Pool Model Research Articles

Green apple detection method based on multi-dimensional feature extraction network model and Transformer module

To enhance fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multi-dimensional feature extraction network and Transformer module. Firstly, an improved DETR network main feature extraction module adopts the ResNet18 network and replaces some residual layers with deformable convolutions (DCNv2), enabling the model to better adapt to pollution-free fruit changes at different scales and angles, while eliminating the impact of microbial contamination on fruit testing; Subsequently, the extended spatial pyramid pooling model (DSPP) and multi-scale residual aggregation module (FRAM) are integrated, which help reduce feature noise and minimize the loss of underlying features during the feature extraction process. The fusion of the two modules enhances the model’s ability to detect objects of different scales, thereby improving the accuracy of near-color fruit detection; At the same time, in order to solve the problems of slow convergence speed and large calculation amount of the basic network model, the convergence speed of the overall network model is improved by replacing the attention mechanism of Transformer. Experimental results show that compared with the original DETR model, the proposed algorithm has improved in AP, AP50 and AP75 indicators, especially in the AP50 indicator, which has the most obvious improvement reaching a detection accuracy of 97.12%. In the meantime, the trained network model is deployed on the picking robot. Compared with the original DETR network model, its average detection accuracy is as high as 96.58%, and the detection speed is increased by about 51%. Mixed sample detection tests were carried out before and after the model deployment, and the detection rate of the proposed method for non-polluted fruits reached more than 0.95. enabling the picking robot to efficiently complete the task of picking green apples. The test results show that the algorithm proposed in this article exhibits great potential in the task of detecting pollution-free near-color fruits by the picking robot. It ensures pollution-free fruit picking and the application of AI in food safety.

Effect of four hemoglobin transfusion threshold strategies in patients with acute myocardial infarction and anemia: a target trial emulation using MINT trial data

Abstract Background The hemoglobin (Hgb) threshold at which red blood cell transfusion affords greatest benefit, without increasing transfusion-associated risks, is uncertain for patients with acute myocardial infarction (MI). The Myocardial Ischemia and Transfusion (MINT) trial recently showed a restrictive transfusion strategy (Hgb threshold <7-8g/dL) resulted in a 15% increased risk of 30-day all-cause death or recurrent MI (death/MI) compared with a liberal strategy (Hgb threshold <10g/dL) in 3,504 adults with acute MI and anemia. However, the transfusion effect at different Hgb thresholds remains uncertain. Purpose To better understand the gradient of effects from Hgb threshold transfusion triggers between 7g/dL and 10g/dL. We estimated the effects of four Hgb transfusion thresholds on 30-day death/MI and death in patients with acute MI and anemia. Methods We used data from the MINT trial and applied an innovative causal inference method, target trial emulation, to assess four Hgb thresholds. We emulated a hypothetical, 4-arm target trial of <10g/dL, <9g/dL, <8g/dL, and <7g/dL transfusion strategies in adults with acute MI and Hgb <10g/dL using a "cloning" procedure. The target trial protocol closely mirrored that of the MINT trial. We conducted a per-protocol analysis of the emulated target trial using censoring and inverse probability (IP) weighting; we estimated hazard ratios (HRs) and 95% confidence intervals (CIs) by fitting pooled logistic regression models with a time-dependent intercept for time free of the outcomes. We estimated IP weighted Kaplan-Meier cumulative incidence curves under each strategy for 30-day outcomes. Results Of the MINT trial participants, 3,492 (99.7%) met eligibility criteria for the hypothetical target trial of four transfusion strategies. The unadjusted rate of 30-day death/MI was lowest in the <10g/dL strategy and highest in the <9g/dL strategy, and the rate of transfusions decreased monotonically as Hgb thresholds decreased. Relative to the <10g/dL strategy, the estimated HRs (95% CI) from the IP weighted model of 30-day death/MI were 1.07 (0.77-1.50) for the <9g/dL strategy, 1.14 (0.84-1.55) for the <8g/dL strategy, and 1.44 (0.99-2.09) for the <7g/dL strategy (Figure). A log-linear estimate over strategy thresholds between 7-10g/dL estimated a 1.12 (95% CI 1.00-1.27) times greater average risk of death/MI and a 1.12 (95% CI 0.92, 1.37) times greater average risk of death over 30 days for each 1g/dL decrease in the Hgb threshold. The IP weighted cumulative incidence curves showed similar increase in risk of death/MI as transfusion thresholds decreased throughout 30 days post-randomization. Conclusions Relative to a <10g/dL transfusion strategy, we estimated a progressively greater risk of 30-day death/MI as transfusion Hgb thresholds decreased within the range of 7-10g/dL. Targeting a Hgb concentration of 10g/dL in patients with a recent acute MI may avoid serious risks associated with anemia.Hazard ratios for 30-day death/MI

Do middle-aged and older people underreport loneliness? experimental evidence from the Netherlands

Despite the growing acknowledgment of the importance of loneliness among older individuals, questionnaire length constraints may hinder the inclusion of common multi-item loneliness scales in surveys. Direct, single-item loneliness measures are a practical alternative, but scholars have expressed concerns that such measures may lead to underreporting. Our aim was to test whether such reservations are justified. We conducted a preregistered list experiment among 2,553 people aged 50 + who participated in the Dutch Longitudinal Internet studies for the Social Sciences (LISS) panel. The list experiment method has been developed to unobtrusively gather sensitive information. We compared the list experiment estimate of the prevalence of frequent loneliness with the corresponding direct question estimate to assess downward bias in the latter. Next to pooled models, we estimated models stratified by gender to assess whether loneliness underreporting differed between women and men. Relying on the direct question, we estimated that 5.9% of respondents frequently felt lonely. Our list experiment indicated that the prevalence of frequent loneliness was 13.1%. Although substantial in magnitude, the difference between both estimates was only marginally significant (Δb: 0.072, 95% CI: − 0.003;0.148, p = .06). No evidence of gender differences was found. Although we cannot be conclusive that loneliness estimates are biased downward when a direct question is used, our results call for caution with direct, single-item measures of loneliness if researchers want to avoid underreporting. Replications are needed to gain more precise insights into the extent to which direct, single-item loneliness measures are prone to downward reporting bias.

The prognostic value of myosteatosis in pancreatic cancer: A systematic review and meta-analysis

Background and aimsThe phenomenon of myosteatosis, characterized by the accumulation of ectopic fat within and surrounding skeletal muscle, has been identified as a potential adverse factor in the prognosis of individuals with cancer. This systematic review and meta-analysis sought to examine the association between myosteatosis and survival rates as well as postoperative complications in patients diagnosed with pancreatic cancer (PC). MethodsA systematic search was conducted on Web of Science, Embase, and Pubmed until March 25, 2024, to identify pertinent articles assessing the prognostic significance of myosteatosis in patients with PC, utilizing the search terms: myosteatosis, PC, and prognosis. The selected studies were utilized to investigate the prognostic impact of myosteatosis on the survival of PC patients. Forest plots and pooled effects models were employed to present the findings of this meta-analysis. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). A total of 565 studies were initially identified from the three databases, with 14 retrospective cohort studies ultimately included in the final quantitative analysis. ResultsThe meta-analysis revealed a significant association between myosteatosis and both overall survival (OS) [Hazard Ratio (HR): 1.55, 95% Confidence Interval (CI): 1.40-1.72, P < 0.001, I2=0.0%] and recurrence-free survival (RFS) (HR 1.48, 95% CI: 1.17-1.86, P = 0.001, I2=0.0%) in patients diagnosed with PC. Subgroup analyses revealed that myosteatosis continued to be a negative prognostic factor in PC across various treatment modalities, patient populations, and myosteatosis assessment methods. Additionally, myosteatosis was identified as a risk factor for postoperative complications, with a pooled odds ratio of 2.20 (95% CI: 1.45-3.35, P < 0.001, I2=37.5%). All included studies achieved NOS scores of 6 or higher, indicating a relatively high level of methodological quality. ConclusionThese results suggest that myosteatosis is significantly associated with both survival outcomes and postoperative complications in patients with PC.

