Objective Of Task Research Articles

Deep learning models fail to capture the configural nature of human shape perception

SummaryA hallmark of human object perception is sensitivity to the holistic configuration of the local shape features of an object. Deep convolutional neural networks (DCNNs) are currently the dominant models for object recognition processing in the visual cortex, but do they capture this configural sensitivity? To answer this question, we employed a dataset of animal silhouettes and created a variant of this dataset that disrupts the configuration of each object while preserving local features. While human performance was impacted by this manipulation, DCNN performance was not, indicating insensitivity to object configuration. Modifications to training and architecture to make networks more brain-like did not lead to configural processing, and none of the networks were able to accurately predict trial-by-trial human object judgements. We speculate that to match human configural sensitivity, networks must be trained to solve a broader range of object tasks beyond category recognition.

Cognitive Task Performance and Subjective Cognitive Symptoms in Individuals With Chronic Fatigue Syndrome or Fibromyalgia: A Cross-Sectional Analysis of the Lifelines Cohort Study.

This study examined cognitive task performance and self-reported cognitive functioning in individuals with chronic fatigue syndrome (CFS) and fibromyalgia (FM) in a population-based sample and investigated the role of mood and anxiety disorders as well as severity of the physical symptoms. This study was performed in 79,966 participants (mean [standard deviation] age = 52.9 [12.6] years, 59.2% women) from the Lifelines general population. Symptoms consistent with the diagnostic criteria for CFS and FM were assessed using questionnaires. Two comparison groups were used: participants with self-reported medical disorders with well-defined pathophysiology (i.e., multiple sclerosis and rheumatic arthritis) and controls without these diseases. Objective task performance was based on the computerized CogState cognitive battery and subjective cognitive symptoms using the concentration subscale of the Checklist Individual Strength. Cognitive task performance was poorer in individuals with CFS versus controls without disease and controls with a medical disorder, although the severity of cognitive dysfunction was mild. Participants meeting the criteria for CFS ( n = 2461) or FM ( n = 4295) reported more subjective cognitive symptoms compared with controls without a medical disorder ( d = 1.53, 95% confidence interval [CI] = 1.49-1.57 for CFS; d = 1.25, 95% CI = 1.22-1.29 for FM) and participants with a medical disease ( d = 0.62, 95% CI = 0.46-0.79 for CFS; d = 0.75, 95% CI = 0.70-0.80 for FM). These differences remained essentially the same when excluding participants with comorbid mood or anxiety disorders or adjusting for physical symptom severity. Subjective cognitive symptoms and, to a lesser extent, suboptimal cognitive task performance are more prevalent in individuals with CFS or FM compared with controls without these conditions.

Multiple handlers, several owner changes and short relationship lengths affect horses’ responses to novel object tests

Despite numerous studies emerging on the human-horse relationship, significant gaps exist in the identification of the horse and handler factors that influence the quality of their relationship. Here, we explore key factors affecting human-animal relationships: the number of regular handlers an animal has, the length of the relationship with the handler, the number of owner changes, and the familiarity of the handler. A total of 76 horses participated in two novel object tasks (walking on novel surfaces and being touch with a novel object) to determine whether horses react differently to novel situations depending on whether they are handled by a familiar or an unfamiliar person. We observed that having multiple regular handlers negatively affected the horse reluctance towards novel surfaces and novel object. In horses used to be handled by multiple persons, 68% were showed reluctant behaviours towards the novel surfaces while 75% of the horses handled by only one person did not show reluctant behaviours. Similarly, 26% of the horses with multiple regular handlers refused to be touched with a novel object while only 13% of the horses with only one regular handler refused to be touched with the object. The relationship length between the horse and the familiar handler decreased the horse reluctance towards the novel surfaces and the novel object. The longer the relationship the less reluctant were the horses. Horses sold more than once were also more reluctant to the novel object. These horses had higher chances to refuse to be touched with the novel object than the horses still owned by their breeder or their first buyer. Finally, older horses (> 18 yo) had higher success at walking on the surface when led by someone familiar (87%) compared to led by someone unfamiliar (15%). Our findings suggest that the horse-human relationship may take time to develop as it is shaped by multiple factors involving the horse’s previous and current interactions with humans that affect their everyday life.

