Classifying Organizations for Food System Ontologies using Natural Language Processing

Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population and integration with food system ontologies. We have created NLP models that can automatically classify organizations with respect to categories associated with environmental issues as well as Standard Industrial Classification (SIC) codes, which are used by the U.S. government to characterize business activities. As input, the NLP models are provided with text snippets retrieved by the Google search engine for each organization, which serves as a textual description of the organization that is used for learning. Our experimental results show that NLP models can achieve reasonably good performance for these two classification tasks, and they rely on a general framework that could be applied to many other classification problems as well. We believe that NLP models represent a promising approach for automatically harvesting information to populate knowledge graphs and aligning the information with existing ontologies through shared categories and concepts.Comment: Presented at IFOW 2023 Integrated Food Ontology Workshop at the Formal Ontology in Information Systems Conference (FOIS) 2023 in Sherbrooke, Quebec, Canada July 17-20th, 202

A nested cohort study of 6,248 early breast cancer patients treated in neoadjuvant and adjuvant chemotherapy trials investigating the prognostic value of chemotherapy-related toxicities.

BACKGROUND: The relationship between chemotherapy-related toxicities and prognosis is unclear. Previous studies have examined the association of myelosuppression parameters or neuropathy with survival and reported conflicting results. This study aims to investigate 13 common chemotherapy toxicities and their association with relapse-free survival and breast cancer-specific survival. METHODS: Chemotherapy-related toxicities were collected prospectively for 6,248 women with early-stage breast cancer from four randomised controlled trials (NEAT; BR9601; tAnGo; Neo-tAnGo). Cox proportional-hazards modelling was used to analyse the association between chemotherapy-related toxicities and both breast cancer-specific survival and relapse-free survival. Models included important prognostic factors and stratified by variables violating the proportional hazards assumption. RESULTS: Multivariable analysis identified severe neutropenia (grades ≥3) as an independent predictor of relapse-free survival (hazard ratio (HR) = 0.86; 95% confidence interval (CI), 0.76-0.97; P = 0.02). A similar trend was seen for breast cancer-specific survival (HR = 0.87; 95% CI, 0.75-1.01; P = 0.06). Normal/low BMI patients experienced more severe neutropenia (P = 0.008) than patients with higher BMI. Patients with fatigue (grades ≥3) showed a trend towards reduced survival (breast cancer-specific survival: HR = 1.17; 95% CI, 0.99-1.37; P = 0.06). In the NEAT/BR9601 sub-group analysis by treatment component, this effect was statistically significant (HR = 1.61; 95% CI, 1.13-2.30; P = 0.009). CONCLUSIONS: This large study shows a significant association between chemotherapy-induced neutropenia and increased survival. It also identifies a strong relationship between low/normal BMI and increased incidence of severe neutropenia. It provides evidence to support the development of neutropenia-adapted clinical trials to investigate optimal dose calculation and its impact on clinical outcome. This is important in populations where obesity may lead to sub-optimal chemotherapy doses

The Relationship between Common Genetic Markers of Breast Cancer Risk and Chemotherapy-Induced Toxicity: A Case-Control Study.

Ninety-four common genetic variants are confirmed to be associated with breast cancer. This study tested the hypothesis that breast cancer susceptibility variants may also be associated with chemotherapy-induced toxicity through shared mechanistic pathways such as DNA damage response, an association that, to our knowledge, has not been previously investigated. The study included breast cancer patients who received neoadjuvant/adjuvant chemotherapy from the Pharmacogenetic SNPs (PGSNPS) study. For each patient, a breast cancer polygenic risk score was created from the 94 breast cancer risk variants, all of which were genotyped or successfully imputed in PGSNPS. Logistic regression was performed to test the association with two clinically important toxicities: taxane- related neuropathy (n = 1279) and chemotherapy-induced neutropenia (n = 1676). This study was well powered (≥96%) to detect associations between polygenic risk score and chemotherapy toxicity. Patients with high breast cancer risk scores experienced less neutropenia compared to those with low risk scores (adjusted p-value = 0.06). Exploratory functional pathway analysis was performed and no functional pathways driving this trend were identified. Polygenic risk was not associated with taxane neuropathy (adjusted p-value = 0.48). These results suggest that breast cancer patients with high genetic risk of breast cancer, conferred by common variants, can safely receive standard chemotherapy without increased risk of taxane-related sensory neuropathy or chemotherapy-induced neutropenia and may experience less neutropenia. As neutropenia has previously been associated with improved survival and may reflect drug efficacy, these patients may be less likely to benefit from standard chemotherapy treatment.This work was supported by 1) PGSNPS: project and fellowship grants received by Jean Abraham from Cancer Research UK, C507/A6306 and C10097/A7484, http://www.cancerresearchuk.org/; 2) Neo-tAnGo funding: Cancer Research UK Research Grant (C57/A4180) and an additional unrestricted educational grant from Eli Lilly Limited who also provided free Gemzar®/gemcitabine; Bristol Myers Squibb Ltd provided free Taxol®/paclitaxel from January 2005 to June 2006 [EudraCT No: 2004-002356-34, ISRCTN 78234870, ClinicalTrials.gov number: NCT00070278]; 3) tAnGo funding: Unrestricted educational grants and free drug from Eli Lilly (GemzarTM) and Bristol Myers Squibb (TaxolTM); and 4) NEAT/BR9601 funding: Project grant from Cancer Research UK (formerly Cancer Research Campaign) 1996-2003: Unrestricted educational grant Pfizer (formerly Pharmacia). HME, JEA, and CC acknowledge funding from the NIHR Cambridge Biomedical Research Centre. JEA acknowledges funding from Addenbrookes Charitable Trust. LD acknowledges funding from Medical Research Council.This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pone.015898

