Predicted Value Research Articles

A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting

Precise wind speed forecasting contributes to wind power consumption and power grid schedule as well as promotes the implementation of global carbon neutrality policy. However, in existing research, the negative impact of outliers on forecasting models is ignored and the inherent shortcomings of the single predictors have not been taken seriously. Moreover, the intrinsic parameters of predictors are set by manual and empirical methods in some research, leading to difficulties in achieving optimal forecasting performance. To solve the shortcomings of existing research, a multi-step short-term wind speed forecasting framework is proposed by incorporating boxplot-medcouple (MC), variational mode decomposition (VMD), phase space reconstruction (PSR), weight-based stacked generalization with enhanced differential evolution slime mold algorithm (DESMA). Firstly, boxplot-MC is employed to achieve outlier detection and correction for preprocessing original wind speed data by analyzing values and trends. Then, the modified data is further adaptively decomposed into multiple subsequences by VMD, after which each subsequence is constructed into feature matrices through PSR. Subsequently, weight-based multi-model fusion strategy in layer-1 of stacked generalization is proposed to integrate the predicting values acquired by three primary learners, of which the weight coefficients are calculated with the error between actual values and predicting values. After that, kernel extreme learning machine (KELM) in layer-2 of stacked generalization is applied to predict the fusion result to obtain forecasting value corresponding to each subsequence. Meanwhile, an enhanced DESMA based on slime mold algorithm (SMA) and differential evolution (DE) is proposed to calibrate the parameters of KELM. Eventually, the final wind speed forecasting results are attained by summing the prediction values of all subsequences. Furthermore, comparative experiments from different aspects are undertaken on real datasets to ascertain the availability of the proposed framework. The experimental results are clarified as follows: (1) outlier detection and correction employing boxplot-MC is dedicated to analyzing values and trends effectively, with which the negative impact of outliers can be weakened while retaining valid data significantly; (2) VMD can prominently reduce the non-smoothness and volatility of wind speed data; (3) weight-based stacked generalization is conducive to exploiting the advantages of individual primary learners, contributing to compensating for instability; (4) DESMA enhances prediction accuracy by optimizing the parameters of KELM. Additionally, the code has been made available at https://github.com/fyc233/a-multi-step-short-term-wind-speed-forecasting-framework.git.

Genomic analysis and prediction of genomic values for distichiasis in Staffordshire bull terriers.

Distichiasis is a condition characterized by aberrant hairs along the eyelid margins. The symptoms are usually mild but can lead to ulcerations and lesions of the cornea in severe cases. It is the most frequently noted ocular disorder in Norwegian Staffordshire bull terriers (SBT), with a prevalence above 18% in the adult population. A complex inheritance is assumed, but there is sparse knowledge about the genetic background of distichiasis in dogs. We have performed a genome-wide association study of distichiasis in SBT and used genomic data in an attempt to predict genomic values for the disorder. We identified four genetic regions on CFA1, CFA18, CFA32 and CFA34 using a mixed linear model association analysis and a Bayesian mixed model analysis. Genomic values were predicted using GBLUP and a Bayesian approach, BayesR. The genomic prediction showed that the 1/4 of dogs with predicted values most likely to acquire distichiasis had a 3.9 -4.0 times higher risk of developing distichiasis compared to the quarter (1/4) of dogs least likely to acquire the disease. There was no significant difference between the two methods used. Four genomic regions associated with distichiasis were discovered in the association analysis, suggesting that distichiasis in SBT is a complex trait involving numerous loci. The four associated regions need to be confirmed in an independent sample. We also used all 95K SNPs for genomic prediction and showed that genomic prediction can be a helpful tool in selective breeding schemes at breed level aiming at reducing the prevalence of distichiasis in SBTs in the future, even if the predictive value of single dogs may be low.

Air pollution prediction system using XRSTH-LSTM algorithm.

