Seasonal effects on the corneoconjunctival microflora in a population of Persian cats in Iran

This study was performed to determine the normal seasonal aerobic and an-aerobic corneoconjunctival bacterial flora in cats. Thirty eyes of 15 clinically normal client-owned Persian cats were evaluated. All cats lived in a similar indoor/outdoor home environment being fed the same diet for the entire year. The cats did not receive any medications and were found to be clinically healthy 1 week prior to each microbial sampling. The cats were not exposed to other cats during the study period. Microbial samples were collected at the same time of day on the first day of the second month of each of the four seasons. During sample collection, a sterile swab was rolled over the corneoconjunctival surface avoiding contact with surrounding skin or hair. Immediately after sample collection, microbiologic aerobic and anaerobic cultures were initiated. Gram-positive bacteria were the most prevalent isolates. The most commonly isolated bacterial organisms across all seasons were Staphylococcus epidermidis (41/95; 43.2%), β-hemolytic streptococcus (18/95; 18.9%), Staphylococcus aureus (17/95; 17.9%), and Escherichia coli (11/95; 11.5%). Twenty-five cultures of a total of 120 (20.8%) were negative. One negative culture was collected in the summer, while 21 cultures were negative in fall and winter. Gram-positive bacteria were the predominant micro-organisms of the normal ocular surface of healthy cats in all seasons in this study. This result is in agreement with previous publications

Antenna Design for Directivity-Enhanced Raman Spectroscopy

Antenna performance can be described by two fundamental parameters: directivity and radiation efficiency. Here, we demonstrate nanoantenna designs in terms of improved directivity. Performance of the antennas is demonstrated in Raman scattering experiments. The radiated beam is directed out of the plane by using a ground plane reflector for easy integration with commercial microscopes. Parasitic elements and parabolic and waveguide nanoantennas with a ground plane are explored. The nanoantennas were fabricated by a series of electron beam evaporation steps and focused ion beam milling. As we have shown previously, the circular waveguide nanoantenna boosts the measured Raman signal by 5.5x with respect to a dipole antenna over a ground plane; here, we present the design process that led to the development of that circular waveguide nanoantenna. This work also shows that the parabolic nanoantenna produces a further fourfold improvement in the measured Raman signal with respect to a circular waveguide nanoantenna. The present designs are nearly optimal in the sense that almost all the beam power is coupled into the numerical aperture of the microscope. These designs can find applications in microscopy, spectroscopy, light-emitting devices, photovoltaics, single-photon sources, and sensing

Mystery of Hepatitis E Virus: Recent Advances in Its Diagnosis and Management

Mysterious aspects of the long presumed to be well-known hepatitis E virus (HEV) have recently surfaced that distinguish it from other hepatotropic viruses. It is a cause of chronic hepatitis in immunosuppressed patients. It has human to human transmission through blood and mantains high seroprevalence in blood donors. HEV has also been found to occur more frequently in the West in those without a history of travel to endemic countries. It has varied extrahepatic manifestations and has multiple non-human reservoirs including pigs and rats. Considering these recent discoveries, it appears odd that HEV is not sought more frequently when working up acute and chronic hepatitis patients. The disease is particularly severe among pregnant women and has a high attack rate in young adults. What adds to its ambiguity is the absence of a well-established diagnostic criteria for its detection and that there is no specific antiviral drug for hepatitis E, except for isolated cases where ribavirin or pegylated interferon alpha has been used with occasional success. This review paper discusses the recent advances in the knowledge of the virus itself, its epidemiology, diagnostic approach and prevention, and the treatment options available

Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan.

Neuropeptide S (NPS) is a naturally occurring appetite stimulant, associated with anxiety, stress, and excitement regulation. Neuropeptide S serves as a hypothalamic energy regulator that enhances food intake with a reduced level of satiety. NPS activates fat angiogenesis and the proliferation of new adipocytes in obesity. NPS has an established role in energy regulation by many pre-clinical investigations; however we have limited data available to support this notion in humans. We found significant association of Neuropeptide S receptor (NPSR1) Asn107Ile (rs324981, A>T) polymorphism with obese male participants. The current investigation carried out genotype screening of NPSR1 allele to assess the spectrum of the Asn107Ile polymorphism in obese and healthy Pakistani individuals. We revealed a significant (p = 0.04) difference between AA vs TT + AT genotype distribution of NPSR1 (SNP rs324981,) between obese and healthy individuals (p = 0.04). In this genotype analysis of (SNP rs324981) of the NPSR1 gene, T allele was marked as risk allele with higher frequency in the obese (38%) compared to its frequency in the controls (25%). Single Nucleotide Polymorphism (SNP, rs324981) Asn107Ile of NPSR1gene, that switches an amino acid from Asn to Ile, has been found associated with increased susceptibility to obesity in Pakistani individuals. Furthermore, molecular simulation studies predicted a lower binding affinity of NPSR1 Asn107Ile variant to NPS than the wild-type consistent with the genotype studies. These molecular simulation studies predict a possible molecular mechanism of this interaction by defining the key amino acid residues. However, a significantly (p<0.0001) lower concentration of NPS was recorded independent of genotype frequencies in obese subjects compared to healthy controls. We believe that large scale polymorphism data of population for important gene players including NPSR1 will be more useful to understand obesity and its associated risk factors

A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion

This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including na&iuml;ve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if&ndash;then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques

A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion

This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques