Observer Bias Research Articles

NIMG-26. DIFFERENTIATION OF IDH-WILDTYPE GLIOBLASTOMA AND TUMEFACTIVE MULTIPLE SCLEROSIS USING PRE-OPERATIVE BRAIN MRI AND DEEP LEARNING: RIGOROUS AND UNBIASED EXPERIMENTAL DESIGN AND ANALYTICAL APPROACH

Abstract Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Misdiagnosis of brain lesions may expose patients to unnecessary anxiety, surgery, or radiotherapy. Noninvasive methods to help with accurate diagnosis of tumor and non-tumor etiologies are needed. tMS subjects were identified by the Mayo Clinic Center for Multiple Sclerosis and Autoimmune Neurology (CMSAN). GBM subjects were matched to tMS by age at diagnosis, index MRI date, and sex. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. To identify potential experimental design bias, MRI technology and clinical characteristics were compared across groups. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 600 epochs, batch size 16, learning rate 10-3, cross entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential epoch cross entropy loss < 0.02. Five-fold cross validation (CV) was utilized to obtain an unbiased estimate of predictive performance (overfitting) and sensitivity of the 3D-DenseNet121 algorithm was evaluated. 224 subjects (114 GBM, 110 tMS) were analyzed: 40% male, median age 46. After one round of CV, the average unbiased estimate of sensitivity and specificity for predicting GBM from the final classification model was 0.86 and 0.73, respectively. To assess the reproducibility of the deep learning algorithm (3D-DenseNet121), the exact same CV folds were rerun resulting in average sensitivity and specificity of 0.82 and 0.75, respectively. Obtaining unbiased estimates of model performance using internal validation is critical before evaluating a final model on an external dataset. Understanding sensitivity of a deep learning algorithm is important for model validity. Differentiation of GBM and tMS appears reasonable with MRI; external validation is in progress.

Observational bias and young massive cluster characterization − II. Can Gaia accurately observe young clusters and associations?

ABSTRACT Observations of clusters suffer from issues such as completeness, projection effects, resolving individual stars, and extinction. As such, how accurate measurements and conclusions are likely to be? Here, we take cluster simulations (Westerlund2- and Orion-type), synthetically observe them to obtain luminosities, accounting for extinction, and the inherent limits of Gaia, then place them within the real Gaia DR3 catalogue. We then attempt to rediscover the clusters at distances of between 500 and 4300 pc. We show the spatial and kinematic criteria that are best able to pick out the simulated clusters, maximizing completeness, and minimizing contamination. We then compare the properties of the ‘observed’ clusters with the original simulations. We looked at the degree of clustering, the identification of clusters and subclusters within the data sets, and whether the clusters are expanding or contracting. Even with a high level of incompleteness (e.g. $\lt 2~{{\ \rm per\ cent}}$ stellar members identified), similar qualitative conclusions tend to be reached compared to the original data set, but most quantitative conclusions are likely to be inaccurate. Accurate determination of the number, stellar membership, and kinematic properties of subclusters are the most problematic to correctly determine, particularly at larger distances due to the disappearance of cluster substructure as the data become more incomplete, but also at smaller distances where the misidentification of asterisms as true structure can be problematic. Unsurprisingly, we tend to obtain better quantitative agreement of properties for our more massive Westerlund2-type cluster. We also make optical style images of the clusters over our range of distances.

Multifractal analysis of cellular ATR-FTIR spectrum as a method for identifying and quantifying cancer cell metastatic levels

Cancer is a leading cause of mortality today. Sooner a cancer is detected, the more effective is the treatment. Histopathological diagnosis continues to be the gold standard worldwide for cancer diagnosis, but the methods used are invasive, time-consuming, insensitive, and still rely to some degree on the subjective judgment of pathologists. Recent research demonstrated that Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy can be used to determine the metastatic potential of cancer cells by evaluating their membrane hydration. In the current study, we demonstrate that the conversion of ATR-FTIR spectra using multifractal transformation generates a unique number for each cell line’s metastatic potential. Applying this technique to murine and human cancer cells revealed a correlation between the metastatic capacity of cancer cells within the same lineage and higher multifractal value. The multifractal spectrum value was found to be independent of the cell concentration used in the assay and unique to the tested lineage. Healthy cells exhibited a smaller multifractal spectrum value than cancer cells. Further, the technique demonstrated the ability to detect cancer progression by being sensitive to the proportional change between healthy and cancerous cells in the sample. This enables precise determination of cancer metastasis and disease progression independent of cell concentration by comparing the measured spectroscopy derived multifractal spectrum value. This quick and simple technique devoid of observer bias can transform cancer diagnosis to a great extent improving public health prognosis worldwide.

