Predictive Test Research Articles

Prediksi Harga Kelapa Sawit Menggunakan Metode Extreme Learning Machine

Palm oil is one of the keys to the Indonesian economy and the main commodity for attracting foreign investment. The palm oil and palm kernel industry generates most of the foreign currency from palm oil. The price of palm oil often goes up and down every month resulting in instability in the income received by people who own oil palm plantations. The aim of predicting palm oil prices is to carry out appropriate planning or steps for palm oil business actors. One way to overcome this problem is to make predictions. One method that can make predictions is the Extreme Learning Machine (ELM). ELM is an artificial neural network method used to predict palm oil prices. The ELM method is a feedforward method with a single hidden layer which is better known as a single hidden layer feedforward neural network (SLFNs). In this research, the best implementation was 5 inputs with 20 neurons in the hidden layer with output in the form of palm oil price predictions. Based on the tests carried out, the research produced the smallest error rate of 0.0027111424247658633 using 20 neurons in the hidden layer so that the latest data prediction test results for 5 price rotations in September rotation 1 were 1400.314191, September rotation 2 were 1846.798921, September rotation 3 amounted to 1505.430419, September rotation 4 amounted to 2301.853412, September rotation 5 amounted to 2645.082489 in palm oil price predictions.

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network.

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

The comprehensive English National Lynch Syndrome Registry: development and description of a new genomics data resource

Lynch Syndrome (LS) is a cancer predisposition syndrome caused by constitutional pathogenic variants in the mismatch repair (MMR) genes. To date, fragmentation of clinical and genomic data has restricted understanding of national LS ascertainment and outcomes, and precluded evaluation of NICE guidance on testing and management. To address this, via collaboration between researchers, the National Disease Registration Service (NDRS), NHS Genomic Medicine Service Alliances (GMSAs), and NHS Regional Clinical Genetics Services, a comprehensive registry of LS carriers in England has been established. For comprehensive ascertainment of retrospectively identified MMR pathogenic variant (PV) carriers (diagnosed prior to January 1, 2023), information was retrieved from all clinical genetics services across England, then restructured, amalgamated, and validated via a team of trained experts in NDRS. An online submission portal was established for prospective ascertainment from January 1, 2023. The resulting data, stored in a secure database in NDRS, were used to investigate the demographic and genetic characteristics of the cohort, censored at July 25, 2023. Cancer outcomes were investigated via linkage to the National Cancer Registration Dataset (NCRD). A total of 11,722 retrospective and 570 prospective data submissions were received, resulting in a comprehensive English National Lynch Syndrome Registry (ENLSR) comprising 9030 unique individuals. The most frequently identified pathogenic MMR genes were MSH2 and MLH1 at 37.2% (n=3362) and 29.1% (n=2624), respectively. 35.9% (n=3239) of the ENLSR cohort received their LS diagnosis before their first cancer diagnosis (presumptive predictive germline test). Of these, 6.3% (n=204) developed colorectal cancer, at a median age of initial diagnosis of 51 (IQR 40-62), compared to 73 years (IQR 64-80) in the general population (p<0.0001). The ENLSR represents the first comprehensive national registry of PV carriers in England and one of the largest cohorts of MMR PV carriers worldwide. The establishment of a secure, centralised infrastructure and mechanism for routine registration of newly identified carriers ensures sustainability of the data resource. This work was funded by the Wellcome Trust, Cancer Research UK and Bowel Cancer UK. The funder of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

(243) Frenular Delta Excision with Neurectomy of Ventro – Lateral Branches of Penile Dorsal Nerves (Alaa Aglan4 Operation)

