Neural Architecture Search (NAS) is the process of automating the design of neural network architectures for a given task. Although NAS automates the process of finding suitable neural network ...architectures for a specific task, the existing NAS algorithms are immensely time-consuming. The main bottleneck in NAS algorithms is the training time for each architecture. This study proposes an Improved Grey Wolf Optimization based on Synaptic Saliency (IGWO-SS), which is much faster than the existing NAS algorithms and provides better final performance. The IGWO-SS algorithm skips training the less promising architectures by creating a relative rank between the architectures based on synaptic saliency. The architectures that are lower in rank are considered less promising than those that are higher in rank. Since the calculation of synaptic saliency is a very fast process, a significant amount of time is saved by skipping training of less promising architectures. This study involves extensive experiments assessing synaptic saliency's effectiveness in improving NAS. The experimental results indicate that the synaptic saliency of an untrained neural network positively correlates with its final accuracy. Hence, it can be used to identify untrained promising neural networks. The experimental results also suggest that the IGWO-SS algorithm is almost <inline-formula> <tex-math notation="LaTeX">10x </tex-math></inline-formula> faster and achieves better final performance than five other bio-inspired algorithms. The IGWO-SS algorithm achieves higher mean accuracy than state-of-the-art NAS algorithms, including - REA, RS, RL, BOHB, DARTSV1, DARTSV2, GDAS, SETN, and ENAS. We hope our work will make NAS more accessible and useful to researchers by reducing the time and resources required to perform NAS.
Remyelination failure in diseases like multiple sclerosis (MS) was thought to involve suppressed maturation of oligodendrocyte precursors; however, oligodendrocytes are present in MS lesions yet lack ...myelin production. We found that oligodendrocytes in the lesions are epigenetically silenced. Developing a transgenic reporter labeling differentiated oligodendrocytes for phenotypic screening, we identified a small-molecule epigenetic-silencing-inhibitor (ESI1) that enhances myelin production and ensheathment. ESI1 promotes remyelination in animal models of demyelination and enables de novo myelinogenesis on regenerated CNS axons. ESI1 treatment lengthened myelin sheaths in human iPSC-derived organoids and augmented (re)myelination in aged mice while reversing age-related cognitive decline. Multi-omics revealed that ESI1 induces an active chromatin landscape that activates myelinogenic pathways and reprograms metabolism. Notably, ESI1 triggered nuclear condensate formation of master lipid-metabolic regulators SREBP1/2, concentrating transcriptional co-activators to drive lipid/cholesterol biosynthesis. Our study highlights the potential of targeting epigenetic silencing to enable CNS myelin regeneration in demyelinating diseases and aging.
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•An epigenetic barrier impedes myelin production in multiple sclerosis lesions•Small-molecule inhibitor ESI1 promotes CNS myelin production and regeneration•ESI1 enhances (re)myelination in aged mice while reversing cognitive decline•ESI1-induced active chromatin landscape and SREBP condensation boost myelinogenesis
Oligodendrocytes in multiple sclerosis lesions exhibit epigenetic silencing that precludes myelin restoration. This study identifies a small-molecule inhibitor that counteracts epigenetic silencing, effectively enhancing myelin production and thereby promoting (re)myelination in animal models and human organoids while reversing age-related cognitive decline.
