NO. 6 BOARD OF EDUCATION. APRIL 16TH, 1851.

Report twelve from the seventh bound volume of ten which documents in part the first nineteen years of The Free Academy, the predecessor of the educational institution, City College of New York. The Free Academy Reports, Catalogues and Addresses. 1847-52.[New York City Board of Education Reports on the Free Academy] includes 24 individual reports. At a time when municipal education constituted primary schooling, citizens united in response to arguments presented by a merchant and Board of Education President, Townsend Harris, for the necessity of an institution that would provide advanced training for future generations of citizens to fully engage in the professions advantageous to an expanding urban center. Includes preliminary reports that commented on the application of resources for the creation of the institution and the annual reports of the faculty, demonstrating accountability to the Board of Education with regard to the operation of the facility., NO. 6 BOARD OF EDUCATION. APRIL 16TH, 1851. [Executive Committee of the Free Academy regarding courses of studies]., RG

Human cardiac tissue in a microperfusion chamber simulating extracorporeal circulation - ischemia and apoptosis studies

<p>Abstract</p> <p>Background</p> <p>After coronary artery bypass grafting ischemia/reperfusion injury inducing cardiomyocyte apoptosis may occur. This surgery-related inflammatory reaction appears to be of extreme complexity with regard to its molecular, cellular and tissue mechanisms and many studies have been performed on animal models. However, finding retrieved from animal studies were only partially confirmed in humans. To investigate this phenomenon and to evaluate possible therapies in vitro, adequate human cardiomyocyte models are required. We established a tissue model of human cardiomyocytes preserving the complex tissue environment. To our knowledge human cardiac tissue has not been investigated in an experimental setup mimicking extracorporeal circulation just in accordance to clinical routine, yet.</p> <p>Methods</p> <p>Cardiac biopsies were retrieved from the right auricle of patients undergoing elective coronary artery bypass grafting before cardiopulmonary bypass. The extracorporeal circulation was simulated by submitting the biopsies to varied conditions simulating cardioplegia (cp) and reperfusion (rep) in a microperfusion chamber. Cp/rep time sets were 20/7, 40/13 and 60/20 min. For analyses of the calcium homoeostasis the fluorescent calcium ion indicator FURA-2 and for apoptosis detection PARP-1 cleavage immunostaining were employed. Further the anti-apoptotic effect of carvedilol [10 μM] was investigated by adding into the perfusate.</p> <p>Results</p> <p>Viable cardiomyocytes presented an intact calcium homoeostasis under physiologic conditions. Following cardioplegia and reperfusion a time-dependent elevation of cytosolic calcium as a sign of disarrangement of the calcium homoeostasis occurred. PARP-1 cleavage also showed a time-dependence whereas reperfusion had the highest impact on apoptosis. Cardioplegia and carvedilol could reduce apoptosis significantly, lowering it between 60-70% (p < 0.05).</p> <p>Conclusions</p> <p>Our human cardiac preparation served as a reliable cellular model tool to study apoptosis in vitro. Decisively cardiac tissue from the right auricle can be easily obtained at nearly every cardiac operation avoiding biopsying of the myocardium or even experiments on animals.</p> <p>The apoptotic damage induced by the ischemia/reperfusion stimulus could be significantly reduced by the cold crystalloid cardioplegia. The additional treatment of cardiomyocytes with a non-selective β-blocker, carvedilol had even a significantly higher reduction of apoptotis.</p

Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome

Characterization of the chloroplast proteome is needed to understand the essential contribution of the chloroplast to plant growth and development. Here we present a large scale analysis by nanoLC-Q-TOF and nanoLC-LTQ-Orbitrap mass spectrometry (MS) of ten independent chloroplast preparations from Arabidopsis thaliana which unambiguously identified 1325 proteins. Novel proteins include various kinases and putative nucleotide binding proteins. Based on repeated and independent MS based protein identifications requiring multiple matched peptide sequences, as well as literature, 916 nuclear-encoded proteins were assigned with high confidence to the plastid, of which 86% had a predicted chloroplast transit peptide (cTP). The protein abundance of soluble stromal proteins was calculated from normalized spectral counts from LTQ-Obitrap analysis and was found to cover four orders of magnitude. Comparison to gel-based quantification demonstrates that ‘spectral counting’ can provide large scale protein quantification for Arabidopsis. This quantitative information was used to determine possible biases for protein targeting prediction by TargetP and also to understand the significance of protein contaminants. The abundance data for 550 stromal proteins was used to understand abundance of metabolic pathways and chloroplast processes. We highlight the abundance of 48 stromal proteins involved in post-translational proteome homeostasis (including aminopeptidases, proteases, deformylases, chaperones, protein sorting components) and discuss the biological implications. N-terminal modifications were identified for a subset of nuclear- and chloroplast-encoded proteins and a novel N-terminal acetylation motif was discovered. Analysis of cTPs and their cleavage sites of Arabidopsis chloroplast proteins, as well as their predicted rice homologues, identified new species-dependent features, which will facilitate improved subcellular localization prediction. No evidence was found for suggested targeting via the secretory system. This study provides the most comprehensive chloroplast proteome analysis to date and an expanded Plant Proteome Database (PPDB) in which all MS data are projected on identified gene models

A Supervised Text Classification System Detects Fontan Patients in Electronic Records with Higher Accuracy than ICD Codes

AbstractBackgroundThe Fontan operation palliates single ventricle heart defects and is associated with significant morbidity and premature mortality. Native anatomy varies; thus, Fontan cases cannot always be identified byInternational Classification of Diseases, Ninth and Tenth Revision, Clinical Modification (ICD-9-CM and ICD-10-CM)codes, making it challenging to create large Fontan patient cohorts. We sought to develop natural language processing (NLP) based machine learning (ML) models, which utilize free text notes of patients, to automatically detect Fontan cases, and compare their performances with ICD code based classification.Methods and ResultsWe included free text notes of 10,935 manually validated patients, of whom 778 (7.1%) were Fontan and 10,157 (92.9%) non-Fontan patients, from two large, diverse healthcare systems. Using 5-fold cross validation, we trained and evaluated multiple ML models, namely support vector machines (SVM) and a transformer based model for language understanding named RoBERTa (2 versions), for automatically identifying Fontan cases based on free text notes. To optimize classifier performances, we experimented with different text representation techniques, including a sliding window strategy to overcome the length limit imposed by RoBERTa. We compared the performances of the ML models to ICD code based classification using the F1score metric. The ICD classification model, SVM, and RoBERTa achieved F1scores of 0.81 (95% CI: 0.79-0.83), 0.95 (95% CI: 0.92-0.97), and 0.89 (95% CI: 0.88-0.85) for the positive (Fontan) class, respectively. SVM obtained the best performance (p&lt;0.05), and both NLP models outperformed ICD code based classification (p&lt;0.05). The novel sliding window strategy improved performance over the base RoBERTa model (p&lt;0.05) but did not outperform SVM. ICD code based classification tended to have more false positives compared to both NLP models.ConclusionsOur proposed NLP models can automatically detect Fontan patients based on clinical notes with higher accuracy than ICD codes. Since the sensitivity of ICD codes is high but the positive predictive value is low, it may be beneficial to apply ICD codes as a filter prior to applying NLP/ML to achieve optimal performance.</jats:sec