MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Evaluation of left ventricular systolic function in patients with iron deficiency anemia based on non-invasive left ventricular pressure-strain loops.

BACKGROUND: Iron deficiency anemia (IDA) is a common health problem worldwide. The objective of this study was to noninvasively and quantitatively evaluate early changes in left ventricular systolic function in patients with IDA using the left ventricular press-strain loop (LV-PSL). METHODS: Sixty-two patients with IDA were selected and divided into two groups based on hemoglobin (Hb) concentration: Group B with Hb > 9 g/dL and group C with 6 g/dL < Hb < 9 g/dL. Thirty-three healthy individuals were used as the control (Group A). The global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global waste work (GWW), global work efficiency (GWE) were derived using LV-PSL analysis. Receiver operating characteristic (ROC) curves were constructed for MW parameters to detect abnormal left ventricular systolic function in IDA patients. RESULTS: Compared to group A, GWI and GCW were reduced in group B (both P < 0.01). Compared with groups B and A, GLS, GWI, GCW and GWE, and E/A were all diminished, and GWW, LVEDV, LVESV, and E/mean e' were all increased in group C (all P < 0.01). GLS was positively correlated with GWI, GCW, and GWE (r = 0.679, 0.681, and 0.447, all P < 0.01), and negatively associated with GWW (r = - 0.411, all P < 0.01). For GWI, area under the ROC curve (AUROC) was 0.783. The optimal GWI threshold for detecting abnormal LV systolic function in IDA was1763 mmHg%, with sensitivity of 0.71 and specificity of 0.78. CONCLUSIONS: LV-PSL allows noninvasive quantitative assessment of early impaired LV systolic function in IDA patients with preserved LV ejection fraction, and GWI has high sensitivity and specificity compared with other parameters.

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.

INTRODUCTION: Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting. OBJECTIVES: This study aims to use metabolomics data to develop HerbMet, a high-performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus. METHODS: We propose HerbMet, an AI-based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D-ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance. RESULTS: Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1-score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1-score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU. CONCLUSIONS: HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.

Memristive True Random Number Generator for Security Applications.

This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption- thus leading us into a case study involving electroless YMnO3 (YMO) memristors as TRNG entropy sources that demonstrate good security properties by being able to produce unpredictable random numbers effectively. The end of our analysis sees us pinpointing challenges: post-processing algorithm optimization coupled with ensuring reliability over time for memristor-based TRNGs aimed at next-generation security applications.

Lightweight Vehicle Detection Based on Improved YOLOv5s.

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

TDG generation in a ski-jump spillway with a fully or partial-flip bucket.

Ski-jump spillways are frequently used as discharge structures for high dams during floods with high energy heads. The selection of bucket types at the end of spillways has a pronounced effect on the hydraulics of jet characteristics, such as trajectories and entrained air features. However, there is no literature reporting how changes in the bucket types influence TDG generation. This study compares the hydraulic characteristics and TDG mass transfer properties of a hydraulic project under construction using both the traditional fully-flip bucket and the partial-flip bucket configurations. The results indicate that, the use of the partial-flip bucket at the end of the spillway significantly disperses the water flow and yields better energy dissipation effects. At low flow rates (lower than 400 m3/s for the dam in this study), there is little difference in the downstream TDG saturation between the traditional fully-flip bucket and the partial-flip bucket, the average difference is 1.6 % in three cases with a low flow rate. However, at high flow rates (higher than 400 m3/s), the partial-flip bucket generates more TDG compared to the traditional fully-flip bucket, reaching up to 6.2 % at the maximum flow rate. This phenomenon stems from significant changes in the hydrodynamics of the stilling basin at high flow rates due to variations in the flip bucket type. When strict control of TDG generation is necessary downstream of dams, the use of the partial-flip bucket should be carefully considered. This is because, at high flow rates, the partial-flip bucket might result in higher TDG saturation than the fully-flip bucket.

Estimating the water quality index based on interpretable machine learning models.

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from -9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments.

An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma.

BACKGROUND AND PURPOSE: Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. METHODS: Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. RESULTS: ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 LI showed a negative correlation (r = - 0.478, r = - 0.369, r = - 0.488, r = - 0.388, all P < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933-0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group (P < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721-0.879). CONCLUSIONS: There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.

Cerebrospinal fluid and plasma metabolomics of acute endurance exercise.

Metabolomics has emerged as a powerful new tool in precision medicine. No studies have yet been published on the metabolomic changes in cerebrospinal fluid (CSF) produced by acute endurance exercise. CSF and plasma were collected from 19 young active adults (13 males and 6 females) before and 60 min after a 90-min monitored outdoor run. The median age, BMI, and VO2 max of subjects was 25 years (IQR 22-31), 23.2 kg/m2 (IQR 21.7-24.5), and 47 ml/kg/min (IQR 38-51), respectively. Targeted, broad-spectrum metabolomics was performed by liquid chromatography, tandem mass spectrometry (LC-MS/MS). In the CSF, purines and pyrimidines accounted for 32% of the metabolic impact after acute endurance exercise. Branch chain amino acids, amino acid neurotransmitters, fatty acid oxidation, phospholipids, and Krebs cycle metabolites traceable to mitochondrial function accounted for another 52% of the changes. A narrow but important channel of metabolic communication was identified between the brain and body by correlation network analysis. By comparing these results to previous work in experimental animal models, we found that over 80% of the changes in the CSF correlated with a cascade of mitochondrial and metabolic changes produced by ATP signaling. ATP is released as a co-neurotransmitter and neuromodulator at every synapse studied to date. By regulating brain mitochondrial function, ATP release was identified as an early step in the kinetic cascade of layered benefits produced by endurance exercise.

Metabolomic and exposomic biomarkers of risk of future neurodevelopmental delay in human milk.

BACKGROUND: The chemical composition of human milk has long-lasting effects on brain development. We examined the prognostic value of the human milk metabolome and exposome in children with the risk of neurodevelopmental delay (NDD). METHODS: This retrospective cohort study included 82 mother-infant pairs (40 male and 42 female infants). A total of 59 milk samples were from mothers with typically developing children and 23 samples were from mothers of children at risk. Milk samples were collected before 9 months of age (4.6 ± 2.5 months, mean ± SD). Neurocognitive development was assessed by maternal report at 14.2 ± 3.1 months using the Ages and Stages Questionnaires-2. RESULTS: Metabolome and exposome profiling identified 453 metabolites and 61 environmental chemicals in milk. Machine learning tools identified changes in deoxysphingolipids, phospholipids, glycosphingolipids, plasmalogens, and acylcarnitines in the milk of mothers with children at risk for future delay. A predictive classifier had a diagnostic accuracy of 0.81 (95% CI: 0.66-0.96) for females and 0.79 (95% CI: 0.62-0.94) for males. CONCLUSIONS: Once validated in larger studies, the chemical analysis of human milk might be added as an option in well-baby checks to help identify children at risk of NDD before the first symptoms appear. IMPACT: Maternal milk for infants sampled before 9 months of age contained sex-specific differences in deoxysphingolipids, sphingomyelins, plasmalogens, phospholipids, and acylcarnitines that predicted the risk of neurodevelopmental delay at 14.2 months of age. Once validated, this early biosignature in human milk might be incorporated into well-baby checks and help to identify infants at risk so early interventions might be instituted before the first symptoms appear.

Martial arts enhances working memory and attention in school-aged children: A functional near-infrared spectroscopy study.

Interventions can improve working memory and attention in school-aged children, but little is known about how regional changes in brain activity promoted by exercise mediate this cognitive improvement. This study focused on the improved neurocognitive functions and intrinsic regional variation within the brain by comparing school-aged children in a martial arts group with those in free-play and rest groups. With a pretest-posttest design, the d2 attention test and N-back tasks were carried out. Functional near-infrared spectroscopy was performed during the pre- and post-intervention N-back tasks and rest. Following the intervention, the d2 attention in all groups remarkably increased, and the attention level of the martial arts group was substantially higher than those of the other two groups. Free-play and martial arts shortened the 1- and 2-back task reaction time and increased the 2-back accuracy rate (AR), and the martial arts group exhibited a significantly higher AR than the other two groups. In addition, the martial arts group showed higher activations in the right orbitofrontal cortex and right Broca's area (r-BA) regions post-intervention 1-back tasks, whereas a strong correlation was observed between 1-back performance and the related brain region. However, under the 2-back task, although the cognitive control was improved, the martial arts group decreased activation in the left frontopolar area and free play decreased the activation in the r-BA and right somatosensory cortex. Together, our findings showed that martial arts could be more conducive to cognitive improvement than physical exercise that requires no cognitive skills and that performing interventions in the earlier stages of childhood may improve the regulation of neural networks involved in cognitive control.

