Experimental investigation of moisture influence on biochar and biochar-soil blends thermophysical properties

Biochar is a carbonaceous and porous material obtained through pyrolysis or gasification. It can be extremely valuable as soil amendment since it increases the organic matter content and fertility, the microbial activity, the water retention, and the crop yields. Moreover, biochar soil application has the potential for long-term carbon sequestration which makes its application to soil interesting even outside agricultural crops. In recent years, the study of the variation of the thermophysical properties of the soil induced by mixing with biochar has attracted interest. In this work, the effect of the water content on thermal conductivity of biochar was investigated by means of the guarded hot plate apparatus λ-Meter EP500e. The same procedure was applied to various mixtures of biochar and soil. Furthermore, the specific heat was measured in order to obtain the thermal diffusivity in the various conditions through a calorimeter. Solar reflectance was also measured following the ASTM C1549 using a solar spectrum reflectometer SSR-ER. The obtained thermophysical properties can be used for the evaluation of the temperature trend of soil at different depths during the seasonal variations

Airborne pathogens diffusion: A comparison between tracer gas and pigmented aerosols for indoor environment analysis

The evaluation of airborne pathogens diffusion is a crucial practice in preventing airborne diseases like COVID-19, especially in indoor environments. Through this transmission route, pathogens can be carried by droplets, droplet nuclei and aerosols and be conveyed over long distances. Therefore, understanding their diffusion is vital for prevention and curbing disease transmission. There are different techniques used for this purpose, and one of the most common is the utilization of tracer gas, however, it has limitations such as the difference in size between the gas molecules and the respiratory droplets, as well as its incapability to take into account evaporation. For this reason, a new method for evaluating the diffusion of respiratory droplets has been developed. This approach involves the use of an ultrasonic emitter to release and disperse pigmented aerosols, and a colorimeter for the following quantitative evaluation. A comparison with the tracer gas technique has been carried out, showing for the pigmented aerosols methodology a response that is dependent on different relative humidity conditions, while there is no clear difference in the dispersion of tracer gas at high or low humidity

Biochar powders coating to improve evaporative cooling in Maisotsenko-cycle systems

This work presents an experimental study on the performance of biochar powder coatings on aluminum surfaces for use in indirect evaporative coolers based on the Maisotsenko cycle. The performance of the biochar coated samples was compared to cellulose-coated aluminum samples and uncoated ones. Results showed that biochar coatings improved the performance of uncoated aluminum, with the 150 μm particle size coating offering performance comparable to cellulose. However, wetting times were longer, which has implications for spraying strategies

The largest prehistoric mound in Europe is the Bronze-Age Hill of Udine (Italy) and legend linked its origin to Attila the Hun

Prehistoric monuments often constitute evident landmarks and sometimes, after falling into disuse, fascinated local people enough to stimulate speculations about their origin over time. According to legend, the Hill of Udine (NE Italy) was built by Attila the Hun's soldiers, but its origin (natural or anthropogenic) has been debated until now. Our research analyzed five new 40-m long stratigraphic cores, investigating for the first time the total thickness of the hill and compared the data with the available archaeological information. Moreover, we considered other hills and mounds in northern Italy and other European regions where folklore traditions relate their origin to Attila. The geoarchaeological and ethnographic data prove that the Hill of Udine is a Bronze Age anthropogenic mound erected between 1400 and 1150 BCE and that, later, folklore has transformed the ancestral memory of its origin into legend. By measuring 30 m in height and over 400,000 m(3) in volume, the flat-topped hill is the largest prehistoric mound in Europe. This discovery reveals unprecedented skills in earth construction and confirms significant anthropogenic modifications of the environment during Bronze Age

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. / Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. / Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases. / Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology

Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB 1.0): development and normative data

Background: Neuropsychological testing plays a cardinal role in the diagnosis and monitoring of Alzheimer’s disease. A major concern is represented by the heterogeneity of the neuropsychological batteries currently adopted in memory clinics and healthcare centers. The current study aimed to solve this issue. Methods: Following the initiative of the University of Washington’s National Alzheimer’s Coordinating Center (NACC), we presented the Italian adaptation of the Neuropsychological Test Battery of the Uniform Data Set (I-UDSNB). We collected data from 433 healthy Italian individuals and employed regression models to evaluate the impact of demographic variables on the performance, deriving the reference norms. Results: Higher education and lower age were associated with a better performance in the majority of tests, while sex affected only fluency tests and Digit Span Forward. Conclusions: The I-UDSNB offers a valuable and harmonized tool for neuropsychological testing in Italy, to be used in clinical and research settings

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Are the ancient forests of the Eastern Po plain large enough for a long term conservation of herbaceous nemoral species?

We analysed the effects of patch configuration and site history on vascular plants in ancient forests of the Eastern Po Plain, documented back to 1740. Despite their reduced size, all the forests are part of Natura 2000 Network and significantly contribute to the maintenance of a threatened habitat and support biological diversity of the Continental biogeographic region. The presence of some functional ecological plant species groups was correlated with patch configuration and age. Habitat quality, in terms of suitability for forest species, was found to be important in explaining the presence of species of high conservation value, but patch age (as an indicator for habitat quality) played a major role too. For core forest species, patch area is a redundant variable in explaining species richness relative to habitat quality and patch age and the extinction of specialists seems to occur mainly in a deterministic way. Even small forest fragments can be very important for maintaining plant species diversity, at least if they are of high habitat quality and if the forest management is appropriate. However, to achieve a long term conservation, management plans should also aim at an improvement of the anthropogenic matrix surrounding forest remnants

Are researchers deliberately bypassing the technology transfer office? An analysis of TTO awareness

Most universities committed to the commercialization of academic research have established technology transfer offices (TTOs). Nonetheless, many researchers bypass these TTOs and take their inventions directly to the marketplace. While TTO bypassing has typically been portrayed as deliberate and undesirable behavior, we argue that it could be unintentional as many researchers may simply be unaware of the TTO’s existence. Taking an information-processing perspective and using data on 3250 researchers in 24 European universities, we examine researcher attributes associated with TTO awareness. Our evidence confirms that only a minority of researchers are aware of the existence of a TTO at their university. TTO awareness is greater among researchers who possess experience as entrepreneurs, closed many research and consulting contracts with industry partners, conduct research in medicine, engineering or life sciences, or occupy postdoctoral positions. Policy implications of these findings are discussed