Ovarian cysts are linked to hormone imbalances and altered gene expressions, but the connection between cysts and ion channel expression is understudied. This study explored the role of TWIK-related acid-sensitive K+ (TASK) channels in bovine ovarian cyst formation. The ovarian follicles were split into small (5 to 10 mm in diameter) and large (> 25 mm in diameter) groups. Among the measured K+, Na+, and Cl- concentrations in follicular fluid (FF) obtained from small-sized follicles (SF) and large-sized follicles (LF), the K+ concentration was significantly lower in LFFF. Quantitative PCR, western blot, and immunocytochemistry data revealed that TASK-3 expression levels significantly decreased by approximately 50% in both LF and granulosa cells obtained from LF (LFGCs) compared to the corresponding controls. The TASK-3 protein was localized to the plasma membrane of GCs. The diameter of LFGCs was larger than that of SFGC. The cell swelling response to exposure to a hypotonic solution (200 mOsm/L) was highly reduced in TASK-3-overexpressing cells compared to vector-transfected cells. TASK-3-knockdown cells showed arrested growth. Senescence markers were detected in LFGCs and TASK-3-knockdown cells. These findings suggest that reduced TASK-3 expression in LF is associated with the inhibition of GC growth, leading to senescence and cyst formation.

Network Simulators (NS) is typically used to study services and applications in complex scenarios due to the infeasibility of deploying real testbeds. This is the case of Mobile Ad hoc Networks (MANETs) which can be extensively used in emergency situations or to gather information without infraestructure. An important factor to get realiable results in the MANETs’ simulations is the wireless channel module of NSs. In this report, we explain how thw wireless channel is implemented in NS-3 and it can be configured.

Recently, deep learning models have been widely applied to modulation recognition, which have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the Iterative Dual Scale Attention Module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5\% at 10dB and 0.6232 at full SNR. The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

Industrial manipulators and parallel robots are often used for tasks like drilling or milling, that require three translational, but only two rotational degrees of freedom (“3T2R”). While kinematic models for specific mechanisms for these tasks exist, a general kinematic model for parallel robots is still missing. This paper presents the definition of the rotational component of kinematic constraints equations for parallel robots based on two reciprocal sets of Euler angles for the end-effector orientation and the orientation residual. The method allows to completely remove the redundant coordinate in 3T2R tasks and to solve the inverse kinematics for general serial and parallel robots with the gradient-descent algorithm. The functional redundancy of robots with full mobility is exploited using nullspace projection.

General Linear Modelling (GLM) has been widely employed to estimate the hemodynamic changes evoked by cognitive processing, which are more likely to be nonlinear than linear. First, this study re-analyzed the fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected in the Mixed-Order Design Dimensional Change Card Sort (DCCS) task. The results indicated that the quadratic equation was better than GLM to model HbO changes in this task. Second, analysis of a new set of data indicated that the Habit-DisHabit design of DCCS was more effective in identifying the neural correlates of cognitive shifting than the Mixed-Order Design. Third, this study found that the Non-users were more attentive and engaged than the Heavy-users, with a slower but more steady increase of brain activation in BA8 and BA9.

This study aims to examine the impact of tablet use on preschoolers’ executive function during the Dimensional Change Card Sort Task (DCCS) task using the functional near-infrared spectroscopy (fNIRS). Altogether 38 Chinese preschoolers (Mage = 5.0 years, SD = 0.69 years, 17 girls) completed the tasks before the COVID-19 lockdown. Eight children never used tablets, while 16 children were diagnosed as the ‘heavy-user'. The results indicated that: (1) the 'Non-user' outperformed the 'Heavy-user' with a significantly higher correct rate in the DCCS task; (2) the two groups differed significantly in the activation of the prefrontal cortex (BA 9): the 'Non-user' pattern is normal and healthy, whereas the 'Heavy-user' pattern is not normal and needs further exploration.

In this paper, we study the effect of parasitic channel on electrical characteristics and power consumptions of vertically stacked gate-all-around (GAA) silicon nanosheet (NS) MOSFETs of the bulk and silicon-on-insulator (SOI) substrates. Through deliberate control of the bottom parasitic channel coverage, we systematically assess its influence on DC, AC, and RF characteristics including power consumption. The observed findings highlight a significant differentiation among the bottom parasitic channels: GAA (i.e., control sample with 100% coverage), omega-shaped gate NS (70% and 80% coverages), and FinFET (60% coverage). Compared with the device with a 60% coverage channel, the device with a 100% coverage channel possesses 3520% and 8030% normalized leakage current improvement for both NFET and PFET under the bulk substrate, respectively. Compared with bulk-substrate devices, the SOI-substrate devices further significantly improve the leakage current. Due to enhanced off-state current leakages in the 100% covered bulk-substrate device, the static power demonstrates a notable improvement (about 98.1%), compared to 60% covered devices. Hence, the utilization of a 60% covered channel will result in a degradation of both device and circuit performance. The observation of GAA NS MOSFET indicates that an increase in the bottom parasitic channel coverage ratio leads to a positive impact on power consumption and leakage currents. The examination presented here is useful in the fabrication of stacked GAA NS MOSFETs.

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

The present era of the Internet of Things (IoT) having intelligent functionalities in solving problems pertaining to realtime mission-critical systems has brought an immense revolution in diverse fields including healthcare and navigation systems. However, to the best of our knowledge, the potential of IoT has not been fully exploited yet in the field of the energy sector. We argue that there is an immense need to shift the traditional mission-critical electric power system architecture to IoT-based fully orchestrated architecture in order to increase efficiency, as billions of investment is reserved for the energy sector globally. Since network orchestration deals with auomating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the architecture. The mission-critical systems with urgent task execution often suffer from issues of missing task deadlines. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of nonrenewable energy resources and maximizing the use of renewable energy resources. Moreover, major components of IoT task orchestration such as task mapping and task scheduling are also enhanced using NLP and PSO optimization modules. The proposed task scheduling algorithm incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid mission-critical system. Furthermore, the comparison with state-of-the-art scheduling algorithms showed that the task starvation rate is reduced to 16% and 12% when compared with RR and FEF algorithms, respectively.

It is difficult to effectively operate a dual-arm robot using only the information written in a cooking recipe. To cope with this problem, this paper proposes a graph based approach on bimanual cooking motion planning from a cooking recipe. In our approach, we first decompose the cooking recipe into graph elements. Then, we try to connect the graph elements taking into account the attributes of the input/output nodes. If two graph elements cannot be connected each other, we search for a graph element that can be inserted between them from the database of graph elements. Since the constructed graph includes the whole sequence of the robot’s motions to perform the cooking task, we can generate a task sequence of a dual-arm manipulator simultaneously performing two different tasks by using two arms. Through experimental study, we show that it is possible to generate robot motions from a cooking recipe and perform the cooking motions while simultaneously moving the left and right arms.

Spatial crowdsourcing emerges as a new computing paradigm that enables mobile users to accomplish spatio- temporal tasks in order to solve human-intrinsic problems. Existing crowdsourcing systems critically use centralized servers for interacting with workers and making task assignment decisions. These systems are hence susceptible to issues such as the single point of failure and the lack of operational transparency. Prior work, therefore, turns to blockchain-based decentralized crowdsourcing systems, yet still suffers from problems of lacking efficient task assignment scheme, requiring a deposit to an untrusted system, low block generation speed, and high transaction fees. To address these issues, we design a blockchain-based decentralized framework for spatial crowdsourcing, which we call SC-EOS. Our system does not rely on any trusted servers, while providing efficient and user-customizable task assignment, low monetary cost, and fast block generation. More importantly, it frees users from making a deposit into an untrusted system. Our framework can also be extended and applied to generic crowdsourcing systems. We implemented the proposed system on the EOS blockchain. Trace-driven evaluations involving real users show that our system attains the comparable task assignment performance against a clairvoyant scheme. It also achieves 10× cost savings than an Ethereum-based implementation.

This study aimed to investigate the efficacy of DìRelaxTM, a nutraceutical formulated to reduce anxiety in dogs. The CBARQ questionnaire, some clinical investigations, and the impossible task test were performed in dogs before and after the treatment. Results showed an ameliorative effect on the performances of treated dogs during the solvable phases, with a significant decrease of the time needed to solve the task. No behavioral difference was found between treated and untreated anxious dogs during the unsolvable phase. According to the results from the C-BARQ questionnaire, some of the behaviors appear improved. In general, this study suggests that DiRelaxTM can be safely administered with no adverse effects and can exercise a beneficial effect on anxious dogs by enhancing their cognitive abilities.

Sensory integration is an essential function of human life, and its decline causes difficulties in the quality of life, especially in older adults. In this study, we proposed an arm-reaching task based on a virtual reality head-mounted display (VR-HMD) system to assess sensory integration in daily life, and examined whether reaching task performance was associated with age and resting-state functional connectivity (rsFC) between the brain regions of interest involved in sensory integration. We hypothesized that performance during a reaching task with multiple cognitive loadings would be affected by aging. The gap angle between the reaching hand and target positions was compared between older and middle-aged adults as a measure of sensory integration function. In addition, rsFC, which was significantly correlated with the gap angles of individual participants in the older adult group, was identified using resting-state functional magnetic resonance imaging data. The mean gap angle was significantly larger in the older adults than in the middle-aged group and was also significantly correlated with connections between the left primary motor area and the left inferior temporal gyrus, temporo-occipital part. Because the connectivity between the areas is known to be related to visuomotor integration, the results suggest the involvement of visuomotor integration in the decline of sensory integration function in old age and the validity of the gap angle during the proposed VR-HMD task as an index for the decline in function.

Some studies investigated the relationship between frequency domain analysis of heart rate variability and the cognitive performance at the Stroop task. We propose a combined assessment also of trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac and motor variables based on a principal component analysis. We also assessed the change on this result after an intervention of two months aiming at improving the perception-action link. At baseline, the PCA correctly identified 3 components: one related to cardiac variables, one to trunk motion and one to the performance of Stroop task. After the intervention, only two components were found with trunk symmetry and range of motion, accuracy and time to complete the Stroop task and low frequency heart rate variability aggregated in a single component by PCA. It suggested that this protocol was effective in investigating the embodied cognition and we defined this approach as “embodimetrics”.

