Boxplot Analysis Research Articles

Comparative anatomy and achene micromorphology assessment of two Cousinia Cass. (Asteraceae) species in view of taxonomy

Comparative anatomy and achene micromorphology assessment of two Cousinia Cass. (Asteraceae) 
 species in view of taxonomy
 Deniz ULUKUŞ 1, Osman TUGAY 2
 ORCID: 0000-0002-9627-5492; 0000-0003-3980-7648
 
 1 Department of Biotechnology, Faculty of Sciences, Selçuk University, Konya, Turkey
 2 Department of Pharmaceutical Botany, Faculty of Pharmacy, Selçuk University, Konya, Turkey 
 
 Abstract
 Cousinia is a member of the monophyletic tribe Cardueae, which is traditionally subdivided into four taxonomic groups; however, the rank and boundaries of these taxa are highly controversial. The purpose of this study was to contribute to the genus taxonomy by determining the comparative anatomical and achene micromorphological features of C. aintabensis Boiss. & Hausskn. and C. birecikensis Hub.-Mor. that are morphologically similar to each other. In anatomical studies, stem, leaf, and midrib features were determined. Paraffin embedding, microtome sectioning, and safranin-fast green staining were used for the samples. The obtained sections were photographed, and the leaf, stem, and midrib characters were measured. The importance of anatomical characters between species was determined by applying Independent sample T-test test to quantitative characters. In addition, box plot and heatmap analyses of the studied species were carried out. Our results showed that the stem epidermis, inner sclerenchyma and phloem layer; For leaves; lower and upper epidermis mesophyll thickness, palisade parenchyma; In midrib; number of vascular bundles, collenchyma, and phloem are important characters that can be used in the differentiation of species. SEM microscope was used for achene micromorphological examinations. C. aintabensis achene surface ornamentation is identified as striate-retipylate. It was observed that C. birecikensis had striate and irregular reticulate-faveolate surface ornamentation.
 
 Keywords: Asteraceae, Anatomy, Cousinia, micromorphology, Türkiye

A Study on the Development of ETEC-IGR Test Devices for the Reliability Evaluation of RCD and ETEC-DPS

In this study, ETEC-IGR was developed in order to test the residual current protective device (RCD) and electric fire prevention system (ETEC-DPS) used in low-pressure electrical facilities. Furthermore, the reliability of the developed product was verified with Minitab PGM. The ETEC-IGR was designed to supply single-phase two-wire power and was equipped with an insulation transformer to protect against surge voltages and ground defects during testing. The results revealed that the load current (IL) may linearly flow from 0 A to 50 A, and the leakage current (IG) and the leakage current of the resistance component (IGR) may vary from 0.0 mA to 30 mA. Furthermore, the standby power was 98.43 W when the developed ETEC-IGR and the ETEC-DPS were connected. Moreover, the standby power was analyzed as 97.59 W when only the developed ETEC-IGR was connected. In addition, the standby power of the ETEC-DPS was interpreted as 0.84 W, and it was judged that the characteristics of the developed product were excellent. In addition, the heat dissipation environment, noise level, winding resistance, insulation resistance, voltage fluctuation rate, and AC/DC withstand voltage test of the developed test device met the relevant regulations. The normal distribution analysis of the developed ETEC-IGR revealed that the standard deviation was 0.3209, AD was 0.520, and P was 0.171. Thus, the data were interpreted as appropriate. In addition, the measured data were found to be more than 95% reliable, and the error was also less than 1 W. Furthermore, the reliability was verified with the 95% confidence interval in the box plot and histogram analysis.

Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia

Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature, rainfall, and normalized difference vegetation index (NDVI). These factors are linked to excesses drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change study. East Asia is very sensitive and susceptible to climate change. In this study, we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk; and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019. The data were studied using a series of several drought indexes, and the data were then classified using a heat map, box and whisker plot analysis, and principal component analysis. The various drought indexes from January to August improved rapidly, except for vegetation health index (VHI) and temperature condition index (TCI). While these indices were constant in September, they increased again in October, but in December, they showed a descending trend. The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984, 1993, 2007, and 2012 among the study years. The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia. The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region. The correlations among monthly precipitation anomaly percentage (NAP), NDVI, TCI, vegetation condition index (VCI), temperature vegetation drought index (TVDI), and VHI indicated considerably positive correlations, while considerably negative correlations were found among the three pairs of NDVI and VHI, TVDI and VHI, and NDVI and TCI. This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within the East Asian region. This study is a step forward in monitoring the seasonal variation of vegetation and variations in drought dynamics within the East Asian region, which will serve and contribute to the better management of vegetation, disaster risk, and drought in the East Asian region.

Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

<p>Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.</p>

Reduction of patient specific quality assurance through plan complexity metrics for VMAT plans with an open-source TPS script

PurposeVolumetric modulated arc therapy (VMAT) is a widespread technique for the delivery of normo-fractionated radiation therapy (NFRT) and stereotactic body radiation therapy (SBRT). It is associated with a significant hardware burden requiring dose rate modulation, collimator movement and gantry rotation synchronisation. Patient specific quality assurance (PSQA) guarantees that the linacs can precisely and accurately deliver the planned dose. However, PSQA requires a significant time allocation and class solutions to reduce this while guaranteeing the deliverability of the plans should be investigated. MethodsIn this study, an in-house developed Eclipse Scripting API (ESAPI) script was used to extract five independent plan complexity metrics from N = 667 VMAT treatment fields. The correlation between metrics and portal dosimetry measurements was investigated with Pearson correlation, box plot analysis and receiver operating characteristic curves, which were used to defined the best performing metric and its threshold. ResultsThe incidence of fields failing the clinical PSQA criteria of 3%/2mm (NFRT) and 3%/1.5mm (SBRT) was low (N = 1). The mean MLC opening was the metric with the highest correlation with the portal dosimetry data and among the best in discriminating the requirement of PSQA. The thresholds of 16.12 mm (NFRT) and 7.96 mm (SBRT) corresponded to true positive rates higher than 90%. ConclusionsThis work presents a quantitative approach to reduce the time allocation for PSQA by identifying the most complex plans demanding a dedicated measurement. The proposed method requires PSQA for approximately 10% of the plans. The ESAPI script is distributed open-source to ease the investigation and implementation at other institutions.

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.

Association between Serum Uric Acid Levels and Bone Mineral Density in Taiwanese Elderly Population.

Osteoporosis is a progressive metabolic bone disorder characterised by a decline in bone mineral density (BMD). Some previous studies have reported a controversial relationship between uric acid and osteoporosis. This cross-sectional study aimed to assess the association between serum uric acid levels and BMD in older adults from Taiwan. Data from participants aged ≥ 60 years were collected from 2008 to 2018. Furthermore, the participants were classified based on uric acid level quartiles. Regression models were used to assess the association between uric acid levels and bone health, including BMD values and risk of at least osteopenia. Crude and adjusted models of potential confounders, including age, sex and body mass index (BMI), were used. Compared with the first quartile of uric acid levels, the odds ratios for osteoporosis decreased in the higher uric acid level groups after adjustment for age, sex and BMI. The boxplot analysis showed that BMD values were higher in the groups with higher uric acid levels; moreover, the results of the multivariable linear regression model showed a consistent pattern. Notably, uric acid levels were positively correlated with BMD values. Higher uric acid levels in the elderly population might decrease the risk of at least osteopenia. As opposed to the anti-hyperuricemic policy for younger adults with a relatively lower risk of osteoporosis, BMD evaluation and urate-lowering therapy, goal adjustments should be considered for older adults with lower uric acid levels.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.

The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. The model resulted in an accuracy of 0.84 (SD=0.06), and an area under the curve of 0.90 (SD=0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher. The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

Design of inverse multiquadric radial basis neural networks for the dynamical analysis of MHD casson nanofluid flow along a nonlinear stretchable porous surface with multiple slip conditions

The present research, a numerical approach to examine magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) by taking inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. i.e. ANNs-IMQ-RBF. The hybridization of genetic algorithms (GAs) and sequential quadratic programming (SQP) is used for learning in ANNs-IMQ-RBF. The PDEs representing the fluid flow are converted into a nonlinear system of dimensionless form of ODEs through an appropriate transformation while effects of variation in the values of Casson parameter (β), Brownian motion parameter (Nb), Prandtl number (Pr), stretching parameter (n), porosity parameter (P), Lewis number (Le) along with temperature slip parameter (λ2) on velocity, temperature and nanofluid concentration are depicted through graphs. The effectiveness, convergence and accuracy of the proposed solver are validated evidently through boxplot analysis, histograms and cumulative distribution function (CDF) plots.

Statistical Analysis for Understanding PM2.5 Air Quality and the Impacts of COVID-19 Social Distancing in Several Provinces and Cities in Vietnam.

Air pollution, especially in urban regions, is receiving increasing attention in Vietnam. Consequently, this work aimed to study and analyze the air quality in several provinces and cities in the country focusing on PM2.5. Moreover, the impacts of COVID-19 social distancing on the PM2.5 level were investigated. For this purpose, descriptive statistic, Box and Whisker plot, correlation matrix, temporal variation, and trend analysis were conducted. R-based program and the R package "openair" were employed for the calculations. Hourly PM2.5 data were obtained from 8 national air quality monitoring sites. The study results indicated that provinces and cities in the North experienced more PM2.5 pollution compared to the Central and South. PM2.5 concentrations at each monitoring site varied significantly. Among monitoring sites, the northern sites showed high PM2.5 correlations with each other than the other sites. Seasonal variation was observed with high PM2.5 concentration in the dry season and low PM2.5 concentration in the wet season. PM2.5 concentration variation during the week was not so different. Diurnal variation showed that PM2.5 concentration rose at peak traffic hours and dropped in the afternoon. There was mainly a decreasing trend in PM2.5 concentration over the studied period. The COVID-19 pandemic has contributed to PM2.5 reduction. In the months implemented social distancing for preventing the epidemic, PM2.5 concentration declined but it would mostly increase in the following months. This study provided updated and valuable assessments of recent PM2.5 air quality in Vietnam.

Integrated omics profiling and network pharmacology uncovers the prognostic genes and multi-targeted therapeutic bioactives to combat lung cancer

Non-small cell lung cancer (NSCLC) is the frequent subtype of lung cancer and the currently used treatment methods, diagnosis, and chemoresistance are relatively ineffective. Determining the pharmacological targets from active biomolecules of medicinal plants has become a frontiers era for biomedical research to develop novel therapies. In view of these scenarios, this pilot study, network pharmacology, cheminformatics, integrative omics, molecular docking and in vitro anti-cancer analysis were performed to unveil the multi-targeted treatment mechanisms of novel plant bioactives to treat lung cancer. Bioactive molecules from medicinal plants were compiled from PubChem. Network pharmacology approach revealed that 29 compounds efficiently target the 390 human and lung cancer associated genes. In addition, comparative analysis was performed and identified the 7 bioactive molecules significantly targeting 8 lung cancer genes. The integrative omics analysis discovered unique genes between the lung cancer and normal lung tissues. These genes were further validated through protein-protein interaction, gene ontology, gene functional and pathway enrichment, boxplot and overall survival analyses to understand the function of unique genes and their involvement in cancer signaling pathways. Survival heatmap analyses identified the significant prognostic genes. Docking results revealed that, lupeol and p-coumaric acid displayed high binding affinities with MIF, CCNB1, FABP4. Hence, we selected these two bioactives for in vitro analysis. Furthermore, these selected bioactives were showed concentration dependent cytotoxicity against the lung adenocarcinoma cells (A549). This holistic study has opened up novel avenues and unravels the cancer prognostic genes which could serve as druggable target and bioactives with anti-cancerous efficacy. Further functional validations are prerequisites to deciphering these bioactives as commercial drug candidates.

Effect of alkali modification on the powder flowability of rapeseed straw cellulose fibers

ABSTRACT Powder flowability of natural reinforced fibers has a significant effect on the processing and properties of the composite. Sodium hydroxide modified rapeseed straw reinforced fibers were prepared in four fiber sizes and the powder flowability before and after modification was tested. The results showed that the alkali modification reduced the total flowability index of the fibers by 6–11 within the test range, with the effect on each flowability index in the order of compression > angle of spatula > angle of repose > miformity. This effect was mainly attributed to morphological, chemical and surface characteristics. Moreover, the slimming effect of the alkali modification reduced the average particle size by 9.4% to 30.8%, exacerbating the anisotropy of the morphological structure. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) analyses showed that pectin, lipids, hemicellulose and impurities were removed, exposing the clean rough fiber surface. Box plot analysis showed that the modification resulted in a uniform and regular distribution of shape factors. This work has valuable implications for the industrial application of straw cellulose fibers in the field of natural fiber composites.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Clustering airborne gamma-ray spectrometry data in Nova Friburgo, State of Rio de Janeiro, southeastern Brazil

The goal of this study was to test different image clustering techniques, using airborne gamma-ray spectrometry data to optimize the interpretation of areas with similar properties in large scale. The methodology applied was based on the comparison of two techniques of data-driven classification (K-means and Gaussian Mixture Models) and a technique of knowledge-driven classification (Simplified RGB) to discriminate domains from the primary variables potassium (K), uranium (eU), and thorium (eTh) obtained from airborne gamma-ray spectrometry surveys. The performance of these three methods was evaluated through pre-processing techniques and post-processing including the best number of clusters/classes, visual interpretation, internal validation, and boxplot analysis. The clustering performance was considered satisfactory through the visual interpretation and comparison with the geological map and the DEM, for all three methods. For a quantitative analysis, the simplest model of unsupervised clustering Gaussian Mixture Models prevailed.

Modelling of Sea Water Level During High Tide Using Statistical Method and Neural Network

Recently, the rise of sea level has caused an increase in rising tides that affected about three million locations around the world. The tide rising phenomenon has been occurring in Peninsular Malaysia since the 20th century. The purpose of this study is to determine the most critical station and forecast three stations located on the West Coast of Peninsular Malaysia. The Box Plot analysis method has been used in this study; the results shown that Bagan Datuk Station is the most critical station. This is due to the maximum tide’s value of Bagan Datuk Station experienced the highest increment of 0.45 m, compared to Port Klang station and Permatang Sedepa Station with only 0.2 m increment in 10 years. However, these three stations are also experiencing rising tides. Thus, the focus of managing coastal structures should be given to all these three stations as well. In addition, for forecasting, the Artificial Neural Network (ANN) forecasting model provides better forecasting results compared to the Autoregressive Integrated Moving Average (ARIMA) model for long-term forecast. In this study, the artificial Neural Network (ANN) forecasting model obtained value of RMSE 0.05642 at Bagan Datuk Station compared to the RMSE value of 0.0928 obtained from the ARIMA model at the same station. Besides, MAE value of ANN method, 0.04387 compared to the MAE value of ARIMA which is worth 0.06391 at Bagan Datuk Station. This study can conclude that the Artificial Neural Network (ANN) forecasting model is better in high tide forecasting.

Clinical and Molecular Features of Highly Durable Response to Azacitidine+Venetoclax in Acute Myeloid Leukemia

