Miracle-dz/UniFAR-FTUs huggingface dataset:
Filling the Gap and Improving Conservation: How IUCN Red Lists and Historical Scientific Data Can Shed More Light on Threatened Sharks in the Italian Seas

Chondrichthyans are one of the most threatened marine taxa worldwide. This is also the case in the Mediterranean Sea, which is considered an extinction hotspot for rays and sharks. The central position of the Italian peninsula makes it an ideal location for studying the status and changes of this sea. There is a lack of biological, ecological and historical data when assessing shark populations, which is also highlighted in the Red List of Threatened Species compiled by the International Union for the Conservation of Nature (IUCN). Historical data can provide important information to better understand how chondrichthyan populations have changed over time. This study aims to provide a clearer understanding of the changes in distribution and abundance of eight shark species in the Italian seas that are currently classified as at risk of extinction by the IUCN. In this respect, a bibliographic review was conducted on items from the 19th century to the first half of the 20th century, focusing on the selected species. The results show that all sharks were considered common until the beginning of the 20th century but have declined since, with a clear negative trend, mainly in the past 70 years. The strong local decline has been attributed to overexploitation, bycatch, habitat loss, depletion of prey items and environmental pollution. Furthermore, historical data also allow us to avoid the issue of a ‘shifting baseline’, in which contemporary abundances are assumed to be “normal”. Using historical data to further our knowledge of the marine environment is becoming increasingly common, and is fundamental in understanding human impact and evaluating mitigation measures to manage and conserve marine species and environments.
Seascape habitat patchiness and hydrodynamics explain genetic structuring of kelp populations

Macroalgae underpin most temperate inshore ecosystems, but increasing macroalgal loss, fragmentation and range contractions are eroding connectivity among populations. Understanding loss, and predicting the likelihood of recovery, is dependent on knowledge of population connectivity and how it is mediated by variability in local seascapes. Although many studies of marine macroalgal connectivity have focussed on influences of geographic distance on genetic structure, the contribution of intervening habitat is rarely considered. We tested the extent to which geographic separation, intervening suitable habitat (reef versus sand and open water) and local hydrodynamics (inferred from particle dispersal models) explained structuring of genetic variation at microsatellite loci in the habitat forming macroalga Lessonia corrugata . Genetic structuring was best explained by the availability of suitable intervening habitat (rocky reef) rather than by geographic separation, although biologically realistic estimates of dispersal probability also became important at smaller spatial scales. Our results indicate that ecological separation can be more influential than geographic distance on population genetic structuring, and this should be considered during the assessment of connectivity and gene flow in marine species.
Critical Health Conditions And Mental Health During Pandemic Covid-19

Despite the few observational studies available in the COVID-19 field, the available literature collected in this research indicates that the pandemic has a multifaceted response from medical professionals dealing with chronic illnesses and mental health, as cited by Abel and McQueen (2020). Amid the confusion and stress of COVID-19, it is imperative to note that patients need medical attention, especially those suffering from chronic illnesses. Patients suffering from heart, lungs, and kidney diseases need to keep seeing their doctor, have their prescription refilled, and stay diet with or without the pandemic (Danhieux et al., 2020). This paper has highlighted two research questions acting as the guide for this literature review. The researcher collected data from renowned electronic databases such as MEDLINE, PMC, and Cochrane Library. It is indicated that the healthcare department needs to review its medical policies to accommodate the proper integration of telemedicine (Sampa et al., 2020). The pandemic has made it clear that the healthcare system needs to adjust its policies and rules around patients suffering from chronic illnesses and mental illnesses.
Middle to Late Pleistocene environments based on stable organic carbon and nitrogen isotopes of loess‐palaeosol sequences from the Carpathian Basin

