowded environments. DBSs provide a promising solution to meet the demanding requirements of high data rates, real-time responsiveness, low latency, and extended network coverage, particularly for real-time e-business applications. A critical challenge in this context involves efficiently allocating the needed number of DBSs to the different hotspot service areas, referred to as cells, to optimize the operator’s total profit under unpredictable user demands, varying area-specific service costs, and price dependence real-time e-service. The objective is to achieve the highest total revenue while minimizing the cost (cost savings) throughout the multi-cell system. This challenge is formulated as a profit-maximization discount return problem, integrating the coverage constraint, the variable cell-dependent operational cost, the e-service-based price and the uncertain demands of users across cells. Traditional optimization methods fail due to environmental uncertainty, which leads to the need to reformulate the problem as a Markov Decision Problem (MDP). We introduce a cloud-based Reinforcement Learning (RL) algorithm for DBS dispatch to address the MDP formulation. This algorithm dynamically adjusts to uncertain per-cell user distributions, considering variable operating costs and service-dependent prices across cells. Through extensive evaluation, the RL-based dispatch approach is compared with reference drone dispatch algorithms, demonstrating superior performance in maximizing operator profit through cost savings by optimizing DBS dispatch decisions based on learned user behaviors, variable operational costs, and e-service types.

Label-Free Interference Imaging of Intracellular Trafficking.

Intracellular cargo trafficking is a highly regulated process responsible for transporting vital cellular components to their designated destinations. This intricate journey has been a central focus of cellular biology for many years. Early investigations leaned heavily on biochemical and genetic approaches, offering valuable insight into molecular mechanisms of cellular trafficking. However, while informative, these methods lack the capacity to capture the dynamic nature of intracellular trafficking. The advent of fluorescent protein tagging techniques transformed our ability to monitor the complete lifecycle of intracellular cargos, advancing our understanding. Yet, a central question remains: How do these cargos manage to navigate through traffic challenges, such as congestion, within the crowded cellular environment? Fluorescence-based imaging, though valuable, has inherent limitations when it comes to addressing the aforementioned question. It is prone to photobleaching, making long-term live-cell imaging challenging. Furthermore, they render unlabeled cellular constituents invisible, thereby missing critical environmental information. Notably, the unlabeled majority likely exerts a significant influence on the observed behavior of labeled molecules. These considerations underscore the necessity of developing complementary label-free imaging methods to overcome the limitations of fluorescence imaging or to integrate them synergistically.In this Account, we outline how label-free interference-based imaging has the potential to revolutionize the study of intracellular traffic by offering unprecedented levels of detail. We begin with a brief introduction to our previous findings in live-cell research enabled by interferometric scattering (iSCAT) microscopy, showcasing its aptitude and adeptness in elucidating intricate nanoscale intracellular structures. As we delved deeper into our exploration, we succeeded in the label-free visualization of the entire lifespan of nanoscale protein complexes known as nascent adhesions (NAs) and the dynamic events associated with adhesions within living cells. Our continuous efforts have led to the development of Dynamic Scattering-particle Localization Interference Microscopy (DySLIM), a generalized concept of cargo-localization iSCAT (CL-iSCAT). This label-free, high-speed imaging method, armed with iSCAT detection sensitivity, empowers us to capture quantitative and biophysical insights into cargo transport, providing a realistic view of the intricate nanoscale logistics occurring within living cells. Our in vivo studies demonstrate that intracellular cargos regularly contend with substantial traffic within the crowded cellular environment. Simultaneously, they employ inherent strategies for efficient cargo transport, such as collective migration and hitchhiking, to enhance overall transport rates─intriguingly paralleling the principle and practice of urban traffic management. We also highlight the synergistic benefits of combining DySLIM with chemical-selective fluorescent methods. This Account concludes with a "Conclusions and Outlook" section, outlining promising directions for future research and developments, with a particular emphasis on the functional application of iSCAT live-cell imaging. We aim to inspire further investigation into the efficient transport strategies employed by cells to surmount transportation challenges, shedding light on their significance in cellular phenomena.

Hybrid Classifier for Crowd Anomaly Detection with Bernoulli Map Evaluation

Automatically detecting unusual behavior in a crowded environment greatly improves public safety. Unusual behaviors are those that depend on the rules established in the environment under consideration and cannot be properly described. This paper proposes a new deep feature-based crowd anomaly detection method. Priorly, the input image is preprocessed using the Weiner filtering method. Subsequently, AlexNet, VGGnet, and ResNet-based deep features are extracted. During this process, all three models were optimally tuned. For optimization, a new hybrid optimization method called Hybrid COOT and Bald Eagle with Bernoulli Map Evaluation (HCBEBME) is introduced in this work. This improves the performance of extracting features from the input. Finally, based on the proposed feature set, anomalies are detected by the hybrid detection model that combines LSTM and Bi-GRU models, respectively. Finally, the performance of the proposed model is validated over the conventional models. The detection accuracy of the suggested approach is 96.59%, whereas the minimal accuracy scores for the other methods BRCASO, GNNN, CNN-BILSTM, LSTM, BIGRU, BILSTM, CNN, and RNN are 93.63%, 79.16%, 87.57%, 73.85%, 70.59%, 77.08%, 81.44%, and 84.66% respectively.

Activity-induced diffusion recovery in crowded colloidal suspensions.

We show that the forces generated by active enzyme molecules are strong enough to influence the dynamics of their surroundings under artificial crowded environments. We measured the behavior of polymer microparticles in a quasi-two-dimensional system under aqueous environment, at various area fraction values of particles. In the presence of enzymatic activity, not only was the diffusion of the suspended particles enhanced at shorter time-scales, but the system also showed a transition from subdiffusive to diffusive dynamics at longer time-scale limits. Similar observations were also recorded with enzyme-functionalized microparticles. Brownian dynamics simulations have been performed to support the experimental observations.

CollaborativeBEV: Collaborative bird eye view for reconstructing crowded environment

Constructing a virtual world for the Metaverse based on real-world data is crucial, yet creating virtual environments for crowded scenes poses challenges in accurately tracking individuals using egocentric wearable cameras due to occlusions caused by crowded pedestrians. To address this, we propose a collaborative perception strategy that leverages multiple agents equipped with multi-view cameras to construct an occupancy map for a crowded environment. To fuse the multi-view perceptions of multiple agents, we propose a Collaborative Bird Eye View fusion network, called CollaborativeBEV (C-BEV), in which, we leverage a depth-based BEV network as a feature extractor, and propose a feature enhancement module to improve perception fusion in overlapping area. A designed loss function is introduced to address data imbalance during training, and a BEV enhancement strategy is proposed to augment the sample pool for training the BEV decoder. Experiment on the Sean2.0 dataset demonstrates that our C-BEV method performs better than the baseline method in terms of a 5.3% IoU increase. Our code will be released on github. https://github.com/RYaNzzZ1/CollaborativeBEV.

CRIME PREVENTION AND WORK MONITORING IN CROWD USING CCTV NETWORK

In an era where surveillance technology is becoming increasingly sophisticated, leveraging Closed-Circuit Television (CCTV) networks for crime prevention and crowd monitoring has gained significant attention. This research aims to explore the potential of CCTV systems in deterring criminal activities and ensuring public safety in crowded environments. By integrating advanced video analytics and machine learning algorithms, the CCTV network can detect suspicious behavior patterns, identify potential threats, and alert law enforcement in real-time. Moreover, by employing work monitoring features, such as crowd density analysis and traffic flow optimization, the system can enhance operational efficiency and mitigate safety risks in various public spaces, including transportation hubs, shopping malls, and urban centers. Ultimately, this study seeks to contribute to the development of proactive security measures and intelligent surveillance solutions for safer communities. Keywords— Artificial Intelligence, CCTV Network, Crime Prevention, Crowd Monitoring, Surveillance.

Flexible Zn-air battery for self-powered aptasensing SARS-CoV-2

It is vital to manufacture the highly efficient flexible and readable self-powered electrochemical aptasensor for the sensitive and selective detection of SARS-CoV-2 S-gene respiratory diseases. In this work, a Zn-air battery (ZAB) was assembled using hollow mesocarbon spheres enveloped with Co-N-C atomic sites (referred to as Co-N-C@mHCSs) as the cathode electrocatalyst and utilized it as the ZAB-driven self-powered aptasensor (SPA) for sensitively and selectively detecting SARS-CoV-2 S-gene from complicated environments. The flexible Co-N-C@mHCS-assembled ZAB had a high open-circuit voltage of 1.411 V and a significant output maximum power density of 178.4 mW cm–2 due to the improved oxygen reduction reaction performance. With a SARS-CoV-2 S gene concentration range of 1.0 fg mL–1 to 10 ng mL–1 and an ultralow detection limit of 0.45 fg mL–1, the developed ZAB-SPA displayed strong selectivity, good stability, and exceptional repeatability. The established ZAB-SPA also offered a broad range of practicability for the on-site detection of the SARS-CoV-2 S-gene by using a mobile phone, as well as promising applicability in a crowded environment.

Binding properties of ruthenium(II) polypyridyl complexes [Ru(bpy)2(L)]2+(L=7-F-dppz and 7OCH3–-dppz) with poly(A)-poly(U) under dilute and molecular crowding conditions

Two ruthenium(II) polypyridyl complexes, [Ru(bpy)2(7-F-dppz)]2+ (Ru1, bpy = 2,2′-bipyridine, 7-F-dppz = 7-fluorodipyrido[3,2-a:2′,3′-c]phenazine) and [Ru(bpy)2(7OCH3–-dppz)]2+ (Ru2, 7OCH3–-dppz = 7‑methoxy-dipyrido-[3,2-a,2′,3′-c]-phenazine), were synthesized. The binding properties of Ru1 and Ru2 to duplex RNA poly(A)-poly(U) have been explored through spectroscopic techniques and viscometry. Spectral titration and viscosity experiments show that the binding mode of Ru1 and Ru2 to double-stranded RNA(dsRNA) in dilute solution and molecular crowded environment are intercalation, and that the binding affinity of Ru1 for poly(A)-poly(U) is greater than that of Ru2. Thermal denaturation experiments showed that two complexes have good thermal stability for dsRNA in two environments. Fluorescence titrations showed that Ru1 and Ru2 could act as molecular "light switch" for duplex poly(A)-poly(U) in both environments. Surprisingly, the binding effect of ligands and poly(A)-poly(U) was significantly reduced under crowded condition, and the thermal stability of RNA duplex was less significant than that of dilute solution. The reason of this result is due to the water activity in molecular crowded environment, the increased viscosity of the medium leading to a decrease in the affinity of the ligand for the RNA duplex, and also the electronic effect of the ligand on the binding affinity of the poly(A)-poly(U). Results of the present work facilitate our understanding of the two environments as well as the interaction between ruthenium(II) polypyridyl complexes and dsRNA is influenced by the electronegativity of the substituents.

Anomalous diffusion of synaptic vesicles and its influences on spontaneous and evoked neurotransmission.

Neurons in the central nervous system communicate with each other by activating billions of tiny synaptic boutons distributed along their fine axons. These presynaptic varicosities are very crowded environments, comprising hundreds of synaptic vesicles. Only a fraction of these vesicles can be recruited in a single release episode, either spontaneous or evoked by action potentials. Since the seminal work by Fatt and Katz, spontaneous release has been modelled as a memoryless process. Nevertheless, at central synapses, experimental evidence indicates more complex features, including non-exponential distributions of release intervals and power-law behaviour in their rate. To describe these features, we developed a probabilistic model of spontaneous release based on Brownian motion of synaptic vesicles in the presynaptic environment. To account for different diffusion regimes, we based our simulations on fractional Brownian motion. We show that this model can predict both deviation from the Poisson hypothesis and power-law features in experimental quantal release series, thus suggesting that the vesicular motion by diffusion could per se explain the emergence of these properties. We demonstrate the efficacy of our modelling approach using electrophysiological recordings at single synaptic boutons and ultrastructural data. When this approach was used to simulate evoked responses, we found that the replenishment of the readily releasable pool driven by Brownian motion of vesicles can reproduce the characteristic binomial release distributions seen experimentally. We believe that our modelling approach supports the idea that vesicle diffusion and readily releasable pool dynamics are crucial factors for the physiological functioning of neuronal communication. KEY POINTS: We developed a new probabilistic model of spontaneous and evoked vesicle fusion based on simple biophysical assumptions, including the motion of vesicles before they dock to the release site. We provide closed-form equations for the interval distribution of spontaneous releases in the special case of Brownian diffusion of vesicles, showing that a power-law heavy tail is generated. Fractional Brownian motion (fBm) was exploited to simulate anomalous vesicle diffusion, including directed and non-directed motion, by varying the Hurst exponent. We show that our model predicts non-linear features observed in experimental spontaneous quantal release series as well as ultrastructural data of synaptic vesicles spatial distribution. Evoked exocytosis based on a diffusion-replenished readily releasable pool might explain the emergence of power-law behaviour in neuronal activity.

Determinants of shared e-scooter usage and their policy implications. findings from a survey in Braga, Portugal

Shared e-scooter systems have become an alternative for micromobility users in cities since 2017. The success of the shared e-scooter service can be related to the provision of more last-mile flexibility and convenience to users. They can also be seen as a replacement for private cars and on-demand ridesharing, especially in highly crowded urban environments. Therefore, the main objective of this research is to disclose the main characteristics that determine shared e-scooter usage and their policy implication in a medium-sized city in the North of Portugal. To meet this purpose, a survey was conducted, and statistical analyses were performed to correlate the sociodemographic characteristics of respondents with the willingness to use shared e-scooters. Results show that gender, origin of the trip, and the main mode of transport affect the usage of e-scooters, thus specific policies should be developed to decrease these inequalities. Special attention needs to be given to the creation and expansion of dedicated areas for e-scooter ridership, zoning, and some traffic calming measures to promote a safe, user-centric, and more pleasant environment for shared e-scooter riders.