Clinical screening tests for balance and mobility often fall short of predicting fall risk. Cognitive distractors and unpredictable external stimuli, common in busy natural environments, contrib-ute to this risk, especially in the elderly. Less is known about the effects of upper sensory-motor coordination such as coordinating one’s hand with an external stimulus. We combined move-ment sonification and affordable inertial motion sensors to develop a task for the precise meas-urement and manipulation of full-body interaction with stimuli in the environment. In a double-task design, we studied how a supra-postural activity affected quiet stance. The supra-postural task consisted of rhythmic synchronization with a repetitive auditory stimulus. The stimulus was attentionally demanding because it was being modulated continuously. The participant’s hand movement was sonified in real-time, and their goal was to synchronize their hand move-ment with the stimulus. In the unpredictable condition, the tempo changed at random points in the trial. A separate sensor recorded postural fluctuations. Young healthy adults were compared to elderly participants without known risk of falling. Results supported the hypothesis that su-pra-postural coordination would entrain postural control. The effect was stronger in the elderly, supporting the idea that diminished reserve capacities reduce the ability to isolate postural con-trol from sensory-motor and cognitive activity.

To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual-task on lower limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. Participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and co-contraction in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle c-contraction between the swing and stance limb compared to the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared to the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.

Benefits of baby swimming have been established, but there is limited knowledge about the methodologies used by practitioners. To address this knowledge gap, this study aimed to categorize and classify the equipment and environmental used in baby swimming programs worldwide. A descriptive categorical content analysis approach was employed to analyse videos of baby swimming programs obtained from YouTube. The framework created was validated for interrater reliability, and a pilot study was conducted to classify videos from different countries based on the equipment and environment used. Results indicated that the physical environments observed were pool tanks and swimming pools with and without depth reduction zones, while the equipment used included assistive devices, toys, and clothes. This study represents a significant first step in the development of a comprehensive tool to systematize and characterize the methodologies used in baby swimming programs globally.

When following a prescribed route, we need to decide at each intersection which way to proceed. The present work addressed several factors that might influence the difficulty of this decision making process. Participants repeatedly followed a route through a virtual maze with twelve or eighteen intersections, and with two or three choices per intersection. One group performed task S, which promoted decision making by the serial order strategy since all intersections looked alike. Another group performed task SA, which allowed the use of the serial order and the associative cue strategy since unique visual cues were presented at each intersection. We found that in both tasks, participants were more accurate in making decisions on routes with twelve rather than eighteen intersections, and more accurate by a similar amount on routes with two rather than three choices. Reaction time in task SA was reciprocal to accuracy; reaction time in task S was generally lower and route-independent. Accuracy in task SA was similar for participants who experienced smooth transport across intersections and those who experienced abrupt transport, while accuracy in task S was lower for participants who experienced smooth rather than abrupt transport across intersections. We conclude that the number of intersections and the number of choices were equivalent determinants of route following difficulty. We further conclude that optic flow during turns at intersections interfered with anticipatory decision making in task S; hence natural (smooth) transport across intersections did not enhance route following performance.

This study aims to examines three elements shape leadership in Leader-Member Exchange (LMX) theory as a relationship and process. LMX quality is important for the company, because it relates to employee behavior and attitudes, including improving employee performance. The research method applied literature review using description logic and systematics. In this article the theory will be observed specially the effect of LMX on employee performance and antecedents of LMX. The results of the study found that the effect of LMX quality on performance is determined by the characteristics of the task as antecedent LMX in the company.

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailable time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailable time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailable time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

Background: Most sepsis patients died during their first episode and the long-term survival post discharge was low. Major adverse cardiovascular events and repeated infections were regarded as the major causes in such conditions. No definite medications were proven to effectively improve the long-term outcome. We aimed to examine the benefit of statins on the long-term outcome of survived sepsis patients. Methods: Between 1999 and 2013, a total of 220,082 patients with the first episode of sepsis hospitalization were included and 134,448 ones (61.09%) survived to discharge. The survived patients who subsequently had statins use of >30 cumulative defined daily doses (cDDDs) post discharge were defined as the statin users. After propensity score matching of 1:5, a total of 7,356 and 36,780 survived patients were retrieved as the study (statin-users) and comparison cohort (non-statin users), respectively. The main outcome was the long term survival post discharge. HR with 95% CI was calculated using the Cox regression model to evaluate the effectiveness of statins with further stratification analyses according to cDDDs. Results: The study cohort, that is, the statin users had an adjusted HR of 0.29 (95% CI, 0.27-0.31) in long term mortality rate compared with the comparison cohort. For statin users with the cDDDs of 30–180, 180–365, and >365, the adjusted HRs were 0.32, 0.22, and 0.16, respectively, (95% CI, 0.30-0.34, 0.19-0.26, and 0.12-0.23, respectively) compared with the non-statin users (defined as statins use <30 cDDDs post discharge), and the P for trend <.0001. In the sensitivity analysis, after excluding the survived patients who died within three and six months post discharge, the adjusted HR of statins use remained significant (0.35, 95% CI 0.32-0.37 and 0.42, 95% CI 0.39-0.45, respectively). Conclusions: Statins decreased the long-term mortality post sepsis. Further randomized control trial deserved to be conducted to confirm this observation.

The enhancement of Fusarium head blight (FHB) resistance is one of the best options to reduce mycotoxin contamination in wheat. This study has aimed to verify that the genotypes with high tolerance to deoxynivalenol could guarantee an overall minimization of the sanitary risk, by evaluating the contamination of regulated, modified and emerging mycotoxins on durum wheat cvs with different degrees of FHB susceptibility, grown under different meteorological condi-tions, in 8 growing seasons in North-West Italy. The years which were characterized by frequent and heavy rainfall in spring, were also those with the highest contamination of deoxynivalenol, zearalenone, moniliformin and enniatins. The most FHB resistant genotypes resulted in the lowest contamination of all the mycotoxins but showed the highest deoxyniva-lenol-3-glucoside/deoxynivalenol ratio and moniliformin/deoxynivalenol ratio. An inverse re-lationship between the amount of deoxynivalenol and the deoxyniva-lenol-3-glucoside/deoxynivalenol ratio was recorded for all the cvs and all the years. Converse-ly, the enniatins/deoxynivalenol ratio had a less intense relationship with cv tolerance to FHB. In conclusion, even though the more tolerant cvs, showed higher relative relationships between modified/emerging mycotoxins and native/target mycotoxins than the susceptible ones, they showed lower absolute levels of contamination of both emerging and modified mycotoxins.

Background/Objectives: Population aging is rapidly increasing, and the importance of preventive medicine has been stressed. Health checkup, diet, and exercise are of paramount importance. This study aimed to evaluate the effectiveness of a personalized dual-task intervention that combined exercise with cognitive tasks in improving physical and cognitive functions among independently living elderly individuals. Methods: Participants were aged &gt;65 years who were mostly independent in their activities of daily living, divided into 2 groups. The group receiving the 20-min robot-assisted session was compared to the group receiving traditional functional restoration training. This randomized trial assessed the impact of this intervention on the 30-s chair stand test score and Montreal Cognitive Assessment-Japanese version score of the participants. Results: Both scores significantly improved in the intervention group, indicating enhanced lower-limb function and cognitive capabilities. Conclusions: These findings suggest that integrating cognitive tasks with physical exercise can be a potent strategy to improve overall well-being in the elderly, offering valuable insights for designing comprehensive preventive health programs tailored to this demographic.

The golden ratio, also known as Phi (ϕ ≈ 1.618034), attracted the interest of mathematicians, artists, and intellectuals for many centuries, probably from when it was discovered in human anthropometry. Even in recent times, researchers found the presence of golden ratio in Renaissance paintings and aesthetic preferences. The reasons behind the fascination with the golden ratio remain unclear, but it has been suggested that stimuli containing this proportion are often perceived as beautiful. However, evidence is conflicting, and the literature struggles to establish the existence of individual preferences for golden ratio. To gain new insights into the nature of these preferences, one hundred participants completed an implicit association task, with either golden ratio or random stimuli presented with positive or negative words. Participants initially categorized the stimuli based on their assigned categories. Then, we assessed their explicit preferences by asking to rate the stimuli in terms of pleasantness and by completing a line bisection task and the Ultimate Game. The results revealed the typical effects observed in implicit association tasks, with improved response times and accuracy when golden ratio stimuli were associated with positive word categories. In contrast, explicit ratings yielded mixed results. We discuss our findings in relation to previous studies that have explored this issue, highlighting the ongoing debate surrounding preferences for the golden ratio.

Age-related changes in behavior and sensory perception have been observed in a wide variety of animal species. In ants and other eusocial insects, workers often progress through an ordered sequence of olfactory-driven behavioral tasks. Notably, these behaviors are plastic, and workers adapt and rapidly switch tasks in response to changing environmental conditions. In the Florida carpenter ant, smaller minors typically perform most of the work needed to maintain the colony while the larger majors are specialized for nest defense and rarely engage in these routine tasks. Here, we investigate the effects of age and task group on olfactory responses to a series of odorant blends in minor and major worker castes. Consistent with their respective roles within the colony, we observed significant age-associated shifts in the olfactory responses of minors as they transitioned between behavioral states, whereas the responses of majors remained consistently low regardless of age. Furthermore, we identified a unitary compound, 3-methylindole, which elicited significantly higher responses and behavioral aversion in minor nurses than in similarly aged foragers suggesting that this compound may play an important role in brood care. Taken together, our results suggest that age- and task-associated shifts in olfactory physiology may play a critical role in the social organization of ant colonies.

How language mediates emotional perception and experience is poorly understood. The present event-related potential (ERP) study examined the explicit and implicit processing of emotional speech to differentiate the relative influences of communication channel, emotion category and task type in the prosodic salience effect. Thirty participants (15 women) were presented with spoken words denoting happiness, sadness and neutrality in either the prosodic or semantic channel. They were asked to judge the emotional content (explicit task) and speakers’ gender (implicit task) of the stimuli. Results indicated that emotional prosody (relative to semantics) triggered larger N100 and P200 amplitudes with greater delta, theta and alpha inter-trial phase coherence (ITPC) values in the corresponding early time windows, and continued to produce larger LPC amplitudes and faster responses during late stages of higher-order cognitive processing. The relative salience of prosodic and semantics was modulated by emotion and task, though such modulatory effects varied across different processing stages. The prosodic salience effect was reduced for sadness processing and in the implicit task during early auditory processing and decision-making but reduced for happiness processing in the explicit task during conscious emotion processing. Additionally, across-trial synchronization of delta, theta and alpha bands predicted the ERP components with higher ITPC values significantly associated with stronger N100, P200 and LPC enhancement. These findings reveal the neurocognitive dynamics of emotional speech processing with prosodic salience tied to stage-dependent emotion- and task-specific effects, which can reveal insights to research reconciling language and emotion processing from cross-linguistic/cultural and clinical perspectives.

Background: Simulations have historically aided training programs by providing a realistic and holistic replication of professional scenarios and procedures. Simulations have developed over the past 40 years to include varying fidelities and modalities of simulation. Learning in a simulation-centered environment has benefits, ranging from improved patient care to specific skills acquisition while catering to students’ numerous and varied learning approaches. Simulation is a multifaceted field that benefits all parties, the teachers, the learners, and the patients. The application of simulation to medical education and its amalgamation with other modes and substitutes allows for a more integrated learning and testing curriculum that advances the current trajectory of medical education. Such developments, however, are limited to resource rich areas, leaving behind low-middle income countries to use traditional, less evolved methodologies and practices. This review aimed to explore different aspects of simulation and focus specifically on low-cost task trainers and their accessibility. Method: The purpose of the study was to assess the accessibility of low-cost task trainers in terms of cost-effectiveness, distribution, validation, and frequency within specific specialties. To do so, 84 PubMed publications have been screened, and 39 filtered research studies have collected the necessary data. After analyzing the papers, we classified the following information – process, specialization, validation (y/n), costs, development location, and year of publication. Results: After carefully analyzing the accumulated data from the selected 39 publications, we found that most studies (i.e., 6 out of 39) were published in 2020. Emergency Medicine was the most common specialty for which low-cost trainers were developed (9 out of 39 procedural simulators); Otolaryngology followed this with 8 out of 39 trainers and general surgery with 7/39 of the task-trainers. The price ranges fluctuated and fell within the price bracket of USD 0 to USD 400 collectively. Our review also uncovered the concentration of development of such innovations solely in high income countries (HICs). Conclusion: Simulation is an invaluable tool applicable to a diverse range of phases of medical education. Future conjunction of simulation with low-cost substitutes along with increased encouragement and enthusiasm towards developing cost effective simulation-based learning environments (SBLEs) with the reserves and requirements of these areas in mind may prove to be a reliable option for low and middle resource settings

Despite the popularity of Zumba in several countries, research is scarce about its impact on older adults. Meanwhile, the integration of cognitive tasks with physical exercises, known as dual-tasking, is an evolving strategy to facilitate activities for older people. This study investigated the perceptions of older adults regarding Zumba and the potential of implementing it in a dual-task program. We conducted a qualitative-descriptive research involving 44 Filipino older adults from August to November 2020. Content analysis was employed to analyze the data. Four themes were identified: moving towards match or mismatch; balancing benefits with burdens; dual-tasking as innovative yet potentially challenging; and overcoming barriers with enablers. While Zumba is an inclusive and beneficial activity, individual and contextual limitations could hinder its suitability for older people. Dual-tasking in Zumba was also recognized as an innovative approach, although challenges should be addressed to promote its utility. Several strategies could support the design of these programs in communities. This is the first study to explore older adults’ perceptions towards Zumba and its potential utilization as a dual-tasking program. Findings could guide the implementation of appropriate Zumba and dual-tasking activities that seek to integrate cognitive and physical training for older adults.

Multi-task Gaussian processes (MTGPs) are a powerful approach for modeling structured dependencies among multiple tasks. Researchers on MTGPs have contributed to enhance this approach in various ways. Current MTGP methods, however, cannot model nonlinear task correlations in a general way. In this paper we address this problem. We focus on spectral mixture (SM) based kernels and propose an enhancement of this type of kernels, called multi-task generalized convolution spectral mixture (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and mixtures dependency, including time and phase delay, not only between different tasks but also within a task at the spectral mixture level. Each task in MT-GCSM has its own generalized convolution spectral mixture kernel (GCSM) with a different number of convolution structures and all spectral mixtures from different tasks are dependent. Furthermore, the proposed kernel uses inner and outer full cross convolution between base spectral mixtures, so that the base spectral mixtures in the tasks are not necessarily aligned. Extensive experiments on synthetic and real-life datasets illustrate the difference between MT-GCSM and other kernels as well as the practical effectiveness of MT-GCSM.

Horses’ ability to adapt to new environments and to acquire new information plays an important role in handling and training. Social learning in particular would be very adaptive for horses as it enables them to flexibly adapt to new environments. In the context of horse handling, social learning from humans has been rarely investigated but could help to facilitate management practices. We assessed the impact of human demonstration on spatial problem-solving abilities in horses using a detour task. In this task, a bucket with a food reward was placed behind a double-detour barrier and horses (n = 16) received a human demonstration or no demonstration. Horses were allocated to two test groups of 8 horses each, which experienced the two treatments in a counterbalanced order. We found that horses did not solve the detour task faster with human demonstration. However, both test groups improved rapidly over trials. Our results suggest that horses prefer to use individual rather than social information when being confronted with a spatial problem-solving task.

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM_2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ_2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

Nanoparticles are of great importance for various scientific and technological applications, drug discovery, ecology, molecular biology, etc. Size is a basic characteristic of any particle, and dynamic light scattering is beyond competition when measuring particle size in dispersions ranging from nanometers to micrometers. Unfortunately, this method has a serious problem in processing and interpreting experimental data. Instrument noise and polydispersity of the sample lead to the situation when one initial autocorrelation function corresponds to several different particle size distributions. Sometimes these different solutions of the inverse task can be found by changing parameters in the software. Sometimes the scientist cannot be suspicious of possible other solutions, having one result from the program. In this article I demonstrate the problem on model and experimental data using three known programs: CONTIN, the Malvern Zetasizer and DynaLS. I also present my own free program Autocor, which allows to test our own hypotheses about the modality and shape of the particle size distribution by optimizing the parameters of our own models with a small number of degrees of freedom.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

Task-Based Instruction (TBI) is a dynamic approach in tertiary language education that emphasizes the integration of authentic and meaningful tasks to enhance language learning outcomes. This study aims to explore the implementation of TBI in the Philippines, investigate its cognitive benefits and transformative nature, identify pedagogical considerations, and provide recommendations for optimization. The study employed a qualitative research design, with data collected from five private schools in Iligan City. A purposive sampling procedure was used to select 20 participants who engaged in interviews, focus group discussions, and thematic analysis. The results revealed that TBI positively influenced language proficiency, cognitive development, learner autonomy, intercultural competence, and pedagogical considerations. Participants reported enhanced language skills and increased confidence in real-life language use. They also highlighted the development of critical thinking, problem-solving, and decision-making skills through engaging in challenging tasks. TBI was found to empower learners by promoting autonomy, ownership, and self-directed learning. Additionally, it cultivated intercultural communicative competence and fostered appreciation for diverse perspectives. Pedagogical considerations included task design, clear instructions, scaffolding, and feedback, while recommendations focused on optimizing TBI, integrating technology-enhanced learning, and supporting professional development for educators. This research contributes to the growing body of literature on TBI in tertiary language education and underscores its potential for creating transformative learning experiences. The findings provide valuable insights for educators, curriculum designers, and policymakers in enhancing language education practices in the Philippines and beyond.

This article considers the basic psychological needs theory and tries to determine how much autonomy support from a coach is required for basic psychological needs and how autonomy, competence, relatedness, and the psychological motivational climate can explain task and ego involvement in a group of young elite ice hockey players. Our three hypotheses were as follows: autonomy support from the coach will have an impact on motivational involvement, all three basic needs will have an impact on motivational involvement, and motivational involvement will be explained by the motivational climate. A total of 175 male elite ice hockey players from Norway, ranging in age from 15 to 18 years old, answered questionnaires about autonomy support, perceived motivational climate, achievement goal orientation, and basic psychological needs. A multiple regression analysis was run to predict ego–task involvement using autonomy support from the coach, the need for autonomy, the need for competence, the need for relatedness, the task climate, and the ego climate. The only two variables that statistically significantly predicted ego–task involvement were autonomy support from the coach and the ego climate. The analysis revealed that the athletes had a higher score on task than ego involvement, but when this was transformed into two variables (high and low) for task and ego involvement, we found that most players scored high for both task and ego involvement. We found that autonomy from the coach had a positive relationship with a high score for players on both task and ego involvement. We also found that the three basic psychological needs had no impact on the motivational involvement of the athletes. Lastly, we found that the ego climate had an impact on the motivational involvement. There was a positive relationship between a high score for the ego climate and a high score for both ego and task involvement.

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both the synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

Trace amine-associated receptors (TAARs) are a family of G protein-coupled receptors present in mammals in the brain and in several peripheral organs. Apart from its olfactory role, TAAR5 is expressed in the major limbic brain areas and regulates brain serotonin functions and emotional behaviors. However, most of its functions remain undiscovered. Given the role of serotonin and limbic regions in some aspects of cognition, we used a temporal decision-making task to unveil a possible role of TAAR5 in cognitive processes. We found that TAAR5 knock-out (KO) mice showed a generally better performance due to a reduced number of errors and displayed a greater rate of improvement at the task than WT littermates. However, task-related parameters, such as time accuracy and uncertainty have not changed significantly. Overall, we show that TAAR5 modulates specific domains of cognition, highlighting a new role in brain physiology.

The development in information technology has played an influential role in transforming the restaurant industry services. Therefore, this research’s main agenda is to investigate factors that motivate employees to adopt and continue using information technology services by integrating two famous information system (IS) theories, namely, task technology fit (TTF) and technology continuance theory (TCT). The extant integrative perspective model details the cause-effect relationship between technology adoption and continuance intention. The positivist paradigm forms the basis of this research design, and the approach followed is quantitative research. Using the stratified random sampling technique, the empirical data was collected from 417 restaurant industry employees in the US (United States) on a five-point Likert scale. The PLS-SEM technique was utilized to analyze data while using Smart PLS 3 because of its suitability and wider application currently in the hospitality sector. Results suggest that the recently developed integrated technology continuance research model has considerable influence on predicting pre- and post-adoption behavior with continuance intention for technology usage within the restaurant industry. All hypotheses were found significant except one for the direct association of hedonic motivation and continuance intention of technology adoption. Moreover, the results revealed that factors like perceived security & information privacy and assisting conditions were the most important factors in determining the usage of information technology with continuance intention. Unlike previous research studies that focus majorly only on issues before adoption of informational technology usage, the current focus on investigating continuance intention toward information technology usage by focusing on factors that can also boost post-adoption behavior and pre-adoption usage information technology.

