Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

BACKGROUND: Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements. METHODS: We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic. RESULTS: Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence. CONCLUSIONS: Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Next-generation matrices for marine metapopulations: The case of sea lice on salmon farms.

Classifying habitat patches as sources or sinks and determining metapopulation persistence requires coupling connectivity between habitat patches with local demographic rates. While methods to calculate sources, sinks, and metapopulation persistence exist for discrete-time models, there is no method that is consistent across modeling frameworks. In this paper, we show how next-generation matrices, originally popularized in epidemiology to calculate new infections after one generation, can be used in an ecological context to calculate sources and sinks as well as metapopulation persistence in marine metapopulations. To demonstrate the utility of the method, we construct a next-generation matrix for a network of sea lice populations on salmon farms in the Broughton Archipelago, BC, an intensive salmon farming region on the west coast of Canada where certain salmon farms are currently being removed under an agreement between local First Nations and the provincial government. The column sums of the next-generation matrix can determine if a habitat patch is a source or a sink and the spectral radius of the next-generation matrix can determine the persistence of the metapopulation. With respect to salmon farms in the Broughton Archipelago, we identify the salmon farms which are acting as the largest sources of sea lice and show that in this region the most productive sea lice populations are also the most connected. The farms which are the largest sources of sea lice have not yet been removed from the Broughton Archipelago, and warming temperatures could lead to increased sea louse growth. Calculating sources, sinks, and persistence in marine metapopulations using the next-generation matrix is biologically intuitive, mathematically equivalent to previous methods, and consistent across different modeling frameworks.

Timing and probability of arrival for sea lice dispersing between salmon farms.

Sea lice are a threat to the health of both wild and farmed salmon and an economic burden for salmon farms. With a free-living larval stage, sea lice can disperse tens of kilometres in the ocean between salmon farms, leading to connected sea louse populations that are difficult to control in isolation. In this paper, we develop a simple analytical model for the dispersal of sea lice (Lepeophtheirus salmonis) between two salmon farms. From the model, we calculate the arrival time distribution of sea lice dispersing between farms, as well as the level of cross-infection of sea lice. We also use numerical flows from a hydrodynamic model, coupled with a particle tracking model, to directly calculate the arrival time of sea lice dispersing between two farms in the Broughton Archipelago, British Columbia, in order to fit our analytical model and find realistic parameter estimates. Using the parametrized analytical model, we show that there is often an intermediate interfarm spacing that maximizes the level of cross-infection between farms, and that increased temperatures will lead to increased levels of cross-infection.

Sustainable protocol-based management of hypertension in one institution in northern Malawi.

INTRODUCTION: Across all WHO regions, Africa has the highest prevalence of hypertension with 46% of the population >25 years estimated to be hypertensive. Blood pressure (BP) control is poor, with <40% of hypertensives diagnosed, <30% of those diagnosed receiving medical treatment, and <20% with adequate control. We report an intervention to improve BP control in a cohort of hypertensive patients attending a single hospital in Mzuzu Malawi, by introducing a limited protocol of four antihypertensive medications taken once-daily. METHODS: A drug protocol based on international guidelines, drug availability in Malawi, cost and clinical effectiveness was developed and implemented. Patients were transitioned to the new protocol as they attended for clinic visits. Records of 109 patients completing at least three visits were assessed for BP control. RESULTS: Two-thirds of patients (n=73) were female and average age at enrolment was 61.6 ± 12.8 years. Median [interquartile range] systolic BP (SBP) was 152 [136;167] mm Hg at baseline and reduced over the follow-up period to 148 [135; 157, p<0.001 vs baseline]. Median diastolic BP (DBP) reduced from 90.0 [82.0; 100] mm Hg to 83.0 [77.0; 91.0], p<0.001 vs baseline. Patients with highest baseline blood pressures benefited most and there were no associations noted between BP responses and either age or gender. DISCUSSION: We conclude that a limited evidence based once-daily drug regimen can improve blood pressure control by comparison with standard management. Cost effectiveness of this approach will also be reported.

Electrospray Ionization Ion Mobility Mass Spectrometry.

