A prediction scheme using perceptually important points and dynamic time warping

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price

A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints

This paper deals with the mean-variance optimization frontier problem when realistic constraints are considered. Our proposed methodology hybridizes a heuristic algorithm with an exact solution approach. A genetic algorithm is applied for the identification of the assets in the portfolio, whilst the asset weights in the portfolios are obtained by a quadratic programming model. The proposed algorithmic framework produces a constrained frontier that actually fulfills the bound and cardinality constraints, unlike other proposals where the frontier is composed of several sub-frontiers, each one considering the cardinality constraint but with different assets in each sub-frontier, thus violating the cardinality constraint. This brings us to propose a surrogate similarity measure for the optimization of the constrained frontier, which differs from a previous proposal where no bound constraints were considered. Regarding the genetic algorithm, we propose an initial population to boost the convergence of the optimization process, whilst the adopted mutation and crossover genetic operators result in feasible individuals. An illustrative example using components of five major stock market indices is provided to demonstrate the effectiveness of the proposed method

Increased proportion of alcohol-related trauma in a South London Major Trauma Centre during lockdown: A cohort study

Purpose Alcohol has been associated with 10%–35% trauma admissions and 40% trauma-related deaths globally. In response to the COVID-19 pandemic, the United Kingdom (UK) entered a state of “lockdown” on 23rd March 2020. Restrictions were most significantly eased on 1st June 2020, when shops and schools re-opened. The purpose of this study was to quantify the effect of lockdown on alcohol-related trauma admissions. Methods All adult patients admitted as “trauma calls” to a London Major Trauma Centre during April 2018 and April 2019 (pre-lockdown, n=316), and 1st April–31st May 2020 (lockdown, n=191) had electronic patient records analysed retrospectively. Patients’ blood alcohol level and records of intoxication were used to identify alcohol-related trauma. Trauma admissions from pre- and post-lockdown cohorts were compared using multiple regression analyses. Results Alcohol-related trauma was present in a significantly higher proportion of adult trauma calls during lockdown (lockdown 60/191 (31.4%), vs. pre-lockdown 62/316 (19.6%); (odds ratio (OR): 0.83, 95% CI: 0.38–1.28, p0.05). Conclusions UK lockdown was independently associated with an increased proportion of alcohol-related trauma. Trauma admissions were increased during the weekend when staffing levels are reduced. With the possibility of further global “waves” of COVID-19, the long-term repercussions of dangerous alcohol-related behaviour to public health must be addressed

Arterial spectral waveform analysis in the prediction of diabetic foot ulcer healing

Objective: We assessed the association between (1) severity of vessel wall calcification, (2) number of patent vessels at the ankle and (3) arterial spectral waveform features, as assessed on a focused ankle Duplex ultrasound (DUS), and healing at 12-months in a cohort of patients who had their diabetic foot ulcers conservatively managed. Research design and methods: Scans performed on 50 limbs in 48 patients were included for analysis. Patient health records were prospectively reviewed for 12-months to assess for the outcome of ulcer healing. Results: We identified that the number of waveform components, peak systolic velocity, systolic rise time and long forward flow as well as the number of vessels patent at the ankle on DUS, may be useful independent predictors of healing, as noted by the trend towards statistical significance. Conclusion: Arterial spectral waveform features may be useful in predicting the chance of diabetic foot ulcer healing

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Subsequence dynamic time warping for charting: bullish and bearish class predictions for NYSE stocks

Advanced pattern recognition algorithms have been historically designed in order to mitigate the problem of subjectivity that characterises technical analysis (also known as ‘charting’). However, although such methods allow to approach technical analysis scientifically, they mainly focus on automating the identification of specific technical patterns. In this paper, we approach the assessment of charting from a more generic point of view, by proposing an algorithmic approach using mainly the dynamic time warping (DTW) algorithm and two of its modifications; subsequence DTW and derivative DTW. Our method captures common characteristics of the entire family of technical patterns and is free of technical descriptions and/or guidelines for the identification of specific technical patterns. The algorithm assigns bullish and bearish classes to a set of query patterns by looking the price behaviour that follows the realisation of similar, in terms of price and volume, historical subsequences to these queries. A large number of stocks listed on NYSE from 2006 to 2015 is considered to statistically evaluate the ability of the algorithm to predict classes and resulting maximum potential profits within a test period that spans from 2010 to 2015. We find statistically significant bearish class predictions that generate on average significant maximum potential profits. However, bullish performance measures are not significant