A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness.

OBJECTIVES: We evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI). METHODS: We created methods for transforming raw data into a data framework for applying machine learning and natural language processing techniques for predicting falls and fractures. Strategies such as inclusion and reporting for multiple races, mixed data sources such as outpatient, inpatient, structured codes, and unstructured notes, and addressing missingness were applied to raw data to promote a reduction in bias. The raw data was carefully curated using validated definitions to create data variables such as age, race, gender, and healthcare utilization. For the formation of these variables, clinical, statistical, and data expertise were used. The research team included a variety of experts with diverse professional and demographic backgrounds to include diverse perspectives. RESULTS: For the prediction of falls, information extracted from radiology reports was converted to a matrix for applying machine learning. The processing of the data resulted in an input of 5,377,673 reports to the machine learning algorithm, out of which 45,304 were flagged as positive and 5,332,369 as negative for falls. Processed data resulted in lower missingness and a better representation of race and diagnosis codes. For fractures, specialized algorithms extracted snippets of text around keywork "femoral" from dual x-ray absorptiometry (DXA) scans to identify femoral neck T-scores that are important for predicting fracture risk. The natural language processing algorithms yielded 98% accuracy and 2% error rate The methods to prepare data for input to artificial intelligence processes are reproducible and can be applied to other studies. CONCLUSION: The life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. When applying artificial intelligence methods, input data must be prepared optimally to reduce algorithmic bias, as biased output is harmful. Building AI-ready data frameworks that improve efficiency can contribute to transparency and reproducibility. The roadmap for the application of AI involves applying specialized techniques to input data, some of which are suggested here. This study highlights data curation aspects to be considered when preparing data for the application of artificial intelligence to reduce bias.

Whole genome sequencing analysis of Limosilactobacillus reuteri from the intestinal tract of mice recovering from ulcerative colitis and preliminary study on anti-inflammatory effects of its derived peptides.

Limosilactobacillus reuteri is an indigenous inhabitant of the animal gut known for its probiotic effects on the host. In our previous study, a large number of L. reuteri strains were isolated from the gastrointestinal tract of mice recovering from ulcerative colitis, from which we randomly selected L. reuteri RE225 for whole genome sequencing to explore its probiotic properties. The results of next-generation sequencing and third-generation single molecule sequencing showed that L. reuteri RE225 contained many genes encoding functional proteins associated with adhesion, anti-inflammatory and pathogen inhibition. And compared to other L. reuteri strains in NCBI, L. reuteri RE225 has unique gene families with probiotic functions. In order to further explore the probiotic effect of the L. reuteri RE225, the derived peptides were identified by LC-MS/MS, and the peptides with tumor necrosis factor-α binding ability were screened by reverse molecular docking and microscale thermophoresis. Finally, cell experiments demonstrated the anti-inflammatory ability of the peptides. Western blotting and qPCR analyses confirmed that the selected peptides might alleviate LPS-induced inflammation in NCM460 cells by inhibiting JAK2/STAT3 pathway activation.

Analysis of antimicrobial biological activity of a marine Bacillus velezensis NDB.

A new strain of Bacillus velezensis NDB was isolated from Xiangshan Harbor and antibacterial test revealed antibacterial activity of this strain against 12 major pathogenic bacteria. The whole genome of the bacterium was sequenced and found to consist of a 4,214,838 bp circular chromosome and a 7410 bp circular plasmid. Furthermore, it was predicted by AntiSMASH and BAGEL4 to have 12 clusters of secondary metabolism genes for the synthesis of the inhibitors, fengycin, bacillomycin, macrolactin H, bacillaene, and difficidin, and there were also five clusters encoding potentially novel antimicrobial substances, as well as three bacteriocin biosynthesis gene clusters of amylocyclicin, ComX1, and LCI. qRT-PCR revealed significant up-regulation of antimicrobial secondary metabolite synthesis genes after 24 h of antagonism with pathogenic bacteria. Furthermore, MALDI-TOF mass spectrometry revealed that it can secrete surfactin non-ribosomal peptide synthase and polyketide synthase to exert antibacterial effects. GC-MS was used to analyze methanol extract of B. velezensis NDB, a total of 68 compounds were identified and these metabolites include 16 amino acids, 17 acids, 3 amines, 11 sugars, 11 alcohols, 1 ester, and 9 other compounds which can inhibit pathogenic bacteria by initiating the antibiotic secretion pathway. A comparative genomic analysis of gene families showed that the specificity of B. velezensis NDB was mainly reflected in environmental adaptability. Overall, this research on B. velezensis NDB provides the basis for elucidating its biocontrol effect and promotes its future application as a probiotic.

Rapid and simultaneous multiepitope antigen-based detection of Enterococcus by microscale thermophoresis and immunomagnetic separation.

Background: Generally, enterococci bacteria cause nosocomial infections and are major indicators of bacterial contamination in marine bathing beach. However, a method for the rapid and simultaneous detection of multiple pathogenic enterococci has not been developed on account of the wide variety of pathogenic enterococci and their existence in complex matrices. Methods: Immunoinformatics tools were used to design a multi-epitope antigen for the detection of various pathogenic enterococci by using the sequence of dltD gene on enterococci lipoteichoic acid (LTA) surface, which is associated with toxicological effects. The multi-epitopes included enterococci such as Enterococcus faecalis, E. gallinarum, E. raffinosus, E. durans, E. faecium, E. hirae, E. thailandicus, E. casseliflavus, E. avium, E. mundtii, E. lactis, E. solitarius, E. pseudoavium, and E. malodoratum. Microscale thermophoresis (MST) and western blot were carried out to detect the affinity between multi-epitope antigens and antibodies and between multi-epitope antibodies and bacteria. Furthermore, the detection of pathogenic enterococci was carried out by using immunomagnetic beads (IMBs) and immune chromatographic test strip (ICTS). Results: The multi-epitope antibody had a satisfactory affinity to the antigen and enterococci. IMBs and ICTS were detected with a minimum of 101 CFU/mL and showed incompatibility for Vibrio parahemolyticus, V. vulnifcus, V. harveyi, V. anguillarum, and Edwardsiella tarda. Implication: The present study demonstrated that the multi-epitope antigens exhibited excellent specificity and sensitivity, making them highly suitable for efficient on-site screening of enterococci bacteria in marine bathing beaches.

Childhood Maltreatment and Gratitude: A Multilevel, Meta-Analytic Review.

During the past decade, research on the association between childhood maltreatment (CM) and gratitude has been accumulating, but there is a lack of systematic, quantitative evaluation of existing literature. The present systematic review aims to fill this gap by conducting a three-level meta-analysis. After a comprehensive search in five English and three Chinese databases, we retrieved 33 effect sizes from 16 studies with a total sample of 13,818 participants. The results showed that CM (aggregated across forms) was negatively and moderately linked to gratitude (r = -.311, 95% CI [-0.382, -0.235], p < .001). Childhood neglect (i.e., physical and emotional neglect) exhibited more substantial effects on gratitude than emotional abuse, while the effects of childhood physical and sexual abuse were insignificant. In addition, the mean effect sizes were larger in studies with younger samples. However, the effect did not vary as functions of CM measurement, methodological rigor, whether the sample included college students only, publication type, or region of the sample. Last, we highlighted the limitations of existing research, proposed agendas for future studies, and discussed practical implications.

Childhood maltreatment and depressed mood in female college students: A daily diary analysis.