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot.

In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients' differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection.

KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction.

With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe responses. In recent years, large-scale pretrained language models have demonstrated their powerful capabilities across various domains. Many studies have leveraged these pretrained models for dialogue tasks to address concerns such as safe response generation. Pretrained models can enhance responses by carrying certain knowledge information after being pre-trained on large-scale data. However, when specific knowledge is required in a particular domain, the model may still generate bland or inappropriate responses, and the interpretability of such models is poor. Therefore, in this paper, we propose the KRP-DS model. We design a knowledge module that incorporates a knowledge graph as external knowledge in the dialogue system. The module utilizes contextual information for path reasoning and guides knowledge prediction. Finally, the predicted knowledge is used to enhance response generation. Experimental results show that our proposed model can effectively improve the quality and diversity of responses while having better interpretability, and outperforms baseline models in both automatic and human evaluations.

EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips.

The pursuit of higher recognition accuracy and speed with smaller model sizes has been a major research topic in the detection of surface defects in steel. In this paper, we propose an improved high-speed and high-precision Efficient Fusion Coordination network (EFC-YOLO) without increasing the model's size. Since modifications to enhance feature extraction in shallow networks tend to affect the speed of model inference, in order to simultaneously ensure the accuracy and speed of detection, we add the improved Fusion-Faster module to the backbone network of YOLOv7. Partial Convolution (PConv) serves as the basic operator of the module, which strengthens the feature-extraction ability of shallow networks while maintaining speed. Additionally, we incorporate the Shortcut Coordinate Attention (SCA) mechanism to better capture the location information dependency, considering both lightweight design and accuracy. The de-weighted Bi-directional Feature Pyramid Network (BiFPN) structure used in the neck part of the network improves the original Path Aggregation Network (PANet)-like structure by adding step branches and reducing computations, achieving better feature fusion. In the experiments conducted on the NEU-DET dataset, the final model achieved an 85.9% mAP and decreased the GFLOPs by 60%, effectively balancing the model's size with the accuracy and speed of detection.

The long non-coding RNA SNHG1 attenuates chondrocyte apoptosis and inflammation via the miR-195/IKK-α axis.

Multiple studies have suggested that long non-coding RNAs (lncRNAs) are involved in the development and progression of osteoarthritis (OA). However, how lncRNA SNHG1 regulates OA remains unknown. This study aimed to explore how SNHG1 regulates chondrocyte apoptosis and inflammation. Our data showed that H2O2-treated chondrocytes exhibited lower expression of SNHG1 and secreted higher levels of IL-6, IL-8, and TNF-α than untreated cells. Further, overexpressing SNHG1 reduced chondrocyte apoptosis and production of inflammatory factors. Additionally, SNHG1 targets miR-195 directly, and IKK-α has direct biding sites for miR-195. Of note, IKK-α acts as an inhibitor of the NF-κB signaling pathway. These findings suggest that SNHG1 can upregulate IKK-α by inhibiting miR-195 and thus, inhibit NF-κB activity. Our in vivo experiments validate our in vitro findings. Thus, under oxidative stress, SNHG1 inhibits the activation of NF-κB to attenuate chondrocyte apoptosis and inflammation via the miR-195/IKK-α axis. Targeting SNHG1 may serve as a potential novel therapeutic approach for OA.

Restoration of a fish-attracting flow field downstream of a dam based on the swimming ability of endemic fishes: A case study in the upper Yangtze River basin.

The construction of fish passage facilities can mitigate the negative effects of dams and other water engineering construction on river connectivity and have a significant positive effect on the conservation of local fish diversity. To attract target fishes into fish passage facilities effectively, the optimal flow velocity range to attract fish must be determined. Three local endemic species of the Mishi Reservoir were considered as the protection targets. However, their swimming abilities remain unclear. Therefore, the induced swimming speed (Uind), critical swimming speed (Ucrit) and burst swimming speed (Uburst) of three fish species were tested. Based on these results, we identified the optimal flow velocity to attract fish, which falls within the range of 0.15-0.51 m/s. A validated three-dimensional hydrodynamic model was used to simulate different schemes. By comparing the flow field simulation results of different schemes, we obtained the optimal measure to restore the flow field, namely, a multiple engineering measure combining increased the fish attraction flow in the fish collection pond and the construction of a spur dike. This study offers a solution for the specific case and enhances the database of swimming characteristics of endemic fish in the upstream reaches of the Yangtze River. It also provides a valuable reference for designing fish-attracting flows and potential measures for restoring flow fields in similar future projects.

