Effect of Lupinus albus as protein supplement on yield, constituents, clotting properties and fatty acid composition in ewes' milk

The effect of feeding lupin seeds (Lupinus albus L.) as an alternative protein source in ewe diets was investigated. Two groups of 18 Sarda ewes were fed two different isonitrogenous diets: with lupin (L) seed, given after 12 h soaking, or soybean meal (SBM) as the main protein source. DMI, variations of body weight and milk production were unaffected by the treatment. Although not statistically significant, in the group fed L diet the production of milk fat and protein was higher. Clotting properties of milk were similar for the two treatments, probably due to the small differences in the milk protein contents. The fatty acid profile of milk was affected by treatment with a larger content of short (14.19 wt% versus 12.26 wt%)- and medium (49.37 wt% versus 47.76 wt%)-chain fatty acids in milk from ewes fed the L diet. CLA content was unaffected by treatment. Triglyceride content of fat from the two diets reflects the milk fatty acid composition. Indeed, milk from L diet showed a higher level of medium-chain triglycerides, which are of particular interest to consumers with concerns over health and heart disease. The inclusion of lupin seed in the diet of lactation ewes can be a means of achieving a more desirable triglyceride profile in milk fat. Milk with enhanced nutritive quality may promote wider market penetration of sheep dairy products

Historical analysis of national subjective wellbeing using millions of digitized books

In addition to improving quality of life, higher subjective wellbeing leads to fewer health problems and higher productivity, making subjective wellbeing a focal issue among researchers and governments. However, it is difficult to estimate how happy people were during previous centuries. Here we show that a method based on the quantitative analysis of natural language published over the past 200 years captures reliable patterns in historical subjective wellbeing. Using sentiment analysis on the basis of psychological valence norms, we compute a national valence index for the United Kingdom, the United States, Germany and Italy, indicating relative happiness in response to national and international wars and in comparison to historical trends in longevity and gross domestic product. We validate our method using Eurobarometer survey data from the 1970s and demonstrate robustness using words with stable historical meanings, diverse corpora (newspapers, magazines and books) and additional word norms. By providing a window on quantitative historical psychology, this approach could inform policy and economic history

Georadar investigations to detect cavities in a historical town damaged by an earthquake of the past

This paper aims to highlight the use of the georadar as a useful prospecting technique to identify the areal density and the geometrical features of the grottoes placed in a historical town characterised by high seismic hazard. <br><br> The town considered here is Rionero in Vulture (Southern Italy) that was hit by several historical earthquakes, among which the 1930 Irpinia earthquake (Me=6.7, Is=VIII MCS). <br><br> For this event a damage map was already available from a previous study (Gizzi and Masini, 2006). This map shows that some sectors of the town suffered higher damage. One factor causing the uneven distribution of the effects is considered to be the presence of grottoes. <br><br> To strengthen this work hypothesis it was necessary to in-depth investigate the subsoil of Rionero in Vulture. Therefore, geophysical data were correlated and integrated with data obtained from field surveys and historical documentary sources. All these investigations allowed to obtain more insights about the influences of the man-made caves on seismic damage

A data-driven energy platform: from energy performance certificates to human-readable knowledge through dynamic high-resolution geospatial maps

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings’ energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process

Association between Antibiotic-Immunotherapy Exposure Ratio and outcome in metastatic Non Small Cell Lung Cancer

OBJECTIVES: Immunotherapy (IO) is effective in metastatic Non Small Cell Lung Cancer (NSCLC). Gut microbiota has an impact on immunity and its imbalance due to antibiotics may impair the efficacy of IO. We investigated this topic in a case series of NSCLC patients treated with IO. MATERIALS AND METHODS: Data about all metastatic NSCLC patients treated with IO between 04/2013 and 01/2018 were collected. Patients were stratified according to antibiotic use during the Early IO Period (EIOP), and according to the Antibiotic-ImmunotherapyExposure Ratio (AIER) defined as "days of antibiotic/days of IO" during the Whole IO Period (WIOP). Survival was estimated using the Kaplan-Meier method. Log-rank test was used to compare the curves. Multivariate analyses were performed with the Cox model. RESULTS: We analyzed 157 patients. Forty-six patients received antibiotics during the WIOP, 27 patients during the EIOP. No differences in either Progression-Free Survival (PFS) or Overall Survival (OS) were observed according to antibiotic use during the EIOP (p\u2009=\u20090.1772 and p\u2009=\u20090.2492, respectively). Considering the WIOP, median AIER was 4.2%. The patients with a higher AIER had worse PFS (p\u2009&lt;\u20090.0001) and OS (p\u2009=\u20090.0004) than the others. Results were significant also after correction for the IO line (p\u2009=\u20090.0018 for PFS) and performance status (p\u2009&lt;\u20090.0001 for PFS, p\u2009=\u20090.0052 for OS). CONCLUSION: Although no difference in outcome were observed with antibiotic use in the EIOP, a detrimental effect became evident for patients with a higher AIER in the WIOP. If its relevance is confirmed, AIER may become an innovative variable for estimating the impact of antibiotics on IO efficacy

Исследование кристаллизации NaCl из водного раствора, находящегося в магнитном поле

In the context of developing single-site stereoselective post-metallocene catalysts, the case for isospecific styrene polymerization catalysts based on methylaluminoxane-activated group 4 metal bis(phenolato) complexes is summarized. In the context of developing single-site stereoselective post-metallocene catalysts, the case for isospecific styrene polymerization catalysts based on methylaluminoxane-activated group 4 metal bis(phenolato) complexes is summarized. Ligands derived from the 1,4-dithiabutanediyl-linked bis(phenol)s have been found to induce stereochemical rigidity by the presence of the hemi-labile sulfide donor functions. Isospecific styrene polymerization was achieved using easily accessible catalyst precursors of the type [MX 2(OC6H2-tBu2-4,6) 2S(CH2)2S] (M = Ti, Zr, Hf; X = Cl, O iPr, CH2Ph). Activating the dibenzyl titanium complex [Ti(CH2Ph)2(OC6H2- tBu2-4,6)2S(CH2)2S] with B(C6F5)3 and AliBu3, controlled isotactic polymerization became possible at lower temperatures. A remarkable dependence of both the activity and stereoselectivity on the ligand substitution pattern was observed. Analogous precursors with the 1,5-dithiapentanediyl-linked bis(phenolato) ligand gave syndiotactic polystyrene with lower activity. © 2004 Elsevier B.V. All rights reserved

Physics potentials with the second Hyper-Kamiokande Detector in Korea

Hyper-Kamiokande consists of two identical water-Cherenkov detectors of total 520 kt, with the first one in Japan at 295 km from the J-PARC neutrino beam with 2.5 degrees off-axis angles (OAAs), and the second one possibly in Korea at a later stage. Having the second detector in Korea would benefit almost all areas of neutrino oscillation physics, mainly due to longer baselines. There are several candidate sites in Korea with baselines of 1000-1300 km and OAAs of 1 degrees-3 degrees. We conducted sensitivity studies on neutrino oscillation physics for a second detector, either in Japan (JD x 2) or Korea (JD + KD), and compared the results with a single detector in Japan. Leptonic charge-parity (CP) symmetry violation sensitivity is improved, especially when the CP is non-maximally violated. The larger matter effect at Korean candidate sites significantly enhances sensitivities to non-standard interactions of neutrinos and mass ordering determination. Current studies indicate the best sensitivity is obtained at Mt. Bisul (1088 km baseline, 1.3 degrees OAA). Thanks to a larger (1000 m) overburden than the first detector site, clear improvements to sensitivities for solar and supernova relic neutrino searches are expected

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice