Evaluation of the effects of the recommended oral dose of diflubenzuron on bovine sperm and oocyte quality using CASA and OPU-IVEP.

Introduction: The aim of this study was to evaluate potential effects of diflubenzuron on the production and quality of gametes, and on in vitro embryo production (IVEP) outcomes, in cattle. Methods: Two experiments were performed, the first to evaluate effects on semen, and the second on cumulus-oocyte complexes (COC) and on IVEP. Nelore (Bos taurus indicus) bulls (n = 14) or heifers (n = 16) were allocated into control (CG) or treatment (DIF) groups. All groups received a mineral mix supplement added (DIF) or not (CG) with diflubenzuron (30 mg/head/day), during 8 weeks. Animals were weighed and blood samples were collected throughout the experimental period. Every other week, bulls were subjected to semen collection and heifers to transvaginal ultrasound-guided follicle aspiration sessions. Semen underwent physical and morphological evaluation, and samples were stored for further computer-assisted sperm analysis. The COC recovered were evaluated according to morphology and those classified as viable were sent to an IVEP laboratory. Results: Diflubenzuron had no effect (P > 0.05) on average body weight or in any blood hematological or biochemical endpoints, regardless of gender. In experiment 1, there was no difference (P > 0.05) between DIF and CG groups for sperm concentration, morphology, or kinetics. In experiment 2, there was also no effect of diflubenzuron on the number of total, viable, or grade I oocytes, as well as on cleavage or blastocyst rates (P > 0.05). Discussion: In summary, the oral administration of diflubenzuron, within the recommended dose, has no short-term negative effects on sperm production and quality or on oocyte yield and developmental potential in vitro, in cattle.

Light spectra of biophilic LED-sourced system modify essential oils composition and plant morphology of Mentha piperita L. and Ocimum basilicum L.

Investigating morphological and molecular mechanisms that plants adopt in response to artificial biophilic lighting is crucial for implementing biophilic approaches in indoor environments. Also, studying the essential oils (EOs) composition in aromatic plants can help unveil the light influence on plant metabolism and open new investigative routes devoted to producing valuable molecules for human health and commercial applications. We assessed the growth performance and the EOs composition of Mentha x piperita and Ocimum basilicum grown under an innovative artificial biophilic lighting system (CoeLux®), that enables the simulation of natural sunlight with a realistic sun perception, and compared it to high-pressure sodium lamps (control) We found that plants grown under the CoeLux® light type experienced a general suppression of both above and belowground biomass, a high leaf area, and a lower leaf thickness, which might be related to the shade avoidance syndrome. The secondary metabolites composition in the plants' essential oils was scarcely affected by both light intensity and spectral composition of the CoeLux® light type, as similarities above 80% were observed with respect to the control light treatments and within both plant species. The major differences were detected with respect to the EOs extracted from plants grown under natural sunlight (52% similarity in M. piperita and 75% in O. basilicum). Overall, it can be speculated that the growth of these two aromatic plants under the CoeLux® lighting systems is a feasible strategy to improve biophilic approaches in closed environments that include both plants and artificial sunlight. Among the two plant species analyzed, O. basilicum showed an overall better performance in terms of both morphological traits and essential oil composition. To increase biomass production and enhance the EOs quality (e.g., higher menthol concentrations), further studies should focus on technical solutions to raise the light intensity irradiating plants during their growth under the CoeLux® lighting systems.

Chemical Profiles, In Vitro Antioxidant and Antifungal Activity of Four Different Lavandula angustifolia L. EOs.

Lavandula angustifolia L., known as lavender, is an economically important Lamiaceae due to the production of essential oils (EOs) for the food, cosmetic, pharmaceutical and medical industries. The purpose of this study was to determine the chemical composition of EOs isolated from four inflorescences of L. angustifolia L. collected in different geographical areas: central-southern Italy (LaCC, LaPE, LaPS) and southern France (LaPRV). The essential oils, obtained by steam distillation from plants at the full flowering stage, were analyzed using gas chromatography coupled with mass spectrometry (GC-MS). More than 70 components identified in each sample showed significant variability among the main constituents. The four EOs analyzed contained the following as main component: linalool (from 30.02% to 39.73%), borneol (13.65% in LaPE and 16.83% in La PS), linalyl acetate (24.34% in LaCC and 31.07% in LaPRV). The EOs were also evaluated for their in vitro antifungal activity against two white rot fungi (Phanerochaete chrysosporium and Trametes cingulata) as potential natural biodeteriogens in the artworks field, and against Sclerotium rolfsii, Botrytis cinerea and Fusarium verticilloides responsible for significant crop yield losses in tropical and subtropical areas. The results confirm a concentration-dependent toxicity pattern, where the fungal species show different sensitivity to the four EOs. The in vitro antioxidant activity by DPPH assay showed better scavenging activity on LaCC (IC50 26.26 mg/mL) and LaPRV (IC50 33.53 mg/mL), followed by LaPE (IC50 48.00 mg/mL) and LaPS (IC50 49.63 mg/mL). The potential application of EOs as a green method to control biodeterioration phenomena on a work of art on wood timber dated 1876 was evaluated.

Effects of EOs vs. Antibiotics on E. coli Strains Isolated from Drinking Waters of Grazing Animals in the Upper Molise Region, Italy.

