Interspecific competition between Snellenius manilae and Meteorus pulchricornis, larval parasitoids of Spodoptera litura.

Snellenius manilae (Ashmead) and Meteorus pulchricornis (Wesmael) (Hymenoptera: Braconidae) are larval endoparasitoids of Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae). Both species preferentially parasitize early-instar S. litura and occupy similar ecological niches. Therefore, competition between the two species may occur. In this study, intrinsic competition and cage experiments were conducted to discuss the interactions between S. manilae and M. pulchricornis. The results indicated that in intrinsic competition, M. pulchricornis was always the dominant species. In cage experiments, when the total number of parasitoids was four, the parasitism rates following the release of one species were significantly higher than the release of two species simultaneously. In addition, parasitism rate of eight M. pulchricornis was also significantly higher than the parasitism rate of the treatment released four S. manilae and four M. pulchricornis simultaneously. Therefore, competition occurs between S. manilae and M. pulchricornis, and M. pulchricornis is typically the superior of the two species. The use of M. pulchricornis as a biological agent for S. litura should be considered.

Patient Phenotyping for Atopic Dermatitis With Transformers and Machine Learning: Algorithm Development and Validation Study.

BACKGROUND: Atopic dermatitis (AD) is a chronic skin condition that millions of people around the world live with each day. Performing research into identifying the causes and treatment for this disease has great potential to provide benefits for these individuals. However, AD clinical trial recruitment is not a trivial task due to the variance in diagnostic precision and phenotypic definitions leveraged by different clinicians, as well as the time spent finding, recruiting, and enrolling patients by clinicians to become study participants. Thus, there is a need for automatic and effective patient phenotyping for cohort recruitment. OBJECTIVE: This study aims to present an approach for identifying patients whose electronic health records suggest that they may have AD. METHODS: We created a vectorized representation of each patient and trained various supervised machine learning methods to classify when a patient has AD. Each patient is represented by a vector of either probabilities or binary values, where each value indicates whether they meet a different criteria for AD diagnosis. RESULTS: The most accurate AD classifier performed with a class-balanced accuracy of 0.8036, a precision of 0.8400, and a recall of 0.7500 when using XGBoost (Extreme Gradient Boosting). CONCLUSIONS: Creating an automated approach for identifying patient cohorts has the potential to accelerate, standardize, and automate the process of patient recruitment for AD studies; therefore, reducing clinician burden and informing the discovery of better treatment options for AD.

Using Natural Language Processing to Extract and Classify Symptoms Among Patients with Thyroid Dysfunction.

In the United States, more than 12% of the population will experience thyroid dysfunction. Patient symptoms often reported with thyroid dysfunction include fatigue and weight change. However, little is understood about the relationship between these symptoms documented in the outpatient setting and ordering patterns for thyroid testing among various patient groups by age and sex. We developed a natural language processing and deep learning pipeline to identify patient-reported outcomes of weight change and fatigue among patients with a thyroid stimulating hormone test. We built upon prior works by comparing 5 open-source, Bidirectional Encoder Representations from Transformers (BERT) to determine which models could accurately identify these symptoms from clinical texts. For both fatigue (f) and weight change (wc), Bio_ClinicalBERT achieved the highest F1-score (f: 0.900; wc: 0.906) compared BERT (f: 0.899; wc: 0.890), DistilBERT (f: 0.852; wc: 0.912), Biomedical RoBERTa (f: 0.864; wc: 0.904), and PubMedBERT (f: 0.882; wc: 0.892).

Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing.

According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions.

Background: Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods: We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion: Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Historical connectivity, contemporary isolation and local adaptation in a widespread but discontinuously distributed species endemic to Taiwan, Rhododendron oldhamii (Ericaceae).

