Cerebrovascular atherosclerosis has been identified as a prominent pathological feature of Alzheimer's disease (AD); the link between vessel pathology and AD risk may also extend to extracranial arteries. This study aimed to determine the effectiveness of using arterial pulse-wave measurements and multilayer perceptron (MLP) analysis in distinguishing between AD and control subjects. Radial blood pressure waveform (BPW) and finger photoplethysmography signals were measured noninvasively for 3 min in 87 AD patients and 74 control subjects. The 5-layer MLP algorithm employed evaluated the following 40 harmonic pulse indices amplitude proportion and its coefficient of variation, and phase angle and its standard deviation. The BPW indices differed significantly between the AD patients (6247 pulses) and control subjects (6626 pulses). Significant intergroup differences were found between mild, moderate, and severe AD (defined by Mini-Mental-State-Examination scores). The hold-out test results indicated an accuracy of 82.86%, a specificity of 92.31%, and a 0.83 AUC of ROC curve when using the MLP-based classification between AD and Control. The identified differences can be partly attributed to AD-induced changes in vascular elastic properties. The present findings may be meaningful in facilitating the development of a noninvasive, rapid, inexpensive, and objective method for detecting and monitoring the AD status.We utilize real-time time-dependent density functional theory and Ehrenfest dynamics scheme to investigate excited-state nonadiabatic dynamics of ligand dissociation of cobalt tricarbonyl nitrosyl, Co(CO)3NO, which is a precursor used for cobalt growth in advanced technologies, where the precursor's reaction is enhanced by electronic excitation. Based on the first-principles calculations, we demonstrate two dissociation pathways of the NO ligand on the precursor. Detailed electronic structures are further analyzed to provide an insight into dynamics following the electronic excitations. This study sheds light on computational demonstration and underlying mechanism of the electronic-excitation-induced dissociation, especially in molecules with complex chemical bonds such as the Co(CO)3NO.Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL models using candidate clinical variables potentially associated with mortality. We partitioned the data into training and evaluation sets with cross-validation. We estimated model performance based on the area under the receiver-operator characteristics curve (AUC) and balanced accuracy. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We identified 375 SCAD patients of which mortality during the index hospitalization was 11.5%. The best-performing DL algorithm identified in-hospital mortality with AUC 0.98 (95% CI 0.97-0.99), compared to other ML models (P  less then  0.0001). For prediction of mortality using ML models in patients with SCAD, the AUC ranged from 0.50 with the random forest method (95% CI 0.41-0.58) to 0.95 with the AdaBoost model (95% CI 0.93-0.96), with intermediate performance using logistic regression, decision tree, support vector machine, K-nearest neighbors, and extreme gradient boosting methods. PF-00835231 price A deep neural network model was associated with higher predictive accuracy and discriminative power than logistic regression or ML models for identification of patients with ACS due to SCAD prone to early mortality.Reconstruction of a critical-sized osseous defect is challenging in maxillofacial surgery. Despite novel treatments and advances in supportive therapies, severe complications including infection, nonunion, and malunion can still occur. Here, we aimed to assess the use of a beta-tricalcium phosphate (β-TCP) scaffold loaded with high mobility group box-1 protein (HMGB-1) as a novel critical-sized bone defect treatment in rabbits. The study was performed on 15 specific pathogen-free New Zealand rabbits divided into three groups Group A had an osseous defect filled with a β-TCP scaffold loaded with phosphate-buffered saline (PBS) (100 µL/scaffold), the defect in group B was filled with recombinant human bone morphogenetic protein 2 (rhBMP-2) (10 µg/100 µL), and the defect in group C was loaded with HMGB-1 (10 µg/100 µL). Micro-computed tomography (CT) examination demonstrated that group C (HMGB-1) showed the highest new bone volume ratio, with a mean value of 66.5%, followed by the group B (rhBMP-2) (31.0%), and group A (Control) (7.1%). Histological examination of the HMGB-1 treated group showed a vast area covered by lamellar and woven bone surrounding the β-TCP granule remnants. These results suggest that HMGB-1 could be an effective alternative molecule for bone regeneration in critical-sized mandibular bone defects.Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores.