Tales of the tides: Pattern-based non-target analysis of the Elbe River.

ABSTRACT Sell‐side analysts commonly transition to buy‐side money managers. I examine whether and how these career transitions shape sell‐side analysts' behavior, relying on the granular career information of 6,310 analysts in the United States. I identify analysts who transition from the sell‐side to the buy‐side and find that these analysts issue inflated recommendations on stocks of interest to their future buy‐side employers. This favoritism is (1) present only during the year preceding their transition, (2) more pronounced among stocks where a single analyst is more influential in moving the stock price; and (3) present only among transitions likely to be strategically planned. Importantly, stocks receiving inflated recommendations experience a significant price decline following the departure of transitioning analysts from the sell‐side. Overall, these findings suggest that analyst career transitions are an important source of conflicts of interest.

Inductive content analysis (ICA) is a useful method for analyzing qualitative data in genetic counseling research. It is particularly relevant when the goal is to examine and improve practices or develop recommendations. Although ICA can be undertaken by a single analyst, ideally there is involvement of multiple analysts (or co-coders). Co-coding can bring many benefits to qualitative analysis that sits within a constructivist paradigm, including developing a representation of the data that is not only understandable to more than one individual but also richer and more nuanced. It also provides an opportunity for mentoring more junior researchers and can be an efficient way to analyze large datasets. However, co-coding requires important planning and consideration, and there is currently a paucity of clear guidance. In this paper, we provide an outline of the small body of existing literature on this topic and propose six flexible step-by-step components of our approach to co-coding in ICA, based on our own work. We have utilized it to analyze reporting practices and perspectives for diagnostic genomic sequencing, informed consent for genetic testing, data sharing and storage, and genomic newborn screening, among other topics. To illustrate these components, we present some example vignettes to show how these procedures can be applied in different scenarios and with different analysts.

The rapid advancement of multi‐omics single‐cell technologies has significantly enhanced our ability to investigate complex biological systems at unprecedented resolution. However, many existing analysis tools are complex, requiring substantial coding expertize, which can be a barrier for computationally less competent researchers. To address this challenge, we present single‐cell analyst, a user‐friendly, web‐based platform to facilitate comprehensive multi‐omics analysis. Single‐cell analyst supports a wide range of data types, including six single‐cell omics: single‐cell RNA sequencing (scRNA‐sequencing), single‐cell assay for transposase accessible chromatin sequencing (scATAC‐seq sequencing), single‐cell immune profiling (scImmune profiling), single‐cell copy number variation, cytometry by time‐of‐flight, and flow cytometry and spatial transcriptomics, and enables researchers to perform integrated analyses without requiring programming skills. The platform offers both online and offline modes, providing flexibility for various use cases. It automates critical analysis steps, such as quality control, data processing, and phenotype‐specific analyses, while also offering interactive, publication‐ready visualizations. With over 20 interactive tools for intermediate analysis, single cell analyst simplifies workflows and significantly reduces the learning curve typically associated with similar platforms. This robust tool accommodates datasets of varying sizes, completing analyses within minutes to hours depending on the data volume, and ensures efficient use of computational resources. By democratizing the complex process of multi‐omics analysis, single‐cell analyst serves as an accessible, all‐encompassing solution for researchers of diverse technical backgrounds. The platform is freely accessible at www.singlecellanalyst.org.

In this study, we developed a simultaneous extraction method for 19 food additives, including sweeteners, preservatives, and antioxidants, to enhance testing efficiency across a wide range of processed foods. Samples underwent 2 or 4 extractions with acetonitrile containing 0.05 w/v% ascorbic acid palmitate, facilitated by the addition of water, phosphoric acid, magnesium sulfate, and sodium chloride. The extracts were then diluted and analyzed using instruments tailored to each specific compound. Recovery tests (n=5 or n=3) on 22 samples, encompassing high-protein, oily, and powdered foods, demonstrated satisfactory recovery rates between 76.6% and 111.6% (RSD: 0.1-6.6%). To validate the method, two concentrations-corresponding to the lower limit of quantification and the maximum permissible level-were added to two or three different samples per additives. This validation was conducted by a single analyst over two parallel runs across five days. The results indicated that the lower limit of quantification achieved trueness of 81.7-102.4%, repeatability of 0.3-6.3%, and within-laboratory reproducibility of 1.0-9.7%. Similarly, for the maximum level of use, trueness ranged from 80.6 to 105.5%, repeatability from 0.4 to 2.8%, and within-laboratory reproducibility from 0.6 to 4.3%, all meeting the target criteria. The developed method proves to be a valuable analytical tool, significantly enhancing the efficiency of food additive analysis.

