Respiratory Phenomics across Multiple Models of Protein Hyperacylation in Cardiac Mitochondria Reveals a Marginal Impact on Bioenergetics

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An integrative framework of coexistence mechanisms in competitive metacommunities

Species distribution in a metacommunity varies according to their traits, the distribution of environmental conditions and connectivity among localities. These ingredients contribute to coexistence across spatial scales via species sorting, patch dynamics, mass effects and neutral dynamics. These mechanisms however seldom act in isolation and the impact of landscape configuration on their relative importance remains poorly understood. We present a new model of metacommunity dynamics that simultaneously considers these four possible mechanisms over spatially explicit landscapes and propose a statistical approach to partition their contribution to species distribution. We find that landscape configuration can induce dispersal limitations that have negative consequences for local species richness. This result was more pronounced with neutral dynamics and mass effect than with species sorting or patch dynamics. We also find that the relative importance of the four mechanisms varies not only among landscape configurations, but also among species, with some species being mostly constrained by dispersal and/or drift and others by sorting. Changes in landscape properties might lead to a shift in coexistence mechanisms and, by extension, to a change in community composition. This confirms the importance of considering landscape properties for conservation and management. Our results illustrate the idea that ecological communities are the results of multiple mechanisms acting at the same time and complete our understanding of spatial processes in competitive metacommunities.     

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.     

Bayesian spatial modeling of moose count data: increasing estimator efficiency and exploring ecological hypotheses

Moose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model-based analytical approach and finite-population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well-documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon-Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets.     

The human–primate interface in the New Normal: Challenges and opportunities for primatologists in the COVID-19 era and beyond

The emergence of SARS-CoV-2 in late 2019 and human responses to the resulting COVID-19 pandemic in early 2020 have rapidly changed many aspects of human behavior, including our interactions with wildlife. In this commentary, we identify challenges and opportunities at human–primate interfaces in light of COVID-19, focusing on examples from Asia, and make recommendations for researchers working with wild primates to reduce zoonosis risk and leverage research opportunities. First, we briefly review the evidence for zoonotic origins of SARS-CoV-2 and discuss risks of zoonosis at the human–primate interface. We then identify challenges that the pandemic has caused for primates, including reduced nutrition, increased intraspecific competition, and increased poaching risk, as well as challenges facing primatologists, including lost research opportunities. Subsequently, we highlight opportunities arising from pandemic-related lockdowns and public health messaging, including opportunities to reduce the intensity of problematic human–primate interfaces, opportunities to reduce the risk of zoonosis between humans and primates, opportunities to reduce legal and illegal trade in primates, new opportunities for research on human–primate interfaces, and opportunities for community education. Finally, we recommend specific actions that primatologists should take to reduce contact and aggression between humans and primates, to reduce demand for primates as pets, to reduce risks of zoonosis in the context of field research, and to improve understanding of human–primate interfaces. Reducing the risk of zoonosis and promoting the well-being of humans and primates at our interfaces will require substantial changes from “business as usual.” We encourage primatologists to help lead the way.     

Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing’s Sarcoma

Ewing’s sarcoma is a malignant pediatric bone tumor with a poor prognosis for patients with metastatic or recurrent disease. Ewing’s sarcoma cells are acutely hypersensitive to poly (ADP-ribose) polymerase (PARP) inhibition and this is being evaluated in clinical trials, although the mechanism of hypersensitivity has not been directly addressed. PARP inhibitors have efficacy in tumors with BRCA1/2 mutations, which confer deficiency in DNA double-strand break (DSB) repair by homologous recombination (HR). This drives dependence on PARP1/2 due to their function in DNA single-strand break (SSB) repair. PARP inhibitors are also cytotoxic through inhibiting PARP1/2 auto-PARylation, blocking PARP1/2 release from substrate DNA. Here, we show that PARP inhibitor sensitivity in Ewing’s sarcoma cells is not through an apparent defect in DNA repair by HR, but through hypersensitivity to trapped PARP1-DNA complexes. This drives accumulation of DNA damage during replication, ultimately leading to apoptosis. We also show that the activity of PARP inhibitors is potentiated by temozolomide in Ewing’s sarcoma cells and is associated with enhanced trapping of PARP1-DNA complexes. Furthermore, through mining of large-scale drug sensitivity datasets, we identify a subset of glioma, neuroblastoma and melanoma cell lines as hypersensitive to the combination of temozolomide and PARP inhibition, potentially identifying new avenues for therapeutic intervention. These data provide insights into the anti-cancer activity of PARP inhibitors with implications for the design of treatment for Ewing’s sarcoma patients with PARP inhibitors.     

Vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase activating peptide (PACAP) receptor specific peptide analogues for PET imaging of breast cancer: In vitro/in vivo evaluation

Vasoactive intestinal peptide and pituitary adenylate cyclase activating peptide have high affinity for VPAC1, VPAC2 and PAC1 receptors overexpressed on human cancer cells. Four potent analogues of these peptides, TP3939, TP3982, TP4200 and TP3805 were labeled with (64)Cu and evaluated ex vivo and in vivo to asses their biological activity and receptor specificity. The ultimate goal is to utilize (64)Cu analogues for positron emission tomography (PET) imaging of breast cancers in humans. Radiochemical purity of each analogue was >92%. The muscle relaxivity assay revealed IC(50) to be 5.3x10(-8) M, 4.4x10(-8) M, 8.1x10(-8) M, 8.1x10(-9) M and Kd values determined by receptor specific cell binding assays were 3.3 nM, 0.33 nM, 0.2 nM and 0.72 nM for TP3805, TP3939, TP3982, and TP4200 respectively. The receptor affinity, using human breast cancer tissues, was 10.93 times greater than normal breast tissues. RT-PCR confirmed increased VPAC1 receptor expression on human breast tumor cells over normal cells and corroborated with autoradiography data. The blood clearance was rapid and in vivo translocation of (64)Cu to plasma protein was <15%. Data demonstrate that these analogues are potent, have uncompromised biological activity and are worthy of further evaluation for accurate PET imaging of human breast cancers and in determining malignant and benign lesions.     

Alternative stable states and regional community structure

Many models of local species interactions predict the occurrence of priority effects due to alternative stable equilibria (ASE). However, few empirical examples of ASE have been shown. One possible explanation for the disparity is that local ASE are difficult to maintain regionally in patch dynamic models. Here we examine two possible mechanisms for regional coexistence of species engaged in local ASE. Biotically generated heterogeneity (e.g., habitat modification that favors further invasion by conspecifics) results in regional exclusion of one species at equilibrium. In contrast, abiotic heterogeneity due to spatial variation in resource supply ratios generates local-scale ASE and ensures regional coexistence with sufficiently broad environmental gradients. Abiotic heterogeneity can result in a species that is the dominant competitor over some of its range being excluded if the area where it is dominant is too small. Biotic heterogeneity can lead to alternative stable landscapes or regional priority effects, while abiotic heterogeneity results in regional determinism. Broad environmental gradients in resource supply favor regional coexistence of species that exhibit local ASE.