Elucidation of the evolutionary processes that constrain or facilitate adaptive divergence is a central goal in evolutionary biology, especially in non-model organisms. We tested whether changes in dynamics of gene flow (historical vs contemporary) caused population isolation and examined local adaptation in response to environmental selective forces in fragmented Rhododendron oldhamii populations. Variation in 26 expressed sequence tag-simple sequence repeat loci from 18 populations in Taiwan was investigated by examining patterns of genetic diversity, inbreeding, geographic structure, recent bottlenecks, and historical and contemporary gene flow. Selection associated with environmental variables was also examined. Bayesian clustering analysis revealed four regional population groups of north, central, south and southeast with significant genetic differentiation. Historical bottlenecks beginning 9168-13,092 years ago and ending 1584-3504 years ago were revealed by estimates using approximate Bayesian computation for all four regional samples analyzed. Recent migration within and across geographic regions was limited. However, major dispersal sources were found within geographic regions. Altitudinal clines of allelic frequencies of environmentally associated positively selected outliers were found, indicating adaptive divergence. Our results point to a transition from historical population connectivity toward contemporary population isolation and divergence on a regional scale. Spatial and temporal dispersal differences may have resulted in regional population divergence and local adaptation associated with environmental variables, which may have played roles as selective forces at a regional scale.

Patient Phenotyping for Atopic Dermatitis with Transformers and Machine Learning.

Background: Atopic dermatitis (AD) is a chronic skin condition that millions of people around the world live with each day. Performing research studies into identifying the causes and treatment for this disease has great potential to provide benefit for these individuals. However, AD clinical trial recruitment is a non-trivial task due to variance in diagnostic precision and phenotypic definitions leveraged by different clinicians as well as time spent finding, recruiting, and enrolling patients by clinicians to become study subjects. Thus, there is a need for automatic and effective patient phenotyping for cohort recruitment. Objective: Our study aims to present an approach for identifying patients whose electronic health records suggest that they may have AD. Methods: We created a vectorized representation of each patient and trained various supervised machine learning methods to classify when a patient has AD. Each patient is represented by a vector of either probabilities or binary values where each value indicates whether they meet a different criteria for AD diagnosis. Results: The most accurate AD classifier performed with a class-balanced accuracy of 0.8036, a precision of 0.8400, and a recall of 0.7500 when using XGBoost (Extreme Gradient Boosting). Conclusions: Creating an automated approach for identifying patient cohorts has the potential to accelerate, standardize, and automate the process of patient recruitment for AD studies; therefore, reducing clinician burden and informing knowledge discovery of better treatment options for AD.

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors.

PURPOSE: Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions. METHODS: Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter. RESULTS: Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve [AUROC]: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner). CONCLUSION: We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.

Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clinical notes from patients discharged home when a clinical decision support system recommended post-acute care. We annotated B2PAC sentences from discharge planning notes and developed an NLP classifier to identify the highest-value B2PAC class (negative patient preferences). Thirteen machine learning models were compared with Amazon's AutoGluon deep learning model. The study included 594 acute care notes from 100 patient encounters (1156 sentences contained 11 B2PAC) in a large academic health system. The most frequent and modifiable B2PAC class was negative patient preferences (18.3%). The best supervised model was Extreme Gradient Boosting (F1: 0.859), but the deep learning model performed better (F1: 0.916). Alerting clinicians of negative patient preferences early in the hospitalization can prompt interventions such as patient education to ensure patients receive the right level of care and avoid negative outcomes.

Identification of a novel HLA-B*40 allele, HLA-B*40:332, in a Korean individual.

HLA-B*40:332 differs from B*40:01:02 by 1 nucleotide difference at nucleotide position 439.

Clonal variation in foliar chemistry of aspen: effects on gypsy moths and forest tent caterpillars.

