Effects of salinity, temperature, light intensity and light regimes on production, growth and reproductive parameters of Apocyclops dengizicus

The effects of salinity, temperature, and light conditions on production and development, longevity, survival and sex ratios of the cyclopoid copepod, A. dengizicus were determined. Seven different salinity levels (5, 10, 15, 20, 25, 30, 35 psu), four temperatures (20, 25, 30, 35 °C), three different light intensities (33.3, 85.3, 162.1 μmol photons/ m^2/ s) and light regimes (24:0, 0:24, and 12:12 h light:dark regime) were employed. The highest production was achieved under 20 psu salinity. The optimum temperature required for the maximum reproduction and shortest development time was 35 °C. The production was highest (p<.05) and development rate of A. dengizicus was shortest (p<.05) under the lowest light intensity (33.3 μmol photons/ m^2/ s). Continuous light (24:0 h LD) showed positive effects on growth and production. Light regimes 24:0 h and 12:12 h LD yielded the highest total production and growth (p<.05), with highest (p<.05) survival percentage. This study demonstrated that A. dengizicus can tolerate wide range of environmental conditions and can be cultured for commercial live feed purposes as well as toxicity studies

Supervised wavelet method to predict patient survival from gene expression data.

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis

Allelic polymorphism of 'Makoei' sheep myostatin gene identified by polymerase chain reaction and single strand conformation polymorphism

Myostatin, a transforming growth factor-beta (TGF-) super family member, has been well documented as a negative regulator of muscle growth and development. Myostatin with 376 amino acids is synthesized as a precursor protein. In this study, polymorphism of myostatin gene in Iranian 'Makoei' sheep breeds was investigated by polymerase chain reaction and single strand conformation polymorphism technique (PCR–SSCP). Genomic DNA was isolated from the blood of 92 sheep. A 417 bp myostatin intron 1 segment was amplified by standard PCR, using the locus specific primers. Four different SSCP patterns, representing four different genotypes, were identified. The frequencies of the observed genotypes were , 0.293, 0.130, and 0.163 for AD, AC, AE, and BC, respectively. Allele frequencies were 0.4185, 0.0815, 0.2283, 0.2065 and 0.0652 for A, B, C, D and E. Observed heterozygosity (Hobs) value was 0.7192. The chi-square test showed significant (P&lt;0.05) deviation from Hardy-Weinberg equilibrium for this locus in studied population.Key words: Myostatin gene, polymerase chain reaction (PCR), single strand conformation polymorphism technique (SSCP), Ovis aries

Ingestion rate and feeding behavior of guppy (Poecilia reticulata Peters) larvae fed on nauplii of Artemia urmiana and Artemia franciscana

Artemia nauplii, despite their marine origin, have a good potential for application in the freshwater ornamental fish culture. In this study, two types of Artemia urmiana and Artemia franciscana were used for feeding of Guppy (Poecilia reticulata Peters) Larvae. Newly hatched fresh nauplii at three different densities of 3, 6, and 12 ind./ml as live feeds were compared for measuring larval ingestion rate. Results showed that type and density of Artemia nauplii had significant effects on ingestion rate (P<0.05). The average ingestion rate in larvae which maintain 12 hours starvation (first set of experiment) were 36, 244, and 664 ind./larvae/day for A. urmiana, correspondingly, for A. franciscana were 160, 480, and 880 ind./larvae/day at densities of 3, 6, and 12 ind./ml, respectively. The average ingestion rate of guppy larvae with 24, 48 and 72 hours feeding preconditions (2^nd, 3^rd and 4^th set of experiment) were significantly (P<0.05) decreased. After starvation, the ingestion rate of guppy larvae fed A. urmiana had range 22-54, 86-102, and 148-188 ind./larvae/day, correspondingly, for A. franciscana 66-100, 100-260, and 200-224 ind./larvae/day at 3, 6, and 12 ind./ml, respectively. The use of suitable densities of 6 and 12 ind./ml from Artemia nauplii could increase efficiency of utilization and also improve Guppy larvae production

Synthesis of fully bio-based and solvent free non-isocyanate poly (ester amide/urethane) networks with improved thermal stability on the basis of vegetable oils

© 2018 Elsevier Ltd The purpose of this study is to synthesize non-isocyanate poly (ester amide/urethane) networks, based entirely on vegetable oil through a green method, i.e., without solvent and having any rigid and aromatic structures to improve their thermal stability. For this purpose, first, three amines were synthesized from castor oil and oleic acid. Second, carbonated sunflower oil (CSFO) was obtained by reaction of epoxidized sunflower oil with CO2 at atmospheric pressure. In the final step, CSFO easily reacted with bio-based amines by melt-blending without catalyst to give corresponding non-isocyanate polyurethane (NIPU) networks. The Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), X-ray powder diffraction (XRD), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) techniques were applied to characterize the structural, thermal and physical features of NIPU networks. In addition, to determine the environmental stability the water absorption amount and the degradation percentage were calculated in the deionized water and phosphate saline buffer, respectively. These NIPU networks showed an excellent thermal stability (T5 wt% between 323 and 386 °C), low water absorption and degradation (4–10 and 1.04–1.40 wt% respectively). The results show the potential of this environmentally friendly strategy for preparing bio-based NIPU for high performances. Furthermore, the presence of an aliphatic ester group and their biodegradability nature may also make them proper for biological and/or biomedical applications

Insights into the molecular interaction between sucrose and α-chymotrypsin

© 2018 Elsevier B.V. One of the most important purposes of enzyme engineering is to increase the thermal and kinetic stability of enzymes, which is an important factor for using enzymes in industry. The purpose of the present study is to achieve a higher thermal stability of α-chymotrypsin (α-Chy) by modification of the solvent environment. The influence of sucrose was investigated using thermal denaturation analysis, fluorescence spectroscopy, circular dichroism, molecular docking and molecular dynamics (MD) simulations. The results point to the effect of sucrose in enhancing the α-Chy stability. Fluorescence spectroscopy revealed one binding site that is dominated by static quenching. Molecular docking and MD simulation results indicate that hydrogen bonding and van der Waals forces play a major role in stabilizing the complex. Tm of this complex was enhanced due to the higher H-bond formation and the lower surface hydrophobicity after sucrose modification. The results show the ability of sucrose in protecting the native structural conformation of α-Chy. Sucrose was preferentially excluded from the surface of α-Chy which is explained by the higher tendency of water toward favorable interactions with the functional groups of α-Chy than with sucrose

Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources