Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques

One of the most important steps of document image processing is binarization. The computational requirements of locally adaptive binarization techniques make them unsuitable for devices with limited computing facilities. In this paper, we have presented a computationally efficient implementation of convolution based locally adaptive binarization techniques keeping the performance comparable to the original implementation. The computational complexity has been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the image size. Experiments over benchmark datasets show that the computation time has been reduced by 5 to 15 times depending on the window size while memory consumption remains the same with respect to the state-of-the-art algorithmic implementation

Quantum dot conjugated nanobodies for multiplex imaging of protein dynamics at synapses

Neurons communicate with each other through synapses, which show enrichment for specialized receptors. Although many studies have explored spatial enrichment and diffusion of these receptors in dissociated neurons using single particle tracking, much less is known about their dynamic properties at synapses in complex tissue like brain slices. Here we report the use of smaller and highly specific quantum dots conjugated with a recombinant single domain antibody fragment (VHH fragment) against green fluorescent protein to provide information on diffusion of adhesion molecules at the growth cone and neurotransmitter receptors at synapses. Our data reveals that QD-nanobodies can measure neurotransmitter receptor dynamics at both excitatory and inhibitory synapses in primary neuronal cultures as well as in ex vivo rat brain slices. We also demonstrate that this approach can be applied to tagging multiple proteins to simultaneously monitor their behavior. Thus, we provide a strategy for multiplex imaging of tagged membrane proteins to study their clustering, diffusion and transport both in vitro as well as in native tissue environments such as brain slices

Neuronal activity mediated regulation of glutamate transporter GLT-1 surface diffusion in rat astrocytes in dissociated and slice cultures.

The astrocytic GLT-1 (or EAAT2) is the major glutamate transporter for clearing synaptic glutamate. While the diffusion dynamics of neurotransmitter receptors at the neuronal surface are well understood, far less is known regarding the surface trafficking of transporters in subcellular domains of the astrocyte membrane. Here, we have used live-cell imaging to study the mechanisms regulating GLT-1 surface diffusion in astrocytes in dissociated and brain slice cultures. Using GFP-time lapse imaging, we show that GLT-1 forms stable clusters that are dispersed rapidly and reversibly upon glutamate treatment in a transporter activity-dependent manner. Fluorescence recovery after photobleaching and single particle tracking using quantum dots revealed that clustered GLT-1 is more stable than diffuse GLT-1 and that glutamate increases GLT-1 surface diffusion in the astrocyte membrane. Interestingly, the two main GLT-1 isoforms expressed in the brain, GLT-1a and GLT-1b, are both found to be stabilized opposed to synapses under basal conditions, with GLT-1b more so. GLT-1 surface mobility is increased in proximity to activated synapses and alterations of neuronal activity can bidirectionally modulate the dynamics of both GLT-1 isoforms. Altogether, these data reveal that astrocytic GLT-1 surface mobility, via its transport activity, is modulated during neuronal firing, which may be a key process for shaping glutamate clearance and glutamatergic synaptic transmission

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

DISC1-dependent Regulation of Mitochondrial Dynamics Controls the Morphogenesis of Complex Neuronal Dendrites

The DISC1 protein is implicated in major mental illnesses including schizophrenia, depression, bipolar disorder, and autism. Aberrant mitochondrial dynamics are also associated with major mental illness. DISC1 plays a role in mitochondrial transport in neuronal axons, but its effects in dendrites have yet to be studied. Further, the mechanisms of this regulation and its role in neuronal development and brain function are poorly understood. Here we have demonstrated that DISC1 couples to the mitochondrial transport and fusion machinery via interaction with the outer mitochondrial membrane GTPase proteins Miro1 and Miro2, the TRAK1 and TRAK2 mitochondrial trafficking adaptors, and the mitochondrial fusion proteins (mitofusins). Using live cell imaging, we show that disruption of the DISC1-Miro-TRAK complex inhibits mitochondrial transport in neurons. We also show that the fusion protein generated from the originally described DISC1 translocation (DISC1-Boymaw) localizes to the mitochondria, where it similarly disrupts mitochondrial dynamics. We also show by super resolution microscopy that DISC1 is localized to endoplasmic reticulum contact sites and that the DISC1-Boymaw fusion protein decreases the endoplasmic reticulum-mitochondria contact area. Moreover, disruption of mitochondrial dynamics by targeting the DISC1-Miro-TRAK complex or upon expression of the DISC1-Boymaw fusion protein impairs the correct development of neuronal dendrites. Thus, DISC1 acts as an important regulator of mitochondrial dynamics in both axons and dendrites to mediate the transport, fusion, and cross-talk of these organelles, and pathological DISC1 isoforms disrupt this critical function leading to abnormal neuronal development

Gaussian-weighted moving-window robust automatic threshold selection

A multi-scale, moving-window method for local thresholding based on Robust Automatic Threshold Selection (RATS) is developed. Using a model for the noise response of the optimal edge detector in this context, the reliability of thresholds computed at different scales is determined. The threshold computed at the smallest scale at which the reliability is suffcient is used. The performance on 2-D images is evaluated on synthetic an natural images in the presence of varying background and noise. Results show the method deals better with these problems than earlier versions of RATS at most noise levels

Stabilization of GABAA Receptors at Endocytic Zones Is Mediated by an AP2 Binding Motif within the GABAA Receptor β3 Subunit

The strength of synaptic inhibition can be controlled by the stability and endocytosis of surface and synaptic GABAA receptors (GABAARs), but the surface receptor dynamics that underpin GABAAR recruitment to dendritic endocytic zones (EZs) have not been investigated. Stabilization of GABAARs at EZs is likely to be regulated by receptor interactions with the clathrin-adaptor AP2, but the molecular determinants of these associations remain poorly understood. Moreover, although surface GABAAR downmodulation plays a key role in pathological disinhibition in conditions such as ischemia and epilepsy, whether this occurs in an AP2-dependent manner also remains unclear. Here we report the characterization of a novel motif containing three arginine residues (405RRR407) within the GABAAR β3-subunit intracellular domain (ICD), responsible for the interaction with AP2 and GABAAR internalization. When this motif is disrupted, binding to AP2 is abolished in vitro and in rat brain. Using single-particle tracking, we reveal that surface β3-subunit-containing GABAARs exhibit highly confined behavior at EZs, which is dependent on AP2 interactions via this motif. Reduced stabilization of mutant GABAARs at EZs correlates with their reduced endocytosis and increased steady-state levels at synapses. By imaging wild-type or mutant super-ecliptic pHluorin-tagged GABAARs in neurons, we also show that, under conditions of oxygen–glucose deprivation to mimic cerebral ischemia, GABAARs are depleted from synapses in dendrites, depending on the 405RRR407 motif. Thus, AP2 binding to an RRR motif in the GABAAR β3-subunit ICD regulates GABAAR residency time at EZs, steady- state synaptic receptor levels, and pathological loss of GABAARs from synapses during simulated ischemia

Impact of dislocations and dangling bond defects on the electrical performance of crystalline silicon thin films

A wide variety of liquid and solid phase crystallized silicon films are investigated in order to determine the performance limiting defect types in crystalline silicon thin-film solar cells. Complementary characterization methods, such as electron spin resonance, photoluminescence, and electron microscopy, yield the densities of dangling bond defects and dislocations which are correlated with the electronic material quality in terms of solar cell open circuit voltage. The results indicate that the strongly differing performance of small-grained solid and large-grain liquid phase crystallized silicon can be explained by intra-grain defects like dislocations rather than grain boundary dangling bonds. A numerical model is developed containing both defect types, dislocations and dangling bonds, describing the experimental results