Electrospray ionization ion mobility mass spectrometry (ESI-IMS-MS) is a rapidly progressing analytical technique for the examination of complex compounds in the gas phase. ESI-IMS-MS separates isomers, provides structural information, and quantitatively identifies peptides, lipids, carbohydrates, polymers, and metabolites in biological samples. ESI-IMS-MS has pharmaceutical, environmental, and manufacturing applications quickly characterizing drugs, petroleum products, and metal macromolecules. This review provides the history of ESI-IMS-MS development and applications to date.

Advances in Activity/Property Prediction from Chemical Structures.

Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.

Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species.

A metabolomic ratio rule-based classification method was developed and programmed for automated metabolite profiling and differentiation of four major cinnamon species using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The computational program identifies key cinnamon metabolites, including proanthocyanidins, cinnamaldehyde, and coumarin, from test samples through LC-MS data processing and assigns cinnamon species by critical metabolite ratios using a stepwise classification strategy. Further, 100% classification accuracy was achieved on the training sample set through critical ratio optimization, and over 95% accuracy was achieved on the validation sample set. The proposed cinnamon classification method exhibited superior accuracy compared to the metabolomic-based PLS-DA modeling method and offered great value for the authentication of cinnamon samples and evaluation of their potential health benefits.

Analysis of Wine and Its Use in Tracing the Origin of Grape Cultivation.

The concentrations of elemental and volatile components in wine and the effect of biological, meteorological, and anthropogenic factors on their levels are important for authentication and quality assurance. Sample preparation for atomic absorption and inductively coupled plasma spectrometries for elemental analysis as well as chromatographic and electronic nose (EN) analytical methods for volatile compounds are reviewed. The International Organization of Vine and Wine (OIV) and countries that produce and import wine developed methods and set limits on metal abundance to ensure that all metal concentrations are well below toxic threshold limits. With the use of data analysis tools, elemental analysis can enable wines to be traced back to their geographic region of origin. When paired with volatile and isotopic analysis the accuracy of this authentication greatly improves. Tracing studies are reviewed to demonstrate the capabilities of these analyses.

A quantitative reliability metric for querying large database.

A redesigned quantitative reliability metric based on the F-distribution (QRMf) is reported for evaluating the reliability of library search. The QRMf provides orthogonal information to the comparison metric (e.g., dot product) and yields a probabilistic result. An intralibrary search can be considered as an idealized search because the top hit, i.e., the closest matching object, will match perfectly. If the search of an unknown object yields the same hit list as the intralibrary search, it would indicate good reliability. For each object in the hit list, a QRMf compares the order of an intralibrary and interlibrary search results and calculates a variance of interlibrary similarity metrics between the records of the intralibrary search and records in the corresponding positions of the interlibrary search. This variance that measures the discordance of the intra and interlibrary search can simply be compared to the variance of the similarity metrics within the interlibrary search results. The ratio of these variances follows an F-distribution that can be used to determine if the discordance is statistically significant and generates the probability based on the cumulative distribution function. The QRMf works for both similarity and dissimilarity and can be used for any queried object and comparison metric that is searched against a database. In this work, the QRMf was used along with the dot product similarity to query the mass spectra of novel synthetic opioids measured by gas chromatography-mass spectrometry (GC/MS). An automated pipeline was devised that used a basis set correction to assist peak detection. The basis was constructed by mass spectra obtained from the blank measurement preceding the analytical run to remove interferences from column bleed and septum degradation. After peak detection, the pipeline applied multivariate curve resolution to the chromatographic peak window to remove background components from the mass spectra. The corrected mass spectra were searched against a customized library for identification. The QRMf can be used along with the similarity metric to detect misidentifications and assist in finding the correct identification when it is not the closest match.

In Situ Determination of Cannabidiol in Hemp Oil by Near-Infrared Spectroscopy.

