Computer-aided diagnosis for (123I)FP-CIT imaging: impact on clinical reporting

BACKGROUND: For (123I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters' performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology. Three experienced (123I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1). Once completed, the process was then repeated (read 2). Immediately after submitting each image score for a second time, the CADx system output was displayed to reporters alongside the image data. With this information available, the reporters submitted a score for the third time (read 3). Comparisons between reads 1 and 2 provided evidence of intra-operator reliability, and differences between reads 2 and 3 showed the impact of the CADx. RESULTS: The performance of all reporters demonstrated a degree of variability when analysing images through visual analysis alone. However, inclusion of CADx improved consistency between reporters, for both clinical and research data. The introduction of CADx increased the accuracy of the radiologists when reporting (unfamiliar) research images but had less impact on the clinical scientist and caused no significant change in accuracy for the clinical data. CONCLUSIONS: The outcomes for this study indicate the value of CADx as a diagnostic aid in the clinic and encourage future development for more refined incorporation into clinical practice

The pharmaceutical use of permethrin: Sources and behavior during municipal sewage treatment

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 Springer Science+Business Media, LLC.Permethrin entered use in the 1970s as an insecticide in a wide range of applications, including agriculture, horticultural, and forestry, and has since been restricted. In the 21st century, the presence of permethrin in the aquatic environment has been attributed to its use as a human and veterinary pharmaceutical, in particular as a pedeculicide, in addition to other uses, such as a moth-proofing agent. However, as a consequence of its toxicity to fish, sources of permethrin and its fate and behavior during wastewater treatment are topics of concern. This study has established that high overall removal of permethrin (approximately 90%) was achieved during wastewater treatment and that this was strongly dependent on the extent of biological degradation in secondary treatment, with more limited subsequent removal in tertiary treatment processes. Sources of permethrin in the catchment matched well with measured values in crude sewage and indicated that domestic use accounted for more than half of the load to the treatment works. However, removal may not be consistent enough to achieve the environmental quality standards now being derived in many countries even where tertiary treatment processes are applied.United Utilities PL

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Absence of knockdown resistance suggests metabolic resistance in the main malaria vectors of the Mekong region

<p>Abstract</p> <p>Background</p> <p>As insecticide resistance may jeopardize the successful malaria control programmes in the Mekong region, a large investigation was previously conducted in the Mekong countries to assess the susceptibility of the main malaria vectors against DDT and pyrethroid insecticides. It showed that the main vector, <it>Anopheles epiroticus</it>, was highly pyrethroid-resistant in the Mekong delta, whereas <it>Anopheles minimus sensu lato </it>was pyrethroid-resistant in northern Vietnam. <it>Anopheles dirus sensu stricto </it>showed possible resistance to type II pyrethroids in central Vietnam. <it>Anopheles subpictus </it>was DDT- and pyrethroid-resistant in the Mekong Delta. The present study intends to explore the resistance mechanisms involved.</p> <p>Methods</p> <p>By use of molecular assays and biochemical assays the presence of the two major insecticide resistance mechanisms, knockdown and metabolic resistance, were assessed in the main malaria vectors of the Mekong region.</p> <p>Results</p> <p>Two FRET/MCA assays and one PCR-RFLP were developed to screen a large number of <it>Anopheles </it>populations from the Mekong region for the presence of knockdown resistance (<it>kdr</it>), but no <it>kdr </it>mutation was observed in any of the study species. Biochemical assays suggest an esterase mediated pyrethroid detoxification in <it>An. epiroticus </it>and <it>An. subpictus </it>of the Mekong delta. The DDT resistance in <it>An. subpictus </it>might be conferred to a high GST activity. The pyrethroid resistance in <it>An. minimus s.l</it>. is possibly associated with increased detoxification by esterases and P450 monooxygenases.</p> <p>Conclusion</p> <p>As different metabolic enzyme systems might be responsible for the pyrethroid and DDT resistance in the main vectors, each species may have a different response to alternative insecticides, which might complicate the malaria vector control in the Mekong region.</p

Polyploidization Altered Gene Functions in Cotton (Gossypium spp.)

