Unbiased Data Research Articles

Dual gradient method for ill-posed problems using multiple repeated measurement data

We consider determining -minimizing solutions of linear ill-posed problems , where is a bounded linear operator from a Banach space to a Hilbert space and is a proper strongly convex penalty function. Assuming that multiple repeated independent identically distributed unbiased data of y are available, we consider a dual gradient method to reconstruct the -minimizing solution using the average of these data. By terminating the method by either an a priori stopping rule or a statistical variant of the discrepancy principle, we provide the convergence analysis and derive convergence rates when the sought solution satisfies certain variational source conditions. Various numerical results are reported to test the performance of the method.

The evolution of the mitochondrial disease diagnostic odyssey

BackgroundMitochondrial diseases often require multiple years and clinicians to diagnose. We lack knowledge of the stages of this diagnostic odyssey, and factors that affect it. Our goals are to report the results of the 2018 Odyssey2 (OD2) survey of patients with a medical diagnosis of mitochondrial disease; and to propose steps to reduce the odyssey going forward, and procedures to evaluate them.MethodsData are from the NIH-funded NAMDC-RDCRN-UMDF OD2 survey (N = 215). The main outcomes are Time from symptom Onset to mitochondrial disease Diagnosis (TOD) and Number of Doctors Seen during this diagnostic process (NDOCS).ResultsExpert recoding increased analyzable responses by 34% for final mitochondrial diagnosis and 39% for prior non-mitochondrial diagnosis. Only one of 122 patients who initially saw a primary care physician (PCP) received a mitochondrial diagnosis, compared to 26 of 86 (30%) who initially saw a specialist (p < 0.001). Mean TOD overall was 9.9 ± 13.0 years, and mean NDOCS 6.7 ± 5.2. Mitochondrial diagnosis brings extensive benefits through treatment changes and increased membership in and support of advocacy groups.ConclusionsBecause TOD is long and NDOCS high, there is great potential for shortening the mitochondrial odyssey. Although prompt patient contact with primary mitochondrial disease specialists, or early implementation of appropriate tests, may shorten the diagnostic odyssey, specific proposals for improvement require testing and confirmation with adequately complete, unbiased data across all its stages, and appropriate methods. Electronic Health Record (EHRs) may help by accessing diagnostic codes early, but their reliability and diagnostic utility have not been established for this group of diseases.

"This Vilest of Trades"

This article examines the League of Nations’ official inquiry into the international question of trafficking, integral to their wider anti-trafficking campaign of the 1920’s. Through this, it assesses the consequences of employing external experts drafted from activist non-governmental organizations to conduct large-scale international research projects. In doing so it argues that the League’s official standards of unbiased data collection and knowledge production was somewhat compromised and that the leaders of the external research group used this official inquiry as a form of trojan horse to challenge and undermine the legitimacy of the system of regulated prostitution. Furthermore, this article unfolds how the research constructed new systems of knowledge about the nature of prostitution, that in turn positioned the system as a global threat with the potential to degenerate the future of humanity.

PLPose: An efficient framework for detecting power lines via key points-based pose estimation

The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and the inherent noisy image backgrounds. Traditional line and edge detection methods do not detect the PLs accurately due to the cluttered backgrounds while the more recent deep learning (DL) CNNs are also not feasible for efficient PL detection due to the coarse bounding boxes and the computationally expensive pixel-based segmentations. Hence, in this study we propose PLPose, a novel framework for detecting the PLs via key points-based pose estimation technique and adapt the MobileNetV3 CNN for this task (kMobileNetV3), by adding a simple key point detection head to predict the PL key points. We also introduce a novel data-centric architecture (kMobileNetV3 + UDP), by adding the unbiased data processing (UDP) module to our kMobileNetV3, for faster and more accurate key point-based PL detection along with novel methods for data annotations and performance evaluation. Evaluations of PLPose on three benchmark PL datasets (PLDM, PLDU and the Mendeley Powerline Dataset) reveal that our proposed framework outperforms the state-of-the-art top-down pose estimation networks (HRNet-w32, HRNet-w32 + UDP and Resnet-50 Simple Baselines) in processing speed (∼29 FPS) and model size (5.23 M) for PL detection. Thus, the comprehensive experimental results demonstrate the effectiveness of our proposed framework. Our code is available from Github (https://www.github.com/rubeea/pl_mmpose).