Towards an orthogonality constraint-based feature partitioning approach to classify veracity and identify stance overlapping of rumors on twitter

The consequences of fake news and rumors have adversely affected social and political stability worldwide. Many such incidents have been reported, which resulted in mass chaos with the loss of lives and property. In response, many researchers have developed models for the veracity detection of rumors on social media. The recent advances in veracity detection models incorporate the use of Deep Neural Networks (DNN) over statistical and traditional machine learning based models. Current veracity detection approaches leverage powerful DNN models such as Transformer, Adversarial Networks, Graph Convolutional Networks (GCN), Variational Autoencoder (VAE) etc., along with exploiting the intuition of Multi-task learning (MTL) approach. In addition, most of these aforesaid well-known models rely on auxiliary (additional) information to a good extent. Presently, these recent models’ dependence on (1) auxiliary information and multiple tasks restrain productivity and incur cost on resources. Moreover, (2) the structural constraints of these models put a limiting effect on model deliverance. These two shortcomings of the recent models result in poor resource utilization and unstructured feature organization of the objective task, resulting in a compromised output of the model. In this paper, we present an efficient Segregated Non-overlapping and Collectively exhaustive DNN model (SeNoCe) which mitigates the effects of poor utilization of resources and enhances the model performance without the aid of auxiliary information or tasks which incur good manual efforts and costs. SeNoCe is capable of utilizing the fine-granularity of implicit features as attention for task identification. SeNoCe reports a major performance improvement over the state-of-the-art techniques on standard benchmark metrics across two real-world rumor datasets. It records a minimum of 24.06−12.8%, 53.1−49.2% improvement in terms of Macro F and Accuracy, respectively over the best performing state-of-the-art.

Social influences in the digital era: When do people conform more to a human being or an artificial intelligence?

The spread of artificial intelligence (AI) technologies in ever-widening domains (e.g., virtual assistants) increases the chances of daily interactions between humans and AI. But can non-human agents influence human beings and perhaps even surpass the power of the influence of another human being? This research investigated whether people faced with different tasks (objective vs. subjective) could be more influenced by the information provided by another human being or an AI. We expected greater AI (vs. other humans) influence in objective tasks (i.e., based on a count and only one possible correct answer). By contrast, we expected greater human (vs. AI) influence in subjective tasks (based on attributing meaning to evocative images). In Study 1, participants (N = 156) completed a series of trials of an objective task to provide numerical estimates of the number of white dots pictured on black backgrounds. Results showed that participants conformed more with the AI's responses than the human ones. In Study 2, participants (N = 102) in a series of subjective tasks observed evocative images associated with two concepts ostensibly provided, again, by an AI or a human. Then, they rated how each concept described the images appropriately. Unlike the objective task, in the subjective one, participants conformed more with the human than the AI's responses. Overall, our findings show that under some circumstances, AI can influence people above and beyond the influence of other humans, offering new insights into social influence processes in the digital era.

Information-Based Sensor Control for Multitask Planning via Multi-Bernoulli Filter

In multitarget tracking, the issue of sensor control is a challenging problem in theoretical analysis and calculation. In this paper, we study the sensor control strategies for multitask planning based on information criteria and propose two sensor control strategies according to the two different task objectives. Initially, we propose a sensor control strategy to improve the overall multitarget tracking performance within the partially observed Markov decision process (POMDP) framework, where the reward function is calculated by the Bhattacharyya distance between the prior and the posterior multitarget densities. In this strategy, we present a target-oriented multi-Bernoulli (TOMB) particle sampling method to approximate the multitarget density and then derive the solution of Bhattacharyya distance in detail. Subsequently, as another important contribution of this paper, we propose a threat-based sensor control strategy, which is still solved under the information theory where the goal is to prioritize multiple threat targets and then to track preferentially the maximum threat target. These strategies are finally used to optimize the sensor trajectory for range-bearing multitarget tracking.