Introduction: In older and/or unfit patients with newly diagnosed AML, the median duration of response to azacitidine+venetoclax (Aza/Ven) is 17.5 months. However, there is wide variability in duration of response, with some patients relapsing quickly and others with highly durable responses. Although several factors including measurable residual disease negativity and the presence of an NPM1 and/or IDH2 mutation have been associated with durable responses, it is currently difficult to predict which patients may achieve very prolonged remissions with Aza/Ven. This uncertainty may be stressful for patients and can make decisions regarding other available therapies such as allogeneic stem cell transplant challenging. We hypothesized that specifically studying the very long-term responders to Aza/Ven (an outlier group we have termed "Super Responders") may identify predictors of durable responses. Methods: To investigate predictors of very long-term response to Aza/Ven, charts of patients with newly diagnosed AML treated with Aza/Ven between January 2015 and December 2021 at a single institution were reviewed. Baseline patient demographic and clinical data was recorded, and responses to Aza/Ven were assessed according to ELN criteria. Patients who proceeded to transplant after responding to Aza/Ven were excluded from this analysis. To identify a group of Super Responders, event-free survival (EFS) was calculated and outliers were identified by Interquartile Range (IQR) box plot analysis. Clinical and disease characteristics of the Super Responders were then compared to the Non-Super Responders using chi-square or Fisher's exact tests for categorical variables and t-tests or Wilcoxon tests for continuous variables. Approval for this retrospective study was obtained from the Institutional Review Board. Results: Charts from 184 patients treated with frontline Aza/Ven for newly diagnosed AML were reviewed. After excluding 40 patients who proceeded to allogeneic transplant after response to Aza/Ven, 144 patients were analyzed. Fifteen outliers (termed Super Responders) with the longest EFS were identified by box plot analysis (Figure 1). These 15 patients also represented the top 10% of EFS (represented by green dots in Figure 1). The n=15 Super Responders had a median EFS of 31.8 months (range 23.7 to 84.6 months) compared to a median EFS of 3.4 months (range 0 to 20.8 months) among the n=129 Non-Super Responders (p<0.001). There was no significant difference in the frequency of secondary or treatment-related AML, SWOG cytogenetic classification, or ELN risk group when comparing the Super Responders to the Non-Super Responders (Table 1). Regarding molecular profile, mutations identified in Super Responders included: TET2 (n=7), SRSF2 (n=5), NPM1 (n=5), IDH2 (n=4), RUNX1 (n=2), IDH1 (n=2), DNMT3A (n=2), PHF6 (n=2), ASXL1 (n=2), TP53 (n=1), SF3B1 (n=1), and FLT3-TKD (n=1). The Super Responders had a higher incidence of IDH mutations, 40% vs 14% (p = .02). The rate of NPM1 mutations was not significantly different between groups, although 0% of subjects with NPM1 mutations in the Super Responder group had concomitant FLT3-ITD mutations compared to 40% in the Non-Super Responder group. No Ras pathway mutations were identified in Super Responders. Conclusions: Traditional prognostic factors including ELN risk group, cytogenetic classification, and having secondary or treatment-related AML do not predict whether a patient will have a highly durable response to Aza/Ven. Consistent with prior reports, IDH mutations (especially IDH2 mutations) are more common in Super Responders. NPM1 mutations did not appear to be more common in Super Responders in our analysis but this may be related to not controlling for the presence of a co-occurring FLT3-ITD mutation. Notably, no Super Responders had FLT3-ITD or Ras pathway mutations, consistent with prior data suggesting that Ras mutations may contribute to resistance to Aza/Ven. Multivariate analyses and analyses looking at the role of co-occurring mutations and measurable residual disease in predicting the likelihood of highly durable response to Aza/Ven are ongoing. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Implementation of boosting algorithms for prediction of punching shear strength of RC column footings

For more accurate and reliable prediction of punching shear strength of reinforced concrete (RC) column footings, machine learning (ML) was utilized. To develop ML-based strength prediction models, an experimental database was built by collecting test results (total 218 specimens without shear reinforcement and subjected to concentric loads) from the literature, and three boosting algorithms including the adaptive boosting (AdaBoost), gradient boosting (GBR), and extreme gradient boosting (XGBoost) were implemented. Input variables were carefully selected based on existing study results, and optimal hyperparameters of the ML-based models were determined based on the grid search with k-fold cross-validation. The ML-based models with the optimal hyperparameters showed highly accurate predictions without any significant and evident bias for all input variables, and outperformed current design codes and existing models. The relative feature importance of input variables showed that the effective footing depth is the most important variable for the prediction performance, and the boxplot analysis, ANOVA test, and t-test results revealed that spring or uniform load supports can be used instead of sand supports to avoid the complexity of the test setup. Finally, based on the study results, a simple design equation was proposed to widen the scope of application and to consider the effects of the shear span-to-depth ratio, steel yield strength, and test setup.

Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation

Objective. In recent years, methods based on U-shaped structure and skip connection have achieved remarkable results in many medical semantic segmentation tasks. However, the information integration capability of this structure is still limited due to the incompatibility of feature maps of encoding and decoding stages at corresponding levels and lack of extraction of valid information in the final stage of encoding. This structural defect is particularly obvious in segmentation tasks with non-obvious, small and blurred-edge targets. Our objective is to design a novel segmentation network to solve the above problems. Approach. The segmentation network named Global Context-Aware Network is mainly designed by inserting a Multi-feature Collaboration Adaptation (MCA) module, a Scale-Aware Mining (SAM) module and an Edge-enhanced Pixel Intensity Mapping (Edge-PIM) into the U-shaped structure. Firstly, the MCA module can integrate information from all encoding stages and then effectively acts on the decoding stages, solving the problem of information loss during downsampling and pooling. Secondly, the SAM module can further mine information from the encoded high-level features to enrich the information passed to the decoding stage. Thirdly, Edge-PIM can further refine the segmentation results by edge enhancement. Main results. We newly collect Magnetic Resonance Imaging of Colorectal Cancer Liver Metastases (MRI-CRLM) dataset in different imaging sequences with non-obvious, small and blurred-edge liver metastases. Our method performs well on the MRI-CRLM dataset and the publicly available ISIC-2018 dataset, outperforming state-of-the-art methods such as CPFNet on multiple metrics after boxplot analysis, indicating that it can perform well on a wide range of medical image segmentation tasks. Significance. The proposed method solves the problem mentioned above and improved segmentation accuracy for non-obvious, small and blurred-edge targets. Meanwhile, the proposed visualization method Edge-PIM can make the edge more prominent, which can assist medical radiologists in their research work well.

Design of inverse multiquadric radial basis neural networks for the dynamical analysis of wire coating problem with Oldroyd 8-constant fluid

Wire coating is a commercial method to insulate wires for mechanical intensity and environmental protection. In this experimental study, the technique of computational intelligence is used for nonlinear wire coating analysis by soaking the wires in Oldroyd 8-constant fluid under a constant pressure gradient with the help of feed forward artificial neural networks (ANNs). The system of partial differential equations generated for the process of wire coating is transformed into a nonlinear dimensionless ordinary differential equation. One of the salient features of ANNs is the mathematical modeling of transformed equations by exploring the unsupervised error. A new scheme based on inverse multiquadric neural networks (IMQNNs) is used with a hybridization process of well-known genetic algorithms (GAs) and sequential quadratic programming (SQP) to obtain expeditious convergence, i.e., IMQNNs-GA-SQP. The applicability of the problem is investigated by altering the values of the dilatant constant, pressure gradient, shear stress, and pseudo-plastic constant, the outcome of which is in the form of varying polymer coating thickness. Comparison of highly accurate results in the shape of error analysis based on absolute errors of obtained results with those of the reference solution, calculated using the Adams numerical technique using MATHEMATICA software, statistical analysis such as root-mean-square error, Theil’s coefficient for inequality, E-R2 (error function based on the coefficient of determination), E-VAF (variance accounted for), E-NSE (Nash–Sutcliffe efficiency), mean absolute error, box plot analyses, and the cumulative distributive function through histogram analyses, is also carried out in this research, which guarantees the effectiveness of the used scheme.

USE OF BITUMEN-TREATED FABRIC MULCH DURING FIRST YEAR REPLANTING OF IMMATURE OIL PALMS

The use of empty fruit bunch (EFB) mulching in oil palm replants has for decades been implemented as best practice across the industry. In this study, several trials were initiated to evaluate the effectiveness of bitumen-treated fabric mulch (BFM) as an alternative for EFB mulching on oil palm growth performance in replants. The trial was conducted in Jeleta Bumi Estate, Sabah for 12 months. The treatments evaluated were mulching using BFM with a total quantity of first-year fertiliser applied underneath (T1) and normal EFB mulching with the standard bi-monthly manuring programme (T2). The treatments were arranged using randomised complete block design (RCBD) with four replications. The palms’ rachis length, petiole cross-section, and total fronds were measured 12 months after planting to calculate the relative leaf area (RLA), true leaf area (TLA), and leaf area index (LAI). Leaves from frond number nine were sampled for nutrient analysis. Concurrently, these treatments were replicated at a semi-commercial scale in five estates totalling 466.36 hectares under T1. Generally, there was no significant difference (P&gt;0.05) in all parameters measured for both vegetative performance and leaf nutrients between T1 and T2. Additionally, box plot analysis indicated a uniform growth pattern across treatments from the calculated RLA, TLA, and LAI. Cost-benefit analysis indicated a saving of up to 62 per cent at 12 months after planting. This includes costs of EFB transportation, weeding and manuring rounds. Concomitantly, labour dependency could be further reduced, particularly of interest under current industry labour shortages. Response by users highlighted in reduced road maintenance, minimised rhinoceros beetle damage and lower frond scorching.