Dawn Research (United States)

We present an analysis of the JWST NIRCam and MIRI morphological properties of 80 massive ($\log_{10}(M_\ast[M_{\odot}])$=11.2$\pm$0.1) dusty star-forming galaxies at $z$$=$2.7$^{+1.2}_{-0.7}$, identified as sub-millimetre galaxies (SMGs) by ALMA, that have been observed as part of the JWST PRIMER project. To compare the structure of these massive, active galaxies to more typical less actively star-forming galaxies, we define two comparison samples. The first of 850 field galaxies matched in specific star-formation rate and redshift and the second of 80 field galaxies matched in stellar mass. We identify 20$\pm$5% of the SMGs as candidate late-stage major mergers, a further 40$\pm$10% as potential minor mergers and 40$\pm$10% which have comparatively undisturbed disk-like morphologies, with no obvious massive neighbours. These rates are comparable to those for the field samples and indicate that the majority of the sub-millimetre-detected galaxies are not late-stage major mergers, but have interaction rates similar to the less-active population at $z$$\sim$2-3. We establish that SMGs have comparable near-infrared sizes to the less active populations, but exhibit lower S\'ersic indices, consistent with bulge-less disks and have more structured morphologies at 2$\mu$m relative to 4$\mu$m. We find evidence for dust reddening as the origin of the morphological differences between the populations, identifying a strong correlation between the F200W$-$F444W pixel colour and the 870$\mu$m surface brightness. We conclude that SMGs and less active galaxies at the same epochs share a common disk-like structure, but the weaker bulge components of the SMGs results in a lower dynamical stability. Consequently, instabilities triggered either secularly or by minor external perturbations result in higher levels of activity (and dust content) in SMGs compared to typical star-forming galaxies. [Abridged]

ObjectiveKawasaki syndrome (KS) is an acute vasculitis that affects children < 5 years of age and leads to coronary artery lesions (CAL) in about 20-25% of untreated cases. Machine learning (ML) is a branch of artificial intelligence (AI) that integrates complex data sets on a large scale and uses huge data to predict future events. The purpose of the present study was to use ML to present the model for early risk assessment of CAL in children with KS by different algorithms.MethodsA total of 158 children were enrolled from Women and Children’s Hospital, Qingdao University, and divided into 70–30% as the training sets and the test sets for modeling and validation studies. There are several classifiers are constructed for models including the random forest (RF), the logistic regression (LR), and the eXtreme Gradient Boosting (XGBoost). Data preprocessing is analyzed before applying the classifiers to modeling. To avoid the problem of overfitting, the 5-fold cross validation method was used throughout all the data.ResultsThe area under the curve (AUC) of the RF model was 0.925 according to the validation of the test set. The average accuracy was 0.930 (95% CI, 0.905 to 0.956). The AUC of the LG model was 0.888 and the average accuracy was 0.893 (95% CI, 0,837 to 0.950). The AUC of the XGBoost model was 0.879 and the average accuracy was 0.935 (95% CI, 0.891 to 0.980).ConclusionThe RF algorithm was used in the present study to construct a prediction model for CAL effectively, with an accuracy of 0.930 and AUC of 0.925. The novel model established by ML may help guide clinicians in the initial decision to make a more aggressive initial anti-inflammatory therapy. Due to the limitations of external validation and regional population characteristics, additional research is required to initiate a further application in the clinic.

To assess the relationship between triptan exposure and risk of chronic migraine (CM) onset in migraine patients with prior acute treatment with triptans, and to quantify this risk with respect to duration of triptan use.

There are many interesting and insightful essay topics for college students but the most important question is what do you know? It's not enough to be a good writer; if you don't know what you are writing about you are doomed to fail. Essay topics should be thought-provoking, as well as both useful and educational. The topics should not simply list what you know, but ask yourself questions, which will inspire you to write additional essays.To begin with, let's consider some of the most popular essay topics for college students. These interesting, informational https://www.the-essays.com/ essay topics will continually keep the reader's attention throughout the essay. Some of the more common essay topics include; crime, the causes of crime, the effects of crime, obesity, poverty, drug abuse and many others. This is not limited to just the US, there are many different types of these topics, which pertain to various parts of the world. It's interesting to know the reasons behind why people do the things they do.Another way to choose essay topics for college students is to find out what types of arguments have been used in your daily life and what is your opinion on those types of arguments. For example, if you have a pet dog and believe in animal rights, you might want to look at some of the argumentative essay topics concerning animal rights. On the other hand, if you think the United States dollar is too strong, you might want to read some of the argumentative topics concerning how the American dollar is failing to gain its value against other currencies. The important thing to remember is, if you can show your opinion, you can prove your point and that is what this class is all about.

Compare the efficacy of rimegepant with nonsteroidal anti-inflammatory drugs (NSAIDs) for the acute treatment of migraine.

Introduction: Literature on the effectiveness of simulation based medical education programs used in acute ischemic stroke (AIS) care is scant. In an effort to improve coordination and door to needle time (DNT) for AIS care, a stroke simulation education training program for neurology nursing staff and neurology residents was implemented in a comprehensive stroke center. Methods: Hospital stroke registry was used for retrospective analysis. The study population was defined as all patients treated with IV-tPA for AIS in the emergency room from October 2008 to September 2014. Simulation training was implemented yearly, for a three month period starting from July 2011. All neurology residents and a group of nurses trained to respond to all AIS cases participated. Simulations were standardized, using deliberate practice with a trained live actor portraying stroke vignettes in the presence of a board certified vascular neurologist. During the period of study, there were no changes in Emergency Department stroke triage protocol, or changes in first provider response to AIS. The data was analyzed using IBM SPSS24 software. Results: We identified 448 patients admitted with AIS who were treated with IV-tPA. The average DNT on univariate analysis before and after intervention was 67.9 and 58.3 minutes [p &lt;0.001]. A multivariate linear regression analysis was performed controlling for age, night/day shift, weekday/weekend, and blood pressure at presentation (&gt;185/110). After controlling for confounders we found that simulation training independently reduced the DNT by 9.64 minutes [95% confidence interval (CI) 4.01 - 15.28, p=0.001]. Amongst other co-variates, only the systolic blood pressure &gt;185 was associated with 14.27 minutes of delay in DNT [95% CI 3.36 - 25.191, p=0.011]. Conclusion: Time to thrombolysis from symptom onset is a critical factor in AIS management and evidence shows improving the DNT could improve patient outcomes. In our six year study, integration of simulation based medical education for AIS reduced the average DNT by 9.64 minutes in multivariate analysis. Simulation based medical education therefore should be considered as a standard process for providers involved in the care of AIS patients receiving thrombolytic treatment.

Introduction: Lipopolysaccharide (LPS) is known to induce endothelial dysfunction causing an increase in permeability and damage intra cellular signaling as well as known to induce autophagy. However, few studies have suggested that autophagy might interrupt cell-cell interaction through adherens junction protein, which leads to endothelial barrier dysfunction. Here we demonstrated that LPS induces autophagy, and hyper permeability of lung endothelial cells and this can be prevented by the combinatorial treatment with 5-Aza 2 deoxycytidine and Tubastatin A (HDAC6 inhibitor) (referred as Aza+TBSA). Hypothesis: We hypothesize that treatment with the combination of Aza+TBSA inhibits autophagy and restores vascular integrity after LPS induced vascular hyper permeability. Methods and Results: In this study, we have used LPS to induce hyper permeability and autophagy and mTOR inhibitor Rapamycin used as a positive control for autophagy in mouse lung endothelial cells (MLECs). Our Western analysis data showed that LPS and Rapamycin induce autophagy, which is indicated by the increased level of known autophagy markers LC3 and beclin-1, and decreased VE-cadherin expression in MLECs induced with LPS or Rapamycin. Where as treatment with Aza+TBSA significantly reduced the effect of LPS induced autophagy markers and increased VE-cadherin expression (Fig. 1A). Our immunofluorescence analysis also showed a decreased expression of LC3 when the LPS induced MLECs was treated with Aza+TBSA ( Fig. 1B ). Conclusions: These data suggest that Aza+TBSA treatment can rescue endothelial barrier dysfunction and prevent autophagy in LPS-induced MLECs, indicating that LPS-induced vascular leakage is dependent on autophagy. These results support a therapeutic potential of Aza+TBSA combination for patients with acute lung injury.