A Reconfigurable Multirobot Cooperation Workcell for Personalized Manufacturing

Most robotic systems designed for mass manufacturing are optimized for a specific type of product. They generally lack the ability to adapt to low-volume customized products. In this article, we present a system based on a modular design for manufacturing personalized medical stent graft implants. The concept is based on learning-by-demonstration by integrating real-time 3-D vision, multirobot collaboration, and personalization to guide the robots to learn and execute tasks continuously with adaptation to different implant geometry. The system is optimized to generate customized and collision-free paths for efficient object manipulation and task completion. We show that the system is generalizable to different stent graft designs and the proposed multirobot system can seamlessly work together with high efficiency without collisions. The results have also suggested its usability for other manipulation tasks, especially for flexible production of customized products where bimanual or multirobot cooperation is required. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this article is the problem of automatic sewing of personalized stent grafts (a tailor-made artificial vessel). Existing personalized stent grafts are mostly hand sewn, which is time consuming and often undersupplied. Automating such process can significantly improve the production and this requires a sewing system that can handle different designs. This article suggests a new schema to design a robotic system to handle personalized designs. The first methodology is modularized design to separate the task into a repetitive part and a personalized part, each handled by a module. The second methodology is to find the best relative pose between the modules such that the robots can complete their task within their working space and with minimum motion. This ensures that a stent graft can be sewn feasibly and with the lowest cost. Computational results show this approach can find optimal solution for different personalized stent grafts and preliminary on robot experiment verifies that this approach is feasible. Please note that this approach is not limited to sewing personalized stent graft. The schema can be applied to solve similar problem of customized product motion planning and system design.

Effects of attention on visual discrimination and awareness: Evidence from eye-specific attention.

The role of attention in visual awareness is a foundational issue for understanding consciousness, but remains highly controversial. Therefore, in two experiments, we investigated whether and how attention modulates visual awareness using a monocular cuing paradigm. Although observers are not aware of which eye received a cue, a monocular cue can attract eye-specific attention to a cued eye. In Experiment 1, we found that eye-specific attention enhanced subjective visual awareness (i.e., awareness reports) as well as objective task performance (i.e., orientation discrimination) of a Gabor target. Importantly, attention enhanced visual awareness only when a decision for orientation discrimination was correct, suggesting that the effect of attention on visual awareness is closely associated with perceptual evidence for orientation discrimination. In Experiment 2, we showed that the modulatory effect of attention on visual awareness was based on contrast gain, which amplified the effective contrast of an attended stimulus, and did not differ from that on orientation discrimination performance qualitatively and quantitatively. Our findings suggest that attention influences visual awareness by amplifying perceptual evidence on which visual awareness and objective performance are commonly based. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ASE

The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only enable humans to perform complex tasks, but also provide powerful priors for guiding their behaviors when learning new tasks. This is in stark contrast to what is common practice in physics-based character animation, where control policies are most typically trained from scratch for each task. In this work, we present a large-scale data-driven framework for learning versatile and reusable skill embeddings for physically simulated characters. Our approach combines techniques from adversarial imitation learning and unsupervised reinforcement learning to develop skill embeddings that produce life-like behaviors, while also providing an easy to control representation for use on new downstream tasks. Our models can be trained using large datasets of unstructured motion clips, without requiring any task-specific annotation or segmentation of the motion data. By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills. We show that a single pre-trained model can be effectively applied to perform a diverse set of new tasks. Our system also allows users to specify tasks through simple reward functions, and the skill embedding then enables the character to automatically synthesize complex and naturalistic strategies in order to achieve the task objectives.

A Versatile Affordance Modeling Framework Using Screw Primitives to Increase Autonomy During Manipulation Contact Tasks

Recent studies utilizing Affordance Templates to perform remote contact manipulation tasks with mobile manipulators have demonstrated their usefulness for modeling complex tasks allowing robots to work in uncertain environments. These efforts largely fall into the &#x201C;supervised autonomy&#x201D; paradigm where a user commands high-level actions and supervises the robot&#x2019;s execution, but is not responsible for the low-level execution details such as controlling the endpoint/joints, managing contact forces, or avoiding collisions. In this work, we present a novel formulation for modeling affordances that features the versatility and generality of screw theory. We also propose a generic framework and algorithm for executing manipulation contact tasks. To thoroughly evaluate the performance of the proposed formulation and framework, we performed various sets of experiments using our mobile manipulator. We showed that this framework is generic enough to successfully manipulate a variety of articulated objects. Moreover, by performing a set of manipulation tasks on a wheel valve as our case study, we demonstrated that our approach lowers task duration (about 5 times) and applied forces and torques significantly (about 2.5 and 3 times, respectively) when compared to direct teleoperation. Further, by performing 90 trials, we show robust performance of the proposed screw-based framework even when there is significant error in the estimated position and orientation (i.e., about 10&#x00A0;cm and 0.35&#x00A0;rd) of the task objects.

Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment

Convolutional neural networks have achieved success in resolving object identification problems. This study contributes a suitable new approach to herb identification for educational and research purposes based on a small dataset and small-sized images. Two self-collected Thai herb datasets with either plain or natural environment backgrounds were used for experimentation to realize this objective. The plain background dataset includes 4,400 images of 11 leaf types, and the natural dataset contains 1,620 images of nine leaf types. The images were divided into a training set containing 75% of the images and a separate test set with the remaining 25%. The experiments included five-fold cross-validation applied to the training set; the InceptionV3, MobileNetV2, ResNet50V2, VGG16, and Xception convolutional neural network models RMSprop and Adam optimizers. Further, dropout rates of 0.3, 0.5, and 0.7 were considered along with five and ten epochs. Transfer learning was applied using pre-trained weights. The model with the best outcome, based on the average accuracy of the cross-validation results on both datasets (the plain background dataset was 94.55%, and the natural dataset was 90.37%), was the VGG16 with the RMSprop optimizer, which exhibited a dropout rate of 0.5 over ten epochs. The model achieved 96.64% and 92.00% accuracy on the plain background training and test sets, and 99.59% and 91.36% on the natural environment training and test sets, respectively. The results show that the method has a high potential for objective tasks and application in identifying herbs based on visual leaf information.

Real Power Loss Reduction by Accipitridae Optimization Algorithm

In this paper, Accipitridae Optimization Algorithm (AOA) is used to solve the power loss lessening problem. Authorizing a satisfactory magnitude of reactive power is effervescent for the steadfast operation of power transmission structures, as the power meagreness may tip to staid power catastrophe and leading power upheavals. Loss reduction problem is intended as one of the prominent conditions for compassionate and financial action of edifice. It is exceptional by appropriate union of the network apparatus castoff to deal with up the power flow with the dispassionate of decreasing the losses and evolution the power outline of the assemblage. Plentiful previous studies unravelled the power problem as the optimization objective tasks premeditated noticeably. Real power loss reduction, Voltage stability expansion and voltage deviation minimization are the key objectives of the work. Hunting methods of Accipitridae are imitated to design the Accipitridae Optimization Algorithm (AOA). Accipitridae does hunting in a single-mode on the prey. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae is formulated. As soon as the prey spot is precise then Accipitridae is geared up for landing and assault, the Accipitridae move down perpendicularly with a preface assault to determine the prey response. AOA initiates the upgrading measures by engendering an arbitrary solution. In the course of the curve of reiteration, the explore stratagem of the AOA is the rational location of the most excellent optimal solution. The authenticity of the Accipitridae Optimization Algorithm (AOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Voltage stability escalated with minimization of power deviance. Then the proportion of actual power loss lessening is amplified.

Impact of background music on the performance of laparoscopy teams

BackgroundAcoustic conditions in the operating room have different impacts on surgeon’s performance. Their effects on the performance of surgical teams are not well documented. We investigated if laparoscopic teams operating under pleasant acoustic conditions would perform better than under noisy conditions.MethodsWe recruited 114 surgical residents and built 57 two-person teams. Each team was required to perform two laparoscopic tasks (object transportation and collaborative suturing) on a simulation training box under music, neutral, and noisy acoustic conditions. Data were extracted from video recordings of each performance for analysis. Task performance was measured by the duration of time to complete a task and the total number of errors, and objective performance scores. The measures were compared over the three acoustic conditions.ResultsA music environment elicited higher performance scores than a noisy environment for both the object transportation (performance score: 66.3 ± 8.6 vs. 57.6 ± 11.2; p < 0.001) and collaborative suturing tasks (78.6 ± 5.4 vs. 67.2 ± 11.1; p < 0.001). Task times in the music and noisy environments was subtracted to produce a music-noisy difference time. Pearson correlation coefficient analysis showed a significant negative relationship between the team experience score and the music-noisy difference time on the object transportation (r = − 0.246, p = 0.046) and collaborative suturing tasks (r = − 0.248, p = 0.044).ConclusionsAs to individuals, music enhances the performance of a laparoscopy team while a noisy environment worsens performance. The negative correlation between team experience and music-noisy difference time suggests that laparoscopy teams composed of experienced surgeons are less likely affected by an acoustic distraction than the noisy teams. Team resistance to acoustic distraction may lead to a new way for assessing team skills.

