Large Sample Sets Research Articles

Testing identifying assumptions in fuzzy regression discontinuity designs

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD‐validity). We derive a new set of testable implications, characterized by a set of inequality restrictions on the joint distribution of observed outcomes and treatment status at the cut‐off. We show that this new characterization exploits all of the information in the data that is useful for detecting violations of FRD‐validity. Our approach differs from and complements existing approaches that test continuity of the distributions of running variables and baseline covariates at the cut‐off in that we focus on the distribution of the observed outcome and treatment status. We show that the proposed test has appealing statistical properties. It controls size in a large sample setting uniformly over a large class of data generating processes, is consistent against all fixed alternatives, and has non‐trivial power against some local alternatives. We apply our test to evaluate the validity of two FRD designs. The test does not reject FRD‐validity in the class size design studied by Angrist and Lavy (1999) but rejects it in the insurance subsidy design for poor households in Colombia studied by Miller, Pinto, and Vera‐Hernández (2013) for some outcome variables. Existing density continuity tests suggest the opposite in each of the two cases.

Unsupervised cross-domain person re-identification by instance and distribution alignment

Most existing person re-identification (re-id) methods assume supervised model training on a separate large set of training samples from the target domain. While performing well in the training domain, such trained models are seldom generalisable to a new independent unsupervised target domain without further labelled training data from the target domain. To solve this scalability limitation, we develop a novel Hierarchical Unsupervised Domain Adaptation (HUDA) method. It can transfer labelled information of an existing dataset (a source domain) to an unlabelled target domain for unsupervised person re-id. Specifically, HUDA is designed to model jointly global distribution alignment and local instance alignment in a two-level hierarchy for discovering transferable source knowledge in unsupervised domain adaptation. Crucially, this approach aims to overcome the under-constrained learning problem of existing unsupervised domain adaptation methods. Extensive evaluations show the superiority of HUDA for unsupervised cross-domain person re-id over a wide variety of state-of-the-art methods on four re-id benchmarks: Market-1501, DukeMTMC, MSMT17 and CUHK03.

Uranium-thorium isotope systematics of cold-seep carbonate and their constraints on geological methane leakage activities

Marine methane hydrates are a huge and dynamic carbon reservoir found mainly at the continental margins, and their stability might be affected by climate-associated pressure and temperature changes on the seafloor. Reconstructing the growth history of cold-seep carbonates, which formed during seafloor methane leakage, could help to constrain methane hydrate stability in the geological past. However, U-Th isotope systematics of these complex carbonate cements have not yet been systematically investigated on the micro-scale, leaving uncertainties in the U-series geochronology which is commonly applied to date the seep carbonates. In this study, we have developed multiple in-situ analytical methods, including U-Th isotope analysis by laser ablation MC-ICPMS, elemental concentration mapping by laser ablation ICPMS, as well as organic distribution mapping by Raman Spectroscopy, to provide insights into the U-series geochemistry and geochronology for the different types of cold-seep carbonates. Our result demonstrates that 238U and 232Th of these carbonates are dominantly derived from seawater and detrital particles, respectively. As the [230Th/232Th] (activity ratio) of micro-domains with high [232Th/238U] is negligibly affected by U decay, we have directly determined the initial [230Th/232Th] of the cold-seep carbonates which is 0.7 ± 0.1 (2 SD, n = 12). In general, U-series isotopes show closed-system behavior within our studied analytical precision in the interior of the seep cements, and the initial [230Th/232Th] derived from the isochron approach is consistent with the in-situ direct determination. A notable exception is a calcitic pipe sample with highly enriched U in its rim which has also experienced post-depositional mobilization. Our method is then applied to a large set of seep carbonate samples from the upper continental slope of the northern South China Sea (SCS). The obtained U-series ages provide evidence that cold seep was likely continuously active since at least ∼72 ka in the northern SCS, but the timing and duration of methane leakage vary between different sites within this region. While the upper continental slope methane hydrates are highly susceptible to destabilization under changing bottom water conditions, our extensive dating on seep carbonates from the SCS further indicates that bottom water P-T conditions could exert contrasting impacts on methane hydrate stability at different depths within the same geological setting. Overall, the in-situ U-series geochronological method developed in our study has the potential to date cold-seep carbonates efficiently and reliably, offering new opportunities to probe into the history of methane leakage in the late Quaternary.