Early Addition of Selexipag to Double Therapy for Pulmonary Arterial Hypertension

A subgroup analysis of a randomized clinical trial established the efficacy of selexipag plus background therapy (monotherapy or double oral therapy [DOT]) vs placebo plus background therapy and found that the addition of selexipag within 6 months had an added benefit. However, the timing of selexipag addition to DOT and the incremental benefit in clinical practice is not well studied. To compare triple oral therapy (TOT) consisting of selexipag, endothelin receptor antagonist (ERA), and phosphodiesterase type 5 inhibitor (PDE5i) vs DOT consisting of ERA and PDE5i. This comparative effectiveness study was conducted using data from the US Komodo claims database to emulate a randomized trial. Patients aged 18 years or older with pulmonary arterial hypertension (PAH) treated with ERA plus PDE5i with records from July 2015 through June 2022 were duplicated to TOT and DOT and artificially censored when observed treatment deviated from assigned treatment. Hypothetical randomization was emulated using inverse probability of treatment weighting, and the study accounted for censoring-induced selection bias using inverse probability of censoring weighting. A pooled logistic model estimated the per-protocol difference between treatment groups. Data were analyzed from November 2022 through July 2023. TOT (addition of selexipag within 3, 6, and 12 months of initiating DOT) vs DOT. Adjusted risk of all-cause hospitalization, PAH-related hospitalization, and PAH-related disease progression over a 2-year follow-up. A total of 2966 patients with PAH (mean [SD] age, 54.3 [14.0] years; 2125 female [71.6%]) met eligibility criteria. Adding selexipag within 6 months of ongoing DOT was associated with a reduction in risk for all-cause hospitalization (adjusted hazard ratio [aHR], 0.82; 95% CI, 0.72-0.94), PAH-related hospitalization (aHR, 0.81; 95% CI, 0.70-0.95), and PAH-related progression (aHR, 0.82; 95% CI, 0.70-0.95) vs DOT alone. There were no associations if selexipag was initiated within 12 months for all-cause hospitalization, PAH-related hospitalization, or PAH-related disease progression. The association remained with a greater decrease in risk for disease progression vs DOT for selexipag initiation within 3 months (aHR, 0.74; 95% CI, 0.61-0.90). This study found that early selexipag addition to ERA plus PDE5i was associated with a reduction in risk of hospitalization and disease progression. These findings suggest that delays in selexipag initiation likely contribute to suboptimal patient and health system outcomes.

In Vitro Hepatic Clearance Evaluations of Per- and Polyfluoroalkyl Substances (PFAS) across Multiple Structural Categories.

Toxicokinetic (TK) assays and in vitro-in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography-mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants.

The Asymetric Relation between Money Supply and Inflation

In this study using yearly data, it is examined if the effect of money supply (broad money) on inflation is asymmetric or not. 38 countries which have 5% and above inflation rate in average during the period of 1989–2018 are investigated through the panel data analyses. The study differs from other researches, which use monetary shocks in explaining the asymmetric relation, in which that it uses broad money change intervals along with control variables to see the asymmetric impact. Using broad money change intervals, it is concluded that the relation between broad money and inflation is explained better in the asymmetric pooled and fixed effect panel data models, compared to the symmetric models. According to the results, the effects of negative and positive changes in money supply on inflation are not symmetric. Moreover, as broad money increases, inflation goes up further. In the light of this information, it is possible to mention an asymmetric relation between broad money and inflation.

Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders.

This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The framework synergistically integrates pyramid pooling and a dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. The framework is specifically designed to effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), and control subjects. The pyramid pooling structure aggregates multilevel global information by pooling features at four scales. SHAP assesses feature importance from the eight sensors. Two encoder architectures handle different feature sets based on their importance, optimizing performance. Besides, the model's robustness is enhanced using the sliding window technique and white noise augmentation on the original data. In 5-fold cross-validation, the model achieved an average accuracy of 96.40%, surpassing that of a single encoder pyramid pooling model by 10.77%. Further optimization of filters in the transformer convolutional layer and pooling size in the pyramid module increased the accuracy to 98.46%. This study offers an efficient tool for identifying the effects of smoking and COPD, as well as a novel approach to utilizing deep learning technology to address complex biomedical issues.

DOES ESG INVESTMENT IMPACT THE FINANCIAL SUSTAINABILITY OF NIGERIAN ENERGY COMPANIES: A PANEL REGRESSION APPROACH?

The destruction of around 5% to 10% of Nigerian mangrove ecosystems and the disappearance of approximately 7,400 square kilometres of rainforest have shifted managerial priorities in Nigeria's energy sector from purely financial gain to increased social responsibility. The rising concerns regarding climate change, environmental risks, social well-being, and sustainability have propelled ESG investment to the forefront of corporate sustainability considerations. The study delves into the influence of ESG investment on the financial sustainability of listed industry players in Nigeria's oil and gas industry. By utilizing quantitative data from sustainability and corporate annual reports of listed firms on the Nigeria Exchange Group from 2013 to 2023, a fixed pooled panel regression model was conducted to statistically test the three hypotheses anchored on the ESG nexus on financial sustainability. The findings indicate that the relationship between environmental investments, measured by environmental emissions, and return on assets (ROA) for the examined listed entities is insignificant. However, the research establishes a notable correlation between social investing practices, quantified by workforce size, and ROA, displaying a positive coefficient. Moreover, the study does not confirm a substantial impact of governance investing practices, measured by board size, on the ROA of the scrutinized corporations. The research acknowledged the impact of ESG on the financial sustainability of Nigeria’s energy sector. The research recommendations include integrating ESG factors into investment strategies, enhancing disclosure and transparency, improving risk management and resilience measures, and collaborating with policymakers.

Debt maturity, governance and investment efficiency: new evidence from emerging market

PurposeThis study investigates the combined influence of corporate governance (CG) and debt maturity (DM) on the investment inefficiency among non-financial 506 NSE-listed firms in India between 2009 and 2022. Additionally, this study also investigates the moderating effect of short-term debt (STD) maturity concerning the relationship between CG and investment inefficiency.Design/methodology/approachUtilizing the residuals extracted from the Biddle et al. (2009) investment model, three different forms of investment inefficiency (investment inefficiency, overinvestment and underinvestment) were measured. To measure the internal governance of firms, a new corporate governance index (CGI) was developed using 65 new governance stipulations, whereas STD was measured as short-term debt divided by total debt. Interaction effects between CG and DM were also estimated. Employing CGI and STD along with firm-specific control variables, many pooled regression models were estimated. Endogeneity issues were addressed through two-stage least squares. Robustness checks were also conducted using the two-step system GMM, alternative measures of dependent and independent variables.FindingsThe findings demonstrate that higher CG and shortened DM increase investment efficiency. This evidence implies that firm-level governance and short-term debt reduce information asymmetry and increase management oversight. Additionally, the evidence suggested that shortened DM and CG complement one another to increase investment efficiency, suggesting companies that utilize STD to a greater (lesser) extent demonstrate a greater (lesser) impact of CG in reducing investment inefficiency.Practical implicationsThis work first advocates the establishment and implementation of robust corporate governance mechanisms to control agency conflicts, moral hazard, adverse selection and limit opportunistic behavior of managers for improving investment efficiency. Second, since interaction effects suggest a complementarity between CG and DM, it is advocated that STDs can be used to achieve optimal investment choices to control moral hazards and adverse selection and discourage suboptimal investment levels.Originality/valueThis work provides new evidence concerning the effects of CG and DM on various forms of corporate investment efficiency (investment inefficiency, overinvestment and underinvestment, using alternate measures) in an emerging economy like India having a unique institutional framework and macroeconomic environment using a newly developed firm-specific CG index for a large sample of companies using recent data.

