Extensions of Picard 2-Stacks and the cohomology groups Ext^i of length 3 complexes

The aim of this paper is to define and study the 3-category of extensions of Picard 2-stacks over a site S and to furnish a geometrical description of the cohomology groups Ext^i of length 3 complexes of abelian sheaves. More precisely, our main Theorem furnishes (1) a parametrization of the equivalence classes of objects, 1-arrows, 2-arrows, and 3-arrows of the 3-category of extensions of Picard 2-stacks by the cohomology groups Ext^i, and (2) a geometrical description of the cohomology groups Ext^i of length 3 complexes of abelian sheaves via extensions of Picard 2-stacks. To this end, we use the triequivalence between the 3-category of Picard 2-stacks and the tricategory T^[-2,0](S) of length 3 complexes of abelian sheaves over S introduced by the second author in arXiv:0906.2393, and we define the notion of extension in this tricategory T^[-2,0](S), getting a pure algebraic analogue of the 3-category of extensions of Picard 2-stacks. The calculus of fractions that we use to define extensions in the tricategory T^[-2,0](S) plays a central role in the proof of our Main Theorem.Comment: 2 New Appendix: in the first Appendix we compute a long exact sequence involving the homotopy groups of an extension of Picard 2-stacks, and in the second Appendix we sketch the proof that the fibered sum of Picard 2-stacks satisfies the universal propert

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Prediction of Academic Performance at Undergraduate Graduation: Course Grades or Grade Point Average?

Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction

Uncovering Dynamic Brain Reconfiguration in MEG Working Memory n-Back Task Using Topological Data Analysis

The increasing availability of high temporal resolution neuroimaging data has increased the efforts to understand the dynamics of neural functions. Until recently, there are few studies on generative models supporting classification and prediction of neural systems compared to the description of the architecture. However, the requirement of collapsing data spatially and temporally in the state-of-the art methods to analyze functional magnetic resonance imaging (fMRI), electroencephalogram (EEG) and magnetoencephalography (MEG) data cause loss of important information. In this study, we addressed this issue using a topological data analysis (TDA) method, called Mapper, which visualizes evolving patterns of brain activity as a mathematical graph. Accordingly, we analyzed preprocessed MEG data of 83 subjects from Human Connectome Project (HCP) collected during working memory n-back task. We examined variation in the dynamics of the brain states with the Mapper graphs, and to determine how this variation relates to measures such as response time and performance. The application of the Mapper method to MEG data detected a novel neuroimaging marker that explained the performance of the participants along with the ground truth of response time. In addition, TDA enabled us to distinguish two task-positive brain activations during 0-back and 2-back tasks, which is hard to detect with the other pipelines that require collapsing the data in the spatial and temporal domain. Further, the Mapper graphs of the individuals also revealed one large group in the middle of the stimulus detecting the high engagement in the brain with fine temporal resolution, which could contribute to increase spatiotemporal resolution by merging different imaging modalities. Hence, our work provides another evidence to the effectiveness of the TDA methods for extracting subtle dynamic properties of high temporal resolution MEG data without the temporal and spatial collapse

The Assessment of Oncological Emergencies Of Chest Diseases

Amaç: Göğüs hastalıkları hastanesi acil servisine başvuran onkoloji hastalarının genel özelliklerini ortaya koymak amaçlandı. Yöntem: Bir aylık dönemde acil servise başvuran malignite tanılı hastaların sosyodemografik verileri ile birlikte başvuru semptomları ve acil tanıları retrospektif olarak incelendi.Bulgular: Çalışmaya alınan 118 hastanın 105 (%84.7)’si erkek, 13 (%15.3)’ü kadın ve yaş ortalaması 61.2 yıl idi. En sık başvuru yapan yaş aralığı 50-59 idi. Hastalarımızda saptanan en sık semptomlar nefes darlığı (%50), ağrı (%27.9), ateş (%14.4) ve hemoptizi (%10.1) idi. Daha az sıklıkta bulantı (%9.3), öksürük-balgam çıkarma (%7.6) ve halsizlik (%5.9) izlendi. Küçük hücreli dışı akciğer karsinomlu hastaların %87.5’i ve küçük hücreli akciğer karsinomunun %23.5’i ileri evre kansere sahip idi. En sık rastlanan acil tanı 49 (%41.5) hastada solunum yetmezliği, 14 (%11.8) hastada kemik metastazı, 13 (%11) hastada beyin metastazı idi.Sonuç: Göğüs hastalıkları acil servisine başvuran hastaların en sık yakınmaları nefes darlığı ve ağrı, en sık acil tanıları solunum yetmezliği ve metastatik hastalıktır. Genel talep palyatif tedaviler içindir, ölüm oranı düşüktür.Objective: It was aimed to reveal the general characteristics of oncology patients referred to emergency service of chest diseases training hospital. Method: A retrospective analysis was performed on the socio-demographic data, the referral symptoms and emergency diagnoses of the subjects diagnosed with malignity who referred to emergency service along one month. Results: Of 118 subjects included in the study, 13 (84.7 %) were women and 105 (84.7 %) were men and the average age was 61.2 years. Frequent age interval was between 50-59. The symptoms most often seen in our cases were dypnea (50 %), pain (27.9 %), fever (14.4 %) and hemoptysis (10.1 %), whereas nausea (9.3%), cough-expectoration (7.6%) and weakness (5.9%) were observed less frequently. 87.5% of non-small cell lung carcinoma and 23.5% of small cell lung carcinoma had advanced stage lung cancer. The most frequent encountered emergency diagnoses were respiratory insufficiency in 49 (41.5%) cases, bone metastasis in 14 (11.8%) and brain metastasis in 13 (11%). Conclusion: It was observed that the most frequent complaints for emergency deferral were dyspnea and pain and the most frequent emergency diagnoses were respiratory insufficiency and metastatic disease. General requirement was for palliative treatments and the mortality was lower