Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy.

Metabolic diseases driven by negative energy balance in dairy cattle contribute to reduced milk production, increased disease incidence, culling, and death. Cow side tests for negative energy balance markers are available but are labor-intensive. Milk sample analysis using Fourier transform infrared spectroscopy (FTIR) allows for sampling numerous cows simultaneously. FTIR prediction models have moderate accuracy for hyperketonemia diagnosis (beta-hydroxybutyrate (BHB) ≥ 1.2 mmol/L). Most research using FTIR has focused on homogenous datasets and conventional prediction models, including partial least squares, linear discriminant analysis, and ElasticNet. Our objective was to evaluate more diverse modeling options, such as deep learning, gradient boosting machine models, and model ensembles for hyperketonemia classification. We compiled a sizable, heterogeneous dataset including milk FTIR and concurrent blood samples. Blood samples were tested for blood BHB, and wavenumber data was obtained from milk FTIR analysis. Using this dataset, we trained conventional prediction models and other options listed above. We demonstrate prediction model performance is similar for convolutional neural networks and ensemble models to simpler algorithm options. Results obtained from this study indicate that deep learning and model ensembles are potential algorithm options for predicting hyperketonemia in dairy cattle. Additionally, our results indicate hyperketonemia prediction models can be developed using heterogeneous datasets.

Full model selection using regression trees for numeric predictions of biomarkers for metabolic challenges in dairy cows.

Dairy cows suffer poor metabolic adaptation syndrome (PMAS)1 during early post-calving periods caused by negative energy balance. Measurement of blood beta-hydroxy butyric acid (BHBA)2 and blood non-esterified fatty acids (NEFA)3 allow early and accurate detection of negative energy balance. Machine learning prediction of blood BHBA and blood NEFA using milk testing samples represents an opportunity to identify at-risk animals, using less labor than direct blood testing methods. Routine milk testing on modern dairies and computer record keeping provide an immense amount of data which can then be used in machine learning models. Previous research for predicting blood metabolites using Fourier-transform infrared spectroscopy (FTIR)4 milk data has focused mainly on individual models rather than a comparison among the models. Full model selection is the process of comparing different combinations of pre-processing methods, variable selection, and statistical learning algorithms to determine which model results in the lowest prediction error for a given dataset. For this project we used a full model selection approach with regression trees (rtFMS)5 . rtFMS uses the cross-validated performance of different model configurations to feed a regression tree for selecting a final model. A total of 384 possible model configurations (algorithms, predictors and data preprocessing options) for each outcome (blood BHBA and blood NEFA) were considered in the rtFMS technique. rtFMS allows direct comparison of multiple modeling approaches reducing bias due to empirical knowledge, modeling habits, or preferences, identifying the model with minimal root mean squared prediction error (RMSE)6 . An elastic net regression model was selected as the best performing model for both biomarkers. The input data for blood BHBA predictions were FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers, obtaining RMSE = 0.354 (0.328-0.392). The best performing model for blood NEFA had input data of FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers filter along with the time of milking, obtaining RMSE = 0.601 (0.564-0.654). The comparison of multiple modeling strategies, conducted by rtFMS, present an option for improved FTIR prediction models of blood BHBA and blood NEFA by reducing error due to human bias. The implementation of rtFMS to design future prediction models can guide model inputs and features. Our prediction models have the potential to increase early detection of metabolic disorders in dairy cows during the transition period.

Prediction model optimization using full model selection with regression trees demonstrated with FTIR data from bovine milk.

Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), naïve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and ß-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms.

Identifying poor metabolic adaptation during early lactation in dairy cows using cluster analysis.

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood ß-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5-50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360-0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650-0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level.

Natural antibodies to oxidation-specific epitopes: innate immune response and venous thromboembolic disease.

Essentials Natural antibodies to oxidation-specific epitopes have antithrombotic properties. We evaluated the relation between natural IgM and IgG antibodies and the venous thrombosis risk. Risk of recurrent thrombosis was higher in patients with low natural IgM antibody levels. The protective effect of high IgM levels suggests a role of innate immune response in thrombosis. SUMMARY: Background and objectives Natural antibodies to oxidation-specific epitopes protect from atherothrombotic events. Whether mechanisms of innate immunity are relevant in the pathogenesis of venous thromboembolism (VTE) is unknown. Patients/Methods We measured plasma levels of immunoglobulin M (IgM) antibodies to oxidized low-density lipoproteins (OxLDL) and phosphocholine (PC) by enzyme linked immune assay in 663 patients with unprovoked VTE, who were prospectively followed after discontinuation of anticoagulation for a median of 8.8 years. The study endpoint was recurrent VTE. Results IgM antibody levels to OxLDL and PC were higher in patients without compared to those with recurrent VTE (n = 174, 26.2%). For each doubling of OxLDL-IgM or PC-IgM the hazard ratio (HR) of recurrence was 0.88 (95% confidence interval [CI], 0.77-1.01) and 0.82 (95% CI, 0.71-0.94), respectively. After 5 years the probability of recurrence in patients with PC-IgM levels in the highest tertile (> 19.6 RLU/100 ms) was 13.0% (95% CI, 8.1-17.6%), compared with 21.1% (95% CI, 14.9-26.9%) in the middle tertile and 20.6% (95% CI, 14.7-26.0%) in the lowest tertile. The corresponding HR was 0.56 (0.39-0.82) for PC-IgM levels in the highest compared with the lowest tertile. Neither immunoglobulin G IgG antibody levels to OxLDL nor those to PC were associated with risk of VTE. Conclusion Levels of natural IgM antibodies to oxidation-specific epitopes are inversely related to the risk of VTE.

