New standards for nutrition science, concepts and methods—novel approach to substantiate cause- and -effect relationships in nutritional science by ranking studies and subsequent statistical modelling

In any scientific field, demonstrating cause-and-effect relationships is of the utmost importance, however difficult to achieve. The present study aims to establish an objective approach to substantiate cause-and-effect relationships. Our approach consisted of ranking published studies and subsequently using the best performing studies to construct and validate a statistical model. For the first part, studies on the association between vitamin D status and COVID-19 severity (morbidity/mortality) in hospitalized patients were identified and ranked using a combination of physiological and statistical relevance, including dose-dependency, power evaluation, confounding, physiological mechanisms, and target population. The various ranking criteria were developed in an iterative process, taking into account the Bradford Hill criteria. For the second part, a two-step statistical modelling strategy was implemented. Firstly, a multivariate model was constructed and secondly, this model was validated using data from at least one other independent study with a similar design. The sensitivity (percentage of correctly detected cases by the model) and specificity (percentage of correctly detected non-cases by the model) was assessed in both studies, and the results of both studies (model-making and model-testing) were compared using the chi-square test with expectation. Five ranking criteria were defined with a maximum score of 67 points. Six studies were selected with scores ranging between 27 and 47 points [1–6]. The highest score was obtained by Hernandez et al., 2021 [1]. Unfortunately, it was not possible to obtain complete independent datasets of these studies. Therefore, to evaluate our approach in cause- and -effect relationships, two datasets were selected of studies on the effects of postbiotic intake on the incidence of pulmonary and gastrointestinal infections in children aged 1 to 4 years [7,8]. A logistic confounding model in combination with a discriminant analysis was applied on the first (model-making) study resulting in an internal sensitivity and specificity of 78% and 100%, respectively (p &lt; 0.001), showing a treatment effect on the reduction of infections (p &lt; 0.001). An external validation of the acquired model in a second independent (model-testing) study showed sensitivity and specificity of 76% and 80% (p &lt; 0.001), again showing a treatment effect (p &lt; 0.001). The sensitivity and specificity were not statistically different indicating similarity of the impact by the explanatory variables in both datasets. Overall, the combination of ranking studies and statistical modelling supports the validation of cause-and-effect relationships using objective criteria. Demonstrating consistency in associations by replication and robustness testing contributes to proof of concept in causative relations.<br/

New standards for nutrition science, concepts and methods—novel approach to substantiate cause- and -effect relationships in nutritional science by ranking studies and subsequent statistical modelling

In any scientific field, demonstrating cause-and-effect relationships is of the utmost importance, however difficult to achieve. The present study aims to establish an objective approach to substantiate cause-and-effect relationships. Our approach consisted of ranking published studies and subsequently using the best performing studies to construct and validate a statistical model. For the first part, studies on the association between vitamin D status and COVID-19 severity (morbidity/mortality) in hospitalized patients were identified and ranked using a combination of physiological and statistical relevance, including dose-dependency, power evaluation, confounding, physiological mechanisms, and target population. The various ranking criteria were developed in an iterative process, taking into account the Bradford Hill criteria. For the second part, a two-step statistical modelling strategy was implemented. Firstly, a multivariate model was constructed and secondly, this model was validated using data from at least one other independent study with a similar design. The sensitivity (percentage of correctly detected cases by the model) and specificity (percentage of correctly detected non-cases by the model) was assessed in both studies, and the results of both studies (model-making and model-testing) were compared using the chi-square test with expectation. Five ranking criteria were defined with a maximum score of 67 points. Six studies were selected with scores ranging between 27 and 47 points [1–6]. The highest score was obtained by Hernandez et al., 2021 [1]. Unfortunately, it was not possible to obtain complete independent datasets of these studies. Therefore, to evaluate our approach in cause- and -effect relationships, two datasets were selected of studies on the effects of postbiotic intake on the incidence of pulmonary and gastrointestinal infections in children aged 1 to 4 years [7,8]. A logistic confounding model in combination with a discriminant analysis was applied on the first (model-making) study resulting in an internal sensitivity and specificity of 78% and 100%, respectively (p &lt; 0.001), showing a treatment effect on the reduction of infections (p &lt; 0.001). An external validation of the acquired model in a second independent (model-testing) study showed sensitivity and specificity of 76% and 80% (p &lt; 0.001), again showing a treatment effect (p &lt; 0.001). The sensitivity and specificity were not statistically different indicating similarity of the impact by the explanatory variables in both datasets. Overall, the combination of ranking studies and statistical modelling supports the validation of cause-and-effect relationships using objective criteria. Demonstrating consistency in associations by replication and robustness testing contributes to proof of concept in causative relations.<br/

Theoretical Analysis of Pre-Receptor Image Conditioning in Weakly Electric Fish

Electroreceptive fish detect nearby objects by processing the information contained in the pattern of electric currents through the skin. The distribution of local transepidermal voltage or current density on the sensory surface of the fish's skin is the electric image of the surrounding environment. This article reports a model study of the quantitative effect of the conductance of the internal tissues and the skin on electric image generation in Gnathonemus petersii (Günther 1862). Using realistic modelling, we calculated the electric image of a metal object on a simulated fish having different combinations of internal tissues and skin conductances. An object perturbs an electric field as if it were a distribution of electric sources. The equivalent distribution of electric sources is referred to as an object's imprimence. The high conductivity of the fish body lowers the load resistance of a given object's imprimence, increasing the electric image. It also funnels the current generated by the electric organ in such a way that the field and the imprimence of objects in the vicinity of the rostral electric fovea are enhanced. Regarding skin conductance, our results show that the actual value is in the optimal range for transcutaneous voltage modulation by nearby objects. This result suggests that “voltage” is the answer to the long-standing question as to whether current or voltage is the effective stimulus for electroreceptors. Our analysis shows that the fish body should be conceived as an object that interacts with nearby objects, conditioning the electric image. The concept of imprimence can be extended to other sensory systems, facilitating the identification of features common to different perceptual systems

Short Communication: Simultaneous Identification of Five κ-Casein (CSN3) Alleles in Domestic Goat by Polymerase Chain Reaction-Single Strand Conformation Polymorphism

Until now, a total of nine polymorphic sites corresponding to six different alleles have been described at the kappa-casein (CSN3) locus in the domestic goat (Capra hircus). A protocol for the rapid and simultaneous genotyping of five goat CSN3 alleles by using polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) technique was developed. Moreover, the developed test was validated by screening the CSN3 variability in four Italian breeds, Garganica, Jonica, Maltese, and Camosciata. Seven different patterns were readily identifiable. These corresponded to five known alleles and two newly identified variants. The G/A substitution at nucleotide position 471, which is not identifiable at the protein level but was found to be very frequent in the typed breeds, is easily detectable by the protocol developed. The PCR-SSCP analysis is a powerful tool for the genetic study of CSN3 variability in domestic goats, allowing both the simultaneous identification of different alleles, and the detection of new variants

Modeling active electrolocation in weakly electric fish

In this paper, we provide a mathematical model for the electrolocation in weakly electric fishes. We first investigate the forward complex conductivity problem and derive the approximate boundary conditions on the skin of the fish. Then we provide a dipole approximation for small targets away from the fish. Based on this approximation, we obtain a non-iterative location search algorithm using multi-frequency measurements. We present numerical experiments to illustrate the performance and the stability of the proposed multi-frequency location search algorithm. Finally, in the case of disk- and ellipse-shaped targets, we provide a method to reconstruct separately the conductivity, the permittivity, and the size of the targets from multi-frequency measurements.Comment: 37 pages, 11 figure

Effect of Udder Health Status and Lactation Phase on the Characteristics of Sardinian Ewe Milk

Mammary involution and inflammation are known to negatively affect milk quality. A trial was carried out to elucidate the mechanism by which udder health status and lactational phase determine compositional modifications in ovine milk. A total of 60 individual milk samples was collected from a group of 20 pluriparous Sardinian ewes from mid to late lactation. Each sample was assessed for its chemical characteristics, quantitative distribution of casein fractions, lactodynamographic characteristics, and enzymatic activity. Udders were classed as healthy, doubtful, or infected on the basis of repeated somatic cell counts, and samples were grouped in 3 classes of days in milk. Results indicated that both udder inflammation and mammary involution can increase plasmin (PL) activity (15.6 vs. 18.4 U/mL in healthy vs. infected udders; 14.0 vs. 20.2 U/mL in phase 1 vs. 3), which is responsible for an evident protein breakdown in milk. Significant differences between groups were observed for several characteristics. With regard to udder heath status, casein index was lower in the infected vs. healthy udders (74.8 vs. 76.6%), and beta(tot)-casein showed a similar trend (43.9 vs. 46.6%). As a consequence of protein degradation, gamma-casein (5.78 vs. 2.82%) and proteolysis index (7.60 vs. 3.82) increased in the infected group with respect to the healthy group. Udder health status also affected milk technological traits. Udder inflammation resulted in longer clotting time (20.7 vs. 16.5 min for infected vs. healthy, respectively) and in poorer curd firmness (35.6 vs. 47.6 mm for infected vs. healthy, respectively). Frequency of samples reactive to rennet was 100, 93, and 67%, respectively, for healthy, doubtful, and infected groups. With regard to lactational phase, a decrease in alpha(s1)-casein (39.13 vs. 29.36%) and beta(1)-casein (23.41 vs. 19.36%) occurred during phase 1 vs. 3, whereas kappa + alpha(s2)-casein increased (12.30 vs. 21.56%, phase 1 vs. 3). Correlation coefficients confirmed the role of PL in protein degradation. It was concluded that PL activity was strongly affected by both lactational phase and udder health status and, in turn, could be an important agent enhancing milk quality detriment

