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NETosis, an antimicrobial form of neutrophil cell death, is considered a primary source of citrullinated autoantigens in rheumatoid arthritis (RA) and immunogenic DNA in systemic lupus erythematosus (SLE). Activation of the citrullinating enzyme peptidylarginine deiminase type 4 (PAD4) is believed to be essential for neutrophil extracellular trap (NET) formation and NETosis. PAD4 is therefore viewed as a promising therapeutic target to inhibit the formation of NETs in both diseases. In this review, we examine the evidence for PAD4 activation during NETosis and provide experimental data to suggest that protein citrullination is not a universal feature of NETs. We delineate two distinct biological processes, leukotoxic hypercitrullination (LTH) and defective mitophagy, which have been erroneously classified as \"NETosis.\" While these NETosis mimics share morphological similarities with NETosis (i.e., extracellular DNA release), they are biologically distinct. As such, these processes can be readily classified by their stimuli, activation of distinct biochemical pathways, the presence of hypercitrullination, and antimicrobial effector function. NETosis is an antimicrobial form of cell death that is NADPH oxidase-dependent and not associated with hypercitrullination. In contrast, LTH is NADPH oxidase-independent and not bactericidal. Rather, LTH represents a bacterial strategy to achieve immune evasion. It is triggered by pore-forming pathways and equivalent signals that cumulate in calcium-dependent hyperactivation of PADs, protein hypercitrullination, and neutrophil death. The generation of citrullinated autoantigens in RA is likely driven by LTH, but not NETosis. Mitochondrial DNA (mtDNA) expulsion, the result of a constitutive defect in mitophagy, represents a second NETosis mimic. In the presence of interferon-α and immune complexes, this process can generate highly interferogenic oxidized mtDNA, which has previously been mistaken for NETosis in SLE. Distinguishing NETosis from LTH and defective mitophagy is paramount to understanding the role of neutrophil damage in immunity and the pathogenesis of human diseases. This provides a framework to design specific inhibitors of these distinct biological processes in human disease.

This study assessed subseasonal global precipitation hindcast quality from all Subseasonal to Seasonal (S2S) prediction project models. Deterministic forecast quality of weekly accumulated precipitation was verified using different metrics and hindcast data considering lead times up to 4 weeks. The correlation scores were found to be higher during the first week and dropped as lead time increased, confining meaningful signals in the tropics mostly due to El Niño–Southern Oscillation and Madden–Julian Oscillation-related effects. The contribution of these two phenomena to hindcast quality was assessed by removing their regressed precipitation patterns from predicted fields. The model’s rank showed ECMWF, UKMO, and KMA as the top scoring models even when using a single control member instead of the mean of all ensemble members. The lowest correlation was shared by CMA, ISAC, and HMCR for most weeks. Models with larger ensemble sizes presented noticeable reduction in correlation when subsampled to fewer perturbed members, showing the value of ensemble prediction. Systematic errors were measured through bias and variance ratio revealing in general large positive (negative) biases and variance overestimation (underestimation) over the tropical oceans (continents and/or extratropics). The atmospheric circulation hindcast quality was also examined suggesting the importance of using a relatively finer spatial resolution and a coupled model for resolving the tropical circulation dynamics, particularly for simulating tropical precipitation variability. The extratropical circulation hindcast quality was found to be low after the second week likely due to the inherent unpredictability of the extratropical variability and errors associated with model deficiencies in representing teleconnections.

Sports sciences are increasingly data-intensive nowadays since computational tools can extract information from large amounts of data and derive insights from athlete performances during the competition. This paper addresses a performance prediction problem in soccer, a popular collective sport modality played by two teams competing against each other in the same field. In a soccer game, teams score points by placing the ball into the opponent’s goal and the winner is the team with the highest count of goals. Retaining possession of the ball is one key to success, but it is not enough since a team needs to score to achieve victory, which requires an offensive toward the opponent’s goal. The focus of this work is to determine if analyzing the first five seconds after the control of the ball is taken by one of the teams provides enough information to determine whether the ball will reach the final quarter of the soccer field, therefore creating a goal-scoring chance. By doing so, we can further investigate which conditions increase strategic leverage. Our approach comprises modeling players’ interactions as graph structures and extracting metrics from these structures. These metrics, when combined, form time series that we encode in two-dimensional representations of visual rhythms, allowing feature extraction through deep convolutional networks, coupled with a classifier to predict the outcome (whether the final quarter of the field is reached). The results indicate that offensive play near the adversary penalty area can be predicted by looking at the first five seconds. Finally, the explainability of our models reveals the main metrics along with its contributions for the final inference result, which corroborates other studies found in the literature for soccer match analysis.