The Impact of Subjectivity on Financial Accounting

In this paper it is attempted to explore current trends in Financial Accounting, that connect research endeavors in the relatively new branch of Behavioral Finance, with the impact of subjectivity on the accounting cycle of each fiscal year, as well as on the preparation of financial statements.&#x0D; The investigation of subjective factors in Financial Accounting expands its scope, while the systematic utilization of its two-way relationship with the field of Neurofinance (unbiased data) may create a broader framework of knowledge for the further improvement of the investment process, through an understanding of the methodology required by accounting operations and rules up until the preparation of financial statements. By extension, a better understanding of how financial markets operate will be beneficial not only for governments wishing to promote savings incentives and responsible management, but also for supervisory authorities seeking to ensure that the market for personal financial services operates rationally and without bias. &#x0D; &#x0D; Keywords: Behavioral Finance, Neurofinance, Financial Accounting&#x0D; JEL Classification: D87, G41, M41

Exploring and verifying the acoustic presence of southern right whales (Eubalaena australis) off Elephant Island, Antarctica.

Passive acoustic monitoring (PAM) can be used to monitor acoustic presence and behaviour of cetaceans, providing continuous, long-term, and seasonally unbiased data. The efficiency of PAM methods, however, depends on the ability to detect and correctly interpret acoustic signals. The upcall is the most prevalent vocalization of the southern right whale (Eubalaena australis) and is commonly used as a basis for PAM studies on this species. However, previous studies report difficulties to distinguish between southern right whale upcalls and similar humpback whale (Megaptera novaeangliae) vocalizations with certainty. Recently, vocalizations comparable to southern right whale upcalls were detected off Elephant Island, Antarctica. In this study, these vocalizations were structurally analyzed, and call characteristics were compared to (a) confirmed southern right whale vocalizations recorded off Argentina and (b) confirmed humpback whale vocalizations recorded in the Atlantic Sector of the Southern Ocean. Based on call features, detected upcalls off Elephant Island could be successfully attributed to southern right whales. Measurements describing slope and bandwidth were identified as the main differences in call characteristics between species. With the newly gained knowledge from this study, additional data can be analyzed providing further insight into temporal occurrence and migratory behaviour of southern right whales in Antarctic waters.

Accelerated design of high entropy alloys by integrating high throughput calculation and machine learning

Designing high entropy alloys (HEAs) from a large compositional space is challenging. The design process is accelerated by integrating computational techniques. The current study developed a framework for the accelerated identification of phases in HEAs by integrating high throughput calculations and machine learning (ML). The 19968 composition information was generated using high throughput CALculation of PHAse Diagrams (CALPHAD) calculations and used as a dataset for the machine learning algorithms. The Synthetic Minority Over-sampling Technique (SMOTE) makes unbiased data, and multiple machine learning algorithms are trained and compared. The Support Vector Machine (SVM) and Artificial Neural Network (NN) show test accuracy of ∼99%. The trained algorithm was used to predict phases in ternary, quaternary and quinary systems. The experimental comparison confirms that the algorithms successfully predicted the phase transitions in HEAs with respect to varying compositions. The developed framework can be adopted for designing new materials.

