A data-centric approach to generative modelling for 3D-printed steel.

The emergence of additive manufacture (AM) for metallic material enables components of near arbitrary complexity to be produced. This has potential to disrupt traditional engineering approaches. However, metallic AM components exhibit greater levels of variation in their geometric and mechanical properties compared to standard components, which is not yet well understood. This uncertainty poses a fundamental barrier to potential users of the material, since extensive post-manufacture testing is currently required to ensure safety standards are met. Taking an interdisciplinary approach that combines probabilistic mechanics and uncertainty quantification, we demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model that enables the quality of a design to be predicted before manufacture. Specifically, the geometric variation in the material can be described by an anisotropic spatial random field with oscillatory covariance structure, and the mechanical behaviour by a stochastic anisotropic elasto-plastic material model. The fitted generative model is validated on a held-out experimental dataset and our results underscore the need to combine both statistical and physics-based modelling in the characterization of new AM steel products

A data-centric approach to generative modelling for 3D-printed steel.

The emergence of additive manufacture (AM) for metallic material enables components of near arbitrary complexity to be produced. This has potential to disrupt traditional engineering approaches. However, metallic AM components exhibit greater levels of variation in their geometric and mechanical properties compared to standard components, which is not yet well understood. This uncertainty poses a fundamental barrier to potential users of the material, since extensive post-manufacture testing is currently required to ensure safety standards are met. Taking an interdisciplinary approach that combines probabilistic mechanics and uncertainty quantification, we demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model that enables the quality of a design to be predicted before manufacture. Specifically, the geometric variation in the material can be described by an anisotropic spatial random field with oscillatory covariance structure, and the mechanical behaviour by a stochastic anisotropic elasto-plastic material model. The fitted generative model is validated on a held-out experimental dataset and our results underscore the need to combine both statistical and physics-based modelling in the characterization of new AM steel products

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Has carbohydrate-restriction been forgotten as a treatment for diabetes mellitus? A perspective on the ACCORD study design

Prior to the discovery of medical treatment for diabetes, carbohydrate-restriction was the predominant treatment recommendation to treat diabetes mellitus. In this commentary we argue that carbohydrate-restriction should be reincorporated into contemporary treatment studies for diabetes mellitus

Low-carbohydrate diet in type 2 diabetes: stable improvement of bodyweight and glycemic control during 44 months follow-up

<p>Abstract</p> <p>Background</p> <p>Low-carbohydrate diets, due to their potent antihyperglycemic effect, are an intuitively attractive approach to the management of obese patients with type 2 diabetes. We previously reported that a 20% carbohydrate diet was significantly superior to a 55–60% carbohydrate diet with regard to bodyweight and glycemic control in 2 groups of obese diabetes patients observed closely over 6 months (intervention group, n = 16; controls, n = 15) and we reported maintenance of these gains after 22 months. The present study documents the degree to which these changes were preserved in the low-carbohydrate group after 44 months observation time, without close follow-up. In addition, we assessed the performance of the two thirds of control patients from the high-carbohydrate diet group that had changed to a low-carbohydrate diet after the initial 6 month observation period. We report cardiovascular outcome for the low-carbohydrate group as well as the control patients who did not change to a low-carbohydrate diet.</p> <p>Method</p> <p>Retrospective follow-up of previously studied subjects on a low carbohydrate diet.</p> <p>Results</p> <p>The mean bodyweight at the start of the initial study was 100.6 ± 14.7 kg. At six months it was 89.2 ± 14.3 kg. From 6 to 22 months, mean bodyweight had increased by 2.7 ± 4.2 kg to an average of 92.0 ± 14.0 kg. At 44 months average weight has increased from baseline g to 93.1 ± 14.5 kg. Of the sixteen patients, five have retained or reduced bodyweight since the 22 month point and all but one have lower weight at 44 months than at start. The initial mean HbA1c was 8.0 ± 1.5%. After 6, 12 and 22 months, HbA1c was 6.1 ± 1.0%, 7.0 ± 1.3% and 6.9 ± 1.1% respectively. After 44 months mean HbA1c is 6.8 ± 1.3%.</p> <p>Of the 23 patients who have used a low-carbohydrate diet and for whom we have long-term data, two have suffered a cardiovascular event while four of the six controls who never changed diet have suffered several cardiovascular events.</p> <p>Conclusion</p> <p>Advice to obese patients with type 2 diabetes to follow a 20% carbohydrate diet with some caloric restriction has lasting effects on bodyweight and glycemic control.</p

Subcellular Distribution of Mitochondrial Ribosomal RNA in the Mouse Oocyte and Zygote

Mitochondrial ribosomal RNAs (mtrRNAs) have been reported to translocate extra-mitochondrially and localize to the germ cell determinant of oocytes and zygotes in some metazoa except mammals. To address whether the mtrRNAs also localize in the mammals, expression and distribution of mitochondrion-encoded RNAs in the mouse oocytes and zygotes was examined by whole-mount in situ hybridization (ISH). Both 12S and 16S rRNAs were predominantly distributed in the animal hemisphere of the mature oocyte. This distribution pattern was rearranged toward the second polar body in zygotes after fertilization. The amount of mtrRNAs decreased around first cleavage, remained low during second cleavage and increased after third cleavage. Staining intensity of the 12S rRNA was weaker than that of the 16S rRNA throughout the examined stages. Similar distribution dynamics of the 16S rRNA was observed in strontium-activated haploid parthenotes, suggesting the distribution rearrangement does not require a component from sperm. The distribution of 16S rRNAs did not coincide with that of mitochondrion-specific heat shock protein 70, suggesting that the mtrRNA is translocated from mitochondria. The ISH-scanning electron microscopy confirms the extra-mitochondrial mtrRNA in the mouse oocyte. Chloramphenicol (CP) treatment of late pronuclear stage zygotes perturbed first cleavage as judged by the greater than normal disparity in size of blastomeres of 2-cell conceptuses. Two-third of the CP-treated zygotes arrested at either 2-cell or 3-cell stage even after the CP was washed out. These findings indicate that the extra-mitochondrial mtrRNAs are localized in the mouse oocyte and implicated in correct cytoplasmic segregation into blastomeres through cleavages of the zygote

Identifying factors relevant in the assessment of return-to-work efforts in employees on long-term sickness absence due to chronic low back pain: a focus group study

ABSTRACT: BACKGROUND: Efforts undertaken during the return to work (RTW) process need to be sufficient to prevent unnecessary applications for disability benefits. The purpose of this study was to identify factors relevant to RTW Effort Sufficiency (RTW-ES) in cases of sick-listed employees with chronic low back pain (CLBP). METHODS: Using focus groups consisting of Labor Experts (LE's) working at the Dutch Social Insurance Institute, arguments and underlying grounds relevant to the assessment of RTW-ES were investigated. Factors were collected and categorized using the International Classification of Functioning, Disability and Health (ICF model). RESULTS: Two focus groups yielded 19 factors, of which 12 are categorized in the ICF model under activities (e.g. functional capacity) and in the personal (e.g. age, tenure) and environmental domain (e.g. employer-employee relationship). The remaining 7 factors are categorized under intervention, job accommodation and measures. CONCLUSIONS: This focus group study shows that 19 factors may be relevant to RTW-ES in sick-listed employees with CLBP. Providing these results to professionals assessing RTW-ES might contribute to a more transparent and systematic approach. Considering the importance of the quality of the RTW process, optimizing the RTW-ES assessment is essential

Pretest probability assessment derived from attribute matching

BACKGROUND: Pretest probability (PTP) assessment plays a central role in diagnosis. This report compares a novel attribute-matching method to generate a PTP for acute coronary syndrome (ACS). We compare the new method with a validated logistic regression equation (LRE). METHODS: Eight clinical variables (attributes) were chosen by classification and regression tree analysis of a prospectively collected reference database of 14,796 emergency department (ED) patients evaluated for possible ACS. For attribute matching, a computer program identifies patients within the database who have the exact profile defined by clinician input of the eight attributes. The novel method was compared with the LRE for ability to produce PTP estimation <2% in a validation set of 8,120 patients evaluated for possible ACS and did not have ST segment elevation on ECG. 1,061 patients were excluded prior to validation analysis because of ST-segment elevation (713), missing data (77) or being lost to follow-up (271). RESULTS: In the validation set, attribute matching produced 267 unique PTP estimates [median PTP value 6%, 1(st)–3(rd )quartile 1–10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1(st)–3(rd )quartile 10–30%]. The areas under the receiver operating characteristic curves were 0.74 (95% CI 0.65 to 0.82) for the attribute matching curve and 0.68 (95% CI 0.62 to 0.77) for LRE. The attribute matching system categorized 1,670 (24%, 95% CI = 23–25%) patients as having a PTP < 2.0%; 28 developed ACS (1.7% 95% CI = 1.1–2.4%). The LRE categorized 244 (4%, 95% CI = 3–4%) with PTP < 2.0%; four developed ACS (1.6%, 95% CI = 0.4–4.1%). CONCLUSION: Attribute matching estimated a very low PTP for ACS in a significantly larger proportion of ED patients compared with a validated LRE

A Dominated Coupling From The Past algorithm for the stochastic simulation of networks of biochemical reactions

<p>Abstract</p> <p>Background</p> <p>In recent years, stochastic descriptions of biochemical reactions based on the Master Equation (ME) have become widespread. These are especially relevant for models involving gene regulation. Gillespie’s Stochastic Simulation Algorithm (SSA) is the most widely used method for the numerical evaluation of these models. The SSA produces exact samples from the distribution of the ME for finite times. However, if the stationary distribution is of interest, the SSA provides no information about convergence or how long the algorithm needs to be run to sample from the stationary distribution with given accuracy. </p> <p>Results</p> <p>We present a proof and numerical characterization of a Perfect Sampling algorithm for the ME of networks of biochemical reactions prevalent in gene regulation and enzymatic catalysis. Our algorithm combines the SSA with Dominated Coupling From The Past (DCFTP) techniques to provide guaranteed sampling from the stationary distribution. The resulting DCFTP-SSA is applicable to networks of reactions with uni-molecular stoichiometries and sub-linear, (anti-) monotone propensity functions. We showcase its applicability studying steady-state properties of stochastic regulatory networks of relevance in synthetic and systems biology.</p> <p>Conclusion</p> <p>The DCFTP-SSA provides an extension to Gillespie’s SSA with guaranteed sampling from the stationary solution of the ME for a broad class of stochastic biochemical networks.</p