ADVANCED DEEP LEARNING COMPARISONS FOR NON-INVASIVE TUNNEL LINING ASSESSMENT FROM GROUND PENETRATING RADAR PROFILES

Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process for a worthwhile maintenance schedule. Nowadays, machine learning tools are in the spotlight because of their outstanding capabilities to deal with data coming from even heterogeneous sources and their ability to extract information from the structural systems, providing highly effective, reliable, and efficient damage classification tools. In the current study, a supervised multi-level damage classification strategy has been developed regarding Ground Penetrating Radar (GPR) profiles for the assessment of tunnel lining conditions. In previous research, the authors firstly considered a convolutional neural network (CNN), adopting the quite popular ResNet-50, initialized through transfer learning. In the present work, further enhancements have been attempted by adopting two configurations of the newest state-of-art advanced neural architectures: the neural transformers. The foremost is the original Vision Transformer (ViT), whose core is an encoder entirely based on the innovative self-attention mechanism and does not rely on convolution at all. The second is an improvement of ViT which merges convolution and self-attention, the Compact Convolution Transformer (CCT). In conclusion, a critical discussion of the different pros and cons of adopting the above-mentioned different architectures is finally provided, highlighting the actual powerfulness of these technologies in the future civil engineering paradigm nevertheless

In the lycophyte Selaginella martensii is the "extra-qT" related to energy spillover? Insights into photoprotection in ancestral vascular plants

Lycophytes are early diverging vascular plants, representing a minor group as compared to the dominating euphyllophytes, mostly angiosperms. Having maximally developed in a CO2-rich atmosphere, extant lycophytes are characterized by a low carbon fixing capacity, which is compensated by a marked ability to induce the non-photochemical quenching of chlorophyll fluorescence (NPQ). Different kinetic components contribute to NPQ, in particular the fast relaxing high-energy quenching qE, the middle relaxing qT, and the slowly relaxing qI. Unlike angiosperms, lycophytes enhance the qT component under high light, originating from an "extra-qT". In this research, we analyze whether in Selaginella martensii the extra-qT can reflect a photosystem (PS) I-based quenching mechanism activated upon saturation of qE capacity. From comparative analyses of fluorescence quenching parameters, carbon fixation, in vivo low- and room-temperature fluorescence spectroscopy, and thylakoid protein phosphorylation, it is proposed that the extra-qT is not mechanistically separate from the ordinary qT. The results suggest a relationship between qT and photoprotective energy spillover to PSI, which is activated upon sensing the excitation energy pressure inside PSII and is possibly facilitated by phosphorylation of Lhcb6, a minor antenna protein of PSII. Energy spillover emphasizes 77K fluorescence emission from PSI core (F714) and becomes more relevant at irradiance levels corresponding to the CO2-limited, potentially photoinhibiting phase of photosynthesis. At the highest irradiances, when Lhcb6 phosphorylation potential has been saturated, the major LHCII increases in turn its phosphorylation level, probably leading to the full exploitation of PSI as a safe excitation sink. It is suggested that the low photosynthetic capacity of lycophytes could allow an easier experimental access to the use of PSI as a safe excitation quencher for PSII, a debated, emerging issue about thylakoid photoprotection in angiosperms.</p

Application of RNA-sequencing to identify biomarkers in broiler chickens prophylactic administered with antimicrobial agents

Antimicrobial (AM) resistance is largely acknowledged as one of the biggest global health and food safety challenges and the overuse of AMs is known to generate resistance in bacteria that may affect both animals and humans. Poultry meat is the second most-produced meat in the European Union and in recent years consumers are becoming more concerned about food safety, traceability, and animal welfare in poultry rearing system, increasingly requiring meats from broilers reared without AMs. In the present study, we performed RNA sequencing to analyze 64 liver and 54 muscle transcriptomic profiles in broilers reared without treatment or treated with different classes of AMs. Moreover, we validated the most differentially expressed genes among the treated groups to detect putative novel biomarkers able to discriminate meats of broilers reared without AMs. The PDK4, IGFBP1, and RHOB genes were identified as putative novel hepatic biomarkers, discriminating broilers treated with AMs compared to broilers reared without treatments. The whole transcriptome changes revealed the liver as a valuable target organ for AM administration screening. In addition, our results suggest a leading effect of the coccidiostat when associated with AMs, influencing several biological processes. Our study showed that RNA sequencing is a powerful and valuable method to detect aberrant regulated genes and to identify biomarker candidates for AM misuse detection in farm animals. Further validation on larger sample size and a wider spectrum of AMs are needed to confirm the viability of the aforementioned biomarkers in poultry population