Multiclass semantic segmentation mediated neuropathological readout in Parkinson's disease

Automated segmentation of anatomical sub-regions with high precision has become a necessity to enable the quantification and characterization of cells/ tissues in histology images. An automated model to do this task is currently unavailable. One area of the brain which requires precise sub-region segmentation and downstream analysis is Substantia Nigra (SN). The loss of dopaminergic (DA) neurons in SN is the primary endpoint for majority of Parkinson's disease (PD) preclinical studies. The scientists rely on manually segmenting anatomical sub-regions of the brain which is extremely time-consuming and prone to labeler-dependent bias. In this study, we employed a UNet-based architecture to segment two sub-regions of SN-dorsal tier of substantia nigra pars compacta (SNCD) and reticulata (SNr). We compared model performance with various combinations of encoders, image sizes and sample selection techniques. The model is trained on approximately one thousand annotated 2D brain images stained with Nissl/ Haematoxylin and Tyrosine Hydroxylase enzyme (TH, indicator of dopaminergic neuron viability). The framework's output are: segmentation of SNr and SNCD irrespective of the tissue staining, quantitative readout for TH intensity indicating DA health status in the segmented regions. With the availability of training data, this model can be expanded to other 2D sub-region segmentation tasks. The shorter turnaround time, high accuracy and unbiased data output of this model will fulfill the ever increasing demands of data analysis in PD preclinical research.

On-skin biosensors for noninvasive monitoring of postoperative free flaps and replanted digits

Severe soft tissue defects and amputated digits are clinically common injuries. Primary treatments include surgical free flap transfer and digit replantation, but these can fail because of vascular compromise. Postoperative monitoring is therefore crucial for timely detection of vessel obstruction and survival of replanted digits and free flaps. However, current postoperative clinical monitoring methods are labor intensive and highly dependent on the experience of nurses and surgeons. Here, we developed on-skin biosensors for noninvasive and wireless postoperative monitoring based on pulse oximetry. The on-skin biosensor was made of polydimethylsiloxane with gradient cross-linking to create a self-adhesive and mechanically robust substrate that interfaces with skin. The substrate was shown to exhibit appropriate adhesion on one side for both high-fidelity measurements of the sensor and low risk of peeling injury to delicate tissues. The other side demonstrated mechanical integrity to facilitate flexible hybrid integration of the sensor. Validation studies using a model of vascular obstruction in rats demonstrated the effectiveness of the sensor in vivo. Clinical studies indicated that the on-skin biosensor was accurate and more responsive than current clinical monitoring methods in identifying microvascular conditions. Comparisons with existing monitoring techniques, including laser Doppler flowmetry and micro-lightguide spectrophotometry, further verified the sensor's accuracy and ability to identify both arterial and venous insufficiency. These findings suggest that this on-skin biosensor may improve postoperative outcomes in free flap and replanted digit surgeries by providing sensitive and unbiased data directly from the surgical site that can be remotely monitored.

Comparison of Compartmental and Non-Compartmental Analysis to Detect Biopharmaceutical Similarity of Intravenous Nanomaterial-Based Rifabutin Formulations.

Pharmacometric analysis is often used to quantify the differences and similarities between formulation prototypes. In the regulatory framework, it plays a significant role in the evaluation of bioequivalence. While non-compartmental analysis provides an unbiased data evaluation, mechanistic compartmental models such as the physiologically-based nanocarrier biopharmaceutics model promise improved sensitivity and resolution for the underlying causes of inequivalence. In the present investigation, both techniques were applied to two nanomaterial-based formulations for intravenous injection, namely, albumin-stabilized rifabutin nanoparticles and rifabutin-loaded PLGA nanoparticles. The antibiotic rifabutin holds great potential for the treatment of severe and acute infections of patients co-infected with human immunodeficiency virus and tuberculosis. The formulations differ significantly in their formulation and material attributes, resulting in an altered biodistribution pattern as confirmed in a biodistribution study in rats. The albumin-stabilized delivery system further undergoes a dose-dependent change in particle size which leads to a small yet significant change in the in vivo performance. A second analysis was conducted comparing the dose fraction-scaled pharmacokinetic profiles of three dose levels of albumin-stabilized rifabutin nanoparticles. The dose strength affects both the nanomaterial-related absorption and biodistribution of the carrier as well as the drug-related distribution and elimination parameters, increasing the background noise and difficulty of detecting inequivalence. Depending on the pharmacokinetic parameter (e.g., AUC, Cmax, Clobs), the relative (percentage) difference from the average observed using non-compartmental modeling ranged from 85% to 5.2%. A change in the formulation type (PLGA nanoparticles vs. albumin-stabilized rifabutin nanoparticles) resulted in a similar level of inequivalence as compared to a change in the dose strength. A mechanistic compartmental analysis using the physiologically-based nanocarrier biopharmaceutics model led to an average difference of 152.46% between the two formulation prototypes. Albumin-stabilized rifabutin nanoparticles tested at different dose levels led to a 128.30% difference, potentially due to changes in particle size. A comparison of different dose strengths of PLGA nanoparticles, on average, led to a 3.87% difference. This study impressively illustrates the superior sensitivity of mechanistic compartmental analysis when dealing with nanomedicines.

