Effects of Osmotic Stress on DNA and Cell Viability in a Desiccation-Sensitive Cell Line

Kc167 is a widely used Drosophila cell line, known to be sensitive to the extreme water loss caused by desiccation. In order to characterize the effects of this desiccation-sensitivity on DNA and cell viability, a series of osmotic stressors of differing concentrations were introduced to the cell line. These cells were then imaged via the Cytation1 cell imaging machine using fluorescence microscopy. Specifically, cells were stained using the DAPI staining solution, a blue fluorescent DNA stain that binds strongly to A-T rich regions within the DNA, forming a fluorescent complex. As DAPI more readily enters the membrane and thereby stains dead cells, instances of apoptosis caused by osmotic stress on cells can be characterized by increasing intensity of fluorescence. Both sucrose and sodium chloride were used to simulate the water loss relevant to that of desiccation. This was done in concentrations of 100mM, 250mM, and 500mM for both sucrose and sodium chloride

Measurement of the reaction \gamma p \TO K^ + \Lambda(1520) at photon energies up to 2.65 GeV

The reaction \gamma p \TO K^+\Lambda(1520) was measured in the energy range from threshold to 2.65 GeV with the SAPHIR detector at the electron stretcher facility ELSA in Bonn. The $\Lambda(1520)$ production cross section was analyzed in the decay modes $pK^-$, $n \bar{K}^0$, $\Sigma^{\pm}\pi^{\mp}$, and $\Lambda\pi^+\pi^-$ as a function of the photon energy and the squared four-momentum transfer $t$. While the cross sections for the inclusive reactions rise steadily with energy, the cross section of the process \gamma p \TO K^+\Lambda(1520) peaks at a photon energy of about 2.0 GeV, falls off exponentially with $t$, and shows a slope flattening with increasing photon energy. The angular distributions in the $t$-channel helicity system indicate neither a $K$ nor a $K^\star$ exchange dominance. The interpretation of the $\Lambda(1520)$ as a $\Sigma(1385)\pi$ molecule is not supported.Comment: 11 pages, 16 figures, 4 table

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery

Predicting the Location of Glioma Recurrence After a Resection Surgery

International audienceWe propose a method for estimating the location of glioma recurrence after surgical resection. This method consists of a pipeline including the registration of images at different time points, the estimation of the tumor infiltration map, and the prediction of tumor regrowth using a reaction-diffusion model. A data set acquired on a patient with a low-grade glioma and post surgery MRIs is considered to evaluate the accuracy of the estimated recurrence locations found using our method. We observed good agreement in tumor volume prediction and qualitative matching in regrowth locations. Therefore, the proposed method seems adequate for modeling low-grade glioma recurrence. This tool could help clinicians anticipate tumor regrowth and better characterize the radiologically non-visible infiltrative extent of the tumor. Such information could pave the way for model-based personalization of treatment planning in a near future

Insecticide Resistance in Malaria Vectors: An Update at a Global Scale

Malaria remains the deadliest vector-borne disease in the world. With nearly half of the world’s population at risk, 216 million people suffered from malaria in 2016, with over 400,000 deaths, mainly in sub-Saharan Africa. Important global efforts have been made to eliminate malaria leading to significant reduction in malaria cases and mortality in Africa by 42% and 66%, respectively. Early diagnosis, improved drug therapies and better health infrastructure are key components, but this extraordinary success is mainly due the use of long-lasting insecticidal nets (LLINs) and indoor residual sprayings (IRS) of insecticide. Unfortunately, the emergence and spread of resistance in mosquito populations against insecticides is jeopardising the effectiveness of the most efficient malaria control interventions. To help establish suitable resistance management strategies, it is vital to better understand the distribution of resistance, its mechanisms and impact on effectiveness of control interventions and malaria transmission. In this chapter, we present the current status of insecticide resistance worldwide in main malaria vectors as well as its impact on malaria transmission, and discuss the molecular mechanisms and future perspectives

Multiple Insecticide Resistance in the Malaria Vector Anopheles funestus from Northern Cameroon Is Mediated by Metabolic Resistance Alongside Potential Target Site Insensitivity Mutations

Background Despite the recent progress in establishing the patterns of insecticide resistance in the major malaria vector Anopheles funestus, Central African populations of this species remain largely uncharacterised. To bridge this important gap and facilitate the implementation of suitable control strategies against this vector, we characterised the resistance patterns of An. funestus population from northern Cameroon. Methods and Findings Collection of indoor-resting female mosquitoes in Gounougou (northern Cameroon) in 2012 and 2015 revealed a predominance of An. funestus during dry season. WHO bioassays performed using F1 An. funestus revealed that the population was multiple resistant to several insecticide classes including pyrethroids (permethrin, deltamethrin, lambda-cyhalothrin and etofenprox), carbamates (bendiocarb) and organochlorines (DDT and dieldrin). However, a full susceptibility was observed against the organophosphate malathion. Bioassays performed with 2015 collection revealed that resistance against pyrethroids and DDT is increasing. PBO synergist assays revealed a significant recovery of susceptibility for all pyrethroids but less for DDT. Analysis of the polymorphism of a portion of the voltage-gated sodium channel gene (VGSC) revealed the absence of the L1014F/S kdr mutation but identified 3 novel amino acid changes I877L, V881L and A1007S. However, no association was established between VGSC polymorphism and pyrethroid/DDT resistance. The DDT resistant 119F-GSTe2 allele (52%) and the dieldrin resistant 296S-RDL allele (45%) were detected in Gounougou. Temporal analysis between 2006, 2012 and 2015 collections revealed that the 119F-GSTe2 allele was relatively stable whereas a significant decrease is observed for 296S-RDL allele. Conclusion This multiple resistance coupled with the temporal increased in resistance intensity highlights the need to take urgent measures to prolong the efficacy of current insecticide-based interventions against An. funestus in this African region

Automated claustrum segmentation in human brain MRI using deep learning

In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may be the delicate and sheet-like structure of the claustrum lying between the insular cortex and the putamen, which makes it not amenable to conventional segmentation methods. Recently, Deep Learning (DL) based approaches have been successfully introduced for automated segmentation of complex, subcortical brain structures. In the following, we present a multi-view DL-based approach to segment the claustrum in T1-weighted MRI scans. We trained and evaluated the proposed method in 181 individuals, using bilateral manual claustrum annotations by an expert neuroradiologist as reference standard. Cross-validation experiments yielded median volumetric similarity, robust Hausdorff distance, and Dice score of 93.3%, 1.41 mm, and 71.8%, respectively, representing equal or superior segmentation performance compared to human intra-rater reliability. The leave-one-scanner-out evaluation showed good transferability of the algorithm to images from unseen scanners at slightly inferior performance. Furthermore, we found that DL-based claustrum segmentation benefits from multi-view information and requires a sample size of around 75 MRI scans in the training set. We conclude that the developed algorithm allows for robust automated claustrum segmentation and thus yields considerable potential for facilitating MRI-based research of the human claustrum. The software and models of our method are made publicly available