Risk of cancer after anticoagulation in patients with unprovoked venous thromboembolism: an observational cohort study.

Essentials Data on long-term cancer risk are controversial in patients with venous thromboembolism (VTE). We assessed long-term rates and risk factors of cancer in patients with VTE. Cancer risk after anticoagulation is not higher in VTE patients than in the general population. VTE recurrence is not predictive of a future cancer diagnosis. SUMMARY: Background Patients with venous thromboembolism (VTE) are at risk of having a subsequent cancer diagnosis. The risk is highest during the first 6 months. Reports on cancer rates thereafter are controversial. We aimed to assess long-term rates and risk factors of cancer in patients with VTE. Methods and Results We followed patients with a first unprovoked VTE after discontinuation of anticoagulation, and excluded those receiving long-term antithrombotic therapy or with major thrombophilia. The study endpoint was the occurrence of cancer. Sixty-two (5.2%) of 1188 patients developed cancer during a median follow-up of 98 months. The cumulative incidence rates of cancer were 0.7% (95% confidence interval [CI] 0.2-1.2%), 3.1% (95% CI 2.0-4.1%) and 9% (95% CI 6.5-11.5) after 1, 5 and 15 years; these were not significantly different from those in the matched general population (0.6%, 3.4%, and 12.2%, respectively). The corresponding standardized incidence ratios (ratio of the observed cancer cases and the number of cases based on national cancer incidence rates) of 1.1 (95% CI 0.5-2.5), 1.0 (95% CI 0.6-1.4) and 0.9 (95% CI 0.7-1.2) did not indicate a difference in cancer incidence between our cohort and the general population. Advancing age (hazard ratio [HR] per decade 1.5, 95% CI 1.2-2.0) and shorter duration of anticoagulation (HR per 1-month decrease 1.3, 95% CI 1.1-1.6) were associated with an increased cancer risk, whereas VTE recurrence was not (HR 1.17, 95% CI 0.66-2.07). Conclusions Asymptomatic patients with unprovoked VTE who have completed anticoagulation therapy do not have a higher cancer risk. The inverse association between the duration of anticoagulation and the incidence of cancer warrants further investigation.

The long-term recurrence risk of patients with unprovoked venous thromboembolism: an observational cohort study.

Essentials Long-term recurrence risk of venous thromboembolism (VTE) is uncertain. We performed a prospective cohort study of 839 patients with first unprovoked VTE. VTE recurrence risk is high, particularly in men with proximal thrombosis or pulmonary embolism. Sex and VTE site determine the recurrence risk and should be considered for patient counseling. SUMMARY: Background The long-term recurrence risk (ltRR) of venous thromboembolism (VTE) is uncertain. Objective To assess the ltRR of patients with first unprovoked VTE. Patients/methods Patients were classified into three categories: distal deep vein thrombosis (DVT), proximal DVT or pulmonary embolism (PE), that is, PE associated with DVT or isolated PE. Patients with major thrombophilia or antithrombotic therapy were excluded. The endpoint was recurrent symptomatic VTE. Results A total of 839 patients were followed for a median of 7.7 years. VTE recurred in 263 patients (31%). After 10 and 20 years, the cumulative ltRR was 32% (95% confidence interval [CI], 29-36) and 44% (95% CI, 38-49) with 3.9 (95% CI, 3.3-4.6) and 3.3 (95% CI, 2.7-4.0) events per 100 patient-years, respectively. The adjusted hazard ratio was 2.1 (95% CI, 1.4-3.2) and 2.1 (95% CI, 1.4-3.2) for patients with proximal DVT or PE compared with patients with distal DVT and was 2.1 (95% CI, 1.6-2.9) for men compared with women. At 10 years, 4.7 (95% CI, 3.8-5.8) events per 100 patient-years occurred in men with proximal DVT or PE, 2.4 (95% CI, 1.2-4.4) in men with distal DVT, 1.9 (95% CI, 1.2-2.8) in women with proximal DVT or PE and 0.9 (95% CI, 0.2-1.9) in women with distal DVT. Conclusion The ltRR of patients with first unprovoked VTE is high and dependent upon sex and VTE site.

Discovery of 505-million-year old chitin in the basal demosponge Vauxia gracilenta.