Optimised protocol for monitoring SARS-CoV-2 in wastewater using reverse complement PCR-based whole-genome sequencing.

Monitoring the spread of viral pathogens in the population during epidemics is crucial for mounting an effective public health response. Understanding the viral lineages that constitute the infections in a population can uncover the origins and transmission patterns of outbreaks and detect the emergence of novel variants that may impact the course of an epidemic. Population-level surveillance of viruses through genomic sequencing of wastewater captures unbiased lineage data, including cryptic asymptomatic and undiagnosed infections, and has been shown to detect infection outbreaks and novel variant emergence before detection in clinical samples. Here, we present an optimised protocol for quantification and sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in influent wastewater, used for high-throughput genomic surveillance in England during the COVID-19 pandemic. This protocol utilises reverse compliment PCR for library preparation, enabling tiled amplification across the whole viral genome and sequencing adapter addition in a single step to enhance efficiency. Sequencing of synthetic SARS-CoV-2 RNA provided evidence validating the efficacy of this protocol, while data from high-throughput sequencing of wastewater samples demonstrated the sensitivity of this method. We also provided guidance on the quality control steps required during library preparation and data analysis. Overall, this represents an effective method for high-throughput sequencing of SARS-CoV-2 in wastewater which can be applied to other viruses and pathogens of humans and animals.

Comparing methods to quantify cover in turfgrass research

AbstractTurfgrass cover can be assessed qualitatively using visual ratings, but quantitative turfgrass cover measurements are desired for producing unbiased data. Digital image analysis and point intercept are two quantitative percent cover data collection methods used in turfgrass research. A potential weakness of digital image analysis is the difficulty in evaluating color variation. Considering this, a series of controlled environment and field experiments were conducted to evaluate the accuracy of the point intercept method compared to using a digital grid, digital image analysis, and visual ratings when utilized in turfgrass research. To explore this topic, four experiments were conducted in Corvallis, OR, and one in New Brunswick, NJ. Results from research conducted in Corvallis, OR, determined that the closest point intercept spacing (2.5 cm) resulted in a percent cover that was not statistically different from the known cover and the lowest variance, but the greatest amount of time required to collect the data. Digital image analysis was the most consistent method for measuring percent cover when a known percent cover was being quantified. The estimate of the percent dollar spot cover was reduced by a single pigment application regardless of the data collection method. In the absence of pigment applications, the point intercept frame, digital image analysis, and visual ratings were well‐correlated methods used to assess dollar spot cover. Digital image analysis was not correlated with the other data collection methods when used to evaluate percent anthracnose cover. The research conducted in New Brunswick, NJ, determined that the point intercept method and digital image analysis are well‐correlated methods for quantifying turfgrass establishment from seed.

