Patterns and mechanisms of stem mortality in Acacia nigrescens induced by elephants and fire

Increasing elephant populations have been implicated in the decline of woody vegetation throughout Africa. The problem is particularly relevant to the Kruger National Park in South Africa, where elephant populations have almost doubled in the last 10 years. One manner in which elephants utilize trees is by stripping their bark. The role of bark stripping in increasing stem vulnerability to fire and the mechanism through which fire damage is mediated were investigated by experimentally removing bark and burning Acacia nigrescens stems. Field surveys were conducted in order to investigate patterns of bark stripping in relation to mortality patterns of large trees occurring subsequent to natural fires. In the experimental study, an increasing probability of mortality was associated with increasing amount of bark removed when trees were burnt. However, when trees were stripped but not burnt, simulating damage to cambium and phloem, none died in the 4-month period over which the experiment ran. This was taken as evidence that fire-induced xylem damage causes stem mortality. However, fire did kill a greater proportion of the remaining stem cambium around the circumference when bark had been removed. The field surveys indicate that bark stripping by elephants is frequent on large stems (44%) and that larger trees are more heavily impacted. The only variable measured that explained mortality patterns well was the percent of bark removed around the stem circumference up to 3m (p = 0.0076). These results indicate that damage to xylem is important in determining post-fire survival and that bark stripping by elephants increases the vulnerability of stems to fire. This increased vulnerability is a result of both increased damage to cambium and damage to exposed xylem. The high proportion of trees stripped by elephants and the increase in vulnerability to mortality associated with bark stripping suggests that unless elephant population growth is curbed, large Acacia nigrescens trees will eventually be eliminated from this ecosystem

The origins and maintenance of species boundaries in Jamesbrittenia O. Kuntze (Scrophulariaceae: Manuleae)

The genus Jamesbrittenia contains 83 species distributed throughout southern Africa. Many species produce attractive flowers and consequently their horticultural potential is currently being explored. Speciation patterns and reproductive isolation were investigated in order to identify trends that may apply at broader scales. Bayesian phylogenetic analysis was performed using plastid (rps 16 and psbA-trnH) and nuclear (GScp) sequence data. Relative divergence times were calculated using a relaxed clock method. Prezygotic isolation, measured as seed set resulting from interspecific crosses, correlated with divergence time. However, recently diverged, highly sympatric taxa deviated from the overall trend. This provides circumstantial evidence for reinforcement of reproductive barriers. Floral dissimilarity and divergence time were found to be useful in predicting hybridization reported in the wild (p<0.0001). Species pairs susceptible to hybridization were identified on the basis of their floral dissimilarity and divergence time in order to prevent potentially hybridizing species from being brought into contact. The inability to detect the dominant mode of speciation confounded interpretation of the results, as it was not possible to determine if the influence of geographic patterns on the evolution of reproductive isolation was a result of the mode of speciation or post-speciation evolutionary changes

Locating and Dating Land Cover Change Events in the Renosterveld, a Critically Endangered Shrubland Ecosystem

Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow for monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of land cover change events in a highly biodiverse, Critically Endangered, shrubland ecosystem—the Renosterveld of South Africa. Using supervised classification of Sentinel 2 data, and subsequent manual verification with very high resolution imagery, I locate all conversion of Renosterveld to non-natural land cover between 2016 and 2020. Land cover change events are further assigned dates using high temporal frequency data from Planet labs. A total area of 478.6 hectares of Renosterveld loss was observed over this period, accounting for 0.72% of the remaining natural vegetation in the region. In total, 50% of change events were dated to within two weeks of their actual occurrence, and 87% to within two months. The Renosterveld loss identified here is almost entirely attributable to conversion of natural vegetation to cropland through ploughing. Change often preceded the planting and harvesting seasons of rainfed annual grains. These results show the potential for new satellite platforms to accurately map land cover change in non-forest ecosystems, and detect change within days of its occurrence. There is potential to use this and similar datasets to automate the process of change detection and monitor change continuously

Continuous Land Cover Change Detection in a Critically Endangered Shrubland Ecosystem Using Neural Networks

Existing efforts to continuously monitor land cover change using satellite image time series have mostly focused on forested ecosystems in the tropics and the Northern Hemisphere. The notable difference in spectral reflectance that occurs following deforestation allows land cover change to be detected with relative accuracy. Less progress has been made in detecting change in low productivity or disturbance-prone vegetation such as grasslands and shrublands where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5–10% of its original extent remaining in small, highly fragmented patches. I demonstrate that classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence and that trained models are suitable for operational continuous monitoring. A dataset of precisely dated vegetation change events between 2016 and 2021 was obtained from daily, high resolution Planet Labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously detect land cover change events in time series of vegetation activity from Sentinel 2 satellite data. The best model correctly identified 89% of land cover change events at the pixel-level, achieving a f-score of 0.93, a 79% improvement over the f-score of 0.52 achieved using a method designed for forested ecosystems based on trend analysis. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for continuous monitoring of habitat loss in non-forest ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated datasets of land cover change events with which to train machine-learning classifiers

The demographic impacts of browsing on woody plants in savannas : from individual branches to whole populations

Includes bibliographical references (p. 121-131).Browsing ungulates can potentially have drastic impacts on vegetation patterns. This is particularly true in African savannas where many large browsers persist at high densities. Most of the theory and models outlining mechanisms of impact on plants and predicting responses are framed in terms of biomass impacts and responses. However, for trees in African savannas, fitness is more closely linked to height than above ground biomass. I evaluate the demographic impacts of browsing, making explicit contrasts with impacts on biomass. The results highlight under- explored intrinsic aspects of plants and browsers that determine the degree of browser impact on plant demography, aspects that have been under-explored due to an emphasis on biomass responses, and provide novel methods to measure and evaluate large-scale browser impacts, which have proved difficult before

The 'antisocial' person: an insight in to biology, classification and current evidence on treatment

<p>Abstract</p> <p>Background</p> <p>This review analyses and summarises the recent advances in understanding the neurobiology of violence and empathy, taxonomical issues on defining personality disorders characterised by disregard for social norms, evidence for efficacy of different treatment modalities and ethical implications in defining 'at-risk' individuals for preventive interventions.</p> <p>Methods</p> <p>PubMed was searched with the keywords 'antisocial personality disorder', 'dissocial personality disorder' and 'psychopathy'. The search was limited to articles published in English over the last 10 years (1999 to 2009)</p> <p>Results</p> <p>Both diagnostic manuals used in modern psychiatry, the <it>Diagnostic and Statistical Manual </it>published by the American Psychiatric Association and the <it>International Classification of Diseases </it>published by the World Health Organization, identify a personality disorder sharing similar traits. It is termed antisocial personality disorder in the diagnostic and statistical manual and dissocial personality disorder in the <it>International Classification of Diseases</it>. However, some authors query the ability of the existing manuals to identify a special category termed 'psychopathy', which in their opinion deserves special attention. On treatment-related issues, many psychological and behavioural therapies have shown success rates ranging from 25% to 62% in different cohorts. Multisystemic therapy and cognitive behaviour therapy have been proven efficacious in many trials. There is no substantial evidence for the efficacy of pharmacological therapy. Currently, the emphasis is on early identification and prevention of antisocial behaviour despite the ethical implications of defining at-risk children.</p> <p>Conclusions</p> <p>Further research is needed in the areas of neuroendocrinological associations of violent behaviour, taxonomic existence of psychopathy and efficacy of treatment modalities.</p

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment