The conservation impacts of ecological disturbance : time-bound estimates of population loss and recovery for fauna affected by the 2019–2020 Australian megafires

Aim: After environmental disasters, species with large population losses may need urgent protection to prevent extinction and support recovery. Following the 2019–2020 Australian megafires, we estimated population losses and recovery in fire-affected fauna, to inform conservation status assessments and management. Location: Temperate and subtropical Australia. Time period: 2019–2030 and beyond. Major taxa: Australian terrestrial and freshwater vertebrates; one invertebrate group. Methods: From > 1,050 fire-affected taxa, we selected 173 whose distributions substantially overlapped the fire extent. We estimated the proportion of each taxon’s distribution affected by fires, using fire severity and aquatic impact mapping, and new distribution mapping. Using expert elicitation informed by evidence of responses to previous wildfires, we estimated local population responses to fires of varying severity. We combined the spatial and elicitation data to estimate overall population loss and recovery trajectories, and thus indicate potential eligibility for listing as threatened, or uplisting, under Australian legislation. Results: We estimate that the 2019–2020 Australian megafires caused, or contributed to, population declines that make 70–82 taxa eligible for listing as threatened; and another 21–27 taxa eligible for uplisting. If so-listed, this represents a 22–26% increase in Australian statutory lists of threatened terrestrial and freshwater vertebrates and spiny crayfish, and uplisting for 8–10% of threatened taxa. Such changes would cause an abrupt worsening of underlying trajectories in vertebrates, as measured by Red List Indices. We predict that 54–88% of 173 assessed taxa will not recover to pre-fire population size within 10 years/three generations. Main conclusions: We suggest the 2019–2020 Australian megafires have worsened the conservation prospects for many species. Of the 91 taxa recommended for listing/uplisting consideration, 84 are now under formal review through national processes. Improving predictions about taxon vulnerability with empirical data on population responses, reducing the likelihood of future catastrophic events and mitigating their impacts on biodiversity, are critical. © 2022 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 30 including Federation University Australia affiliate “Diana Kuchinke” is provided in this record*

The Status of Radiation Damage Experiments

Experiments have been on-going for about two years to determine the effects that radiation damage have on the physical and chemical properties of candidate titanate ceramics for the immobilization of plutonium. We summarize the results of these experiments in this document

Tepotinib in Non–Small-Cell Lung Cancer with MET Exon 14 Skipping Mutations

BACKGROUND: A splice-site mutation that results in a loss of transcription of exon 14 in the oncogenic driver MET occurs in 3 to 4% of patients with non-small-cell lung cancer (NSCLC). We evaluated the efficacy and safety of tepotinib, a highly selective MET inhibitor, in this patient population. METHODS: In this open-label, phase 2 study, we administered tepotinib (at a dose of 500 mg) once daily in patients with advanced or metastatic NSCLC with a confirmed MET exon 14 skipping mutation. The primary end point was the objective response by independent review among patients who had undergone at least 9 months of follow-up. The response was also analyzed according to whether the presence of a MET exon 14 skipping mutation was detected on liquid biopsy or tissue biopsy. RESULTS: As of January 1, 2020, a total of 152 patients had received tepotinib, and 99 patients had been followed for at least 9 months. The response rate by independent review was 46% (95% confidence interval [CI], 36 to 57), with a median duration of response of 11.1 months (95% CI, 7.2 to could not be estimated) in the combined-biopsy group. The response rate was 48% (95% CI, 36 to 61) among 66 patients in the liquid-biopsy group and 50% (95% CI, 37 to 63) among 60 patients in the tissue-biopsy group; 27 patients had positive results according to both methods. The investigator-assessed response rate was 56% (95% CI, 45 to 66) and was similar regardless of the previous therapy received for advanced or metastatic disease. Adverse events of grade 3 or higher that were considered by investigators to be related to tepotinib therapy were reported in 28% of the patients, including peripheral edema in 7%. Adverse events led to permanent discontinuation of tepotinib in 11% of the patients. A molecular response, as measured in circulating free DNA, was observed in 67% of the patients with matched liquid-biopsy samples at baseline and during treatment. CONCLUSIONS: Among patients with advanced NSCLC with a confirmed MET exon 14 skipping mutation, the use of tepotinib was associated with a partial response in approximately half the patients. Peripheral edema was the main toxic effect of grade 3 or higher. (Funded by Merck [Darmstadt, Germany]; VISION ClinicalTrials.gov number, NCT02864992.)

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Spatial Text Mining: an Enhanced Text Mining Framework for Extracting Disaster Relevant Social Media Data

Includes Framework, Maps, Photographs, Tables, Charts, Imagery and Bibliography.In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. To find disaster relevant social media data and automatically categorize them into different classes (e.g. damage or donation), current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these classification approaches have not been perfected due to the variability and uncertainty in language used on social media. Therefore, more clues or signals are necessary to improve purely text-based approaches. A disaster relevant social media post is highly sensitive to the location and time of the post. Thus, additional features related to space and time could be useful for differentiating relevant posts. However, there has been no systematic study to explore the extent of how spatial features can aid text classification. To fill the research gap, this study proposed a spatial text mining framework to incorporate spatial information derived from social media and authoritative meteorological datasets, along with the text information, for classifying disaster relevant social media posts. This approach assesses the textual content using common text mining methods and the spatiotemporal relationship of the post to the disaster event. An assessment of the framework utilized geo-tagged social media posts and meteorological data for the 2012 Hurricane Sandy disaster event. The study designed and demonstrated how diverse types of spatial features, including wind, flooding, and proximity, can be derived from the data and then used to enhance text mining. Additionally, different temporal features are also derived and integrated into text classification. This study used a common classification scheme for classifying disaster relevant social media posts into different categories. Commonly used machine learning algorithms, including Naive Bayes and Support Vector Machine classifiers, assessed the accuracy within the enhanced text-mining framework. Finally, integrating textual, spatial, and temporal features to generate different classification models identified the features with the greatest influence in the classification. The experimental results indicate that proximity (spatial), disaster status (i.e., spatiotemporal relationship of the hurricane and social media post) features help improve the overall accuracy of the classification. The results from this study address the need for incorporating spatial data when using social media in disaster management applications

Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features

To find disaster relevant social media messages, current approaches utilize natural language processing methods or machine learning algorithms relying on text only, which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when posted. Meanwhile, a disaster relevant social media message is highly sensitive to its posting location and time. However, limited studies exist to explore what spatial features and the extent of how temporal, and especially spatial features can aid text classification. This paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets, along with the text information, for classifying disaster relevant social media posts. This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data, and then used to enhance text mining. The deep learning-based method and commonly used machine learning algorithms, assessed the accuracy of the enhanced text-mining method. The performance results of different classification models generated by various combinations of textual, spatial, and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification

The promise of selective MET inhibitors in non-small cell lung cancer with MET exon 14 skipping

Inhibidor de MET; NSCLCInhibidor de MET; NSCLCMET inhibitor; NSCLCDysregulated activation of the MET tyrosine kinase receptor is implicated in the development of solid tumors and can arise through several mechanisms, including gene amplification, overexpression of the receptor and/or its ligand hepatocyte growth factor (HGF), and the acquisition of activating mutations. The most common activating mutations cause exon 14 to be skipped during MET mRNA splicing. This in-frame deletion, known as MET exon 14, results in production of a shortened receptor that lacks a juxtamembrane domain but retains affinity for HGF. However, the negative regulatory function located within this protein sequence is lost, leading to receptor accumulation on the cell surface and prolonged activation by HGF. MET mutations causing exon 14 skipping appear to be true oncogenic drivers and occur in patients and tumors with distinct characteristics. Increasing evidence suggests that tumors carrying such mutations are sensitive to MET inhibition, raising the hope that selective MET inhibitors will provide patients with optimal anticancer activity with minimal toxicity. We discuss the prospects for selective MET inhibitors in the treatment of non-small cell lung cancer harboring MET exon 14 skipping.This work was supported by Merck KGaA, Darmstadt, Germany . No grant number is applicable

New methods for estimating the tuberculosis case detection rate in high-HIV prevalence countries: the example of Kenya

OBJECTIVE: To develop new methods for estimating the sputum smear-positive tuberculosis case detection rate (CDR) in a country where infection with HIV is prevalent. METHODS: We estimated the smear-positive tuberculosis CDR in HIV-negative and HIV-positive adults, and in all adults in Kenya. Data on time trends in tuberculosis case notification rates and on HIV infection prevalence in adults and in tuberculosis patients were used, along with data on tuberculosis control programme performance. FINDINGS: In 2006, the estimated smear-positive tuberculosis CDR in HIV-negative adults was 79% (95% confidence interval, CI: 64-94) and in HIV-positive adults, 57% (95% CI: 26-88), giving a weighted mean of 68% (95% CI: 49-87). The separate estimate for all smear-positive tuberculosis cases was 72% (95% CI: 53-91), giving an overall average for the three estimates of 70% (95% CI: 58-82). As the tuberculosis CDR in 1996 was 57% (95% CI: 47-67), the estimated increase by 2006 was 13 percentage points (95% CI: 6-20), or 23%. This increase was accompanied by a more than doubling of the resources devoted to tuberculosis control in Kenya, including facilities and staff. CONCLUSION: Using three approaches to estimate the tuberculosis CDR in a country where HIV infection is prevalent, we showed that expansion of the tuberculosis control programme in Kenya led to an increase of 23% in the CDR between 1996 and 2006. While the methods developed here can be applied in other countries with a high prevalence of HIV infection, they rely on precise data on trends in such prevalence in the general population and among tuberculosis patients