Temperature distribution gradient in metal powder bed additive manufacturing (MPBAM) directly controls the mechanical properties and dimensional accuracy of the build part. Experimental approach and ...numerical modeling approach for temperature in MPBAM are limited by the restricted accessibility and high computational cost, respectively. Analytical models were reported with high computational efficiency, but the developed models employed a moving coordinate and semi-infinite medium assumption, which neglected the part dimensions, and thus reduced their usefulness in real applications. This paper investigates the in-process temperature in MPBAM through analytical modeling using a stationary coordinate with an origin at the part boundary (absolute coordinate). Analytical solutions are developed for temperature prediction of single-track scan and multi-track scans considering scanning strategy. Inconel 625 is chosen to test the proposed model. Laser power absorption is inversely identified with the prediction of molten pool dimensions. Latent heat is considered using the heat integration method. The molten pool evolution is investigated with respect to scanning time. The stabilized temperatures in the single-track scan and bidirectional scans are predicted under various process conditions. Close agreements are observed upon validation to the experimental values in the literature. Furthermore, a positive relationship between molten pool dimensions and powder packing porosity was observed through sensitivity analysis. With benefits of the absolute coordinate, and high computational efficiency, the presented model can predict the temperature for a dimensional part during MPBAM, which can be used to further investigate residual stress and distortion in real applications.
Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive thoughts and urges and repetitive, intentional behaviors that cause significant distress and impair ...functioning. The OCD Collaborative Genetics Association Study (OCGAS) is comprised of comprehensively assessed OCD patients with an early age of OCD onset. After application of a stringent quality control protocol, a total of 1065 families (containing 1406 patients with OCD), combined with population-based samples (resulting in a total sample of 5061 individuals), were studied. An integrative analyses pipeline was utilized, involving association testing at single-nucleotide polymorphism (SNP) and gene levels (via a hybrid approach that allowed for combined analyses of the family- and population-based data). The smallest P-value was observed for a marker on chromosome 9 (near PTPRD, P=4.13 × 10(-)(7)). Pre-synaptic PTPRD promotes the differentiation of glutamatergic synapses and interacts with SLITRK3. Together, both proteins selectively regulate the development of inhibitory GABAergic synapses. Although no SNPs were identified as associated with OCD at genome-wide significance level, follow-up analyses of genome-wide association study (GWAS) signals from a previously published OCD study identified significant enrichment (P=0.0176). Secondary analyses of high-confidence interaction partners of DLGAP1 and GRIK2 (both showing evidence for association in our follow-up and the original GWAS study) revealed a trend of association (P=0.075) for a set of genes such as NEUROD6, SV2A, GRIA4, SLC1A2 and PTPRD. Analyses at the gene level revealed association of IQCK and C16orf88 (both P<1 × 10(-)(6), experiment-wide significant), as well as OFCC1 (P=6.29 × 10(-)(5)). The suggestive findings in this study await replication in larger samples.
Two types of nanosilica (NS) particles with different average particle sizes (20
nm and 80
nm in diameter, respectively) were used to fabricate epoxy–silica nanocomposites (ESNs) in this study. No ...significant differences in fracture behavior were observed between the epoxies filled with 20
nm NS particles and the epoxies filled with 80
nm NS particles. Interestingly, both types of NS particles were found to be more efficient in toughening epoxies than micron size glass spheres. As with micron size glass spheres, the fracture toughness of the ESNs were affected by the crosslink density of the epoxy matrix, i.e. a lower crosslinked matrix resulted in a tougher ESN. The increases in toughness in both types of ESNs were attributed to a zone shielding mechanism involving matrix plastic deformation. Moreover, the use of Irwin's formalized plastic zone model precisely described the relationship between the fracture toughness, yield strength and the corresponding plastic zone size of the various ESNs examined.