Quaking aspen (Populus tremuloides) exhibits striking intraspecific variation in concentrations of phenolic glycosides, compounds that play important roles in mediating interactions with herbivorous insects. This research was conducted to assess the contribution of genetic variation to overall phenotypic variation in aspen chemistry and interactions with gypsy moths (Lymantria dispar) and forest tent caterpillars (Malacosoma disstria). Thirteen aspen clones were propagated from field-collected root material. Insect performance assays, measuring survival, development, growth, and food utilization indices, were conducted with second and/or fourth instars. Leaf samples were assayed for water, nitrogen, total nonstructural carbohydrates, condensed tannins, and phenolic glycosides. Results showed substantial among-clone variation in the performance of both insect species. Chemical analyses revealed significant among-clone variation in all foliar constituents and that variation in allelochemical contents differed more than variation in primary metabolites. Regression analyses indicated that phenolic glycosides were the dominant factor responsible for among-clone variation in insect performance. We also found significant genetic trade-offs between growth and defense among aspen clones. Our results suggest that genetic factors are likely responsible for much of the tremendous phenotypic variation in secondary chemistry exhibited by aspen, and that the genetic structure of aspen populations may play important roles in the evolution of interactions with phytophagous insects.

Genetic variations in ATP2B1, CSK, ARSG and CSMD1 loci are related to blood pressure and/or hypertension in two Korean cohorts.

Blood pressure, one of the important vital signs, is affected by multiple genetic and environmental factors. Recently, several genome-wide association (GWA) studies have successfully identified genetic factors that influence blood pressure and hypertension risk. In this study, we report results of the Korean Association REsource (KARE, 8842 subjects) GWA study on blood pressure and hypertension risk. In all, 10 single-nucleotide polymorphisms (SNPs) that showed significant association with hypertension were further analysed for replication associations in the Health2 project (7861 subjects). Among these 10 SNPs, 3 were replicated in the Health2 cohort for an association with systolic or diastolic blood pressure. The most significant SNP (rs17249754 located in ATPase, Ca(++) transporting, plasma membrane 1 (ATP2B1)) has been previously reported, and the other two SNPs are rs1378942 in the c-src tyrosine kinase (CSK) gene and rs12945290 in the arylsulphatase G (ARSG) gene. An additional hypertension case-control study confirmed that rs17249754 (in ATP2B1) increases hypertension risk in both the KARE and Health2 (meta-analysis, P-value=4.25 x 10(-9)) cohorts. One more SNP, rs995322, located in the CUB and Sushi multiple domains 1 (CSMD1), is also associated with increased risk of hypertension (meta-analysis, P-value=1.00 x 10(-4)). Despite the difficulty of obtaining replication results for a complex trait genetic association between blood pressure and hypertension, we were able to identify consistent genetic factors in both the Korean cohorts in ATP2B1, CSK, ARSG and CSMD1 genes.

Non-synonymous single-nucleotide polymorphisms associated with blood pressure and hypertension.

In this study, we determined the association of 1180 non-synonymous single-nucleotide polymorphisms (SNPs) with systolic blood pressure (SBP) and hypertensive status. A total of 8842 subjects were taken from two community-based cohorts--Ansung (n=4183) and Ansan (n=4659), South Korea--which had been established for genome-wide association studies (GWAS). Five SNPs (rs16835244, rs2286672, rs6265, rs17237198 and rs7312017) were significantly associated (P-values: 0.003-0.0001, not corrected for genome-wide significance) with SBP in both cohorts. Of these SNPs, rs16835244 and rs2286672 correlated with risk for hypertension. The rs16835244 SNP replaces Ala288 in arginine decarboxylase (ADC) with serine, and rs2286672 replaces Arg172 in phospholipase D2 (PLD2) with cysteine. A comparison of peptide sequences between vertebrate homologues revealed that the SNPs identified occur at conserved amino-acid residues. In silico analysis of the protein structure showed that the substitution of a polar residue, serine, for a non-polar alanine at amino-acid residue 288 affects a conformational change in ADC, and that Arg172 in PLD2 resides in the PX domain, which is important for membrane trafficking. These results provide insights into the function of these non-synonymous SNPs in the development of hypertension. The study investigating non-synonymous SNPs from GWAS not only by statistical association analysis but also by biological relevance through the protein structure might be a good approach for identifying genetic risk factors for hypertension, in addition to discovering causative variations.