Aims: Adolescence is a critical period because the brain is involved in the process of maturation that entails cognitive functions. On the way of maturation, an individual’s inhibitory control undergoes many changes and becomes vulnerable to different thrill-seeking like smoking, drinking, and so on. Smoking is highly prevalent among teenagers that are trying to take control of their behaviors in order to join society. They experience antisocial behavior too which is a trait that can lead adolescents to addiction. This trait is an inevitable part of psychopathy. Inhibitory deficits and psychopathy have been widely reported in addiction studies. The current study tried to investigate the relationship between psychopathic traits and behavioral inhibition between male smokers and non-smoker teenage students.Materials & Methods: Statistical sample of this research is 62 teenage students aged 17 years that are divided into smoker and non-smoker groups. The participants have been chosen through random sampling from the population of 10 high schools. The data have been gathered in Kordkoy and Gorgan in Golestan province. Behavioral bias has been measured by Go/NoGo task and psychopathic traits through youth psychopathic traits inventory. Also, the short form of Wechsler Adult Intelligence Scale test has been executed and used as a control variable.Findings: A meaningful difference has been found between the performance of smoker and non-smoker groups in Go/No Go task and psychopathic traits that are smokers performed weaker in comparison with non-smokers and psychopathic traits of smokers were meaningfully higher than non-smokers. On the other hand, there was no significant difference between these two groups in their Wechsler Adult Intelligence Scale scores.Conclusion: The results have shown that smokers have higher psychopathic traits and lower behavioral inhibition when compared with their non-smoker peers. According to the results of current research, smoking can decline the cognitive functions.

Recent studies suggest that the developmental curves in adolescence, related to the development of executive functions, could be fitted to a non-linear trajectory of development with progressions and retrogressions. Therefore, the present study proposes to analyze the pattern of development in Perceptual Inhibition (PI), considering all stages of adolescence (early, middle, and late) in intervals of one year. To this aim, we worked with a sample of 275 participants between 10 and 25 years, who performed a joint visual and search task (to measure PI). We have fitted exGaussian functions to the probability distributions of the mean response time across the sample and performed a covariance analysis (ANCOVA). The results showed that the 10- to 13-year-old groups performed similarly in the task and differ from the 14- to 19-year-old participants. We found significant differences between the older group and all the rest of the groups. We discuss the important changes that can be observed in relation to the nonlinear trajectory of development that would show the PI during adolescence.

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The assigning task to the lecturer is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign fixed tasks (the time and required skills to be fixed) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that optimal makes decisions require the trade-off presence of trade-offs between two or more conflicting objectives. To solve this, we use the Compromise Programming approach to multi-objective programming. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, the model and algorithm tested with real scheduling data collected at the Computing Fundamental Department, FPT University, Hanoi, Vietnam.

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

Background: Amyloid beta peptides (Aβ) have been identified as the main pathogenic agents in Alzheimer’s disease (AD). Soluble Aβ oligomers, rather than monomer or insoluble amyloid fibrils, show membrane-binding capacity to red blood cells (RBCs) and trigger several morphological and functional alterations in RBCs that can result in impaired oxygen transport and delivery. Since bioactive lipids have been recently proposed as potent protective agents against Aβ toxicity, we investigated the role of sphingosine-1-phosphate (S1P) in signaling pathways involved in the mechanism underlying ATP release in Aβ-treated RBCs. Methods: In RBCs following different treatments, ATP, 2,3 DPG, cAMP levels, and caspase 3 activity were determined by spectrophotometric and immunoassay. Results: S1P rescued the inhibition of ATP release from RBCs triggered by Aβ through a mechanism involving caspase-3 and restoring 2,3 DPG and cAMP levels within the cell. Conclusions: These findings reveal the molecular basis of S1P protection against Aβ in RBCs and suggest new therapeutic avenues in AD.

Sleep quality is known to affect automatic and executive brain functions such as gait control and cognitive processing. This study aimed to assess the effect of dual tasks on gait spatiotemporal parameters among young adults with good and poor sleep quality in the United Arab Emirates. In total, 65 young adults with a mean age of 21.1±2.5 were assessed for gait analysis during single-task and dual-task conditions. The participants’ sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and gait was assessed using the BTS Gaitlab System. The participants were asked to walk at natural speed as a single-task condition, followed by walking while performing a cognitive task as a dual-task condition. The parameters assessed included the gait velocity (m/s), cadence (steps/min), step width (m), and stride length (m). The DTC on each gait parameter was calculated. The Mann–Whitney U test was used to compare the differences in the DTC on gait variables between the good and poor sleep quality groups, and the Spearman correlation test was used to assess the correlation between total PSQI scores and the DTC. At a significance level of p&lt;0.05, a significant difference in cadence between the two sleep quality groups was observed, in addition to a positive correlation between sleep quality and the DTC effect on gait mean velocity, cadence, and stride length. Our findings also revealed a greater DTC on participants with poorer sleep quality. These findings contribute to our understanding of the significance of sleep quality in gait performance while multitasking in younger populations.

The purpose of this study is to explore the effects of dual-task training, including cognitive tasks, on cognitive and bodily functioning and β-amyloid levels in Alzheimer's dementia patients. The subjects were 34 inpatients diagnosed with Alzheimer's dementia at a nursing hospital located in Gyeongsansi, South Korea. The patients were randomly divided into a dual-task group (n = 16) and a single-task group (n = 18). The dual-task group performed cognitive tasks at the same time as exercising tasks, while the single-task group performed only exercise tasks. Each group was trained for 30 minutes three times a week for eight weeks. The Mini-Mental State Examination was used to measure the patients’ cognitive function. Static and dynamic balance were measured to evaluate bodily functioning. Static balance was measured using Biorescue, while dynamic balance was measured using the Berg Balance Scale. Blood analysis was performed to measure levels of β-amyloid, which is known to cause Alzheimer's dementia. Both groups exhibited statistically significant improvements in gait function after the training (p < .05). The dual-task group exhibited statistically significant differences in cognitive function, static and dynamic balance function, and β-amyloid levels after training (p < .05). A significant difference was observed between the two groups (p < .05). Dual-task activities were found to be effective in improving cognitive and bodily functioning and reducing β-amyloid levels in Alzheimer's dementia patients. Therefore, dual-task training is thought to be an effective method of treating and preventing Alzheimer's dementia.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

Ageing is associated with a decline in both Nrf2 activity and DNA repair efficiency, leading to the accumulation of DNA damage and an increased risk of cancer. Understanding the mechanisms behind the increased level of damaged DNA during ageing is crucial for developing interventions to mitigate age-related cancer risk and improve overall health in older individuals. (Poly)phenols are plant-derived bioactive compounds that undergo metabolism into simpler phenolic catabolites following ingestion. While these compounds have been associated with various health benefits, further analysis of their bioavailable catabolites is necessary. To this end, four phenolic catabolites derived from colonic microbiota were investigated for their ability to reduce DNA damage in response to oxidative challenge and modulate the Nrf2-Antixoidant Response Element pathway. Normal and adenocarcinoma colonocyte cell lines (CCD 841 CoN, & HT29 respectively) were exposed to sub cytotoxic concentrations (>10 µM,) of hydroxylated (4-hydroxybenzoic acid, 3-(3’-hydroxyphenyl) propionic acid) and non-hydroxylated (benzoic acid or 3-phenylpropionic acid) phenolic acids. Using the COMET assay, each catabolite demonstrated significant (p<0.001) geno-protective activity, albeit with varying potency. They also significantly modulated expression of key genes in the Nrf2-ARE pathway, including Nrf2, Heme oxygenase-1 (HO-1), and NAD(P)H dehydrogenase quinone 1 (NQO1). Hydroxylation of the phenolic acids appeared to have a limited effect with respect to bioactivity. Overall, the colon derived phenolic metabolites, when assessed at physiologically relevant concentrations, reduced DNA damage in both normal and adenocarcinoma colonic cells in response to oxidative challenge, mediated – at least in part via upregulation of the Nrf2-ARE pathway.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

To compare the quality of low-energy VMIs obtained with three DECT platforms according to phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were assessed on VMIs from 40 to 70keV. Highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26-cm (10.2±1.3%) and 31-cm (7.0±2.5%), and the opposite for 36-cm (-4.2±2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f50) values were found with DSCT. For all energy levels, f50 values were higher with SFCT-2nd than SFCT-1st for 26-cm (3.2±0.4%) and the opposite for 31-cm (-6.9±0.5%) and 36-cm (-5.6±0.7%). The lowest d’ values were found with SFCT-1st. For all energy levels, d’ values were lower with SFCT-2nd than with DSCT for 26-cm (-6.2±0.7%), similar for 31-cm (-0.3±1.9%) and higher for 36-cm (5.4±2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had lower noise magnitude and higher detectability for the 26-cm but the opposite was true for the 36-cm.

