Automated call detection for acoustic surveys with structured calls of varying length

Funding: Y.W. is partly funded by the China Scholarship Council (CSC) for Ph.D. study at the University of St Andrews, UK.1. When recorders are used to survey acoustically conspicuous species, identification calls of the target species in recordings is essential for estimating density and abundance. We investigate how well deep neural networks identify vocalisations consisting of phrases of varying lengths, each containing a variable number of syllables. We use recordings of Hainan gibbon (Nomascus hainanus) vocalisations to develop and test the methods. 2. We propose two methods for exploiting the two-level structure of such data. The first combines convolutional neural network (CNN) models with a hidden Markov model (HMM) and the second uses a convolutional recurrent neural network (CRNN). Both models learn acoustic features of syllables via a CNN and temporal correlations of syllables into phrases either via an HMM or recurrent network. We compare their performance to commonly used CNNs LeNet and VGGNet, and support vector machine (SVM). We also propose a dynamic programming method to evaluate how well phrases are predicted. This is useful for evaluating performance when vocalisations are labelled by phrases, not syllables. 3. Our methods perform substantially better than the commonly used methods when applied to the gibbon acoustic recordings. The CRNN has an F-score of 90% on phrase prediction, which is 18% higher than the best of the SVM or LeNet and VGGNet methods. HMM post-processing raised the F-score of these last three methods to as much as 87%. The number of phrases is overestimated by CNNs and SVM, leading to error rates between 49% and 54%. With HMM, these error rates can be reduced to 0.4% at the lowest. Similarly, the error rate of CRNN's prediction is no more than 0.5%. 4. CRNNs are better at identifying phrases of varying lengths composed of a varying number of syllables than simpler CNN or SVM models. We find a CRNN model to be best at this task, with a CNN combined with an HMM performing almost as well. We recommend that these kinds of models are used for species whose vocalisations are structured into phrases of varying lengths.Publisher PDFPeer reviewe

Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually. Digital recorders allow surveyors to gather large volumes of data at low cost, but identifying target species vocalisations in these data is non-trivial. Machine learning (ML) methods are often used to do the identification. They can process large volumes of data quickly, but they do not detect all vocalisations and they do generate some false positives (vocalisations that are not from the target species). Existing wildlife abundance survey methods have been designed specifically to deal with the first of these mistakes, but current methods of dealing with false positives are not well-developed. They do not take account of features of individual vocalisations, some of which are more likely to be false positives than others. We propose three methods for acoustic spatial capture-recapture inference that integrate individual-level measures of confidence from ML vocalisation identification into the likelihood and hence integrate ML uncertainty into inference. The methods include a mixture model in which species identity is a latent variable. We test the methods by simulation and find that in a scenario based on acoustic data from Hainan gibbons, in which ignoring false positives results in 17% positive bias, our methods give negligible bias and coverage probabilities that are close to the nominal 95% level.Comment: 35 pages, 5 figure

Musashi-2 controls cell fate, lineage bias, and TGF-β signaling in HSCs

Hematopoietic stem cells (HSCs) are maintained through the regulation of symmetric and asymmetric cell division. We report that conditional ablation of the RNA-binding protein Msi2 results in a failure of HSC maintenance and engraftment caused by a loss of quiescence and increased commitment divisions. Contrary to previous studies, we found that these phenotypes were independent of Numb. Global transcriptome profiling and RNA target analysis uncovered Msi2 interactions at multiple nodes within pathways that govern RNA translation, stem cell function, and TGF-β signaling. Msi2-null HSCs are insensitive to TGF-β–mediated expansion and have decreased signaling output, resulting in a loss of myeloid-restricted HSCs and myeloid reconstitution. Thus, Msi2 is an important regulator of the HSC translatome and balances HSC homeostasis and lineage bias

Boredom and engagement at work: do they have different antecedents and consequences?