Abstract Introduction Neurectomy is one of the surgical options used for treatment of premature ejaculation, variable degrees of neurectomy could be done, aggressive neurectomy may cause severe demenishion in sensations as occurs in cases of perovic technique for penis elongation or in cases of sliding techniques used in treatment of peyronie`s disease also it increases the risk of erectile dysfunction. In our old trials in phalloplasty and penile implantation we were doing neurectomy routinely with few exceptions, the success rate at that time was 78% and some side effects were reported e.g neuroma, numbness, and recurrence. After application of prediction test (spraying local anesthesia procomail at frenular area (temporary excision) and local injection around the chosen nerve/s) the picture has been changed. as we could predict the expected gained time post-surgery. The classic neurectomy is related to neural supply of glans. once we was doing classic Alaa Aglan operation (cutting bulbospongiosus muscles bilaterally plus frenular delta excision) but we found the muscles congenitally absent so we excised part of frenular delta with neurectomy to neural supply to the same area (i.e. ventro – lateral branches of dorsal nerves) the results were satisfactory to the patient. Many patients were afraid of muscles cutting at our early trials, so this operation was the alternative option, dorsal neurectomy could be done with it according to prediction tests in some cases (usually with implants). Objective Presentation of new operation for treatment of premature ejaculation (Alaa Aglan4). Methods Study was done between 3/12/2011 – 8/2/2017 (5 years following up), 247 patients were operated (after prediction test), partial excision of frenular delta plus neurectomy to ventral branches of RT and LT penile dorsal nerves. Neurectomy was done at middle third of penile shaft before branching. Dissection of proximal part till penis root and retraction of the proximal end and placing it at penis root area (to avoid reunion or recurrence). Results Numbness was reported in 13 patients disappeared within 3 months, neuroma was obvious in 7 patients treated by vitamin B injection in 5 patient’s. Re-excision of 2mm of proximal neural end (under local anesthesia) in 3 patients. No recurrence was reported. The dissection of nerves and covering the proximal end with inverted skin graft or silicone cap as a barrier between the nerves ends to prevent reunion and recurrence was done outside this study, but this technique is simple and easy. Erectile dysfunction was not reported in all patients. after 4 months all patients were satisfied by the ejaculation time. Conclusions Alaa Aglan4 operation is simple, effective, safe and permanent treatment for premature ejaculation guided by prediction test. Disclosure No.

An Evolved Transformer Model for ADME/Tox Prediction

Drug discovery aims to keep fueling new medicines to cure and palliate many ailments and some untreatable diseases that still afflict humanity. The ADME/Tox (absorption, distribution, metabolism, excretion/toxicity) properties of candidate drug molecules are key factors that determine the safety, uptake, elimination, metabolic behavior and effectiveness of drug research and development. The predictive technique of ADME/Tox drastically reduces the fraction of pharmaceutics-related failure in the early stages of drug development. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, artificial intelligence techniques such as Graphormer are showing increasing promise and usefulness to perform custom models for molecule modeling tasks. However, Graphormer and other transformer-based models do not consider the molecular fingerprint, as well as the physicochemicals that have been proved effective in traditional computational drug research. Here, we propose an enhanced model based on Graphormer which uses a tree model that fully integrates some known information and achieves better prediction and interpretability. More importantly, the model achieves new state-of-the-art results on ADME/Tox properties prediction benchmarks, surpassing several challenging models. Experimental results demonstrate an average SMAPE (Symmetric Mean Absolute Percentage Error) of 18.9 and a PCC (Pearson Correlation Coefficient) of 0.86 on ADME/Tox prediction test sets. These findings highlight the efficacy of our approach and its potential to enhance drug discovery processes. By leveraging the strengths of Graphormer and incorporating additional molecular descriptors, our model offers improved predictive capabilities, thus contributing to the advancement of ADME/Tox prediction in drug development. The integration of various information sources further enables better interpretability, aiding researchers in understanding the underlying factors influencing the predictions. Overall, our work demonstrates the potential of our enhanced model to expedite drug discovery, reduce costs, and enhance the success rate of our pharmaceutical development efforts.

Time-Dependent Prediction Models for Individual Prognosis of Chronic Postsurgical Pain following Knee Replacement Based on an Extensive Multivariable Data Set

(1) Background: Clinically useful prediction models for chronic postsurgical pain (CPSP) in knee replacement (TKA) are lacking. (2) Methods: In our prospective, multicenter study, a wide-ranging set of 91 variables was collected from 933 TKA patients at eight time points up to one year after surgery. Based on this extensive data pool, simple and complex prediction models were calculated for the preoperative time point and for 6 months after surgery, using least absolute shrinkage and selection operator (LASSO) 1se and LASSO min, respectively. (3) Results: Using preoperative data only, LASSO 1se selected age, the Revised Life Orientation Test on pessimism, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)—subscore pain and the Timed “Up and Go” Test for prediction, resulting in an area under the curve (AUC) of 0.617 and a Brier score of 0.201, expressing low predictive power only. Using data up to 6 months after surgery, LASSO 1se included preoperative Patient Health Questionnaire-4, Knee Injury and Osteoarthritis Outcome Score (KOOS)—subscore pain (pain) 3 months after surgery (month), WOMAC pain 3 and 6 months, KOOS subscore symptoms 6 months, KOOS subscore sport 6 months and KOOS subscore Quality of Life 6 months. This improved the predictive power to an intermediate one (AUC 0.755, Brier score 0.168). More complex models computed using LASSO min did little to further improve the strength of prediction. (4) Conclusions: Even using multiple variables and complex calculation methods, the possibility of individual prediction of CPSP after TKA remains limited.