Improved accuracy of continuum surface flux models for metal additive manufacturing melt pool simulations

Computational modeling of the melt pool dynamics in laser-based powder bed fusion metal additive manufacturing (PBF-LB/M) promises to shed light on fundamental mechanisms of defect generation. These processes are accompanied by rapid evaporation so that the evaporation-induced recoil pressure and cooling arise as major driving forces for fluid dynamics and temperature evolution. The magnitude of these interface fluxes depends exponentially on the melt pool surface temperature, which, therefore, has to be predicted with high accuracy. The present work utilizes a diffuse interface finite element model based on a continuum surface flux (CSF) description of interface fluxes to study dimensionally reduced thermal two-phase problems representative for PBF-LB/M in a finite element framework. It is demonstrated that the extreme temperature gradients combined with the high ratios of material properties between metal and ambient gas lead to significant errors in the interface temperatures and fluxes when classical CSF approaches, along with typical interface thicknesses and discretizations, are applied. It is expected that this finding is also relevant for other types of diffuse interface PBF-LB/M melt pool models. A novel parameter-scaled CSF approach is proposed, which is constructed to yield a smoother temperature field in the diffuse interface region, significantly increasing the solution accuracy. The interface thickness required to predict the temperature field with a given level of accuracy is less restrictive by at least one order of magnitude for the proposed parameter-scaled approach compared to classical CSF, drastically reducing computational costs. Finally, we showcase the general applicability of the parameter-scaled CSF to a 3D simulation of stationary laser melting of PBF-LB/M considering the fully coupled thermo-hydrodynamic multi-phase problem, including phase change.

THE ROLE OF DOMESTIC INVESTMENT IN POVERTY REDUCTION IN 11 PROVINCES IN JAVA, BALI, NTT, NTB AND MALUKU

This study aims to investigates the role of domestic investment in reducing poverty in 11 provinces in Indonesia, specifically Java, Bali, East Nusa Tenggara, West Nusa Tenggara, and Maluku. This study employs a quantitative framework, utilizing panel data analysis to investigate trends across selected regions. The findings support region-specific economic strategies that boost growth and reduce poverty by encouraging local investment to reduce poverty and improve living standards. The research highlights the importance of domestic investment in promoting inclusive growth and improving social welfare in Indonesia. This research represents a novel inquiry, as the study of the panel data pool model across 11 provinces provides a wider perspective by emphasizing the importance of domestic investment in promoting inclusive growth and improving social welfare in Indonesia.

Effect of statin use on long-term survival after diagnosis of colorectal cancer: Emulating hypothetical target trials using a nationwide real-world database.

82 Background: Observational studies suggest that statin use after colorectal cancer (CRC) diagnosis is associated with improved survival. However, potential bias due to misalignment of time zero in observational studies may distort associations and therefore requires validation using robust epidemiological methods. We aimed to emulate a hypothetical randomized trial—a target trial—for estimating per-protocol effects of statin initiation in patients with incident colorectal cancer on long-term survival. Methods: To emulate a target trial using real-world data, we extracted information from 103,005 patients diagnosed with CRC between 2005 and 2015 from the Korean National Health Insurance (KNHI) database, encompassing health records for the entire Korean population. We excluded individuals who had been prescribed statins for at least 180 days before the CRC diagnosis or those with missing or incomplete demographic information. We set a 180-day grace period and compared the group that initiated statin treatment within 180 days after the CRC diagnosis with the non-statin group using the “clone-censor-weight” method. Patients' demographic and socioeconomic information, along with colorectal cancer treatment, were included as baseline covariates. Additionally, the Charlson Comorbidity Index (CCI) score was included both as a baseline covariate and as a time-varying covariate. Hazard ratios (HRs) for all-cause mortality and CRC-specific mortality within 60 months of baseline were calculated using pooled logistic regression models. Results: Among the 103,005 patients diagnosed with CRC, 88,516 were finally analyzed after the exclusion process. A total of 2,071 (2.3%) individuals initiated statin treatment during the grace period and 86,445 (97.7%) patients did not. Statin initiators, compared with statin noninitiators tended to be older (mean age: 65.7 vs. 62.6) and had a higher CCI score at baseline (mean CCI score: 1.38 vs.1.26). Individuals who initiated statin treatment did not reduce the hazard of all-cause mortality (The adjusted HR 1.01, 95% CI 0.98-1.04) and CRC-specific mortality (The adjusted HR 1.01, 95% CI 0.97-1.04). Conclusions: The results of the analysis after removing potential biases showed that statin was not related to improving survival in CRC patients. The study results conflict with those of previous observational studies but are consistent with the findings of other recently conducted target trial emulations.

Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition

Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in input spaces. To address this challenge, this paper introduces a novel approach for constructing an MF surrogate for predicting melt pool geometry by integrating models of varying complexity, that operate on heterogeneous input spaces. The first thermal model incorporates five input parameters i.e., laser power, scan velocity, powder flow rate, carrier gas flow rate, and nozzle height. In contrast, the second thermal model can only handle laser power and scan velocity. A mapping is established between the heterogeneous input spaces so that the five-dimensional space can be morphed into a pseudo two-dimensional space. Predictions are then blended using a Gaussian process-based co-kriging method. The resulting heterogeneous multi-fidelity Gaussian process (Het-MFGP) surrogate not only improves predictive accuracy but also offers computational efficiency by reducing evaluations required from the high-dimensional, high-fidelity thermal model. The tested Het-MFGP yields an R2 of 0.975 for predicting melt pool depth. This surpasses the comparatively modest R2 of 0.592 achieved by a GP trained exclusively on high-dimensional, high-fidelity data. Similarly, in the prediction of melt pool width, the Het-MFGP excels with an R2 of 0.943, outshining the GP's performance, which registers a lower R2 of 0.588. The results underscore the benefits of employing Het-MFGP for modeling melt pool behavior in L-DED. The framework successfully demonstrates how to leverage multimodal data and handle scenarios where certain input parameters may be difficult to model or measure.

Data-driven prediction of future melt pool from built parts during metal additive manufacturing

Smart manufacturing in metal additive manufacturing (MAM) relies on real-time process optimization through the prediction of unbuilt parts using data from built parts. Melt pool dynamics are intimately associated with the development of various microstructures during the MAM. In this paper, we demonstrate the idea by establishing a machine learning (ML) model to predict the melt pool of future parts, based on the experimental data from the National Institute of Standards and Technology (NIST) where 80 % is used for the training (built parts) and 20 % for testing (future parts). The ML model integrates both data denoising and predictive modeling. First, a convolutional neural network (CNN) is used to process a large dataset of raw melt pool images, enabling the automatic removal of noises (e.g., splash and plume) and thereof extraction of high-quality melt pool data. Following that, a novel data-driven melt pool model based on multi-layer perceptron (MLP) is trained by incorporating raw, long scanning history as input features, which best accounts for the effects of printing history (e.g., intertrack heating) on melt pool development. It takes complete advantage of MLP in handling high-dimensional regression problems in conjunction with a large dataset. Upon testing under various manufacturing conditions, the average relative error magnitude (AREM) of predicting melt pool size drops to 2.8 %, compared to 14.8 % of the prior art – the Neighboring Effect Modeling Method model (NBEM). This research thus represents a significant step towards reliable melt-pool-guided AM process optimization for smart manufacturing, enabled by advanced, flexible, and maximum use of ML techniques.

Inexpensive high fidelity melt pool models in additive manufacturing using generative deep diffusion

Defects in Laser Powder Bed Fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects. Therefore, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by upscaling lightweight coarse mesh simulations. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. We predict the melt pool depth within 3 μm based on low-fidelity input data 4× coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.

CRISPR screens in 3D tumourspheres identified miR-4787-3p as a transcriptional start site miRNA essential for breast tumour-initiating cell growth

Our study employs pooled CRISPR screens, integrating 2D and 3D culture models, to identify miRNAs critical in Breast Cancer (BC) tumoursphere formation. These screens combine with RNA-seq experiments allowing identification of miRNA signatures and targets essential for tumoursphere growth. miR-4787-3p exhibits significant up-regulation in BC, particularly in basal-like BCs, suggesting its association with aggressive disease. Surprisingly, despite its location within the 5’UTR of a protein coding gene, which defines DROSHA-independent transcription start site (TSS)-miRNAs, we find it dependant on both DROSHA and DICER1 for maturation. Inhibition of miR-4787-3p hinders tumoursphere formation, highlighting its potential as a therapeutic target in BC. Our study proposes elevated miR-4787-3p expression as a potential prognostic biomarker for adverse outcomes in BC. We find that protein-coding genes positively selected in the CRISPR screens are enriched of miR-4787-3p targets. Of these targets, we select ARHGAP17, FOXO3A, and PDCD4 as known tumour suppressors in cancer and experimentally validate the interaction of miR-4787-3p with their 3’UTRs. Our work illuminates the molecular mechanisms underpinning miR-4787-3p’s oncogenic role in BC. These findings advocate for clinical investigations targeting miR-4787-3p and underscore its prognostic significance, offering promising avenues for tailored therapeutic interventions and prognostic assessments in BC.

Imaging of Existing and Newly Translated Proteins Elucidates Mechanisms of Sarcomere Turnover.

How the sarcomeric complex is continuously turned over in long-living cardiomyocytes is unclear. According to the prevailing model of sarcomere maintenance, sarcomeres are maintained by cytoplasmic soluble protein pools with free recycling between pools and sarcomeres. We imaged and quantified the turnover of expressed and endogenous sarcomeric proteins, including the giant protein titin, in cardiomyocytes in culture and in vivo, at the single cell and at the single sarcomere level using pulse-chase labeling of Halo-tagged proteins with covalent ligands. We disprove the prevailing protein pool model and instead show an ordered mechanism in which only newly translated proteins enter the sarcomeric complex while older ones are removed and degraded. We also show that degradation is independent of protein age and that proteolytic extraction is a rate-limiting step in the turnover. We show that replacement of sarcomeric proteins occurs at a similar rate within cells and across the heart and is slower in adult cells. Our findings establish a unidirectional replacement model for cardiac sarcomeres subunit replacement and identify their turnover principles.

Later-age neutering causes lower risk of early-onset urinary incontinence than early neutering-a VetCompass target trial emulation study.

There is growing evidence supporting clinically important associations between age at neutering in bitches and subsequent urinary incontinence (UI), although much of this evidence to date is considered weak. Target trial emulation is an innovative approach in causal inference that has gained substantial attention in recent years, aiming to simulate a hypothetical randomised controlled trial by leveraging observational data. Using anonymised veterinary clinical data from the VetCompass Programme, this study applied the target trial emulation framework to determine whether later-age neutering (≥ 7 to ≤ 18 months) causes decreased odds of early-onset UI (diagnosed < 8.5 years) compared to early-age neutering (3 to < 7 months). The study included bitches in the VetCompass database born from January 1, 2010, to December 31, 2012, and neutered between 3 and 18 months old. Bitches were retrospectively confirmed from the electronic health records as neutered early or later. The primary outcome was a diagnosis of early-onset UI. Informed from a directed acyclic graph, data on the following covariates were extracted: breed, insurance status, co-morbidities and veterinary group. Inverse probability of treatment weighting was used to adjust for confounding, with inverse probability of censoring weighting accounting for censored bitches. The emulated trial included 612 early-age neutered bitches and 888 later-age neutered bitches. A pooled logistic regression outcome model identified bitches neutered later at 0.80 times the odds (95% CI 0.54 to 0.97) of early-onset UI compared with bitches neutered early. The findings show that later-age neutering causes reduced odds of early-onset UI diagnosis compared with early-age neutering. Decision-making on the age of neutering should be carefully considered, with preference given to delaying neutering until after 7 months of age unless other major reasons justify earlier surgery. The study is one of the first to demonstrate successful application of the target trial framework to veterinary observational data.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.