Stable organic carbon and nitrogen isotopes can be used to interpret past vegetation patterns and ecosystem qualities. Here we present these proxies for two loess‐palaeosol sequences from the southern Carpathian Basin to reconstruct the palaeoenvironment during the past 350 ka and establish regional commonalities and differences. Before now, isotopic studies on loess sequences from this region were only conducted on deposits from the last glacial cycle. We conducted methodological tests involving the complete decalcification of the samples prior to stable isotope analyses. Two decalcification methods (fumigation method and wet chemical acidification), different treatment times, and the reproducibility of carbon isotope analyses were tested. Obtained results indicate that the choice of the decalcification method is important for organic carbon stable isotope analyses of loess‐palaeosol sequences because ratios vary by more than 10‰ between the wet chemical and fumigation methods, due to incomplete carbonate removal by the latter. Therefore, we suggest avoiding the fumigation method for studies on loess‐palaeosol sequences. In addition, our data show that samples with TOC content <0.2% bear increased potential for misinterpretation of their carbon isotope ratios. For our sites, C3‐vegetation is predominant and no palaeoenvironmental shifts leading to a change of the dominant photosynthesis pathway can be detected during the Middle to Late Pleistocene. Furthermore, the importance of further stable nitrogen isotope studies is highlighted, since this proxy seems to reflect past precipitation patterns and reveals favourable conditions in the southern Carpathian Basin, especially during interstadials.
Aerosol Effective Radiative Forcing in the Online Aerosol Coupled CAS-FGOALS-f3-L Climate Model

The effective radiative forcing (ERF) of anthropogenic aerosol can be more representative of the eventual climate response than other radiative forcing. We incorporate aerosol–cloud interaction into the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System (CAS-FGOALS-f3-L) by coupling an existing aerosol module named the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) and quantified the ERF and its primary components (i.e., effective radiative forcing of aerosol-radiation interactions (ERFari) and aerosol-cloud interactions (ERFaci)) based on the protocol of current Coupled Model Intercomparison Project phase 6 (CMIP6). The spatial distribution of the shortwave ERFari and ERFaci in CAS-FGOALS-f3-L are comparable with that of most available CMIP6 models. The global mean 2014–1850 shortwave ERFari in CAS-FGOALS-f3-L (−0.27 W m−2) is close to the multi-model means in 4 available models (−0.29 W m−2), whereas the assessing shortwave ERFaci (−1.04 W m−2) and shortwave ERF (−1.36 W m−2) are slightly stronger than the multi-model means, illustrating that the CAS-FGOALS-f3-L can reproduce the aerosol radiation effect reasonably well. However, significant diversity exists in the ERF, especially in the dominated component ERFaci, implying that the uncertainty is still large.
Recyclabl Metal (Ni, Fe) Cluster Designed Catalyst for Cellulose Pyrolysis to Upgrade Bio-Oil

A new recyclable catalyst for pyrolysis has been developed by combining calculations and experimental methods. In order to understand the properties of the new cluster designed catalysts, cellulose (a major component of plants) as a biomass model compound was pyrolyzed and catalyzed with different cluster designed catalysts. The NiaFeb (2 ≤ a + b ≤ 6) catalyst clusters structures were calculated by using Gaussian and Materials Studio software to determine the relationships between catalyst structure and bio-oil components, which is essential to design cluster designed catalysts that can improve bio-oil quality. GC-MS analysis of the bio-oil was used to measure the effects on the different catalyst interactions with cellulose. It was found that the NiFe cluster designed catalysts can increase the yield of bio-oil from 35.8% ± 0.9% to 41.1% ± 0.6% and change the bio-oil composition without substantially increasing the water content, while substantially decreasing the sugar concentration from 40.1% ± 1.3% to 27.5% ± 0.9% and also producing a small amount of hydrocarbon compounds. The catalyst with a high Ni ratio also had high Gibbs free energy, ΔG, likely also influencing the decrease of sugar and acid while increasing the ketone concentrations. These results indicate the theoretical calculations can enhance the design next-generation cluster designed catalysts to improve bio-oil composition based upon experiments.
Filogeografia molecular de Corynespora cassiicola, um fungo polífago e causador da mancha alvo

viii INTRODUÇÃO . 1 MATERIAIS E MÉTODOS . 4 1. Composição da coleção e isolamento fúngicos . 4 2. Extração de DNA genômico e reações de amplificação de genes específicos via PCR . 5 3. Alinhamento das sequências de DNA e de aminoácidos . 7 4. Montagem dos bancos de dados . 8 5. Análises de filogenia Bayesiana . 9 6. Análises filogeográficas, índices de diversidade molecular e teste de neutralidade . 9 7. Análise da presença de recombinantes . 10 8. Detecção dos mutantes resistentes . 10 RESULTADOS . 11 1. Detecção das isoformas da cassiicolina dentro da coleção . 11 2. Análise filogenética dos genes da cassiicolina . 12 3. As análises filogenéticas revelaram três linhagens genealógicas . 15 4. Associação entre linhagens filogenéticas e isoformas da cassiicolina . 17 5. Análises de rede – genes nucleares . 17 6. Análises de rede – gene mitocondrial cyb . 19 7. Medidas de diversidade nucleotídica e análise de recombinação . 19 8. Presença das mutações pontuais E198A, F200Y e G143A . 20 discussão . 21 1. As três linhagens não estão associadas a hospedeiros ou origem geográfica dos isolados . 21 2. Associação entre isoformas da cassiicolina e as três linhagens . 22 3. Ocorrência de mutações e implicações para o manejo do patógeno . 23 REFERÊNCIAS . 25 MATERIAL SUPLEMENTAR . 30
Material Consumption and Carbon Emissions Associated with the Infrastructure Construction of 34 Cities in Northeast China