Globally, there are significant worries about the rise in air pollution (AP) from substances that are harmful to human health, different living forms, and unfavorable environmental imbalances. To overcome the problem, AI-based prediction model is the need of the hour. Therefore, an attempt was made to develop a novel AP prediction system based on Xavier Reptile Switan-h-based Long-Short Term Memory (XRSTH-LSTM), which undergoes fine-tuning at various steps such as pre-processing, attribute extraction, and air-quality index prediction, in order to reduce computational cost and also to increase accuracy as well as precision. The dataset used to train the proposed methodology is Air Quality Data in India (2015-2020), taken from publically available sources Kaggle. The dataset includes information on the AQI and air quality at different stations in numerous Indian cities at hourly and daily intervals. The accuracy has been calculated using MSE, MAPE, RMSE, precision, recall, and F-measure. The robustness of the proposed model is tested using parameters such as negative predicted value and Mathew correlation coefficient. The proposed model is found to efficiently process air quality with an improved accuracy of 98.52% and precision of 99.79%, which is 0.74% higher than the existing state-of-the-art model. The testing findings showed that the proposed approach worked better than the current models and offered a higher rate of accuracy in predicting air pollution.

Prediction of sea ice area based on the CEEMDAN-SO-BiLSTM model.

This article proposes a combined prediction model based on a bidirectional long short-term memory (BiLSTM) neural network optimized by the snake optimizer (SO) under complete ensemble empirical mode decomposition with adaptive noise. First, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the sea ice area time series data into a series of eigenmodes and perform noise reduction to enhance the stationarity and smoothness of the time series. Second, this article used a bidirectional long short-term memory neural network optimized by the snake optimizer to fully exploit the characteristics of each eigenmode of the time series to achieve the prediction of each. Finally, the predicted values of each mode are superimposed and reconstructed as the final prediction values. Our model achieves a good score of RMSE: 1.047, MAE: 0.815, and SMAPE: 3.938 on the test set.

An 8-gene predicting survival model of hepatocellular carcinoma (HCC) related to pyroptosis and cuproptosis

BackgroundThe study aimed to establish a prognostic survival model with 8 pyroptosis-and-cuproptosis-related genes to examine the prognostic effect in patients of hepatocellular carcinoma (HCC).MethodsWe downloaded gene expression data and clinical information of HCC patients from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). The clustering analysis and cox regression with LASSO were used for constructing an 8 PCmRNAs survival model. Using TCGA, ICGC and GEO cohort, the overall survival (OS) between high- and low- risk group was determined. We also evaluated independent prognostic indicators using univariate and multivariate analyses. The relatively bioinformatics analysis, including immune cell infiltration, function enrichment and drug sensitivity analyses, was performed as well. The gene expression of 8 PCmRNAs in vitro were validated in several HCC cell lines by qRT-PCR and Western blot. The relationship between GZMA and Fludarabine were further checked by CCK-8 assay.ResultsThe survival prognostic model was constructed with ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA using data from TCGA cohort. The ICGC and GEO cohort were used for model validation. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Risk scores had the highest predictable value for survival among Stage, Age, Gender and Grade. Most Immune cells and immune functions were decreased in high-risk group. Besides, function enrichment analyses showed that steroid metabolic process, hormone metabolic process, collagen − containing extracellular matrix, oxidoreductase activity and pyruvate metabolism were enriched. Potential drugs targeted different PCDEGs like Nelarabine, Dexamethasone and Fludarabine were found as well. ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NOD1 were upregulated while NLRP6 and GZMA were downregulated in most HCC cell lines. The potential therapy of Fludarabine was demonstrated when GZMA was low expressed in Huh7 cell line.ConclusionWe constructed a novel 8-gene (ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA) prognostic model and explored potential functional information and microenvironment of HCC, which might be worthy of clinical application. In addition, several potential chemotherapy drugs were screened and Fludarabine might be effective for HCC patients whose GZMA was low expressed.

Prevalence of small airway dysfunction in the Swiss PneumoLaus Cohort.