Evaluating Edge Credibility in Evolving Noisy Social Networks

Despite the massive surge of evolving social network analysis in popularity, existing research usually represent the observed social interactions among individuals as completely credible edges. However, due to information inaccuracy, individual non-response and dropout, and sampling biases in observations, the evolving noisy social network that coexists true edges and spurious edges is pervasive in actual applications, where the ignoration of credibility otherness of observed edges could lead to the wrong estimates of social properties and misleading conclusions. To discover credible edge information to shape correct social interactions among individuals, we propose a universal and explainable multiple-neighbor evolutional filtering method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> ) to evaluate how credible of observed edges to ‘truly’ exist in the evolving noisy social network. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> consists of an evolutional extractor and a filtering evaluator. To resist the noisy disturbance, the evolutional extractor exploits the evolutional states of edges from the perspective of evolution mechanisms within multiple-neighbor ranges, which applies different link prediction algorithms to fit the evolution mechanism in the formation of each edge. Further, the filtering evaluator reconstructs Kalman filter to predict and refine the evolutional states of edges based on their evolving local structures. As a result, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> combines the evolutional extractor and the filtering evaluator to analyze the evolutional fluctuations of the observed edges to evaluate their credibility. Extensive experiments on real-world datasets demonstrate that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEFM</i> can effectively and reasonably evaluate edge credibility in evolving noisy social networks.

Temperature-Humidity-Bias Testing and Life Prediction Modeling for Electrochemical Migration in Aerosol-Jet Printed Circuits

Abstract Aerosol-Jet Printing (AJP) technology, applied to the manufacturing of printed hybrid electronics (PHE) devices, has the capability to fabricate highly complex structures with resolution in the tens-of-microns scale, creating new possibilities for the fabrication of electronic devices and assemblies. The widespread use of AJP in fabricating PHE and package-level electronics necessitates a thorough assessment of not only the performance of AJP printed electronics but also their reliability under different kinds of life-cycle operational and environmental stresses. One important hindrance to the reliability and long-term performance of such AJP electronics is electrochemical migration (ECM). ECM is an important failure mechanism in electronics under temperature and humidity conditions because it can lead to conductive dendritic growth, which can cause dielectric breakdown, leakage current, and unexpected short circuits. In this paper, the ECM propensity in conductive traces printed with AJP process, using silver-nanoparticle (AgNP) based inks, was experimentally studied using temperature-humidity-bias (THB) testing of printed test coupons. Conductive dendritic growth with complex morphologies was observed under different levels of temperature, humidity, and electric bias in the THB experiments. Weibull statistics are used to quantify the failure data, along with the corresponding confidence bounds to capture the uncertainty of the Weibull distribution. A nonmonotonic relationship between time-to-failure and electric field strength was noticed. An empirical acceleration model for ECM is proposed, by combining the classical Peck's model with a quadratic polynomial dependence on electric field strength. This model provides good estimate of acceleration factors for use conditions where the temperature, humidity, and electrical field are within the tested range, but should be extrapolated with care beyond the tested range.

Spoil characterisation using UAV-based optical remote sensing in coal mine dumps

The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line. Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil offloading continues over time. Generally, the conventional in-situ coal spoil characterisation is inefficient, laborious, hazardous, and prone to experts' observation biases. To this end, this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle (UAV) based optical remote sensing. The textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters (i.e., fabric structure and relative density/consistency) in the proposed workflow. The raw images were converted to an orthomosaic using structure from motion aided processing. Then, structural descriptors were computed per pixel to enhance feature modalities of the spoil materials. Finally, machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention. The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

Large language models show human-like content biases in transmission chain experiments.