Helminth-Induced Human Gastrointestinal Dysbiosis: a Systematic Review and Meta-Analysis Reveals Insights into Altered Taxon Diversity and Microbial Gradient Collapse.

ABSTRACTHigh-throughput 16S rRNA sequencing has allowed the characterization of helminth-uninfected (HU) and helminth-infected (HI) gut microbiomes, revealing distinct profiles. However, there have been no qualitative or quantitative syntheses of these studies, which show marked variation in participant age, diet, pathogen of interest, and study location. A predefined minimally biased search strategy identified 23 studies in humans. For each of these studies, we qualitatively addressed the effects of helminth infection on within-individual (alpha) and between-individual (beta) fecal microbiome diversity, infection-associated microbial taxa, the effect of helminth clearance on microbiome composition, microbiome composition as a predictor of infection status or treatment outcome, and treatment-specific effects on the fecal microbiome. Concomitantly, we performed a meta-analysis on a subset of 7 of these studies containing raw, paired-end 16S reads and individual-level metadata, comprising 424 pretreatment or untreated HI individuals and 497 HU controls. After reducing the batch effect and adjusting for age, our data demonstrated that intestinal helminth parasites can alter the host gut microbiome by increasing alpha diversity and promoting taxonomic reassortment and gradient collapse. Most strongly influencing the microbiome composition were the helminths found in the large intestine, Enterobius vermicularis and Trichuris trichiura, suggesting that this influence appears to be specific to soil-transmitted helminths (STH) species and host anatomical niche. In summary, using a large and diverse sample set captured in the meta-analysis, we were able to evaluate the influence of individual helminth species as well as species-species interactions, each of which explained a significant portion of the variation in the microbiome.

Performance of Cleanroom Garment Fabrics When Processed in an Industrial Laundry, Comparing Irradiation and Autoclave Sterilisation

EU GMP Annex 1 requires that “reusable garments should be replaced based at a set frequency determined by qualification, or if damage is identified.” [1] In the UK, most cleanroom garments supplied to the pharmaceutical and healthcare sectors are washed and sterilised by gamma irradiation. This study compares cleanroom garment fabric performance across the lifespan of multiple fabrics. Previous research has shown that cleanroom garment fabrics terminally sterilised by gamma irradiation remain suitable for use for up to 50 processes, however, these studies often focus on a limited number of samples. This study uses a large sample set, analysing the performance up to 100 processes and compares the performance effects of gamma irradiation vs autoclaving, as an alternative sterilisation method. Multiple market leading cleanroom garment fabrics were washed and dried using a standard industrial cleanroom laundry process and sterilised by either gamma irradiation or autoclave. They were tested for particle barrier efficiency, abrasion resistance, pore size, and tensile strength as new, then at set process counts throughout their life, 10, 20, 30, 50, 70 and 100 processes. A process is equal to one wash/dry/sterilisation cycle. The results show that not all cleanroom garment fabrics deteriorate equally and that some market leading fabrics may not provide adequate performance throughout life, even if they are suitable when new. They also show that autoclaving is comparable with irradiation in durability and performance over a fabric’s life, in some cases performing better than irradiation above process counts of 50.

The Temporal Focus of Mind-Wandering and Chinese Adolescents' Well-Being.