This study aimed to demonstrate the empirical distinctiveness of boredom at work and work engagement in relation to their potential antecedents (job demands and job resources) and consequences (psychological distress and turnover intention) based on the Job Demands-Resources model. A three-wave longitudinal survey was conducted among registered monitors of an Internet survey company in Japan. The questionnaire included scales for boredom at work, work engagement, psychological distress, and turnover intention as well as participants' job characteristics and demographic variables. The hypothesized model was evaluated via structural equation modeling with 1,019 participants who were employed full-time. As expected, boredom at work was negatively associated with quantitative job demands and job resources and positively associated with psychological distress and turnover intention. In contrast, work engagement was positively associated with job resources and negatively associated with turnover intention. Thus, boredom at work and work engagement had different potential antecedents and were inversely related to employee well-being and organizational outcomes. However, contrary to expectations, qualitative job demands were not significantly associated with boredom at work. Further investigation is needed to understand the relationship between boredom and qualitative job demands, which require sustained cognitive load and the use of higher skills

Cross-talk between PRMT1-mediated methylation and ubiquitylation on RBM15 controls RNA splicing

RBM15, an RNA binding protein, determines cell-fate specification of many tissues including blood. We demonstrate that RBM15 is methylated by protein arginine methyltransferase 1 (PRMT1) at residue R578 leading to its degradation via ubiquitylation by an E3 ligase (CNOT4). Overexpression of PRMT1 in acute megakaryocytic leukemia cell lines blocks megakaryocyte terminal differentiation by downregulation of RBM15 protein level. Restoring RBM15 protein level rescues megakaryocyte terminal differentiation blocked by PRMT1 overexpression. At the molecular level, RBM15 binds to pre-mRNA intronic regions of genes important for megakaryopoiesis such as GATA1, RUNX1, TAL1 and c-MPL. Furthermore, preferential binding of RBM15 to specific intronic regions recruits the splicing factor SF3B1 to the same sites for alternative splicing. Therefore, PRMT1 regulates alternative RNA splicing via reducing RBM15 protein concentration. Targeting PRMT1 may be a curative therapy to restore megakaryocyte differentiation for acute megakaryocytic leukemia

The CXCL16-CXCR6 axis in glioblastoma modulates T-cell activity in a spatiotemporal context

IntroductionGlioblastoma multiforme (GBM) pathobiology is characterized by its significant induction of immunosuppression within the tumor microenvironment, predominantly mediated by immunosuppressive tumor-associated myeloid cells (TAMCs). Myeloid cells play a pivotal role in shaping the GBM microenvironment and influencing immune responses, with direct interactions with effector immune cells critically impacting these processes.MethodsOur study investigates the role of the CXCR6/CXCL16 axis in T-cell myeloid interactions within GBM tissues. We examined the surface expression of CXCL16, revealing its limitation to TAMCs, while microglia release CXCL16 as a cytokine. The study explores how these distinct expression patterns affect T-cell engagement, focusing on the consequences for T-cell function within the tumor environment. Additionally, we assessed the significance of CXCR6 expression in T-cell activation and the initial migration to tumor tissues.ResultsOur data demonstrates that CXCL16 surface expression on TAMCs results in predominant T-cell engagement with these cells, leading to impaired T-cell function within the tumor environment. Conversely, our findings highlight the essential role of CXCR6 expression in facilitating T-cell activation and initial migration to tumor tissues. The CXCL16-CXCR6 axis exhibits dualistic characteristics, facilitating the early stages of the T-cell immune response and promoting T-cell infiltration into tumors. However, once inside the tumor, this axis contributes to immunosuppression.DiscussionThe dual nature of the CXCL16-CXCR6 axis underscores its potential as a therapeutic target in GBM. However, our results emphasize the importance of carefully considering the timing and context of intervention. While targeting this axis holds promise in combating GBM, the complex interplay between TAMCs, microglia, and T cells suggests that intervention strategies need to be tailored to optimize the balance between promoting antitumor immunity and preventing immunosuppression within the dynamic tumor microenvironment