Abstract 5793: Met confers radioresistance-associated aggressiveness through enhancing PLXDC2-mediated cancer stem cell plasticity

Abstract The development of radioresistance in head and neck squamous cell carcinoma (HNSCC) remains a significant problem in cancer treatment, contributing to the lack of improvement in survival trends in the past decades. One of the clinical challenges is that radioresistance often promotes tumor aggressiveness. However, the underlying mechanisms and molecular determinants are largely unknown. We report here that radioresistance-associated HNSCC aggressiveness is effectively exacerbated by c-Met but can be suppressed by its genetic knockdown and pharmacological inactivation. Through unbiased RNAseq data, we further uncovered that the resulting upregulation of c-Met signaling increases the expression of PLXDC2. a critical gene associated with the tumor microenvironment. Mechanistically, PLXDC2 is upregulated in radioresistant HNSCC cells through c-Met mediated activation of ERK1/2-ELK1 signaling cascade. Most importantly, PLXDC2 modulates cancer cell plasticity by inducing epithelial-mesenchymal transition (EMT) and the emergence of cancer stem cell (CSC) subpopulation. Additionally, depletion of PLXDC2 overcomes c-Met-mediated radioresistance by reversing the EMT progress and blunting the self-renewal capacity of CSCs. Therapeutically, a combination of the c-Met selective inhibitor SU11274 with radiation remarkably induces tumor shrinkage and constrains tumor metastasis to lymph nodes in vivo. Our study is novel in being the first to explore the role and mechanism of the c-Met-PLXDC2 axis in radioresistance-associated HNSCC aggressiveness and the first to our knowledge to evaluate how to take advantage of blocking this signaling to overcome radioresistance in preclinical mouse models of HNSCC. Citation Format: Fanghui Chen, Liwei Lang, Chloe Shay, Georgia Chen, Nabil Saba, Yong Teng. Met confers radioresistance-associated aggressiveness through enhancing PLXDC2-mediated cancer stem cell plasticity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5793.

Abstract 2794: A Quantitative Systems Pharmacology (QSP) model to characterize dose-dependent antitumor activity of AZD5305, PARP1 selective inhibitor, across multiple xenograft models

Abstract AZD5305 is a potent and selective PARP1 inhibitor and trapper which is hypothesized to improve therapeutic index over first generation nonselective PARP inhibitors. AZD5305 demonstrated significant and sustained antitumor activity in multiple BRCA1/2 mutant xenograft models. Here we present a mechanistic Quantitative Systems Pharmacology (QSP) model to analyze dose-dependent antitumor activity of AZD5305 (0.01-10 mg/kg) across a selection of xenograft models with different homologous recombination repair (HRR) status (Capan-1, DLD-1 BRCA2 KO, HBCx-9, HBCx-17, MDA-MB-436 and SUM149PT). A QSP model was developed based on a system of ordinary differential equations (ODEs) to address formation and repair of trapped PARP-DNA fragments and longitudinal changes in tumor size as a function of pharmacokinetic (PK) profiles in individual animals. Tumor growth data as well as intratumoral PARylation inhibition from xenograft models were utilized for model development and qualification. Model parameters characterizing intrinsic tumor growth and cancer cell sensitivity to accumulated DNA damage, were set to be different across xenograft models, to provide unbiased data reproduction. Sensitivity analyses were performed to identify model parameters which have the most impact on differential antitumor activity observed across various xenograft models. Maximal antitumor efficacy was seen at 0.1 to 1 mg/kg AZD5305, depending on the tumor model. Exposures at 1 mg/kg were similar to those causing peak PARP1 trapping in vitro. The QSP model adequately captures antitumor activity across different xenograft models. Simulations indicate antitumor activity of AZD5305 was driven mainly by differences in the HRR status-related model parameter (khrr). Xenograft models with HRR deficiency such as HBCx-17, DLD-1 BRCA2 KO and MDA-MB-436 (with a very low khrr) were the most sensitive to AZD5305 and treatment led to tumor regressions. In contrast, tumor models with partial sensitivity, such as HBCx-9, Capan-1, SUM149PT (with khrr up to 1000-fold higher than in the sensitive tumors), AZD5305 only achieved tumor growth inhibition. Dosing AZD5305 at 0.03 mg/kg daily was associated with tumor regression in HBCx-17 and MDA-MB-436 xenografts, whereas 1 mg/kg daily dosing was required to achieve tumor regression in the DLD-1 BRCA2 KO model, and maximal tumor growth inhibition in less sensitive models. Further biomarker analyses to assess functional HRR status (e.g. via RAD51 foci score) in these xenograft models is ongoing to validate model estimated khrr parameters. The calibrated model was used to predict antitumor activity of AZD5305 at clinically relevant exposures observed in the phase I clinical study PETRA. Model-based simulations indicated near maximal efficacy at clinical doses equivalent to 1 mg/kg AZD5305 exposure in xenograft models. Citation Format: Ganesh Moorthy, Veronika Voronova, Cesar Pichardo, Kirill Peskov, Giuditta Illuzzi, Anna Staniszewska, Mark Albertella, Holly Kimko. A Quantitative Systems Pharmacology (QSP) model to characterize dose-dependent antitumor activity of AZD5305, PARP1 selective inhibitor, across multiple xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2794.