The health and safety of grazing animals was the subject of microbiological monitoring on natural source of drinking waters in the upper Molise region, Italy. Surface water samples, on spring-summer season, were collected and submitted to analyses using sterile membrane filtration, cultural medium, and incubation. The level of environmental microbial contamination (Total viable microbial count, yeasts and fungi) and faecal presence (Total and faecal coliforms, E. coli, and Salmonellae spp.) were carried out. By the selective microbiological screening, twenty-three E. coli strains from drinking waters were isolated and submitted to further studies to evaluate antibiotic resistance by antibiograms vs. three animal and two diffuse human antibiotics. Furthermore, after a fine chemical characterization by GC and GC-MS, three Essential Oils (EOs) of aromatic plants (Timus vulgaris, Melaleuca alternifolia, Cinnamomun verum) aromatograms were performed and results statistically compared. The effects of EOs vs. antibiotics on E. coli strains isolated from drinking waters showed a total absence of microbial resistance. In our experimental conditions, even if some suggestions will be further adopted for better managements of grazing animals, because the health and safety represent a guarantee for both animals and humans.

Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.

Estimating COVID-19-induced excess mortality in Lombardy, Italy.

We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.

The unresolved question on COVID-19 virus origin: The three cards game?

The ongoing discussion about the real origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) feeds acrimonious debates. Where did SARS-CoV-2 come from? Was SARS-CoV-2 transmitted in the wild from an animal to a person before exploding in Wuhan or was it an engineered virus that escaped from research or a laboratory in Wuhan? Right now, we still don't know enough whether SARS-CoV-2 is human-made or not, and lab-leak theories remain essentially speculative. Many recent studies have pointed out several plausible scenarios. Anyhow, currently, even if suspicions by some about the possibility of lab-leak hypothesis still remain, the consensus view is that the pandemic probably started from a natural source and, to determine the real origin of the SARS-CoV-2 virus, further research is needed.

Two years of COVID-19 pandemic: The Italian experience of Statgroup-19.

The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, starting with monitoring changes and evaluating risks. The "what" and the "why" of these changes represent fundamental research questions to provide timely and effective tools to manage the evolution of the epidemic. Answers to such questions need appropriate statistical models and visualization tools. Here, we give an overview of the role played by Statgroup-19, an independent Italian research group born in March 2020. The group includes seven statisticians from different Italian universities, each with different backgrounds but with a shared interest in data analysis, statistical modeling, and biostatistics. Since the beginning of the COVID-19 pandemic the group has interacted with authorities and journalists to support policy decisions and inform the general public about the evolution of the epidemic. This collaboration led to several scientific papers and an accrued visibility across various media, all made possible by the continuous interaction across the group members that shared their unique expertise.

Chemical Profile, In Vitro Biological Activity and Comparison of Essential Oils from Fresh and Dried Flowers of Lavandula angustifolia L.

The chemical composition of essential oils (EOs) from dried and fresh flowers of Lavandula angustifolia L. (lavender), named LA 2019 and LA 2020, respectively, grown in central Italy was analyzed and compared by GC and GC-MS. For both samples, 61 compounds were identified, corresponding to 97.9% and 98.1% of the total essential oils. Explorative data analysis, performed to compare the statistical composition of the samples, resulted in a high level of global similarity (around 93%). The compositions of both samples were characterized by 10 major compounds, with a predominance of Linalool (35.3-36.0%), Borneol (15.6-19.4%) and 1,8-Cineole (11.0-9.0%). The in vitro antibacterial activity assay by disk diffusion tests against Bacillus subtilis PY79 and Escherichia coli DH5α showed inhibition of growth in both indicator strains. In addition, plate counts revealed a bactericidal effect on E. coli, which was particularly noticeable when using oil from the fresh lavender flowers at the highest concentrations. An in vitro antifungal assay showed that the EOs inhibited the growth of Sclerotium rolfsii, a phytopathogenic fungus that causes post-harvest diseases in many fruits and vegetables. The antioxidant activity was also assessed using the ABTS free radical scavenging assay, which showed a different antioxidant activity in both EOs. In addition, the potential application of EOs as a green method to control biodeterioration phenomena on an artistic wood painting (XIX century) was evaluated.

Nowcasting COVID-19 incidence indicators during the Italian first outbreak.

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.

An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions.

The availability of intensive care beds during the COVID-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short-term prediction of COVID-19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area-specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave-last-out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.

Winter survival of microbial contaminants in soil: an in situ verification.

The aim of the research was to evaluate, at site scale, the influence of freezing and freeze/thaw cycles on the survival of faecal coliforms and faecal enterococci in soil, in a climate change perspective. Before the winter period and during grazing, viable cells of faecal coliforms and faecal enterococci were detected only in the first 10 cm below ground, while, after the winter period and before the new seasonal grazing, a lower number of viable cells of both faecal indicators was detected only in some of the investigated soil profiles, and within the first 5 cm. Taking into consideration the results of specific investigations, we hypothesise that the non-uniform spatial distribution of grass roots within the studied soil can play an important role in influencing this phenomenon, while several abiotic factors do not play any significant role. Taking into account the local trend in the increase of air temperature, a different distribution of microbial pollution over time is expected in spring waters, in future climate scenarios. The progressive increase in air temperature will cause a progressive decrease in freeze/thaw cycles at higher altitudes, minimising cold shocks on microbial cells, and causing spring water pollution also during winter.

Predicting the geographical distribution of two invasive termite species from occurrence data.

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.