Analysis of Oct4-dependent transcriptional networks regulating self-renewal and pluripotency in human embryonic stem cells

The POU domain transcription factor OCT4 is a key regulator of pluripotency in the early mammalian embryo and is highly expressed in the inner cell mass of the blastocyst. Consistent with its essential role in maintaining pluripotency, Oct4 expression is rapidly downregulated during formation of the trophoblast lineage. To enhance our understanding of the molecular basis of this differentiation event in humans, we used a functional genomics approach involving RNA interference-mediated suppression of OCT4 function in a human ESC line and analysis of the resulting transcriptional profiles to identify OCT4-dependent genes in human cells. We detected altered expression of >1,000 genes, including targets regulated directly by OCT4 either positively (NANOG, SOX2, REX1, LEFTB, LEFTA/EBAF DPPA4, THY1, and TDGF1) or negatively (CDX2, EOMES, BMP4, TBX18, Brachyury [T], DKK1, HLX1, GATA6, ID2, and DLX5), as well as targets for the OCT4-associated stem cell regulators SOX2 and NANOG. Our data set includes regulators of ACTIVIN, BMP, fibroblast growth factor, and WNT signaling. These pathways are implicated in regulating human ESC differentiation and therefore further validate the results of our analysis. In addition, we identified a number of differentially expressed genes that are involved in epigenetics, chromatin remodeling, apoptosis, and metabolism that may point to underlying molecular mechanisms that regulate pluripotency and trophoblast differentiation in humans. Significant concordance between this data set and previous comparisons between inner cell mass and trophectoderm in human embryos indicates that the study of human ESC differentiation in vitro represents a useful model of early embryonic differentiation in humans

Transient cooling of a lithium-ion battery module during high-performance driving cycles using distributed pipes - A numerical investigation

Transient effects are often excluded from the design and analysis of battery thermal management systems (BTMS). However, electric vehicles are subjected to significant dynamic loads causing transient battery heating that is not encountered in a steady state. To evaluate the significance of such effects, this paper presents a time-dependent analysis of the battery cooling process, based on an existing cooling system that satisfactorily operates in steady conditions. To resemble realistic conditions, the temporal variations in the battery power withdrawal are inferred from different standard driving cycles. Computational fluid dynamics is then utilized to predict the coolant and battery temperatures inside a battery module for a period of 900 s. It is shown that, for air cooling, the batteries temperature can exceed the safe limit. For example, in a high-performance driving cycle, after 200 s, the battery temperature goes beyond the critical value of 308 K. Nonetheless, the temperatures are always within the safe region when liquid is used to cool the battery module. Also, during a high-performance cycle where the flow rate is 1.230 g/s, the battery temperature decreased below the critical threshold and reached 304 K. In addition, to maintain the temperature of the batteries below the critical threshold during NYCC traffic and US06 driving cycles, a maximum coolant pressure inlet of 1.52 and 0.848 g/s, equivalent to 100 Pa and 50 Pa, respectively, are required. The temporal changes in Nusselt number distribution over the battery module, induced by the acceleration of the vehicle during the driving cycles, are also discussed. It is concluded that the assumption of a steady state might lead to the non-optimal design of BTMSs

Transient cooling of a lithium-ion battery module during high-performance driving cycles using distributed pipes - A numerical investigation

Transient effects are often excluded from the design and analysis of battery thermal management systems (BTMS). However, electric vehicles are subjected to significant dynamic loads causing transient battery heating that is not encountered in a steady state. To evaluate the significance of such effects, this paper presents a time-dependent analysis of the battery cooling process, based on an existing cooling system that satisfactorily operates in steady conditions. To resemble realistic conditions, the temporal variations in the battery power withdrawal are inferred from different standard driving cycles. Computational fluid dynamics is then utilized to predict the coolant and battery temperatures inside a battery module for a period of 900 s. It is shown that, for air cooling, the batteries temperature can exceed the safe limit. For example, in a high-performance driving cycle, after 200 s, the battery temperature goes beyond the critical value of 308 K. Nonetheless, the temperatures are always within the safe region when liquid is used to cool the battery module. Also, during a high-performance cycle where the flow rate is 1.230 g/s, the battery temperature decreased below the critical threshold and reached 304 K. In addition, to maintain the temperature of the batteries below the critical threshold during NYCC traffic and US06 driving cycles, a maximum coolant pressure inlet of 1.52 and 0.848 g/s, equivalent to 100 Pa and 50 Pa, respectively, are required. The temporal changes in Nusselt number distribution over the battery module, induced by the acceleration of the vehicle during the driving cycles, are also discussed. It is concluded that the assumption of a steady state might lead to the non-optimal design of BTMSs

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected to occur from the out-of-distribution risks which arise from situations outside the AV’s training experience. Traditional vehicle insurance products (for human-driven vehicles) rely on large data sets of drivers’ background and historical incidents. However, the lack of such datasets for AVs makes it imperative to exploit the ability to deploy AVs in simulated environments. In this paper, the data collected by deploying Autonomous Driving Systems (ADSs) in simulated environments is used to develop models to answer two questions: (1) how risky a road Section is for an AV to drive? and (2) how does the risk profile vary with different (SAE levels) of ADSs? A simulation pipeline was built on the CARLA (Car Learning to Act): an open-source simulator for autonomous driving research. The environment was specified using parameters such as weather, lighting, traffic density, traffic flow, no. of lanes, etc. A metric - risk factor was defined as a combination of harsh accelerations/braking, inverse Time to Collision, and inverse Time Headway to capture the crashes and near-crashes. To assess the difference between ADSs, two ADSs: OpenPilot (Level 2/3) and Pylot (Level 4) were implemented in the simulator. The results (from data and model predictions) show that the trends in the relation between the environment features and risk factor for an AV are similar to those observed for human drivers (e.g., risk increases with traffic flow). The models also showed that junctions were a risk hot-spot for both ADSs. The feature importance of the model revealed that the Level 2/3 ADS is more sensitive to no. of lanes and the Level 4 ADS is sensitive to traffic flow. Such differences in feature importance provide valuable insights into the risk characteristics of different ADSs. In the future, this base model will be extended to include other features (other than the environment), e.g., take over requests, and also address the deficiencies of the current simulation data in terms of insensitivity to weather and lighting

Particulate number emissions during cold-start with diesel and biofuels: A special focus on particle size distribution

The share of biofuels in the transportation sector is increasing. Previous studies revealed that the use of biofuels decreases the size of particles (which is linked to an increase in particulate toxicity). Current emission regulations do not consider small particles (sub-23 nm); however, there is a focus in future emissions regulations on small particles. These and the fact that within cold-start emissions are higher than during the warmed-up operation highlight the importance of a research that studies particulate matter emissions during cold-start. This research investigates the influence of biofuel on PN and PM concentration, size distribution, median diameter and cumulative share at different size ranges (including sub-23 nm and nucleation mode) during cold-start and warm-up operations using diesel and 10, 15 and 20% mixture (coconut biofuel blended with diesel). During cold-start, between 19 and 29% of total PN and less than 0.8% of total PM were related to the nucleation mode (sub-50 nm). Out of that, the share of sub-23 nm was up to 9% for PN while less than 0.02% for PM. By using biofuel, PN increased between 27 and 57% at cold-start; while, the increase was between 4 and 19% during hot-operation. The median diameter also decreased at cold-start and the nucleation mode particles (including sub-23 nm particles) significantly increased. This is an important observation because using biofuel can have a more adverse impact within cold-start period which is inevitable in most vehicles’ daily driving schedules.<br/

Enhancement of an Air-Cooled Battery Thermal Management System Using Liquid Cooling with CuO and Al2O3 Nanofluids under Steady-State and Transient Conditions

Lithium-ion batteries are a crucial part of transportation electrification. Various battery thermal management systems (BTMS) are employed in electric vehicles for safe and optimum battery operation. With the advancement in power demand and battery technology, there is an increasing interest in enhancing BTMS’ performance. Liquid cooling is gaining a lot of attention recently due to its higher heat capacity compared to air. In this study, an air-cooled BTMS is replaced by a liquid cooled with nanoparticles, and the impacts of different nanoparticles and flow chrematistics are modeled. Furthermore, a unique approach that involves transient analysis is employed. The effects of nanofluid in enhancing the thermal performance of lithium-ion batteries are assessed for two types of nanoparticles (CuO and Al2O3) at four different volume concentrations (0.5%, 2%, 3%, and 5%) and three fluid velocities (0.05, 0.075, and 0.1 m/s). To simulate fluid flow behavior and analyze the temperature distribution within the battery pack, a conventional k-ε turbulence model is used. The results indicate that the cooling efficiency of the system can be enhanced by introducing a 5% volume concentration of nanofluids at a lower fluid velocity as compared to pure liquid. Al2O3 and CuO reduce the temperature by 7.89% and 4.73% for the 5% volume concentration, respectively. From transient analysis, it is also found that for 600 s of operation at the highest power, the cell temperature is within the safe range for the selected vehicle with nanofluid cooling. The findings from this study are expected to contribute to improving BTMS by quantifying the benefits of using nanofluids for battery cooling under both steady-state and transient conditions

