bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effect of Mindfulness-Based Interventions on Stress, Depression and Anxiety During the Perinatal Period in Women Without Pre-existing Stress, Depressive or Anxiety Disorders: a Systematic Review and Meta-analysis of Controlled Trials
Authors: Corbally Linda , Wilkinson Mick 
Published: July 2021

Abstract
Objectives
The objective of this systematic review and meta-analysis was to examine controlled trial evidence for the effectiveness of mindfulness-based interventions on stress, anxiety and depression in the perinatal period in women without pre-existing mental health issues.

Methods
Six databases were searched for studies exploring the effects of mindfulness-based interventions on mental health outcomes of women during the perinatal period. Quality of both controlled trial meeting inclusion criteria were assessed using a tool specifically designed for meta-analyses of mindfulness-based interventions. Effect sizes were extracted for measures of mindfulness, depression, stress and anxiety outcomes. Effects were pooled in separate meta-analyses for all outcomes except anxiety which lacked sufficient studies.

Results
Twelve studies were analysed. Pooled effects suggest that mindfulness-based interventions cause small but clear increases in mindfulness and reductions in depression in women without pre-existing disorders. Effects of mindfulness-based interventions on other outcomes were unclear and confounded by heterogeneity.

Conclusions
Available controlled trial evidence suggests that mindfulness-based interventions improve mindfulness and decrease symptoms of depression during pregnancy in women without pre-existing mental health issues and might be a useful approach to prevent or attenuate the development of depression in the perinatal period.
An IoT enabled system for enhanced air quality monitoring and prediction on the edge
Authors: Moursi Ahmed Samy , El-Fishawy Nawal , Djahel Soufiene , Shouman Marwa Ahmed 
Published: July 2021

AbstractAir pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry Pi to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry Pi.
COVID-19 and Aspiration Pneumonia: Similar Pulmonary Findings with Different Diagnoses—a Pitfall in [18F]FDG PET/CT
Authors: Liberini Virginia , Grimaldi Serena , Huellner Martin W. , Giunta Francesca , Bachi Costanza , Dall’Armellina Sara , Onesti Federica , Ceci Francesco , Boccomini Carola , Icardi Massimiliano , Deandreis Désirée 
Published: July 2021

AbstractSince December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a worldwide pandemic. Especially in the centers most affected by the pandemic, symptoms (such as fever, cough, myalgia, or fatigue) and/or radiological signs (such as ground-glass opacity) typically related to COVID-19 often diverted clinicians’ attention from other diseases. Despite the urgency to recognize and cure SARS-CoV-2 infection, a plethora of differential diagnoses must be considered, and other diseases must be equally and promptly treated, as described in this case report.
FAIR TRANSITION FROM DEFINED BENEFIT TO TARGET BENEFIT
Authors: Zhu Xiaobai , Hardy Mary , Saunders David 
Published: July 2021

AbstractTarget benefit (TB) plans that incorporate intergenerational risk sharing have been demonstrated to be welfare improving over the long term. However, there has been little discussion of the short-term benefits for members in a defined benefit (DB) plan that is transitioning to TB. In this paper, we adopt a two-step approach that is designed to ensure the long-term sustainability of the new plan, without unduly sacrificing the benefit security of current retirees. We propose a cohort-based transition plan for reducing intergenerational inequity. Our study is based on simulations using an economic scenario generator with some theoretical results under simplified settings.
Small Vessel Vasculitis in Biopsies Of Anti-mog Encephalitis
Authors: Chaharyn Bradley M. , Dunham Christopher P. 
Published: July 2021

We report the neuropathology of two pediatric brain biopsy cases associated with serum anti-myelin oligodendrocyte glycoprotein (MOG) positivity. Descriptions of anti-MOG associated neuropathology are limited, with initial reports describing various patterns of inflammatory demyelination. Our first patient presented with confusion, speech abnormalities and personality changes following a treated strep throat infection. Our second patient had a past medical history of neurofibromatosis type 1 (NF1) and presented with hypersomnolence and focal neurological deficits. MRI abnormalities included diffuse scattered T2 FLAIR hyperintensities +/- enhancement. CSF was positive for anti-MOG antibodies in both cases, while one case exhibited additional anti-NMDA-R antibodies. Brain biopsies revealed vasocentric mononuclear inflammation featuring a predominance of lymphocytes that included intramural forms, as well as diffuse microglial activation, but no evidence of microglial nodules or microorganisms. One case demonstrated mild perivascular demyelination. The prevailing pattern in both cases was suggestive of “small vessel childhood primary angiitis of the central nervous system” (SVcPACNS). Our results parallel recent reports of anti-MOG neuropathology describing small vessel vasculitis, contrary to initial and subsequent reports that describe “encephalitis”. The foregoing suggests that the neuropathology associated with serum anti-MOG positivity may be broader than first appreciated. Moreover, this pattern of vasculitis might have implications for the natural history of this nascent disorder.Learning ObjectivesDefine anti-MOG encephalitis.Recognize the pathologic spectrum of reported cases of anti-MOG encephalitis.Contrast the pathologic features of pediatric and adult CNS vasculitis.Describe the histologic overlap of vasculitis and encephalitis.
Spectrum Of White Matter Changes In Ischemic Lesions
Authors: Alturkustani Murad , Ang Lee -Cyn 
Published: July 2021

Morphological studies on cerebral ischemia concentrate mainly on the grey matter and white matter changes are regarded as secondary or overlapping injuries. Immunohistochemical (IHC) studies to highlight the combination of various cellular changes in ischemic white matter but have not well documented. We selected 11 archival cases of 3 different ischemic processes (i.e. large vessel occlusion, small vessel occlusion, and hypoperfusion) with survival period range 2-35 days from the ischemic event. The white matter was examined using HE-LFB histochemistry, APP, GFAP, and HLA-DR immunostains focusing on myelin, axonal, astrocytic and microglial changes respectively. The various white matter changes are probably reflective of the different mechanism, duration, severity and extent of ischemia. The APP-IHC shows patchy axonal expression, swelling, and finally complete axonal loss. HLADR-IHC highlights early microglial injuries (fragmentation of processes), complete cell loss, and subsequent replacement by cells of macrophage phenotype. Surrounding the ischemic areas are reactive microglia. Astrocytic changes range from fragmentation of processes (clasmatodendrosis) to different stages of cell loss. Astrocytic swelling tends to occur with cerebral edema. Large vessel occlusion results in complete tissue loss while in small vessel disease the damage is more selective. The injury is generally more subtle in hypoperfusion but can be pronounced focally. Our study has documented the spectrum of white matter injury in different scenarios of cerebral ischemia.Learning ObjectiveDescribe the cellular and immunohistochemical changes in the ischemic white matter
Cortical Dysplasia Teaching Pathology to a Machine
Authors: Cosma Delaney , Khan Ali , Hammond Robert 
Published: July 2021

Many patients with epilepsy do not achieve adequate pharmacologic control of their seizures and must consider surgical options. Many such patients undergo temporal lobectomy and experience a marked reduction in the frequency and severity of their seizures. However, many are less fortunate. One suspected factor for the latter group is the limited ability of clinical imaging to delineate subtle epileptogenic abnormalities, leading to subtotal resection of lesional tissue. A long range goal in this field is to increase the sensitivity and specificity of detecting such abnormalities by “training” MRI with pathology, feature analysis and machine learning. A key component of this is the ability to segment histopathology to facilitate its mapping to co-registered MRI. A foundational step in this process is to determine whether or not algorithms are capable of detecting the architectural abnormalities in cortical dysplasia on the basis of these segmentations. In brief, reliable semi-automated segmentations were developed to extract a number of features including neuron size, clustering, eccentricity, field-fraction and polarity. Feature analyses using t-Distributed Stochastic Neighbor Embedding (t-SNE) plots demonstrate a non-random association between selected features and diagnostic categories. These results indicate that automated algorithms are capable of distinguishing dysplastic from normal cortex on the basis of semi-automated segmentations.Learning ObjectivesDescribe the value of segmentation in image analysisDefine the role of feature analysis such as t-SNE in high dimensionality histopathology data
Epidemiologic trends of hepatitis A in different age groups and regions of China from 1990 to 2018: observational population-based study
Authors: Zheng Bo , Wen Zexuan , Pan Jinhua , Wang Weibing 
Published: July 2021