Over the past three decades, China has experienced rapid economic growth along with a rapid increase in urbanization and living standards, leading to a boom in infrastructure demand. A large part of China’s newly constructed infrastructure is through urban construction; thus, cities have become a major source of material consumption and carbon emissions. Understanding the relationship between material consumption, carbon emissions, and the economic growth of cities is key to ensuring that the construction of infrastructure satisfies the needs for both economic development and dematerialization. In this study, we first accounted for material consumption and the carbon emissions of infrastructure construction of 34 cities in Northeast China and characterized spatial and temporal changes from 2010–2017. The material use and carbon emissions of infrastructure construction declined by 34.6% and 30.2% during this period. Specifically, material consumption decreased from 305.2 million tonnes to 199.6 million tonnes, and carbon emissions decreased from 77.7 million tonnes to 54.3 million tonnes. Furthermore, we used a decoupling indicator to evaluate the decoupling of material consumption or carbon emissions from GDP in these cities. We found that most cities have achieved the absolute decoupling of material consumption and carbon emissions from GDP over the study period. Finally, we proposed several policy recommendations for promoting the sustainable development of the infrastructure of cities. To ensure that cities realize low-carbon urbanization, policymakers need to promote modular buildings and low-emission construction materials. This paper also serves as a practical reference for the improvement of relevant materials and carbon emissions management strategies for other developing regions.
Irregular Shelterwood Cuttings Promote Viability of European Yew Population Growing in a Managed Forest: A Case Study from the Starohorské Mountains, Slovakia

The increasing probability of Taxus baccata (L.) decline given climate change brings forth many uncertainties for conservation management decisions. In this article, the authors present the effects of applying regeneration cuttings since the year 2000 on the viability of the understory yew population. By collecting data from a stand located at the centre of the largest population of European yew in Slovakia, containing approximately 160,000 individuals, and analysing tree-ring records from 38 sampled trees, the improved performance of yews, including stem growth, seed production, and number of regenerated individuals, was revealed. Thinning the canopy by removing 15% of the growing stock volume per decade, combined with the subsequent irregular shelterwood cuttings, was assessed as a useful strategy. Moreover, lower radial growth of females compared to males, but simultaneously their similar response to climate, suggests a possible trade-off between reproduction and growth. Release cuttings of up to 30% of the standing volume in the vicinity of the female trees, executed in the rainy summers following warmer winters, and consistent elimination of deer browsing, can further enhance the positive effects of applied cuts on yew viability. Overall, the suggested active measures could be considered as an effective option to preserve the unique biodiversity of calcareous beech-dominated forests in Central Europe.
Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

In forest ecosystems, different types of regression models have been frequently used for the estimation of aboveground biomass, where Ordinary Least Squares (OLS) regression models are the most common prediction models. Yet, the relative performance of Bayesian and OLS models in predicting aboveground biomass of shrubs, especially multi-stem shrubs, has relatively been less studied in forests. In this study, we developed the biomass prediction models for Caragana microphylla Lam. which is a widely distributed multi-stems shrub, and contributes to the decrease of wind erosion and the fixation of sand dunes in the Horqin Sand Land, one of the largest sand lands in China. We developed six types of formulations under the framework of the regression models, and then, selected the best model based on specific criteria. Consequently, we estimated the parameters of the best model with OLS and Bayesian methods with training and test data under different sample sizes with the bootstrap method. Lastly, we compared the performance of the OLS and Bayesian models in predicting the aboveground biomass of C. microphylla. The performance of the allometric equation (power = 1) was best among six types of equations, even though all of those models were significant. The results showed that mean squared error of test data with non-informative prior Bayesian method and the informative prior Bayesian method was lower than with the OLS method. Among the tested predictors (i.e. plant height and basal diameter), we found that basal diameter was not a significant predictor either in OLS or Bayesian methods, indicating that suitable predictors and well-fitted models should be seriously considered. This study highlights that Bayesian methods, the bootstrap method and the type of allometric equation could help to improve the model accuracy in predicting shrub biomass in sandy lands.
Examining the joint effects of heatwaves, air pollution, and green space on the risk of preterm birth in California

