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"Wade, T."
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Immunotherapeutic approaches for small-cell lung cancer
Immune-checkpoint inhibitors (ICIs) are approved in the first-line and third-line settings for patients with extensive-stage or relapsed small-cell lung cancer (SCLC), respectively. In the first-line setting, the addition of the anti-programmed cell death 1 ligand 1 (PD-L1) antibody atezolizumab to chemotherapy improves overall survival (OS). In patients with relapsed disease, data from nonrandomized trials have revealed promising responses, although a significant improvement in OS over that obtained with conventional chemotherapy was not achieved in a randomized trial in this setting. Substantial research interest exists in identifying predictive biomarkers that could guide the use of ICIs in patients with SCLC. PD-L1 expression is typically low or absent in SCLC, which has precluded its use as a predictive biomarker. Tumour mutational burden might have some predictive value, although blood-based measures of tumour mutational burden did not have predictive value in patients receiving atezolizumab plus chemotherapy in the first-line setting. After three decades, ICIs have finally enabled an improvement in OS for patients with SCLC; however, a substantial amount of research remains to be done, including identifying the optimal therapeutic strategy and predictive biomarkers. In this Review, we describe the available data on clinical efficacy, the emerging evidence regarding biomarkers and ongoing clinical trials using ICIs and other immunotherapies in patients with SCLC.Patients with small-cell lung cancer (SCLC) have historically received chemotherapy, typically with poor survival outcomes. In the past few years, the combination of immune-checkpoint inhibitors (ICIs) with chemotherapy has provided a more effective alternative to chemotherapy alone. Nonetheless, durations of survival are often short, and no robust biomarkers of response are available. In this Review, the authors provide a summary of the efficacy and safety of ICIs in patients with SCLC, and also highlight potential novel immunotherapeutic approaches that are currently in the early stages of investigation.
Journal Article
Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States
Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment exhibit a well-known summertime warm bias in mid-latitude land regions – most notably in the central contiguous United States (CUS). The dominant source of this bias is still under debate. Using validated datasets and both coupled and off-line modeling, we find that the CUS summertime warm bias is driven by the incorrect partitioning of evapotranspiration (ET) into its canopy transpiration and soil evaporation components. Specifically, CMIP6 ESMs do not effectively use available rootzone soil moisture for summertime transpiration and instead rely excessively on shallow soil and canopy-intercepted water storage to supply ET. As such, expected summertime precipitation deficits in CUS induce a negative ET bias into CMIP6 ESMs and a corresponding positive temperature bias via local land-atmosphere coupling. This tendency potentially biases CMIP6 projections of regional water stress and summertime air temperature variability under elevated CO
2
conditions.
Summertime warm bias in the central United States persists in Earth System Models. This bias is dominated by land physics related to transpiration and evaporation partitioning. Improved land physics can constrain projected climate uncertainty.
Journal Article
Pervasive transcription: illuminating the dark matter of bacterial transcriptomes
It has recently emerged that pervasive transcription is widespread in bacteria and is caused by transcription from non-canonical promoters and terminator readthrough. However, whether the resultant transcripts have any functional role is unclear. In this Opinion article, Wade and Grainger argue that pervasive transcripts are likely to be important for the regulation of gene expression and genome evolution.
The conventional view of transcription posits that mRNAs are generated from the coding DNA strand and are delineated by gene boundaries; however, recent reports have mapped transcription start sites to unexpected locations in bacterial genomes, including the non-coding strand. The resultant RNAs were previously dismissed as artefacts, but models that describe such events as 'pervasive transcription' are now gaining support. In this Opinion article, we discuss our current understanding of pervasive transcription, its genetic origin and its regulation. On the basis of existing observations, we propose that RNAs that result from pervasive transcription are more than 'transcriptional noise' and have important functions in gene regulation and genome evolution.
Journal Article
Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models
Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.
Journal Article
Comprehensive Mapping of the Escherichia coli Flagellar Regulatory Network
Flagellar synthesis is a highly regulated process in all motile bacteria. In Escherichia coli and related species, the transcription factor FlhDC is the master regulator of a multi-tiered transcription network. FlhDC activates transcription of a number of genes, including some flagellar genes and the gene encoding the alternative Sigma factor FliA. Genes whose expression is required late in flagellar assembly are primarily transcribed by FliA, imparting temporal regulation of transcription and coupling expression to flagellar assembly. In this study, we use ChIP-seq and RNA-seq to comprehensively map the E. coli FlhDC and FliA regulons. We define a surprisingly restricted FlhDC regulon, including two novel regulated targets and two binding sites not associated with detectable regulation of surrounding genes. In contrast, we greatly expand the known FliA regulon. Surprisingly, 30 of the 52 FliA binding sites are located inside genes. Two of these intragenic promoters are associated with detectable noncoding RNAs, while the others either produce highly unstable RNAs or are inactive under these conditions. Together, our data redefine the E. coli flagellar regulatory network, and provide new insight into the temporal orchestration of gene expression that coordinates the flagellar assembly process.
Journal Article
A randomised controlled trial of three psychological treatments for anorexia nervosa
There is a lack of evidence pointing to the efficacy of any specific psychotherapy for adults with anorexia nervosa (AN). The aim of this study was to compare three psychological treatments for AN: Specialist Supportive Clinical Management, Maudsley Model Anorexia Nervosa Treatment for Adults and Enhanced Cognitive Behavioural Therapy.
A multi-centre randomised controlled trial was conducted with outcomes assessed at pre-, mid- and post-treatment, and 6- and 12-month follow-up by researchers blind to treatment allocation. All analyses were intention-to-treat. One hundred and twenty individuals meeting diagnostic criteria for AN were recruited from outpatient treatment settings in three Australian cities and offered 25-40 sessions over a 10-month period. Primary outcomes were body mass index (BMI) and eating disorder psychopathology. Secondary outcomes included depression, anxiety, stress and psychosocial impairment.
Treatment was completed by 60% of participants and 52.5% of the total sample completed 12-month follow-up. Completion rates did not differ between treatments. There were no significant differences between treatments on continuous outcomes; all resulted in clinically significant improvements in BMI, eating disorder psychopathology, general psychopathology and psychosocial impairment that were maintained over follow-up. There were no significant differences between treatments with regard to the achievement of a healthy weight (mean = 50%) or remission (mean = 28.3%) at 12-month follow-up.
The findings add to the evidence base for these three psychological treatments for adults with AN, but the results underscore the need for continued efforts to improve outpatient treatments for this disorder. Trial Registration Australian New Zealand Clinical Trials Registry (ACTRN 12611000725965) http://www.anzctr.org.au/.
Journal Article
The Conditional Bias of Extreme Precipitation in Multi‐Source Merged Data Sets
Multi‐source data merging via weighted average (WA) is widely applied to enhance large‐scale precipitation estimates. However, these data sets usually contain substantial conditional biases with respect to extreme precipitation (EP) events—undermining their utility for extreme event analysis. Nevertheless, the main source of such EP biases remains unknown. Here, we demonstrate that WA algorithms are responsible for less than 1% of total EP biases. Instead, EP biases originate from the multi‐source precipitation inputs, which are not adequately adjusted prior to WA. Specifically, current data‐merging frameworks only correct the monthly means or statistical distributions of the remote sensing/reanalysis precipitation inputs prior to WA. Such procedures are insufficient for adjusting EP timing uncertainties, which eventually propagate into the WA‐based merged data set as an EP bias. Therefore, developing algorithms that iteratively adjust EP timing and intensity errors should be prioritized in future precipitation merging frameworks. Plain Language Summary Remote sensing (RS) and reanalysis systems are crucial for estimating large‐scale precipitation. Weighted averaging (WA) of different data sets can enhance overall precipitation estimation accuracy and has been widely applied for generating global precipitation data sets. However, WA algorithms often lead to biases for extreme precipitation (EP). Such issues undermine the usefulness of WA‐based precipitation data sets for flood forecasting. This study investigates the sources of EP biases in WA frameworks, based on surface precipitation gauge observations and numerical experiments. Results show that the WA algorithms themselves contribute less than 1% to EP biases. Instead, most EP bias is related to RS/reanalysis data correction procedures. Specifically, current WA methods only adjust the monthly means or general statistical distributions of the input data. However, EP occurrence errors are often neglected during the precipitation correction. This means that the timing and location of EP as estimated by different data sets are not entirely consistent, leading to substantial biases when they are averaged. Therefore, to improve the accuracy of EP estimates, it is important to develop preprocessing methods that better account for both the timing and intensity errors of extreme events. Key Points We investigate sources of bias in extreme precipitation (EP) estimates provided by commonly used data merging frameworks We demonstrate that EP biases arise from the neglect of EP timing error correction and not the merging algorithm Algorithms that iteratively adjust the EP intensities and timing errors should be prioritized in future merging frameworks
Journal Article