Artificial neural network-based standalone tunable RF sensor system.

An artificial neural network (ANN) based tunable automated standalone RF sensor system is proposed to realize an improved sensing module involving a quite accurate solution of the non-linear inverse characterization problem. The presented tunable sensor system is quite novel as it alleviates the need for any active tuning circuitry. Moreover, the proposed unified design topology facilitates a relatively higher tuning range (1900 MHz) than that of the earlier reported (580 MHz) capacitor-based tunable complementary split-ring resonator (CSRR). The higher tuning range of structures resulted from the improved design configuration comprising a modified CSRR design coupled with a modified microstrip line. The obtained dielectric sensitivity is ∼8.8%. The numerically generated S-parameters of various dielectric samples are used here as a training dataset for the ANN, which is trained using the Levenberg-Marquardt backpropagation algorithm in combination with the Bayesian regularization. Finally, several standard test samples at different unloaded tuned frequencies are measured to record the corresponding resonant frequency and magnitude of the S-parameter in order to process them using the proposed ANN-based sensor system. It is found that the developed ANN-based sensor system provides a reasonably accurate value of the extracted complex permittivity over the frequency range under consideration, which basically removes the need for designing multiple resonant structures unlikely to the conventional resonant sensors.

Streptococcus uberis ST439 and ST475 induce differential inflammatory responses in a mouse intramammary infection model.

Streptococcus uberis causing mastitis is a growing challenge to the dairy industry. Molecular, epidemiological and population structure studies have revealed clonal diversity among the infecting strains. In this study, mouse intramammary infection model was used to uncover the host immune response to two epidemiologically important live strains of S. uberis (SU1and SU2) obtained from subclinical case of mastitis possessing specific and unique multi locus sequence types (ST), pulsed field gel electrophoresis (PFGE) pulsotypes and virulence profiles. Temporal (2h, 4h, 8h, 12h, 24h and 48h) expression of key inflammatory mediators (IL2, IL4, IL6, IL12, TNFα, IFNγ, GMCSF, TLR2, TLR4, TLR9, TLR11, TLR12, CD14, IL1ß, RANTES, Lactoferrin, and CXCl1) by reverse transcription and probe-based quantitative real-time PCR showed relative mRNA levels higher (p<0.05) in response to SU2 compared with SU1 with 24h PI serving as a critical point for the deviating behavior (SU1 versus SU2). Further employing the predicted biological processes under the influence of this pool of tested genes, the delineation of gene regulatory networks suggested SU1-favoring its persistence in the host environment; in contrast, SU2-which elevated gene expression indicating towards pathogen clearance or immune surveillance. This study suggested how these unique strains could manipulate the host immune response to influence the severity of mastitis; our results expand the available information on host pathogen interaction and provide a firm foundation needing further investigations to gain control over this pathogen.

Molecular characterization of Streptococcus agalactiae and Streptococcus uberis isolates from bovine milk.

Streptococci are one among the major mastitis pathogens which have a considerable impact on cow health, milk quality, and productivity. The aim of the present study was to investigate the occurrence and virulence characteristics of streptococci from bovine milk and to assess the molecular epidemiology and population structure of the Indian isolates using multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE). Out of a total of 209 bovine composite milk samples screened from four herds (A-D), 30 Streptococcus spp. were isolated from 29 milk samples. Among the 30 isolates, species-specific PCR and partial 16S rRNA gene sequence analysis identified 17 Streptococcus agalactiae arising from herd A and 13 Streptococcus uberis comprising of 5, 7, and 1 isolates from herds B, C, and D respectively. PCR based screening for virulence genes revealed the presence of the cfb and the pavA genes in 17 and 1 S. agalactiae isolates, respectively. Similarly, in S. uberis isolates, cfu gene was present in six isolates from herd C, the pau A/skc gene in all the isolates from herds B, C, and D, whereas the sua gene was present in four isolates from herd B and the only isolate from herd D. On MLST analysis, all the S. agalactiae isolates were found to be of a novel sequence type (ST), ST-483, reported for the first time and is a single locus variant of the predicted subgroup founder ST-310, while the S. uberis isolates were found to be of three novel sequence types, namely ST-439, ST-474, and ST-475, all reported for the first time. ST-474 was a double locus variant of three different STs of global clonal complex ST-143 considered to be associated with clinical and subclinical mastitis, but ST-439 and ST-475 were singletons. Unique sequence types identified for both S. agalactiae and S. uberis were found to be herd specific. On PFGE analysis, identical or closely related restriction patterns for S. agalactiae ST-483 and S. uberis ST-439 in herds A and B respectively, but an unrelated restriction pattern for S. uberis ST-474 and ST-475 isolates from herds D and C respectively, were obtained. This signifies that the isolates of particular ST may exhibit related PFGE patterns suggesting detection of a faster molecular clock by PFGE than MLST. Since all the isolates of both the species belonged to novel sequence types, their epidemiological significance in global context could not be ascertained, however, evidence suggests that they have uniquely evolved in Indian conditions. Further research would be useful for understanding the role of these pathogens in bovine sub-clinical mastitis and implementing effective control strategies in India.

Histone H3K14 and H4K8 hyperacetylation is associated with Escherichia coli-induced mastitis in mice.

Mastitis is a multietiological complex disease, defined as inflammation of parenchyma of mammary glands. Bacterial infection is the predominant cause of mastitis, though fungal, viral and mycoplasma infections also have been reported. Based on the severity of the disease, mastitis can be classified into subclinical, clinical and chronic forms. Bacterial pathogens from fresh cow milk were isolated and classified by standard microbiological tests and multiplex PCR. Epidemiological studies have shown that Escherichia coli is the second largest mastitis pathogen after Staphylococcus aureus in India. Based on Enterobacterial Repetitive Intergenic Consensus (ERIC)-PCR profile and presence of virulence genes, a field isolate of E. coli was used for intramammary inoculation in lactating mice. Histopathological examination of hematoxylin and eosin stained sections showed severe infiltration of polymorphonuclear neutrophils, mononuclear inflammatory cells in the alveolar lumen and also in interstitial space, and necrosis of alveolar epithelial cells after 24 h. Western blot and immunohistochemical analysis of mice mammary tissues showed significant hyperacetylation at histone H3K14 residue of both mammary epithelial cells and migrated inflammatory cells. Quantitative real-time PCR and genome-wide gene expression profile in E. coli infected mice mammary tissue revealed differential expression of genes related to inflammation, immunity, antimicrobial peptide expression, acute phase response and oxidative stress response. Expression of milk proteins was also suppressed. ChIP assay from paraffinized tissues showed selective enrichment of acetylated histone H3K14 and H4K8 at the promoters of overexpressed genes. These data suggest that E. coli infection in mice mammary tissue leads to histone hyperacetylation at the promoter of immune genes, which is a pre-requisite for the expression of inflammatory genes in order to mount a drastic immune response.