The relationship between vertebral compression rates and systemic inflammatory scores in osteoporotic vertebral fractures.

There are many scientific reports on systemic inflammation scores (SIS) associated with decreased bone mineral density in osteoporotic vertebral disease. However, there are no studies on the association of inflammation scores with the risk of collapse in osteoporotic vertebral collapse fractures. The aim of this study was to examine the correlation between the product of platelet and neutrophil counts (PPN), platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), and systemic immune inflammation index (SII) derived from complete blood count analysis in cases of osteoporotic vertebral fractures and fracture severity based on vertebral collapse rates. This study is a retrospective analysis of a cohort of 50 patients aged 50 years or older who presented with osteoporotic vertebral fractures and underwent kyphoplasty at our clinic from 2018 to 2023. The study included both men and women. Computed tomography (CT) and magnetic resonance imaging (MRI) were used to diagnose and differentiate osteoporotic vertebral compression fractures from burst fractures and pathologic fractures. All compression rate measurements were performed with CT. The compression rate of the most affected vertebra (MAV-CR) was calculated. Groups were divided into two categories based on their compression rates: <50% and ≥50%. Initial PPN, PLR, NLR, and SII parameters were used as systemic inflammation scores. No statistically significant differences were found between MAV-CR groups in PPN, PLR, NLR, and SII parameters (p>0.05). No statistically significant correlation was observed between inflammation scores and MAV-CR groups (p>0.05). In this comparison, no significant difference was observed between the selected CBC parameters and the groups divided according to the compression rate (WBC: p=0.725, PC: p=0.069, NC: p=0.732, LC: p=0.513). ROC analysis was performed to analyze the diagnostic tests (AUC=0.372 for PPN, AUC=0.509 for PLR, AUC=0.525 for NLR, and AUC=0.435 for SII). None of the systemic inflammation scores had any predictive value for osteoporotic vertebral collapse fractures. Although it has been established in the scientific literature that systemic inflammation scores are associated with osteoporotic vertebral fractures, our analysis indicates no statistically significant correlation between the parameters of PPN, PLR, NLR, and SII and the severity of compression fractures in individuals with osteoporotic vertebral fractures. In this study, using systemic inflammation scores as a predictive test for the severity of osteoporotic vertebral fractures does not seem appropriate.

PEMANFAATAN MACHINE LEARNING UNTUK OPTIMASI HARGA DENGAN MENGGUNAKAN MODEL ARTIFICIAL NEURAL NETWORK PADA PT ASIA GARMENT ACCESSORIES

Today's market is characterized by intense competition, as relatively many similar goods are in the market. One way to win the competition is with a pricing strategy. PT Asia Garment's Accessories is a company engaged in the garment accessories industry, with products such as lace, mote, embroidery, and fabric materials. Pricing is determined by the quality of the product, the level of time efficiency, and the difficulty of the work. The purpose of the research is how to get price optimization using the Artificial Neural Network model. In this study, the method used for data collection is the observation, interviews, and literature studies. According to the results of the price prediction test using Python and machine learning, it is concluded that the price offered is a best price for several products including lace and imported applications, because the predicted price is higher than the original price. Predictions made using ANN produce an accuracy output of 87% that can be said to be good accuracy.

THE ANALYSIS OF STUDENT'S DIFFICULTIES IN COMPREHENDING MACRO AND MICRO-SKILLS OF LISTENING

The research explored the challenges of students undertaking the TOEFL prediction test, specifically in the listening section. The study utilized descriptive qualitative research and employed a questionnaire. To attain proficiency in listening comprehension, students must grasp macro and micro skills, as Brown’s taxonomy outlines. The study demonstrated that students encounter difficulty in listening comprehension due to four major factors: sound, vocabulary, grammar, and pragmatic awareness. The findings indicated that students' struggle to understand spoken language lies in micro-skills, particularly at lower proficiency levels of recognizing word sounds. Consequently, introducing micro-skills is essential to enhance students' language knowledge.

