Sheltonvoss2698

The distinction between a semantic memory system, encompassing conceptual knowledge, and an episodic memory system, characterized by specific episodes, is one of the most important theoretical proposals in cognitive science. However, the distinction between systems has rarely been discussed in relation to spontaneous thought that comes to mind with reduced cognitive effort and intentionality. In this review, we propose that the growing research on spontaneous thought can contribute to current discussions on the interaction between the episodic and semantic systems. Firstly, we review research that shows that, as in deliberate retrieval, spontaneous thoughts are influenced by both episodic and semantic memory, as reflected by the mix of semantic and episodic elements in descriptions of spontaneous thoughts, as well as semantic priming effects in spontaneous thoughts. We integrate the current evidence based on the interplay between cues and semantic activation. Namely, we suggest that cues are key to access episodic memory and modulate the frequency of spontaneous thought, while semantic activation modulates the content of spontaneous thought. Secondly, we propose that spontaneous retrieval is a privileged area to explore the question of functional independence between systems, because it provides direct access to the episodic system. We review the evidence for spontaneous thought in semantic dementia, which suggests that episodic and semantic systems are functionally independent. We acknowledge the scarcity of evidence and suggest that future studies examine the contents of spontaneous thought descriptions and their neural correlates to test the functional relationship and inform the interaction between episodic and semantic systems.

Shock is common in critically ill and injured patients. Survival during shock is highly dependent on rapid restoration of tissue oxygenation with therapeutic goals based on cardiac output (CO) optimization. Despite the clinical availability of numerous minimally invasive monitors of CO, limited supporting performance data are available.

Following approval of the University of Saskatchewan Animal Research Ethics Board, we assessed the performance and trending ability of PiCCOplus™, FloTrac™, and CardioQ-ODM™ across a range of CO states in pigs. Akt inhibitor In addition, we assessed the ability of invasive mean arterial blood pressure (iMAP) to follow changes in CO using a periaortic transit-time flow probe as the reference method. Statistical analysis was performed with function-fail, bias and precision, percent error, and linear regression at all flow, low-flow (> 1 standard deviation [SD] below the mean), and high-flow (> 1 SD above the mean) CO conditions.

We made a total of 116,957 paired CO measurements. Trs including iMAP, but at low flows iMAP (correlation coefficient, 0.58; 99% CI, 0.58 to 0.60) was superior to all minimally invasive CO monitors (all comparisons P < 0.001).

None of the minimally invasive monitors of CO performed well at all tested flows. Invasive mean arterial blood pressure most closely tracked CO change at critical flow states.

None of the minimally invasive monitors of CO performed well at all tested flows. Invasive mean arterial blood pressure most closely tracked CO change at critical flow states.The aggravating deforestation, industrialization, and urbanization are becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal variability in land surface temperature (LST) and its link with elevation in the Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001-2020) were used. The pixel-based linear regression model was used to explore the spatiotemporal variability of LST changes. Furthermore, Sen's slope and Mann-Kendall trend test were used to determine the magnitude of temporal shifts of the areal average LST and evaluate trends in areal average LST, respectively. Coefficient of variation (CV) was also used to analyze spatial and temporal variability in seasonal and annual LST. The seasonal LST CV varied from 1.096-10.72%, 0.7-11.06%, 1.29-14.76%, and 2.19-10.35% for average autumn (September to November), summer (June to August), spring (March to May), and winter (December to February) seasons, respectively. The highest inter-annual variability was observed in the eastern, northern, and south-western districts than that in the other parts. The seasonal spatial LST trend varied from -0.7-0.16, -0.4-0.224, 0.6-0.19, and -0.6-0.32 for average autumn, summer, spring, and winter seasons, respectively. Besides, the annual spatial LST slope varied from -0.58 to 0.17. Negative slopes were found in the central, mid-western, and mid-northern districts in annual LST, unlike the other parts. The annual variations of mean areal LST decreased insignificantly at the rate of 0.046°C year-1 (P less then 0.05). However, the inter-annual variability trend of annual LST increased significantly. Generally, the LST is tremendously variable in space and time and negatively correlated with elevation.We studied the ability of Argyrochosma formosa growing in an arsenic heavily contaminated site to accumulate this metalloid; morphological characteristics and translocation of arsenic were evaluated in the organs. Population census of wild specimens of A. formosa was done, and 14 samples of ferns and rhizosphere soil were collected randomly. We recorded morphological characteristics with scanning electronic microscopy (SEM); concentrations of As in organs of fern plants (root, rhizome, and fronds) were evaluated with inductively coupled plasma-optic emission spectrometry (ICP-OES). Two hundred ninety-four individuals at different stages of development were identified, indicating the establishment of fern on the site. Morphological characteristics of A. formosa in fern plant organs did not show structural effects, compared with herbarium plants. Arsenic distribution in fern plant tissues was 192.2-763.6 mg/kg, 188-1017 mg/kg, and 113-2008 mg/kg, in roots, rhizomes, and fronds, respectively. The calculated bioaccumulation factor in fronds ranged from 2 to 7 and the translocation factor from 0.6 to 2.1. Our data suggest that A. formosa is an arsenic-tolerant species and propose it for phytoremediation on contaminated sites with As concentrations similar to that of the studied location. Further studies should be performed to evaluate the mechanisms of accumulation of As in plant tissues.This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development.Municipal solid waste is typically managed in developing countries through various disposal methods, such as sanitary landfills or dumpsites. Alternatively, waste to energy (WTE) systems have been recently adopted to provide sustainable waste management and diversify the energy mix. The abundance of remotely sensed datasets and derivatives, along with the rapid development of artificial intelligence, can offer an effective solution for WTE site selection. In this study, an analytical hierarchy process (AHP)-based framework supported by multiple machine learning algorithms (gradient boosted tree (GBT), decision tree (DT), and support vector machines (SVMs)) was established to explore the optimum location for WTE facilities. Various social, legal, environmental, economic, morphological, and land cover parameters were considered under 11 thematic geospatial raster layers. The proposed framework was applied to the 1.5-million-capita city of Sharjah, United Arab Emirates. A novel approach was developed to incorporate Gaussian dispersion modeling for the expected air pollution emissions from a WTE facility. The results showed that the accuracy performance sequence of the algorithms was 94.6, 93.9, and 91.8% for GBT, DT, and SVM, respectively. It was found that the distance from existing landfills had the most critical impact on the optimum location of the WTE facility, followed by the distance from coastline and elevation. The AHP consistency check revealed an acceptable overall criteria consistency index and the ratio of 0.0344 and 0.019, respectively. The results showed that 16.6% of Sharjah was considered extremely highly suitable areas. This research supports decision-makers in developing local guidelines for siting WTE facilities and determining the most suitable locations for such projects.This study aimed to estimate morbidity risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019. A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using 1-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM). Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity. The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme, and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively. This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.