showed the highest diagnostic sensitivity, and the κIgG index presented the highest ability to exclude multiple sclerosis. Conclusion This study provides novel information about the diagnostic significance of four markers combined in the κIgG index. More investigations in larger study groups are needed to confirm that the κIgG index can reflect the intrathecal synthesis of immunoglobulins and may improve the diagnosis of multiple sclerosis.In this study, we investigated using coconut shell concrete (CSC) in double-skin steel plate sandwich beams, i.e., steel-concrete-steel (SCS) under flexure. Two cases-without and with shear studs to interconnect the bottom tension and top compression plates-were considered. Conventional concrete (CC) was used for comparison purposes. The effect of quarry dust (QD) in place of river sand (RS) was considered. Therefore, four mixes named as CC, conventional concrete produced using QD (CCQ), CSC and coconut shell concrete produced using QD (CSCQ) were used. Three different steel plate thicknesses were considered (4 mm, 6 mm and 8 mm). In total, twelve SCS specimens were tested to evaluate the flexural performance under two-point static loads. Study parameters include partial and fully composite, ultimate moment and failures, deflection characteristics, ductility property, cracking behavior and strains in both tension and compression plates. It was found that the moment carrying capacity of the SCS sandwich beams increased when the thickness of the steel plate increased. Our results provided evidence that using QD in place of RS augmented the strength of beams. Theoretical deflections were underestimated the experimental deflection, except in one case. The SCS beams showed good ductility behavior. The SCS beams exhibited crack widths at yielding well below guideline values. Chalcone synthase (CHS) is a key enzyme in the flavonoid pathway, participating in the production of phenolic phytoalexins. The rice genome contains 31 CHS family genes (OsCHSs). The molecular characterization of OsCHSs suggests that OsCHS8 and OsCHS24 belong in the bona fide CHSs, while the other members are categorized in the non-CHS group of type III polyketide synthases (PKSs). see more Biochemical analyses of recombinant OsCHSs also showed that OsCHS24 and OsCHS8 catalyze the formation of naringenin chalcone from p-coumaroyl-CoA and malonyl-CoA, while the other OsCHSs had no detectable CHS activity. OsCHS24 is kinetically more efficient than OsCHS8. Of the OsCHSs, OsCHS24 also showed the highest expression levels in different tissues and developmental stages, suggesting that it is the major CHS isoform in rice. In oschs24 mutant leaves, sakuranetin content decreased to 64.6% and 80.2% of those in wild-type leaves at 2 and 4 days after UV irradiation, respectively, even though OsCHS24 expression was mostly suppressed. Instead, the OsCHS8 expression was markedly increased in the oschs24 mutant under UV stress conditions compared to that in the wild-type, which likely supports the UV-induced production of sakuranetin in oschs24. These results suggest that OsCHS24 acts as the main CHS isozyme and OsCHS8 redundantly contributes to the UV-induced production of sakuranetin in rice leaves.The authors performed a narrative review on Severe Acute Respiratory Syndrome- CoronaVirus-2 ( SARS-CoV-2) and all infectious agents with the primary endpoints to illustrate the most accepted models of safety protocols in dentistry and oral medicine, and to propose an easy view of the problem and a comparison (pre- vs post-COVID19) for the most common dental procedures. The outcome is forecast to help dentists to individuate for a given procedure the differences in terms of safety protocols to avoid infectious contagion (by SARS-CoV-2 and others dangerous agents). An investigation was performed on the online databases Pubmed and Scopus using a combination of free words and Medical Subject Headings (MESH) terms "dentist" OR "oral health" AND "COVID-19" OR "SARS-CoV-2" OR "coronavirus-19". After a brief excursus on all infectious agents transmittable at the dental chair, the authors described all the personal protective equipment (PPE) actually on the market and their indications, and on the basis of the literature, they compared (before and after COVID-19 onset) the correct safety procedures for each dental practice studied, underlining the danger of underestimating, in general, dental cross-infections. The authors have highlighted the importance of knowing exactly the risk of infections in the dental practice, and to modulate correctly the use of PPE, in order to invest adequate financial resources and to avoid exposing both the dental team and patients to preventable risks.Touch plays a crucial role in humans' nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human-robot interaction (HRI).