Symmetry-Broken Perturbation Theory to Large Orders in Antiferromagnetic Phases.

We introduce a spin-symmetry-broken extension of the connected determinant algorithm [Riccardo Rossi, Determinant diagrammatic Monte Carlo algorithm in the thermodynamic limit, Phys. Rev. Lett. 119, 045701 (2017).PRLTAO0031-900710.1103/PhysRevLett.119.045701]. The resulting systematic perturbative expansions around an antiferromagnetic state allow for numerically exact calculations directly inside a magnetically ordered phase. We show new precise results for the magnetic phase diagram and thermodynamics of the three-dimensional cubic Hubbard model at half-filling. With detailed computations of the order parameter in the low to intermediate-coupling regime, we establish the Néel phase boundary. The critical behavior in its vicinity is shown to be compatible with the O(3) Heisenberg universality class. By determining the evolution of the entropy with decreasing temperature through the phase transition we identify the different physical regimes at U/t=4. We provide quantitative results for several thermodynamic quantities deep inside the antiferromagnetic dome up to large interaction strengths and investigate the crossover between the Slater and Heisenberg regimes.

Evolution and engineering of pathways for aromatic O-demethylation in Pseudomonas putida KT2440.

Biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However, aromatic compounds derived from lignin commonly contain methoxy groups, and O-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Several enzyme families catalyze aromatic O-demethylation, but they are rarely compared in vivo to determine an optimal biocatalytic strategy. Here, two pathways for aromatic O-demethylation were compared in Pseudomonas putida KT2440. The native Rieske non-heme iron monooxygenase (VanAB) and, separately, a heterologous tetrahydrofolate-dependent demethylase (LigM) were constitutively expressed in P. putida, and the strains were optimized via adaptive laboratory evolution (ALE) with vanillate as a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ∼1.8x faster than those harboring the heterologous LigM pathway. Enzyme kinetics and transcriptomics studies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, the reductase for vanillate O-demethylase, PP_3494, a global regulator of vanillate catabolism, and fghA, involved in formaldehyde detoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms, yielding a platform strain for efficient O-demethylation of lignin-related aromatic compounds to value-added products.

Predicting inhibitor development using a random peptide phage-display library approach in the SIPPET cohort.

ABSTRACT: Inhibitor development is the most severe complication of hemophilia A (HA) care and is associated with increased morbidity and mortality. This study aimed to use a novel immunoglobulin G epitope mapping method to explore the factor VIII (FVIII)-specific epitope profile in the SIPPET cohort population and to develop an epitope mapping-based inhibitor prediction model. The population consisted of 122 previously untreated patients with severe HA who were followed up for 50 days of exposure to FVIII or 3 years, whichever occurred first. Sampling was performed before FVIII treatment and at the end of the follow-up. The outcome was inhibitor development. The FVIII epitope repertoire was assessed by means of a novel random peptide phage-display assay. A least absolute shrinkage and selection operator (LASSO) regression model and a random forest model were fitted on posttreatment sample data and validated in pretreatment sample data. The predictive performance of these models was assessed by the C-statistic and a calibration plot. We identified 27 775 peptides putatively directed against FVIII, which were used as input for the statistical models. The C-statistic of the LASSO and random forest models were good at 0.78 (95% confidence interval [CI], 0.69-0.86) and 0.80 (95% CI, 0.72-0.89). Model calibration of both models was moderately good. Two statistical models, developed on data from a novel random peptide phage display assay, were used to predict inhibitor development before exposure to exogenous FVIII. These models can be used to set up diagnostic tests that predict the risk of inhibitor development before starting treatment with FVIII.

A Longitudinal Multiinstitutional Study of Vulvar Lichen Sclerosus: From Childhood to Perimenopause.