BACKGROUND: Childhood maltreatment has well-documented relations with depressed mood. However, few studies have used a daily diary methodology to investigate the association between all five forms of childhood maltreatment (emotional, physical, and sexual abuse, emotional and physical neglect) and depressed mood, and the mechanisms underlying the association remain unclear. OBJECTIVE: The current study sought to examine the associations of multiple forms of childhood maltreatment with depressed mood via a 14-day daily diary methodology, and investigate the mediating effects of self-compassion and perceived social support. METHODS: A sample of 220 Chinese female college students (Mage = 19.13 years) participated in this study and completed questionnaires regarding childhood maltreatment, self-compassion, perceived social support and depressed mood. RESULTS: The multilevel regression analysis indicated that only emotional abuse was slightly associated with depressed mood, while emotional neglect, physical abuse, physical neglect, or sexual abuse were not associated with depressed mood. The multilevel mediation analysis further revealed that self-compassion and perceived social support independently mediated the association of childhood emotional abuse with depressed mood. CONCLUSIONS: Overall, these results emphasize the specific association between childhood emotional abuse and daily depressed mood, and further support self-compassion and perceived social support as explanatory mechanisms linking childhood emotional abuse with later depressed mood.

Suppressed Auger recombination and enhanced emission of InP/ZnSe/ZnS quantum dots through inner shell manipulation.

Understanding the influence of the inner shell on fluorescence blinking and exciton dynamics is essential to promote the optical performances of InP-based quantum dots (QDs). Here, the fluorescence blinking, exciton dynamics, second-order correlation function g2(τ), and ultrafast carrier dynamics of InP/ZnSe/ZnS QDs regulated by the inner ZnSe shell thickness varying from 2 to 7 monolayers (MLs) were systematically investigated. With an inner ZnSe shell thickness of 5 MLs, the photoluminescence quantum yield (PL QY) can reach 98% due to the suppressed blinking and increased probability of multiphoton emission. The exciton dynamics of InP/ZnSe/ZnS QDs with different inner shells indicates that two decay components of neural excitons and charged trions are competitive to affect the photon emission behavior. The probability density distributions of the ON and OFF state duration in the blinking traces demonstrate an effective manipulation of the inner ZnSe shell in the non-radiative processes via defect passivation. Accordingly, the radiative recombination dominates the exciton deactivation and the non-radiative Auger recombination rate is remarkably reduced, leading to a QY close to unity and a high PL stability for InP/ZnSe/ZnS QDs with 5 MLs of the ZnSe shell. These results provide insights into the photophysical mechanism of InP-based QDs and are significant for developing novel semiconductor PL core/shell QDs.

Comparison of Lactiplantibacillus plantarum isolates from the gut of mice supplemented with different types of nutrients: a genomic and metabolomic study.

Many studies have focused on the influence of dietary supplements on gut microbiota composition, but limited research have reported their effects on specific bacterial species in the gut. Lactiplantibacillus plantarum is one of the most widely studied probiotics, with a wide range of sources and good environmental adaptability. In this study, in order to elucidate the adaptation strategies of L. plantarum to the gut of mice supplemented with carbohydrates, peptides and minerals, whole genome resequencing and intracellular metabolites detection were performed, and high-frequency mutant genes and differential metabolites were screened. The results suggested different types of dietary supplements do have different effects on L. plantarum from the gut of mice. Additionally, KEGG annotation unveiled that the effects of these dietary supplements on the gene level of L. plantarum primarily pertained to environmental information processing, while the differential metabolites were predominantly associated with metabolism. This study provided new perspectives on the adaptive mechanism of L. plantarum in response to the host's gut environment, suggesting that the diversity of the genome and metabolome of L. plantarum was correlated with dietary supplements. Furthermore, this study offered useful guidance in the effective utilization of dietary supplements.

An accurate and flexible image clamping center locating algorithm for micro-gripper.

In the process of microassembly, aligning the end effectors with the micro-parts using image information is the basis of automated assembly. In order to realize the flexible and accurate clamping center locating of the micro-gripper with various shapes of jaws, this paper proposes an iterative-based processing algorithm. First, the locating problem is transformed into a multi-parameter optimization problem through the geometric analysis of the clamping process. Second, an iterative optimal algorithm based on the block coordinate descent is developed, in which a scaling golden section (SGS) scheme is proposed to calculate the iteration scaling parameters. Third, the lookup table and variable threshold iteration techniques are utilized to further improve the performance of the SGS scheme. Simulation results show that the proposed algorithm can efficiently locate the clamping center for various types of jaws with sub-pixel accuracy. Finally, a microassembly experiment is carried out to demonstrate the effectiveness of the algorithm.