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Understanding how RNA binding proteins control the splicing code is fundamental to human biology and disease. Here, we present a comprehensive study to elucidate how heterogeneous nuclear ...ribonucleoparticle (hnRNP) proteins, among the most abundant RNA binding proteins, coordinate to regulate alternative pre-mRNA splicing (AS) in human cells. Using splicing-sensitive microarrays, crosslinking and immunoprecipitation coupled with high-throughput sequencing (CLIP-seq), and cDNA sequencing, we find that more than half of all AS events are regulated by multiple hnRNP proteins and that some combinations of hnRNP proteins exhibit significant synergy, whereas others act antagonistically. Our analyses reveal position-dependent RNA splicing maps, in vivo consensus binding sites, a surprising level of cross- and autoregulation among hnRNP proteins, and the coordinated regulation by hnRNP proteins of dozens of other RNA binding proteins and genes associated with cancer. Our findings define an unprecedented degree of complexity and compensatory relationships among hnRNP proteins and their splicing targets that likely confer robustness to cells.
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► Thousands of binding sites and splicing events identified for major hnRNP proteins ► HnRNP proteins act cooperatively, regulating cassette exons in similar directions ► HnRNP proteins cross- and autoregulate ► HnRNP proteins regulate other RNA binding proteins and cancer related genes
Heterogeneous nuclear ribonucleoparticle (hnRNP) proteins are among the most abundant RNA binding proteins (RBPs) and regulate cellular RNA levels. Through the integration of several genomic approaches, Huelga, Yeo, and colleagues have identified thousands of binding sites, splicing events, and regulated genes for major hnRNP proteins A1, A2/B1, F, H1, M, and U. HnRNP proteins cross- and autoregulate each other and a whole network of RBPs. These findings define an unprecedented degree of complexity and compensatory relationships among hnRNP proteins.
Residual stress plays an important role in controlling the performance of high-precision martensitic steel parts, as it affects the properties of the material in various ways. At present, the dynamic ...evolution of the phase transition mechanism in residual stress generation is not yet fully understood, and there have been few quantitative studies on the effect of phase transitions on the residual stress generated in the micro-grinding process. In this study, flow stress was modeled by considering the specific volume and yield stress in the phase-transition process, and a user-defined constitutive model was developed. A finite-element model that simulates the movement and application of thermo-mechanical loading and phase transitions on the surface and subsurface of the machined material was developed to predict the residual stress generated by micro-grinding. The accuracy of the simulations and the effect of phase transitions on the residual stress were experimentally verified. The results showed that the effect of phase transitions on the residual stress was mainly reflected in the tangential subsurface. This study used a novel approach in the analysis of residual stress induced by micro-grinding and established two process optimization criteria for the reduction of residual stress. The results of this study provide a more comprehensive understanding of the phase transition and residual stress mechanisms governing the grinding process, which could potentially be useful for improving the reliability of high-strength martensitic steel components.
Selective laser melting (SLM) is an emerging additive manufacturing (AM) technology for metals. Intricate three-dimensional parts can be generated from the powder bed by selectively melting the ...desired location of the powders. The process is repeated for each layer until the part is built. The necessary heat is provided by a laser. Temperature magnitude and history during SLM directly determine the molten pool dimensions, thermal stress, residual stress, balling effect, and dimensional accuracy. Laser-matter interaction is a crucial physical phenomenon in the SLM process. In this paper, five different heat source models are introduced to predict the three-dimensional temperature field analytically. These models are known as steady state moving point heat source, transient moving point heat source, semi-elliptical moving heat source, double elliptical moving heat source, and uniform moving heat source. The analytical temperature model for all of the heat source models is solved using three-dimensional differential equations of heat conduction with different approaches. The steady state and transient moving heat source are solved using a separation of variables approach. However, the rest of the models are solved by employing Green's functions. Due to the high temperature in the presence of the laser, the temperature gradient is usually high which has a substantial impact on thermal material properties. Consequently, the temperature field is predicted by considering the temperature sensitivity thermal material properties. Moreover, due to the repeated heating and cooling, the part usually undergoes several melting and solidification cycles, and this physical phenomenon is considered by modifying the heat capacity using latent heat of melting. Furthermore, the multi-layer aspect of the metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the layers have an impact on heat transfer mechanisms. The proposed temperature field models based on different heat source approaches are validated using experimental measurement of melt pool geometry from independent experimentations. A detailed explanation of the comparison of models is also provided. Moreover, the effect of process parameters on the balling effect is also discussed.
A mix locally recurrent neural network was used to create a proportional-integral-derivative (PID)-like neural network nonlinear adaptive controller for uncertain multivariable ...single-input/multi-output system. It is composed of a neural network with no more than three neural nodes in hidden layer, and there are included an activation feedback and an output feedback, respectively, in a hidden layer. Such a special structure makes the exterior feature of the neural network controller able to become a P, PI, PD, or PID controller as needed. The closed-loop error between directly measured output and expected value of the system is chosen to be the input of the controller. Only a group of initial weights values, which can run the controlled closed-loop system stably, are required to be determined. The proposed controller can update weights of the neural network online according to errors caused by uncertain factors of system such as modeling error and external disturbance, based on stable learning rate. The resilient back-propagation algorithm with sign instead of the gradient is used to update the network weights. The basic ideas, techniques, and system stability proof were presented in detail. Finally, actual experiments both of single and double inverted pendulums were implemented, and the comparison of effectiveness between the proposed controller and the linear optimal regulator were given.
Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures ...across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.
To enhance the process stability and densification, semi-melt step has been introduced when fabricating the TiC/high Nb–TiAl nanocomposite via electron beam melting. The homogenous TiAl matrix ...microstructure with dispersed nano-scale carbides was realised. During the EBM melt, most TiC nanoparticles dissolved and Ti2AlC formed with near-spherical and rod-like shapes. The particles had an influence on solidification behaviour and the subsequent microstructural degradation. High Nb–TiAl nanocomposites with 1.2 wt% TiC addition exhibited a duplex microstructure with dispersed carbides, while a nearly lamellar microstructure (carbide-free) was found in samples with 0.6 and 0.8 wt% TiC. Furthermore, a lower scanning speed resulted in higher relative density, greater Al loss, increased α2-phase but reduced carbide fractions. The microhardness of 433 ± 10 HV0.2, ultimate tensile strength of 657 ± 155 MPa and fracture toughness of 8.1 ± 0.1 MPa√m in 1.2 wt% TiC/high Nb–TiAl nanocomposite processed by EBM are very promising. In addition, the compressive yield strength of 1085 ± 55 MPa, fracture strength of 2698 ± 34 MPa and strain to fracture of 26.1 ± 1.0%, are superior to those processed by conventional means. The strengthening and toughening mechanisms have been interpreted on the basis of crack path observation.
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•Electron beam melted TiC/high Nb–TiAl nanocomposite.•Semi-melt step helps to achieve good process stability and densification.•A homogeneous TiAl matrix microstructure with dispersed nano-scale carbides leading to promising mechanical properties.•The evolution of nano-TiC, solidification microstructure and degradation have been elucidated.
Summary
Our network meta-analysis analyzed the effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on fracture risk. By combining data from randomized controlled trials, we found that ...GLP-1 RAs were associated with a decreased bone fracture risk, and exenatide is the best option agent with regard to the risk of fracture. This study is registered with PROSPERO (CRD42018094433).
Introduction
Data on the effects of GLP-1 RAs on fracture risk are conflicted. This study aimed to analyze the available evidence on the effects of GLP-1 RAs on fracture risk in type 2 diabetes mellitus patients.
Methods
Electronic databases were searched for relevant published articles, and unpublished studies presented at
ClinicalTrials.gov
were searched for relevant clinical data. All analyses were performed with STATA 12.0 and R software (Version 3.4.4). We estimated the risk ratio (RR) and 95% confidence interval (CI) by combining RRs for fracture effects of included trials.
Results
There were 54 eligible random control trials (RCTs) with 49,602 participants, including 28,353 patients treated with GLP-1 RAs. Relative to placebo, exenatide (RR, 0.17; 95% CI 0.03–0.67) was associated with lowest risk of fracture among other GLP-1 RAs. Exenatide had the highest probability to be the safest option with regard to the risk of fracture (0.07 ‰), followed by dulaglutide (1.04%), liraglutide (1.39%), albiglutide (5.61%), lixisenatide (8.07%), and semaglutide (18.72%). A statistically significant inconsistency was observed in some comparisons.
Conclusion
The Bayesian network meta-analysis suggests that GLP-1 RAs were associated with a decreased bone fracture risk compared to users of placebo or other anti-hyperglycemic drugs in type 2 diabetes mellitus patients, and exenatide is the best option agent with regard to the risk of fracture.
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