OBJECTIVE: The main outcome of this study was the evaluation of clinical characteristics, comorbidities, and therapeutic approaches in patients with vulvar lichen sclerosus (VLS) aged from childhood to perimenopause. Secondly, it was intended to compare these characteristics according to the menarchal status. METHODS: Patients less than 45 years of age with a diagnosis of VLS from January 2002 to June 2022 in 10 referral centers were included in this retrospective longitudinal study. The univariate analysis compared the dependent variables according to menarchal status. RESULTS: One hundred eighty-six patients met the inclusion criteria. At diagnosis, between 25% and 40% of premenarchal patients reported signs related to subepithelial hemorrhage. A significantly greater presence of bleeding ( p < .005), easy bruising ( p = .028), fissures ( p = .008), petechiae/splinter hemorrhages ( p < .001), and bleeding/blistering or open sores ( p = .011) was observed in premenarchal patients with respect to the postmenarchal group. The perineum ( p = .013) and the perianal region ( p < .001) were significantly more involved in the premenarchal group. Topical calcineurin inhibitors were more used in the premenarchal population ( p = .004), whereas vitamin E oil and moisturizers were more used in the postmenarchal population ( p = .047). CONCLUSIONS: Vulvar lichen sclerosus is a chronic condition that can cause vulvar changes that result in severe morbidity and affects sexual function and quality of life, even before menopause. Vulvar lichen sclerosus continues to be misdiagnosed in this population. This may lead to an average delay from symptom onset to diagnosis. Evaluating clinical manifestations of VLS in premenarchal and postmenarchal age allowed us to find different clinical characteristics between the 2 periods suggestive of the diagnosis.

Predictive Power of Dependence and Clinical-Social Fragility Index and Risk of Fall in Hospitalized Adult Patients: A Case-Control Study.

OBJECTIVES: Accidental falls are among the leading hospitals' adverse events, with incidence ranging from 2 to 20 events per 1.000 days/patients. The objective of this study is to assess the relationship between in-hospital falls and the score of 3 DEPendence and Clinical-Social Fragility indexes. METHODS: A monocentric case-control study was conducted by retrieving data of in-hospital patients from the electronic health records. RESULTS: Significant differences between the mean scores at the hospital admission and discharge were found. The BRASS scale mean (SD) values at the admission and at the discharge were also significantly higher in cases of in-hospital falls: at the admission 10.2 (±7.7) in cases versus 7.0 (±8.0) in controls ( P = 0.003); at the discharge 10.0 (±6.4) versus 6.7 (±7.5) ( P = 0.001). Barthel index mean (SD) scores also presented statistically significant differences: at the admission 60.3 (±40.6) in cases versus 76.0 (±34.8) in controls ( P = 0.003); at discharge 51.3 (±34.9) versus 73.3 (±35.2) ( P = 0.000).Odds ratios were as follows: for Barthel index 2.37 (95% CI, 1.28-4.39; P = 0.003); for Index of Caring Complexity 1.45 (95% CI, 0.72-2.91, P = 0. 255); for BRASS index 1.95 (95% CI, 1.03-3.70, P = 0.026). With BRASS index, the area under the curve was 0.667 (95% CI, 0.595-0.740), thus indicating a moderate predictive power of the scale. CONCLUSIONS: The use of only Conley scale-despite its sensitivity and specificity-is not enough to fully address this need because of the multiple and heterogeneous factors that predispose to in-hospital falls. Therefore, the combination of multiple tools should be recommended.

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors.

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review.

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in December 2019 in Wuhan city, in the Hubei province of China. Since then, it has spread practically all over the world, disrupting many human activities. In temperate climates overwhelming evidence indicates that its incidence increases significantly during the cold season. Italy was one of the first nations, in which COVID-19 reached epidemic proportions, already at the beginning of 2020. There is therefore enough data to perform a systematic investigation of the correlation between the spread of the virus and the environmental conditions. The objective of this study is the investigation of the relationship between the virus diffusion and the weather, including temperature, wind, humidity and air quality, before the rollout of any vaccine and including rapid variation of the pollutants (not only their long term effects as reported in the literature). Regarding them methodology, given the complexity of the problem and the sparse data, robust statistical tools based on ranking (Spearman and Kendall correlation coefficients) and innovative dynamical system analysis techniques (recurrence plots) have been deployed to disentangle the different influences. In terms of results, the evidence indicates that, even if temperature plays a fundamental role, the morbidity of COVID-19 depends also on other factors. At the aggregate level of major cities, air pollution and the environmental quantities affecting it, particularly the wind intensity, have no negligible effect. This evidence should motivate a rethinking of the public policies related to the containment of this type of airborne infectious diseases, particularly information gathering and traffic management.

Comparative Impact Analysis of Low-Deductible Insurance Versus In-house Hospital Assumption of Risk and Management on Medical Malpractice Claims.