The tyrosine phosphatase PTPROt is a suggested tumor suppressor (TS) in B-cell chronic lymphocytic leukemia (CLL), and its expression is reduced in this disease. In order to examine how reduced ...PTPROt expression affects CLL in vivo we induced CLL in PTPROt-targeted mice. Unexpectedly, loss of both Ptprot alleles delayed disease detection and progression and lengthened survival relative to mice carrying two intact alleles, indicating that PTPROt fulfills a novel tumor-promoting role in CLL. Tumor cells from mice lacking PTPROt exhibited reduced B-cell receptor (BCR)-induced signaling, as well as increased apoptosis and autophagy. Inhibition of BCR/Src signaling in CLL cells induced their apoptosis, indicating that these findings are linked causally. These results suggest a cell-autonomous mechanism for the weakened CLL phenotype of PTPROt-deficient mice and uncover non-redundant roles for PTPROt in support of BCR signaling and survival of CLL cells. In contrast, loss of only one Ptprot allele induced earlier detection and progression of CLL and reduced survival, consistent with a tumor-suppressing role for PTPROt. Tumor cells from mice lacking one or both Ptprot allele exhibited increased interleukin-10 (IL-10) expression and signaling, factors known to support CLL; cells lacking one Ptprot alleles exhibited normal BCR signaling and cell death rates. We conclude that loss of one Ptprot allele promotes CLL, most likely by activating IL-10 signaling. Loss of both Ptprot alleles also reduces BCR signaling and increases cell death rates, offsetting the IL-10 effects and reducing the severity of the disease. PTPROt thus functions as an obligate haploinsufficient TS in CLL, where its expression levels determine its role as a promoter or inhibitor of the tumorigenic process in mice. Partial loss of PTPROt generates the strongest disease phenotype, suggesting that its intermediate expression levels in CLL are selected for.
Bananas, one of the most widely consumed fruits globally, are highly susceptible to various leaf spot diseases, leading to significant economic losses in banana production. In this article, we ...present the Banana Leaf Spot Diseases (BananaLSD) dataset, an extensive collection of images showcasing three prevalent diseases affecting banana leaves: Sigatoka, Cordana, and Pestalotiopsis. The dataset was used to develop the BananaSqueezeNet model 1. The BananaLSD dataset contains 937 images of banana leaves collected from banana fields, which were then further augmented to generate another 1600 images. The images were acquired using three smartphone cameras in diverse real-world conditions. The dataset has potential for reuse in the development of machine learning models that can help farmers identify symptoms early. It can be useful for researchers working on leaf spot diseases and serve as motivation for further researches.
Abstract
Background
Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study’s purpose was ...to compare volumetric to 2D methods.
Methods
An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images.
Results
There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15–25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD.
Conclusions
Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.
Intracranial hemorrhage is a medical condition that involves bleeding within the skull or brain tissue. It has mainly five subtypes - epidural hemorrhage, subdural hemorrhage, subarachnoid ...hemorrhage, intraparenchymal hemorrhage, and intraventricular hemorrhage. In order to ensure a successful outcome for a patient, a timely and accurate detection of intracranial hemorrhage is crucial. Despite this, there is a shortage of radiologists, especially in rural areas, which can lead to a delay in diagnosis. In this work, we proposed an automatic way of diagnosing intracranial hemorrhage from a CT scan. We have optimized the DenseNet architecture using Bayesian Optimization (BO) to diagnose intracranial hemorrhage effectively. Using BO, we identified the optimal learning rate, optimizer, and number of dense nodes for the DenseNet architecture. Our proposed model can analyse whether hemorrhage is present in a CT scan and, if it is present, what its subtype is. The optimized DenseNet model was able to achieve a very high accuracy of 98.02% on the test set. By ensuring accurate and reliable diagnoses, our method will assist doctors in making better-informed decisions and providing better care for their patients.