A comparison between grey-box models and neural networks for indoor air temperature prediction in buildings

Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air temperature prediction tests have been performed on simulated and measured data from buildings with different thermal insulation and thermal mass during both heating and cooling seasons. Results show that neural networks have, on average, a better prediction performance than grey-box models. Both modelling approaches are affected by the building characteristics and by the season considered. The grey-box models require less training data, although the latter seems to play a role only in the worse-performing tests. When user setpoint changes in the testing phase, the LSTM neural network shows a significant drop in the root mean square error. In conclusion, although LSTM outperforms grey-box models on average, the reduced training data and higher reliability under normal operating conditions, as well as their linearity, make RC models a strong alternative for predictive controllers.

Ppe.RPT/SSC-1: from QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach

Genomic regions associated with ripening time (RPT) and soluble solids concentration (SSC) were mapped using a pedigreed population including multiple F1 and F2 families from the Clemson University peach breeding program (CUPBP). RPT and SSC QTLs were consistently identified in two seasons (2011 and 2012) and the average datasets (average of two seasons). A target region spanning 10,981,971–11,298,736 bp on chromosome 4 of peach reference genome used for haplotype analysis revealed four haplotypes with significant differences in trait values among different diplotype combinations. Favorable alleles at the target region for both RPT and SSC were determined and a DNA test for predicting RPT and SSC was developed. Two Kompetitive Allele Specific PCR (KASP) assays were validated on 84 peach cultivars and 163 seedlings from the CUPBP, with only one assay (Ppe.RPT/SSC-1) needed to predict between early and late-season ripening cultivars and low and high SSC. These results advance our understanding of the genetic basis of RPT and SSC and facilitate selection of new peach cultivars with the desired RPT and SSC.

Numerical simulation and experimental testing for static failure prediction in additively manufactured below-knee prosthetic sockets.

The socket of a transtibial prosthesis is a structural part customized to a patient's amputated residual lower limb. The free-form geometry of the socket can be suitable for additive manufacturing (AM) to save time and cost. However, the mechanical fracture of additively manufactured lower limb prostheses is not yet fully understood. A novel experimental method and numerical approach by finite element method (FEM) to test the strength and fracture behavior of a lower limb prosthetic socket of acrylonitrile butadiene styrene (ABS), reverse-engineered using computer-aided design (CAD) from the actual amputee's residual limb and manufactured using fused filament fabrication (FFF) are proposed in the present work. The mechanical behavior, von Mises stress distribution, and the damage status of layered AM sockets of different thicknesses were simulated by FEM using Hashin's transversely isotropic mechanical damage model, initially developed for composite materials. The experimental work showed that the fracture failure initiated at the corner of the lobe in the 4 mm thickness socket at a failure load of 918.5 N. The FEM results predicted this failure load to be 896.6 N, with only a 2.45% error as compared to the experiment. The failure loads predicted by FEM in the sockets with thicknesses of 3, 5, and 6 mm were 618.1, 1008.6, and 1105.2 N, respectively. The present work provides a dependable method for testing a below-knee prosthetic socket against static failure and arriving at a factor-of-safety (FoS) based socket thickness selection for any amputee.

Magnetic resonance imaging improves specificity of placenta accreta spectrum requiring hysterectomy compared to ultrasound