ATP binding to Nerve Growth Factor (NGF) and pro-Nerve Growth Factor (proNGF): an endogenous molecular switch modulating neurotrophins activity.

ATP has recently been reconsidered as a molecule with functional properties which go beyond its recognized role of the energetic driver of the cell. ATP has been described as an allosteric modulator as well as a biological hydrotrope with anti-aggregation properties in the crowded cellular environment. The role of ATP as a modulator of the homeostasis of the neurotrophins (NTs), a growth factor protein family whose most known member is the nerve growth factor (NGF), has been investigated. The modulation of NTs by small endogenous ligands is still a scarcely described area, with few papers reporting on the topic, and very few reports on the molecular determinants of these interactions. However, a detailed atomistic description of the NTs interaction landscape is of urgent need, aiming at the identification of novel molecules as potential therapeutics and considering the wide range of potential pharmacological applications for NGF and its family members. This mini-review will focus on the unique cartography casting the interactions of the endogenous ligand ATP, in the interaction with NGF as well as with its precursor proNGF. These interactions revealed interesting features of the ATP binding and distinct differences in the binding mode between the highly structured mature NGF and its precursor, proNGF, which is characterized by an intrinsically unstructured domain. The overview on the recent available data will be presented, together with the future perspectives on the field.

Real and In Silico Microgels Show Comparable Bulk Moduli Below and Above the Volume Phase Transition.

The compressibility of soft colloids influences their phase behavior and flow properties, especially in concentrated suspensions. Particle compressibility, which is proportional to the reciprocal of the bulk modulus K, is a key parameter for soft polymer-based particles that can be compressed in crowded environments. Here, microgels with different degrees of cross-linking, i.e.,softness, are investigated below and above their volume phase transition temperature (VPTT). By combining molecular dynamics simulations with small-angle neutron scattering with contrast variation, a change in the particle bulk moduli of two orders of magnitude is observed. The degree of cross-linking has a significant impact on the bulk modulus of the swollen microgel, while above the VPTT the values of K are almost independent of the cross-linking density. The excellent agreement between experimental results and simulations also highlight that the model microgels from computer simulationspossess both the internal architecture and the elastic properties of real polymeric networks. This paves the way to a systematic use of simulations to investigate the behavior of dense microgelsuspensions below and above their VPTT.

NURBs Based Multi-robots Path Planning with Obstacle Avoidance

The primary problem for multi-robot displacement and motion phase solving requires that the robots prevent themselves from colliding with each other as well as stationary obstacles. In certain situations, robot conflict is unavoidable if one robot views its neighbors as immovable obstacles. Hence, this paper proposes a new NURBs (Non-Uniform Rational B-spline) based algorithm for multi-robot path planning in a crowded environment. First, the proposed technique finds each robot's free, smooth, optimal path while avoiding collision with the existing obstacles. Secondly, the prospect of possible collision between the preplanned trajectories will be computed to allow the robots to navigate in the same workspace and coordinate between them. Then, each robot's time to arrive at potential collision sites is computed based on its speed. As a result, the robots involved in the collision must choose whether to use the robot priority technique to prevent the collision. Simulation results under different scenarios and comparisons with previous works are provided to validate the work. The obtained results prove that the proposed approach is accurate (as the robot's instantaneous speed is taken into consideration), fast (as there is no need to broadcast the robots’ positions), the robots’ paths are optimal and smooth (to avoid jerk movements), and the approach ensures that the robots will not be trapped by local minima problem.

Dynamic Structures and Fast Transition Kinetics of Oxidized G-Quadruplexes.

8-oxoguanines (8-oxoG) in cells form compromised G-quadruplexes (GQs), which may vary GQ mediated gene regulations. By mimicking molecularly crowded cellular environment using 40% DMSO or sucrose, here it is found that oxidized human telomeric GQs have stabilities close to the wild-type (WT) GQs. Surprisingly, while WT GQs show negative formation cooperativity between a Pt(II) binder and molecularly crowded environment, positive cooperativity is observed for oxidized GQ formation. Single-molecule mechanical unfolding reveals that 8-oxoG sequence formed more diverse and flexible structures with faster folding/unfolding transition kinetics, which facilitates the Pt(II) ligand to bind the best-fit structures with positive cooperativity. These findings offer new understanding on structures and properties of oxidized G-rich species in crowded environments. They also provide insights into the design of better ligands to target oxidized G-rich structures formed under oxidative cell stress.