A Roadmap to Artificial Intelligence (AI): Methods for Designing and Building AI ready Data for Women's Health Studies.

Objectives: Evaluating methods for building data frameworks for application of AI in large scale datasets for women's health studies. Methods: We created methods for transforming raw data to a data framework for applying machine learning (ML) and natural language processing (NLP) techniques for predicting falls and fractures. Results: Prediction of falls was higher in women compared to men. Information extracted from radiology reports was converted to a matrix for applying machine learning. For fractures, by applying specialized algorithms, we extracted snippets from dual x-ray absorptiometry (DXA) scans for meaningful terms usable for predicting fracture risk. Discussion: Life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. For applying AI, data must be prepared optimally to reduce algorithmic bias. Conclusion: Algorithmic bias is harmful for research using AI methods. Building AI ready data frameworks that improve efficiency can be especially valuable for women's health.

Comprehensive analysis of metabolomics on flesh quality of yellow catfish (Pelteobagrus fulvidraco) fed plant-based protein diet.

Background: To investigate the mechanism of plant protein components on nutritional value, growth performance, flesh quality, flavor, and proliferation of myocytes of yellow catfish (Pelteobagrus fulvidraco). Methods: A total of 540 yellow catfish were randomly allotted into six experimental groups with three replicates and fed six different diets for 8 weeks. Results and Conclusions: The replacement of fish meal with cottonseed meal (CM), sesame meal (SEM), and corn gluten meal (CGM) in the diet significantly reduced growth performance, crude protein, and crude lipid, but the flesh texture (hardness and chewiness) was observably increased. Moreover, the flavor-related amino acid (glutamic acid, glycine, and proline) contents in the CM, SEM, and CGM groups of yellow catfish muscle were significantly increased compared with the fish meal group. The results of metabolomics showed that soybean meal (SBM), peanut meal (PM), CM, SEM, and CGM mainly regulated muscle protein biosynthesis by the variations in the content of vitamin B6, proline, glutamic acid, phenylalanine, and tyrosine in muscle, respectively. In addition, Pearson correlation analysis suggested that the increased glutamic acid content and the decreased tyrosine content were significantly correlated with the inhibition of myocyte proliferation genes. This study provides necessary insights into the mechanism of plant proteins on the dynamic changes of muscle protein, flesh quality, and myocyte proliferation in yellow catfish.

Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing.

BACKGROUND: Musculoskeletal pain is common in the Veterans Health Administration (VHA), and there is growing national use of chiropractic services within the VHA. Rapid expansion requires scalable and autonomous solutions, such as natural language processing (NLP), to monitor care quality. Previous work has defined indicators of pain care quality that represent essential elements of guideline-concordant, comprehensive pain assessment, treatment planning, and reassessment. OBJECTIVE: Our purpose was to identify pain care quality indicators and assess patterns across different clinic visit types using NLP on VHA chiropractic clinic documentation. METHODS: Notes from ambulatory or in-hospital chiropractic care visits from October 1, 2018 to September 30, 2019 for patients in the Women Veterans Cohort Study were included in the corpus, with visits identified as consultation visits and/or evaluation and management (E&M) visits. Descriptive statistics of pain care quality indicator classes were calculated and compared across visit types. RESULTS: There were 11,752 patients who received any chiropractic care during FY2019, with 63,812 notes included in the corpus. Consultation notes had more than twice the total number of annotations per note (87.9) as follow-up visit notes (34.7). The mean number of total classes documented per note across the entire corpus was 9.4 (standard deviation [SD] = 1.5). More total indicator classes were documented during consultation visits with (mean = 14.8, SD = 0.9) or without E&M (mean = 13.9, SD = 1.2) compared to follow-up visits with (mean = 9.1, SD = 1.4) or without E&M (mean = 8.6, SD = 1.5). Co-occurrence of pain care quality indicators describing pain assessment was high. CONCLUSION: VHA chiropractors frequently document pain care quality indicators, identifiable using NLP, with variability across different visit types.

