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Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022., Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance., Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models., Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)., Competing Interests: KS, HG, RG, HJ, RN, BP, FS, JD, DW, SA, AU, GG, ER, NR, DS, YW, NW, LW, JT, GO, TS, DT, LP, EK, JM, MB, NL, JM, JS, JW, JF, BL, IR, JB, RW, IV, VT, MS, MZ, CP, BR, NB, SM, LC, GF, IM, DO, PA, AM, VE, AK, DK, LP, BD, ML, SS, JD, SM, VP, GR, BB, GA, NP, GZ, WB, SH, TH, DS, MG, JA, DM, SA, EA, DL, CP, JB, AR, TZ, AK, JZ, FP, FD, MC, GL, PG, BT, FB, GJ, MM, AF, AS, PM, AA, BH, BL, MM, PP, SV, RB, FD, AG, KG, MG, BK, AM, KN, JN, MR, FR, MS, ES, JZ, JK, BP, KH, YK, MS, BP, MB, MF, RI, TK, TO, JB, SF No competing interests declared, PB, HG, SS, BS Affiliated with IEM, Inc. The author has no financial interests to declare

Proximal operators are of particular interest in optimization problems dealing with non-smooth objectives because in many practical cases they lead to optimization algorithms whose updates can be computed in closed form or very efficiently. A well-known example is the proximal operator of the vector l 1 norm, which is given by the soft-thresholding operator. In this paper we study the proximal operator of the mixed l 1,∞ matrix norm and show that it can be computed in closed form by applying the well-known soft-thresholding operator to each column of the matrix. However, unlike the vector l 1 norm case where the threshold is constant, in the mixed l 1,∞ norm case each column of the matrix might require a different threshold and all thresholds depend on the given matrix. We propose a general iterative algorithm for computing these thresholds, as well as two efficient implementations that further exploit easy to compute lower bounds for the mixed norm of the optimal solution. Experiments on large-scale synthetic and real data indicate that the proposed methods can be orders of magnitude faster than state-of-the-art methods.

Purpose: Diagnosis of prostate cancer (CaP) is based on digital rectal examination (DRE) and/or elevated prostate specific antigen (PSA) level. This approach lacks sensitivity and specificity and is associated with many negative biopsies, high rate of diagnosing clinically insignificant disease and lacks accuracy to predict clinically significant (CS) cancer. The addition of multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy reduces the detection of low-grade tumors while improving the detection of CS CaP. Most studies that evaluated mpMRI performance did not separate the DRE status of the examined patients. Therefore, the aim of our study is to investigate whether mpMRI provides similar advantages in detection of CaP according to the DRE findings., Materials and Methods: This prospective study included patients with clinically suspected CaP that were referred to MRI-fusion biopsy from 2014 to 2019. All patients had mpMRI of the prostate with an index lesion of PIRADS ≥3. Analysis was done comparing systemic and targeted biopsy. Patients were divided into two groups according to the DRE findings (positive or negative DRE) and the primary outcomes were compared between the 2 study groups: detection rate of CaP and the detection rate of CS disease defined as Gleason score ≥ 7., Results: The final study cohort included 86 patients: 47 with negative DRE and 39 with positive DRE. Overall cancer detection rate was higher in patients with a positive DRE (70.3% vs 48.9%, P <0.05). In the region of interest a higher overall detection rate and of CS disease was found in those with abnormal DRE (51.3% vs. 40.4% and 48.6% vs. 34.0% respectively). The systematic biopsy analysis showed an overall lower detection rate in the negative DRE group (8.5% vs. 18.9 %). The targeted biopsies detected more cancer and significant tumors per core in patients with positive DRE (29.2% vs. 18.5% and 22.1% vs. 14.5% respectively)., Conclusions: Patients submitted to fusion biopsy and have a positive DRE are diagnosed more often with CaP, have higher grade disease and larger tumors. In patients suspicious for CaP and having a significant lesion on mpMRI one should combine targeted and systematic biopsy regardless of the DRE status., Competing Interests: Conflict of interest None of the contributing authors have any conflict of interest, including specific financial interests or relationships and affiliations relevant to the subject matter or materials discussed in the manuscript. All the listed co-authors approve the submission of this manuscript, and the content of the manuscript has not been published, submitted for publication or is under consideration for publication with any other journal., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Patients with chronic spontaneous urticaria (CSU), an autoimmune disorder, show increased skin expression of IL-17A and can benefit from treatment with the anti-IL-17A biologic secukinumab. The mechanisms that drive IL-17A expression in CSU are currently unknown, but may involve Semaphorin5A (Sema5A). Objective: To explore the expression, role, and effects of Sema5A in CSU and its link to IL-17A. Material and Methods: We investigated patients with CSU and healthy controls for skin expression of expressing peripheral T cells. Results: Sema5A was highly expressed in the skin of CSU patients as compared to healthy control skin. Both CD4+ T cells and mast cells in CSU skin expressed Sema5A, and many of them expressed both Sema5A and IL-17A. Patients with CSU had significantly higher rates of IL-17A-expressing CD4+ T cells as compared to healthy controls. Incubation with Sema5A increased the rates of IL-17A-expressing CD4+ T cells in healthy controls to CSU levels. Conclusion: Sema5A may drive the expression and effects of IL-17A in CSU. Further studies in larger cohorts are needed to confirm the role of Sema5A in the pathogenesis of CSU and to explore its potential as a therapeutic target.

In order to investigate the role of helix α1 in the different biochemical properties between class I and class II Glucose Isomerases, a histidine and a phenylalanine residue were inserted at position 17 and 19 of Streptomyces sp. SK Glucose Isomerase (SKGI). In addition, W16 was substituted by a histidine. The H17/F19 insertion displaced the optimal pH of SKGI from 6.5 to 7-8 and slightly decreased the thermostability. As for the W16H mutant, a shift in optimal pH of SKGI from 6.5 to 6 was observed along with a decrease in the enzyme thermostability at 85°C with a half-life time reduced twice compared to the wild-type enzyme. Three-dimensional structure analysis suggested that the insertion of a histidine at position 17 results in the formation of new hydrogen bond with D287, thereby preventing it from deprotonating the O2 hydroxyl of the sugar at low pH, while the substitution W16H induced opposite effect by preventing hydrogen bond formation between D287 and W16 and thereby probably facilitating the hydrogen transfer during the isomerization reaction. The findings highlight the essential role of helix α1, which bears the three introduced mutations, in the acid-tolerance and the thermostability of SKGI and of glucose isomerases in general., (Copyright © 2014 Elsevier B.V. All rights reserved.)