The relationships between the temporal focus of mind-wandering (i.e., past-oriented and future-oriented mind-wandering) and well-being are important issues for adolescents, which may have significant implications on their well-being and self-identity development. However, few studies tested the temporal focus of mind-wandering and its emotional consequences in adolescents. In the present study, we conducted two studies using self-reported questionnaires from large sample sets to examine the relationships between the temporal focus of mind-wandering and hedonic (pleasure attainment) and eudaimonic (meaning pursuing) well-being among Chinese adolescents. Study 1 preliminarily tested the relationships between the temporal focus of mind-wandering and hedonic well-being among adolescents (n = 1273) suggesting that both past-oriented mind-wandering (PMW) and future-oriented mind-wandering (FMW) were positively correlated with hedonic well-being. Study 2 used a new sample (n = 986) and included another aspect of well-being (i.e., eudaimonic well-being), showing that PMW and FMW were both positively correlated with hedonic and eudaimonic well-being. Moreover, self-reflection mediated the relationships between FMW and hedonic/eudaimonic well-being, whereas self-reflection did not act as a mediator in the relationships between PMW and well-being. The present findings indicated that both PMW and FMW are beneficial for Chinese adolescents' well-being, and emphasized the mediating role of self-reflection in the relationships between FMW and well-being.

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning.

The problem of automating the data analysis of microplastics following a spectroscopic measurement such as focal plane array (FPA)-based micro-Fourier transform infrared (FTIR), Raman, or QCL is gaining ever more attention. Ease of use of the analysis software, reduction of expert time, analysis speed, and accuracy of the result are key for making the overall process scalable and thus allowing nonresearch laboratories to offer microplastics analysis as a service. Over the recent years, the prevailing approach has been to use spectral library search to automatically identify spectra of the sample. Recent studies, however, showed that this approach is rather limited in certain contexts, which led to developments for making library searches more robust but on the other hand also paved the way for introducing more advanced machine learning approaches. This study describes a model-based machine learning approach based on random decision forests for the analysis of large FPA-μFTIR data sets of environmental samples. The model can distinguish between more than 20 different polymer types and is applicable to complex matrices. The performance of the model under these demanding circumstances is shown based on eight different data sets. Further, a Monte Carlo cross validation has been performed to compute error rates such as sensitivity, specificity, and precision.

Developing test color samples to compute color fidelity of light sources for printing matter

Based on the clustering optimization of test color samples and a psychophysical experiment, the objective and subjective color fidelity of light sources for printing matter is evaluated, and an improved evaluation method thus has been proposed. Firstly, for representing the output characteristics of printing press, the International Color Consortium standard color target samples (ICC SCTS), which is measured in the process of color management, is used as a large color sample set. In a 6D spectral color space proposed in this paper, the optimized color sample set (OCSS) is obtained by Self Organizing Maps Neural Networks (SOMNN) clustering algorithm from ICC SCTS. Taking OCSS, ICC SCTS, and standard color sample set (SCSS) as the test color sample set, three objective color fidelity indexes (CFIs) CIE-Ra, Ra,2012, and CIE-Rf of 1202 light sources are calculated. The correlation metrics of the CFIs show that the OCSS highly improved the objective accuracy of color fidelity evaluation for printing matters. Secondly, in the psychophysical experiment, 20 observers have evaluated the visual color difference of the OCSS under the illumination of nine pairs of test and reference light sources. The subjective CFIs are calculated by using the visual color difference of OCSS obtained from the psychophysical experiment. In order to improve the subjective and objective consistency of CFIs, a polynomial modified model for objective color difference of OCSS is proposed. By the optimization of test color samples and the modification of color difference calculation, the method developed in this paper can be effectively and conveniently applied to the subjective and objective evaluation of light source for printing matters.

Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease.

One of the potential benefits of using data-independent acquisition (DIA) proteomics protocols is that information not originally targeted by the study may be present and discovered by subsequent analysis. Herein, we reanalyzed DIA data originally recorded for global proteomic analysis to look for isomerized peptides, which occur as a result of spontaneous chemical modifications to long-lived proteins. Examination of a large set of human brain samples revealed a striking relationship between Alzheimer's disease (AD) status and isomerization of aspartic acid in a peptide from tau. Relative to controls, a surprising increase in isomer abundance was found in both autosomal dominant and sporadic AD samples. To explore potential mechanisms that might account for these observations, quantitative analysis of proteins related to isomerization repair and autophagy was performed. Differences consistent with reduced autophagic flux in AD-related samples relative to controls were found for numerous proteins, including most notably p62, a recognized indicator of autophagic inhibition. These results suggest, but do not conclusively demonstrate, that lower autophagic flux may be strongly associated with loss of function in AD brains. This study illustrates that DIA data may contain unforeseen results of interest and may be particularly useful for pilot studies investigating new research directions. In this case, a promising target for future investigations into the therapy and prevention of AD has been identified.