OBJECTIVES: Claims management is critical to ensure the safe and high-quality medical care for which liability insurers and/or hospitals are responsible. The aim of this research is to determine whether increasing hospital malpractice risk exposure, with increasing deductibles, has an impact on malpractice claims and payouts. METHODS: The study was conducted at a single tertiary hospital, the Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. Payouts on closed reported and registered claims were analyzed during 4-study periods, which ranged from 1.5 million euro annual aggregate deductibles entirely managed by the insurance company to 5 million euro annual aggregate deductibles entirely managed by the hospital. We retrospectively analyzed 2034 medical malpractice claims submitted between January 1, 2007, and August 31, 2021. Four periods were examined depending on the claims management model adopted, ranging from total outsourcing to the insurer (period A) to an almost total hospital assumption of risk method (period D). RESULTS: We found that progressive hospital assumption of risk is associated with a decrease in the incidence of medical malpractice claims (average variation per year: -3.7%; P = 0.0029 if the 2 initial periods and the 2 last periods-characterized by the highest risk retention-are respectively aggregated and compared), an initial decrease in the mean claims cost followed by an increase that is still lower than the national increase (-5.4% on average), and an increase in the total claims cost (when compared with the period where the insurer solely managed claims). We also found that the rate of increase in payouts was less than the national average. CONCLUSIONS: The assumption of more malpractice risk by the hospital was associated with the adoption of numerous patient safety and risk management initiatives. The decrease in claims incidence could be due to the implementation of patient safety policies, while the cost increase could be attributed to inflation and rising costs of healthcare services and claims. Notably, only the hospital assumption of risk model with a high-deductible insurance coverage is sustainable for the studied hospital, while also being profitable for the insurer. In conclusion, as hospitals progressively assumed more risk and management responsibility of malpractice claims, there was a progressive decrease in the total number of claims, and a less rapid rise in claim payouts as compared with the national average. Even a small assumption of risk appeared to elicit meaningful changes in claim filings and payouts.

The Effect of Partial Sleep Deprivation and Time-on-Task on Young Drivers' Subjective and Objective Sleepiness.

Despite sleepiness being considered one of the main factors contributing to road crashes, and even though extensive efforts have been made in the identification of techniques able to detect it, the assessment of fitness-to-drive regarding driving fatigue and sleepiness is still an open issue. In the literature on driver sleepiness, both vehicle-based measures and behavioral measures are used. Concerning the former, the one considered more reliable is the Standard Deviation of Lateral Position (SDLP) while the PERcent of eye CLOSure over a defined period of time (PERCLOS) seems to be the most informative behavioral measure. In the present study, using a within-subject design, we assessed the effect of a single night of partial sleep deprivation (PSD, less than 5 h sleeping time) compared to a control condition (full night of sleep, 8 h sleeping time) on SDLP and PERCLOS, in young adults driving in a dynamic car simulator. Results show that time-on-task and PSD affect both subjective and objective sleepiness measures. Moreover, our data confirm that both objective and subjective sleepiness increase through a monotonous driving scenario. Considering that SDLP and PERCLOS were often used separately in studies on driver sleepiness and fatigue detection, the present results have potential implications for fitness-to-drive assessment in that they provide useful information allowing to combine the advantages of the two measures for drowsiness detection while driving.

Biological risk in Italian prisons: data analysis from the second to the fourth wave of COVID-19 pandemic.

BACKGROUND: The management of COVID-19 in Italian prisons triggered considerable concern at the beginning of the pandemic due to numerous riots which resulted in inmate deaths, damages and prison breaks. The aim of this study is to shed some light, through analysis of the infection and relevant disease parameters, on the period spanning from the second to the fourth wave of the outbreak in Italy's prisons. METHODS: Reproductive number (Rt) and Hospitalisation were calculated through a Eulerian approach applied to differential equations derived from compartmental models. Comparison between trends was performed through paired t-test and linear regression analyses. RESULTS: The infection trends (prevalence and Rt) show a high correlation between the prison population and the external community. Both the indices appear to be lagging 1 week in prison. The prisoners' Rt values are not statistically different from those of the general population. The hospitalisation trend of inmates strongly correlates with the external population's, with a delay of 2 weeks. The magnitude of hospitalisations in prison is less than in the external community for the period analysed. CONCLUSIONS: The comparison with the external community revealed that in prison the infection prevalence was greater, although Rt values showed no significant difference, and the hospitalisation rate was lower. These results suggest that the consistent monitoring of inmates results in a higher infection prevalence while a wide vaccination campaign leads to a lower hospitalisation rate. All three indices demonstrate a lag of 1 or 2 weeks in prison. This delay could represent a useful time-window to strengthen planned countermeasures.

Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning.

Calibration is a key part of the development of a diagnostic. Standard approaches require the setting up of dedicated experiments under controlled conditions in order to find the calibration function that allows one to evaluate the desired information from the raw measurements. Sometimes, such controlled experiments are not possible to perform, and alternative approaches are required. Most of them aim at extracting information by looking at the theoretical expectations, requiring a lot of dedicated work and usually involving that the outputs are extremely dependent on some external factors, such as the scientist experience. This work presents a possible methodology to calibrate data or, more generally, to extract the information from the raw measurements by using a new unsupervised physics-informed deep learning methodology. The algorithm allows to automatically process the data and evaluate the searched information without the need for a supervised training by looking at the theoretical expectations. The method is examined in synthetic cases with increasing difficulties to test its potentialities, and it has been found that such an approach can also be used in very complex behaviours, where human-drive results may have huge uncertainties. Moreover, also an experimental test has been performed to validate its capabilities, but also highlight the limits of this method, which, of course, requires particular attention and a good knowledge of the analysed phenomena. The results are extremely interesting, and this methodology is believed to be applied to several cases where classic calibration and supervised approaches are not accessible.

Nanomaterials biotransformation: In planta mechanisms of action.

Research on engineered nanomaterials (ENMs) exposure has continued to expand rapidly, with a focus on uncovering the underlying mechanisms. The EU largely limits the number and the type of organisms that can be used for experimental testing through the 3R normative. There are different routes through which ENMs can enter the soil-plant system: this includes the agricultural application of sewage sludges, and the distribution of nano-enabled agrochemicals. However, a thorough understanding of the physiological and molecular implications of ENMs dispersion and chronic low-dose exposure remains elusive, thus requiring new evidence and a more mechanistic overview of pathways and major effectors involved in plants. Plants can offer a reliable alternative to conventional model systems to elucidate the concept of ENM biotransformation within tissues and organs, as a crucial step in understanding the mechanisms of ENM-organism interaction. To facilitate the understanding of the physico-chemical forms involved in plant response, synchrotron-based techniques have added new potential perspectives in studying the interactions between ENMs and biota. These techniques are providing new insights on the interactions between ENMs and biomolecules. The present review discusses the principal outcomes for ENMs after intake by plants, including possible routes of biotransformation which make their final fate less uncertain, and therefore require further investigation.

Nostrana, frustra, fiorata: migration patterns and the semantics of consumption in the Alps, mid-17th to late 18th centuries.

Labour migrants were a widespread phenomenon in the Alps during the early modern period and impacted the materiality of everyday life in the mountains. This article investigates traces of these movements in linguistic usage by exploring the way in which goods were described by actors from the Three Leagues, in present-day Switzerland and Italy. Provenances of goods were given by using toponyms that indicated the place of origin the more precise, the closer the location was to the Alps. These geographical terms informed about specific visual and tactile qualities and were introduced together with other technical vocabulary via specialized merchants and spread via shops to customers of the upper echelons. Small-scale retailers and occasional dealers made use of less detailed descriptions that can also be found in the accounts of their clients which resembled the language used in informal correspondences. These channels could be activated to gain more detailed information and thanks to the wide-spread networks of migrant labourers, knowledge was exchanged with and via the Alps. This exchange of information appears, however, to have become less intense when migration patterns changed in the aftermath of the French Revolutionary Wars.

Driving fatigue increases after the Spring transition to Daylight Saving Time in young male drivers: A pilot study.

The Spring transition to Daylight Saving Time (DST) has been associated with several health and road safety issues. Previous literature has focused primarily on the analysis of historical crash and hospitalization data, without investigating specific crash contributing factors, such as driving fatigue. The present study aims to uncover the effects of DST-related circadian desynchrony and sleep deprivation on driving fatigue, by means of a driving simulator experiment. Eighteen participants (all males, age range 21-30 years, mean = 24.2, SD = 2.9) completed two 50-minute trials (at one week distance, same time and same day of the week) on a monotonous highway environment, the second one taking place in the week after the Spring transition to DST. Driving fatigue was evaluated by analysing several different variables (including driving-based, physiological and subjective indices) and by comparison with a historical cohort of pertinent, matched controls who had also undergone two trials, but in the absence of any time change in between. Results showed a considerable rise in fatigue levels throughout the driving task in both trials, but with significantly poorer performance in the post-DST trial, documented by a worsening in vehicle lateral control and an increase in eyelid closure. However, participants seemed unable to perceive this decrease in their alertness, which most likely prevented them from implementing fatigue-coping strategies. These findings indicate that DST has a detrimental effect on driving fatigue in young male drivers in the week after the Spring transition, and provide valuable insights into the complex relationship between DST and road safety.