Background. Exposure to high air temperature in late pregnancy is increasingly recognized as a risk factor for preterm birth (PTB). However, the combined effects of heatwaves with air pollution and green space are still unexplored. In the context of climate change, investigating the interaction between environmental factors and identifying communities at higher risk is important to better understand the etiological mechanisms and design targeted interventions towards certain women during pregnancy. Objectives. To examine the combined effects of heatwaves, air pollution and green space exposure on the risk of PTB. Methods. California birth certificate records for singleton births (2005–2013) were obtained. Residential zip code-specific daily temperature during the last week of gestation was used to create 12 definitions of heatwave with varying temperature thresholds and durations. We fit multi-level Cox proportional hazard models with time to PTB as the outcome and gestational week as the temporal unit. Relative risk due to interaction (RERI) was applied to estimate the additive interactive effect of air pollution and green space on the effect of heatwaves on PTB. Results. In total, 1 967 300 births were included in this study. For PM2.5, PM10 and O3, we found positive additive interactions (RERIs >0) between heatwaves and higher air pollution levels. Combined effects of heatwaves and green space indicated negative interactions (RERIs <0) for less intense heatwaves (i.e. shorter duration or relatively low temperature), whereas there were potential positive interactions (RERIs >0) for more intense heatwaves. Conclusion. This study found synergistic harmful effects for heatwaves with air pollution, and potential positive interactions with lack of green space on PTB. Implementing interventions, such as heat warning systems and behavioral changes, targeted toward pregnant women at risk for high air pollution and low green space exposures may optimize the benefits of reducing acute exposure to extreme heat before delivery.
Applying Petitmengin’s Explicitation Interview Method to Elicit the Lived Experience of Breathing Upon Waking by an Individual With Cystic Fibrosis

Breathing is an act that most people do not consciously evoke unless there is a presence of illness that affects the respiratory system. Adults generally take in 12 to 15 breaths per minute without even a thought about the body’s mechanics that allow for proper oxygenation and ventilation. However, for those with pulmonary compromise, breathing becomes a very conscious, deliberate, and sometimes laborious act. The purpose of this paper is to demonstrate the use of the explicitation method by Petitmengin by interviewing a subject to elicit the lived experience of breathing by an individual with end-stage cystic fibrosis (CF). To apply the interview method, the following phenomenological question guided the interviewer’s approach: What is the lived experience of breathing upon waking for an individual with CF? This paper includes a transcription of the interview followed by a self-critique, textual analysis, and discussion of the implications to health care.
For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth.

RATIONALE, AIMS AND OBJECTIVES Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias. METHODS In this historical and conceptual article, we focus on two main problems: first, the data and the problem of its validity; second, the inference drawn from the data by AI, and the establishment of correlations through the use of algorithms. We use examples from the contemporary use of mobile health (mHealth), i.e. the practice of medicine and public health supported by mobile or wearable devices such as mobile phones or smart watches. RESULTS We show that the validity of the data and of the inferences drawn from these mHealth data are likely to be biased. As biases are insensitive to the size of the sample, even if the sample is the whole population, artificial intelligence and big data cannot avoid biases and even tend to increase them. CONCLUSIONS The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.
Valuation model for Chinese convertible bonds with soft call/put provision under the hybrid willow tree

The Chinese convertible bond market has been developing rapidly in the last 10 years. However, some special characteristics of the Chinese convertible bond, such as the soft call/put provision, cause huge difficulty in the valuation. In this paper, we establish a new valuation model for the Chinese convertible bond, based on the available Chinese market data, through a hybrid willow tree approach with consideration of the underlying stock price, stochastic interest rate, and credit risk of the issuer. We employ the Brownian bridge to handle the special characteristics. Finally, we examine our model prices for the daily market closing prices for 20 Chinese convertible bonds traded from 2007 to 2017. The empirical results show the effectiveness of our valuation model under the historical and implied volatilities of the underlying stock price.