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The article examines that Anthony Trollope, George Meredith, Mark Rutherford, and others followed Disraeli formula, mixing narratives of male self-development and political careerism. Topics include reports that scholars have demonstrated that women had a significant impact on electoral politics itself as canvassing, organizing fund-raising events, lobbying for votes, even orchestrating electoral chicanery.

The standard image of First World War soldiers is of men in open trenches: waiting to attack or be attacked; walking, sitting, sleeping, dead. Ford's Parade's End includes such scenes. But it is a different kind of image which predominates in his war writings and often produces its most memorable passages: images of houses or house-like shelters. The mind seeks protection in such structures; but they offer little security against the destructiveness outside, against the bombardments, gas, shrapnel, bullets. Ford wrote that the experience of war revealed: 'men's dwellings were thin shells that could be crushed as walnuts are crushed. ... all things that lived and moved and had volition and life might at any moment be resolved into a scarlet viscosity seeping into the earth of torn fields [...]'. This realisation works in two ways. The soldier's sense of vulnerability provokes fantasies of home, solidity, sanctuary, while for the returnee soldier, domestic architecture summons war-visions of its own annihilation: 'it had been revealed to you', adds Ford, 'that beneath Ordered Life itself was stretched, the merest film with, beneath it, the abysses of Chaos'. It is now customary to read war literature through trauma theory. Building on analyses of Ford's use of repression, but drawing instead on object relations theory, I argue that Ford's houses of war are not screen memories but images of the failure of repression to screen off devastating experiences. The abysses of Chaos can be seen through the screen or projected upon it. Attending to Ford's handling of this theme enables a new reading of his war writing and a new case for its coherence. The essay will connect the opening of No More Parades (in a hut, during a bombardment) with the war poem 'The Old Houses of Flanders'; the postwar poem A House; the memoir It Was the Nightingale (quoted above); and the otherwise puzzling, fictionalised memoir No Enemy, structured in terms of 'Four Landscapes' and 'Certain Interiors'. [ABSTRACT FROM AUTHOR]

Discusses the problems with autobiographical fictions and fictional autobiographies, focusing on works such as those of Mark Rutherford. Inadequacy of the anonymity or pseudonymity as descriptive terms; Analysis of several autobiographies including 'The Autobiography of Mark Rutherford,' 'Clara Hopgood' and 'Miriam's Schooling and Other Papers.'

Reducing methane (CH4) emissions from the oil and gas (O&G) sector is key to mitigating climate change in the near-term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release to the atmosphere. MethaneSAT data will help advocacy and policy efforts to help track methane reduction commitments and targets made by countries and industry. Here we introduce the CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite with similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of the MethaneAIR data shows that measurement precision is typically better than 2 % for 20 × 20 m2 pixel resolution, with no strong dependence on geophysical variables such as surface reflectance. We show that detector focus drifts over the course of each flight likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory tabulated instrument spectral response function in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 %. MethaneAIR retrievals were also intercompared with those of TROPOMI; the latitudinal gradients for the two datasets are in good agreement, with a 2.5 ppb mean bias between instruments. We evaluate the accuracy of MethaneAIR estimates of point source emissions using observations made over the Permian O&G basin, based on the integrated mass enhancement approach coupled with a plume-masking algorithm based on total variational denoising. We estimate that the median point source detection threshold is 100–150 kg h−1 at the aircraft's nominal 12 km above-surface observation altitude, based on an ensemble WRF large eddy simulations used to mimic the campaign conditions with the threshold for quantification about 2× the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source we infer emissions from a large O&G processing facility, and estimate a leak rate between 1.6 and 2.1 %, higher than any previously-reported emission from a facility of its size. We also identify a ruptured pipeline that alone would constitute 2 % of estimated basin emissions, two weeks before it was found by its operator, highlighting the importance of regular monitoring from the future satellite mission. The results showcase the capability of MethaneAIR to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with fine spatial resolution over large swaths on the ground. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130 × 400 m2), thereby delivering quantitative data on basin-wide methane emissions. [ABSTRACT FROM AUTHOR]

Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been developed to study neurite morphology in a high-throughput and comprehensive manner. These approaches include computer-automated algorithms known as 'convolutional neural networks' (CNNs)—powerful models capable of learning complex tasks without the biases of hand-crafted models. Nevertheless, their complexity often relegates them to functioning as 'black boxes.' Therefore, research in the field of explainable AI is imperative to comprehend the relationship between CNN image analysis output and predefined morphological parameters of neurite growth in order to assess the applicability of these machine learning approaches. In this study, drawing inspiration from the field of automated feature selection, we investigate the correlation between quantified metrics of neurite morphology and the image analysis results from NeuriteNet—a CNN developed to analyze neurite growth. NeuriteNet accurately distinguishes images of neurite growth based on different treatment groups within two separate experimental systems. These systems differentiate between neurons cultured on different substrate conditions and neurons subjected to drug treatment inhibiting neurite outgrowth. By examining the model's function and patterns of activation underlying its classification decisions, we discover that NeuriteNet focuses on aspects of neuron morphology that represent quantifiable metrics distinguishing these groups. Additionally, it incorporates factors that are not encompassed by neuron morphology tracing analyses. NeuriteNet presents a novel tool ideally suited for screening morphological differences in heterogeneous neuron groups while also providing impetus for targeted follow-up studies. [ABSTRACT FROM AUTHOR]

The aim of the study is to provide a comprehensive assessment of incidence and survival trends of epithelial ovarian cancer (EOC) by histological subtype across seven high income countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the United Kingdom). Data on invasive EOC diagnosed in women aged 15 to 99 years during 1995 to 2014 were obtained from 20 cancer registries. Age standardized incidence rates and average annual percentage change were calculated by subtype for all ages and age groups (15‐64 and 65‐99 years). Net survival (NS) was estimated by subtype, age group and 5‐year period using Pohar‐Perme estimator. Our findings showed marked increase in serous carcinoma incidence was observed between 1995 and 2014 among women aged 65 to 99 years with average annual increase ranging between 2.2% and 5.8%. We documented a marked decrease in the incidence of adenocarcinoma "not otherwise specified" with estimates ranging between 4.4% and 7.4% in women aged 15 to 64 years and between 2.0% and 3.7% among the older age group. Improved survival, combining all EOC subtypes, was observed for all ages combined over the 20‐year study period in all countries with 5‐year NS absolute percent change ranging between 5.0 in Canada and 12.6 in Denmark. Several factors such as changes in guidelines and advancement in diagnostic tools may potentially influence the observed shift in histological subtypes and temporal trends. Progress in clinical management and treatment over the past decades potentially plays a role in the observed improvements in EOC survival. [ABSTRACT FROM AUTHOR]

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image. This study presents the Hair Cell Analysis Toolbox (HCAT), which automates common image analysis tasks such as counting hair cells, classifying them by subtype and determining their optimal frequency. HCAT removes a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. [ABSTRACT FROM AUTHOR]

The small, idyllic family farms that come to mind at the first mention of farming are all but gone, replaced by enormous factories that churn out animals at record speed, with little regard for their health and welfare. These factory farms produce a host of issues, including pervasive water and air pollution, particularly in vulnerable agricultural communities like those of the San Joaquin Valley in California. While the detriments of the factory farm model are numerous, contribution to climate change in particular has garnered significant attention. Animal agriculture in the U.S. produces 36% of the country's methane, a greenhouse gas significantly more potent than carbon dioxide. Despite the myriad of problems posed by factory farms, industry has focused its attention on the climate change impacts of these enormous operations, and now purports to have the solution--anaerobic digesters. This technology captures methane from animal waste and produces biogas, an energy source that can be used much like natural gas. States like California heavily incentivize this otherwise cost prohibitive technology. This Article argues anaerobic digesters are a false solution to factory farms. It posits that by promoting them, California fails to acknowledge the greater environmental threat factory farms pose to the environment and the environmental justice concerns surrounding the expansion of animal agriculture spurred by digesters. Accordingly, this Article examines a number of possible solutions to the recent growth of anaerobic digesters in California, including potential improvements to California's environmental justice legislation, possible redress using environmental litigation, and a possible challenge to the funding driving anaerobic digester growth in California. Ultimately, this Article concludes that California should cease supporting anaerobic digesters and should focus instead on funding holistic solutions to factory farm issues in order to safeguard California's most vulnerable communities. [ABSTRACT FROM AUTHOR]

Introduction: Lung cancer has a poor prognosis that varies internationally when assessed by the two major histological subgroups (non-small cell (NSCLC) and small cell (SCLC)).Method: 236 114 NSCLC and 43 167 SCLC cases diagnosed during 2010-2014 in Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK were included in the analyses. One-year and 3-year age-standardised net survival (NS) was estimated by sex, histological type, stage and country.Results: One-year and 3-year NS was consistently higher for Canada and Norway, and lower for the UK, New Zealand and Ireland, irrespective of stage at diagnosis. Three-year NS for NSCLC ranged from 19.7% for the UK to 27.1% for Canada for men and was consistently higher for women (25.3% in the UK; 35.0% in Canada) partly because men were diagnosed at more advanced stages. International differences in survival for NSCLC were largest for regional stage and smallest at the advanced stage. For SCLC, 3-year NS also showed a clear female advantage with the highest being for Canada (13.8% for women; 9.1% for men) and Norway (12.8% for women; 9.7% for men).Conclusion: Distribution of stage at diagnosis among lung cancer cases differed by sex, histological subtype and country, which may partly explain observed survival differences. Yet, survival differences were also observed within stages, suggesting that quality of treatment, healthcare system factors and prevalence of comorbid conditions may also influence survival. Other possible explanations include differences in data collection practice, as well as differences in histological verification, staging and coding across jurisdictions. [ABSTRACT FROM AUTHOR]

Here, we report a confirmed sighting of brown hyaena outside of Mossel Bay, Western Cape and discuss how this improves our understanding of brown hyaena species distribution and conservation. Ambiguous records exist due to lack of differentiation between the three possible Hyaenidae species: brown hyaena ( I Hyaena brunnea i ), spotted hyaena ( I Crocuta crocuta i ) and Aardwolf ( I Proteles cristata i ) (East & Hofer, 1998). The brown hyaena ( I Parahyaena brunnea i ) (Thunberg, 1820) is a medium-sized carnivore and one of the three hyaenid species to historically occur in the Western Cape, South Africa (Hofer & Mills, 1998). [Extracted from the article]

Analyzes the novel `Clara Hopgood,' by Mark Rutherford. Overview the novel; Information on the main character; Arguments raised by the author.

Focuses on the collection of Mark Rutherford's novels, `Miriam's Schooling and Other Papers.' Implications for the study of historical fiction; William Hale White's creation of Mark Rutherford; Rewritings of the Bible; Human experience.