PWN: enhanced random walk on a warped network for disease target prioritization

BackgroundExtracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks.ResultsWe developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge.ConclusionsWe showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

A robust fusion-extraction procedure with summary statistics in the presence of biased sources

Summary Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption or model misspecification. Thus there is a need for robust data combination methods that can be used with biased sources. In this paper, a robust data fusion-extraction method is proposed. Unlike existing methods, the proposed method can be applied in the important case where researchers have no knowledge of which data sources are unbiased. The proposed estimator is easy to compute and employs only summary statistics; hence it can be applied in many different fields, such as meta-analysis, Mendelian randomization and distributed systems. The proposed estimator is consistent, even if many data sources are biased, and is asymptotically equivalent to the oracle estimator that uses only unbiased data. Asymptotic normality of the proposed estimator is also established. In contrast to existing meta-analysis methods, the theoretical properties are guaranteed for our estimator, even if the number of data sources and the dimension of the parameter diverge as the sample size increases. Furthermore, the proposed method provides consistent selection for unbiased data sources with probability approaching 1. Simulation studies demonstrate the efficiency and robustness of the proposed method empirically. The method is applied to a meta-analysis dataset to evaluate surgical treatment for moderate periodontal disease and to a Mendelian randomization dataset to study the risk factors for head and neck cancer.

Comparing GPS collar and fecal sampling using detection dogs for habitat selection analysis in brocket deer (Mazama)

Abstract Habitat use data are key to understanding species ecology and extinction risk. However, such information is lacking for the elusive deer species of Neotropical region. In this context, fecal sampling has emerged as an alternative tool, in which development and evaluation are essential to obtaining unbiased ecological data. We aimed to compare data from GPS-tracked animals and fecal sampling using scat detection dogs to evaluate the noninvasive performance of this method in habitat selection analysis. We carried out the study in the Brazilian Pantanal, where we monitored six free-living Gray Brocket Deer (Mazama gouazoubira) with GPS collars for 1 year (average of 584 GPS locations/animal) and collected fecal samples (n = 649) simultaneously along a set of transects designed for a scat detection dog survey. We evaluated habitat selection using the chi-square test in an availability/utilization analysis and submitted both data to a bootstrap procedure to assess its precision and accuracy with increasing sample size. GPS data indicated habitat selection at a fine utilization scale, in which savanna and cerrado were preferred and open grassland habitat was avoided. Exclusive fecal sampling also indicated habitat selection, revealing the preference for cerrado and avoidance of open grassland. The GPS and fecal habitat utilization estimates did not differ significantly and fecal sampling increased precision and accuracy with increased sample size, reaching minimal values once n = 200 which should be considered a sufficient survey effort. The similarity between the two methods suggested the reliability of fecal sampling, as long as a standardized sampling design is used. This noninvasive sampling framework can provide previously unavailable ecological data for threatened Neotropical deer as well as other elusive species.

The bias for statistical significance in sport and exercise medicine.

We aimed to examine the bias for statistical significance using published confidence intervals in sport and exercise medicine research. Observational study. The abstracts of 48,390 articles, published in 18 sports and exercise medicine journals between 2002 and 2022, were searched using a validated text-mining algorithm that identified and extracted ratio confidence intervals (odds, hazard, and risk ratios). The algorithm identified 1744 abstracts that included ratio confidence intervals, from which 4484 intervals were extracted. After excluding ineligible intervals, the analysis used 3819 intervals, reported as 95 % confidence intervals, from 1599 articles. The cumulative distributions of lower and upper confidence limits were plotted to identify any abnormal patterns, particularly around a ratio of 1 (the null hypothesis). The distributions were compared to those from unbiased reference data, which was not subjected to p-hacking or publication bias. A bias for statistical significance was further investigated using a histogram plot of z-values calculated from the extracted 95 % confidence intervals. There was a marked change in the cumulative distribution of lower and upper bound intervals just over and just under a ratio of 1. The bias for statistical significance was also clear in a stark under-representation of z-values between -1.96 and +1.96, corresponding to p-values above 0.05. There was an excess of published research with statistically significant results just below the standard significance threshold of 0.05, which is indicative of publication bias. Transparent research practices, including the use of registered reports, are needed to reduce the bias in published research.

Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

BackgroundPrenatal stress (PS) is considered as a risk factor for many mental disorders. PS-induced transcriptomic alterations may contribute to the functional dysregulation during brain development. Here, we used RNA-seq to explore changes of gene expression in the mouse fetal brain after prenatal exposure to chronic unpredictable mild stress (CUMS).ResultsWe compared the stressed brains to the controls and identified groups of significantly differentially expressed genes (DEGs). GO analysis on up-regulated DEGs revealed enrichment for the cell cycle pathways, while down-regulated DEGs were mostly enriched in the neuronal pathways related to synaptic transmission. We further performed cell-type enrichment analysis using published scRNA-seq data from the fetal mouse brain and revealed cell-type-specificity for up- and down-regulated DEGs, respectively. The up-regulated DEGs were highly enriched in the radial glia, while down-regulated DEGs were enriched in different types of neurons. Cell deconvolution analysis further showed altered cell fractions in the stressed brain, indicating accumulation of neuroblast and impaired neurogenesis. Moreover, we also observed distinct brain-region expression pattern when mapping DEGs onto the developing Allen brain atlas. The up-regulated DEGs were primarily enriched in the dorsal forebrain regions including the cortical plate and hippocampal formation. Surprisingly, down-regulated DEGs were found excluded from the cortical region, but highly expressed on various regions in the ventral forebrain, midbrain and hindbrain.ConclusionTaken together, we provided an unbiased data source for transcriptomic alterations of the whole fetal brain after chronic PS, and reported differential cell-type and brain-region vulnerability of the developing brain in response to environmental insults during the pregnancy.

Global, regional, and national incidence and prevalence of systemic sclerosis

ObjectivesTo estimate the global and country-specific unbiased epidemiological data of SSc. MethodsEpidemiological studies were systematically searched in four databases. A Bayesian hierarchical linear mixed model was constructed to estimate epidemiological data. Results82 studies were included and epidemiological data on SSc were missing for 83.9% of countries worldwide. The global SSc incidence and newly diagnosed population were estimated to be 8.64 per 100,000 person-years (1.78–23.57) and 0.67 million (0.14–1.84) people annually, respectively. Regarding prevalence, the global SSc prevalence and affected population were 18.87 per 100,000 persons (1.55–25.28) and 1.47 million (0.12–1.97) people, respectively. Relatively higher incidence and prevalence were observed in females, adults, and high-income level countries. ConclusionsWe provide a comprehensive synthesis of SSc epidemiology and fill data gaps in most countries. Especially in low- and middle-income countries, epidemiological studies of SSc are insufficient. Further large-scale and standardized reported epidemiological investigations of SSc are imperative.