Population-Based Surveillance for Birth Defects Potentially Related to Zika Virus Infection - 22 States and Territories, January 2016-June 2017.

Zika virus infection during pregnancy can cause congenital brain and eye abnormalities and is associated with neurodevelopmental abnormalities (1-3). In areas of the United States that experienced local Zika virus transmission, the prevalence of birth defects potentially related to Zika virus infection during pregnancy increased in the second half of 2016 compared with the first half (4). To update the previous report, CDC analyzed population-based surveillance data from 22 states and territories to estimate the prevalence of birth defects potentially related to Zika virus infection, regardless of laboratory evidence of or exposure to Zika virus, among pregnancies completed during January 1, 2016-June 30, 2017. Jurisdictions were categorized as those 1) with widespread local transmission of Zika virus; 2) with limited local transmission of Zika virus; and 3) without local transmission of Zika virus. Among 2,004,630 live births, 3,359 infants and fetuses with birth defects potentially related to Zika virus infection during pregnancy were identified (1.7 per 1,000 live births, 95% confidence interval [CI] = 1.6-1.7). In areas with widespread local Zika virus transmission, the prevalence of birth defects potentially related to Zika virus infection during pregnancy was significantly higher during the quarters comprising July 2016-March 2017 (July-September 2016 = 3.0; October-December 2016 = 4.0; and January-March 2017 = 5.6 per 1,000 live births) compared with the reference period (January-March 2016) (1.3 per 1,000). These findings suggest a fourfold increase (prevalence ratio [PR] = 4.1, 95% CI = 2.1-8.4) in birth defects potentially related to Zika virus in widespread local transmission areas during January-March 2017 compared with that during January-March 2016, with the highest prevalence (7.0 per 1,000 live births) in February 2017. Population-based birth defects surveillance is critical for identifying infants and fetuses with birth defects potentially related to Zika virus regardless of whether Zika virus testing was conducted, especially given the high prevalence of asymptomatic disease. These data can be used to inform follow-up care and services as well as strengthen surveillance.

Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan.

Land use land cover (LULC) dynamics is an important aspect of environmental studies. Lahore is one of the wide-ranging urban cities in the world experiencing rapid development in the form of unplanned urban growth and industrialization, which leads to many adverse consequences. This research focuses on the study of spatio-temporal variability of urbanization and its impact on the water quality index (WQI) in Lahore city using remote sensing (RS) and geographical information systems (GIS). Landsat images (Landsat 7 ETM+, Landsat 8 OLI) between 2005 to 2021 were used to observe the changes in urban growth over seventeen years. GIS is used to create the LULC, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) maps, to study the urbanization impact on the WQI. The results of this study indicate that the groundwater quality of metropolitan Lahore city has significantly dropped within 17 years. The extent of the built-up area has been expanded from 22.4% to 953.04% with an increase in the poor WQI area from 1.95% to 37.89%, reveals a general decline in groundwater quality with urbanization. Indeed, the trends observed by the linear regression modelling showed a positive and negative correlation (R2 = 0.67 and -0.74) of WQI with % of urban and vegetation areas respectively. GIS and RS tools have been found effective in assessing spatio-temporal phenomena of urbanization and its impact on groundwater quality. Furthermore, this research would be very helpful in making decisions for managing groundwater resources and illegal urban expansion in Lahore city.

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson's ratio of selected rock types.

This research focuses on the predictive modeling between rocks' dynamic properties and the optimization of neural network models. For this purpose, the rocks' dynamic properties were measured in terms of quality factor (Q), resonance frequency (FR), acoustic impedance (Z), oscillation decay factor (α), and dynamic Poisson's ratio (v). Rock samples were tested in both longitudinal and torsion modes. Their ratios were taken to reduce data variability and make them dimensionless for analysis. Results showed that with the increase in excitation frequencies, the stiffness of the rocks got increased because of the plastic deformation of pre-existing cracks and then started to decrease due to the development of new microcracks. After the evaluation of the rocks' dynamic behavior, the v was estimated by the prediction modeling. Overall, 15 models were developed by using the backpropagation neural network algorithms including feed-forward, cascade-forward, and Elman. Among all models, the feed-forward model with 40 neurons was considered as best one due to its comparatively good performance in the learning and validation phases. The value of the coefficient of determination (R2 = 0.797) for the feed-forward model was found higher than the rest of the models. To further improve its quality, the model was optimized using the meta-heuristic algorithm (i.e. particle swarm optimizer). The optimizer ameliorated its R2 values from 0.797 to 0.954. The outcomes of this study exhibit the effective utilization of a meta-heuristic algorithm to improve model quality that can be used as a reference to solve several problems regarding data modeling, pattern recognition, data classification, etc.

Assessment of genetic diversity among Pakistani wheat (Triticum aestivum L) advanced breeding lines using RAPD and SDS-PAGE

Genetic diversity was assessed among 32 advanced wheat breeding lines included in the National Uniform Wheat Yield Trials (2006-07) of Pakistan using molecular (DNA) and biochemical (SDS-PAGE) markers. Of the 72 RAPD primers used for initial screening, 15 were found polymorphic. A total of 140 bands (61.4 percent polymorphic) were generated by the 15 random decamer primers. Genetic similarity coefficients ranged from 0.81 to 0.94 for rainfed and from 0.70 to 0.93 for the normal seeding date group. Cluster analysis using the unweighted pair group method of arithmetic averages (UPGMA) clustered the 32 advanced wheat breeding lines into one major and three small groups. Maximum level of polymorphism (90 percent) was observed for the primer OPA-05. Lines N9 and N11 showed the least genetic similarities (0.70-0.82 and 0.71-0.83, respectively) with rest of the lines studied. Line RF1 had the maximum similarity (0.81-0.94) with other lines. Wheat lines included in the normal seeding date were relatively distantly related than those in the rainfed group. Seed storage protein analysis produced 19 subunits ranging from 29-120KDa. Similarity coefficients ranged from 0.53 to 1.0 for the normal seeding date and from 0.47 to 1.0 for the rainfed group. High molecular weight subunits (particularly 120KDa) showed greater polymorphism than the lower molecular weight subunits. Narrow genetic base was observed in wheat lines included in the rainfed group. DNA fingerprinting of advanced breeding lines may help to avoid release of varieties with narrow genetic base in the future.