Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

Radiomics for differentiation of gliomas from primary central nervous system lymphomas: a systematic review and meta-analysis

Background and objective

Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms.

Methods

A systematic review of the literature and a meta-analysis were conducted, encompassing 12 studies and a total of 1779 patients, focusing on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test.

Results

The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 - 0.91) and 87% (95% CI = 0.83 - 0.91), respectively. The best-performing features were the ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (ACC 87% - 95%). Most studies applied a cross-validation analysis (92%).

Conclusion

The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used.

A Mobile App for Detecting Potato Crop Diseases

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.7%, with MobileNetv2. Based on the results obtained, an offline mobile application was developed, supported on devices with Android 4.1 or later, also featuring an information section on the 27 diseases affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of diseases affecting the same plant.

Discovering potential inhibitors of Raf proto-oncogene serine/threonine kinase 1: a virtual screening approach towards anticancer drug development

Raf proto-oncogene serine/threonine kinase 1 (RAF1 or c-Raf) is a serine/threonine protein kinase crucial in regulating cell growth, differentiation, and survival. Any disruption or overexpression of RAF1 can result in neoplastic transformation and other disorders such as cardiomyopathy, Noonan syndrome, leopard syndrome, etc. RAF1 has been identified as a potential therapeutic target in drug development against various complex diseases, including cancer, due to its remarkable role in disease progression. Here, we carried out a multitier virtual screening study involving different in-silico approaches to discover potential inhibitors of RAF1. After applying the Lipinski rule of five, we retrieved all phytocompounds from the IMPPAT database based on their physicochemical properties. We performed a molecular docking-based virtual screening and got top hits with the best binding affinity and ligand efficiency. Then we screened out the selected hits using the PAINS filter, ADMET properties, and other druglike features. Eventually, PASS evaluation identifies two phytocompounds, Moracin C and Tectochrysin, with appreciable anti-cancerous properties. Finally, all-atom molecular dynamics simulation (MDS) followed by interaction analysis was performed on the elucidated compounds in complex with RAF1 for 200 ns to investigate their time-evolution dynamics and interaction mechanism. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and Dynamical Cross-Correlation Matrix (DCCM) analyses then followed these results from the simulated trajectories. According to the results, the elucidated compounds stabilize the RAF1 structure and lead to fewer conformational alterations. The results of the current study indicated that Moracin C and Tectochrysin could serve as potential inhibitors of RAF1 after required validation.Communicated by Ramaswamy H. Sarma.

Automated deep learning model for estimating intraoperative blood loss using gauze images

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze's overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images

AIM

To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages, limitations, and possible solutions common to all tasks.

METHODS

We searched three academic databases, including PubMed, Web of Science, and Ovid, with the date of August 2022. We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords, of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.

RESULTS

Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance, including diabetic retinopathy, glaucoma, age-related macular degeneration, retinal vein occlusions, retinal detachment, and other peripheral retinal diseases. Compared to fundus images, the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200° in a single exposure, which can observe more areas of the retina.

CONCLUSION

The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

Genome-wide characterization of SDR gene family and its potential role in seed dormancy of Brassica napus L

Rapeseed (Brassica napus L.) with short or no dormancy period are easy to germinate before harvest (pre-harvest sprouting, PHS). PHS has seriously decreased seed weight and oil content in B. napus. Short-chain dehydrogenase/ reductase (SDR) genes have been found to related to seed dormancy by promoting ABA biosynthesis in rice and Arabidopsis. In order to clarify whether SDR genes are the key factor of seed dormancy in B. napus, homology sequence blast, protein physicochemical properties, conserved motif, gene structure, cis-acting element, gene expression and variation analysis were conducted in present study. Results shown that 142 BnaSDR genes, unevenly distributed on 19 chromosomes, have been identified in B. napus genome. Among them, four BnaSDR gene clusters present in chromosome A04、A05、C03、C04 were also identified. These 142 BnaSDR genes were divided into four subfamilies on phylogenetic tree. Members of the same subgroup have similar protein characters, conserved motifs, gene structure, cis-acting elements and tissue expression profiles. Specially, the expression levels of genes in subgroup A, B and C were gradually decreased, but increased in subgroup D with the development of seeds. Among seven higher expressed genes in group D, six BnaSDR genes were significantly higher expressed in weak dormancy line than that in nondormancy line. And the significant effects of BnaC01T0313900ZS and BnaC03T0300500ZS variation on seed dormancy were also demonstrated in present study. These findings provide a key information for investigating the function of BnaSDRs on seed dormancy in B. napus.

The Fe-S cluster biosynthesis in Enterococcus faecium is essential for anaerobic growth and gastrointestinal colonization

The facultative anaerobic Gram-positive bacterium Enterococcus faecium is a ubiquitous member of the human gut microbiota. However, it has gradually evolved into a pathogenic and multidrug resistant lineage that causes nosocomial infections. The establishment of high-level intestinal colonization by enterococci represents a critical step of infection. The majority of current research on Enterococcus has been conducted under aerobic conditions, while limited attention has been given to its physiological characteristics in anaerobic environments, which reflects its natural colonization niche in the gut. In this study, a high-density transposon mutant library containing 26,620 distinct insertion sites was constructed. Tn-seq analysis identified six genes that significantly contribute to growth under anaerobic conditions. Under anaerobic conditions, deletion of sufB (encoding Fe-S cluster assembly protein B) results in more extensive and significant impairments on carbohydrate metabolism compared to aerobic conditions. Consistently, the pathways involved in this utilization-restricted carbohydrates were mostly expressed at significantly lower levels in mutant compared to wild-type under anaerobic conditions. Moreover, deletion of sufB or pflA (encoding pyruvate formate lyase-activating protein A) led to failure of gastrointestinal colonization in mice. These findings contribute to our understanding of the mechanisms by which E. faecium maintains proliferation under anaerobic conditions and establishes colonization in the gut.

A comprehensive review of discovery and development of drugs discovered from 2020-2022

To fully evaluate and define the new drug molecule for its pharmacological characteristics and toxicity profile, pre-clinical and clinical studies are conducted as part of the drug research and development process. The average time required for all drug development processes to finish various regulatory evaluations ranges from 11.4 to 13.5 years, and the expense of drug development is rising quickly. The development in the discovery of newer novel treatments is, however, largely due to the growing need for new medications. Methods to identify Hits and discovery of lead compounds along with pre-clinical studies have advanced, and one example is the introduction of computer-aided drug design (CADD), which has greatly shortened the time needed for the drug to go through the drug discovery phases. The pharmaceutical industry will hopefully be able to address the present and future issues and will continue to produce novel molecular entities (NMEs) to satisfy the expanding unmet medical requirements of the patients as the success rate of the drug development processes is increasing. Several heterocyclic moieties have been developed and tested against many targets and proved to be very effective. In-depth discussion of the drug design approaches of newly found drugs from 2020 to 2022, including their pharmacokinetic and pharmacodynamic profiles and in-vitro and in-vivo assessments, is the main goal of this review. Considering the many stages these drugs are going through in their clinical trials, this investigation is especially pertinent. It should be noted that synthetic strategies are not discussed in this review; instead, they will be in a future publication.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Complementary use of visual and olfactory cues to assess capture of Bactrocera dorsalis (Hendel): Implementation and field verification via an IoT-based automatic monitoring system

This study examined the effect of combining visual and olfactory cues to attract oriental fruit flies (OFFs). Six different colored light-emitting diodes (LEDs) served as a visual attractant and methyl eugenol served as olfactory bait to lure male flies. An internet of things (IoT)-based pest monitoring system, consisting of sensor nodes, a gateway, and automatic counting traps, was deployed in the field to automatically collect environmental data and pest counts. The results of the calibrated experiments indicated that green, yellow, or red LEDs exhibited better performance in attracting flies than white, purple, or blue LEDs or no LEDs. With an accurate combination of visual and olfactory cues, the proposed IoT-based pest monitoring system may be an effective tool in agricultural pest management, given its advantages for efficiently capturing OFFs in a labor and time saving manner, providing accurate information regarding increases in pest populations, and enabling long-term, real-time data collection.

Wireless Sensor Networks for Precision Agriculture: A Review of NPK Sensor Implementations

The integration of Wireless Sensor Networks (WSNs) into agricultural areas has had a significant impact and has provided new, more complex, efficient, and structured solutions for enhancing crop production. This study reviews the role of Wireless Sensor Networks (WSNs) in monitoring the macronutrient content of plants. This review study focuses on identifying the types of sensors used to measure macronutrients, determining sensor placement within agricultural areas, implementing wireless technology for sensor communication, and selecting device transmission intervals and ratings. The study of NPK (nitrogen, phosphorus, potassium) monitoring using sensor technology in precision agriculture is of high significance in efforts to improve agricultural productivity and efficiency. Incorporating Wireless Sensor Networks (WSNs) into the ongoing progress of proposed sensor node placement design has been a significant facet of this study. Meanwhile, the assessment based on soil samples analyzed for macronutrient content, conducted directly in relation to the comparison between the NPK sensors deployed in this research and the laboratory control sensors, reveals an error rate of 8.47% and can be deemed as a relatively satisfactory outcome. In addition to fostering technological innovations and precision farming solutions, in future this research aims to increase agricultural yields, particularly by enabling the cultivation of certain crops in locations different from their original ones.

Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion

This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning machine method. By providing abundant machining information, the machining parameters and force signal data are fused on the feature level to further improve ELM prediction accuracy. An ultra-precision milling experiment was designed and conducted to verify our proposed data-fusion-based ELM method. The results show that the ELM with data fusion outperforms other state-of-art methods in surface roughness prediction. It achieves an impressively low mean absolute percentage error of 1.6% while requiring a mere 18 s for model training.

A Novel Data Augmentation Method Based on CoralGAN for Prediction of Part Surface Roughness

Deep learning networks can be applied to the field of intelligent prediction of part surface roughness. However, the surface roughness samples of parts have the problems of high collection cost, unbalanced categories, and complicated data distribution, which inevitably limit the application of deep learning network models in the field of intelligent prediction of part surface roughness. To solve these problems, this article proposes a novel data augmentation method based on CoralGAN for prediction of part surface roughness, which introduces the domain adaptive method deep coral function to help optimize the network parameters of the generator of generative adversarial network (GAN). Specifically, the vibration signal collected during processing is converted into frequency spectrum data and input into CoralGAN. The training of the generator is guided by coral loss, that is, the distance between the covariances of the real samples and generated samples features, not just the statistical consistency of the traditional GAN. Experiments have been carried out on the three-axis vertical machining center. Research shows that the proposed method can improve the prediction accuracy of part surface roughness to 95.5%.

Miraculin-based sweeteners in the protein-engineering era: an alternative for developing more efficient and safer products

The current sweeteners available are very efficient in providing sweet taste. However, they are associated with several chronic diseases. Some glycoproteins, such as miraculins, are extremely interesting from a biotechnological point of view because they perform the bitter into sweet taste modifying function excellently, in addition to being safer as food. In contrast, purifying and synthesizing these proteins represents a major challenge for the food industry, as these proteins are large and complex molecules, which would make the final product expensive and economically unviable. In this context, emerging techniques from computational biology and molecular modelling have been promoting a remarkable revolution in protein bioengineering. Bioinspired peptides can provide many possibilities in sweeteners development through rational design. Once these peptides are smaller molecules than an entire protein, its synthesis on a large scale tends to be much easier and more economical, besides presenting a potential for better bioavailability in the organism. The techniques discussed here allow, through sophisticated pipelines and algorithms, to perform the rational design of mimetic peptides and with smaller size, which can carry out the activation of sweet taste of miraculins and to be more viable for industrial production. In this review, the premises and tools for the elaboration of synthetic peptides bioinspired in proteins with sweetening activity that mimic this action will be emphasized.Communicated by Ramaswamy H. Sarma.

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

OCT-based deep-learning models for the identification of retinal key signs

A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice. Images from the local hospital database were retrospectively selected from 2017 to 2022. Images were labeled by two retinal specialists and included central fovea cross-section OCTs. Nine models were developed using the Visual Geometry Group 16 architecture to distinguish healthy versus abnormal retinas and to identify eight different retinal abnormality signs. A total of 21,500 OCT images were screened, and 10,770 central fovea cross-section OCTs were included in the study. The system achieved high accuracy in identifying healthy retinas and specific pathological signs, ranging from 93 to 99%. Accurately detecting abnormal retinal signs from OCT images is crucial for patient care. This study aimed to identify specific signs related to retinal pathologies, aiding ophthalmologists in diagnosis. The high-accuracy system identified healthy retinas and pathological signs, making it a useful diagnostic aid. Labelled OCT images remain a challenge, but our approach reduces dataset creation time and shows DL models' potential to improve ocular pathology diagnosis and clinical decision-making.

Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D'Ivoire

Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d'Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network.

New lead compounds identification against KRas mediated cancers through pharmacophore-based virtual screening and in vitro assays

Cancer remains the leading cause of mortality and morbidity in the world, with 19.3 million new diagnoses and 10.1 million deaths in 2020. Cancer is caused due to mutations in proto-oncogenes and tumor-suppressor genes. Genetic analyses found that Ras (Rat sarcoma) is one of the most deregulated oncogenes in human cancers. The Ras oncogene family members including NRas (Neuroblastoma ras viral oncogene homolog), HRas (Harvey rat sarcoma) and KRas are involved in different types of human cancers. The mutant KRas is considered as the most frequent oncogene implicated in the development of lung, pancreatic and colon cancers. However, there is no efficient clinical drug even though it has been identified as an oncogene for 30 years. Therefore there is an emerging need to develop potent, new anticancer drugs. In this study, computer-aided drug designing approaches as well as experimental methods were employed to find new and potential anti-cancer drugs. The pharmacophore model was developed from an already known FDA approved anti-cancer drug Bortezomib using the software MOE. The validated pharmacophore model was then used to screen the in-house and commercially available databases. The pharmacophore-based virtual screening resulted in 26 and 86 hits from in-house and commercial databases respectively. Finally, 6/13 (in-house database) and 24/64 hits (commercial databases) were selected with different scaffolds having good interactions with the significant active residues of KRasG12D protein that were predicted as potent lead compounds. Finally, the results of pharmacophore-based virtual screening were further validated by molecular dynamics simulation analysis. The 6 hits of the in-house database were further evaluated experimentally. The experimental results showed that these compounds have good anti-cancer activity which validate the protocol of our in silico studies. KRasG12D protein is a very important anti-cancer target and potent inhibitors for this target are still not available, so small lead compound inhibitors were identified to inhibit the activity of this protein by blocking the GTP-binding pocket.Communicated by Ramaswamy H. Sarma.

A Deep Recurrent Learning-Based Region-Focused Feature Detection for Enhanced Target Detection in Multi-Object Media

Target detection in high-contrast, multi-object images and movies is challenging. This difficulty results from different areas and objects/people having varying pixel distributions, contrast, and intensity properties. This work introduces a new region-focused feature detection (RFD) method to tackle this problem and improve target detection accuracy. The RFD method divides the input image into several smaller ones so that as much of the image as possible is processed. Each of these zones has its own contrast and intensity attributes computed. Deep recurrent learning is then used to iteratively extract these features using a similarity measure from training inputs corresponding to various regions. The target can be located by combining features from many locations that overlap. The recognized target is compared to the inputs used during training, with the help of contrast and intensity attributes, to increase accuracy. The feature distribution across regions is also used for repeated training of the learning paradigm. This method efficiently lowers false rates during region selection and pattern matching with numerous extraction instances. Therefore, the suggested method provides greater accuracy by singling out distinct regions and filtering out misleading rate-generating features. The accuracy, similarity index, false rate, extraction ratio, processing time, and others are used to assess the effectiveness of the proposed approach. The proposed RFD improves the similarity index by 10.69%, extraction ratio by 9.04%, and precision by 13.27%. The false rate and processing time are reduced by 7.78% and 9.19%, respectively.

Correlation between Severity of Idiopathic Epiretinal Membrane and Irvine-Gass Syndrome

A higher risk of pseudophakic cystoid macular edema (PCME) has been reported in patients with preoperative idiopathic epiretinal membrane (ERM); however, whether the formation of PCME depends on the grade of ERM has not been well established. We conducted a retrospective case-control study of 87 eyes of 78 patients who were preoperatively diagnosed with idiopathic ERM and had undergone cataract surgery. Patients were divided into two groups: PCME and non-PCME groups. After cataract surgery, the ERM status was graded using the Gass and Govetto classifications. Both the central macular thickness (CMT) and ERM grade increased after surgery, and higher preoperative CMT and ERM grades were found in the PCME group. The association between higher-grade ERM and the development of PCME was significant in the Govetto classification (grade 2, odds ratio (OR): 3.13; grade 3, OR: 3.93; and grade 4, OR: 16.07). The study results indicate that close attention should be given to patients with ERM with the presence of an ectopic inner foveal layer before cataract surgery.

Comparative genomics and DNA methylation analysis of Pseudomonas aeruginosa clinical isolate PA3 by single-molecule real-time sequencing reveals new targets for antimicrobials

Introduction

Pseudomonas aeruginosa (P.aeruginosa) is an important opportunistic pathogen with broad environmental adaptability and complex drug resistance. Single-molecule real-time (SMRT) sequencing technique has longer read-length sequences, more accuracy, and the ability to identify epigenetic DNA alterations.

Methods

This study applied SMRT technology to sequence a clinical strain P. aeruginosa PA3 to obtain its genome sequence and methylation modification information. Genomic, comparative, pan-genomic, and epigenetic analyses of PA3 were conducted.

Results

General genome annotations of PA3 were discovered, as well as information about virulence factors, regulatory proteins (RPs), secreted proteins, type II toxin-antitoxin (TA) pairs, and genomic islands. A genome-wide comparison revealed that PA3 was comparable to other P. aeruginosa strains in terms of identity, but varied in areas of horizontal gene transfer (HGT). Phylogenetic analysis showed that PA3 was closely related to P. aeruginosa 60503 and P. aeruginosa 8380. P. aeruginosa's pan-genome consists of a core genome of roughly 4,300 genes and an accessory genome of at least 5,500 genes. The results of the epigenetic analysis identified one main methylation sites, N6-methyladenosine (m6A) and 1 motif (CATNNNNNNNTCCT/AGGANNNNNNNATG). 16 meaningful methylated sites were picked. Among these, purH, phaZ, and lexA are of great significance playing an important role in the drug resistance and biological environment adaptability of PA3, and the targeting of these genes may benefit further antibacterial studies.

Disucssion

This study provided a detailed visualization and DNA methylation information of the PA3 genome and set a foundation for subsequent research into the molecular mechanism of DNA methyltransferase-controlled P. aeruginosa pathogenicity.

The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use

Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians' time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.

Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes

Understanding the biological roles of all genes only through experimental methods is challenging. A computational approach with reliable interpretability is needed to infer the function of genes, particularly for non-coding RNAs. We have analyzed genomic features that are present across both coding and non-coding genes like transcription factor (TF) and cofactor ChIP-seq (823), histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) to predict ontology-based functions of genes. Our approach for gene function prediction was reliable (>90% balanced accuracy) for 486 gene-sets. PubMed abstract mining and CRISPR screens supported the inferred association of genes with biological functions, for which our method had high accuracy. Further analysis revealed that TF-binding patterns at promoters have high predictive strength for multiple functions. TF-binding patterns at the promoter add an unexplored dimension of explainable regulatory aspects of genes and their functions. Therefore, we performed a comprehensive analysis for the functional-specificity of TF-binding patterns at promoters and used them for clustering functions to reveal many latent groups of gene-sets involved in common major cellular processes. We also showed how our approach could be used to infer the functions of non-coding genes using the CRISPR screens of coding genes, which were validated using a long non-coding RNA CRISPR screen. Thus our results demonstrated the generality of our approach by using gene-sets from CRISPR screens. Overall, our approach opens an avenue for predicting the involvement of non-coding genes in various functions.

A structural analysis of physician agency and pharmaceutical demand

This paper examines the significance of physician agency in medical providers' prescription choices. Physician agency is considered as medical providers' responses to the price and markup percentage of prescription drugs. Their preferences are allowed to be heterogeneous using a random coefficient logit model. Using a sample of anti-diabetic prescriptions with metformin from a population-based database in Taiwan, empirical results reveal that physician owners, privately-owned medical providers, small medical providers and the medical providers facing less competition are more likely to prescribe drugs with higher profit margins. The aggregate pharmaceutical demand is also found to increase with the markup, which is allowed to be endogenous in the estimation. Price elasticity estimates suggest medical providers are quite responsive to pharmaceutical price changes in Taiwan. Counterfactual analysis reveals the potential impact of physician agency is economically significant. Removing markups and lowering pharmaceutical prices are found to be more welfare enhancing than restricting physicians' dispensing services.

Real-Time Detection and Classification of Scirtothrips dorsalis on Fruit Crops with Smartphone-Based Deep Learning System: Preliminary Results

This study proposes a deep-learning-based system for detecting and classifying Scirtothrips dorsalis Hood, a highly invasive insect pest that causes significant economic losses to fruit crops worldwide. The system uses yellow sticky traps and a deep learning model to detect the presence of thrips in real time, allowing farmers to take prompt action to prevent the spread of the pest. To achieve this, several deep learning models are evaluated, including YOLOv5, Faster R-CNN, SSD MobileNetV2, and EfficientDet-D0. EfficientDet-D0 was integrated into the proposed smartphone application for mobility and usage in the absence of Internet coverage because of its smaller model size, fast inference time, and reasonable performance on the relevant dataset. This model was tested on two datasets, in which thrips and non-thrips insects were captured under different lighting conditions. The system installation took up 13.5 MB of the device's internal memory and achieved an inference time of 76 ms with an accuracy of 93.3%. Additionally, this study investigated the impact of lighting conditions on the performance of the model, which led to the development of a transmittance lighting setup to improve the accuracy of the detection system. The proposed system is a cost-effective and efficient alternative to traditional detection methods and provides significant benefits to fruit farmers and the related ecosystem.

The classification of stages of epiretinal membrane using convolutional neural network on optical coherence tomography image

BACKGROUND AND OBJECTIVE

The gold standard for diagnosing epiretinal membranes is to observe the surface of the internal limiting membrane on optical coherence tomography images. The stages of the epiretinal membrane are used to decide the condition of the health of the membrane. The stages are not detected because some of them are similar. To accurately classify the stages, a deep-learning technology can be used to improve the classification accuracy.

METHODS

A combinatorial fusion with multiple convolutional neural networks (CNN) algorithms are proposed to enhance the accuracy of a single image classification model. The proposed method was trained using a dataset of 1947 optical coherence tomography images diagnosed with the epiretinal membrane at the Taichung Veterans General Hospital in Taiwan. The images consisted of 4 stages; stages 1, 2, 3, and 4.

RESULTS

The overall accuracy of the classification was 84%. The combination of five and six CNN models achieves the highest testing accuracy (85%) among other combinations, respectively. Any combination with a different number of CNN models outperforms any single CNN algorithm working alone. Meanwhile, the accuracy of the proposed method is better than ophthalmologists with years of clinical experience.

CONCLUSIONS

We have developed an efficient epiretinal membrane classification method by using combinatorial fusion with CNN models on optical coherence tomography images. The proposed method can be used for screening purposes to facilitate ophthalmologists making the correct diagnoses in general medical practice.

A Multilevel Study of Eupatorin and Scutellarein as Anti-Amyloid Agents in Alzheimer's Disease

Today, Alzheimer's disease (AD)-the most common neurodegenerative disorder, which affects 50 million people-remains incurable. Several studies suggest that one of the main pathological hallmarks of AD is the accumulation of abnormal amyloid beta (Aβ) aggregates; therefore, many therapeutic approaches focus on anti-Aβ aggregation inhibitors. Taking into consideration that plant-derived secondary metabolites seem to have neuroprotective effects, we attempted to assess the effects of two flavones-eupatorin and scutellarein-on the amyloidogenesis of Aβ peptides. Biophysical experimental methods were employed to inspect the aggregation process of Aβ after its incubation with each natural product, while we monitored their interactions with the oligomerized Aβ through molecular dynamics simulations. More importantly, we validated our in vitro and in silico results in a multicellular organismal model-namely, Caenorhabditis elegans-and we concluded that eupatorin is indeed able to delay the amyloidogenesis of Aβ peptides in a concentration-dependent manner. Finally, we propose that further investigation could lead to the exploitation of eupatorin or its analogues as potential drug candidates.

An Analysis of the Influence of Surface Roughness and Clearance on the Dynamic Behavior of Deep Groove Ball Bearings Using Artificial Neural Networks

The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and sorting them into classes according to the vibration level. In this paper, based on experimental research, models are created to predict the vibration class and analyze the dynamic behavior of new ball bearings. The models are based on artificial neural networks. A feedforward multilayer perceptron (MLP) was applied, and a backpropagation learning algorithm was used. A specific method of training groups of artificial neural networks was applied, where each network provided an answer to the input within the group, and the final answer was the mean value of the answers of all networks in the group. The models achieved a prediction accuracy of over 90%. The main aim of the research was to construct models that are able to predict the vibration class of a new ball bearing based on the geometric parameters of the bearing rings. The models are also applied to analyze the influence of surface roughness of the raceways and the internal radial clearance on bearing vibrations.

PREDICTING VISUAL OUTCOME AFTER SURGERY IN PATIENTS WITH IDIOPATHIC EPIRETINAL MEMBRANE USING A NOVEL CONVOLUTIONAL NEURAL NETWORK

PURPOSE

To develop a deep convolutional neural network that enables the prediction of postoperative visual outcomes after epiretinal membrane surgery based on preoperative optical coherence tomography images and clinical parameters to refine surgical decision making.

METHODS

A total of 529 patients with idiopathic epiretinal membrane who underwent standard vitrectomy with epiretinal membrane peeling surgery by two surgeons between January 1, 2014, and June 1, 2020, were enrolled. The newly developed Heterogeneous Data Fusion Net was introduced to predict postoperative visual acuity outcomes (improvement ≥2 lines in Snellen chart) 12 months after surgery based on preoperative cross-sectional optical coherence tomography images and clinical factors, including age, sex, and preoperative visual acuity. The predictive accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network model were evaluated.

RESULTS

The developed model demonstrated an overall accuracy for visual outcome prediction of 88.68% (95% CI, 79.0%-95.7%) with an area under the receiver operating characteristic curve of 97.8% (95% CI, 86.8%-98.0%), sensitivity of 87.0% (95% CI, 67.9%-95.5%), specificity of 92.9% (95% CI, 77.4%-98.0%), precision of 0.909, recall of 0.870, and F1 score of 0.889. The heatmaps identified the critical area for prediction as the ellipsoid zone of photoreceptors and the superficial retina, which was subjected to tangential traction of the proliferative membrane.

CONCLUSION

The novel Heterogeneous Data Fusion Net demonstrated high accuracy in the automated prediction of visual outcomes after weighing and leveraging multiple clinical parameters, including optical coherence tomography images. This approach may be helpful in establishing personalized therapeutic strategies for epiretinal membrane management.

BNP-Track: A framework for multi-particle superresolved tracking

When tracking fluorescently labeled molecules (termed "emitters") under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or "aliasing") introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets.

An Explainable Fully Dense Fusion Neural Network with Deep Support Vector Machine for Retinal Disease Determination

Retinal issues are crucial because they result in visual loss. Early diagnosis can aid physicians in initiating treatment and preventing visual loss. Optical coherence tomography (OCT), which portrays retinal morphology cross-sectionally and noninvasively, is used to identify retinal abnormalities. The process of analyzing OCT images, on the other hand, takes time. This study has proposed a hybrid approach based on a fully dense fusion neural network (FD-CNN) and dual preprocessing to identify retinal diseases, such as choroidal neovascularization, diabetic macular edema, drusen from OCT images. A dual preprocessing methodology, in other words, a hybrid speckle reduction filter was initially used to diminish speckle noise present in OCT images. Secondly, the FD-CNN architecture was trained, and the features obtained from this architecture were extracted. Then Deep Support Vector Machine (D-SVM) and Deep K-Nearest Neighbor (D-KNN) classifiers were proposed to reclassify those features and tested on University of California San Diego (UCSD) and Duke OCT datasets. D-SVM demonstrated the best performance in both datasets. D-SVM achieved 99.60% accuracy, 99.60% sensitivity, 99.87% specificity, 99.60% precision and 99.60% F1 score in the UCSD dataset. It achieved 97.50% accuracy, 97.64% sensitivity, 98.91% specificity, 96.61% precision, and 97.03% F1 score in Duke dataset. Additionally, the results were compared to state-of-the-art works on the both datasets. The D-SVM was demonstrated to be an efficient and productive strategy for improving the robustness of automatic retinal disease classification. Also, in this study, it is shown that the unboxing of how AI systems' black-box choices is made by generating heat maps using the local interpretable model-agnostic explanation method, which is an explainable artificial intelligence (XAI) technique. Heat maps, in particular, may contribute to the development of more stable deep learning-based systems, as well as enhancing the confidence in the diagnosis of retinal disease in the analysis of OCT image for ophthalmologists.

Automated extraction of pod phenotype data from micro-computed tomography

INTRODUCTION

Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial.

METHODS

In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape (Brassica napus) mature pods.

RESULTS

With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds.

DISCUSSION

This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7-46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.

Simple and Convenient Method for Assessing the Severity of Bleeding during Endoscopic Prostate Surgery and the Relationships between Its Corresponding Surgical Outcomes

Bleeding during endoscopic prostate surgery is often overlooked, and appropriate measurement techniques are rarely applied. We proposed a simple and convenient method for assessing the severity of bleeding during endoscopic prostate surgery. We determined the factors affecting bleeding severity and whether they affected the surgical results and functional outcomes. Records from March 2019 to April 2022 were obtained for selected patients who underwent endoscopic prostate enucleation through either 120-W Vela XL Thulium:YAG laser or bipolar plasma enucleation of the prostate. The bleeding index was measured using the following equation: irrigant hemoglobin (Hb) concentration (g/dL) × irrigation fluid volume (mL)/preoperative blood Hb concentration (g/dL) × enucleated tissue (g). Our research revealed that patients who underwent surgery employing the thulium laser, those aged over 80 years, and those with a preoperative maximal flow rate (Qmax) of more than 10 cc/s experienced less surgical bleeding. The patients' treatment outcomes differed depending on the severity of the bleeding. Enucleating prostate tissue was easier in the patients with less severe bleeding, who also had a lower risk of developing urinary tract infections and an improved Qmax.

NEW ARTIFICIAL INTELLIGENCE ANALYSIS FOR PREDICTION OF LONG-TERM VISUAL IMPROVEMENT AFTER EPIRETINAL MEMBRANE SURGERY

PURPOSE

To predict improvement of best-corrected visual acuity (BCVA) 1 year after pars plana vitrectomy for epiretinal membrane (ERM) using artificial intelligence methods on optical coherence tomography B-scan images.

METHODS

Four hundred and eleven (411) patients with Stage II ERM were divided in a group improvement (IM) (≥15 ETDRS letters of VA recovery) and a group no improvement (N-IM) (<15 letters) according to 1-year VA improvement after 25-G pars plana vitrectomy with internal limiting membrane peeling. Primary outcome was the creation of a deep learning classifier (DLC) based on optical coherence tomography B-scan images for prediction. Secondary outcome was assessment of the influence of various clinical and imaging predictors on BCVA improvement. Inception-ResNet-V2 was trained using standard augmentation techniques. Testing was performed on an external data set. For secondary outcome, B-scan acquisitions were analyzed by graders both before and after fibrillary change processing enhancement.