Pennsylvania Folklife Vol. 22, Folk Festival Supplement

• Sounds of the Folk Festival: A Visitor\u27s Walking Tour • Herbs at Kutztown • Amish Weddings • Food Varieties at the Festival • Festival Highlights • Folk Festival Program • Taverns and Tavern Lore of Dutchland • The Lure of Tinsmithing • Folk Whittling in Pennsylvania • The Dance in Pennsylvania - Current Status: Folk-Cultural Questionnaire No. 30https://digitalcommons.ursinus.edu/pafolklifemag/1054/thumbnail.jp

Phase II randomized preoperative window-of-opportunity study of the PI3K inhibitor pictilisib plus anastrozole compared with anastrozole alone in patients with estrogen receptor-positive breast cancer

Purpose: Preclinical data support a key role for the PI3K pathway in estrogen receptor-positive breast cancer and suggest that combining PI3K inhibitors with endocrine therapy may overcome resistance. This preoperative window study assessed whether adding the PI3K inhibitor pictilisib (GDC-0941) can increase the antitumor effects of anastrozole in primary breast cancer and aimed to identify the most appropriate patient population for combination therapy. Patients and Methods: In this randomized, open-label phase II trial, postmenopausal women with newly diagnosed operable estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers were recruited. Participants were randomly allocated (2:1, favoring the combination) to 2 weeks of preoperative treatment with anastrozole 1 mg once per day (n = 26) or the combination of anastrozole 1 mg with pictilisib 260 mg once per day (n = 49). The primary end point was inhibition of tumor cell proliferation as measured by change in Ki-67 protein expression between tumor samples taken before and at the end of treatment. Results: There was significantly greater geometric mean Ki-67 suppression of 83.8% (one-sided 95% CI, ≥ 79.0%) for the combination and 66.0% (95% CI, ≤ 75.4%) for anastrozole (geometric mean ratio [combination: anastrozole], 0.48; 95% CI, ≤ 0.72; P = .004). PIK3CA mutations were not predictive of response to pictilisib, but there was significant interaction between response to treatment and molecular subtype (P =.03);for patients with luminal B tumors, the combination:anastrozole geometric mean ratio of Ki-67 suppression was 0.37 (95% CI, ≤ 0.67; P = .008), whereas no significant Ki-67 response was observed for pictilisib in luminal A tumors (1.01; P = .98). Multivariable analysis confirmed Ki-67 response to the combination treatment of patients with luminal B tumors irrespective of progesterone receptor status or baseline Ki-67 expression. Conclusion: Adding pictilisib to anastrozole significantly increases suppression of tumor cell proliferation in luminal B primary breast cancer

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis

Pennsylvania Folklife Vol. 31, No. 4

• Of Baskets and Basket Makers • Egg Decorating at the Kutztown Folk Festival • Spatterware • Scrimshaw • Folk Musical Instruments at the Kutztown Folk Festival • Puppets: Fun at the Festival • Festival Focus • Folk Festival Programs • Festival Focus on Quilts • The Kutztown Folk Festival\u27s Calico Seamstresses • Summer Drinks of the Pennsylvania Dutch • The Folk Festival\u27s Lace Maker • The Country Cemetery: Connection Between Past and Present • Coopering • The Dialect of the Pennsylvania Dutchhttps://digitalcommons.ursinus.edu/pafolklifemag/1096/thumbnail.jp

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.

BACKGROUND: There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. METHODS: We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. RESULTS: Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). CONCLUSIONS: A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT00070278 ; 03/10/2003.We acknowledge funding from Cancer Research UK and NIHR Cambridge Biomedical Research Centre. HRA is an NIHR Academic Clinical Lecturer supported by a Career Development Fellowship from the Pathological Society of Great Britain and Northern Ireland and a Starter Grant for Clinical Lecturers from the Academy of Medical Sciences.This is the final version of the article. It first appeared from BioMed Central via https://doi.org 10.1186/s13058-016-0682-