Classification System Research Articles

Exploring the Branching Pattern of the Posterior Division of the Internal Iliac Artery: An Analysis Based on 75 Computed Tomography Angiographies.

The internal iliac artery stands as the main blood supplier of the pelvis, serving as the primary source of blood for the pelvic viscera while also nourishing the musculoskeletal framework within. The arterial anatomy of the pelvis exhibits a vast array of variations, especially regarding the branching pattern of the internal iliac arteries. The posterior division of the internal iliac artery (PDIIA) may also have variable topography, especially regarding the location of its origin in the pelvic region. A retrospective study was carried out to determine the anatomical variations, prevalence, and morphometric data of the PDIIA and its branches. A total of 75 computed tomography angiographies were analyzed. The most prevalent branch of the PDIIA was the superior gluteal artery, as it was present in 114 of the studied cases (77.03%). The median diameter of the PDIIA at its origin was 6.66mm. The median cross-sectional area of the PDIIA at its origin was set to be 34.59 mm2. Our study highlights the critical significance of understanding the PDIIA and its branches in surgical interventions aimed at managing pelvic hemorrhage. The present study provides valuable insights into the precise localization and characteristics of the PDIIA and its branches, which are essential for surgical procedures targeting specific vessels to control bleeding effectively. Owing to the high level of variability of the branching pattern of the PDIIA, a novel classification system consisting of six types was created.

Degree of food processing and breast cancer risk: a prospective study in 9 European countries

AbstractRecent epidemiological studies have suggested a positive association between ultra-processed food consumption and breast cancer risk, although some studies also reported no association. Furthermore, the evidence regarding the associations between intake of food with lower degrees of processing and breast cancer risk is limited.Thus, we investigated the associations between dietary intake by degree of food processing and breast cancer risk, overall and by breast cancer subtypes in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.Dietary intake of EPIC participants was assessed via questionnaires at baseline. More than 11,000 food ingredients were classified into four groups of food processing levels using the NOVA classification system: unprocessed/minimally processed (NOVA 1), culinary ingredients (NOVA 2), processed (NOVA 3) and ultra-processed (NOVA 4). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of breast cancer per standard deviation increase in daily consumption (grams) of foods from each NOVA group.The current analysis included 14,933 breast cancer cases, diagnosed among the 318,686 EPIC female participants, (median follow-up of 14.9 years). No associations were found between breast cancer risk and the level of dietary intake from NOVA 1 [HR per 1 SD=0.99 (95% CI 0.97 – 1.01)], NOVA 2 [HR per 1 SD =1.01 (95% CI 0.98 – 1.03)] and NOVA 4 [HR per 1 SD =1.01 (95% CI 0.99 – 1.03)] foods. However, a positive association was found between NOVA 3 and breast cancer risk [HR per 1 SD =1.05 (95% CI 1.03 – 1.07)] which became non-significant after adjustment for alcohol intake [HR per 1 SD =1.01 (95% CI 0.98 – 1.05)] or when beer and wine were excluded from this group [HR per 1 SD =0.99 (95% CI 0.97 – 1.01)]. The associations did not differ by breast cancer subtype, menopausal status or body mass index. Findings from this large-scale prospective study suggest that the positive association between processed food intake and breast cancer risk was likely driven by alcoholic beverage consumption. Graphical Abstract

Impact of care coordination on service utilisation for children with medically complex cerebral palsy.

Complex care programmes for children with medically complex cerebral palsy (CP) exist; however, evidence for their impact is limited. This study (i) explored the impact of The Royal Children's Hospital Complex Care Hub (CCH) on hospital service utilisation rates over a 3-year period for children with medically complex CP compared with those eligible but received routine care, and (ii) compared health, disability and socio-demographic characteristics of children and their families in both groups. Electronic medical record data from 78 children (mean age 9.43 years, females n = 37) with medically complex CP who accessed CCH services, and 92 (mean age 10.86 years, females, n = 39) who received routine care were included. Multivariable regression was used to analyse service utilisation: number of emergency department (ED) presentations, length/number of inpatient and intensive care unit admissions and number/type of hospital appointments. Critical health-care needs, functioning/disability profile and child/family demographics for each group were compared. More children in the CCH group had a mixed motor type (73.1% vs. 15.2%), were classified within Gross Motor Function Classification System level V (76.9% vs. 34.8%), had respiratory, nutrition and social support needs and epilepsy. Children receiving CCH services had higher service utilisation rates; ED presentations (rate ratio (RR) = 1.81, 95% confidence interval (CI): 1.09-3.01), inpatient admissions (RR = 2.77, 95% CI: 2.01-3.83), outpatient encounters (RR = 1.69, 95% CI: 1.31-2.18) and telephone encounters (RR = 6.05, 95% CI: 4.56-8.02). Children with medically complex CP accessing a complex care service have higher service utilisation rates yet have more complex clinical presentations and higher support needs.

Integrated analysis and experiments uncover the function of disulfidptosis in predicting immunotherapy effectiveness and delineating immune landscapes in uterine corpus endometrial carcinoma

IntroductionRecently, a novel type of metabolic-regulated cell demise titled disulfidptosis has been discovered. Studies have demonstrated its importance in immune responses against cancer and its impact on the proliferation of cancer cells. Nonetheless, the precise mechanism and roles of disulfidptosis are not fully understood, particularly regarding the prognosis for individuals with uterine corpus endometrial carcinoma (UCEC).MethodsIn this research, a distinctive disulfidptosis pattern was developed in UCEC, and by utilizing Non-negative Matrix Factorization (NMF) on 23 disulfidptosis related genes within the TCGA database, 3 distinct subgroups were distinguished. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA and GEO database, specifically from UCEC patients. Cell line experiments and immunohistochemical (IHC) staining were conducted to validate the role of the LRPPRC in proliferation, migration and invasion.ResultsThe genetic features and immune microenvironment of these subgroups were examined. It is worth mentioning that these subgroups offer important insights into comprehending the tumor microenvironment (TME) and the response of patients to immunotherapy and chemotherapy. Moreover, a disulfidptosis model was developed and validated, demonstrating a high level of accuracy in predicting the prognosis and outcomes of immunotherapy in UCEC patients. Additionally, a novel biomarker, LRPPRC, was identified, which can server as a promising predictor for forecasting prognosis in UCEC patients, with validation through tissue microarray staining and cell line experiments.DiscussionThis study has designed a classification system and a disulfidptosis model for UCEC, in addition to identifying a new biomarker, LRPPRC, for UCEC. These advancements serve as reliable and positive indicators for predicting outcomes and the efficacy of immunotherapy for each UCEC patient.

Future perspectives: Current trends and controversies of meat alternatives classified as ultra-processed foods.

This study reviewed the current state of meat alternatives including plant-based or cell-based ingredients and discussed the contentious factor surrounding the classification of meat alternatives as ultra-processed foods (UPFs). The term UPFs refers to foods undergoing extensive industrial processing and containing additives such as flavors, colors, emulsifiers, and preservatives. There is growing concern regarding the potential adverse effects of UPF consumption on health, nutrition, and sociodemographic factors. Additionally, this study examined the market potential, drivers, and barriers associated with different types of meat alternatives. In light of barriers focused on UPFs, meat alternatives can be disputed in that they undergo extensive processing and are highly processed, including numerous ingredients, while meat alternatives offer potential solutions to the environmental, ethical, and health issues associated with animal meat consumption. Consequently, it is important to distinguish them from other UPFs, which are known to have detrimental effects on health. Therefore, this paper proposed a reassessment of the UPF classification system, the establishment of uniform nutritional profiles for meat alternatives, and the dissemination of their beneficial impacts. These measures are necessary to validate the exclusion of meat alternatives from the UPF category and to promote their development and adoption.

Prognostic Value of a Classification System for Iliofemoral Stenting in Patients with Chronic Venous Obstruction

This retrospective, multicentre study aimed to assess the prognostic value of a proposed classification system for chronic venous obstruction (CVO) patients undergoing successful interventional procedures. This study analysed data from 13 vascular centres, including 1 033 patients with CVO treated between 2015 - 2019. The patients were classified into five category types: 1 - non-thrombotic iliac vein lesion; 2 - CVO of iliac segment; 3 - CVO of iliofemoral segment above common femoral vein confluence; 4 - CVO of iliofemoral segment extending into the femoral vein (FV) or deep femoral vein (DFV); and 5 - CVO of iliofemoral segment involving both DFV and FV. Stent deployment, complications, and follow ups were evaluated. Univariate and multivariate analyses were performed to identify predictors of primary patency loss. Mean age of the patients was 44.0 ± 14.7 years, with 59.9% being women. A median of two stents were used for unilateral cases and five stents for bilateral cases. At 12 months of follow up, primary patency rates for types 1 - 5 were 94.9%, 90.3%, 80.8%, 60.6%, and 39.4%, respectively. These rates were strongly correlated with the extension of CVO and showed significant differences between each type. Univariate analysis identified predictors of primary patency loss as the type of CVO, history of deep vein thrombosis, and the total number of stents. In the multivariate analysis, the significant independent predictors of primary patency loss were the type of CVO and the total number of stents. The proposed anatomical classification of iliofemoral CVO will help to predict intervention outcomes and facilitate comparison of stent outcomes in future studies. However, further evaluation and validation in prospective studies are needed to confirm the utility of this classification.

Severe heterotopic ossification after total hip arthroplasty in male patients under 70years of age: effectiveness of prophylactic protocol.

This study aims to evaluate the incidence of clinically significant heterotopic ossification (HO) in primary total hip arthroplasty (THA), comparing outcomes with and without the adoption of an HO prophylactic protocol in male patients under 70years of age. The prophylactic protocol involved the administration of 50mg of Indomethacin twice daily for 3 weeks. HO presence was classified according to the Brooker classification system, considering "severe" clinically significant HO (Brooker grade 3 and 4). Two hundred and seventy-nine patients were included in our study, and an overall HO rate of 68.2% versus a rate of 61.5% was found respectively in patients not subjected and subjected to prophylactic protocol, without significant difference (PR 0.062). However, patients not subjected to the HO prophylactic protocol exhibited a severe HO rate of 22.4% compared to 7.7% in the prophylactic group, with a statistically significant difference (P = 0.008). Our study demonstrated that prophylactic protocol adoption is significantly associated with lower rate of severe HO in male patients under 70years of age. Currently, there are no orthopedic guidelines for the prevention and management of HO after THA, but in the absence of contraindications, the adoption of a prophylactic protocol for HO should always be considered in high-risk patients.

Epidemiology, Classification and Treatment of Femur and Tibia Fractures around Total Knee Arthroplasty (Literature Review)

Summary. Periprosthetic fractures around total knee arthroplasty (TKA) are difficult to treat due to complex fracture morphology, high proportions of injuries associated with osteopenia, and the variability of injury patterns. Periprosthetic fractures associated with TKA are defined as fractures around the knee joint (femur, tibia, or patella) occurring within 5 cm of the intramedullary stem of the prosthesis or 15 cm of the joint. The incidence is estimated to be between 0.3% and 2.5% after primary TKA and up to 28% after revision TKA. There are several surgical and nonsurgical risk factors associated with postoperative periprosthetic fractures. Distal femoral periprosthetic fractures following TKA are the most common and reported to occur in 0.2–1.8% of patients after primary TKA. Tibial periprosthetic fractures have a reported prevalence of 0.07–0.1% in primary and 0.36% in revision knee arthroplasties. Patella fractures are the second most common periprosthetic fractures following TKA, with prevalence ranging from 0.68% to 1.19%, and are usually associated with a resurfaced patella. Several classification systems of periprosthetic fractures around TKA have been published and some treatment recommendations have been provided. However, most systems are subject to criticism and use different classification systems for the tibia, patella, and the femur.A variety of treatment methods are available for periprosthetic fractures. Conservative treatment can include protected weight bearing, splinting, or traction. Surgical treatment commonly involves closed intramedullary nailing or open reduction and internal fixation by plates. In cases of significant bone loss, a revision TKA may be indicated. In general, treatment must be guided based on the stability of the implant and the remaining bone quality, as determined by the presence of osteolysis and the location of the fracture.

Derotational distal femoral osteotomy yields better outcomes in patellar subluxation with proximal femoral torsion compared with distal femoral torsion: A retrospective comparative study.

Controversy exists regarding the origin of femoral torsion, and specific treatment rules regarding the optimal position of femoral osteotomy in patients with recurrent patellar subluxation and excessive femoral torsion are scarce. To establish a novel classification system for such patients, and to compare clinical and radiological outcomes after distal derotational femoral osteotomy (DDFO) between femoral torsion at proximal (neck and shaft) and distal levels. Between January 2014 and June 2019, patients who underwent DDFO were retrospectively reviewed. The segmental torsion analysis was performed to establish a novel classification system, and classify included patients into two groups: 35 patients in proximal torsion group and 38 patients in distal torsion group. These patients were followed-up for at least 3years. Clinical evaluations included functional outcomes, physical examinations, quality of life, activity level, satisfaction, and complications. Radiological outcomes included patellofemoral osteoarthritis, congruence, and alignment. Type I was defined as the proximal torsion. Type II was defined as the distal torsion. Proximal torsion group had lower postoperative femoral torsion (12.6 ± 2.6° vs. 14.8 ± 3.6°; P = .004) and higher surgical correction angle (21.6 ± 5.0° vs. 19.1 ± 3.0°; P = .009). All clinical and radiological outcomes improved significantly in both groups, but proximal torsion group had significantly higher quality of life (EQ-5D-5L: 0.96 ± 0.06 vs. 0.91 ± 0.07; P = .003. 92.0 ± 6.0 vs. 88.7 ± 5.8; P = .021) and Tegner activity score (5.2 ± 1.5 vs. 4.5 ± 1.4; P = .040), and fewer patellofemoral osteoarthritis (8.6% vs. 26.3%; P = .048). Two patients in the distal torsion group had subjective patellar instability. The percentage of patients with anterior knee pain was higher in the distal torsion group. A novel classification system for patients with recurrent patellar subluxation and excessive femoral torsion based on segmental femoral torsion analysis was established. DDFO was more appropriate for patients with proximal torsion, yielding higher surgical correction angle, and better clinical and radiological outcomes. Cohort study; Level of evidence, 3.

A NOVEL WAY TO CLASSIFY THE SLAKE DURABILITY INDEX OF ROCKS

Slaking, the disintegration of rocks upon exposure to moisture and air, poses significant challenges in engineering applications, such as in slope stability and embankment design. This study improves the slake durability index test by introducing several enhancements to better reflect the field conditions of rocks. A new test methodology is developed which includes a revised slake durability classification table, a novel Discard Method for calculating weight retention in the test drum, and an updated classification chart. The improved methodology considers parameters such as porosity and strength, evaluated through index, Schmidt hammer rebound and point load tests. The new classification table categorizes rocks into durability classes according to the field observations and laboratory test results. The Discard Method improves accuracy by eliminating broken pieces from the durability calculation, addressing the limitations in the suggested method. A Configured Discard Method equation further defines the durability estimation by combining porosity, strength and slake durability data. Field investigations and laboratory tests conducted with various rock types demonstrate that the new classification system provides more accurate and practical assessments of rock durability. This study highlights the importance of effective porosity and strength impact which enhance the reliability of slake durability evaluations, offering a revised classification framework that simplifies field assessments while maintaining the accuracy. Future research should focus on developing these methods and expanding their applicability to diverse geological contexts by including more material to test.

Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986–2020)

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management.

Three-dimensional assessment of root canal morphology of primary dentition using cone beam computed tomography.

Root canal therapy in primary teeth is exceedingly complex due to the unique internal geometry of the pulp cavity. The purpose of this study was to evaluate variations in primary teeth root canal morphology and applicability of the Ahmed etal. (Int Endod J, 50, 2017, 761) alphanumeric system for classifying them. A retrospective analysis of 2024 primary teeth from an existing CBCT database was performed for assessing variations in root canal morphology. Descriptive statistics and the chi-squared test were used for data analysis. The maxillary canines exhibited splitting levels of 5.9% (right) and 6.6% (left), and merging levels of 1% (both sides); maxillary lateral incisors showed splitting levels of 2.6% (right) and 0.8% (left), with no merging observed; mandibular second molars had splitting levels of 1.5% (right) and 0.8% (left), with no merging observed; and mandibular lateral incisors exhibited a merging level of 0.9% (right), with no splitting observed. This study provides new insights into the variations in root canal morphology of primary teeth, particularly highlighting the variations found in primary canines. The Ahmed etal. (Int Endod J, 50, 2017, 761) classification system was found to be a useful tool for categorizing these variations.

MicroRΝΑ analysis in patients with myelodysplastic neoplasms. Possible implications in risk stratification

MiRNAs have been identified as participants in leukemogenesis by controlling several cellular functions, such as differentiation, proliferation, and apoptosis. Their role in myelodysplastic neoplasms (MDS) pathogenesis is researched due to implementations in early identification, classification, and therapeutical options. IPSS-R, being the most widely used MDS classification, underestimates early biological events that can alter the disease’s prognosis. The purpose of this study is to determine whether miRNA levels are aligned to MDS risk stratification groups and can therefore be used as diagnostic biomarkers. To evaluate miRNAs as possible biomarkers, we measured the levels of miR-181a-2-3p, miR-124-3p, miR-550a-3p, miR-155-5p, miR-151a-3p, and miR-125b-5p by a quantitative real-time PCR in bone marrow samples of 41 MDS patients. In conclusion, in myeloid malignancies, genomic characteristics may provide a wider apprehension of its clinical course and prognosis. MiRNAs constitute a possible diagnostic biomarker and therapeutic target, allowing intermediate-risk patients that express high levels of specific miRNAs to be re-classified and receive more advanced therapeutic agents. In our study, an association between high levels of miRNAs and worsening outcomes is established, supporting the need for further incorporation of molecular data into currently used classification systems.

Analysis of Cesarean Section Rates Using Robson Ten Group Classification System in A Tertiary Care Hospital of Peshawar: A Cross-Sectional Study

Objective: To observe CS rates and assess them using Robson's Ten Groups Categorization System in a government tertiary medical facility in Peshawar, Pakistan Study Design and Setting: From 1st March to 31st August 2023, a cross-sectional study was carried out at the Khyber Teaching Hospital’s department of obstetrics and gynecology in Peshawar, Pakistan Methodology:. The research cohort consisted of 1250 women with CS who were hospitalized throughout the specified study period. Information on maternal features and pregnancy-related details was collected for every patient. Results: 4227 women sought labor and delivery services during the course of the research. It was discovered that the CS rate was 29.5%. Most common were Groups 1 (7.57%), 3 (8.65%), and 5 (54.66%), which together accounted for around 69% of all CS occurrences. Group 5's CS rate was 80.7%, but subgroup 5.1's (previous CS) women experienced repeat CS at a rate of 65.14%. 95% babies were alive and 5% were stillborns. Conclusion: Noticeable raise in the caesarean deliveries rate, leading to substantial worse influence in terms of health, finances, and society. Previous CS is the most prevalent sign of CS.CS if performed on primigravidae with a valid indication, the rate of CS may be managed.

Reasons for encounter in video contacts at a Danish out-of-hours primary care service: a questionnaire study

ObjectiveTo investigate reasons for encounter in telephone triage contacts to an out-of-hours primary care service for which general practitioners (GPs) use video consultations (video contact), overall and stratified for patient...

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

Hardware Design Strategies Oriented to Edge Computing and AgriFood Electronics for Image Processing using Cellular Automata

There has been a growing interest from academia and industry in developing circuits and systems for edge computing and quality control tasks in food production lines, where image-processing is frequently required. This paper outlines the required considerations for designing a fruit classification system based on image-processing using Cellular Automata (CA) models and integrating it into reconfigurable hardware (HW) such as Field Programmable Gate Arrays (FPGAs). Parallel processing in CA requires numerous processing elements to be implemented and mapping CA models to HW generally comes with limitations. Homogeneous CA arrays are easier to design and implement in HW but can be resource-demanding. To fill this gap, this study explores different alternatives for the HW implementation of CA models, particularly trading computational-parallelism for a more optimized use of the available HW resources. We conducted experimental tests of the designed HW system using the Digilent Nexys development board, and the operation was validated against software-based benchmarks for image-processing, particularly concerning edge-detection. The presented study provides a broader range of design solutions for the HW implementation of two-dimensional CA models and a better understanding of their advantages and disadvantages. The results show that solutions focusing on instruction-parallelism add some complexity to the conception and require more design effort, compared to homogeneous CA models composed of identical cells. However, the instruction-parallel design solutions can significantly improve the HW resource utilization, especially when implementing computationally intensive CA rules in FPGAs.

The Effect of Video-Based Action Observation Training and Live Action Observation Training on Motor Function, Activity Participation, and Secondary Outcome Measures in Children With Spastic Diparetic Cerebral Palsy: A Randomized Controlled Study.

This study aimed to investigate the effects of Video-Based Action Observation Training and Live Action Observation Training on motor function, activity participation, and secondary outcome measures in children with spastic diparetic cerebral palsy (CP). Thirty-nine children with spastic diparetic cerebral palsy, aged 5-14 years, with Gross Motor Function Classification System I-III, were distributed in equal numbers to any of the Video-Based Action Observation Training (conventional physiotherapy + Video-Based Action Observation Training), Live Action Observation Training (conventional physiotherapy + Live Action Observation Training), and control (conventional physiotherapy) groups through stratified randomization. For 8 weeks, action observation training groups received 20 minutes of conventional physiotherapy followed by 20 minutes of action observation training, and the control group received 40 minutes of conventional physiotherapy. Primary outcome measures were Gross Motor Function Measurement and Child and Adolescent Scale of Participation, secondary outcome measures were Pediatric Berg Balance Scale, timed-up-and-go test, five times sit-to-stand test (FTSST), Gillette Functional Assessment Questionnaire (GFAQ), and 1-minute walk test (1MWT). Improvements were observed in all other evaluation parameters of the groups except Gross Motor Function Measurement-lying and rolling (P = .066) in the Live Action Observation Training Group, and lying and rolling (P = .317) and crawling and kneeling (P = .063) motor subtests and Gillette Functional Assessment Questionnaire-walking scale (P = .513) in the control group. Comparisons of the increases in all other measurements between the groups, except for the dimensions of Gross Motor Function Measurement-lying and rolling (P = .172), were statistically significant (P < .05) and this difference was in favor of action observation training. It was found that 2 different AOTs applied in addition to conventional physiotherapy in children with spastic diparetic cerebral palsy were more effective on all outcomes than was conventional physiotherapy alone.

A Comprehensive Survey of Studies on Predicting Anatomical Therapeutic Chemical Classes of Drugs

Drug classification plays a crucial role in contemporary drug discovery, design, and development. Determining the Anatomical Therapeutic Chemical (ATC) classes for new drugs is a laborious, costly, and intricate process, often requiring multiple clinical trial phases. Computational models offer significant benefits by accelerating drug evaluation, reducing complexity, and lowering costs; however, challenges persist in the drug classification system. To address this, a literature survey of computational models used for predicting ATC classes was conducted, covering research from 2008 to 2024. This study reviews numerous research articles on drug classification, focusing on drug descriptors, data sources, tasks, computational methods, model performance, and challenges in predicting ATC classes. It also examines the evolution of computational techniques and their application in identifying ATC classes. Finally, the study highlights open problems and research gaps, suggesting areas for further investigation in ATC class prediction. CCS Concepts: Applied computing → Life and medical sciences → Bioinformatics