Recent years have witnessed the adoption of differential privacy (DP) in practical database systems like PINQ, FLEX, and PrivateSQL. Such systems allow data analysts to query sensitive data while providing a rigorous and provable privacy guarantee. However, the existing design of these systems does not distinguish data analysts of different privilege levels or trust levels. This design can have an unfair apportion of the privacy budget among the data analyst if treating them as a single entity, or waste the privacy budget if considering them as non-colluding parties and answering their queries independently. In this paper, we propose DProvDB, a fine-grained privacy provenance framework for the multi-analyst scenario that tracks the privacy loss to each single data analyst. Under this framework, when given a fixed privacy budget, we build algorithms that maximize the number of queries that could be answered accurately and apportion the privacy budget according to the privilege levels of the data analysts.

Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflicting priorities that must share the same privacy loss budget. This motivates the problem of equitable budget-sharing for multi-analyst differential privacy. Our previous work defined desiderata that any mechanism in this space should satisfy and introduced methods for budget-sharing in the offline case where queries are known in advance. We extend our previous work on multi-analyst differentially private query answering to the case of online query answering, where queries come in one at a time and must be answered without knowledge of the following queries. We demonstrate that the unknown ordering of queries in the online case results in a fundamental limit in the number of queries that can be answered while satisfying the desiderata. In response, we develop two mechanisms, one which satisfies the desiderata in all cases but is subject to the fundamental limitations, and another that randomizes the input order ensuring that existing online query answering mechanisms can satisfy the desiderata.

Multiparametric quantitative renal MRI may provide noninvasive radiologic biomarkers of chronic kidney disease (CKD) based on investigations in animal models and adults. We aimed to (1) obtain normative multiparametric quantitative MRI data from the kidneys of healthy children and young adults, (2) compare MRI measurements between healthy control participants and patients with CKD, and (3) determine if MRI measurements correlate with clinical and laboratory data as well as histology. This was a prospective, case-control study of 20 healthy controls and 12 CKD patients who underwent percutaneous renal biopsy ranging from 12 to 23years of age between October 2018 and March 2020. Kidney function was documented and pathology assessed for fibrosis/inflammation. Utilizing a field strength of 1.5T, we examined renal T1, T2, and T2* relaxation mapping, MR elastography (MRE), and diffusion-weighted imaging (DWI). A single analyst made all manual measurements for quantitative MRI pulse sequences. Independent measurements from cortex, medulla, and whole kidney were obtained by drawing regions of interest on single slices from the upper, mid, and lower kidney. A weighted average was calculated for each kidney; if two kidneys, the right and left were averaged. Continuous variables were compared with Mann-Whitney U test; bivariate relationships were assessed using Spearman rank-order correlation. Median estimated glomerular filtration rate (eGFR) was 112.3ml/min/1.73 m2 in controls (n = 20, 10 females) and 55.0ml/min/m2 in CKD patients (n = 12, 2 females) (p < 0.0001). Whole kidney (1333 vs. 1291ms; p = 0.018) and cortical (1212 vs 1137ms; p < 0.0001) T1 values were higher in CKD patients. Cortical T1 values correlated with eGFR (rho = -0.62; p = 0.0003) and cystatin C (rho = 0.58; p = 0.0007). Whole kidney (1.87 vs. 2.02 10-3 mm2/s; p = 0.007), cortical (1.89 vs. 2.04 10-3 mm2/s; p = 0.008), and medullary (1.87 vs. 1.98 10-3 mm2/s; p = 0.0095) DWI apparent diffusion coefficients (ADC) were lower in CKD patients. Whole kidney ADC correlated with eGFR (rho = 0.45; p = 0.012) and cystatin C (rho = -0.46; p = 0.009). Cortical histologic inflammation correlated with DWI ADC (rho = -0.71; p = 0.011). Renal T1 relaxation and DWI ADC measurements differ between pediatric healthy controls and CKD patients, correlate with laboratory markers of CKD, and may have histologic correlates.

The purpose of this study was to evaluate agreement between eye banks (EBs) and an image analysis reading center on endothelial cell density (ECD) determinations using the same image analysis method. The Cornea Image Analysis Reading Center (CIARC) determined ECD with a single experienced analyst on EB-obtained central endothelial images from donors intended for keratoplasty from 2 eye banks, Eversight and Lions VisionGift, using the Konan center analysis method. The EBs performed ECD determination on their respective sets of images using the same analysis method with experienced eye bank technicians. The mean age of the 200 donors was 54 years (range 30-75 years). Seventy (35%) of the 200 patients were women, and 57 (29%) were diabetic. The mean ECD was 10 cells/mm2 greater by the EBs than by CIARC (P = 0.39), with 95% limits of agreement of [-304 to 323 cells/mm2]. The mean difference was not substantially changed when the difference between EBs and CIARC ECD was adjusted for sex, donor age, donor diabetes, CV, HEX, number of cells analyzed, and EBs as a random effect (estimated mean difference of 20 cells/mm2 after adjustment in a linear mixed model; P = 0.73). The EB-determined preoperative ECD was within 10% of the CIARC-determined ECD for 178 (89%) image sets, with 15 (8%) higher by >10% and 7 (3%) lower by >10%. Well-trained eye bank technicians achieve comparable results for ECD determination with an experienced image analyst from an image analysis reading center when the same image analysis method is used.

Abstract Background Patients with Marfan syndrome (MFS) may develop aortic dissection due to progressive dilatation in the entire aorta. Increased aortic stiffness, i.e.a. decreased distensibility has been shown to often precede these dismal sequelae. Therefore, we investigated longitudinal changes in aortic distensibility throughout the entire aorta by means of Cardiac Magnetic Resonance (CMR) imaging in patients with MFS. Methods This retrospective study included all MFS patients with four CMR examinations performed between 1996 and 2012. Aortic distensibility was measured and calculated by a single analyst, in the ascending, proximal- and distal descending, and abdominal aorta. Changes in distensibility were studied using linear mixed-effects regression models. Furthermore, we investigated the association between distensibility and age, sex, blood pressure, medication use, FBN1 mutation type, and previous aortic root surgery. Results In total, 35 MFS patients (age at inclusion 28 [IQR 23–32] years, 54% male) were included. Mean aortic distensibility was low in the ascending and proximal descending aorta (resp. 3.25±1.87, 3.91±1.73x10–3 mmHg–1) at the first scan. Distensibility decreased significantly over time at level 2, 3, and 4 (resp. p=0.021, p=0.002, p=0.038) (Figure 1). The rate of distensibility loss per year (x10–3 mmHg–1/year) was respectively 0.04 and 0.06 in the proximal- and distal descending aorta. Men seemed to have a lower but more stable distensibility, whereas women showed a higher distensibility at younger age, but a faster deterioration rate over time (difference in distensibility loss per year between men and women: 0.08, p=0.038). Distensibility did not correlate significantly with medication use, FBN1 mutation type or previous aortic root surgery. Conclusion Patients with MFS have low distensibility at all levels of the aorta at young age, which keeps decreasing over time. Men had lower distensibility at younger age than women. Distensibility was stably low in men, while still deteriorating over time in women. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): AMC FoundationHorstingstuit Foundation

Analyst Optimism and Buy-Side Institutions: Evidence from Analyst Transition from Sell-Side to Buy-Side

Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rock-bursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.

Seemingly inconsistent analyst revisions

Body composition is associated with important clinical and functional outcomes in colon cancer patients. Colon cancer patients often undergo computed tomography (CT) in routine clinical care. These images may then be used to assess body composition to potentially identify individuals who may benefit most from physical activity (PA) intervention. Developing reliable and accurate ways to measure body composition is a prerequisite to using CT-generated body composition to inform disease management. PURPOSE: To determine inter- and intra-rater relative reliability of CT to measure body composition in colon cancer patients in a randomized controlled trial (PA vs. usual care). METHODS: 25 CT scans were randomly selected from 10 men and 8 women (59.1±9.7yrs), all post-primary treatment for stage II-III colon cancer. Manual image analysis was conducted for each single CT image slice using SliceOmatic software (Tomovision, Montreal, Canada) to mark the third lumbar vertebra and segment/quantify muscle (MUS), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and the muscle attenuation coefficient (MA). Inter-rater reliability was assessed by estimating the agreement between measures from a) 2 trained manual analysts and b) a manual analyst and automated software (Voronoi Health Analytic ABACS L3 Module), respectively. Intra-rater reliability was evaluated by estimating the agreement between measures by the same manual analyst one month apart. Inter- and intra-class correlation coefficients (ICCs) were calculated with ICC ≥ 0.9 deemed excellent reliability. RESULTS: ICCs were excellent for both measures of inter-rater reliability (analyst 1 vs. 2: MUS=0.999, IMAT=0.928, VAT=1.000, SAT=0.999, MA=0.999; manual vs. automated: MUS=0.981, IMAT=0.710, VAT=0.997, SAT=0.992, MA=0.992), and intra-rater reliability (MUS=1.000, IMAT=0.971, VAT=1.000, SAT=0.999, MA=1.000) (all p<0.01). CONCLUSION: Body composition analyses using clinical CT scans, SliceOmatic software, and a trained analyst is feasible for a single analyst across time, between two separate analysts, and between a manual analyst/automated software. Relatively reliable CT analyses of body composition is possible in stage II-III, post-primary treatment colon cancer patients.

Asthma as an outcome: Exploring multiple definitions of asthma across birth cohorts in the Environmental influences on Child Health Outcomes Children's Respiratory and Environmental Workgroup

Simultaneous Analyst Coverage and the Reduction of the Risk-Arb Spread

Background: The determination of cyanocobalamin (vitamin B12) and biotin has always been challenging because of the lack of a chromophore for biotin and trace level input for vitamin B12 in supplements. Microbiological assay methods are currently used for quantitation. However, these methods are time consuming, may lack specificity, and have high imprecision. Objective: Our laboratory developed and validated an LC method for the simultaneous quantitation of vitamin B12 and biotin. Methods: This LC method uses a single quadruple mass analyzer to detect biotin and vitamin B12 at m/z 245.10 and m/z 678.29, respectively. Results: The mass analyzer allows for low limits of quantitation (biotin: 1 ng/mL; vitamin B12: 4 ng/mL). Precision results showed that injections are repeatable without the use of an internal standard (RSD < 5%). Single analyst (n = 5: RSD < 3%), within lab (n = 10: RSD < 8%), and multilab (n = 20: RSD < 13%) precision results were also much better than those reported by microbiological assay methods. Linearity was evaluated between 92.00 ng/mL and 9200 ng/mL (R² 09916) for biotin and between 4.846 ng/mL and 484.6 ng/mL (R² 0.9999) for vitamin B12. The method is accurate between 20 ng/mL and 60 ng/mL for vitamin B12 and between 400 ng/mL and 1200 ng/mL for biotin. Conclusions: The results show a simple, accurate, and precise method for the quantitation of vitamin B12 and biotin. Highlights: This work demonstrates that single quadrupole mass analyzers can be successfully used to quantify trace level analytes in quality control laboratories.

Abstract In many real world scenarios, terms of service allow a producer of a service to collect data from its users. Producers value data but often only compensate users for their data indirectly with reduced prices for the service. This work considers how a producer (data analyst) may offer differential privacy as a premium service for its users (data subjects), where the degree of privacy offered may itself depend on the user data. Along the way, it strengthens prior negative results for privacy markets to the pay-for-privacy setting and develops a new notion of endogenous differential privacy. A positive result for endogenous privacy is given in the form of a class of mechanisms for privacy-as-a-service markets that 1) determine ɛ using the privacy and accuracy preferences of a heterogeneous body of data subjects and a single analyst, 2) collect and distribute payments for the chosen level of privacy, and 3) privately analyze the database. These mechanisms are endogenously differentially private with respect to data subjects’ privacy preferences as well as their private data, they directly elicit data subjects’ true preferences, and they determine a level of privacy that is efficient given all parties’ preferences.

Development of an automatable method for the measurement of endo-1,4-β-xylanase activity in barley malt and initial investigation into the relationship between endo-1,4-β-xylanase activity and wort viscosity

Abstract Many data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart‐driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta‐visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high‐level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.