Revisiting the moduli space of semistable GG-bundles over elliptic curves

We show that the moduli space of semistable G-bundles on an elliptic curvefor a reductive group G is isomorphic to a power of the elliptic curve modulo acertain Weyl group which depend on the topological type of the bundle. Thisgeneralises a result of Laszlo to arbitrary connected components and recoversthe global description of the moduli space due to Friedman--Morgan--Witten andSchweigert. The proof is entirely in the realm of algebraic geometry and worksin arbitrary characteristic.<br

Reactivity of 2-formylphenylboronic acid toward secondary aromatic amines in amination–reduction reactions

The synthesis of 2-(arylaminomethyl)phenylboronic acid via an amination–reduction reaction has been investigated within a model system comprising 2-formylphenylboronic acid and N-ethylaniline. Adoption of the appropriate reaction conditions influences the reactivity of 2-formylphenylboronic acid, enabling efficient synthesis of so-far unobtainable 2-(arylaminomethyl)phenylboronic compounds. The first crystal structure of the aromatic amine derivative has been determined and described

AFM, SEM and TEM Studies on Porous Anodic Alumina

Porous anodic alumina (PAA) has been intensively studied in past decade due to its applications for fabricating nanostructured materials. Since PAA’s pore diameter, thickness and shape vary too much, a systematical study on the methods of morphology characterization is meaningful and essential for its proper development and utilization. In this paper, we present detailed AFM, SEM and TEM studies on PAA and its evolvements with abundant microstructures, and discuss the advantages and disadvantages of each method. The sample preparation, testing skills and morphology analysis are discussed, especially on the differentiation during characterizing complex cross-sections and ultrasmall nanopores. The versatility of PAAs is also demonstrated by the diversity of PAAs’ microstructure

In vitro degradation behavior and cytocompatibility of Mg–Zn–Zr alloys

Zinc and zirconium were selected as the alloying elements in biodegradable magnesium alloys, considering their strengthening effect and good biocompatibility. The degradation rate, hydrogen evolution, ion release, surface layer and in vitro cytotoxicity of two Mg–Zn–Zr alloys, i.e. ZK30 and ZK60, and a WE-type alloy (Mg–Y–RE–Zr) were investigated by means of long-term static immersion testing in Hank’s solution, non-static immersion testing in Hank’s solution and cell-material interaction analysis. It was found that, among these three magnesium alloys, ZK30 had the lowest degradation rate and the least hydrogen evolution. A magnesium calcium phosphate layer was formed on the surface of ZK30 sample during non-static immersion and its degradation caused minute changes in the ion concentrations and pH value of Hank’s solution. In addition, the ZK30 alloy showed insignificant cytotoxicity against bone marrow stromal cells as compared with biocompatible hydroxyapatite (HA) and the WE-type alloy. After prolonged incubation for 7 days, a stimulatory effect on cell proliferation was observed. The results of the present study suggested that ZK30 could be a promising material for biodegradable orthopedic implants and worth further investigation to evaluate its in vitro and in vivo degradation behavior

A mixed-methods feasibility study of a novel AI-enabled, web-based, clinical decision support system for the treatment of major depression in adults

Background:- The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in treating adults with major depression. Methods:- A single arm, naturalistic follow-up study aimed at assessing the acceptability and useability of the software. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. Study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust. 7 physicians and 17 patients, of which 14 completed, consented to participate. Results:- 86 % of physicians (6/7) felt the information provided by the CDSS provided more comprehensive understanding of patient situations. 71 % (5/7) felt the information was helpful. 86 % of physicians (6/7) reported the AI/predictive model was useful when deciding treatment. 62 % of patients (8/13) reported improved care due to the tool, and 46 %(6/13) reported a significantly or somewhat improved physician-patient relationship 54 % reported no change. 71 % of physicians (5/7) and 62 % of patients (8/13) rated trusting the tool. Limitations:- Small sample size and treatment changes prior to CDSS introduction limits ability to verify impact on outcomes. Conclusions:- Qualitative results from 12 patient exit interviews are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while deciding treatment. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships. (Study identifier: NCT04061642)

Evaluating the clinical feasibility of an artificial intelligence–powered, web-based clinical decision support system for the treatment of depression in adults: longitudinal feasibility study

Background:- Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. Objective:- This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction. Methods:- Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Results:- Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. Conclusions:- Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies

Lateral stability of passenger car/caravan combinations

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