Abstract
This study characterises changes in the incidence and mortality of hepatitis A in different age groups and provinces of China from 1990 to 2018, and evaluates the effect of the nation-wide expanded programme on immunisation (EPI). A mathematical model was used to estimate the relative change in incidence and mortality in different provinces and age groups. Interrupted time series regression was applied to evaluate the impacts of the inclusion of vaccination in the EPI during 2007–2018. The geographic clustering of hepatitis A incidence was assessed using global Moran's I and changing trends over time were estimated using joinpoint regression analysis. Both the incidence (odds ratio (OR) for overall relative change: 0.86; 95% confidence interval (CI): 0.85–0.87; P < 0.0001) and the mortality rate (OR for overall relative change: 0.84; 95% CI: 0.83–0.85; P < 0.0001) decreased. Most age groups had significant declines in reported incidence over time. The incidence and mortality of hepatitis A significantly reduced after inclusion of hepatitis A vaccine in EPI, showing that the EPI strategy had a continuous effect on the decreasing trend of hepatitis A burden. Increasing the coverage rate of the vaccine and improving hygiene conditions are the key measures for the control of hepatitis A in China.
Socio-economic factors associated with the incidence of Shiga-toxin producing Escherichia coli (STEC) enteritis and cryptosporidiosis in the Republic of Ireland, 2008–2017
Authors: Cleary E. , Boudou M. , ÓhAiseadha C. , McKeown P. , Garvey P. , O'Dwyer J. , Hynds P. 
Published: July 2021

Abstract
The Republic of Ireland (ROI) currently reports the highest incidence rates of Shiga-toxin producing Escherichia coli (STEC) enteritis and cryptosporidiosis in Europe, with the spatial distribution of both infections exhibiting a clear urban/rural divide. To date, no investigation of the role of socio-demographic profile on the incidence of either infection in the ROI has been undertaken. The current study employed bivariate analyses and Random Forest classification to identify associations between individual components of a national deprivation index and spatially aggregated cases of STEC enteritis and cryptosporidiosis. Classification accuracies ranged from 78.2% (STEC, urban) to 90.6% (cryptosporidiosis, rural). STEC incidence was (negatively) associated with a mean number of persons per room and percentage of local authority housing in both urban and rural areas, addition to lower levels of education in rural areas, while lower unemployment rates were associated with both infections, irrespective of settlement type. Lower levels of third-level education were associated with cryptosporidiosis in rural areas only. This study highlights settlement-specific disparities with respect to education, unemployment and household composition, associated with the incidence of enteric infection. Study findings may be employed for improved risk communication and surveillance to safeguard public health across socio-demographic profiles.
Early-life disease environment and adult height in historical populations
Authors: Casal Bruno , Rivera Berta , Mourelle Estefanía 
Published: July 2021

AbstractNutrition and the incidence of diseases during early life are considered environmental factors that determine people’s height when they become adults. While there is extensive literature focusing on the relationship between physical growth, general mortality and infant mortality rates, few studies analyse the impact of certain disease groups on the final height of historical populations. Using regional mortality rates by causes of death, the main objective of this study is to determine the onset of the disease environment during early life for populations born in Spain between 1916 and 1930, and its relationship with the stature reached at 21 years of age. A population-averaged model is performed on epidemic-infectious, gastrointestinal, and congenital diseases during the gestation period and first year of life. The disease burden in early life had a statistically significant negative effect on adult stature. These results support the premise that an improvement in the disease environment could lead to a greater number of short children surviving and therefore a decrease in the average height.
Developmental variation in testosterone:cortisol ratio alters cortical- and amygdala-based cognitive processes
Authors: Lew Jimin , Jones Sherri Lee , Caccese Christina , Orfi Isobel , Little Charlotte , Botteron Kelly N. , McCracken James T. , Nguyen Tuong-Vi 
Published: July 2021

AbstractTestosterone (T) and cortisol (C) are the end products of neuroendocrine axes that interact with the process of shaping brain structure and function. Relative levels of T:C (TC ratio) may alter prefrontal–amygdala functional connectivity in adulthood. What remains unclear is whether TC-related effects are rooted to childhood and adolescence. We used a healthy cohort of 4–22-year-olds to test for associations between TC ratios, brain structure (amygdala volume, cortical thickness (CTh), and their coordinated growth), as well as cognitive and behavioral development. We found greater TC ratios to be associated with the growth of specific brain structures: 1) parietal CTh; 2) covariance of the amygdala with CTh in visual and somatosensory areas. These brain parameters were in turn associated with lower verbal/executive function and higher spatial working memory. In sum, individual TC profiles may confer a particular brain phenotype and set of cognitive strengths and vulnerabilities, prior to adulthood.
Examining Conditions Supporting the Development of Anomalous Charge Structures in Supercell Thunderstorms in the Southeastern United States
Authors: Stough Sarah M. , Carey Lawrence D. , Schultz Christopher J. , Cecil Daniel J. 
Published: July 2021

AbstractHypotheses regarding favorable conditions for anomalous charging have primarily resulted from studies within the Great Plains region of the United States, where the efficiency of warm precipitation processes is thought to be fundamental. Rare observations of anomalous charge structures in the Southeastern region challenge existing conceptual models used to explain anomalous charging. As a rigorous evaluation of conditions that support anomalous charge structures, environmental characteristics and bulk kinematic and microphysical properties of two normal and two anomalous supercell thunderstorms observed in the Southeast were compared. Within the anomalous supercells, greater quantities of precipitation ice were identified at higher altitudes and colder temperatures, suggesting a greater depth of riming growth and increased vertical transport of rimed hydrometeors. Deeper anomalous supercell updrafts were larger and stronger in the upper mixed‐phase and glaciated regions. However, normal supercells were characterized by more robust low‐level updrafts, resulting in comparable warm cloud residence times that suggested warm precipitation processes were not necessarily less efficient in the anomalous supercells. Indications of enhanced mixed‐phase liquid water content in favor of anomalous charging were observed in the anomalous supercells, though contrasts in related environmental parameters were not as large as observed in other comparative studies. Anomalous supercell environments were characterized by increased instability, shallower warm cloud depth, as well as lower relative humidity in the 700–500 mb layer. Evidence of impacts from dry air in anomalous storm structures suggested that water vapor content may have affected particle‐scale charge transfer in support of anomalous charge structure development.
Training Challenges and Opportunities amid the COVID-19 Pandemic: Perspectives From Pediatric Psychology Interns
Authors: Iskander Jeannette M. , Schaefer Megan R. , Kamara Dana , Tams Sean T. , Yardley Heather L. 
Published: July 2021

Objective: The COVID-19 pandemic presented many personal and professional challenges to pediatric psychology interns as they navigated new policies and provided clinical care via telehealth to patients and families during an unprecedented time. Method: This commentary explores the experiences of 4 pediatric psychology interns and their training director during the height of the pandemic with an emphasis on challenges, opportunities, and lessons learned relevant to internship training. Results: There were numerous challenges in adapting and completing training during the pandemic, such as the rapid transition to telehealth, loss of training opportunities, providing care to inpatient and high-risk patients, and increased personal stress and social isolation. Despite these challenges, there were unique opportunities that arose. Interns were given opportunities to learn a new modality of care (telehealth), expand their breadth of training, practice innovation, receive live supervision in unobtrusive ways, and actively support their medical colleagues. Conclusions: Looking toward the future of health care, interns will significantly benefit from the skills learned during this pandemic, particularly as skills in providing evidence-based care via telehealth will continue to benefit psychologists seeking to increase access to care for patients and families. Institutions providing pediatric psychology internship training may also benefit from incorporating these training opportunities and lessons learned into future internship training in order to best support interns’ professional and personal needs. Implications for Impact Statement Navigating internship during a crisis (i.e., the COVID-19 pandemic) may present both challenges and opportunities for interns and training programs. Training programs may consider ways to adapt their programs to ensure that training competencies are addressed.
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
Authors: Zhang Yijie , Liu Tairan , Singh Manmohan , Çetintaş Ege , Luo Yilin , Rivenson Yair , Larin Kirill V. , Ozcan Aydogan 
Published: July 2021

AbstractOptical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.