All over the world, bananas are one of the most common fruits. It accounts for nearly 16% of global fruit production. However, every year, a large amount of banana yield losses occur due to different ...diseases of the banana leaf. It is essential to identify these diseases at an early stage in order to increase banana production. A visual inspection is the most common method of identifying banana leaf diseases. With a visual inspection, errors are common, time is a factor, and expertise is required. This study shows how deep learning and Bayesian optimization can be used to effectively diagnose banana leaf diseases from images without any human intervention. We collected the Banana Leaf Spot Diseases (BananaLSD) dataset from various locations in Bangladesh. The dataset consists of images of three banana leaf diseases: Pestalotiopsis, Sigatoka, and Cordana. Our proposed BananaSqueezeNet model performed exceptionally well in diagnosing banana leaf diseases from the images with an overall accuracy of 96.25%, precision of 96.53%, recall of 96.25%, specificity of 98.75%, F1-score of 96.17%, and MCC of 95.13%. The BananaSqueezeNet model outperforms some state-of-the-art convolutional neural networks that include EfficientNetB0, MobileNetV3, ResNet-101, ResNet-50, InceptionNet-V3, and VGG16. The BananaSqueezeNet model also detected seven other diseases that affect banana leaves, fruits, and stems, including banana fruit scarring beetle, black sigatoka, bacterial soft rot, pseudo stem weevil, yellow sigatoka, banana aphids, and panama disease, with an accuracy of 95.13%. BananaSqueezeNet will enable banana growers to detect banana diseases early, and we hope that it will ultimately lead to an increase in banana production in Bangladesh and around the world.
Coronary artery disease (CAD) is common in patients with cirrhosis who underwent orthotopic liver transplantation (OLT) evaluation and stress echocardiogram (echo) has a low sensitivity in these ...patients. This study aimed to assess the impact of vascular and valvular calcification on the ability to identify CAD before OLT. We performed a case-control study of 88 patients with and 97 without obstructive CAD who underwent OLT evaluation. All patients had a preoperative stress echo, abdominal computed tomography, and cardiac catheterization. A series of nested logistic regression models of CAD were fit by adding independent variables of vascular (including coronary) calcification, aortic and mitral valve calcification, age, gender, and history of diabetes mellitus requiring insulin to a baseline model of abnormal stress echo. Compared with stress echo alone, identification of the presence or absence of vascular and valvular calcification on routine preoperative computed tomography and echo improved the diagnostic performance for the detection of CAD based on coronary angiogram when combined with stress echo in patients with cirrhosis who underwent OLT evaluation (area under the curve 0.58 vs 0.73, p <0.001), which is even further improved when age, gender, and history of diabetes mellitus requiring insulin are considered (area under the curve 0.58 vs 0.80, p <0.001). Achieving target heart rate (p = 0.92) or rate–pressure product >25,000 (p = 0.63) did not improve the ability of stress echo to identify CAD. In conclusion, the use of abdominal vascular, coronary artery, and valvular calcification, along with stress echo, improves the ability to identify and rule out obstructive CAD before OLT compared with stress echo alone.
Danesh F, Vahid A, Jahanbani J, Mashhadiabbas F, Arman E. Effect of white mineral trioxide aggregate compared with biomimetic carbonated apatite on dentine bridge formation and inflammatory response ...in a dental pulp model. International Endodontic Journal, 45, 26–34, 2012.
Aim To evaluate the effects of apatite precipitation on the biocompatibility and hard tissue induction properties of white mineral trioxide aggregate (WMTA) in a dental pulp model.
Methodology Pulp exposures were created on the axial walls of 32 sound canine teeth of eight dogs. Four additional sound teeth served as controls. The pulps were capped either with WMTA or apatite derivatives biomimetic carbonated apatite (BCAp) in the interaction of WMTA with a synthetic tissue fluid and restored with zinc oxide–eugenol cement. After 7 and 70 days, the animals were killed, and the histological specimens taken from the teeth were stained with haematoxylin and eosin for histomorphological evaluation. The Brown and Brenn technique was employed to stain bacteria. The data were subjected to nonparametric Kruskall–Wallis analysis and Mann–Whitney U_tests.
Results Biomimetic carbonated apatite did not induce hard tissue bridge formation. WMTA performed significantly better than BCAp in this respect at both periods (P < 0.05). BCAp was associated with a significantly greater inflammatory response as compared with WMTA after 7 days (P < 0.05). Both materials were associated with similar reactions after 70 days (P > 0.05).
Conclusions White mineral trioxide aggregate induced hard tissue formation via a mechanism other than that postulated via apatite formation.
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