Sponges are probably the earliest branching animals, and their fossil record dates back to the Precambrian. Identifying their skeletal structure and composition is thus a crucial step in improving our understanding of the early evolution of metazoans. Here, we present the discovery of 505-million-year-old chitin, found in exceptionally well preserved Vauxia gracilenta sponges from the Middle Cambrian Burgess Shale. Our new findings indicate that, given the right fossilization conditions, chitin is stable for much longer than previously suspected. The preservation of chitin in these fossils opens new avenues for research into other ancient fossil groups.

Three-dimensional chitin-based scaffolds from Verongida sponges (Demospongiae: Porifera). Part II: Biomimetic potential and applications.

In order to evaluate the biomedical potential of three-dimensional chitinous scaffolds of poriferan origin, chondrocyte culturing experiments were performed. It was shown for the first time that freshly isolated chondrocytes attached well to the chitin scaffold and synthesized an extracellular matrix similar to that found in other cartilage tissue engineering constructs. Chitin scaffolds also supported deposition of a proteoglycan-rich extracellular matrix of chondrocytes seeded bioconstructs in an in vivo environment. We suggest that chitin sponge scaffolds, apart from the demonstrated biomedical applications, are highly optimized structures for use as filtering systems, templates for biomineralization as well as metallization in order to produce catalysts.

Three-dimensional chitin-based scaffolds from Verongida sponges (Demospongiae: Porifera). Part I. Isolation and identification of chitin.

Marine invertebrate organisms including sponges (Porifera) not only provide an abundant source of biologically active secondary metabolites but also inspire investigations to develop biomimetic composites, scaffolds and templates for practical use in materials science, biomedicine and tissue engineering. Here, we presented a detailed study of the structural and physico-chemical properties of three-dimensional skeletal scaffolds of the marine sponges Aiolochroia crassa, Aplysina aerophoba, A. cauliformis, A. cavernicola, and A. fulva (Verongida: Demospongiae). We show that these fibrous scaffolds have a multilayered design and are made of chitin. (13)C solid-state NMR spectroscopy, NEXAFS, and IR spectroscopy as well as chitinase digestion and test were applied in order to unequivocally prove the existence of alpha-chitin in all investigated species.

Chitin-based scaffolds are an integral part of the skeleton of the marine demosponge Ianthella basta.

The skeletons of demosponges, such as Ianthella basta, are known to be a composite material containing organic constituents. Here, we show that a filigree chitin-based scaffold is an integral component of the I. basta skeleton. These chitin-based scaffolds can be isolated from the sponge skeletons using an isolation and purification technique based on treatment with alkaline solutions. Solid-state (13)C NMR, Raman, and FT-IR spectroscopies, as well as chitinase digestion, reveal that the isolated material indeed consists of chitin. The morphology of the scaffolds has been determined by light and electron microscopy. It consists of cross-linked chitin fibers approximately 40-100 nm in diameter forming a micro-structured network. The overall shape of this network closely resembles the shape of the integer sponge skeleton. Solid-state (13)C NMR spectroscopy was used to characterize the sponge skeleton on a molecular level. The (13)C NMR signals of the chitin-based scaffolds are relatively broad, indicating a high amount of disordered chitin, possibly in the form of surface-exposed molecules. X-ray diffraction confirms that the scaffolds isolated from I. basta consist of partially disordered and loosely packed chitin with large surfaces. The spectroscopic signature of these chitin-based scaffolds is closer to that of alpha-chitin than beta-chitin.

New mitomycin analogs produced by directed biosynthesis.

When the normal fermentation medium for the production of mitomycin C with Streptomyces caespitosus is supplemented with a number of primary amines, two new types of mitomycin analogs described as Type I and Type II are produced. Type I analogs are related to mitomycin C with the amine substitution at position C7 on the mitosane ring. Type II analogs also contain the same substitutions at C7 but the conformation of the mitosane ring is related to mitomycin B having an OH at positions C9a and a methyl substituted aziridine. The products obtained from the supplementation of the medium with methylamine, ethylamine, propylamine, propargylamine and 2-methylallylamine were isolated and characterized. In all cases the Type I analogs are more active in a prophage induction test and against L1210 lymphatic leukemia in mice. A number of other amines have been tested and shown to yield new products that have not yet been isolated. No secondary amines are incorporated.

Microbial models of mammalian metabolism O-demethylations of papaverine.

1. Papaverine is converted to its 4'-, 6- and 7-desmethyl metabolites when incubated with hepatic microsomal preparations from phenobarbital-induced rats. The 7-desmethyl compound was predominantly formed in these studes. When incubated with guinea-pig microsomal preparations, papaverine was converted to its 6- and 4'-desmethyl metabolites in approximately equal amounts. 2. The O-demethylation of papaverine was studied with sixty micro-organisms. Ten organisms (including Aspergillus Cunninghamella and Streptomyces species) were found to actively metabolize this drug in a manner similar to that of mammals. 3. A strain of Aspergillus alliaceus was used to produce gram quantitites of the mammalian metabolite, 6-desmethylpapaverine. Preparative amounts of 4'-desmethylpapaverine, a major mammalian metabolite of papaverine in vivo, was produced with a strain of Cunninghamella echinulata.