Dexmedetomidine alleviates acute lung injury by promoting Tregs differentiation via activation of AMPK/SIRT1 pathway.

OBJECTIVES: To explore the anti-inflammatory effect and the potential mechanism of dexmedetomidine in ARDS/ALI. MATERIALS AND METHODS: C57BL/6 mice and EL-4 cells were used in this research. The ALI model was established by CLP. The level of inflammatory cytokines in the lung and blood, the severity of lung injury, the expression of Foxp3, and the proportion of Tregs were detected before and after dexmedetomidine treatment. The expression of the AMPK/SIRT1 after dexmedetomidine treatment was detected in vivo and in vitro. After blocking the AMPK/SIRT1 pathway or depleting Tregs in vivo, the level of the inflammatory response, tissue injury, and Tregs differentiation were detected again to clarify the effect of dexmedetomidine. RESULTS: Dexmedetomidine significantly reduced systemic inflammation and lung injury in CLP mice. Dexmedetomidine enhanced the Foxp3 expression in the lungs and the frequency of Tregs in the spleen. Dexmedetomidine up-regulated the protein expression of p-AMPK and SIRT1 in lungs and EL-4 cells and facilitated the differentiation of naïve CD4+ T cells into Tregs in vitro. Meanwhile, DEX also increased the expression of Helios in Treg cells. CONCLUSIONS: DEX could improve ARDS/ALI by facilitating the differentiation of Tregs from naïve CD4+ T cells via activating the AMPK/SIRT1 pathway.

The emerging roles of next-generation metabolomics in critical care nutrition.

Critical illness leads to millions of deaths worldwide each year, with a significant surge due to the COVID-19 pandemic. Patients with critical illness are frequently associated with systemic metabolic disorders and malnutrition. The idea of intervention for critically ill patients through enteral and parenteral nutrition has been paid more and more attention gradually. However, current nutritional therapies focus on evidence-based practice, and there have been lacking holistic approaches for nutritional support assessment. Metabolomics is a well-established omics technique in system biology that enables comprehensive profiling of metabolites in a biological system and thus provides the underlying information expressed and modulated by all other omics layers. In recent years, with the development of high-resolution and accurate mass spectrometry, metabolomics entered a new "generation", promoting its broader applications in critical care nutrition. In this review, we first described the technological development and milestones of next-generation metabolomics in the past 20 years. We then discussed the emerging roles of next-generation metabolomics in advancing our understanding of critical care nutrition, such as nutritional deficiency risk evaluation, metabolic mechanisms of nutritional therapies, and novel nutrition target identification.

Fabrication of Ni-Rich 58NiTi and 60NiTi from Elementally Blended Ni and Ti Powders by a Laser Powder Bed Fusion Technique: Their Printing, Homogenization and Densification.

Compared to the equiatomic or near-equiatomic NiTinol alloys, Ni-rich NiTi alloys are suitable to be employed in structural applications as they exhibit higher hardness and are dimensionally stable. This research aimed to process two different grades of Ni-rich NiTi alloys, 58NiTi and 60NiTi, from Ni-Ti powder mixtures having about 58 wt.% and 60 wt.% Ni, respectively. This was performed by a laser powder bed fusion technique. At the first stage of this research, the printability of the used powder mixtures was investigated by applying different sets of printing parameters. Two appropriate sets were then selected to print the samples. Microstructural study of the printed parts revealed the existence of inhomogeneity in the microstructures. In addition, depending on the applied set of parameters, some amounts of cracks and pores were also present in the microstructure of these parts. Postprinting hot isostatic pressing procedures, performed at different temperatures, were developed to cause the reaction of phases, homogenize the parts, and possibly eliminate the existing flaws from the samples. Effects of these applied treatments on the microstructure, phase composition, density, dimensional integrity, and hardness of parts were sequentially studied. In essence, 58NiTi and 60NiTi parts having phase compositions complying with those of the equilibrium phase diagram were obtained in this research. However, the mentioned cracks and pores, formed in the microstructure of as-printed parts, could not be fully removed by postprocessing treatments.