Effects of object size and task goals on reaching kinematics in a non-immersive virtual environment.

Object size (large vs. small) and task goal (reach at a comfortable pace vs. reach as fast as possible) are well-accepted task constraints that influence reaching kinematics. However, it is unclear whether these two constraints affect reaching in the virtual environment (VE). The purpose of this study was to examine the effects of these two task constraints in VE. Forty-one healthy adults (11 males; mean age 24.9years old) participated in this study. All adults were asked to play bubble popping games that ran on our virtual reality (VR) platform (SuperPop VR™), which can precisely measure reaching kinematics (including duration, straightness, jerkiness, and speed) in real time. Participants were blinded to the study purposes, and two task constraints were manipulated: bubble size (small vs. large) and goal (comfortable vs. fast). Participants began with their dominant hand for each condition and alternated hands after each condition was tested three times. A repeated ANOVA was used for analyses. Reaches for small bubbles had longer duration, faster speed, and jerkier with more curved trajectories than reaches for large bubbles; reaches for small bubbles also had larger elbow and shoulder range of motion than reaches for large bubbles. In addition, reaches during participants' comfortable pace had longer duration, slower speed, and larger shoulder range of motion than reaches during their fast pace. Additionally, participants' non-dominant hands had faster speed than their dominant hands. Our findings confirmed that object size and task goals affected reaching kinematics even during VR games. When designing a VR evaluation and intervention program for clinical populations, it is important to consider the virtual object size and task goals as factors in influencing a participant's performance.

Low Vitamin K Intake Negatively Affects C57BL6 Mouse Survival and Cognition

ObjectivesLow dietary intake of vitamin K (VK), which is common among older adults, has been associated with age-related dementia and cognitive impairment. Aging and neurodegenerative diseases negatively affect hippocampal neurogenesis, and result in cognitive deficits, such as in learning and memory. The current study investigated the effects of low vitamin K intake on the potential factors affecting the cognitive functions in a C57BL6 mouse model.MethodsMiddle-aged (7–9 mo) C57BL6 mice (Charles River, n = 60) were randomly assigned by sex and diet (n = 15/group): low vitamin K males (LVKM, 80 μg phylloquinone (PK)/kg diet), control diet males (CM, 1 mg PK/kg diet), low vitamin K females (LVKF), and control diet females (CFM) for 6 months. All animals were fed ad lib, and their body weight were recorded 1–2 times/week. At the completion of the 6-mo feeding period, behavioral tests, including the rotarod, novel object task, and Morris water maze, were performed. Tissue samples were collected at the time of sacrifice (3 weeks after the behavioral tests) and PK content in liver samples were measured by HPLC.ResultsLVKM had significantly lower survival rate compared to CM (53.3% vs. 93.3% in control, P < 0.05). LVKM/F also had reduced body weight increases compared to the CM/F over the 6 mo feeding period (P < 0.05). The liver PK content in LVKM/F animals was significantly lower than that of CM/F (male:6.2 ± 1.1 vs 30.2 ± 7.0 pmol/g, P < 0.001; female: 10.9 ± 2.4 vs 47.3 ± 10.3 pmol/g, P < 0.001). On the novel object task, LVKM/F showed reduced recognition memory compared to CM/F, while no significant differences were seen between LVKM/F and CM/F on the rotarod test. Additionally, LVKM had a non-statistical trend for a greater time to locate the platform in the Morris water maze, suggesting impaired spatial memory.ConclusionsLow vitamin K intake significantly impaired survival and weight gain of C57BL6 mice, especially among males. Additionally, low vitamin K negatively impacted the learning- and memory-related cognitive functions. Future studies are required to establish the mechanisms, underlying these observations.Funding SourcesSupported by USDA/ARS intramural grant, and the Robert and Margaret Patricelli Family Foundation