Studies of discrete pride in the workplace are both few and on the rise. We examined what has, to date, been yet unstudied: the impact that a leader’s expressions of authentic and hubristic pride can have on the followers at that moment, and on their feelings about their task, leader, and group. Students working in groups building Lego structures rated their perceived leader regarding expressions of pride, both authentic and hubristic. Students who perceived the leader as expressing more authentic pride rated the task, group (satisfaction and cohesion), and leader more positively; while the reverse was generally true for perceptions of expressions of hubristic pride. We found these effects both at the individual level, and at the group level. We also predicted and found moderation for the type of task worked on, creative or detailed. Implications abound for leader emotional labor and emotion management.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

Spinocerebellar ataxia type 3 is a neurodegenerative disease caused by an abnormal expansion of CAG repeats in the disease gene ATXN3, leading to prolonged polyglutamine (polyQ) tracts in the respective protein ataxin-3. The polyQ-expanded protein forms polyQ-containing aggregates in several brain regions which leads to neuronal cell loss. Autophagy is thought to play a significant role in clearing these polyQ-containing protein aggregates. Therefore, the autophagy mechanism was investigated in SCA3 with focus on the autophagic protein p62/sequestosome-1. Human and mouse RNA sequencing data were analyzed to determine RNA expression level of autophagic genes. Additionally, autophagy was studied in the SCA3 304Q knock-in mouse model and through several cell culture experiments using HEK-293T cells transfected with ATXN3 of differ-ent polyQ lengths. For cell culture experiments, autophagy was induced with rapamycin treat-ment, proteasomal degradation was blocked with MG132 and degradation of autophagic pro-teins was investigated using cycloheximide treatment. Here, we report an increased autophagy impairment with SCA3 disease progression seen in HEK-293T cells transfected with ATXN3 plasmid with expanded polyQ tracts, higher aged 304Q-knock-in mice, or human post-mortem brain material. Furthermore, co-localized aggregation of the autophagic protein p62 and the dis-ease protein ATXN3 was observed in the course of SCA3 disease. Therefore, we suggest a signifi-cant role for p62 in aggregate formation seen in the neurodegenerative disease SCA3.

To understand the biological relevance and mode of action of artificial protein ligands, crystal structures with their protein targets are essential. Here, we describe and investigate all known crystal structures that contain a so-called “molecular tweezer” or one of its derivatives with an attached natural ligand on the respective target protein. The aromatic ring system of these compounds is able to include lysine and arginine side chains, supported by one or two phosphate groups that are attached to the half-moon shaped molecule. We analyse the corresponding crystal structures with 14-3-3 proteins in complex with mono- and diphosphate tweezers. Furthermore, we solved crystal structures of two different tweezer variants in complex with the enzyme 1-Pyrroline-5-carboxyl-dehydrogenase (P5CDH), and found that the tweezers are bound to a lysine and methionine side chain, respectively. The different binding modes and their implications for affinity and specificity are discussed, as well as the general problems in crystallizing protein complexes with artificial ligands.

For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affect the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination resulting in a large baseline error, leads to the modulation error in azimuth and the slope error in range for timely Digital Elevation Model (DEM) generation. In order to address this problem, a baseline estimation method based on external DEM is proposed in this paper. Firstly, according to the characteristic of the real-time orbit of GF-3 images, orbit fitting is executed to remove the non-linear error factor. Secondly, the height errors are obtained in slant-range plane between Shuttle Radar Topography Mission (SRTM) DEM and the GF-3 generated DEM after orbit fitting. At the same time, the height errors are used to estimate the baseline error which has a linear variation. In this way, the orbit error can be calibrated by the estimated baseline error. Finally, DEM generation is performed by using the modified baseline and orbit. This procedure is implemented iteratively to achieve a higher accuracy DEM. Based on the results of GF-3 interferometric SAR data for Hebei, the effectiveness of the proposed algorithm is verified and the accuracy of GF-3 real-time DEM products can be improved extensively.

The pivotal role of the basolateral amygdala (BLA) in the emotional modulation of hippocampal plasticity and memory consolidation is well established. Specifically, multiple studies have demonstrated that the activation of the noradrenergic (NA) system within BLA governs these modulatory effects. However, most of the current evidence has been obtained by direct infusion of synthetic NA or beta-adrenergic agonists. In the present study, we aimed to investigate the effect of endogenous NA release in the BLA, induced by a natural aversive stimulus (coyote urine), on memory consolidation for a neutral hippocampal-dependent task. We combined a weak version of the object location task (OLT) with mild predator odor exposure (POE) for our experiments. To investigate the role of the BLA in memory modulation, a subset of the animals (Wistar rats) were treated with the non-selective beta-blocker propranolol at the end of the behavioral procedures. Hippocampal tissue was collected either 90 minutes after drug infusion or 24 hours after the OLT test. The samples were used to determine the levels of phosphorylated CREB (pCREB) and activity-regulated cytoskeleton-associated protein (Arc) - two molecular markers of experience-dependent changes in neuronal activity. The result suggests that POE is a behavioral paradigm suitable for studying the interaction between BLA and the hippocampus in memory prioritization and selectivity.

Dialogue State Tracking is an essential component in multi-domain dialogue systems, aiming to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample has limitations and introduces noise. In this paper, we propose an External Slot Relation Memory-based Dialogue State Tracking model (ER-DST). By utilizing an external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Experimental results on the MultiWOZ2.0 and MultiWOZ2.1 benchmarks demonstrate significant improvements while reducing the time complexity to O(n).

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Background and Aims. Most research on Parkinson's disease (PD) focuses on describing symp-toms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may potentially change the course of the disease can be attempted early on. While gait disturb-ances are less pronounced in the early stages of the disease, the prevalence, and severity increase with disease progression. Therefore, postural instability and gait difficulties could be identified as sensitive biomarkers. The aim was to evaluate the discriminatory power of the Trail-Walking Test (Schott, 2015) as a potential diagnostic instrument to improve the predictive power of the clinical evaluation concerning the severity of the disease and record the different aspects of walking. Methods. 20 older healthy (M = 72.4 years, SD = 5.53) adults and 46 older adults with PD and the motor phenotypes postural instability/gait difficulty (PIGD; M = 69.7 years, SD = 8.68) and tremor dominant (TD; M = 68.2 years, SD = 8.94) participated in the study. The participants performed a motor-cognitive dual task (DT) of increasing cognitive difficulty in which they had to walk a given path (condition 1), walk to numbers in ascending order (condition 2), and walk to numbers and letters alternately and in ascending order (condition 3). Results. With an increase in the cognitive load, the time to complete the tasks (seconds) become longer in all groups, F(1.23, 73.5) = 121, p < .001, ɳ2p = .670. PD-PIGD shows the longest times in all conditions of the TWT, F(2, 60) = 8.15, p < .001, ɳ2p = .214. Mutual interferences in the cognitive and motor domain can be observed. How-ever, clear group-specific patterns cannot be identified. A differentiation between the motor phenotypes of PD is especially feasible with the purely motor condition (TWT-M; AUC = . 685, p = 0.44). Conclusions. PD patients with PIGD must be identified by valid, well-evaluated clinical tests that allow a precise assessment of the disease's individual fall risk, the severity of the dis-ease, and the prognosis of progression. The TWT covers various aspects of mobility, examines the relationship between cognitive functions and walking, and enables differentiation of the motor phenotypes of PD.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

To define and evaluate the areas of consideration concerning in identifying the critical factors that top universities in Nueva Ecija, Philippines can be used for triangulating the courses of actions that can be applied to improve the current practices of universities towards its combat to the COVID–19 disease is the primary objective of this study. The researchers used a descriptive design of methodology by using questionnaire–checklist to scientifically describe the situation, problems, phenomenon, or program, or provide information about certain issues related to the virus outbreak. The respondents of the study were faculty and staff of five established universities in Nueva Ecija, Philippines wherein the researchers employed a non–probability sampling technique to be logically assumed as the representative of the entire population. The results of the study shown that the top universities in Nueva Ecija have made efforts to ensure the safety of university workers by complying with the Inter–Agency Task Force (IATF) protocols. It can be inferred, in reality, that there are some areas that must be improved especially when it comes to ensuring the welfare of the personnel who are still reporting to work even in this time of the pandemic. The researchers suggested an enhancement plan that can be adapted by these universities to resolve the concerns of the faculty and staff especially in reducing the spread of the virus without sacrificing the day–to–day transactions of the academic institutions.

Background and objectives: The language-induced grip force modulation (GFM) can be used to better understand the link between the language and motor functions as an expression of the embodied language. However, the contribution of each brain hemisphere to the language-induced GFM is still unclear. Using six different action verbs as stimuli, this study evaluated the GFM of the left hand in unimanual task to characterize the left- and right-hemisphere contributions. Materials and Methods: The left-hand GFM of 20 healthy consistent right-handers subjects was evaluated using the verbs “to write”, “to hold”, “to pull”(left-lateralized central processing actions), “to draw”, “to tie”, and “to drive” (bi-hemispheric central processing actions) as linguistic stimuli. The time between the word onset and the first interval of statistical significance regarding the baseline (RT) was also measured. Results: The six verbs produced language-induced GFM. The modulation intensity was similar for the six verbs, but the RT was variable. The verbs “to draw”, “to tie”, and “to drive”, whose central processing of the described action is bihemispheric showed a longer Rt compared to the other verbs. Conclusions: The possibility that an action is performed by the left-hand does not interfere with the occurrence of GFM when this action verb is employed as linguistic stimulus. Therefore, the language-induced GFM seems mainly rely on the left hemisphere, and the engagement of the right hemisphere seems to slow down the increase in the GFM intensity.

Breast cancer develops upon sequential acquisition of driver mutations in mammary epithelial cells; however how these mutations collaborate to transform normal cells remains unclear in most cases. We aimed to reconstitute this process in a particular case. To this end, we combined the activated form of the PI 3-kinase harboring the H1047R mutation with the inactivation of the histone lysine methyl-transferase KMT2D in the non-tumorigenic human mammary epithelial cell line MCF10A. We found that PI 3-kinase activation promoted cell cycle progression, especially when growth signals were limiting, as well as cell migration, both in a collective monolayer and as single cells. Furthermore, we showed that KMT2D inactivation had relatively little influence on these processes, except for single cell migration that KMT2D inactivation promoted in synergy with PI 3-kinase activation. Combination of these two genetic alterations induced expression of the ARPC5L gene that encodes a subunit of the Arp2/3 complex. ARPC5L depletion fully abolished the enhanced migration persistence exhibited by double-mutant cells. Our reconstitution approach in MCF10A has thus revealed both the cell function, single cell migration, and the underlying Arp2/3-dependent mechanism, which are synergistically regulated when KMT2D inactivation is combined with the activation of the PI 3-kinase.

Among cross-sectional imaging modalities, computed tomography (CT) is more available and faster; however, magnetic resonance (MR) performs better in terms of sensitivity and specificity, due to its higher contrast resolution. The appearance of peritoneal deposits on CT and MR depends mainly on the primary tumour histology: in case of unknown primary tumour (3-5% of cases), their behaviour at imaging may provide insights on the tumour origin. The time-point of tumour evolution, previous or ongoing treatments and the peritoneal spaces where they occur, also play an important role in determining the appearance of peritoneal deposits. Thus, knowledge of peritoneal anatomy and fluid circulation is mandatory in detection and characterization of peritoneal deposits. Several benign and malignant conditions may show similar imaging features that overlap those of PC, making the differential diagnosis challenging. Clinical history, laboratory findings and previous imaging examinations must be considered to achieve the correct diagnosis.

Cellulose nanocrystals (CNCs) are a kind of nano-inclusions that have experienced tremendous expansion in the material industry due to their outstanding mechanical qualities, sizable surface area, and capacity for functional tuning. Due to their vast potential, however, present practises fall short of fully using them. For example, employing CNCs in various matrices with various surface chemistries can be challenging; this problem tends to get worse if the focus is furthered on bio-based applications. This paper reviews the use of CNCs as fillers in natural and man-made polymers; we have explored in depth the production, characterisation, and of CNCs from various sources and their inclusion into various matrices. Surface alterations and the introduction of CNCs in biodegradable polymer can have a significant impact on several industrial behemoths such as tissue engineering and biomedical applications; therefore, pursue of current manuscript is extremely warranted. Throughout the manuscript various assembly techniques that involves alteration and adjustment of polymer network in building up a hydrogel with higher fracture energy and mechanical properties are also included. From rheological perspective the hydrogel processing is also discussed with some models routinely used in the literature to describe these hydrogels. Finally, bio-based hydrogels mechanically reinforced with CNCs such as Xanthan Gum, Alginate, protein, and polysaccharides were discussed.

Background: Monoclonal antibodies against PD-1 or PD-L1 are established in clinical practice for the treatment of both early and advanced/metastatic triple-negative breast cancer. Beyond the established immune checkpoints (ICPs) (PD-1 and CTLA-4) additional ICPs such as lymphocyte activation gene-3 (LAG-3) are subject of current research. In the present retrospective gene ex-pression analysis, we evaluated the prognostic significance of LAG-3 in 461 patients with early breast cancer. In addition, we examined whether there was a correlation between the different ICPs and CD8 expression. Methods: Using microarray-based gene expression analysis, we examined the prognostic signif-icance for metastasis-free survival (MFS) of LAG-3 mRNA expression in the whole cohort of 461 breast cancer patients and among different molecular subtypes. Correlations were analyzed us-ing Spearman-Rho correlation coefficient. Results: In the whole cohort, LAG-3 expression had no significant impact on MFS (p = 0.712, Log Rank). In the subgroup analyses, there was a trend, that higher LAG-3 expression was associated with favorable outcome in the luminal B (p = 0.217), basal-like (p = 0.370) and HER2 (p = 0.089) subtypes, although significance was not reached. In contrast, in multivariate Cox regression analysis adjusted for age, tumor size, axillary nodal status, histological grade of differentiation and the proliferation marker Ki-67, LAG-3 showed a significant influence on MFS (HR 0.574; 95% CI 0.369–0.894; p = 0.014). High LAG-3 significantly correlated with CD8 ( = 0.571; p < 0.001). Conclusion: LAG-3 expression had an independent impact on MFS. In addition to PD-1 and PD-L1, further immune checkpoints such as LAG-3 could serve as therapeutic targets in breast cancer.

Salivarian trypanosomes are extracellular parasites causing anthroponotic and zoonotic infections. Anti-parasite vaccination in considered the only sustainable method for global trypanosomosis control. Unfortunately, not a single field applicable vaccine solution has been successful so far. Active destruction of the host’s adaptive immune system by trypanosomes is believed to contribute to this problem. Here we show Trypanosome brucei brucei infection results in the lasting obliteration of immunological memory, including vaccine-induced memory against non-related pathogens. Using the well-established DTPa vaccine model in combination with a T. b. brucei infection and diminazene diaceturate anti-parasite treatment scheme, our result demonstrate that while the latter ensured the full recovery from the T. b. brucei infection, it failed to restore an efficacious anti-pertussis vaccine recall response. DTPa vaccine failure coincided with a shift in the IgG1/IgG2a anti-pertussis antibody ratio in favor of the latter, and a striking impact on all spleen immune cell populations. Interestingly, an increased plasma IFNy level in DTPa vaccinated trypanosome infected mice did result in a temporary antibody-independent improvement of early-stage trypanosomosis control. In conclusion, our results are the first to show that trypanosome inflicted immune damage is permanent, and is not restored by successful anti-parasite treatment.

NS-3 is one of the most used Network Simulators because it includes different modules, applications and protocols to simulate a wide range of network. Since new proposals usually involve a large number of simulations, a procedure to automatize the simulation process and extract statistics is very useful. "Statistical Framework" (SF) is an important module of NS-3 because it fulfills this need. SF allows us to collect simulation data and obtain statistical reports for NS-3. In this report, we present a detailed description of statistical framework module and two useful examples of its configuration.

Network Simulators is typically used to study services and applications in complex scenarios due to the infeasibility of deploying real testbeds. Many problems can be solved by using network simulators such as NS-3. With this in mind, the aim of this article is to introduce new NS-3 users through detailed information. It is sometimes difficult to handle by new users the traditional manuals developed by NS-3 project official website. In this article, NS-3 for communication network and Eclipse Integrated Development Environment (IDE) for powerful programming language are integrated step-by-step, explaining the main features of these open source software packages and concluding with an example simulation. Our effort is to make it easy for a beginner to be part of the NS-3 research community and to maintain an open environment of knowledge.

The co-occurrence of depression and obesity has become a significant public health concern worldwide. Recent studies have shown that metabolic dysfunction, which is commonly observed in obese individuals and characterized by inflammation, insulin resistance, leptin resistance, and hypertension, is a critical risk factor for depression. This dysfunction may induce structural and functional changes in the brain, ultimately contributing to depression's development. Given that obesity and depression mutually increase each other's risk of development by 50-60%, there is a need for effective interventions that address both conditions. The comorbidity of depression with obesity and metabolic dysregulation is thought to be related to chronic low-grade inflammation, characterized by increased circulating levels of pro-inflammatory cytokines, and C-reactive protein (CRP). As pharmacotherapy fails in at least 30-40% of cases to adequately treat major depressive disorder, a nutritional approach is emerging as a promising alternative. Omega-3 polyunsaturated fatty acids (n-3 PUFAs) are a promising dietary intervention that can reduce inflammatory biomarkers, particularly in patients with high levels of inflammation, including pregnant women with gestational diabetes, patients with type 2 diabetes mellitus, and overweight individuals with major depressive disorder. Further effort directed to implement these strategies in clinical practice could contribute to improved outcomes in patients with depression and comorbid obesity and/or metabolic dysregulation.

Rationale: The 14-3-3 protein family is known to interact with many proteins in non-cardiac cell types to regulate multiple signaling pathways, particularly those relating to energy and protein homeostasis; and the 14-3-3 network is a therapeutic target of critical metabolic and proteostatic signaling in cancer and neurological diseases. Although the heart is critically sensitive to nutrient and energy alterations, and multiple signaling pathways coordinate to maintain the cardiac cell homeostasis, neither the structure of cardiac 14-3-3 protein interactome, nor potential functional roles of 14-3-3 protein-protein interactions (PPIs) in heart has been explored. Objective: To establish the comprehensive landscape and characterize the functional role of cardiac 14-3-3 PPIs. Methods and Results: We evaluated both RNA expression and protein abundance of 14-3-3 isoforms in mouse heart, followed by co-immunoprecipitation of 14-3-3 proteins and mass spectrometry in left ventricle. We identified 52 proteins comprising the cardiac 14-3-3 interactome. Multiple bioinformatic analyses indicated that more than half of the proteins bound to 14-3-3 are related to mitochondria; and the deduced functions of the mitochondrial 14-3-3 network are to regulate cardiac ATP production via interactions with mitochondrial inner membrane proteins, especially those in mitochondrial complex I. Binding to ribosomal proteins, 14-3-3 proteins likely coordinate protein synthesis and protein quality control. Localizations of 14-3-3 proteins to mitochondria and ribosome were validated via immunofluorescence assays. The deduced function of cardiac 14-3-3 PPIs is to regulate cardiac metabolic homeostasis and proteostasis. Conclusions: Thus, the cardiac 14-3-3 interactome may be a potential therapeutic target in cardiovascular metabolic and proteostatic disease states, as it already is in cancer therapy.

This study introduces a fruit harvesting mechanism powered by a single motor, designed for integration with unmanned aerial vehicles (UAVs). The mechanism performs reciprocating motion by converting linear motion into rotational motion. Consequently, the end-effector can execute multi-dimensional kinematic trajectories, including biaxial and rotational movements, synchronized with the motor's position. These axial and rotational motions facilitate the gripper's ability to reach, retrieve, and detach fruit from branches during the harvesting process. Notably, a critical consideration in designing this fruit harvesting mechanism is to generate the necessary torque at the end-effector while minimizing reaction forces and torque that could destabilize the UAV during flight. With these considerations in mind, this preliminary study aimed to harvest a Fuji apple and conducted dynamic analysis. We constructed a prototype of the single motor-driven fruit harvesting mechanism using a suitable servo motor. To assess its mechanical performance and evaluate its impact on the hexacopter, we developed both a specific test platform featuring a six-spherical-prismatic-spherical parallel structure and a virtual environmental flight simulator. Overall, the results demonstrate the successful harvesting of a Fuji apple weighing approximately 300 g by the single motor-driven fruit harvesting mechanism, with no adverse effects observed on the hexacopter's operation.

Transcranial photobiomodulation (tPBM) has been suggested as a non-invasive neuromodulation tool. Repetitive administration of light-emitting diode (LED)-based tPBM for several weeks significantly improved human cognition. To understand the electrophysiological effects of LED-tPBM on the human brain, we investigated alterations by repeated tPBM in vigilance performance and brain networks using electroencephalography (EEG) in healthy participants. Active and sham LED-based tPBM were administered to the right forehead of young participants twice a week for four weeks. The participants performed a psychomotor vigilance task (PVT) during each tPBM/sham experiment. A 64-electrode EEG system recorded electrophysiological signals from each participant during the first and last visits in the 4-week study. Topographical maps of EEG power enhanced by tPBM were statistically compared for the repeated tPBM effect. A new data-processing framework combining the group singular value decompensation (gSVD) with eLORETA was implemented to identify EEG brain networks. The reaction time of PVT in the tPBM-treated group was significantly improved over four weeks compared to that in the sham group. We observed acute increases in EEG delta and alpha powers during 10-min LED-tPBM while the participants performed the PVT task. We also found that theta, beta, and gamma EEG powers significantly increased globally after four weeks of LED-tPBM. Combining gSVD with eLORETA enabled us to identify EEG brain networks and the corresponding network power changes by repeated 4-week tPBM. This study clearly demonstrated that 4-week prefrontal LED-tPBM can neuromodulate several key EEG networks, implying a possible causal effect between modulated brain networks and improved psychomotor vigilance outcomes.

With the rapid development of the Internet of Things, massive amounts of data will be generated at the network edge, resulting in increased latency of the traditional cloud computing model. Cloud service providers deploy edge servers near the users to improve the quality of service. Due to the large scale of networks, the inefficiency of edge server deployment and task scheduling will result in excessive latency and severe workload imbalance among edge servers. In this paper, we propose a Server Deployment and Task Scheduling (SDTS) framework in multi-edge collaborative computing, which can effectively reduce latency and ensure workload balancing. First, a metropolitan area network (MAN) is divided into dense candidate regions (DCRs) based on the density characteristics of mobile end devices, and then servers are deployed independently in each sub-region. Then, a two-step clustering algorithm is designed to deploy servers as close as possible to the mobile devices. Finally, a task scheduling algorithm is presented based on an edge scheduling layer in DCRs, which can leverage collaboration between edge servers to improve the performance of the entire edge computing system. The experimental results based on the Cellular Base Station (CBS) dataset of Shanghai Telecom indicate that the proposed approach outperforms several representative strategies in terms of latency and workload balancing.

Suicide and suicidal behavior are major public health concerns and affect 3-9% of the population worldwide. Despite growing evidence, there are still few effective interventions available to reduce suicide risk. In this article, we describe theoretical models of suicide ideation and behavior and propose to examine the possible effectiveness of a new and innovative preventive strategy. A model of suicidal intrusion (mental imagery related to suicide, also referred to as suicidal flash-forwards) is presented describing one of the assumed mechanisms in the etiology of suicide and the mechanism of therapeutic change. We provide a brief rationale for an Eye Movement Dual Task (EMDT) treatment for suicidal intrusions describing techniques that can be used to target these suicidal mental images and thoughts to reduce overall behavior. Based on the available empirical evidence for the mechanisms of suicidal intrusions, this approach appears to be a promising new treatment to prevent suicidal behavior as it potentially targets one of the linking pins between suicidal ideation and suicidal actions.

Although multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) that results in many neurological disabilities, optic neuritis (ON) is developed in some MS patients. However, the molecular mechanism of ON remains unknown. Galectins, β-galactoside-binding lectins, are involved in various pathophysiology. We have previously shown that galectin-3 (gal-3) is associated with the pathogenesis of experimental autoimmune encephalomyelitis (EAE), its animal model for human MS. In the current study, the expression of gal-3 was explored in the visual pathway of the EAE mice to clarify the pathogenesis of ON. Immunohistochemical analysis demonstrated that expression of gal-3 was increased in the visual pathway of the EAE mice during the peak disease, compared with naïve and EAE mice during the chronic disease. Expression of gal-3 was observed in mainly microglia/macrophages and astrocytes in the visual pathway of the EAE mice. In addition, gal-3+Iba-1+ cells with the phagocytic activities reflected by immunostaining with cathepsin-D were accumulated at demyelinating lesions in the visual pathway during peak disease of EAE. Moreover, NLRP3 expression was also detected in most gal-3+Iba-1+ cells. These results indicate a considerable possibility that gal-3 regulates the NLRP3 signaling in microglia/macrophages, followed by neuroinflammatory demyelination in ON. By contrast, astrocytic expression of gal-3 was also observed from the peak to the chronic disease. Taken together, the current study suggests a critical role of gal-3 in the pathogenesis of ON and proposes gal-3 in glial cells as a potential therapeutic target for ON.

The patient was a 44-year-old woman with suspected Stevens-Johnson syndrome due to Baktar® (sulfamethoxazole trimethoprim) medication received at our outpatient dermatology clinic. Histopathological examination of the epidermis, dermis, and subcutaneous adipose tissue samples showed numerous necrotic keratinocytes in the epidermis. Apoptotic nuclei were visualized as diaminobenzidine brown deposits with immunoperoxidase staining for cleaved caspase-3. Cleaved-caspase3-positive findings were consistent with eosinophilic material that appeared to be necrotic cells within the epidermis. Therefore, these eosinophilic materials must be apoptotic bodies. Generally speaking, and especially in Japan, these materials are considered necrotic keratinocytes. To our best knowledge, no studies have used apoptotic immunohistochemical markers to examine whether these structures are apoptotic.

Crohn's disease is a non-specific inflammatory bowel disease, which is a chronic condition that affects the ileum and/or large intestine. At the same time, it can also involve any other part of the human body, that is, from the mouth to the anus. The symptoms are very bothersome and cause a significant reduction in quality of life and sometimes even crippling permanent damage to the gastrointestinal tract, which requires enteral or parenteral nutrition for life. The purpose of this study was to investigate tissue metalloproteinases as markers of recurrent Crohn's disease. The experimental groups included 31 patients and 10 patients with normal Crohn's disease, ranging in age from 23-70 years, with a mean age of 40.4. Collected tissues were frozen and then fragmented tissues were homogenized with Ripa Lysis buffer on ice. The supernatant was collared and four metalloproteinases - MMP 3, MMP 7, MMP 8 and MMP 9 - were an-alized by Enzyme-linked Immunosorbent Assay using SEA101HU, SEA102Hu, SEA103Hu and SEA553Hu kits (Cloud-Clone Corp., Kata, TX, USA). All chemical analyses were performed in triplicate. Metalloproteinase content was expressed as mean ± standard deviation. Metalloproteinase 3 and metalloproteinase 8) significantly influenced the possibility of Crohn recurrence

The antibodies targeting invasive proteins of Plasmodium falciparum (Pf) merozoites is a clever strategy against malaria parasite. The recent finding that Plasmodium merozoites surface enolase is a target for growth neutralizing antibodies has generated interest in identifying its re-ceptor (s) on erythrocytes. In the present study, several biochemical experiments were undertaken involving recombinant Pf enolase and antibody against it to search for its cognate receptor. The binding of rPfeno to erythrocytes was selectively sensitive to trypsin but resistant to neuramini-dase. Failure of yeast enolase binding to human red blood cells under similar conditions indicat-ed high specificity of parasite Pfeno. The mass spectrometric and immunological analyses of pro-teins obtained in pull downs and co-immunoprecipitation samples led to identification of band 3 as an interactor/receptor for Plasmodium enolase. Structural characterization of sequences of band 3 - rPfeno interacting regions revealed C-terminal exocytic regions of band 3 to be binding to Pfeno. Similarly, band 3 binding regions of rPfeno were also identified. Identification of receptor-ligand (band 3: Pfeno) pair paves the way for antimalarial anti-Pf enolase-based vaccine, anti-Pf enolase antibodies as drug and also for developing novel small molecule-based invasion inhibi-tory therapeutics including peptidomimetics that could disrupt the band 3: Pfeno protein-protein interactions.

Abstract: Oxidative metabolism is crucial for leukemic stem cell (LSC) function and drug resistance in acute myeloid leukemia (AML). Mitochondrial metabolism also affects the immune system and therefore the antitumor response. Modulation of oxidative phosphorylation (OxPHOS) has emerged as a promising approach to improve therapy outcome for AML patients. However, the effect of mitochondrial inhibitors on the immune compartment in the context of AML is yet to be explored. Immune checkpoints such as the ecto-nucleotidase CD39 and programmed dead ligand 1 (PD-L1) have been reported to be expressed in AML and linked to chemoresistance and poor prognosis. In the present study, we first demonstrated that a novel selective electron transfer chain complex (ETC) I inhibitor, EVT-701, decreased OxPHOS metabolism of murine and human cytarabine (AraC)-resistant leukemic cell lines. Furthermore, we showed that, while AraC induced immune response regulation by increasing CD39 expression and by reinforcing interferon-γ/PD-L1 axis, EVT-701 reduced CD39 and PD-L1 expression in vitro in a panel of both murine and human AML cell lines, especially upon AraC treatment. Altogether, this work uncovers a non-canonical function of ETCI in controlling CD39 and PD-L1 immune checkpoints, thereby improving the anti-tumor response in AML.

Let $\mathcal{N}$ be an arbitrary class of matroids, closed under isomorphism. For $k$ a positive integer, we say that $M \in \mathcal{N}$ is \emph{$k$-minor-irreducible} if $M$ has no minor $N \in \mathcal{N}$ such that $1\leq\left|E\left(M\right)\right|-\left|E\left(N\right)\right|\leq k $. Tutte's Wheels and Whirls Theorem establish that, up to isomorphism, there are only two families of 1-minor-irreducible matroids in the class of 3-connected matroids. More recently, Lemos classified the 3-minor-irreducibles with at least 14 elements in the class of triangle-free 3-connected matroids. Here we prove a local characterization for the 2-minor-irreducible matroids with at least 11 elements in the class of triangle-free 3-connected matroids. This local characterization is used to establish two new families of 2-minor-irreducible matroids in this class.

Thanks to the pioneering studies of Østerud and co-workers it is now increasingly understood that natural polyphenols present in marine oils play an essential role in protecting omega-3 lipids from oxidation and autooxidation ensuring that no proinflammatory products are formed after intake as it often happens with assumption of refined omega-3 concentrates. Strong antioxidants exerting multiple biological functions, olive biophenols are ideally suited to functionalize marine oils creating a synergy which has the potential to foster public health across the world. This study identifies suitable avenues to advance the sustainable production of health-beneficial formulations based on newly obtained natural marine oils and olive phenolic extracts. Important educational outcomes for bioeconomy educators conclude the study.

This document describes the theoretical basis of the algorithms used to determine properties of snow and ice from the measurements of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites within the Pre-operational Sentinel-3 snow and ice products (SICE) project: http://snow.geus.dk/. The code used for the SICE retrieval and its documentation can be found at https://github.com/GEUS-SICE/pySICE. The algorithms were developed after the work from Kokhanovsky et al. (2018, 2019, 2020).