Non-targeted Lipidomics Using a Robust and Reproducible Lipid Separation Using UPLC with Charged Surface Hybrid Technology and High-Resolution Mass Spectrometry.

Lipids play an important role in the energy storage, cellular signaling, and pathophysiology of diseases such as cancer, neurodegenerative diseases, infections, and diabetes. Due to high importance of diverse lipid classes in human health and disease, manipulating lipid abundance and composition is an important target for metabolic engineering. The extreme structural diversity of lipids in real biological samples is challenging for analytical techniques due to large difference in physicochemical properties of individual lipid species. This chapter describes lipidomic analysis of large sample sets requiring reliable and robust methodology. Rapid and robust methods facilitate the support of longitudinal studies allowing the transfer of methodology between laboratories. We describe a high-throughput reversed-phase LC-MS methodology using Ultra Performance Liquid Chromatography (UPLC®) with charged surface hybrid technology and accurate mass detection for high-throughput non-targeted lipidomics. The methodology showed excellent specificity, robustness, and reproducibility for over 100 LC-MS injections.

Abstract 10479: Genome Wide Association Study For Acute Kidney Injury In Patients Undergoing Off-pump Coronary Artery Bypass Graft Surgery

Introduction: In this study, we evaluated the genetic predisposition to AKI using genome-wide association study (GWAS) in patients undergoing elective off-pump-CABG (OP-CABG). Methods: Patients of South Asian ancestry were categorized into four cohorts as follows: (A) patients with normal renal function undergoing OP-CABG (eGFR>60ml/min/m2) (n=754; AKI - 81, Non-AKI-673); (B) patients with renal dysfunction (eGFR<60ml/min/m2; not needing dialysis) undergoing OP-CABG (n=263; AKI-169, non-AKI-89); (C) patients with renal dysfunction with no symptomatic/known heart disease (n=92); (D) age-matched healthy control population (with normal kidney function, without symptomatic/known heart disease) (n=826). Renal function was considered to be normal, if the serum creatinine was less than 1.3mg% and/or estimated glomerular filtration rate was greater than 60ml/min/1.7 m 2 . Genotyping was performed using Infinium Global Screening Array-24 v3.0 BeadChip from Illumina, which included 6,54,027 markers. GWAS analysis was performed using age, genders and top10 principal components as covariates using logistic regression using PLINK 2.0. Postoperative AKI was defined based on Kidney Disease Improving Global Outcomes guidelines. Results: There were 754 participants with good renal function undergoing OP-CABG; among them, 81 subjects developed AKI postoperatively. The GWAS analysis revealed 607 markers with a suggestive significance p-value threshold of 1e-4. A modest signal was observed on chromosome 11 region, covering genes such as COX8A, NAA40, RCOR. The gene-based test in FUMA annotation revealed genes, MARCOD2, CNTN4, MAGI2 etc. as top hits. However, none of the markers remained significant after multiple testing corrections using Bonferroni and Benjamini-Hochberg. Conclusions: The GWAS revealed an association signal on chromosome 11, which is suggestive and needs to be pursued further in a larger sample set.

Structure-Activity Relationship of Graphene-Based Materials: Impact of the Surface Chemistry, Surface Specific Area and Lateral Size on Their In Vitro Toxicity.