RESULTS

The overall performance of the DLC showed a sensitivity of 87.3% and a specificity of 86.2%. Regression analysis showed a difference in preoperative images prevalence of ectopic inner foveal layer, foveal detachment, ellipsoid zone interruption, cotton wool sign, unprocessed fibrillary changes (odds ratio = 2.75 [confidence interval: 2.49-2.96]), and processed fibrillary changes (odds ratio = 5.42 [confidence interval: 4.81-6.08]), whereas preoperative BCVA and central macular thickness did not differ between groups.

CONCLUSION

The DLC showed high performances in predicting 1-year visual outcome in ERM surgery patients. Fibrillary changes should also be considered as relevant predictors.

Efficient optimization based thresholding technique for analysis of alzheimer MRIs

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques.Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector Machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues.Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

Taylor-Gorilla troops optimized deep learning network for surface roughness estimation

In order to guarantee the desired quality of machined products, a reliable surface roughness assessment is essential. Using a surface profile metre with a contact stylus, which can produce accurate measurements of surface profiles, is the most popular technique for determining the surface roughness of machined items. One of the limitations of this technique is the work piece surface degradation brought on by mechanical contact between the stylus and the surface. Hence, in this paper, a roughness assessment technique based on the suggested Taylor-Gorilla troops optimizer-based Deep Neuro-Fuzzy Network (Taylor-GTO based DNFN) is proposed for estimating the surface roughness. Pre-processing, data augmentation, feature extraction, feature fusion, and roughness estimation are the procedures that the suggested technique uses to complete the roughness estimate procedure. Roughness estimation is performed using DNFN that has been trained using Taylor-GTO, which was created by combining the Taylor series with the Gorilla troop's optimizer. The created Taylor-GTO based DNFN model has minimum Mean Absolute Error, Mean Square Error, and RMSE of 0.403, 0.416, and 1.149, respectively.

Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis

Coconut cultivation is a promising agricultural activity. But to keep the coconut plants pest-free, the detection of various pest damage in coconut plants is of utmost importance for the cultivators. The processes that the cultivators use to detect pest damage in coconut plants are conventional methods, experts' views, or some laboratory techniques. But these procedures are not adequate in the detection of coconut damage identification. In this study, 16 different color and texture features are reported for 1265 coconut pest damage images by extracting the color and texture features of the damage images in the color and grey domain after the damage segmentation using the thresholding technique. The Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) techniques are applied to extract the texture features of the damages and two Artificial Neural Network (ANN) architectures are reported to classify the extracted data features of the damages into 5 different classes such as Eriophyid_Mite, Rhinoceros_Beetle, Red_Palm_Weevil, Rugose_Spiraling_White_fly, and Rugose_in_Mature with an average testing accuracy of almost 100% respectively. To compare the results with the other machine learning techniques, the Support Vector Machine(SVM), Decision Tree (DT), and Naïve Bayes (NB) are also introduced for damage identification where the SVM methods also report almost 100% accuracy on the fuse features of GLCM and GLRLM. The results of the ANN and SVM are compared by finding the confusion matrix, precision, recall, and f-1 score of the ANN model with the DT and NB classifier. The ANN and SVM outperform in all matrices and they can be used as the base model for further study of coconut pest damage identification using deep learning techniques.

A New Pest Detection Method Based on Improved YOLOv5m

Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38% F1 score, and 96.4% Mean Average Precision (mAP). Meanwhile, the proposed model is significantly better than the original YOLOv3, YOLOv4, and YOLOv5m models. The improved YOLOv5m model shows greater robustness and effectiveness in detecting pests, and it could more precisely detect different pests from the dataset.

iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis

Background: Epiretinal membranes (ERM) have been found to be common among individuals >50 years old. However, the severity grading assessment for ERM based on optical coherence tomography (OCT) images has remained a challenge due to lacking reliable and interpretable analysis methods. Thus, this study aimed to develop a two-stage deep learning (DL) system named iERM to provide accurate automatic grading of ERM for clinical practice. Methods: The iERM was trained based on human segmentation of key features to improve classification performance and simultaneously provide interpretability to the classification results. We developed and tested iERM using a total of 4547 OCT B-Scans of four different commercial OCT devices that were collected from nine international medical centers. Results: As per the results, the integrated network effectively improved the grading performance by 1−5.9% compared with the traditional classification DL model and achieved high accuracy scores of 82.9%, 87.0%, and 79.4% in the internal test dataset and two external test datasets, respectively. This is comparable to retinal specialists whose average accuracy scores are 87.8% and 79.4% in two external test datasets. Conclusion: This study proved to be a benchmark method to improve the performance and enhance the interpretability of the traditional DL model with the implementation of segmentation based on prior human knowledge. It may have the potential to provide precise guidance for ERM diagnosis and treatment.

Anti-viral drug discovery against monkeypox and smallpox infection by natural curcumin derivatives: A Computational drug design approach

Background

In the last couple of years, viral infections have been leading the globe, considered one of the most widespread and extremely damaging health problems and one of the leading causes of mortality in the modern period. Although several viral infections are discovered, such as SARS CoV-2, Langya Henipavirus, there have only been a limited number of discoveries of possible antiviral drug, and vaccine that have even received authorization for the protection of human health. Recently, another virial infection is infecting worldwide (Monkeypox, and Smallpox), which concerns pharmacists, biochemists, doctors, and healthcare providers about another epidemic. Also, currently no specific treatment is available against Monkeypox. This research gap encouraged us to develop a new molecule to fight against monkeypox and smallpox disease. So, firstly, fifty different curcumin derivatives were collected from natural sources, which are available in the PubChem database, to determine antiviral capabilities against Monkeypox and Smallpox.