Inflammation is an essential protective response against injury or infection. Physiological inflammation eliminates the pathogen, promotes tissue repair and healing. An exaggerated, out of control inflammation, however, can become pathological. Inflammation can generate secondary cell damage, inflame the vessels (endothelitis), activate coagulation processes. Among these pathogenetic factors (cell damage, inflammation, endothelitis, coagulopathies), self-amplification mechanisms can be created, spreading beyond the initial site, up to Multiple Organ Failure (MOF) and host death. If the inflammation does not resolve in a physiological way, the remodeling of the tissues can be maladaptive and lead to the onset of chronic inflammatory degenerative diseases. Diseases such as sepsis, burns, polytrauma, severe forms of influenza or COVID-19, are characterized by a condition of hyperinflammation, associated with a condition of immunosuppression. The initial events triggered by the pathogen (cell damage, interferon response in the case of viruses) ignite the inflammation by activating the inflammasome, the transcription factor NFkB, the release of pro-inflammatory eicosanoids (Prostaglandins, Leukotrienes, Thromboxanes) by neutrophils and macrophages. Hence, the cells of the innate immune system produce pro-inflammatory cytokines. Indeed, the ‘’eicosanoid storm’’ precedes the ‘’cytokine storm’’. Eicosanoids are a group of potent endogenous lipid mediators derived from omega-6 fatty acids Arachidonic Acid (AA). Eicosanoids include a group of molecules with pro-inflammatory (Prostaglandins, Leukotriens) and pro-coagulant (Thromboxanes) action. In addition, Arachidonic Acid (AA) is the source of Lipoxins (LXs). Lipoxins belong to a group of molecules collectively referred to as specialized pro-solving mediators (SPMs) which also include molecules derived from w-3 eicosapentaenoic acid (EPA): Resolvins (ReV-E sieres) and w-3 docohexanoic (DHA): Resolvins D-series (ReV D-series); Protectins (PTs); Maresins (MaRs). SPMs are important for the resolution phase of inflammation to take place properly. Their deficiency could be involved in both acute uncontrolled inflammation and chronic inflammation. The active regulation of the acute inflammatory process, integrating the precursors of Specialized Pro-resolving Mediators (SPMs), such as ⍵-6 and ⍵-3 in balanced ratio, or the SPMs themselves, could be a complementary therapeutic approach useful for taming the "storm of cytokines '' which characterizes exaggerated forms of inflammation. ⍵-3 and ⍵-6 are part of already widely used, readily available, inexpensive and safe supplements. Resolvins have already been included in clinical trials for various other inflammatory diseases (eye diseases, periodontal diseases).

A shrimp oil rich in omega-3 lipids and carotenoids is obtained in remarkably high 5 wt% yield extending to pink shrimp processing waste (head and carapace) the circular economy approach to extract fish oil from fish processing by-products using d-limonene. Biobased limonene, a powerful antimicrobial and antioxidant agent, is an excellent solvent for both lipids and astaxanthin-based carotenoids preventing oxidative degradation during the extraction cycle including solvent and oil separation at 85°C. A new low cost route is established to extract valued marine oil from biowaste annually made available in over 2.2 million tonnes.

The efficacy of chemotherapy depends on sensitivity and intrinsic or acquired drug resistance of cancer cells. The n-3 long chain polyunsaturated fatty acids (n-3 LCPUFAs) are considered chemosensitizing agents and revertants of multidrug resistance by pleiotropic mechanisms. The specific mechanisms are not fully understood, but nowadays, it is widely accepted that there are a complex network of mechanisms, including alteration in gene expression, modulation of cellular proliferation and differentiation, induction of apoptosis, generation of reactive oxygen species and lipid peroxidation. A crucial mechanism in the control of cell drug uptake and efflux is related to n-3 LCPUFA influence on membrane lipid composition. The incorporation of docosahexaenoic acid in the lipid rafts produces significant changes in their physical-chemical properties affecting content and functions of transmembrane proteins, such as growth factors, receptors and ATP-binding cassette transporters. Of note, n-3 LCPUFAs often impact on the lipid compositions more in chemoresistant cells than in chemosensitive cells, suggesting their adjuvant role in cancer treatment.

Hypercholesterolemia is a major risk for the development of cardiovascular diseases (CVDs), the main cause of mortality worldwide, and it is characterized by high levels of circulating cholesterol. The drugs currently available for hypercholesterolemia control have several side effects, so it is necessary to develop new effective and safer therapies. Seaweeds serve as sources of several bio-active compounds with claimed beneficial effects. Eisenia bicyclis (Aramé) and Porphyra tenera (Nori) are edible seaweeds which were previously recognized as rich in bioactive compounds. In the present study, we aim to evaluate the anti-hypercholesterolemia effect of these two seaweeds ex-tracts and their health potential. Both extracts, but more efficiently Aramé extract demonstrated to have liver 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) inhibitory activity and to reduce approximately 30 % cholesterol permeation through human Caco-2 cells simulating the intestinal lining, targets for hypercholesterolemia treatments. An untargeted metabolomic assay on human intestinal Caco-2 and liver Hep-G2 cells lines exposed to Aramé and Nori extracts revealed changes in the cell’s metabolism as indicators of the extract’s health beneficial effects. The metabolic pathways affected by exposure to both extracts were associated to lipid metabolism, such as phospholipids, and fatty acid metabolism, pathways of amino acid, cofactors and vitamins and cellular respiration metabolism. More profound for Aramé treated cells, but also seen for Nori ex-posed cells, the metabolites modifications were associated with the protection against CVDs and other diseases and, to the improvement of cells oxidative stress tolerance. The results obtained for the anti-hypercholesterolemia properties, in addition to the revelation of the positive impact on cells metabolism, are an important contribution for further evaluation of these seaweeds extracts as functional foods or for CVDs prevention.

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

Currently, studies are being conducted on the possible role of the cytoprotective effect of biologically active substances in conditions of cerebral hypoxia or cardiomyopathies. At the same time, oxidative stress is considered as one of the important mechanisms of cellular cytotoxicity and a target for the action of cytoprotectors. The aim of this study is to search for derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones. The probability of cytoprotective action was assessed by two tests by measuring cell viability (with neutral red dye and MTT test). It was found that some derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones under the conditions of our experiment have a pronounced cytoprotective activity, providing better cell survival in vitro, including the MTT test and conditions of blood hyperviscosity. To correlate the obtained results in vitro, molecular docking of the synthesized derivatives was also carried out. The standard drug omeprazole (co-crystallized with the enzyme) was used as a standard. It was shown that all synthesized derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones had higher affinity for the selected protein than the standard gastro-cytoprotector omeprazole. The studied derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones also fully satisfy Lipinski's rule of thumb of five (RO5), which increases their chances for possible use as orally active drugs with a good ability to absorption and moderate lipophilicity. Thus, the results obtained make it possible to evaluate derivatives of 3-(arylmethyl)-6-methyl-4-phenylpyridin-2(1H)-ones as having a relatively high cytoprotective potential.

The major auxin, indole-3-acetic acid (IAA), is associated with a plethora of growth and developmental processes including embryo development, expansion growth, cambial activity, and the induction of lateral root growth. Accumulation of the auxin precursor indole-3-acetamide (IAM) induces stress related processes by stimulating abscisic acid (ABA) biosynthesis. How IAM signaling is controlled is, at present, unclear. Here, we characterize an ami1/rooty double mutant, that we initially generated to study the metabolic and phenotypic consequences of a genetic blockade of the indole glucosinolate and IAM pathways in Arabidopsis thaliana. Our mass spectrometric analyses of the mutant revealed that the combination of the two mutations is not sufficient to fully prevent the conversion of IAM to IAA. The detected strong accumulation of IAM was, however, recognized to substantially impair seed development. We further show by genome-wide expression studies that the double mutant is broadly affected in its translational capacity, and that a small number of cell proliferation and plant growth regulating transcriptional circuits are repressed by the high IAM content in the seed. In accordance with the previously described growth reduction in response to elevated IAM levels, our data support the hypothesis that IAM is a growth repressing counterpart to IAA.

Abstract Introduction: Visual perceptual skills are essential for independent participation in self-care tasks, educational, work and leisure time activities. The effect of HIV on the visual perceptual skills is not well understood among children in low resource settings like Zimbabwe. Methods: A cross sectional comparative study was done with 30 children living with HIV and 30 children living without HIV residing in Harare urban area. The TVPS-3 was used to assess their visual perceptual skills. SPSS version 22, STATISTICA 13 and Microsoft 2016 were used for data analysis. Results: Both groups of children had mean percentile ranks below 50 on their TVPS-3 scores. Children without HIV generally performed better than those with HIV but the difference was not statistically significant in most cases. Through univariate analysis, only performance on Spatial Relations significantly differed between the two groups. Both groups had lowest scores in Basic visual perceptual skills. Age and school grade were the independent predictors of the children’s performances in the study. Conclusion: There is need for Occupational therapy services in public primary schools and in the pediatric Opportunistic Infections clinics in hospitals to be part of the health team which caters for children with visual perceptual challenges.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

The design of torsional springs for Series Elastic Actuators (SEAs) is challenging, especially when it comes to balancing good stiffness characteristics and efficient torque robustness. This study focuses on the design of a lightweight, low-cost, and compact torsional spring for use in rotary series elastic actuator (ES-RSEA) of lumbar support exoskeleton. The exoskeleton is used as an assistive device to prevent lower back injuries. The torsion spring was designed following design for manufacturability (DFM) principles, focusing on minimal space and weight. The design process consisted of determining the potential topology and optimizing the selected topology parameters through finite element method (FEM) to reduce equivalent stress. The prototype was made using a waterjet cutting process with low-cost material (AISI-4140-alloy) and tested using a custom-made test rig. The results showed that the torsion spring had a linear torque-displacement relationship with 99% linearity, and the deviation between FEM simulation and experimental measurements was less than 2%. The torsion spring has a maximum torque capacity of 45.7 Nm and a stiffness of 440 Nm/rad. The proposed torsion spring is a promising option for lumbar support exoskeletons and similar applications requiring high stiffness, low weight-to-torque ratio, and cost-effectiveness.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.