Production of Ethanol from Jaggery

Abstract: This exploration depicts about the Development of Ethanol by Jaggery. We use Jaggery which assists us with creating the Ethanol. Ecologically maintainable energy sources are called for because of contemporaneous advancement in businesses alongside the fast speed of urbanization. Ethanol created from biomass can be thought as a spotless and most secure fluid fuel and an option in contrast to fossil and oil fills are they have given exceptional natural, key financial advantages. For as far back as decade, it has been seen that there is a rising pattern found in bioethanol creation which has made an upgrade to go for progression in bioethanol creation advancements. A few feed stocks have been utilized for the bioethanol creation yet the secondage bioethanol has focused on the lignocellulose biomass. Plenteous lignocellulose biomass on the planet can be tapped for Ethanol creation, yet it will require huge advances in the ethanol creation process from lignocellulose as a result of a few specialized and monetary obstacles tracked down in business scale. The principal objective of the ongoing task is to decrease the purposes of Fuel in the public arena since it isn't eco-accommodating for nature. Trial studies have been done to enhance the pre-treatment process for expanding the proficiency of bacterial hydrolysis, the effective transformation of glucose from Jaggery corrupting microorganisms and to change over sugars delivered to Ethanol by utilizing Maturation process. Processing, refining, aging and parchedness associated with the Creation of Ethanol. In the aging system, the yeast breaks down the glucose into sucrose and fructose. The Yeast Saccharomyces Cerevisiae was utilized for aging cycle, which helped in changing over the jaggery into sugar and isolated in refining process. This audit will incorporate the ongoing status of bioethanol creation. During the examination we got 250ml of Ethanol from 1kg of Jaggery blended in with 1liter of water. As far as their monetary and ecological practicality alongside some exploration holes as well as strategy ramifications. Keywords: Bioethanol, biomass, Lignocelluloses, Yeast, Jaggery;

Crowdsourcing Team Formation With Worker-Centered Modeling.

Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.

Activity Monitoring During the Instruction of Gross Anatomy

Over the past decades, a compendium of various physical activities has been compiled that ranges from more vigorous activities in the occupational and recreational domains, to mundane household tasks and chores. These have been developed to allow users to understand their individual activity profile for intake and caloric expenditure. Teaching gross anatomical dissection courses is physically demanding, and anecdotally, has a different activity profile than teaching other courses. The purpose of this study was to quantify the basic activity profile of gross anatomical instruction and compare these measure to other occupation tasks. For a period of 4‐years, faculty and laboratory staff (n=52; 35.6 years + 8) were provided commercially available activity monitors (Fitbit Charge, Fitbit, San Francisco, CA) and were worn during all gross anatomy lab dissections (272 min/session + 38) in a semester‐long class. Data were collected and reported after each class session including, steps, caloric expenditure and time of activity. The average caloric expenditure/session was 569.47 kCal (SD = 33.8), or 126.55 kCal/hour during the instruction of gross anatomy lab experiences and was found to be similar to general carpentry (145 kCal/hour) and custodial work (136 kCal/hour). However, the profession that was most similar was nursing. A recent study has shown (Allan, et al, Journal of Research in Nursing, 2019) that ward nurses on 12‐hour shifts exhibit caloric expenditure of 1512kCal/shift, or 126.75 kCal/hour. When the objective tasks are compared, its not surprising that these professions exhibit similar results. Both repeatedly perform work from station‐to‐station throughout the day requiring above average amounts of standing and walking. Further, the work performed at a station also requires a mixture of finite precision and moving of the subject/patient. Although these data aren’t unique or novel, it may be of particular interest to those that perform this occupation skill. Guiding the dissection experiences for medical and allied health students is a vigorous and strenuous job that requires the background and familiarity human anatomical structures with physical demands that are more extensive than a typical class or laboratory setting.