A Weighted-LSM Method to Improve Classification and Concentration Evaluation from Laser-Induced Fluorescence Spectra.

The detection of biological agents using optical systems is an open field of research. Currently, different spectroscopic techniques allow to detect and classify chemical agents while a fast and accurate technique able to identify biological agents is still under investigation. Some optical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) or Laser-Induced Fluorescence (LIF), are already used as classification methods. However, the presence of background, spectrum similarities and other confounders make these techniques not very specific. This work shows a new method to achieve better performances in terms of classification and concentration evaluations. The method is based on the Weighted Least Square Minimization method. In fact, by using ad hoc weights, the LSM looks at specific features of the spectra, resulting in higher accuracy. In order to make a systematic analysis, numerical tests have been conducted. With these tests, the authors were able to highlight the various advantages and drawbacks of the new methodology proposed. Then, the method was applied to some LIF measurements to investigate the applicability of the method to preliminary experimental cases. The results show that, by using this new weighted LSM, it is possible to achieve better classification and concentration evaluation performances. Finally, the possible application of the new method is discussed.

Potential long-term effects of Covid-19 on telecommuting and environment: An Italian case-study.

To contain the sudden spread of SARS-CoV-2, many governments encouraged people to work from home, generating an unprecedented diffusion of this activity. Furthermore, Covid-19 has induced drastic changes in everyday life and travel habits, which might persist in the future. This paper aims to understand and estimate the potential long-term impacts of telework on the environment due to the pandemic, by analyzing factors affecting the frequency of telecommuting, the mode choice for traveling to work, and pollutant emissions generated by these trips. Data from a mobility survey administered in Padova (Italy) was used. Results indicate that Covid-19 could cause a rebound effect reversing the positive impacts of working from home, since, even if the number of trips could be reduced, many shifts towards non-sustainable travel modes could occur. The promotion of telework should be combined with measures fostering sustainable travel habits to pave the way towards a future green mobility.

Driving simulator performance worsens after the Spring transition to Daylight Saving Time.

Circadian desynchrony and sleep deprivation related to the Spring transition to Daylight Saving Time (DST) have been associated with several unfavorable outcomes, including an increase in road traffic accidents. As previous work has mainly focused on analyzing historical crash/hospitalization data, there is virtually no literature investigating the effects of DST on specific driving performance indicators. Here, the effect of the Spring transition to DST on driving performance was investigated by means of a driving simulator experiment, in which participants completed two trials (one week distance, same time and day of the week) on exactly the same simulated route, the second trial taking place in the week after the transition to DST. Results were compared to those of a control group (who also underwent two trials, both before the DST transition), and documented significant worsening of driving performance after DST, as measured by a comprehensive set of simulator-derived indices.

Cadmium Sulfide Quantum Dots Adversely Affect Gametogenesis in Saccharomyces cerevisiae.

In the last decades, nanotechnology-based tools have attracted attention in the scientific community, due to their potential applications in different areas from medicine to engineering, but several toxicological effects mediated by these advanced materials have been shown on the environment and human health. At present, the effects of engineered nanomaterials on gametogenesis have not yet been well understood. In the present study, we addressed this issue using the yeast Saccharomyces cerevisiae as a model eukaryote to evaluate the effects of cadmium sulfide quantum dots (CdS QDs) on sporulation, a process equivalent to gametogenesis in higher organisms. We have observed that CdS QDs cause a strong inhibition of spore development with the formation of aberrant, multinucleated cells. In line with these observations, treatment with CdS QDs down-regulates genes encoding crucial regulators of sporulation process, in particular, the transcription factor Ndt80 that coordinates different genes involved in progression through the meiosis and spore morphogenesis. Down-regulation of NDT80 mediated by CdS QDs causes a block of the meiotic cell cycle and a return to mitosis, leading to the formation of aberrant, multinucleated cells. These results indicate that CdS QDs inhibit gametogenesis in an irreversible manner, with adverse effects on cell-cycle progression.