Image synthesis of apparel stitching defects using deep convolutional generative adversarial networks

In industrial manufacturing, the detection of stitching defects in fabric has become a pivotal stage in ensuring product quality. Deep learning-based fabric defect detection models have demonstrated remarkable accuracy, but they often require a vast amount of training data. Unfortunately, practical production lines typically lack a sufficient quantity of apparel stitching defect images due to limited research-industry collaboration and privacy concerns. To address this challenge, this study introduces an innovative approach based on DCGAN (Deep Convolutional Generative Adversarial Network), enabling the automatic generation of stitching defects in fabric. The evaluation encompasses both quantitative and qualitative assessments, supported by extensive comparative experiments. For validation of results, ten industrial experts marked 80% accuracy of the generated images. Moreover, Fréchet Inception Distance also inferred promising results. The outcomes, marked by high accuracy rate, underscore the effectiveness of proposed defect generation model. It demonstrates the ability to produce realistic stitching defective data, bridging the gap caused by data scarcity in practical industrial settings.

Novel variable neighborhood search heuristics for truck management in distribution warehouses problem

Logistics and sourcing management are core in any supply chain operation and are among the critical challenges facing any economy. The specialists classify transport operations and warehouse management as two of the biggest and costliest challenges in logistics and supply chain operations. Therefore, an effective warehouse management system is a legend to the success of timely delivery of products and the reduction of operational costs. The proposed scheme aims to discuss truck unloading operations problems. It focuses on cases where the number of warehouses is limited, and the number of trucks and the truck unloading time need to be manageable or unknown. The contribution of this article is to present a solution that: (i) enhances the efficiency of the supply chain process by reducing the overall time for the truck unloading problem; (ii) presents an intelligent metaheuristic warehouse management solution that uses dispatching rules, randomization, permutation, and iteration methods; (iii) proposes four heuristics to deal with the proposed problem; and (iv) measures the performance of the proposed solution using two uniform distribution classes with 480 trucks' unloading times instances. Our result shows that the best algorithm is O I S ~ , as it has a percentage of 78.7% of the used cases, an average gap of 0.001, and an average running time of 0.0053 s.

Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform

Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Understanding the role of AGPs in cardiovascular and neurodegenerative diseases is vital for developing new diagnostic tools and therapeutic approaches. Considering the significance of AGPs, in this research, we first time established a computational model using deep learning for identifying AGPs. First, we constructed a sequence-based dataset. Second, we explored features by designing a novel feature encoder, called position-specific scoring matrix-decomposition-discrete cosine transform (PSSM-DC-DCT) and existing descriptors including Dipeptide Deviation from Expected Mean (DDE) and bigram-position-specific scoring matrix (Bi-PSSM). Third, each feature set is fed into two-dimensional convolutional neural network (2D-CNN) and machine learning classifiers. Finally, the performance of each learning model is validated by 10-fold cross-validation (CV). The experimental results demonstrate that 2D-CNN with proposed novel feature descriptor achieved the highest success rate on both training and testing datasets. In addition to being an accurate predictor for identification of angiogenic proteins, our proposed method (Deep-AGP) might be fruitful in understanding cancer, cardiovascular, and neurodegenerative diseases, development of their novel therapeutic methods and drug designing.

Prediction of antifreeze proteins using machine learning

Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.

The association between salivary zinc levels and dysgeusia in COVID-19 patients

OBJECTIVE

Zinc insufficiency has been proposed to play a role in taste and smell impairment in Coronavirus disease 2019 (COVID-19) patients. Therefore, this study aimed at comparing salivary zinc levels in COVID-19 patients with and without dysgeusia.

PATIENTS AND METHODS

A total of 127 participants were recruited for this study. The patients were divided into three groups based on their COVID-19 test results and taste impairment. Groups I and II were COVID-19 positive with and without taste loss, respectively. Group III included the negative control participants. Salivary zinc levels were measured at baseline in all groups and three months after baseline in groups I and II. Wilcoxon signed-rank test was used to compare the zinc levels between baseline and three months after baseline within each group. Mann-Whitney U test was used to compare zinc levels between groups with different degrees of taste loss.

RESULTS

Salivary zinc levels were significantly lower in the COVID-19 positive group with taste loss compared to levels in the group without taste loss or the negative controls (p<0.005). Three months after baseline, salivary zinc levels were significantly elevated in both COVID-19 positive groups (p<0.001).

CONCLUSIONS

COVID-19 patients with dysgeusia had significantly lower levels of salivary zinc than positive and negative controls. Zinc levels were elevated after recovery, which may indicate that salivary zinc is directly associated with taste abnormalities and COVID-19 outcomes. This study showed that taste impairment is associated with lower salivary zinc levels in COVID-19 patients.

Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting

DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.

Combination of baseline FDG PET/CT total metabolic tumour volume and gene expression profile have a robust predictive value in patients with diffuse large B-cell lymphoma

PURPOSE

This study evaluated the predictive significance of total metabolic tumour volume (TMTV) measured on baseline FDG PET/CT and its value in addition to gene expression profiling using a new method of gene analysis (rapid reverse transcriptase multiplex ligation-dependent probe amplification assay, RT-MLPA) in patients with diffuse large B-cell lymphoma treated with R-CHOP or R-CHOP-like chemotherapies.

METHODS

The analysis included 114 patients. TMTV was measured using a 41% SUVmax threshold and tumours were classified into GCB or ABC subtypes according to the RT-MLPA assay.

RESULTS

The median follow-up was 40 months. the 5-year progression-free survival (PFS) was 54% and the 5-year overall survival (OS) was 62%. The optimal TMTV cut-off value was 261 cm³. In 59 patients with a high TMTV the 5-year PFS and OS were 37% and 39%, respectively, in comparison with 72% and 83%, respectively, in 55 patients with a low TMTV (p = 0.0002 for PFS, p < 0.0001 for OS). ABC status was significantly associated with a worse prognosis. TMTV combined with molecular data identified three groups with very different outcomes: (1) patients with a low TMTV whatever their phenotype (n = 55), (2) patients with a high TMTV and GCB phenotype (n = 33), and (3) patients with a high TMTV and ABC phenotype (n = 26). In the three groups, 5-year PFS rates were 72%, 51% and 17% (p < 0.0001), and 5-year OS rates were 83%, 55% and 17% (p < 0.0001), respectively. In multivariate analysis, TMTV, ABC/GCB phenotype and International Prognostic Index were independent predictive factors for both PFS and OS (p < 0.05 for both).

CONCLUSIONS

This integrated risk model could lead to more accurate selection of patients that would allow better individualization of therapy.

Central line associated blood stream infection rate after intervention and comparing outcome with national healthcare safety network and international nosocomial infection control consortium data

Background:

Benchmarking of central line associated blood stream infection (CLABSI) rates remains a problem in developing countries due to the variations in surveillance practices and/or infection risk as non-availability of national data.

Aim:

The aim of the following study was to find out the CLABSI rate before and after central line (CL) bundle intervention and compare the outcome with international surveillance data.

Subjects and Methods:

This prospective longitudinal cohort study on adult intensive care unit patients was conducted at Hera General Hospital, Makkah Saudi Arabia from January 1 to December 31, 2012. Five key components of bundle were selected; hand hygiene, maximal barrier precautions upon insertion, skin antisepsis, optimum site selection and daily review of line necessity with prompt removal of unnecessary lines. Post-intervention CLABSI rate was compared with National Healthcare Safety Network (NHSN) and International Nosocomial Infection Control Consortium (INICC) rates. Statistical Package for the Social Sciences (SPSS) 14.0 software (SPSS Inc., 233 South Wacker Drive, 11^th floor Chicago, USA) was used for statistical analysis included regression analysis for correlation. Statistical significance was set at P < 0.05.

Results:

CLABSI rate was reduced from 10.1 to 6.5 per 1000 CL days after interventions and had significant correlation with overall bundle compliance rate 87.6% (P = 0.02) On benchmarking, CLABSI rate after the intervention was similar to mean pool value of INICC (6.8) while higher than NHSN (3.1). The most common microorganisms isolated were; methicillin-resistant Staphylococcus aureus (30.8%), Acinetobacter baumanii (23.3%) and Enterococcus faecalis (15.4%).

Conclusion:

We found that INICC data was a better benchmarking tool comparative to NHSN because it represents the countries that are developing the surveillance system. A multicenter national study is recommended.

Tympanosclerosis: review of literature and incidence among patients with middle-ear infection

The aim of the study was to review the literature of tympanosclerosis especially its pathogenesis, to study the general incidence of tympanosclerosis among patients with chronic suppurative otitis media (CSOM), its association with cholesteatoma and also the type of hearing loss as well as its relation to the degree and site of tympanosclerosis. Seven hundred and seventy-five patients with CSOM were studied retrospectively. A full history was taken and thorough ENT examinations were carried out. Pure tone audiograms (PTA) of all patients were done and analysed. The operative finding of tympanosclerosis as well as middle-ear status were inspected. The incidence of tympanosclerosis was found to be 11.6 per cent (90 patients out of 775 CSOM cases). Most tympanosclerosis cases had dry ear, (85.6 per cent). Of the 57.8 per cent who had myringosclerosis, their PTA showed an AB gap 20-40 dB. When sclerosis affect both the tympanic membrane and middle ear, 61 per cent of patients had an AB gap > 40 dB. The association of cholesteatoma and tympanosclerosis may be regarded as uncommon, 2.2 per cent. The exact aetiology and pathogenesis of tympanosclerosis is as yet not well known. Our study concentrated on the clinical picture of tympanosclerosis among patients with CSOM. The majority of hearing loss associated with tympanosclerosis was of the conductive type.

Malignant change in a chronic skin lesion induced by an intravenous cannula

A cutaneous wound subjected to continuous irritation has an increased potential for malignant degeneration. The types of trauma that may give rise to the initial injury are diverse and have been well documented. We report a case of one such lesion in a 65-year-old man who had a persistent right forearm wound for over three years. The wound arose from the site of venous cannula puncture. Malignant transformation occurred in a manner comparable to that seen in other chronic lesions (Marjolin's ulcer).