Surgery is the first-line treatment for early, localized, or operable breast cancer. Regional anesthesia during mastectomy may offer the prevention of postoperative pain. One potential protocol is the combination of serratus anterior plane block (SAM block) with pectoral nerve block I (PECS I), but the results and potential benefits are limited. Our study compared general anesthesia with or without SAM block + PECS I during radical mastectomy with axillary node dissection and breast reconstruction using evaluations of pain, opioid consumption, side effects and serum levels of interleukin (IL)-1beta, IL-6 and IL-10. This is a prospective, randomized controlled trial. Fifty patients were randomized to general anesthesia only or general anesthesia associated with SAM block + PECS I (25 per group). The association of SAM block + PECS I with general anesthesia reduced intraoperative fentanyl consumption, morphine use and visual analog pain scale scores in the post-anesthetic care unit (PACU) and at 24 h after surgery. In addition, the anesthetic protocol decreased side effects and sedation 24 h after surgery compared to patients who underwent general anesthesia only. IL-6 levels increased after the surgery compared to baseline levels in both groups, and no differences in IL-10 and IL-1 beta levels were observed. Our protocol improved the outcomes of mastectomy, which highlight the importance of improving mastectomy protocols and focusing on the benefits of regional anesthesia.

This paper evaluates subseasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (weeks 1–4) during different seasons. Overall, forecast evaluation indicates more skillful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in weeks 3–4 linked to lower association and larger underestimation of predicted variance compared to weeks 1–2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in weeks 3–4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be overconfident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely, El Niño–Southern Oscillation, Indian Ocean dipole, and the Madden–Julian oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden–Julian oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.

Understanding how models represent sub-seasonal rainfall variations and what influences model skill is essential for improving sub-seasonal forecasts and their applications. Here, empirical orthogonal function (EOF) analysis is employed to investigate weekly Eastern Africa short rains variability from October to December. The observed leading EOF modes are identified as (i) a monopole-like rainfall pattern with anomalies impacting southern Ethiopia, Kenya, and northern Tanzania; and (ii) a dipole-like rainfall pattern with contrasting anomalies between Tanzania and the northeastern sector of Eastern Africa. An examination of the links between the leading modes and specific climate drivers, namely, the Madden–Julian Oscillation (MJO), El Niño–Southern Oscillation, and Indian Ocean Dipole (IOD), shows that the MJO and IOD have the highest correlations with the two rainfall modes and indicates that the monopole (dipole)-like rainfall pattern is associated with MJO convective anomalies in the tropical Indian Ocean and western Pacific (Maritime Continent and Western Hemisphere). Assessments of model ability to capture and predict the leading modes show that the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office models outperform the National Centers for Environmental Prediction model at forecast horizons from one to four weeks ahead. Amongst the drivers examined, the MJO has the largest impact on the forecast skill of rainfall modes within the ECMWF model. If MJO-related variability is reliably represented, the ECMWF model is more skilful at predicting the main modes of weekly rainfall variability over the region. Our findings can support model developments and enhance anticipatory planning efforts in several sectors, such as agriculture, food security, and energy.

The ever-increasing requirement of land for food production causes habitat loss and biodiversity decline. Human activities like agriculture are responsible for increases in global temperature, which may preclude species’ survival if they cannot adapt to new climatic conditions or track suitable ones. Although negative impacts of climate change may act in synergy with agriculture when dispersion routes are blocked by croplands, agriculture is important to local economies. Therefore, the demand for land conversion causes conflict among stakeholders and decision makers. But can we benefit both economy and environment? Here we propose an approach to help find a balance between agriculture expansion and biodiversity conservation. We used suitable areas for agriculture to identify priority places to implement monocultures. We modeled species distributions to avoid sites with high conservation value and used species dispersal ability to minimize the distance between present-day and future suitable areas for species persistence. We used a decision-support tool to find a balance between economic development and species conservation, and we conclude that land use conversion is a threat for species persistence given that negative impacts caused by crops could be exacerbated by climate change. Unguided agriculture expansion into future species distribution areas is possible due to severe decreases in the areas for species to persist in the future. Facing this scenario, applying ecological knowledge to guide agriculture expansion is urgent if we want to spare species future distribution area in the Cerrado.