Recent evidence identified exposure to particulate matter of size ≤2.5 µm (PM2.5) as a risk factor for high prevalence of small airway dysfunction (SAD). We assessed the prevalence of SAD in a European region with low air pollution levels. SAD was defined as a maximum mid-expiratory flow (MMEF) <65% of predicted value (PV) or MMEF <lower limit of normal (LLN) measured by spirometry in the Swiss PneumoLaus cohort. We performed bivariate and multivariable analysis with MMEF criteria, age, sex, body mass index, respiratory symptoms and smoking status. Mean PM2.5 values were obtained from a Swiss national database. Among 3351 participants (97.6% Caucasian, 55.7% female sex, mean age 62.7 years), we observed MMEF <65% PV in 425 (12.7%) and MMEF <LLN in 167 (5.0%) individuals. None of the participants had both MMEF <LLN and ≥65% PV. MMEF <65% PV and MMEF <LLN were significantly associated with age, smoking status, cough, sputum and dyspnoea, whereas a positive association with MMEF <65% PV was observed for individuals aged >65 years only. In an area where ambient PM2.5 concentration was <15 µg·m-3 during the observation period (2010 and 2020), ≥72% of participants with SAD were ever-smokers. The observed low prevalence of SAD of 5.0-12.7% depending on criteria employed may be related to lower PM2.5 exposure. Smoking was the main factor associated with SAD in an area with low PM2.5 exposure. Employing a MMEF threshold <65% PV carries a risk of SAD overdiagnosis in elderly individuals.

Evaluation of a point-of-care calcium device in bovine plasma and serum

Hypocalcemia is a common metabolic disease in dairy cows, and it is defined as total calcium (tCa) blood concentration <2.0 mmol/L. The alternatives for the gold standard test to measure tCa in bovine blood are limited. Therefore, our objective was to compare the performance of the calcium (Ca) point-of-care compact analyzer (POC; ARKRAY Inc.) device with the gold standard method to measure bovine blood tCa concentration. Blood samples (n = 151) from dairy cows were collected within 24 h postpartum from multiparous and primiparous dairy cows for serum and plasma. Then, serum and plasma were stored at −80°C until further analyses with the gold standard method on an automatic analyzer (Cobas C501 analyzer; Roche Diagnostics) and the POC device. The tCa blood concentration was measured in the laboratory in plasma and serum samples using both methods within 10 mo of sample collection. Correlation coefficients (Spearman), coefficients of variation (CV, %), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, Passing and Bablok regression, and Bland-Altman agreement test were performed between the gold standard and the POC device. The range and median tCa plasma concentrations measured with the POC device were 1.1 to 2.8 mmol/L and 2.4 mmol/L, respectively. The range and median tCa serum concentrations measured with the POC device were 1.1 to 2.7 mmol/L and 2.3 mmol/L, respectively. The tCa blood concentrations range and median with the gold standard were 1.1 to 2.6 mmol/L and 2.3 mmol/L. The hypocalcemia prevalence of our study population was 11.2%. The CV were 1.89% and 0.55% for low and high tCa in plasma samples measured with the POC, respectively. The CV were 2.57% and 1.58% for low and high tCa in serum, respectively. The Spearman correlation coefficient showed a strong correlation between the gold standard and the POC device for both serum and plasma tCa concentration. The sensitivity of the POC device for both plasma (41.1%) and serum (64.7%) Ca was poor. However, the specificity of the POC device was perfect in plasma (99.2%) and serum (99.2%). The PPV in plasma and serum were 87.5% and 91.6%, respectively. Negative predicted values were 93.0% and 95.6% in plasma and serum. The mean (95% CI) difference between the gold standard and the POC device in plasma and serum were 0.35 (−0.52, 1.23) mmol/L and 0.19 (−0.53, 0.92) mmol/L, respectively. Finally, we observed a strong correlation between the POC device and the gold standard method for tCa plasma and serum. However, the clinical application of the POC device should be carefully considered because its ability to detect cows with hypocalcemia in serum or plasma samples was poor. However, the device performed better than previously analyzed POC devices and needs further improvement to be a valuable tool for the dairy industry.

Intergenerational and Transgenerational Effects of Drought Stress on Winter Wheat (Triticum aestivum L.).

The environments where the progenitors are grown have the potential to affect the expression of traits in their offspring. Currently, there are various hypotheses regarding the evolutionary and ecological importance of stress memory effects. There is uncertainty regarding its occurrence, persistence, predictability, and adaptive value. In this study, fifteen winter wheat cultivars were grown under drought and well-watered (control) treatments for two seasons to produce seeds with all possible combinations of drought exposure histories. A comprehensive analysis to estimate transgenerational (grandparental effects), intergenerational (parental effects), and their combined memory effects on offspring traits under both control and drought moisture treatments was performed. There were significant memory effects in most of the evaluated traits ranging from +787% to -39.0% changes in both seed quality and plant traits. The expression of stress memory was highly dependent on generation and number of exposures, trait and season. Under drought treatment, the combination of grandparental and parental stress memories was additive in all traits, but their strength were variable when considered separately. Stress memory enhanced the performance of offspring under similar stressful conditions: increased plant height, above-ground biomass, number of grains per plant, grain weight per plant and water potential. This study offers valuable new insights into the occurrence of drought stress memory, the complexities of the effects, possible physiological and metabolic alterations explaining the detected differences, and impacts towards a clearer understanding of their generation and context-dependency. This article is protected by copyright. All rights reserved.

MOHP-EC: A Multiobjective Hierarchical Prediction Framework for Urban Rail Transit Passenger Flow

For sophisticated management, advertisement placement, and epidemic prevention control of urban rail transit (URT), accurate and real-time predictions of passenger flows at different levels are of great importance. Unlike traditional prediction tasks, hierarchical prediction (HP) requires that the hierarchical constraints be satisfied as much as possible (the sum of the predicted passenger flow of child nodes should nearly equal the parent node) to achieve realistic predictions. This article proposes a multiobjective HP (MOHP) framework with an error compensation (EC) mechanism for predicting URT passenger flow with a hierarchical structure. Three components are included: the initial prediction module, the EC module, and the hierarchical coordination module. In the initial prediction module, the initial passenger flow prediction of each layer is carried out. The EC model is developed based on proportional-integral-derivative control to compensate for the initial predicted value of every layer. As a final step, a trainable HP model is constructed based on deep learning to coordinate the prediction values of each layer. As examples, we construct three scenarios of passenger flow hierarchy based on the URT system in Wuxi, China. The constructed prediction framework is used to conduct experimental analyses. As a result, the MOHP-EC prediction framework could satisfy the hierarchical constraints and use the passenger flow hierarchical information to reduce prediction errors. The mean absolute error was reduced by 35%, and the root-mean-square error was reduced by 39%.

Predição da Cotação Real/Bitcoin usando a Rede Neural Long Short Term Memory

Cryptocurrencies have become an important financial asset for com- panies, governments and civilians, being accepted for financial transactions in place of traditional currencies. However, cryptocurrencies, which are attractive for some reasons such as not having government control, have high volatility, making price predictions important in order to have a basis for deciding when to buy and when to sell this type of asset, in order to maximize profits. As pre- diction by people demands a specialist, which is expensive and also subject to biases and errors, several studies have investigated the application of artificial intelligence computational techniques for prediction. This study investigates the application of an LSTM neural network for predicting the price of the Bitcoin cryptocurrency in Reais, analyzing parameters of the neural network, together with the network modeling for the prediction of a single value for a given future time horizon and the prediction of multiple values for multiple future instants of time. The results obtained indicate that the single output model has a good performance for short-term loss, mainly in the prediction for the next day (horizon 1) with a mean absolute percentage error (MAPE) of 2.57% in its best configuration, however this error is not linearly scalable when the prediction horizon is increased. The multiple-output model produced higher MAPE values for short-term predictions (horizons 1, 3 and 5), however the error increase rate is lower than that of the simple horizon model.&#x0D;

Zn Extraction from Zinc-Containing Sludge Using Ultrasonic Treatment Leaching with ChCl-MA DES

The recovery of zinc from metallurgical dust sludge is a crucial component of using solid waste as a resource in the metallurgical process, and deep eutectic solvent–ultrasonic synergistic enhanced leaching is an efficient method of doing so with excellent economic effects. The leaching rate of zinc is used as the value of response in this study, along with the four process conditions of leaching temperature, leaching time, liquid–solid ratio, and ultrasonic power. By building a regression model, the relationship between the various parameter components is investigated, and a strategy for optimization is then chosen and confirmed. The findings indicate that, for the parameters of temperature 40 °C, ultrasonic power 90 W, liquid–solid ratio 7:1 g/L, stirring speed 250 rpm, and leaching duration 80 min, the prediction value of the regression model of the zinc leaching rate is 98.47%. The average zinc leaching rate obtained by the 3 parallel verification experiments was 98.49%; the deviation from the regression model’s predicted value was 0.02%. This demonstrated that the experimental results were consistent with those predicted by the regression model, the experimental results were reliable and trustworthy, and the optimization scheme was reasonable and accurate. Compared with the conventional leaching method (leaching rate: 91.61%), the method under ultrasound increased the zinc leaching rate by 6.88%.

DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization

Deep generative models have demonstrated superior performance in lossless compression on identically distributed data. However, in real-world scenarios, data to be compressed are of various distributions and usually cannot be known in advance. Thus, commercially expected neural compression must have strong Out-of-Distribution (OoD) generalization capabilities. Compared with traditional compression methods, deep learning methods have intrinsic flaws for OoD generalization. In this work, we make the attempt to tackle this challenge via exploiting a zoo of Deep Autoregressive models (DAMix). We build a model zoo consisting of autoregressive models trained on data from diverse distributions. In the test phase, we select useful expert models by a simple model evaluation score and adaptively aggregate the predictions of selected models. By assuming the outputs from each expert model are biased in favor of their training distributions, a von Mises-Fisher based filter is proposed to recover the value of unbiased predictions that provides more accurate density estimations than a single model. We derive the posterior of unbiased predictions as well as concentration parameters in the filter, and a novel temporal Stein variational gradient descent for sequential data is proposed to adaptively update the posterior distributions. We evaluate DAMix on 22 image datasets, including in-distribution and OoD data, and demonstrate that making use of unbiased predictions has up to 45.6% improvement over the single model trained on ImageNet.

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.

Use of CAPTURE to Identify Individuals Who May or May Not Require Treatment for COPD.

The COPD Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE) tool was developed to identify patients with undiagnosed COPD with FEV1 <60% predicted or risk of exacerbation as treatment criteria. To test the ability of CAPTURE to identify patients requiring treatment because of symptoms or risk of exacerbation or hospitalization. Data were from COMPASS, a prospective study in China of COPD, chronic bronchitis without airflow limitation (post-bronchodilator FEV1/FVC >0.70), and healthy never-smokers. CAPTURE was tested as questions alone and with peak expiratory flow (PEF). Sensitivity, specificity, positive and negative predicted values (PPV and NPV) were calculated for CAT ≥10 vs <10, mMRC ≥2 vs <2, and ≥1 moderate exacerbation or hospitalization vs none in the previous year. COPD patients (n=1696), mean age 65±7.5 years, 90% males, post-bronchodilator FEV1 66.5±20.1% pred; controls (n=307), age 60.2±7.0 years, 65% males, FEV1/FVC 0.78±0.04. CAPTURE using PEF showed the best combination of sensitivity and specificity. Sensitivity and specificity to detect CAT ≥10 were 68.5% and 64.0%, respectively; mMRC ≥2 (85.6% and 61.0%), ≥1 moderate exacerbation (63.5% and 55.6%) and hospitalization (70.2% and 59.4%). PPV ranged 15.6% (moderate exacerbations) to 47.8% (CAT). NPV ranged 80.8% (CAT) to 95.6% (mMRC). CAPTURE has good sensitivity to identify COPD patients who may require treatment because of elevated symptoms, risk of exacerbations or hospitalization, including those with FEV1 >60% predicted. High NPV values show it can also exclude those who may not require treatment. GSK (208630).

Computed Tomography Is Predictive of Significant Neurologic Injury in Children Supported on Extracorporeal Membrane Oxygenation.

Computed Tomography Is Predictive of Significant Neurologic Injury in Children Supported on Extracorporeal Membrane Oxygenation.

Are Virtually Designed 3D Printed Surgical Splints Accurate Enough for Maxillary Reposition as an Intermediate Orthognathic Surgical Guide.

The accuracy of the virtually-designed 3D-printed surgical splints requires investigation for the practical use of surgical plan in the operating room. This study aimed to compare the validity of the 3D-printed and the conventional intermediate splints and evaluate the outcomes after the surgical application of the 3D-printed splint compared with the predicted values. In this study, ten patients with dentofacial deformity were recruited. Participants were analyzed by the conventional surgical planning and virtual surgical planning. The intermediate surgical splints were created by the conventional and 3D-printing methods. Maxillary movements in 3 spatial directions were measured in an articulator after the application of both splints. Correlation and agreement between the two methods were tested by intraclass correlation coefficient (ICC). After the confirmation of 3D printed splint validity for each patient, the surgery was performed using 3D printed splints. It is assumed that ideally cephalometric prediction values are going to be obtained using conventional acrylic splints (gold standard). So, as a second objective, the outcome of the surgically-applied 3D-printed splint was evaluated and compared with the predicted values and finally analyzed by the paired t-test. Based on the observations, there was an excellent agreement between the virtually-designed 3D-printed and conventional intermediate surgical splints (ICC ranged between 0.83 and 0.99 for linear values). There was a good cumulative agreement of ICC greater than 0.80. Overall, the mean linear measurements were not different between conventional and 3D-printed splint on the articulator. Also, there were no significant differences between the linear and angular measurements of 2D-cephalometric prediction and postoperation values. The results showed cautiously the acceptable accuracy of the 3D-printed splints for several parameters in three spatial dimensions within the laboratory and clinical settings.

Circulating chromatin conformation signatures to enhance PSA positive predictive value and overall accuracy for prostate cancer detection.

e17016 Background: Prostate cancer (PCa) has a high lifetime prevalence (one out of six men), but currently there is no widely accepted screening programme. Widely used prostate specific antigen (PSA) test at cut-off of 3.0 ng/mL does not have sufficient accuracy for detection of any prostate cancer, resulting in numerous unnecessary prostate biopsies in men with benign disease and false reassurance in some men with PCa. We have recently identified circulating chromosome conformation signatures (CCSs, Episwitch PCa test) allowing PCa detection and risk stratification in line with standards of clinical PCa staging. The purpose of this study was to determine whether combining the Episwitch PCa test with the PSA test will increase its diagnostic accuracy. Methods: n = 109 whole blood samples of men enrolled in the PROSTAGRAM screening pilot study and n = 38 samples of patients with established PCa diagnosis and cancer-negative controls from Imperial College NHS Trust were used. Samples were tested for PSA, and the presence of CCSs in the loci encoding for of DAPK1, HSD3B2, SRD5A3, MMP1, and miRNA98 associated with high-risk PCa identified in our previous work. Results: PSA &gt; 3 ng/mL alone showed a low positive predicted value (PPV) of 0.14 and a high negative predicted value (NPV) of 0.93. EpiSwitch alone showed a PPV of 0.91 and a NPV of 0.32. Combining PSA and Episwitch tests has significantly increased the PPV to 0.81 although reducing the NPV to 0.78. Furthermore, integrating PSA, as a continuous variable (rather than a dichotomised 3 ng/mL cut-off), with EpiSwitch in a new multivariant stratification model, Prostate Screening EpiSwitch (PSE) test, has yielded a remarkable combined PPV of 0.93 and NPV of 0.95 when tested on the independent prospective cohort. Conclusions: Our results demonstrate that combining the standard PSA readout with circulating chromosome conformations (PSE test) allows for significantly enhanced PSA PPV and overall accuracy for PCa detection. The PSE test is accurate, rapid, minimally invasive, and inexpensive, suggesting significant screening diagnostic potential to minimise unnecessary referrals for expensive and invasive MRI and/or biopsy testing, with further extended prospective blinded validation of the new combined signature in a screening cohort with low cancer prevalence as the next step for PSE adoption in PCa screening.

Fetal ultrasonography of the distal limb: a new tool to assess equine fetal age and bone development

In Quarter Horses (QH), fetal age can be predicted within 2 weeks prior to 200 days of gestation via ultrasonography, but as pregnancy advances, accuracy decreases (Renaudin.et.al. Reprod Dom Anim. 2022;57: 743-753). Recently, fetal ultrasonographic measurement of the first phalanx (P1) length was introduced as a novel biometric parameter during late gestation, and the time of apparition and closure of P1’s epiphyses were found to be potential markers of bone maturation in utero (Renaudin.et.al. AAEP.2021;67:264-265). The aim of this study was to determine if bone characteristics of the second phalanx (P2), distal metacarpal 3 (MC3), proximal and distal sesamoid can predict fetal age. Ten healthy pregnant mares (9 QH and 1 Thoroughbred carrying a QH fetus) with known ovulation dates were used. Fetuses were examined by transrectal ultrasound every 2 weeks from 9 months of gestation until parturition. At each examination, the length of the ossified portion of P2’s diaphysis was measured, the presence or absence of P2’s and distal MC3’s epiphyses and the time of appearance of the proximal and distal sesamoid bones were documented. P2 length correlated strongly with days of gestation (P < 0.0001; r 2 = 0.75) and a correlation equation was established: y = 0.1413x -24.89 (y = predicted value of P2 length; x = fetal age). The P2 proximal epiphysis and MC3 distal epiphysis appeared on ultrasound between 285 and 306 days of gestation (mean: 294 days),and 260 and 294 days of gestation (mean: 268 days) respectively and did not close before parturition. P2 distal epiphysis was never imaged. Proximal and distal sesamoids were first observed between 286 and 306 days of gestation (mean: 295 days) and 300 and 329 days of gestation (mean: 317 days) respectively except for the smallest foal where they appeared at 331 and 335 days respectively. In conclusion, P2 length can be used in addition to P1 length as a biometric parameter in late gestation. Distal MC3’s, P1’s and P2’s epiphyses, and sesamoid bones diaphyses appear in a chronological order with MC3 distal epiphysis appearing first, P1’s second, P2’s third, proximal sesamoids fourth and distal sesamoid last. Distal limb ossification can be considered complete in QHs when the MC3 distal epiphysis, P1 and P2 proximal epiphysis and all sesamoid bones are present. Clinically, fetal ultrasonography of the distal limb may be used to assess fetal age and in the decision-making process of not inducing parturition if in utero bone development is not complete. This project was supported by the Center for Equine Health with funds provided by the State of California satellite wagering fund and contributions by private donors.

Kinetic Monte Carlo Convergence Demands for Thermochemical Recycling Kinetics of Vinyl Polymers with Dominant Depropagation

As societal interest in recycling of plastics increases, modeling thermochemical recycling of vinyl polymers, e.g., via pyrolysis or reactive extrusion, becomes increasingly important. A key aspect remains the reliability of the simulation results with fewer evaluation studies regarding convergence as in the polymerization or polymer reaction engineering field. Using the coupled matrix-based Monte Carlo (CMMC) framework, tracking the unzipping of individual chains according to a general intrinsic reaction scheme consisting of fission, β-scission, and termination, it is however illustrated that similar convergence demands as in polymerization benchmark studies can be employed, i.e., threshold values for the average relative error predictions on conversion and chain length averages can be maintained. For this illustration, three theoretical feedstocks are considered as generated from CMMC polymer synthesis simulations, allowing to study the effect of the initial chain length range and the number of defects on the convergence demands. It is shown that feedstocks with a broader chain length distribution and a long tail require a larger Monte Carlo simulation volume, and that the head–head effects play a key role in the type of degradation mechanism and overall degradation rate. A minimal number of chains around 5 × 105 is needed to properly reflect the degradation kinetics. A certain degree of noise can be allowed at the higher carbon-based conversions due to the inevitable decrease in number of chains.

Unanticipated broad phylogeny of BEN DNA-binding domains revealed by structural homology searches

Organization of protein sequences into domain families is a foundation for cataloging and investigating protein functions. However, long-standing strategies based on primary amino acid sequences are blind to the possibility that proteins with dissimilar sequences could have comparable tertiary structures. Building on our recent findings that in silico structural predictions of BEN family DNA-binding domains closely resemble their experimentally determined crystal structures, we exploited the AlphaFold2 database for comprehensive identification of BEN domains. Indeed, we identified numerous novel BEN domains, including members of new subfamilies. For example, while no BEN domain factors had previously been annotated in C.elegans, this species actually encodes multiple BEN proteins. These include key developmental timing genes of orphan domain status, sel-7 and lin-14, the latter being the central target of the founding miRNA lin-4. We also reveal that the domain of unknown function 4806 (DUF4806), which is widely distributed across metazoans, is structurally similar to BEN and comprises a new subtype. Surprisingly, we find that BEN domains resemble both metazoan and non-metazoan homeodomains in 3D conformation and preserve characteristic residues, indicating that despite their inability to be aligned by conventional methods, these DNA-binding modules are probably evolutionarily related. Finally, we broaden the application of structural homology searches by revealing novel human members of DUF3504, which exists on diverse proteins with presumed or known nuclear functions. Overall, our work strongly expands this recently identified family of transcription factors and illustrates the value of 3D structural predictions to annotate protein domains and interpret their functions.