Survey of milk protein polymorphism in the "Bovina Rossa Siciliana"

The “Rossa Siciliana” is an autochthonous small Sicilian dairy cattle population, accounting for 5-7,000 individuals.This breed is characterized by high ability to live on poor pasture lands, moderate milk production, and traditionally linked to cheese production. Aim of this work was to investigate milk protein polymorphisms in this population. A total of 62 individual milk samples were collected from 19 extensive farms spread in the “Parco dei Nebrodi” area (Messina). All samples were analyzed by isoelectrofocusing (IEF) with pH range 2.5-6. High variability was found at the CSN1S1, CSN2, CSN3, LGB and LALBA loci, while the CSN1S2 locus was monomorphic for the A allele. The allele frequencies and the Hardy-Weinberg equilibrium were estimated using the GENEPOP software, further the casein haplotype frequencies and the occurrence of the linkage disequilibrium were computed with the EH software, taking into account the association among loci. Joint analysis at all loci showed that the population is not in Hardy-Weinberg equilibrium (Chi-square=19.0, pvalue=0.0407), probably due to genetic drift. Of the three alleles detected at CSN1S1 locus, CSN1S1*B and CSN1S1*C had high frequencies (0.734 and 0.258 respectively), as reported for most breeds. The CSN1S1*D allele was detected with a low frequency (0.008). High frequencies were found for CSN2*A2 (0.573) and CSN3*B (0.637). High frequencies were observed for LALBA*B (0.903) and LGB*B (0.815). Strong linkage disequilibrium was detected for the polymorphic casein loci CSN1S1-CSN2-CSN3. For the casein haplotype only the hypothesis of association among loci was considered. Out of the 27 expected, only four haplotypes had a frequency higher than 0.10. The most frequent haplotype was BA2B (0.236), followed by CA2B (0.214), BA1A (0.168), BA1B (0.131), and BA2A (0.094). The high frequency of BA2B haplotype and the possible relation with production traits are under investigation

Chromosomal assignment of the ovine hairless (hr) gene by fluorescence in situ hybridization.

Congenital hypotrichosis in mammalian species consists of partial or complete absence of a hair coat at birth. Affected individuals having a partial hair coat at birth may loose it subsequently.The aim of this paper was to physically map the ovine hr gene using fluorescence in situ hybridization (FISH).A preliminary study on an internet data bank (http://www.informatics.jax.org) showed that the regions of the hr gene on murine chromosome 14 and human chromosome 8 present homology with ovine chromosome 2

Casein haplotype structure in five Italian goat breeds

The aim of this work was to investigate the genetic structure of the casein gene cluster in 5 Italian goat breeds and to evaluate the haplotype variability within and among populations. A total of 430 goats from Vallesana, Roccaverano, Jonica, Garganica, and Maltese breeds were genotyped at alphas1-casein (CSN1S1), alphas2-casein, (CSN1S2), beta-casein (CSN2), and kappa-casein (CSN3) loci using several genomic techniques and milk protein analysis. Casein haplotype frequencies were estimated for each breed. Principal component analysis was carried out to highlight the relationship among breeds. Allele and haplotype distributions indicated considerable differences among breeds. The haplotype CSN1S1*F- CSN1S2*F-CSN3*D occurred in all breeds with frequencies >0.100 and was the most common haplotype in the Southern breeds. A high frequency of CSN1S1*0-CSN1S2*C-CSN3*A haplotype was found in Vallesana population (0.162). Principal component analysis clearly separated the Northern and Southern breeds by the first component. The variability of the caprine casein loci and variety of resulting haplotypes should be exploited in the future using specific breeding programs aiming to preserve biodiversity and to select goat genetic lines for specific protein production