Cotton (Gossypium spp.) is an important crop plant that is widely grown to produce both natural textile fibers and cottonseed oil. Cotton fibers, the economically more important product of the cotton plant, are seed trichomes derived from individual cells of the epidermal layer of the seed coat. It has been known for a long time that large numbers of genes determine the development of cotton fiber, and more recently it has been determined that these genes are distributed across At and Dt subgenomes of tetraploid AD cottons. In the present study, the organization and evolution of the fiber development genes were investigated through the construction of an integrated genetic and physical map of fiber development genes whose functions have been verified and confirmed. A total of 535 cotton fiber development genes, including 103 fiber transcription factors, 259 fiber development genes, and 173 SSR-contained fiber ESTs, were analyzed at the subgenome level. A total of 499 fiber related contigs were selected and assembled. Together these contigs covered about 151 Mb in physical length, or about 6.7% of the tetraploid cotton genome. Among the 499 contigs, 397 were anchored onto individual chromosomes. Results from our studies on the distribution patterns of the fiber development genes and transcription factors between the At and Dt subgenomes showed that more transcription factors were from Dt subgenome than At, whereas more fiber development genes were from At subgenome than Dt. Combining our mapping results with previous reports that more fiber QTLs were mapped in Dt subgenome than At subgenome, the results suggested a new functional hypothesis for tetraploid cotton. After the merging of the two diploid Gossypium genomes, the At subgenome has provided most of the genes for fiber development, because it continues to function similar to its fiber producing diploid A genome ancestor. On the other hand, the Dt subgenome, with its non-fiber producing D genome ancestor, provides more transcription factors that regulate the expression of the fiber genes in the At subgenome. This hypothesis would explain previously published mapping results. At the same time, this integrated map of fiber development genes would provide a framework to clone individual full-length fiber genes, to elucidate the physiological mechanisms of the fiber differentiation, elongation, and maturation, and to systematically study the functional network of these genes that interact during the process of fiber development in the tetraploid cottons

Large-Scale Gene Disruption in Magnaporthe oryzae Identifies MC69, a Secreted Protein Required for Infection by Monocot and Dicot Fungal Pathogens

To search for virulence effector genes of the rice blast fungus, Magnaporthe oryzae, we carried out a large-scale targeted disruption of genes for 78 putative secreted proteins that are expressed during the early stages of infection of M. oryzae. Disruption of the majority of genes did not affect growth, conidiation, or pathogenicity of M. oryzae. One exception was the gene MC69. The mc69 mutant showed a severe reduction in blast symptoms on rice and barley, indicating the importance of MC69 for pathogenicity of M. oryzae. The mc69 mutant did not exhibit changes in saprophytic growth and conidiation. Microscopic analysis of infection behavior in the mc69 mutant revealed that MC69 is dispensable for appressorium formation. However, mc69 mutant failed to develop invasive hyphae after appressorium formation in rice leaf sheath, indicating a critical role of MC69 in interaction with host plants. MC69 encodes a hypothetical 54 amino acids protein with a signal peptide. Live-cell imaging suggested that fluorescently labeled MC69 was not translocated into rice cytoplasm. Site-directed mutagenesis of two conserved cysteine residues (Cys36 and Cys46) in the mature MC69 impaired function of MC69 without affecting its secretion, suggesting the importance of the disulfide bond in MC69 pathogenicity function. Furthermore, deletion of the MC69 orthologous gene reduced pathogenicity of the cucumber anthracnose fungus Colletotrichum orbiculare on both cucumber and Nicotiana benthamiana leaves. We conclude that MC69 is a secreted pathogenicity protein commonly required for infection of two different plant pathogenic fungi, M. oryzae and C. orbiculare pathogenic on monocot and dicot plants, respectively

Copy Number Variation and Transposable Elements Feature in Recent, Ongoing Adaptation at the Cyp6g1 Locus