AT1R‐mediated neurodegeneration is part of the etiology of hypertension‐associated cognitive impairment

Hypertension has now been closely associated to cognitive impairment, due to its contribution to cerebrovascular dysfunction. To investigate whether there is neuronal mechanism, hypertension was induced in C57BL/6j male mice (6 months) by DOCA‐salt treatment (1.5 mg/g, s.c., 28 days). Firstly, behavioral study, which contains object location behavioral task, novel object recognition, Barnes maze, and nesting score test, was performed to assess cognitive function, meanwhile, blood pressure (BP) was monitored by tail‐cuff device. In NT (non‐transgenic) mice, DOCA+1%NaCl increases BP significantly, before vs. after DOCA (systolic BP): 114.8 ±0.94 vs. 133.7 ±3.3 mmHg. In whole, compared to sham controls, the treated NT mice performed worse in all four behavioral tests, indicating cognition decline after DOCA‐salt treatment. Later, brain tissues were collected to evaluate possible degenerative alterations in neurons by transmission electron microscope (TEM) imaging, Golgi‐Cox staining, fluorescence‐activated cell sorting (FACS) and qRT‐PCR. It was shown via the TEM that DOCA‐salt hypertension induces mitochondrial damage and morphological changes in prefrontal cortical neurons, meanwhile, Golgi‐Cox staining revealed decreased numbers of dendritic spines, suggesting the onset of neurodegeneration in DOCA‐salt hypertension. In both prefrontal cortex and hippocampus, the mRNA levels of BDNF (brain‐derived neurotrophic factor) and PI4KIIIβ (phosphatidylinositol 4‐kinase IIIβ) were found to be significantly lower in hypertensive NT mice. Using FACS, neurons were sorted from those regions, then analyzed via qRT‐PCR, which identified that the downregulated expression of PI4KIIIβ takes place in neurons. Using laser speckle contrast imaging system, we detected only decreased functional hyperemia, but no impairment in basal cerebral blood flow of the hypertensive mice, suggesting that the neurodegeneration is not caused by cerebral ischemia. We previously demonstrated that neuronal angiotensin type 1 receptors (AT1R) plays a pivotal role in the maintenance of neurogenic hypertension, and here we hypothesized that activation of AT1R could also exacerbate hypertension‐induced reduction in neuronal function. Particularly, in the hippocampal CA1 of adult mice, treatment of angiotensin II (Ang‐II, 500 nM, bath application) induces a significant inhibition in the induction of long‐term potentiation (LTP), while pre‐treatment with losartan (5 μM, 15 min), the AT1R antagonist, can efficiently block this Ang‐II‐induced inhibition, supporting the involvement of neuronal AT1R. Indeed, in mice with selective deletion of neuronal AT1aR, DOCA‐salt‐induced neurodegeneration was attenuated, as evidenced by significantly higher mRNA levels of cortical BDNF and PI4KIIIβ, compared with the NT. In summary, our data suggest that neuron‐expressing AT1R participates in the development of hypertension‐associated cognitive impairment, independently of vascular AT1R.

Beneficial Effects of Behavioral Parent Training on Inhibitory Control in Children With Attention-Deficit/Hyperactivity Disorder: A Small-Scale Randomized Controlled Trial.

The purpose of this study was to examine whether the beneficial effects of behavioral parent training (BPT), as an indirect type of psychosocial treatment, are extended to cognitive manifestations beyond behavioral symptoms of attention-deficit/hyperactivity disorder (ADHD). Although previous studies of community families have shown an association between parenting quality and a child’s cognitive functions, little is known about the effects of BPT on cognitive manifestations in children with ADHD. In this study, we focused on inhibitory control among cognitive domains, which is considered to be the most malleable to direct types of psychosocial treatment for ADHD. We hypothesized that inhibitory control is affected by BPT, which uses parents as the primary agents of change to help their children. Thirty school-age children (6–12 years old) with ADHD and their parents (mothers) participated and were randomly assigned to either the standard BPT or waitlist control group. Using two objective laboratory-based tasks of inhibitory control (i.e., go/no-go and single response selection tasks), we assessed baseline and post-treatment response inhibition to suppress task-irrelevant responses and response selection to select task-relevant responses. In addition to decreased ADHD symptoms and negative parenting, the BPT group exhibited significantly improved performance in the single response selection task, but not in the go/no-go task, compared with the waitlist control group. Although tentative, these findings partially support our hypothesis that BPT has beneficial effects on the cognitive inhibitory control of ADHD, highlighting the potential for supportive environmental modifications to advance cognitive development in children with ADHD.