Validation of the severity index by cardiac catheterization and Doppler echocardiography in patients with aortic sclerosis and stenosis

The severity index is a new echocardiographic measure that is thought to be an accurate indicator of aortic leaflet pathology in patients with AS. However, it has not been validated against cardiac catheterization or Doppler echocardiographic measures of AS severity nor has it been applied to patients with aortic sclerosis. The purposes of this study were to compare the severity index to invasive hemodynamics and Doppler echocardiography across the spectrum of calcific aortic valve disease, including aortic sclerosis and AS. 48 patients with aortic sclerosis and AS undergoing echocardiography and cardiac catheterization comprised the study population. The aortic valve leaflets were assessed for mobility (scale 1 to 6) and calcification (scale 1 to 4) and the severity index was calculated as the sum of the mobility and calcification scores according to the methods of Bahler et al. The severity index increased with increasing severity of aortic valve disease; the severity indices for patients with aortic sclerosis, mild to moderate AS and severe AS were 3.38 ± 1.06, 6.45 ± 2.16 and 8.38 ± 1.41, respectively. The aortic jet velocity by echocardiography and the square root of the maximum aortic valve gradient by cardiac catheterization correlated well with the severity index (r = 0.84, p < 0.0001; r = 0.84, p < 0.0001, respectively). These results confirm that the severity index correlates with hemodynamic severity of aortic valve disease and may prove to be a useful measure in patients with aortic sclerosis and AS

Limnological survey on the most important rivers in the Iranian southern soasts of the Caspian Sea in the Guilan Province with emphasize on the pollutants (Hevigh, Karkanrud and Shafarud rivers)

In order to create a reliable ecological database for Guilans running waters(north Iran near the Caspian sea), at the first step 3 important rivers containing Hevigh, Karkanrud and Shafarud, were selected and limnological survey, during autumn 1380 and summer1381 were seasonally carried out for them. Results showed that the key physico-chemical parameters such as dissolved O2, BOD5, pH, Phosphate, Nitrate, Nitrite, ammonium and in all of the three rivers water were in their normal range suggesting good water quality. Chrysophyta, Chlorophyta, Cyanophyta and Euglenophyta were respectively the most prevalent phytoplankton taxa and Zooplankton fauna of the rivers were fixed and sessile species belong to Protozoa and Rotatoria. 42 benthic macroinvertebrates taxa(Hevigh:36,Karkanrud:22 and Shafarud:30) were identified in these 3 rivers which individuals belong to order Diptera compromise the most divers and frequent one. Regarding macroinvertebrates diversity and indicator groups, it seems that Hevigh river has relatively better water quality than the two other. 23 fish species belong to 9 family,7 order and 2 class , were identified in these rivers during the study period which cyprinid were the most diverse and prevalent of them. Albornoides sp., Capoeta capoeta and Neogobios sp. were the most widespread and frequent species of the identified fishes in all of them. Bacteriological survey including Coliform and E.Colie count showed that the lower parts of the rivers near the estuaries were more infected and the bottom sediments had more bacterial count during the all sampling period. However the total coliform count never exceed of 200 colonies per 100cc , suggesting no risk for direct contact (swimming and washing), according to EPA standards. Chromium, Cadmium and Mercury had very low concentrations in the rivers water but Cupper had relatively high concentration (up to 1.788 mg/l in Hevigh river) amongst the studied heavy metals. The maximum and minimum concentrations of detergent materials or surfactant (LAS) were respectively observed in Shafarud (0.047 mg/l) and Hevigh (0.014 mg/l). According to the results of the study even though all the three rivers water had an overall reasonable quality, but some signs of destructions and degradations such as sedimentation, relatively increase of nutrient, increased concentrations of some pollutants, which all results in the low diversity of macroinvertebrates and prevents migration of anadromous fishes, calls for a continuing monitoring program and precise control for these regions