Introduction: Results from randomized controlled trials (RCTs) of bone marrow cell (BMC) therapy for cardiac repair have been discordant. The true impact of BMC therapy in patients with chronic ischemic heart disease (CIHD) and ischemic heart failure remain unclear. Hypothesis: We hypothesized that BMC injection would improve clinical outcomes and left ventricular (LV) parameters in patients with CIHD. Methods: We performed a systematic review and meta-analysis of data from RCTs of BMC therapy on LV ejection fraction (LVEF), LV end-systolic volume (LVESV), LV end-diastolic volume (LVEDV), infarct scar size, and clinical outcomes in patients with CIHD. Database searches based on specified criteria identified 15 eligible RCTs (enrolling 648 CIHD patients). Changes in predefined parameters were analyzed with random-effects meta-analysis. Peto odds ratio (OR) was calculated for clinical outcomes. Results: Compared with standard therapy, BMC transplantation improved LVEF (3.84%; 95% confidence interval [CI], 2.30 to 5.38; P&lt;0.00001), reduced scar size (-5.90%; 95% CI, -8.03 to -3.78; P&lt;0.00001) and LVESV (-10.37 ml; 95% CI, (-18.56 to -2.18; P=0.01). Reduction in LVEDV (-1.84 ml; 95% CI, -9.13 to 5.46; P=0.62) was not significant. The improvement in LVEF in BMC-treated patients was also significant (1.78%; 95% CI: 0.63 to 2.93; P=0.002) in a separate analysis of RCTs that used MRI. Importantly, BMC therapy reduced all-cause mortality (OR: 0.35; 95% CI 0.16 to 0.73; P=0.005) and incidence of cerebrovascular accident (CVA) (OR: 0.07; 95% CI, 0.01 to 0.55; P=0.01) and tended to reduce the incidence of heart failure (OR: 0.36; 95% CI 0.11 to 1.14; P=0.08) in patients with CIHD (Table). Conclusions: BMC therapy improves LVEF, LVESV, and infarct scar size in patients with CIHD. The improvement in LVEF was also confirmed in meta-analysis of RCTs using MRI. Importantly, these numerically small benefits translated into improved clinical outcomes in BMC-treated CIHD patients.

Background: To improve the patient experience and delivery of patient care, a Stroke Center Patient Advisory Board was created in April 2011. A theme that emerged was the need for psychological and emotional support as an inpatient and after discharge. Common symptoms associated with acute stroke include cognitive impairment, depression, anxiety, and reduced independence in daily activities. Per Advisory Board feedback, the subtle emotional and cognitive changes associated with stroke were not being identified and discussed during acute hospitalization. Literature suggests that 2/3 of patients have cognitive impairment after stroke, and similar rates of post-stroke depression are seen in first 3 months. Both cognitive impairment and mood disorders are associated with impaired function, increased mortality, and increased length of acute and rehabilitation stay after stroke. Purpose: Establish new best practice to better identify cognitive impairment and depression to more fully address patients’ needs prior to discharge. Methods: A Neuropsychological Continuum of Care was created. A routine neuropsychological (cognitive &amp; emotional) screen was done for all patients admitted with an ischemic or hemorrhagic stroke, including those with TIA. After screening, psychoeducation is given to patients and/or families on cognitive strengths and weaknesses, emotional needs and expected recovery. Recommendations are made regarding discharge and compensatory strategies. The findings inform the need for outpatient services and referrals are made to Psychiatry for medication consultation if needed. Results: Over first 12 months, 849 patients with stroke/TIA were referred for inpatient neuropsychological consultation, as compared to 0 inpatient stroke referrals the prior 12 month period. Neuropsychologists provide psychoeducation and neuropsychological intervention during inpatient stay. Discussion: The development and implementation of this new standard of care has resulted in greater ability to educate and support patients and families in the critical acute stroke period. Early post-stroke cognitive and emotional concerns are identified to inform treatment and discharge planning.

A test structure for analysis of temperature distribution in CMOS LSI is presented. Fundamental thermal properties of LSI chip have been measured and discussed with simulation results. The test structure consists of 24 sensor blocks, each of which has a resistor as an on-chip heater, a p-n diode or n-MOSFET array for temperature sensing and selector switches. Dependence of heating time and distance from the resistor are analyzed as well as transient phenomena. Temperature $T$ decreases with the distance $L$ , is proportional to the reciprocal of $L~(1/L)$ , and empirical equations have been proposed. The results of both p-n diode and n-MOSFET sensors are nearly the same. A thermal simulation gives an $L$ dependence of $T$ similar to the measured result. $T$ of 160 pin QFP becomes lower than that of 80 pin due to its larger outline, but $L$ dependence of $T$ is similar. The abrupt and gradual change of $T$ at the heater switching suggests both fast and slow processes in thermal conduction. The test structure can provide an effective methodology for analysis of fundamental thermal properties in LSIs packaged in various ways.