Understanding headache classification coding within the veterans health administration using ICD-9-CM and ICD-10-CM in fiscal years 2014-2017.

OBJECTIVES: Understand the continuity and changes in headache not-otherwise-specified (NOS), migraine, and post-traumatic headache (PTH) diagnoses after the transition from ICD-9-CM to ICD-10-CM in the Veterans Health Administration (VHA). BACKGROUND: Headache is one of the most commonly diagnosed chronic conditions managed within primary and specialty care clinics. The VHA transitioned from ICD-9-CM to ICD-10-CM on October-1-2015. The effect transitioning on coding of specific headache diagnoses is unknown. Accuracy of headache diagnosis is important since different headache types respond to different treatments. METHODS: We mapped headache diagnoses from ICD-9-CM (FY 2014/2015) onto ICD-10-CM (FY 2016/2017) and computed coding proportions two years before/after the transition in VHA. We used queries to determine the change in transition pathways. We report the odds of ICD-10-CM coding associated with ICD-9-CM controlling for provider type, and patient age, sex, and race/ethnicity. RESULTS: Only 37%, 58% and 34% of patients with ICD-9-CM coding of NOS, migraine, and PTH respectively had an ICD-10-CM headache diagnosis. Of those with an ICD-10-CM diagnosis, 73-79% had a single headache diagnosis. The odds ratios for receiving the same code in both ICD-9-CM and ICD-10-CM after adjustment for ICD-9-CM and ICD-10-CM headache comorbidities and sociodemographic factors were high (range 6-26) and statistically significant. Specifically, 75% of patients with headache NOS had received one headache diagnoses (Adjusted headache NOS-ICD-9-CM OR for headache NOS-ICD-10-CM = 6.1, 95% CI 5.89-6.32. 79% of migraineurs had one headache diagnoses, mostly migraine (Adjusted migraine-ICD-9-CM OR for migraine-ICD-10-CM = 26.43, 95% CI 25.51-27.38). The same held true for PTH (Adjusted PTH-ICD-9-CM OR for PTH-ICD-10-CM = 22.92, 95% CI: 18.97-27.68). These strong associations remained after adjustment for specialist care in ICD-10-CM follow-up period. DISCUSSION: The majority of people with ICD-9-CM headache diagnoses did not have an ICD-10-CM headache diagnosis. However, a given diagnosis in ICD-9-CM by a primary care provider (PCP) was significantly predictive of its assignment in ICD-10-CM as was seeing either a neurologist or physiatrist (compared to a generalist) for an ICD-10-CM headache diagnosis. CONCLUSION: When a veteran had a specific diagnosis in ICD-9-CM, the odds of being coded with the same diagnosis in ICD-10-CM were significantly higher. Specialist visit during the ICD-10-CM period was independently associated with all three ICD-10-CM headaches.

Comparison of the gut microbiota and metabolism in different regions of Red Swamp Crayfish (Procambarus clarkii).

Background: The gut microbiota is very important for maintaining the homeostasis and health of crustaceans. Many factors affect the gut microbiota of crustaceans, one of which is temperature. However, it is currently unclear how temperature affects the gut microbiota and metabolites of Procambarus clarkii. Methods: Using metagenomic sequencing and gas chromatography-mass spectrometry (GC-MS) techniques, the gut microbiota and metabolites of P. clarkii from Hubei (HB), Jiangsu (JS), Shandong (SD), and Zhejiang (ZJ) in China were investigated. Results: Under the impact of temperature, the gut microbiota and metabolites of P. clarkii exhibit a specific trend of change. The primary pathogenic bacteria affecting P. clarkii are Citrobacter, Enterobacterium, and Aeromonas, which are affected by temperature. Two metabolites, namely, sugars and amino acids, are regulated by temperature. Implication: This study demonstrated that the gut microbiota and gut metabolites of P. clarkii were considerably affected by temperature. It provides a theoretical basis for the systematic study of P. clarkii and provides a basis for a healthy culture of P. clarkii.

Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis.