As the use of large language models (LLMs) grows, it is important to examine whether they exhibit biases in their output. Research in cultural evolution, using transmission chain experiments, demonstrates that humans have biases to attend to, remember, and transmit some types of content over others. Here, in five preregistered experiments using material from previous studies with human participants, we use the same, transmission chain-like methodology, and find that the LLM ChatGPT-3 shows biases analogous to humans for content that is gender-stereotype-consistent, social, negative, threat-related, and biologically counterintuitive, over other content. The presence of these biases in LLM output suggests that such content is widespread in its training data and could have consequential downstream effects, by magnifying preexisting human tendencies for cognitively appealing and not necessarily informative, or valuable, content.

Assessing the trophic ecology of Southern Ocean Myctophidae: the added value of DNA metabarcoding

Lanternfishes (Myctophidae) are key components of mesopelagic fish communities globally. In the Southern Ocean, incomplete information on myctophid diets limits our understanding of their energetics, interactions, and wider ecosystem impact. Traditional microscopic methods of diet analysis have relatively coarse prey resolution and possible taxonomic and observer biases. Deoxyribonucleic acid (DNA) metabarcode sequencing promises higher taxonomic and temporal resolution, but uncertainty remains in comparing this with microscopy-based analyses. Here, we applied 18S DNA metabarcode sequencing to stomach contents from 20 Electrona antarctica individuals that had previously been examined via microscopic analysis. Across all fish analysed, crustacean and gastropod taxa dominated the prey identified via both methods, with broad agreement between methods on the relative abundance of different prey items. DNA metabarcode sequencing recovered greater taxonomic diversity and resolution, particularly for soft-bodied prey items and small crustaceans. DNA sequencing results also more clearly differentiated diet between individuals collected from different environments. Overall, our findings illustrate how DNA-based methods are complementary to, and consistent with, traditional methods and can provide additional, high-resolution data on a range of trophic interactions.

Assessment of treatment-specific tethering survival bias for the juvenile blue crab Callinectes sapidus in a simulated salt marsh.

The blue crab (Callinectes sapidus) is ecologically and economically important in Chesapeake Bay. Nursery habitats, such as seagrass beds, disproportionately contribute individuals to the adult segment of populations. Salt marshes dominated by smooth cordgrass Spartina alterniflora are intertidal nursery habitats which may serve as a refuge from predation for juvenile blue crabs. However, the effects of various characteristics of salt marshes on nursery metrics, such as survival, have not been quantified. Comparisons of juvenile survival between salt marshes and other habitats often employ tethering to assess survival. Although experimental bias when tethering juvenile prey is well recognized, the potential for habitat-specific bias in salt marshes has not been experimentally tested. Using short-term mesocosm predation experiments, we tested if tethering in simulated salt marsh habitats produces a habitat-specific bias. Juvenile crabs were tethered or un-tethered and randomly allocated to mesocosms at varying simulated shoot densities and unstructured sand. Tethering reduced survival, and its effect was not habitat specific, irrespective of shoot density, as evidenced by a non-significant interaction effect between tethering treatment and habitat. Thus, tethering juvenile blue crabs in salt marsh habitat did not produce treatment-specific bias relative to unvegetated habitat across a range of shoot densities; survival of tethered and un-tethered crabs was positively related to shoot density. These findings indicate that tethering is a useful method for assessing survival in salt marshes, as with other nursery habitats including seagrass beds, algae and unstructured sand.

How to design and implement a Group Poem activity

Museum-based learning activities provide interactive and innovative ways to integrate the arts and humanities into medical education. Like other museum-based activities, the Group Poem supports the development of multiple clinically relevant skills and attributes, such as observation, communication, perspective-taking, empathy, and implicit bias awareness. In this paper, we present a step-by-step guide for educators seeking to design and implement a museum-based Group Poem activity for medical learners. The overall ‘task’ of the activity is for learners to collectively create a poem that they perform for others, a process that participants find to be engaging and meaningful to their formation as physicians. In this paper, we provide specific directions on pre-selecting the works of art, preparing the supplies, dividing into small groups, providing iterative instructions to learners, managing the timing of the session, and debriefing the activity. Although designed to be experienced in an art museum, we note that the Group Poem activity can also be conducted in the classroom or virtually using photographic or digital reproductions of artwork.

Does a diagnosis of depression influence observer ratings of pain severity? The mediating role of causal attributions of pain and pain genuineness.

Researchers have been increasingly investigating observer and patient characteristics that may influence the assessment of pain in others. While rates of psychiatric conditions are high in chronic pain populations, surprisingly little attention has been given to if (and why) a comorbid psychiatric diagnosis may influence the estimation of pain in others. Using an experimental vignette paradigm, the current study examined whether a diagnostic label of major depressive disorder (MDD) would impact observer pain estimates of a woman with chronic pain, and whether causal attributions of pain and pain genuineness might help explain these effects. Participants (n = 188) were given a vignette describing a female patient with chronic pain (who either had MDD or no mental health concerns), viewed a brief video clip of the patient, and then were asked to provide a variety of ratings about the woman's pain. Results of a serial multiple mediation analysis revealed that participants in the MDD condition made greater psychological attributions for the woman's pain, which was associated with lower perceptions of pain genuineness, which was then associated with lower estimates of pain intensity. These findings suggest that a diagnosis of depression may indirectly influence observer estimates of another person's pain by heightening psychological attributions of pain, and making their pain seem less genuine. Further research is needed to elucidate the complex processes underlying pain estimation, including patient and observer characteristics, biases, and heuristics, in order to improve quality of care for those living with persistent pain.'

Developing a Drift Rate Distribution for Technosignature Searches of Exoplanets

A stable-frequency transmitter with relative radial acceleration to a receiver will show a change in received frequency over time, known as a “drift rate.” For a transmission from an exoplanet, we must account for multiple components of drift rate: the exoplanet’s orbit and rotation, the Earth’s orbit and rotation, and other contributions. Understanding the drift rate distribution produced by exoplanets relative to Earth, can (a) help us constrain the range of drift rates to check in a Search for Extraterrestrial Intelligence project to detect radio technosignatures, and (b) help us decide validity of signals-of-interest, as we can compare drifting signals with expected drift rates from the target star. In this paper, we modeled the drift rate distribution for ∼5300 confirmed exoplanets, using parameters from the NASA Exoplanet Archive (NEA). We find that confirmed exoplanets have drift rates such that 99% of them fall within the ±53 nHz range. This implies a distribution-informed maximum drift rate ∼4 times lower than previous work. To mitigate the observational biases inherent in the NEA, we also simulated an exoplanet population built to reduce these biases. The results suggest that, for a Kepler-like target star without known exoplanets, ±0.44 nHz would be sufficient to account for 99% of signals. This reduction in recommended maximum drift rate is partially due to inclination effects and bias toward short orbital periods in the NEA. These narrowed drift rate maxima will increase the efficiency of searches and save significant computational effort in future radio technosignature searches.

Color Polymorphism of Unfeathered Parts of the Great White Egret Ardea alba alba in Europe: An Analysis of the Internet Images

Polymorphism has been described in many avian taxa including Ardeidae. This study investigated the spatio-temporal distribution of color polymorphic forms (with regard to the color of the unfeathered parts (legs/lores)) of the Great White Egret Ardea alba alba in internet images from Europe. In total, 140 images of presumed polymorphs from 16 countries (from 2006 to 2021) have been collected and examined. Eight coloration schemes of visible unfeathered parts were distinguished. Since some polymorphs may represent the subspecies Eastern Great Egret Ardea alba modesta, the ratio of beak length to total head length was compared among individuals representing the nominal and Eastern Great Egret subspecies. The ratio distributions were similar in all compared groups; thus, polymorph egrets may represent both of these subspecies. Most polymorph images originate from the Netherlands and the southwestern Norwegian coast. This may have resulted from specific local diets and contact with hormone-disrupting compounds. Nevertheless, some observation biases, such as the presence of local breeding population or high numbers of nature photographers, could have affected our observation pattern. Some analyzed features also followed temporal patterns. Most polymorphs were observed during the breeding season, suggesting disruptions of sex hormones as a possible cause. Studies of spatio-temporal patterns of polymorph occurrence may allow us to obtain insights into the causes of large variation in coloration schemes in polymorphic species.

FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning.

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO (Fullhydro Large-scale structure simulations with All-sky Mapping for the Interpretation of Next Generation Observations) cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller volume simulations, we model how the galaxy stellar mass function (SMF) and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the SMF. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters.

Unchanged frequency and decreasing magnitude of outbursts from ice-dammed lakes in Alaska

Glacial lakes can form and grow due to glacial retreat, and rapid lake drainage can produce destructive floods. Outburst flood compilations show a temporal increase in frequency; however, recent studies highlight the role of observational bias, creating uncertainty about current and future glacial-lake hazards. Here, we focus on the Alaska region, which generated a third of previously documented outbursts globally. Using multitemporal satellite imagery, we documented 1150 drainages from 106 ice-dammed lakes between 1985 and 2020. Documented events became more frequent over time, however, accounting for increasing image availability reveals no significant increase occurred. Most lakes decreased in area and volume, suggesting a reduction in regional flood hazard. Our satellite-based approach documented 60% more events in a 35-year period than had previously been documented over 100 years. This suggests that outburst floods have historically been underreported and warrants systematic study of other regions.

Quantifying Poynting Flux in the Quiet Sun Photosphere

Poynting flux is the flux of magnetic energy, which is responsible for chromospheric and coronal heating in the solar atmosphere. It is defined as a cross product of the electric and magnetic fields, and in ideal MHD conditions it can be expressed in terms of the magnetic field and plasma velocity. Poynting flux has been computed for active regions and plages, but estimating it in the quiet Sun (QS) remains challenging due to resolution effects and polarimetric noise. However, with the upcoming DKIST capabilities, such estimations will become more feasible than ever before. Here, we study QS Poynting flux in SUNRISE/IMaX observations and MURaM simulations. We explore two methods for inferring transverse velocities from observations—FLCT and a neural network–based method DeepVel—and show DeepVel to be the more suitable method in the context of small-scale QS flows. We investigate the effect of azimuthal ambiguity on Poynting flux estimates, and we describe a new method for azimuth disambiguation. Finally, we use two methods for obtaining the electric field. The first method relies on an idealized Ohm’s law, whereas the second is a state-of-the-art inductive electric field inversion method PDFI_SS. We compare the resulting Poynting flux values with theoretical estimates for chromospheric and coronal energy losses and find that some of the Poynting flux estimates are sufficient to match the losses. Using MURaM simulations, we show that photospheric Poynting fluxes vary significantly with optical depth, and that there is an observational bias that results in underestimated Poynting fluxes due to an unaccounted shear term contribution.

Statistical bias correction for CESM-simulated PM2.5

Global climate models are good tools for simulating transnational and interregional transport of pollutants such as PM2.5, which is of growing interest and importance, for example in human health and socio-economic development studies. However, reliable estimates of PM2.5 are very challenging for such relatively coarse and simplified models, and even state of the art models fare poorly in matching satellite observations in many highly polluted, and some almost pristine environments. This work describes a novel bias correction method based on multiple linear regression (MLR) modelling. The target data we aim for is global satellite-based data and the PM2.5 precursors simulated by the Community Earth System Model Version 1.2.2. The statistical method greatly reduced the simulation biases of PM2.5 worldwide compared with satellite-derived PM2.5, especially in highly-polluted regions, such as northern China, the Indo-Gangetic plains, the Democratic Republic of Congo and northwestern Brazil. Root-mean-square differences (RMSD) between continental-averaged observations and simulations are reduced from 75% to 9%. The ensemble RMSD for 13 countries exemplified here is reduced from 116% to 3%. One virtue of the MLR method is that details of the classification of internal mixed modes of each aerosol and their spatial differences are not required. The MLR coefficients are designed to be highly aerosol- and country-dependent, so they provide new perspectives of relative importance of each aerosol to local PM2.5 and offer clues on observational and simulation biases. The bias-correction method is easily applied for air pollutants simulated by global climate models due to its low computational cost.

Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase.

Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on histological analysis of prostate biopsy samples, an activity that is both time-consuming and prone to observer bias. Previous studies have demonstrated that immunostaining of cytokeratin, p63, and racemase can significantly improve the sensitivity and the specificity of PCa detection compared to traditional H&E staining. This study introduces a novel approach that combines diagnosis-specific immunohistochemical (IHC) staining and deep learning techniques to provide reliable stratification of prostate glands. Our approach leverages a customized segmentation network, called K-PPM, that incorporates adaptive kernels and multiscale feature integration to enhance the functional information of IHC. To address the high class-imbalance problem in the dataset, we propose a weighted adaptive patch-extraction and specific-class kernel update. Our system achieved noteworthy results, with a mean Dice Score Coefficient of 90.36% and a mean absolute error of 1.64 % in specific-class gland quantification on whole slides. These findings demonstrate the potential of our system as a valuable support tool for pathologists, reducing workload and decreasing diagnostic inter-observer variability. Our study presents innovative approaches that have broad applicability to other digital pathology areas beyond PCa diagnosis. As a fully automated system, this model can serve as a framework for improving the histological and IHC diagnosis of other types of cancer.

Outskirts of Abell 1795: Probing gas clumping in the intracluster medium

Contact. The outskirts of galaxy clusters host complex interactions between the intracluster and circumcluster media. During the evolution of clusters, ram-pressure stripped gas clumps from infalling substructures break the uniformity of the gas distribution, potentially leading to observational biases at large radii. However, assessing the contribution of gas clumping poses observational challenges and requires robust X-ray measurements in the background-dominated regime of the cluster outskirts. Aims. The main objectives of this study are to isolate faint gas clumps from field sources and from the diffuse emission in the Abell 1795 galaxy cluster, then to probe their impact on the observed surface brightness and thermodynamic profiles. Methods. We performed an imaging analysis on deep Chandra ACIS-I observations of the Abell 1795 cluster outskirts, extending out to ∼1.5r200 with full azimuthal coverage. We built the 0.7 − 2.0 keV surface brightness distribution from the adaptively binned image of the diffuse emission and looked for clumps in the form of &gt; + 2σ surface brightness outliers. Our classification of the clump candidates was based primarily on Chandra and SDSS data. Benefiting from the point source list resolved by Chandra, we extracted the thermodynamic profiles of the intracluster medium from the associated Suzaku XIS data out to r200 using multiple point source and clump candidate removal approaches. Results. We identified 24 clump candidates in the Abell 1795 field, most of which are likely to be associated with background objects, including active galactic nuclei, galaxies, and clusters or groups of galaxies, as opposed to intrinsic gas clumps. These sources had minimal impact on the surface brightness and thermodynamic profiles of the cluster emission. After correcting for clump candidates, the measured entropy profile still deviates from a pure gravitational collapse, suggesting complex physics at play in the outskirts, which may include potential electron–ion non-equilibrium and non-thermal pressure support.

A population of Type Ibc supernovae with massive progenitors

If high-mass stars (≳20 − 25 M⊙) are the progenitors of stripped-envelope (SE) supernovae (SNe), their massive ejecta should lead to broad, long-duration lightcurves. Instead, literature samples of SE SNe have reported relatively narrow lightcurves corresponding to ejecta masses between 1 − 4 M⊙ that favor intermediate-mass progenitors (≲20 − 25 M⊙). Working with an untargeted sample from a single telescope to better constrain their rates, we searched the Palomar Transient Factory (PTF) and intermediate-PTF (iPTF) sample of SNe for SE SNe with broad lightcurves. Using a simple observational marker of g- or r-band lightcurve stretch compared to a template to measure broadness, we identified eight significantly broader Type Ibc SNe after applying quantitative sample selection criteria. The lightcurves, broad-band colors, and spectra of these SNe are found to evolve more slowly relative to typical Type Ibc SNe, proportional with the stretch parameter. Bolometric lightcurve modeling and their nebular spectra indicate high ejecta masses and nickel masses, assuming radioactive decay powering. Additionally, these objects are preferentially located in low-metallicity host galaxies with high star formation rates, which may account for their massive progenitors, as well as their relative absence from the literature. Our study thus supports the link between broad lightcurves (as measured by stretch) and high-mass progenitor stars in SE SNe with independent evidence from bolometric lightcurve modeling, nebular spectra, host environment properties, and photometric evolution. In the first systematic search of its kind using an untargeted sample, we used the stretch distribution to identify a higher than previously appreciated fraction of SE SNe with broad lightcurves (∼13%). Correcting for Malmquist and lightcurve duration observational biases, we conservatively estimate that a minimum of ∼6% of SE SNe are consistent with high-mass progenitors. This result has implications for the progenitor channels of SE SNe, including late stages of massive stellar evolution, the origin of the observed oxygen fraction in the universe, and formation channels for stellar-mass black holes.