Up-regulation of IL-23p19 expression in rheumatoid arthritis synovial fibroblasts by IL-17 through PI3-kinase-, NF-kappaB- and p38 MAPK-dependent signalling pathways.

OBJECTIVE: To investigate the expression of interleukin (IL)-23p19 in human rheumatoid arthritis (RA) synovial fibroblasts and its up-regulation by IL-17 stimulation, and to define the signal pathways involved in the regulation of IL-23p19 expression in RA synovial fibroblasts. METHODS: Synovial fluid (SF) and serum levels of IL-23p19 in RA were determined by enzyme-linked immunosorbent assays. The levels of IL-23p19 mRNA and protein were measured after the RA synovial fibroblasts were treated with recombinant human IL-17 and various inhibitors of intracellular signal pathway molecules using reverse transcription (RT) polymerase chain reaction (PCR), real-time PCR and western blotting. RESULTS: Levels of IL-23p19 in the sera and SF were much higher in RA patients than in osteoarthritis patients or healthy controls. The expression of IL-23p19 mRNA and protein was enhanced in RA synovial fibroblasts by IL-17 stimulation. Such effects of IL-17 were completely blocked by inhibitors of phosphatidylinositol (PI)-kinase/Akt, nuclear factor (NF)-kappaB and p38 mitogen-activated protein kinase (MAPK). In accordance with the expression of IL-23p19, the phosphorylation of IkappaB, Akt and p38 MAPK in synovial fibroblasts also increased after IL-17 stimulation. CONCLUSION: IL-23p19 is over-expressed in RA synovial fibroblasts and IL-17 appears to up-regulate the expression of IL-23p19 in RA synovial fibroblasts via PI3-kinase/Akt, NF-kappaB- and p38-MAPK-mediated pathways. These results suggest that a disruption of interaction between IL-17 and IL-23p19 may provide a new therapeutic approach in the treatment of RA.

Evidence for the existence of some dissociation in an otherwise strong linkage disequilibrium between mitochondrial and chloroplastic genomes in Cyclobalanopsis glauca.

Variations in mitochondrial DNA in Cyclobalanopsis glauca (Thunb. ex Murray) Oerst. were studied in 140 trees from 32 populations collected from within the tree's natural range. By sequencing two mitochondrial DNA intron fragments (nad4/3-nad4/4r and nad7/2-nad7/3r), we revealed a total of 1788 bp and five polymorphic sites which allowed us to distinguish six mitotypes. The mitochondrial DNA markers provided replicated data to support population phylogeographical scenarios suggested previously using chloroplastic DNA markers. The gene genealogical tree of mitochondrial DNA was partially congruent with the chloroplastic DNA tree owing to the slower mutation rate and different mutational direction. Significant linkage disequilibrium existed between the two organellar genomes. Further paring analyses between fragments synthesized using different primers, accompanied by exclusion of polymorphic sites, showed that the random association could be attributed specifically to one of the polymorphic sites of the petG-trnP fragment of the chloroplastic genome, and the three polymorphic sites of the nad4/3-nad4/4r fragment of the mitochondrial genome. The former was inferred to derive from paternal leakage, and the latter from recurrent mutation. These polymorphic sites were also responsible for uncoupling of the combined gene tree of mitotype and chlorotype. In conclusion, specific fragments found in this study contribute to the incomplete congruence of the two organellar lineages that otherwise associate well phylogeographically.

Allelic frequency of the MCP-1 promoter -2518 polymorphism in the Korean population and in Korean patients with rheumatoid arthritis, systemic lupus erythematosus and adult-onset Still's disease.

The frequency of the monocyte chemoattractant protein-1 (MCP-1) -2518 G-type polymorphism in Koreans is significantly higher than the frequencies reported for Caucasians and Afro-Americans. The G- vs. A-allele profile in patients with systemic autoimmune diseases is similar to that in healthy Koreans, and does not appear to contribute to elevated MCP-1 production in patients.