The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the health status of the rotor system. Previous studies are mostly based on the premise that training set and testing set have the same categories. However, because the actual operating conditions of mechanical equipment are complex and changeable, the real diagnostic tasks usually have greater diversity than the pre-acquired datasets. The inconsistency of the categories of training set and testing set makes it easy for convolutional neural network to identify the unknown fault data as normal data, which is very fatal to equipment health management. To overcome the above problem, this article proposes a new method, Huffman-convolutional neural network, to improve the generalization ability of the model in detection task with various operating conditions. First, a new Huffman pooling kernel is designed according to the Huffman coding principle and the Huffman pooling layer structure is introduced in the convolutional neural network to enhance the model's ability to extract common features of data under different conditions. Second, a new objective function is proposed based on softmax loss, intra-class loss, and inter-class loss to improve the Huffman-convolutional neural network's ability to distinguish different classes of data and aggregate the same class of data. Third, the proposed method is tested on three different datasets to verify the generalization ability of the Huffman-convolutional neural network in diagnosis tasks with multi-operating conditions. Compared with other traditional methods, the proposed method has better performance and greater potential in multi-condition fault diagnosis and anomaly detection tasks with inconsistent class spaces.

Effect of the STRIDE fall injury prevention intervention on falls, fall injuries, and health-related quality of life.

BACKGROUND: Falls are common in older adults and can lead to severe injuries. The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial cluster-randomized 86 primary care practices across 10 health systems to a multifactorial intervention to prevent fall injuries, delivered by registered nurses trained as falls care managers, or enhanced usual care. STRIDE enrolled 5451 community-dwelling older adults age ≥70 at increased fall injury risk. METHODS: We assessed fall-related outcomes via telephone interviews of participants (or proxies) every 4 months. At baseline, 12 and 24 months, we assessed health-related quality of life (HRQOL) using the EQ-5D-5L and EQ-VAS. We used Poisson models to assess intervention effects on falls, fall-related fractures, fall injuries leading to hospital admission, and fall injuries leading to medical attention. We used hierarchical longitudinal linear models to assess HRQOL. RESULTS: For recurrent event models, intervention versus control incidence rate ratios were 0.97 (95% confidence interval [CI], 0.93-1.00; p = 0.048) for falls, 0.93 (95% CI, 0.80-1.08; p = 0.337) for self-reported fractures, 0.89 (95% CI, 0.73-1.07; p = 0.205) for adjudicated fractures, 0.91 (95% CI, 0.77-1.07; p = 0.263) for falls leading to hospital admission, and 0.97 (95% CI, 0.89-1.06; p = 0.477) for falls leading to medical attention. Similar effect sizes (non-significant) were obtained for dichotomous outcomes (e.g., participants with ≥1 events). The difference in least square mean change over time in EQ-5D-5L (intervention minus control) was 0.009 (95% CI, -0.002 to 0.019; p = 0.106) at 12 months and 0.005 (95% CI, -0.006 to 0.015; p = 0.384) at 24 months. CONCLUSIONS: Across a standard set of outcomes typically reported in fall prevention studies, we observed modest improvements, one of which was statistically significant. Future work should focus on patient-, practice-, and organization-level operational strategies to increase the real-world effectiveness of interventions, and improving the ability to detect small but potentially meaningful clinical effects. CLINICALTRIALS: gov identifier: NCT02475850.

The Response and Survival Mechanisms of Staphylococcus aureus under High Salinity Stress in Salted Foods.

Staphylococcus aureus (S. aureus) has a strong tolerance to high salt stress. It is a major reason as to why the contamination of S. aureus in salted food cannot be eradicated. To elucidate its response and survival mechanisms, changes in the morphology, biofilm formation, virulence, transcriptome, and metabolome of S. aureus were investigated. IsaA positively regulates and participates in the formation of biofilm. Virulence was downregulated to reduce the depletion of nonessential cellular functions. Inositol phosphate metabolism was downregulated to reduce the conversion of functional molecules. The MtsABC transport system was downregulated to reduce ion transport and signaling. Aminoacyl-tRNA biosynthesis was upregulated to improve cellular homeostasis. The betaine biosynthesis pathway was upregulated to protect the active structure of proteins and nucleic acids. Within a 10% NaCl concentration, the L-proline content was upregulated to increase osmotic stability. In addition, 20 hub genes were identified through an interaction analysis. The findings provide theoretical support for the prevention and control of salt-tolerant bacteria in salted foods.

Association Between Exposure to Complementary and Integrative Therapies and Opioid Analgesic Daily Dose Among Patients on Long-term Opioid Therapy.

OBJECTIVES: To describe the association between exposure to selected complementary and integrative health (CIH) modalities and the trajectory of prescribed opioid analgesic dose within a national cohort of patients receiving long-term opioid therapy (LTOT) in the Veterans Health Administration (VHA). MATERIALS AND METHODS: Using national data from VHA electronic health records between October 1, 2017 and September 30, 2019, CIH use was analyzed among 57,437 patients receiving LTOT within 18 VHA facilities serving as evaluation sites of VHA's Whole Health System of Care. Using linear mixed effects modeling controlling for covariates, opioid dose was modeled as a function of time, CIH exposure, and their interaction. RESULTS: Overall, 11.91% of patients on LTOT used any of the focus CIH therapies; 43.25% of those had 4 or more encounters. Patients used acupuncture, chiropractic care, and meditation modalities primarily. CIH use was associated with being female, Black, having a mental health diagnosis, obesity, pain intensity, and baseline morphine-equivalent daily dose. Mean baseline morphine-equivalent daily dose was 40.81 milligrams and dose decreased on average over time. Controlling for covariates, patients with any CIH exposure experienced 38% faster dose tapering, corresponding to a mean difference in 12-month reduction over patients not engaging in CIH of 2.88 milligrams or 7.06% of the mean starting dose. DISCUSSION: Results support the role of CIH modalities in opioid tapering. The study design precludes inference about the causal effects of CIH on tapering. Analyses did not consider the trend in opioid dose before cohort entry nor the use of other nonopioid treatments for pain. Future research should address these questions and consider tapering-associated adverse events.

A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem.

Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent years due to the promising development of artificial intelligent technology. Many intelligent diagnosis methods work well requiring massive historical data of the diagnosed object. However, it is hard to get sufficient fault data in advance in real diagnosis scenario and the diagnosis model constructed on such small dataset suffers from serious overfitting and losing the ability of generalization, which is described as small sample problem in this paper. Focus on the small sample problem, this paper proposes a new intelligent fault diagnosis framework based on dynamic model and transfer learning for rolling element bearings race faults. In the proposed framework, dynamic model of bearing is utilized to generate massive and various simulation data, then the diagnosis knowledge learned from simulation data is leveraged to real scenario based on convolutional neural network (CNN) and parameter transfer strategies. The effectiveness of the proposed method is verified and discussed based on three fault diagnosis cases in detail. The results show that based on the simulation data and parameter transfer strategies in CNN, the proposed method can learn more transferable features and reduce the feature distribution discrepancy, contributing to enhancing the fault identification performance significantly.

Comprehensive evaluation and analysis of the salinity stress response mechanisms based on transcriptome and metabolome of Staphylococcus aureus.

Staphylococcus aureus possesses an extraordinary ability to deal with a wide range of osmotic pressure. This study performed transcriptomic and metabolomic analyses on the potential mechanism of gradient salinity stress adaptation in S. aureus ZS01. The results revealed that CPS biosynthetic protein genes were candidate target genes for directly regulating the phenotypic changes of biofilm. Inositol phosphate metabolism was downregulated to reduce the conversion of functional molecules. The gluconeogenesis pathway and histidine synthesis were downregulated to reduce the production of endogenous glucose. The pyruvate metabolism pathway was upregulated to promote the accumulation of succinate. TCA cycle metabolism pathway was downregulated to reduce unnecessary energy loss. L-Proline was accumulated to regulate osmotic pressure. Therefore, these self-protection mechanisms can protect cells from hypertonic environments and help them focus on survival. In addition, we identified ten hub genes. The findings will aid in the prevention and treatment strategies of S. aureus infections.