Neighborhood Urban Environmental Quality Conditions Are Likely to Drive Malaria and Diarrhea Mortality in Accra, Ghana

Background. Urbanization is a process which alters the structure and function of urban environments. The alteration in the quality of urban environmental conditions has significant implications for health. This applies both to the ecology of insect vectors that may transmit diseases and the burden of disease. Study Objectives. To investigate the relationship between malaria and infectious diarrhea mortality and spatially varied neighborhood environmental quality conditions in a low-income economy. Design. A one time point spatial analysis of cluster-level environmental conditions and mortality data using principal component analysis (PCA), one-way analysis of variance (ANOVA) and generalized linear models (GLMs). Methods. Environmental variables were extracted from the Ghana Census 2000 database while mortality data were obtained from the Ghana Births and Deaths Registry in Accra over the period 1998–2002. Results. Whereas there was a strong evidence of a difference in relative mortality of malaria across urban environmental zones of differing neighborhood environmental conditions, no such evidence of mortality differentials was observed for diarrhea. In addition, whereas bivariate analyses showed a weak to strong evidence of association between the environmental variables and malaria mortality, no evidence of association was found between diarrhea mortality and environmental variables. Conclusion. We conclude that environmental management initiatives intended for infectious disease control might substantially reduce the risk of urban malaria mortality and to a less extent that for urban diarrhea mortality in rapidly urbanizing areas in a low-income setting

Association of Sociodemographic, Psychopathological and Gambling-Related Factors with Treatment Utilization for Pathological Gambling

Background/Aims: Only a small percentage of pathological gamblers utilizes professional treatment for gambling problems. Little is known about which social and gambling-related factors are associated with treatment utilization. The aim of this study was to look for factors associated with treatment utilization for pathological gambling. Methods: The study followed a sampling design with 3 different recruitment channels, namely (1) a general population-based telephone sample, (2) a gambling location sample and (3) a project telephone hotline. Pathological gambling was diagnosed in a telephone interview. Participants with pathological gambling (n = 395) received an in-depth clinical interview concerning treatment utilization, comorbid psychiatric disorders and social characteristics. Results: Variables associated with treatment were higher age [odds ratio (OR) 1.05, 95% confidence interval (CI) 1.03-1.08], an increased number of DSM-IV criteria for pathological gambling (OR 1.34, 95% CI 1.06-1.70), more adverse consequences from gambling (OR 1.10, 95% CI 1.03-1.16) and more social pressure from significant others (OR 1.17, 95% CI 1.07-1.27). Affective disorders were associated with treatment utilization in the univariate analysis (OR 1.81, 95% CI 1.19-2.73), but multivariate analysis showed that comorbid psychiatric disorders were not independently associated. Conclusion: These results indicate that individuals with more severe gambling problems utilize treatment at an older age when more adverse consequences have occurred. Further research should focus on proactive early interventions

Mineralization of vegetable oils used for thermal weed control in arable soils

Hot vegetable oil can be used for weed control as an alternative to the use of herbicides. We analysed the temporal development of vegetable oil mineralization in soil and tested the role of nutrient supply on oil mineralization. Further, we investigated the effect of oil application on mineralization of native soil organic carbon (SOC), i.e. the priming effect. In a laboratory experiment, three oil dosages (0.1, 1.0 and 3.0ml per 35g soil) were applied to three arable soils and soil respiration was measured hourly. Both a C3-sunflower oil and a C4-corn oil were used in order to differentiate oil-derived CO2 from SOC-derived CO2. The results revealed that after 42days of incubation, 9.6 to 39.7% of the applied oil was mineralized which, however, also primed the mineralization of SOC by a factor of 2.2 to 4.2. The higher the applied oil amount, the lower was the percentage of oil-C mineralization, but the higher was the priming effect. The addition of fertilizer (0.29mgNg(-1) soil and 0.048mgPg(-1) soil) increased oil-C mineralization to 39.9 to 50.9%. We conclude that oil can temporarily accumulate in soil, especially in case of low nutrient supply. As the addition of oil stimulates SOC mineralization, a decrease of native SOC stocks may occur, which needs further quantification in long-term field experiments.Peer reviewe

Ultra-compact tunable fiber laser for coherent anti-Stokes Raman imaging

This work describes the construction of an ultra-compact narrowband fiber laser source for coherent anti-Stokes Raman scattering microscopy of Raman tags, that is, for addressing Raman resonances of deuterated molecules and alkyne tags in the spectral range from 2080 to 2220 cm−1. A narrowband and fast electronically tunable cw seed source based on a semiconductor optical amplifier (SOA) emitting around 1335 nm has been employed to seed four-wave mixing (FWM) in an endlessly single mode fiber (ESM) pumped by a ps pulse duration Yb-fiber laser. A conversion efficiency of 50% is demonstrated. This compact fiber optical parametric amplifier (FOPA) has been used to perform coherent anti-Stokes Raman imaging experiments of crystalline deuterated palmitic acid

Assessment of Problematic Internet Use by the Compulsive Internet Use Scale and the Internet Addiction Test: A Sample of Problematic and Pathological Gamblers

This study aims to analyze psychometric properties and validity of the Compulsive Internet Use Scale (CIUS) and the Internet Addiction Test (IAT) and, second, to determine a threshold for the CIUS which matches the IAT cut-off for detecting problematic Internet use. A total of 292 subjects with problematic or pathological gambling (237 men, 55 women) aged 14-63 years and with private Internet use for at least 1 h per working or weekend day were recruited via different recruitment channels. Results include that both scales were internally consistent (Cronbach's α = 0.9) and had satisfactory convergent validity (r = 0.75; 95% CI 0.70-0.80). The correlation with duration of private Internet use per week was significantly higher for the CIUS (r = 0.54) compared to the IAT (r = 0.40). Among all participants, 25.3% were classified as problematic Internet users based on the IAT with a cut-off ≥40. The highest proportion of congruent classified cases results from a CIUS cut-off ≥18 (sensitivity 79.7%, specificity 79.4%). However, a higher cut-off (≥21) seems to be more appropriate for prevalence estimation of problematic Internet use

Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application

Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning

Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner

Noise Considerations for Tomographic Reconstruction of Single-Projection Digital Holographic Interferometry-Based Radiation Dosimetry

Optical Calorimetry (OC) is a 2D Digital Holographic Interferometry (DHI)-based measurement technique with potential applications for the 3D dosimetry of ultra-high dose rate (FLASH) radiation therapy beams through tomographic reconstruction. This application requires accurate measurements of DHI signals in environments with low signal-to-noise ratios (SNRs) in order to accurately measure absorbed energy to a medium per unit mass (Dose). However, tomographic reconstruction accuracy is sensitive to noise in the measurements. In this study, a virtual model of an OC dosimeter was used to characterize and model major sources of noise within a DHI setup, allowing for the modelled noise sources to be selectively reduced. The tomographic reconstruction of the 3D dose distribution was achieved using the inverse Abel transform. Reducing the noise contribution from atmospheric turbulence and mechanical vibration by one half improved the central axis reconstruction error from 6.5% to 1.3% and 1.1%, respectively, and the mean dose difference from 2.9% to 0.4% and 0.3%, respectively. This indicates the potential of the tomographic DHI-based 3D OC dosimeter to reconstruct accurate 3D dose distributions from a single projection if the specified sources of noise can be reduced to acceptable levels. The used methodology is applicable to any application of tomographic DHI where reconstruction quality is highly sensitive to noise