Predictive toxicity and structure–activity relationships (SARs) are raising interest since the number of nanomaterials has become unmanageable to assess their toxicity with a classical case-by-case approach. Graphene-based materials (GBMs) are among the most promising nanomaterials of this decade and their application might lead to several innovations. However, their toxicity impact needs to be thoroughly assessed. In this regard, we conducted a study on 22 GBMs to investigate their potential SARs by performing a complete physicochemical characterization and in vitro toxicity assessment (on RAW264.7 cells). We used GBMs of variable lateral size (0.5–38 µm), specific surface area (SSA, 30–880 m²/g), and surface oxidation (2–17%). We observed that reduced graphene oxides (RGOs) were more reactive than graphene nanoplatelets (GNPs), potentially highlighting the role of GBM’s surface chemistry and surface defects density in their biological impact. We also observed that for GNPs, a smaller lateral size caused higher cytotoxicity. Lastly, GBMs showing a SSA higher than 200 m²/g were found to induce a higher ROS production. Mechanistic explanations are proposed in the discussion. In conclusion, pairing a full physicochemical characterization with a standardized toxicity assessment of a large set of samples allowed us to clarify SARs and provide an additional step toward safe-by-design GBMs.

86 Extensive FAP expression analysis in 23 tumor indications and potential application in defining the patient population in FAP-targeting cancer immunotherapies

BackgroundFibroblast activation protein alpha (FAP) is frequently over-expressed in the tumor microenvironment (TME) while exhibiting limited expression in normal tissues. FAP expression was reported to be immunosuppressive in tumor mouse models and generally associated with worse prognosis in clinical studies. Therefore, it is important to understand the context in which FAP both exhibits immunosuppressive characteristics and be a useful target for immunotherapy.MethodsComprehensive immunohistochemistry (IHC) analyses on formalin-fixed paraffin-embedded tissue specimens with emphasis on lymph nodes and primary and metastatic tumor lesions spanning a wide range of indications were undertaken in this study. FAP staining of tumor tissues was performed with an optimized IHC robust-prototype-assay (RPA) and manually scored. The area (normal stroma & neoplastic) staining positively relative to the total tumor area at each intensity level was recorded and an H-score calculated (FAP-intensity score).These were supplemented by gene expression analysis using public as well as Roche phase 1, 2 and 3 cancer immunotherapy (CIT) clinical trial data sets.ResultsAnalysing FAP expression on normal tissue confirmed the general absence of FAP apart from a subset of pancreatic islet cells. Unlike the more homogenous expression of typical protein targets on tumor cells, FAP expression in the TME is heterogeneous in both pattern and intensity, requiring the analysis of a large sample set. Therefore, we evaluated 1216 samples from 23 tumor indications and 70 sub-indications. FAP expression exhibited a significant spread ranging from indications with highly abundant expression to those with low coverage.Using data from matching IHC and gene expression samples we confirmed FAP mRNA expression to significantly correlate with RPA H-scores (Spearman correlation: 0.62) (N=289, P=1.2E-31). Gene expression data from 12 atezolizumab clinical studies, including standard of care (SOC) randomized studies, with more than 6000 samples from 4 major indications were interrogated for the association between FAP expression and clinical response as evaluated by overall and progression free survival. This analysis suggests that FAP expression is generally associated with higher hazard ratios across all atezolizumab-treated samples (OS: 95% CI 1.04–1.09; PFS: 1.04–1.08), with the highest effect observed in Renal Cell Carcinoma (OS: 95% CI 1.08–1.31; PFS: 1.05–1.21), indicating a potential role of FAP in limiting CIT.ConclusionsData from these analyses can tailor indication and patient enrichment strategies for achieving optimal FAP-targeting. We propose to select indications with FAP-levels that are high enough to enable drug accumulation, yet low enough to reduce immunosuppressive effects that can hamper successful immunotherapy.

Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse-to-fine convolutional neural network.

Brachytherapy combined with external beam radiotherapy (EBRT) is the standard treatment for cervical cancer and has been shown to improve overall survival rates compared to EBRT only. Magnetic resonance (MR) imaging is used for radiotherapy (RT) planning and image guidance due to its excellent soft tissue image contrast. Rapid and accurate segmentation of organs at risk (OAR) is a crucial step in MR image-guided RT. In this paper, we propose a fully automated two-step convolutional neural network (CNN) approach to delineate multiple OARs from T2-weighted (T2W) MR images. We employ a coarse-to-fine segmentation strategy. The coarse segmentation step first identifies the approximate boundary of each organ of interest and crops the MR volume around the centroid of organ-specific region of interest (ROI). The cropped ROI volumes are then fed to organ-specific fine segmentation networks to produce detailed segmentation of each organ. A three-dimensional (3-D) U-Net is trained to perform the coarse segmentation. For the fine segmentation, a 3-D Dense U-Net is employed in which a modified 3-D dense block is incorporated into the 3-D U-Net-like network to acquire inter and intra-slice features and improve information flow while reducing computational complexity. Two sets of T2W MR images (221 cases for MR1 and 62 for MR2) were taken with slightly different imaging parameters and used for our network training and test. The network was first trained on MR1 which was a larger sample set. The trained model was then transferred to the MR2 domain via a fine-tuning approach. Active learning strategy was utilized for selecting the most valuable data from MR2 to be included in the adaptation via transfer learning. The proposed method was tested on 20 MR1 and 32 MR2 test sets. Mean± SD dice similarity coefficients are 0.93± 0.04, 0.87± 0.03, and 0.80± 0.10 on MR1 and 0.94± 0.05, 0.88± 0.04, and 0.80± 0.05 on MR2 for bladder, rectum, and sigmoid, respectively. Hausdorff distances (95th percentile) are 4.18± 0.52, 2.54± 0.41, and 5.03± 1.31mm on MR1 and 2.89± 0.33, 2.24± 0.40, and 3.28± 1.08mm on MR2, respectively. The performance of our method is superior to other state-of-the-art segmentation methods. We proposed a two-step CNN approach for fully automated segmentation of female pelvic MR bladder, rectum, and sigmoid from T2W MR volume. Our experimental results demonstrate that the developed method is accurate, fast, and reproducible, and outperforms alternative state-of-the-art methods for OAR segmentation significantly (p<0.05).

Cultivation of common bacterial species and strains from human skin, oral, and gut microbiota

BackgroundGenomics-driven discoveries of microbial species have provided extraordinary insights into the biodiversity of human microbiota. In addition, a significant portion of genetic variation between microbiota exists at the subspecies, or strain, level. High-resolution genomics to investigate species- and strain-level diversity and mechanistic studies, however, rely on the availability of individual microbes from a complex microbial consortia. High-throughput approaches are needed to acquire and identify the significant species- and strain-level diversity present in the oral, skin, and gut microbiome. Here, we describe and validate a streamlined workflow for cultivating dominant bacterial species and strains from the skin, oral, and gut microbiota, informed by metagenomic sequencing, mass spectrometry, and strain profiling.ResultsOf total genera discovered by either metagenomic sequencing or culturomics, our cultivation pipeline recovered between 18.1–44.4% of total genera identified. These represented a high proportion of the community composition reconstructed with metagenomic sequencing, ranging from 66.2–95.8% of the relative abundance of the overall community. Fourier-Transform Infrared spectroscopy (FT-IR) was effective in differentiating genetically distinct strains compared with whole-genome sequencing, but was less effective as a proxy for genetic distance.ConclusionsUse of a streamlined set of conditions selected for cultivation of skin, oral, and gut microbiota facilitates recovery of dominant microbes and their strain variants from a relatively large sample set. FT-IR spectroscopy allows rapid differentiation of strain variants, but these differences are limited in recapitulating genetic distance. Our data highlights the strength of our cultivation and characterization pipeline, which is in throughput, comparisons with high-resolution genomic data, and rapid identification of strain variation.

Circulating metabolome does not predict development of aortic stenosis

Abstract Background Calcific aortic valve disease (CAVD) is the most common valvular heart disease in Western world. CAVD is ranging from mild aortic valve sclerosis to severe obstructive aortic stenosis (AS). The development of AS has been associated with several risk factors including age, sex and hypertension. However, there is limited knowledge about factors that predict the development of aortic stenosis. Purpose We investigated if the circulating metabolite profile can predict the development of aortic stenosis in Finnish males. Methods We did a non-targeted LC-MS metabolomics analysis to baseline (1984–1989) serum samples from a prospective population-based Kuopio Ischemic Heart Disease risk factor study (KIHD) cohort of 2682 random Finnish males aged from 42 to 60 years. During the follow-up (until year 2020), 53 subjects developed either moderate (peak flow gradient 36–64mmHg or mean flow gradient 20–40mmHg) or severe aortic valvular stenosis (peak flow gradient over 64mmHg or mean gradient over 40mmHg). The AS patients were collected from the KIHD database using appropriate ICD-10 -codes for aortic valvular disease (from baseline to the end of the year 2017) and the diagnosis was checked manually using hospital medical records of the individuals. Results The AS patients seemed to have altered lipid metabolism and possibly altered composition of gut microbiota, since several acylcarnitines (e.g. octanoylcarnitine [Cohen's d=−0.40], decanoylcarnitine [d=−0.43], layroylcarnitine [d=−0.41], and oleoylcarnitine [d=−0.40]), and branched chain amino acids (BCAA, e.g. leucine [d=0.39], and isoleucine [d=0.49]) had p-values below 0.05. However, after correction for multiple testing, there were no significant differences between the cases and controls. Conclusions The present preliminary results, in need of verification with a larger set of samples, suggest that subjects, who will later develop AS might have reduced levels of acylcarnitines and increased levels of BCAA when compared to matched controls. However, these changes do not have large effects sizes and are likely not good candidates for biomarkers to predict future diagnosis of AS. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The Finnish Cultural Foundation, The Finnish Foundation for Cardiovascular Research.

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

Oil and the U.S. stock market: Implications for low carbon policies

We extend the existing understanding of the relation between oil prices and stock markets in two ways: (1) by evaluating the effects of the oil market on the U.S. stock market, at an aggregate level and for all forty-nine U.S. industry specific portfolios, and (2) by scrutinizing the dynamic nature of this relation within a Structural Vector Autoregression (SVAR) specification for a large set of rolling samples with fixed size. Results indicate that the effect of oil prices on the U.S. stock market depends on the type and timing of the shock. An oil supply shock generally does not have a statistically measurable effect on stock market performance. Conversely, an aggregate demand shock has a positive effect on nearly all sectors while an oil-specific demand shock has a negative effect on stock returns for most industries. These results suggest that investors can shift the portfolios consistent with smaller effects of oil-related shocks and the costs of carbon taxes and/or tradeable permits may be smaller than commonly thought if stock prices represent the net present value of profits.

Needle insertion planning for obstacle avoidance in robotic biopsy

Abstract Understanding the underlying pathology in different tissues and organs is crucial when fighting pandemics like COVID-19. During conventional autopsy, large tissue sample sets of multiple organs can be collected from cadavers. However, direct contact with an infectious corpse is associated with the risk of disease transmission and relatives of the deceased might object to a conventional autopsy. To overcome these drawbacks, we consider minimally invasive autopsies with robotic needle placement as a practical alternative. One challenge in needle based biopsies is avoidance of dense obstacles, including bones or embedded medical devices such as pacemakers. We demonstrate an approach for automated planning and visualising suitable needle insertion points based on computed tomography (CT) scans. Needle paths are modeled by a line between insertion and target point and needle insertion path occlusion from obstacles is determined by using central projections from the biopsy target to the surface of the skin. We project the maximum and minimum CT attenuation, insertion depth, and standard deviation of CT attenuation along the needle path and create two-dimensional intensity-maps projected on the skin. A cost function considering these metrics is introduced and minimized to find an optimal biopsy needle path. Furthermore, we disregard insertion points without sufficient room for needle placement. For visualisation, we display the color-coded cost function so that suitable points for needle insertion become visible. We evaluate our system on 10 post mortem CTs with six biopsy targets in abdomen and thorax annotated by medical experts. For all patients and targets an optimal insertion path is found. The mean distance to the target ranges from (49.9 ± 12.9)mm for the spleen to (90.1 ± 25.8)mm for the pancreas.

Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

High-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.