Material and method

Preliminarily, the molecular docking experiment of fifty different curcumin derivatives were conducted, and the majority of the substances produced the expected binding affinities. Then, twelve curcumin derivatives were picked up for further analysis based on the maximum docking score. After that, the density functional theory (DFT) was used to determine chemical characterizations such as the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), softness, and hardness, etc.

Results

The mentioned derivatives demonstrated docking scores greater than 6.80 kcal/mol, and the most significant binding affinity was at -8.90 kcal/mol, even though 12 molecules had higher binding scores (-8.00 kcal/mol to -8.9 kcal/mol), and better than the standard medications. The molecular dynamic simulation is described by root mean square deviation (RMSD) and root-mean-square fluctuation (RMSF), demonstrating that all the compounds might be stable in the physiological system.

Conclusion

In conclusion, each derivative of curcumin has outstanding absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics. Hence, we recommended the aforementioned curcumin derivatives as potential antiviral agents for the treatment of Monkeypox and Smallpox virus, and more in vivo investigations are warranted to substantiate our findings.

Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography

PURPOSE

The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features.

METHODS

Six hundred macular OCT images from eyes with ERM and no visually significant media opacity or other retinal diseases were obtained. Those with best-corrected visual acuity ≤20/50 were classified as "profound visual impairment," while those with best-corrected visual acuity >20/50 were classified as "less visual impairment." Ninety percent of images were used as the training data set and 10% were used for testing. Two convolutional neural network models (ResNet-50 and ResNet-18) were adopted for training. The t-distributed stochastic neighbor-embedding approach was used to compare their performances. The Grad-CAM technique was used in the heat map generative phase for feature analysis.

RESULTS

During the model development, the training accuracy was 100% in both convolutional neural network models, while the testing accuracy was 70% and 80% for ResNet-18 and ResNet-50, respectively. The t-distributed stochastic neighbor-embedding approach found that the deeper structure (ResNet-50) had better discrimination on OCT characteristics for visual impairment than the shallower structure (ResNet-18). The heat maps indicated that the key features for visual impairment were located mostly in the inner retinal layers of the fovea and parafoveal regions.

CONCLUSIONS

Deep learning algorithms could assess the extent of visual impairment from OCT images in patients with ERM. Changes in inner retinal layers were found to have a greater impact on visual acuity than the outer retinal changes.

Study on In-Situ Tool Wear Detection during Micro End Milling Based on Machine Vision

Most in situ tool wear monitoring methods during micro end milling rely on signals captured from the machining process to evaluate tool wear behavior; accurate positioning in the tool wear region and direct measurement of the level of wear are difficult to achieve. In this paper, an in situ monitoring system based on machine vision is designed and established to monitor tool wear behavior in micro end milling of titanium alloy Ti6Al4V. Meanwhile, types of tool wear zones during micro end milling are discussed and analyzed to obtain indicators for evaluating wear behavior. Aiming to measure such indicators, this study proposes image processing algorithms. Furthermore, the accuracy and reliability of these algorithms are verified by processing the template image of tool wear gathered during the experiment. Finally, a micro end milling experiment is performed with the verified micro end milling tool and the main wear type of the tool is understood via in-situ tool wear detection. Analyzing the measurement results of evaluation indicators of wear behavior shows the relationship between the level of wear and varying cutting time; it also gives the main influencing reasons that cause the change in each wear evaluation indicator.

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

Short-Term In Vitro ROS Detection and Oxidative Stress Regulators in Epiretinal Membranes and Vitreous from Idiopathic Vitreoretinal Diseases

Background

A plethora of inflammatory, angiogenic, and tissue remodeling factors has been reported in idiopathic epiretinal membranes (ERMs). Herein we focused on the expression of a few mediators (oxidative, inflammatory, and angiogenic/vascular factors) by means of short-term vitreal cell cultures and biomolecular analysis.

Methods

Thirty-nine (39) ERMs and vitreal samples were collected at the time of vitreoretinal surgery and biomolecular analyses were performed in clear vitreous, vitreal cell pellets, and ERMs. ROS products and iNOS were investigated in adherent vitreal cells and/or ERMs, and iNOS, VEGF, Ang-2, IFNγ, IL18, and IL22 were quantified in vitreous (ELISA/Ella, IF/WB); transcripts specific for iNOS, p65NFkB, KEAP1, NRF2, and NOX1/NOX4 were detected in ERMs (PCR). Biomolecular changes were analyzed and correlated with disease severity.

Results

The higher ROS production was observed in vitreal cells at stage 4, and iNOS was found in ERMs and increased in the vitreous as early as at stage 3. Both iNOS and NOX4 were upregulated at all stages, while p65NFkB was increased at stage 3. iNOS and NOX1 were positively and inversely related with p65NFkB. While NOX4 transcripts were always upregulated, NRF2 was upregulated at stage 3 and inverted at stage 4. No significant changes occurred in the release of angiogenic (VEGF, Ang-2) and proinflammatory (IL18, IL22 and IFNγ) mediators between all stages investigated.

Conclusions

ROS production was strictly associated with iNOS and NOX4 overexpression and increased depending on ERM stadiation. The higher iNOS expression occurred as early as stage 3, with respect to p65NFkB and NRF2. These last mediators might have potential prognostic values in ERMs as representative of an underneath retinal damage.

An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model's effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production.