Cannabidiol (CBD, 1) is an active component of hemp oil and many other products that offers diverse health benefits. Near-infrared spectroscopy (NIRS) coupled with chemometrics was utilized to quantify the CBD (1) concentration in the hemp oil through the containing glass vial. NIRS provided a fast and cost-effective tool to measure chemical profiles for the hemp oil samples with various concentrations of CBD (1) and its acid precursor, i.e., cannabidiolic acid (CBDA, 2). The measured NIR spectra were transformed by using a Savitzky-Golay first-derivative filter to remove baseline drift. Two self-optimizing chemometric methods, super partial least-squares regression (sPLSR) and self-optimizing support vector elastic net (SOSVEN), were applied to construct automatically multivariate models that predict the concentrations of CBD (1) and total CBD (sum of 1 and 2 concentrations) of the hemp oil samples. The SOSVEN had validation errors of 6.4 mg/mL for the prediction of CBD (1) concentration and 6.6 mg/mL for the prediction of total CBD concentration, which are significantly lower than the errors given by sPLSR. Other than the lower validation errors, SOSVEN has another advantage over sPLSR in that it builds a multivariate model while selecting spectral features at the same time. These results demonstrated that NIR spectroscopy combined with chemometrics can be used as a rapid and cost-effective approach to determine the CBD (1) and total CBD concentrations in hemp oil. Manufacturers would benefit from the fast and reliable approach in quality assurance.

A Systematic Review of Clinical Prediction Rules to Predict Hospitalisation in Children with Lower Respiratory Infection in Primary Care and their Validation in a New Cohort.

Background: Our goal was to identify existing clinical prediction rules for predicting hospitalisation due to lower respiratory tract infection (LRTI) in children in primary care, guiding antibiotic therapy. A validation of these rules was then performed in a novel cohort of children presenting to primary care in Malawi with World Health Organisation clinically defined pneumonia. Methods: MEDLINE & EMBASE databases were searched for studies on the development, validation and clinical impact of clinical prediction models for hospitalisation in children with lower respiratory tract infection between January 1st1946-June 30th 2021. Two reviewers screened all abstracts and titles independently. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews & Meta-Analyses guidelines. The BIOTOPE cohort (BIOmarkers TO diagnose PnEumonia) recruited children aged 2-59 months with WHO-defined pneumonia from two primary care facilities in Mzuzu, Malawi. Validation of identified rules was undertaken in this cohort. Findings: 1023 abstracts were identified. Following the removal of duplicates, a review of 989 abstracts was conducted leading to the identification of one eligible model. The CHARMS checklist for prediction modelling studies was utilized for evaluation. The area under the curve (AUC) of the STARWAVe rule for hospitalisation in BIOTOPE was found to be 0.80 (95% C.I of 0.75-0.85). The AUC of STARWAVe for a confirmed diagnosis of bacterial pneumonia was 0.39 (95% C.I 0.25-0.54). Interpretation: This review highlights the lack of clinical prediction rules in this area. The STARWAVe rule identified was useful in predicting hospitalisation from bacterial infection as defined. However, in the absence of a gold standard indicator for bacterial LRTI, this is a reasonable surrogate and could lead to reductions in antibiotic prescription rates, should clinical impact studies prove its utility. Further work to determine the clinical impact of STARWAVe and to identify diagnostic tests for bacterial LRTI in primary care is required.

Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer.

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

A Techno-economic Analysis for Integrating an Electrochemical Reactor into a Lignocellulosic Biorefinery for Production of Industrial Chemicals and Hydrogen.

In this study, we present a techno-economic analysis for integrating an electrochemical reactor into a lignocellulosic biorefinery for the purpose of converting biorefinery lignin to higher-value industrial chemicals with co-generation of hydrogen. We consider how the electrochemical reactor impacts the manufacturing costs for producing biofuel and determine a break-even value for the lignin oxidation product stream, which is the minimum lignin conversion product stream value that renders the cost to produce biofuel the same as in the typical biorefinery concept. We conclude that at low extents of lignin conversion, the break-even product stream value is likely too high for the process to be feasible. However, at higher extents of lignin conversion, the break-even product stream value may be between $1.00 and $2.00/kg, depending on capital cost and other manufacturing costs like depreciation. Potential markets for the biomass conversion products include resin manufacturing, where the products would compete with petroleum-derived resin precursors.

Self-Optimizing Support Vector Elastic Net.

Chemometrics is widely used to solve various quantitative and qualitative problems in analytical chemistry. A self-optimizing chemometrics method facilitates scientists to exploit the advantages of chemometrics. In this report, a parameter-free support vector elastic net that self-optimizes two key regularization constants, i.e., λ for L2 regularization and t for L1 regularization, is developed and referred to as self-optimizing support vector elastic net (SOSVEN). Response surface modeling (RSM) and bootstrapped Latin partitions (BLPs) are incorporated for the optimization. Responses at a set of design points over the ranges of the two factors are evaluated with an internal BLP validation using a calibration set. A 2-dimensional interpolation with a cubic spline fits a response surface to determine the best condition that gives the best-estimated response. The SOSVEN with RSM had comparable performances with the one tuned by grid search, while the RSM is more efficient. The developed SOSVEN was compared with two parameter-free chemometrics methods, super partial least-squares regression (sPLSR) and super support vector regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS-DA) and support vector classification (SVC) for classification. For calibration, the SOSVEN with RSM worked equivalently well or better than the other two self-optimizing methods for the evaluations using meat and hemp oil data sets. For classification, a reference wine data set and mass spectra of different marijuana extracts were used. The three classifiers had similar performances to identify the cultivars of wines with nearly 98% of accuracy. The SOSVEN significantly outperformed sPLS-DA and SVC to classify the mass spectra of marijuana extracts with an overall accuracy of 97%. These results demonstrated excellent abilities of SOSVEN for classification and calibration.

Study on Human Urinary Metabolic Profiles after Consumption of Kale and Daikon Radish using a High-resolution Mass Spectrometry-Based Non-targeted and Targeted Metabolomic Approach.

In the present study, urine samples were collected from healthy human volunteers to determine the metabolic fates of phenolic compounds and glucosinolates after a single meal of kale and daikon radish. The major glucosinolates and phenolic compounds in kale and daikon radish were measured. The urinary metabolome after feeding at different time periods was investigated. A targeted metabolite analysis method was developed based on the known metabolic pathways for glucosinolates and phenolic compounds. Using a targeted approach, a total of 18 metabolites were found in urine: 4 from phenolic compounds and 14 from glucosinolates. Among these metabolites, 4-methylsulfinyl-3-butenyl isothiocyanate, 4-methylsulfinyl-3-butenyl isothiocyanate-cysteine, and 4-methylsulfinyl-3-butenylglucosinolate-N-acetyl cysteine were reported for the first time in human urine. The combination of non-targeted and targeted metabolomic approaches can gain a full metabolite profile for human dietary intervention studies.

Analysis of Phenolic Compositions in Cranberry Dietary Supplements using UHPLC-HRMS.

The potential human health benefits of American cranberry (Vaccinium macrocarpon Ait.) leads to the popularity of its dietary supplements in the U.S. market. However, the qualities of the cranberry dietary supplements (CDSs) have never been carefully evaluated. In this study, the phenolic components in ten different CDSs were analyzed using ultra-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). The study found quercetin and myricetin aglycones in most CDSs, but not in cranberry fruits, despite otherwise similar phenolic profiles between CDS and cranberry fruits in general. One supplement with high levels of B-type proanthocyanidins and non-cranberry flavonol rutin was determined to be adulterated by other botanical extracts. The CDSs only possessed 4% to 11% of the phenolic contents comparing to their claimed fresh cranberry equivalents, emphasizing the urgency of standardized product quality control and labelling for CDS manufacture and marketing.

Metabolomic profiling and comparison of major cinnamon species using UHPLC-HRMS.

The metabolomic profiles of four major species of cinnamon (Cinnamomum verum, C. burmannii, C. loureiroi, and C. cassia) were investigated by ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). Thirty-six metabolites were tentatively characterized, belonging to various compound groups such as phenolic glycosides, flavan-3-ols, phenolic acids, terpenes, alkaloids, and aldehydes. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) on the HRMS data matrix resulted in a clear separation of the four cinnamon species. Coumarin, cinnamaldehyde, methoxycinnamaldehyde, cinnamoyl-methoxyphenyl acetate, proanthocyanidins, and other components varied among the four species. Such variations were used to develop a step-by-step strategy for differentiating the four cinnamon species based on their levels of pre-selected components. This study suggests a significant variation in the phytochemical compositions of different cinnamon species, which have a direct influence on cinnamon's health benefit potentials. Graphical Abstract.

Quantitative analysis of proanthocyanidins in cocoa using cysteamine-induced thiolysis and reversed-phase UPLC.

The thiolysis of B-type proanthocyanidins in cocoa by cysteamine was evaluated and optimized for its application in cocoa proanthocyanidin quantification. Four thiolysis products consisting of epicatechin, catechin, and their thioethers formed with cysteamine were separated and characterized by reversed-phase UPLC with photo diode array (PDA) detection and high-resolution mass spectrometry (HRMS). A thiolysis time of 20 min under 60 °C temperature was determined as the optimal condition for cocoa proanthocyanidin depolymerization. The optimized thiolysis condition was applied to four cocoa bean samples for proanthocyanidin quantification, using commercially available procyanidin B2 dimer as a reference standard. Satisfactory linearity and quantification and detection limits were achieved for the calibration curves, and proanthocyanidin contents determined by thiolysis were found to be higher than those determined by a published method based on normal-phase HPLC with fluorescence detection. Results in this study suggest promising application potential of cysteamine as an odorless thiolysis agent in routine quantitative analysis of B-type proanthocyanidins. Graphical abstract.

Integrating Radio-Over-Fiber Communication System and BOTDR Sensor System.

In this paper, we propose and experimentally demonstrate for the first time, the integration of a radio-over-fiber (RoF) communication system and a Brillouin optical time-domain reflectometry (BOTDR) distributed sensor system using a single optical fiber link. In this proof-of-concept integrated system, the communication system is composed of three modulation formats of quadrature phase-shift keying (QPSK), 16-quadrature amplitude modulation (16-QAM) and 64-QAM, which are modulated onto an orthogonal frequency division multiplexing (OFDM) signal. Whereas, the BOTDR sensor system is used for strain and/or temperature monitoring over the fiber distance with a spatial resolution of 5 m using a 25 km single-mode silica fiber. The error vector magnitude (EVM) is analyzed in three modulation formats in the presence of various BOTDR input pump powers. Using QPSK modulation, optimized 18 dBm sensing and 10 dBm data power, the measured EVM values with and without bandpass filter are 3.5% and 14.5%, respectively. The proposed system demonstrates a low temperature measurement error (±0.49 °C at the end of 25 km) and acceptable EVM values, which were within the 3GPP requirements. The proposed integrated system can be effectively applied for practical applications, which significantly reduces the fiber infrastructure cost by effective usage of a single optical fiber link.

Analysis of cranberry proanthocyanidins using UPLC-ion mobility-high-resolution mass spectrometry.

Cranberry proanthocyanidin oligomers were investigated using ultra performance liquid chromatography-ion mobility-high-resolution mass spectrometry (UPLC-IM-HRMS). A total of 304 individual A-type and B-type proanthocyanidins, including 40 trimers, 68 tetramers, 53 pentamers, 54 hexamers, 49 heptamers, 28 octamers, and 12 nonamers, were characterized. A-type proanthocyanidins appeared to dominate the cranberry proanthocyanidins. As the degree of polymerization increased, the abundance of molecules with multiple A-type double inter-flavan linkage or having doubly charged ions also increased. Under the same charge state, the drift times of proanthocyanidin ions increased with their degree of polymerization or the number of double inter-flavan linkages. For the same proanthocyanidin molecules, doubly charged ions had shorter drift times compared to their singly charged counterparts, which lead to separated trendlines in the ion mobility-mass plot. While consistent ion mobility was observed for most proanthocyanidins with the same degree of polymerization, coeluted isomeric ions of trimer and tetramer were detected by their unique drift times. Incorporation of ion mobility into HRMS proved to be of great value to characterize and analyze proanthocyanidins from complex sample matrices. Graphical abstract.