The increased transcription of the Cyp6g1 gene of Drosophila melanogaster, and consequent resistance to insecticides such as DDT, is a widely cited example of adaptation mediated by cis-regulatory change. A fragment of an Accord transposable element inserted upstream of the Cyp6g1 gene is causally associated with resistance and has spread to high frequencies in populations around the world since the 1940s. Here we report the existence of a natural allelic series at this locus of D. melanogaster, involving copy number variation of Cyp6g1, and two additional transposable element insertions (a P and an HMS-Beagle). We provide evidence that this genetic variation underpins phenotypic variation, as the more derived the allele, the greater the level of DDT resistance. Tracking the spatial and temporal patterns of allele frequency changes indicates that the multiple steps of the allelic series are adaptive. Further, a DDT association study shows that the most resistant allele, Cyp6g1-[BP], is greatly enriched in the top 5% of the phenotypic distribution and accounts for ∼16% of the underlying phenotypic variation in resistance to DDT. In contrast, copy number variation for another candidate resistance gene, Cyp12d1, is not associated with resistance. Thus the Cyp6g1 locus is a major contributor to DDT resistance in field populations, and evolution at this locus features multiple adaptive steps occurring in rapid succession

Local selection in the presence of high levels of gene flow: Evidence of heterogeneous insecticide selection pressure across Ugandan Culex quinquefasciatus populations

Background: Culex quinquefasciatus collected in Uganda, where no vector control interventions directly targeting this species have been conducted, was used as a model to determine if it is possible to detect heterogeneities in selection pressure driven by insecticide application targeting other insect species. Methodology/Principal findings: Population genetic structure was assessed through microsatellite analysis, and the impact of insecticide pressure by genotyping two target-site mutations, Vgsc-1014F of the voltage-gated sodium channel target of pyrethroid and DDT insecticides, and Ace1-119S of the acetylcholinesterase gene, target of carbamate and organophosphate insecticides. No significant differences in genetic diversity were observed among populations by microsatellite markers with HE ranging from 0.597 to 0.612 and low, but significant, genetic differentiation among populations (FST = 0.019, P = 0.001). By contrast, the insecticide-resistance markers display heterogeneous allelic distributions with significant differences detected between Central Ugandan (urban) populations relative to Eastern and Southwestern (rural) populations. In the central region, a frequency of 62% for Vgsc-1014F, and 32% for the Ace1-119S resistant allele were observed. Conversely, in both Eastern and Southwestern regions the Vgsc-1014F alleles were close to fixation, whilst Ace1-119S allele frequency was 12% (although frequencies may be underestimated due to copy number variation at both loci). Conclusions/Significance: Taken together, the microsatellite and both insecticide resistance target-site markers provide evidence that in the face of intense gene flow among populations, disjunction in resistance frequencies arise due to intense local selection pressures despite an absence of insecticidal control interventions targeting Culex

The Indian cobra reference genome and transcriptome enables comprehensive identification of venom toxins

Snakebite envenoming is a serious and neglected tropical disease that kills ~100,000 people annually. High-quality, genome-enabled comprehensive characterization of toxin genes will facilitate development of effective humanized recombinant antivenom. We report a de novo near-chromosomal genome assembly of Naja naja, the Indian cobra, a highly venomous, medically important snake. Our assembly has a scaffold N50 of 223.35 Mb, with 19 scaffolds containing 95% of the genome. Of the 23,248 predicted protein-coding genes, 12,346 venom-gland-expressed genes constitute the \u27venom-ome\u27 and this included 139 genes from 33 toxin families. Among the 139 toxin genes were 19 \u27venom-ome-specific toxins\u27 (VSTs) that showed venom-gland-specific expression, and these probably encode the minimal core venom effector proteins. Synthetic venom reconstituted through recombinant VST expression will aid in the rapid development of safe and effective synthetic antivenom. Additionally, our genome could serve as a reference for snake genomes, support evolutionary studies and enable venom-driven drug discovery