Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain

Human spermbots for patient-representative 3D ovarian cancer cell treatment

Cellular micromotors are attractive for locally delivering high concentrations of drug, and targeting hard-to-reach disease sites such as cervical cancer and early ovarian cancer lesions by non-invasive means. Spermatozoa are highly efficient micromotors perfectly adapted to traveling up the female reproductive system. Indeed, bovine sperm-based micromotors have shown potential to carry drugs toward gynecological cancers. However, due to major differences in the molecular make-up of bovine and human sperm, a key translational bottleneck for bringing this technology closer to the clinic is to transfer this concept to human material. Here, we successfully load human sperm with Doxorubicin (DOX) and perform treatment of 3D cervical cancer and patient-representative ovarian cancer cell cultures, resulting in strong anticancer cell effects. Additionally, we define the subcellular localization of the chemotherapeutic drug within human sperm, using high-resolution optical microscopy. We also assess drug effects on sperm motility and viability over time, employing sperm samples from healthy donors as well as assisted reproduction patients. Finally, we demonstrate guidance and release of human drug-loaded sperm onto cancer tissues using magnetic microcaps, and show the sperm microcap loaded with a second anticancer drug, camptothecin (CPT), which unlike DOX is not suitable for directly loading into sperm due to its hydrophobic nature. This co-drug delivery approach opens up novel targeted combinatorial drug therapies for future applications. © 2020 The Royal Society of Chemistry

Predicting Kinetochore Localization Using Pix2Pix Image Translation of Spindle Pole Body Foci

Recent advances in machine learning promise to revolutionize biological investigations by creating new methods for image analysis and generation. Deep learning models known as generative adversarial networks (GANs) have shown enormous promise in scientific applications, such as through super-resolution GANs that can artificially upscale image quality. However, GANs have not been applied extensively to predictive image modeling of cellular structures, such as the kinetochore. The kinetochore is the protein complex where spindle microtubules attach during cell division and is crucial in ensuring proper chromosome segregation. Here, we examine whether the Pix2Pix architecture, a type of GAN designed for paired-image translation, can be repurposed to generate predictive fluorescent microscopy images that can localize kinetochore proteins in the budding yeast S. cerevisiae. As a proof-of-concept, this architecture’s ability to create generated images that matched the ground truth based only on synthetic input images of a different microscopy channel was evaluated. Subsequently, this architecture’s robustness was evaluated through its performance on synthetic images with added noise, and then on actual microscopy images of the kinetochore. By comparing generated images with their ground truth targets on various metrics, we find that this architecture is well-suited to generative image modeling of the kinetochore, despite the loss of some fine detail mapping in later tests. Further refinements can improve the network’s accuracy and extend its applicability to other kinetochore protein complexes and during different phases of cell division. The development of predictive image modeling architectures such as the Pix2Pix can elucidate the spatial and temporal localization of pathological errors in cell division that result from kinetochore dysfunction, informing further investigations into the causes of human diseases such as cancer.Bachelor of Scienc

AI-Assisted Forward Modeling of Biological Structures

The rise of machine learning and deep learning technologies have allowed researchers to automate image classification. We describe a method that incorporates automated image classification and principal component analysis to evaluate computational models of biological structures. We use a computational model of the kinetochore to demonstrate our artificial-intelligence (AI)-assisted modeling method. The kinetochore is a large protein complex that connects chromosomes to the mitotic spindle to facilitate proper cell division. The kinetochore can be divided into two regions: the inner kinetochore, including proteins that interact with DNA; and the outer kinetochore, comprised of microtubule-binding proteins. These two kinetochore regions have been shown to have different distributions during metaphase in live budding yeast and therefore act as a test case for our forward modeling technique. We find that a simple convolutional neural net (CNN) can correctly classify fluorescent images of inner and outer kinetochore proteins and show a CNN trained on simulated, fluorescent images can detect difference in experimental images. A polymer model of the ribosomal DNA locus serves as a second test for the method. The nucleolus surrounds the ribosomal DNA locus and appears amorphous in live-cell, fluorescent microscopy experiments in budding yeast, making detection of morphological changes challenging. We show a simple CNN can detect subtle differences in simulated images of the ribosomal DNA locus, demonstrating our CNN-based classification technique can be used on a variety of biological structures

Single-Cell Analysis of Experience-Dependent Transcriptomic States in Mouse Visual Cortex

Activity-dependent transcriptional responses shape cortical function. However, we lack a comprehensive understanding of the diversity of these responses across the full range of cortical cell types, and how these changes contribute to neuronal plasticity and disease. Here we applied high-throughput single-cell RNA-sequencing to investigate the breadth of transcriptional changes that occur across cell types in mouse visual cortex following exposure to light. We identified significant and divergent transcriptional responses to stimulation in each of the 30 cell types characterized, revealing 611 stimulus-responsive genes. Excitatory pyramidal neurons exhibit inter- and intra-laminar heterogeneity in the induction of stimulus responsive genes. Non-neuronal cells demonstrated clear transcriptional responses that may regulate experience-dependent changes in neurovascular coupling and myelination. Together, these results reveal the dynamic landscape of stimulus-dependent transcriptional changes that occur across cell types in visual cortex, which are likely critical for cortical function and may be sites of de-regulation in developmental brain disorders

Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease.

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this programme is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P = 2.4 × 10-22) and are associated with the polygenic diseases osteoporosis (P = 1.8 × 10-13) and osteoarthritis (P = 1.6 × 10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease

CETP and Inflammation in lipid metabolism and atherosclerosis

The studies described in this thesis show that inflammation and CETP are both important factors in lipid metabolism and atherosclerosis. In the first part of this thesis we showed that high dietary cholesterol can induce hepatic inflammation via disturbed cholesterol homeostasis and ER stress, revealing new targets for the treatment of metabolic inflammation. Next, we demonstrated that intervention in both systemic and vascular inflammation can reduce atherosclerosis progression and/ or induce regression, highlighting the importance of developing drugs targeting the inflammatory component of atherosclerotic disease. In the second part of this thesis we showed that CETP inhibition per se may be anti-atherogenic, but that combination therapy of the CETP inhibitor torcetrapib with atorvastatin may have obscured its atheroprotective effect. Furthermore, we showed that the VLDL-increasing effect of CETP largely explains its atherogenic effect, at least in APOE*3-Leiden.CETP mice, and that CETP inhibition may negatively affect lesion stability. Our data suggest that CETP inhibition may not be the most optimal strategy to increase HDL-C levels and thereby reduce atherosclerosis. We anticipate that strategies improving HDL functionality, rather than raising the HDL level, are more likely to effectively reduce CVD.UBL - phd migration 201

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period