BackgroundMagnetic resonance imaging (MRI) has been increasingly used as an adjunct to ultrasound (US) imaging for Placenta Accreta Spectrum (PAS) assessment and preoperative surgical planning, but its value is not yet established. The US-based Placenta Accreta Index (US-PAI) is a well-validated standardized approach for PAS evaluation. PAS-MR markers have been outlined in a joint guideline from Society of Abdominal Radiology and European Society of Urogenital Radiology. ObjectiveTo compare PAS-MR parameters and US-PAI in pregnancies at high risk for PAS and assess MRI's additional diagnostic value for PAS requiring cesarean hysterectomy. Study designThis was a single-center retrospective study of pregnant patients who received MRI in addition to US due to suspected PAS. US-PAI and PAS-MR parameters were obtained. Student's t-test and Fisher's exact test between the primary outcome (hysterectomy vs. no hysterectomy) were performed. Diagnostic performance between MRI and US-PAI were assessed using multivariable logistic regression, receiver operating characteristics (ROC) curve, DeLong test, McNemar test and relative predictive value test. ResultsA total of 82 patients were included in the study; 41 of whom required hysterectomy. All patients who underwent hysterectomy had International Federation of Gynecology and Obstetrics (FIGO) clinical evidence of PAS at the time of delivery. Multiple parameters of US-PAI and PAS-MR predicted hysterectomy, with greatest dimension of invasion by MRI as the best quantitative predictor. At 96% sensitivity for hysterectomy, cutoff values were 3.5 for US-PAI and 2.5 cm for greatest dimension of invasion by MRI. Using this sensitivity, greatest dimension of invasion by MRI had higher specificity (p = 0.0016) and positive predictive value (PPV) (p = 0.0018) than those of US-PAI, indicating an improved diagnostic threshold.Conclusion: In a suspected high-risk group for PAS, MRI identifies more patients who will not need hysterectomy compared to US-based criterion only. MRI has the potential to aid patient counseling, surgical planning, and delivery timing, including preterm delivery decisions for patients with PAS requiring hysterectomy.

Prediction of peak oxygen consumption using cardiorespiratory parameters from warmup and submaximal stage of treadmill cardiopulmonary exercise test.

This study investigates the quality of peak oxygen consumption (VO2peak) prediction based on cardiac and respiratory parameters calculated from warmup and submaximal stages of treadmill cardiopulmonary exercise test (CPET) using machine learning (ML) techniques and assesses the importance of respiratory parameters for the prediction outcome. The database consists of the following parameters: heart rate (HR), respiratory rate (RespRate), pulmonary ventilation (VE), oxygen consumption (VO2) and carbon dioxide production (VCO2) obtained from 369 treadmill CPETs. Combinations of features calculated based on the HR, VE and RespRate time-series from different stages of CPET were used to create 11 datasets for VO2peak prediction. Thirteen ML algorithms were employed, and model performances were evaluated using cross-validation with mean absolute percentage error (MAPE), R2 score, mean absolute error (MAE), and root mean squared error (RMSE) calculated after each iteration of the validation. The results demonstrated that incorporating respiratory-based features improves the prediction of VO2peak. The best results in terms of R2 score (0.47) and RMSE (5.78) were obtained for the dataset which included both cardiac- and respiratory-based features from CPET up to 85% of age-predicted HRmax, while the best results in terms of MAPE (10.5%) and MAE (4.63) were obtained for the dataset containing cardiorespiratory features from the last 30 seconds of warmup. The study showed the potential of using ML models based on cardiorespiratory features from submaximal tests for prediction of VO2peak and highlights the importance of the monitoring of respiratory signals, enabling to include respiratory parameters into the analysis. Presented approach offers a feasible alternative to direct VO2peak measurement, especially when specialized equipment is limited or unavailable.

Disintegration testing for orally disintegrating tablets (ODTs): An overview

Prescribing orally disintegrating tablet (ODT) dosage form is a common practice. For several populations, including geriatrics and pediatrics, ODTs are one of the most desired dosage forms. They are solid dosage forms designed to disintegrate or dissolve very quickly in contact with saliva as soon as they are placed in oral cavity of a patient. According to United States Pharmacopeia (USP), every dosage form has to pass some quality control tests to ensure uniformity and performance of this dosage form. For ODTs, disintegration test is fundamental to ascertain time required for tablets to break down and release their contents for dissolution and absorption. Additionally, it is a crucial predictive test for determining in vitro/in vivo association. There are no mandatory universal disintegration testing standards for ODTs, however, as stated in the USP. Instead, recent USP refers to individual manufacturer monograph specifications which may vary from one monograph to another. This article discusses several developed disintegration tests and methods for ODTs such as OD-mate testing, basket rack assembly, CCD camera, texture analyzer (TA), special disintegration equipment, prototype disintegration tester (PDT), simple approach, and modified wetting test and elaborates on their advantages and disadvantages.

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height.

English Distance Teaching Based on SPOC Classroom and Online Mixed Teaching Mode

At present, the English Small Private Online Course (SPOC) online mixed teaching model has problems in evaluating students' learning and organizing teaching papers. For example, the evaluation is chaotic and unable to meet the key points of organizing the paper. Starting from the thinking chain that accepting learning outcomes can promote learning behavior, a score prediction method and test paper generation algorithm (TPGA) based on a learning evaluation diagnostic model are designed. Among them, the performance prediction algorithm is designed by combining multiple linear regression (MLR) and random forest (RF). The TPGA is based on students' learning status. The research results show that most of the predicted values output by the performance prediction model are not significantly different from the actual values. They are within a reasonable range. Meanwhile, under the influence of TPGA, the number of students in the experimental group is higher in the 70-80 and 80-90 segments, with 27 and 6, respectively. The experimental group has a higher average score rate on each type of question and knowledge point. Both models have high student satisfaction, indicating that the results oriented online mixed learning strategy designed in the study can effectively improve students' learning outcomes.

Applications of Digital Polymerase Chain Reaction (dPCR) in Molecular and Clinical Testing.

Digital polymerase chain reaction (dPCR) is an accurate and sensitive molecular method that can be used in clinical diagnostic, prognostic, and predictive tests. The key component of the dPCR method is the partitioning of a single reaction into many thousands of droplets, nanochannels or other nano- or picoliter-sized reactions. This results in high enough sensitivity to detect rare nucleic acid targets and provides an absolute quantification of target sequences or alleles compared to other PCR-based methods. An increasing number of dPCR platforms have been introduced commercially in recent years and more are being developed. These platforms differ in the method of partitioning, degree of automation, and multiplexing capabilities but all can be used in similar ways for sensitive and highly accurate quantification of a variety of nucleic acid targets. Currently, clinical applications of dPCR include oncology, microbiology and infectious disease, genetics, and prenatal/newborn screening. Commercially available tests for clinical applications are being developed for variants with diagnostic, prognostic, and therapeutic significance in specific disease types. The power of dPCR technology relies on the partitioning of the reactions and results in increased sensitivity and accuracy compared to qPCR. More recently, the sensitivity of dPCR has been applied to the detection of known variants in cell-free DNA and circulating tumor DNA. Future clinical applications of dPCR include liquid biopsy, treatment resistance detection, screening for minimal residual disease, and monitoring allograft engraftment in transplanted patients.

Urban community governance and machine learning: practice and prospects for intelligent decision making

Abstract Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.

Skeletal Muscle Area on CT: Determination of an Optimal Height Scaling Power and Testing for Mortality Risk Prediction.

BACKGROUND. Sarcopenia is commonly assessed on CT by use of the skeletal muscle index (SMI), which is calculated as the skeletal muscle area (SMA) at L3 divided by patient height squared (i.e., a height scaling power of 2). OBJECTIVE. The purpose of this study was to determine the optimal height scaling power for SMA measurements on CT and to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. METHODS. This retrospective study included 16,575 patients (6985 men, 9590 women; mean age, 56.4 years) who underwent abdominal CT from December 2012 through October 2018. The SMA at L3 was determined using automated software. The sample was stratified into two groups: 5459 patients without major medical conditions (based on ICD-9 and ICD-10 codes) who were included in the analysis for determining the optimal height scaling power and 11,116 patients with major medical conditions who were included for the purpose of testing this power. The optimal scaling power was determined by allometric analysis (whereby regression coefficients were fitted to log-linear sex-specific models relating height to SMA) and by analysis of statistical independence of SMI from height across scaling powers. Cox proportional hazards models were used to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. RESULTS. In allometric analysis, the regression coefficient of log(height) in patients 40 years old and younger was 1.02 in men and 1.08 in women, and in patients older than 40 years old, it was 1.07 in men and 1.10 in women (all p < .05 vs regression coefficient of 2). In analyses for statistical independence of SMI from height, the optimal height scaling power (i.e., those yielding correlations closest to 0) was, in patients 40 years old and younger, 0.97 in men and 1.08 in women, whereas in patients older than 40 years old, it was 1.03 in men and 1.09 in women. In the Cox model used for testing, SMI predicted all-cause mortality with a higher concordance index using of a height scaling power of 1 rather than 2 in men (0.675 vs 0.663, p < .001) and in women (0.664 vs 0.653, p < .001). CONCLUSION. The findings support a height scaling power of 1, rather than a conventional power of 2, for SMI computation. CLINICAL IMPACT. A revised height scaling power for SMI could impact the utility of CT-based sarcopenia diagnoses in risk assessment.