Effect of Agaricus blazei Murill on exploratory behavior of mice-model

Increased anxiety and depressive symptoms have reported to be its association with long term illness. Because of having unwanted effects of newly available drugs, patients administering anxiolytic drugs usually discontinue the treatment before they are completely recovered. Therefore, there is a serious need to develop new anxiolytic drugs. The anxiolytic effect of hydro-alcoholic extract of Agaricus blazei in animal models was assessed. 24 male mice (Mus musculus genus) were included in the study. Four groups were prepared and each group contained six animals. The groups were vehicle control, positive control (diazepam 1.0 mg/kg, i.p.) as well as two treatment groups receiving Agaricus blazei hydro-alcoholic extract at a dose of 136.50 mg/kg and 273.0 mg/kg orally. The Marble burying test, Nestlet shredding test and Light and Dark box test used to assess anxiolytic activity. Mice administered with diazepam 1.0 mg/kg, i.p. while hydro-alcoholic extract of AbM (136.50 and 273.0 mg/kg, respectively) was administered via oral route which exhibited marked reduction in number of marbles-burying as compared to vehicle control group. Mice administered with diazepam 1.0 mg/kg, i.p. and Oral administration of hydro-alcoholic extract of AbM (136.50 and 273.0 mg/kg, respectively) exhibited significant decrease in nestlet shredding in comparison to vehicle control group. The oral administration of hydro-alcoholic extract at a dose of 136.5mg/kg and 273mg/kg showed elevation in time spent in light box and was comparable to standard treated group while time spent by mice following oral administration of hydro-alcoholic extract of Agaricus blazei at a dose of 273.0 mg/kg also showed elevation and was found to be more near to standard treated group (diazepam 1 mg/kg, i.p.).

Deep reinforcement learning based control for Autonomous Vehicles in CARLA

Nowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

Entry vehicle control system design for the Tianwen-1 mission

The entry vehicle for the Tianwen-1 mission successfully landed on the surface of Mars at 7:18 AM BJT on May 15, 2021. This successful landing made China the first country to orbit, land, and release a rover in their first attempt at the Mars exploration. The guidance, navigation, and control (GNC) system plays a crucial role in the entry, descent, and landing (EDL) phases. This study focused on the attitude control component of the GNC system design. The EDL phase can be divided into several sub-phases, namely the angle of attack control phase, lift control phase, parachute descent phase, and powered descent phase. Each sub-phase has unique attitude control requirements and challenges. This paper introduces the key aspects of designing attitude controllers for each phase. Furthermore, flight results are presented and analyzed.

Insulin exposed endometrial epithelial cells cultured in a microfluidic device alters transcripts involved in translation that may contribute to reduced implantation capacity of the endometrium.

Obesity is a rapidly growing public health issue among women of reproductive age. It is also associated with decreased reproductive function including implantation failure. Implantation failure can result from a myriad of factors including impaired gametes and endometrial dysfunction. The mechanisms of how obesity-related hyperinsulinaemia disrupts endometrial function and implantation are poorly understood. Our study aims to investigate potential mechanisms by which insulin alters endometrial transcript expression, which may affect endometrial receptivity. Ishikawa cells mimicking human endometrial epithelium were seeded into a microfluidics organ-on-chip device to produce an in vitro endometrium. Syringe pump was attached to the microfluidics device to deliver three varying treatments into Ishikawa cells: 1) media control 2) vehicle control (PBS acidified to pH3 with acetic acid) 3) Insulin (2mg/mL) at a constant flow rate of 1uL/min for 24 hours to mimic secretion in vivo. Three biological replicates were obtained. Insulin-induced transcriptomic response of the in vitro endometrium was quantified via RNA sequencing, and subsequently analysed using DAVID and Webgestalt to identify Gene Ontology (GO) terms and signalling pathways. A Total of 29 transcripts showed differential expression levels across two comparison groups (control v vehicle control; vehicle control v insulin). There were nine transcripts significantly differentially expressed in vehicle control v insulin group (p<0.05). Functional annotation analysis of transcripts altered by insulin (n=9) identified three significantly enriched GO terms: SRP-dependent cotranslational protein targeting to membrane, poly(A) binding, and RNA binding (p<0.05). Over-representation analysis found three significantly enriched signalling pathways relating to insulin-induced transcriptomic response: protein export, glutathione metabolism, and ribosome pathways (p<0.05). Insulin-induced dysregulation of biological functions and pathways highlight potential mechanisms by which high insulin concentrations within maternal circulation may perturb endometrial receptivity.

Assessment of the Toxicity and Carcinogenicity of Double-Walled Carbon Nanotubes in the Rat Lung After Intratracheal Instillation: A Two-Year Study

Background Considering the expanding industrial applications of carbon nanotubes (CNTs), safety assessment of these materials is far less than needed. Very few long-term in vivo studies have been carried out. This is the first 2-year in vivo study to assess the effects of double walled carbon nanotubes (DWCNTs) in the lung and pleura of rats after pulmonary exposure. Methods Rats were divided into six groups: Untreated, Vehicle, 3 DWCNT groups (0.12mg/rat, 0.25mg/rat and 0.5mg/rat), and MWCNT-7 (0.5mg/rat). The test materials were administrated by intratracheal - intrapulmonary spraying (TIPS) every other day for 15 days. Rats were observed without further treatment until sacrifice at weeks 52 and 104. Results DWCNT were biopersistent in the rat lung and induced marked pulmonary inflammation with a significant increase in macrophage count and levels of the chemotactic cytokines CCL2 and CCL3. In addition, the 0.5 mg DWCNT treated rats had significantly higher pulmonary collagen deposition compared to the vehicle controls. The development of carcinomas in the lungs of rats treated with 0.5 mg DWCNT (4/24) was not quite statistically higher (p = 0.0502) than the vehicle control group (0/25), however, the overall incidence of lung tumor development, bronchiolo-alveolar adenoma and bronchiolo-alveolar carcinoma combined, in the lungs of rats treated with 0.5 mg DWCNT (7/24) was statistically higher (p < 0.05) than the vehicle control group (1/25). Notably, two of the rats treated with DWCNT, one in the 0.25 mg group and one in the 0.5mg group, developed pleural mesotheliomas. However, both of these lesions developed in the visceral pleura, and unlike the rats administered MWCNT-7, rats administered DWCNT did not have elevated levels of HMGB1 in their pleural lavage fluids. Conclusions Our results demonstrate that DWCNTs are biopersistent in the rat lung and induce chronic inflammation. Moreover, rats treated with 0.5 mg DWCNT developed pleural fibrosis. While our results do not show that DWCNT is carcinogenic in the rat lung, total tumor incidence was significantly increased in the 0.5 mg DWCNT group. Taken together, these findings demonstrate that the possibility that at least some types of DWCNTs are fibrogenic and carcinogenic cannot be ignored.

S100A9 is a functional effector of infarct wall thinning after myocardial infarction

Neutrophils infiltrate into the left ventricle (LV) early after myocardial infarction (MI) and launch a pro-inflammatory response. Along with neutrophil infiltration, LV wall thinning due to cardiomyocyte necrosis also peaks at day 1 in the mouse model of MI. To understand the correlation, we examined a previously published dataset that included day 0 (n=10) and MI day 1 (n=10) neutrophil proteome and echocardiography assessments. Out of 123 proteins, 4 proteins positively correlated with the infarct wall thinning index (1/wall thickness): histone 1.2 (r=0.62, p=0.004), S100A9 (r=0.60, p=0.005), histone 3.1 (r=0.55, p=0.01), and fibrinogen (r=0.47, p=0.04). As S100A9 was the highest ranked secreted protein, we hypothesized that S100A9 is a functional effector of infarct wall thinning. We exogenously administered S100A8/A9 at the time of MI to mice (C57BL/6J, male, 3-6 months of age, n=7M (D1), and n=5M (D3)) and compared to saline vehicle control treated mice (n=6M (D1) and n=6M (D3)) at MI days 1 and 3. At MI day 3, the S100A8/A9 group showed a 22% increase in the wall thinning index compared to saline (p=0.02), along with higher dilation and lower ejection fraction. The decline in cardiac physiology occurred subsequent to increased neutrophil and macrophage infiltration at MI day 1 and increased macrophage infiltration at D3. Our results reveal that S100A9 is a functional effector of infarct wall thinning.

Human factors, artificial intelligence and autonomous cars: Perspectives for a complex implementation

The centuries-old near-inseparable human/automobile relationship faces a revolution thanks to artificial intelligence gradually creating new paradigms in terms of personal urban mobility. Still, would we be prepared to relinquish our vehicle control to autonomous systems? The main objective of this work is to elucidate the main elements of the complex relationship between human factors and artificial intelligence in the development and establishment of autonomous vehicles. Thus, this paper adopted a basic methodology with a qualitative approach with an exploratory objective and technical procedures, as well as technical procedures of a documentary and bibliographic nature. Notice that autonomous systems present plausible functioning in controlled environments, even so, in an environment with several variables and an almost infinite possibility of combinations, enforced the occurrence of failures and compromised the structuring of a mental model, based on human factors, applicable to artificial intelligence. That explains the little importance given to human factors in the planning of human/autonomous machine interactions.

Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control

Reinforcement learning (RL) is a booming area in artificial intelligence. The applications of RL are endless nowadays, ranging from fields such as medicine or finance to manufacturing or the gaming industry. Although multiple works argue that RL can be key to a great part of intelligent vehicle control related problems, there are many practical problems that need to be addressed, such as safety related problems that can result from non-optimal training in RL. For instance, for an RL agent to be effective it should first cover all the situations during training that it may face later. This is often difficult when applied to the real-world. In this work we investigate the impact of RL applied to the context of intelligent vehicle control. We analyse the implications of RL in path planning tasks and we discuss two possible approaches to overcome the gap between the theorical developments of RL and its practical applications. Specifically, firstly this paper discusses the role of Curriculum Learning (CL) to structure the learning process of intelligent vehicle control in a gradual way. The results show how CL can play an important role in training agents in such context. Secondly, we discuss a method of transferring RL policies from simulation to reality in order to make the agent experience situations in simulation, so it knows how to react to them in reality. For that, we use Arduino Yún controlled robots as our platforms. The results enhance the effectiveness of the presented approach and show how RL policies can be transferred from simulation to reality even when the platforms are resource limited.