An effective deep learning network for detecting and classifying glaucomatous eye

Glaucoma is a well-known complex disease of the optic nerve that gradually damages eyesight due to the increase of intraocular pressure inside the eyes. Among two types of glaucoma, open-angle glaucoma is mostly happened by high intraocular pressure and can damage the eyes temporarily or sometimes permanently, another one is angle-closure glaucoma. Therefore, being diagnosed in the early stage is necessary to safe our vision. There are several ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy but require time and expertise. Using deep learning approaches could be a better solution. This study focused on the recognition of open-angle affected eyes from the fundus images using deep learning techniques. The study evolved by applying VGG16, VGG19, and ResNet50 deep neural network architectures for classifying glaucoma positive and negative eyes. The experiment was executed on a public dataset collected from Kaggle; however, every model performed better after augmenting the dataset, and the accuracy was between 93% and 97.56%. Among the three models, VGG19 achieved the highest accuracy at 97.56%

Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM?

This paper challenges the existing victim-focused counter-based RowHammer detection mechanisms by experimentally demonstrating a novel multi-sided fault injection attack technique called Threshold Breaker. This mechanism can effectively bypass the most advanced counter-based defense mechanisms by soft-attacking the rows at a farther physical distance from the target rows. While no prior work has demonstrated the effect of such an attack, our work closes this gap by systematically testing 128 real commercial DDR4 DRAM products and reveals that the Threshold Breaker affects various chips from major DRAM manufacturers. As a case study, we compare the performance efficiency between our mechanism and a well-known double-sided attack by performing adversarial weight attacks on a modern Deep Neural Network (DNN). The results demonstrate that the Threshold Breaker can deliberately deplete the intelligence of the targeted DNN system while DRAM is fully protected.Comment: 7 pages, 6 figure

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models

Radiotherapy-Induced Cherenkov Luminescence Imaging in a Human Body Phantom

Radiation therapy produces Cherenkov optical emission in tissue, and this light can be utilized to activate molecular probes. The feasibility of sensing luminescence from a tissue molecular oxygen sensor from within a human body phantom was examined using the geometry of the axillary lymph node region. Detection of regions down to 30-mm deep was feasible with submillimeter spatial resolution with the total quantity of the phosphorescent sensor PtG4 near 1 nanomole. Radiation sheet scanning in an epi-illumination geometry provided optimal coverage, and maximum intensity projection images provided illustration of the concept. This work provides the preliminary information needed to attempt this type of imaging in vivo

Complete Genome Sequence of Crohn's Disease-Associated Adherent-Invasive E. coli Strain LF82

International audienceBACKGROUND: Ileal lesions of Crohn's disease (CD) patients are abnormally colonized by pathogenic adherent-invasive Escherichia coli (AIEC) able to invade and to replicate within intestinal epithelial cells and macrophages. PRINCIPAL FINDINGS: We report here the complete genome sequence of E. coli LF82, the reference strain of adherent-invasive E. coli associated with ileal Crohn's disease. The LF82 genome of 4,881,487 bp total size contains a circular chromosome with a size of 4,773,108 bp and a plasmid of 108,379 bp. The analysis of predicted coding sequences (CDSs) within the LF82 flexible genome indicated that this genome is close to the avian pathogenic strain APEC_01, meningitis-associated strain S88 and urinary-isolated strain UTI89 with regards to flexible genome and single nucleotide polymorphisms in various virulence factors. Interestingly, we observed that strains LF82 and UTI89 adhered at a similar level to Intestine-407 cells and that like LF82, APEC_01 and UTI89 were highly invasive. However, A1EC strain LF82 had an intermediate killer phenotype compared to APEC-01 and UTI89 and the LF82 genome does not harbour most of specific virulence genes from ExPEC. LF82 genome has evolved from those of ExPEC B2 strains by the acquisition of Salmonella and Yersinia isolated or clustered genes or CDSs located on pLF82 plasmid and at various loci on the chromosome. CONCLUSION: LF82 genome analysis indicated that a number of genes, gene clusters and pathoadaptative mutations which have been acquired may play a role in virulence of AIEC strain LF82

Perturbative moduli stabilization and string inflation

This Master thesis is focused on the study of cosmological implications of type IIB string compactifications. In particular we will consider brane-antibrane inflation which is one of the first and most-studied realizations of inflation in string theory. The model however suffers from two main issues: ($i$) the lack of a fully supersymmetric formulation of the effective field theory in terms of a K\"ahler potential and a superpotential; ($ii$) the infamous $\eta$-problem due to the non-perturbative stabilization of the volume modulus. We will follow the idea of Burgess and Quevedo which proposed a supersymmetric formulation of the effective action of brane-antibrane inflation together with a mechanism to stabilize the volume mode at perturbative level based on RG-effects. In these perturbative corrections are however just field theory-inspired without a proper string theory motivation. In this Master thesis we will put these perturbative corrections on more solid grounds by exploiting all known results in $\alpha'$ and string loop corrections to the effective action of type IIB string compactifications. In this way we will improve the results of Burgess and Quevedo finding a way to stabilize the moduli in brane-antibrane inflation just using known perturbative corrections. Our results open up the possibility to implement a hybrid inflation scenario where the volume mode can act as a waterfall field to end inflation, leading to a post-inflation minimum where the supersymmetry breaking scale is much lower than the inflationary scale

Development of Galectin-3 Targeting Drugs for Therapeutic Applications in Various Diseases

Galectin-3 (Gal3) is one of the most studied members of the galectin family that mediate various biological processes such as growth regulation, immune function, cancer metastasis, and apoptosis. Since Gal3 is pro-inflammatory, it is involved in many diseases that are associated with chronic inflammation such as cancer, organ fibrosis, and type 2 diabetes. As a multifunctional protein involved in multiple pathways of many diseases, Gal3 has generated significant interest in pharmaceutical industries. As a result, several Gal3-targeting therapeutic drugs are being developed to address unmet medical needs. Based on the PubMed search of Gal3 to date (1987–2023), here, we briefly describe its structure, carbohydrate-binding properties, endogenous ligands, and roles in various diseases. We also discuss its potential antagonists that are currently being investigated clinically or pre-clinically by the public and private companies. The updated knowledge on Gal3 function in various diseases could initiate new clinical or pre-clinical investigations to test therapeutic strategies, and some of these strategies could be successful and recognized as novel therapeutics for unmet medical needs

Phosphorylated Proteins from Serum: A Promising Potential Diagnostic Biomarker of Cancer

Cancer is a fatal disease worldwide. Each year ten million people are diagnosed around the world, and more than half of patients eventually die from it in many countries. A majority of cancer remains asymptomatic in the earlier stages, with specific symptoms appearing in the advanced stages when the chances of adequate treatment are low. Cancer screening is generally executed by different imaging techniques like ultrasonography (USG), mammography, CT-scan, and magnetic resonance imaging (MRI). Imaging techniques, however, fail to distinguish between cancerous and non-cancerous cells for early diagnosis. To confirm the imaging result, solid and liquid biopsies are done which have certain limitations such as invasive (in case of solid biopsy) or missed early diagnosis due to extremely low concentrations of circulating tumor DNA (in case of liquid biopsy). Therefore, it is essential to detect certain biomarkers by a noninvasive approach. One approach is a proteomic or glycoproteomic study which mostly identifies proteins and glycoproteins present in tissues and serum. Some of these studies are approved by the Food and Drug Administration (FDA). Another non-expensive and comparatively easier method to detect glycoprotein biomarkers is by ELISA, which uses lectins of diverse specificities. Several of the FDA approved proteins used as cancer biomarkers do not show optimal sensitivities for precise diagnosis of the diseases. In this regard, expression of phosphoproteins is associated with a more specific stage of a particular disease with high sensitivity and specificity. In this review, we discuss the expression of different serum phosphoproteins in various cancers. These phosphoproteins are detected either by phosphoprotein enrichment by immunoprecipitation using phosphospecific antibody and metal oxide affinity chromatography followed by LC-MS/MS or by 2D gel electrophoresis followed by MALDI-ToF/MS analysis. The updated knowledge on phosphorylated proteins in clinical samples from various cancer patients would help to develop these serum phophoproteins as potential diagnostic/prognostic biomarkers of cancer

DNN-Defender: An in-DRAM Deep Neural Network Defense Mechanism for Adversarial Weight Attack

With deep learning deployed in many security-sensitive areas, machine learning security is becoming progressively important. Recent studies demonstrate attackers can exploit system-level techniques exploiting the RowHammer vulnerability of DRAM to deterministically and precisely flip bits in Deep Neural Networks (DNN) model weights to affect inference accuracy. The existing defense mechanisms are software-based, such as weight reconstruction requiring expensive training overhead or performance degradation. On the other hand, generic hardware-based victim-/aggressor-focused mechanisms impose expensive hardware overheads and preserve the spatial connection between victim and aggressor rows. In this paper, we present the first DRAM-based victim-focused defense mechanism tailored for quantized DNNs, named DNN-Defender that leverages the potential of in-DRAM swapping to withstand the targeted bit-flip attacks. Our results indicate that DNN-Defender can deliver a high level of protection downgrading the performance of targeted RowHammer attacks to a random attack level. In addition, the proposed defense has no accuracy drop on CIFAR-10 and ImageNet datasets without requiring any software training or incurring additional hardware overhead.Comment: 10 pages, 11 figure

JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning

In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models' performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide