The importance of regeneration processes on forest biodiversity in old-growth forests in the Pacific Northwest.

Forest diversity is the outcome of multiple species-specific processes and tolerances, from regeneration, growth, competition and mortality of trees. Predicting diversity thus requires a comprehensive understanding of those processes. Regeneration processes have traditionally been overlooked, due to high stochasticity and assumptions that recruitment is not limiting for forests. Thus, we investigated the importance of seed production and seedling survival on forest diversity in the Pacific Northwest (PNW) using a forest gap model (ForClim). Equations for regeneration processes were fit to empirical data and added into the model, followed by simulations where regeneration processes and parameter values varied. Adding regeneration processes into ForClim improved the simulation of species composition, compared to Forest Inventory Analysis data. We also found that seed production was not as important as seedling survival, and the time it took for seedlings to grow into saplings was a critical recruitment parameter for accurately capturing tree species diversity in PNW forest stands. However, our simulations considered historical climate only. Due to the sensitivity of seed production and seedling survival to weather, future climate change may alter seed production or seedling survival and future climate change simulations should include these regeneration processes to predict future forest dynamics in the PNW. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Markov chain stochastic DCA and applications in deep learning with PDEs regularization.

This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-convex functions) programs where endogenous uncertainty is involved and i.i.d. (independent and identically distributed) samples are not available. Instead, we assume that it is only possible to access Markov chains whose sequences of distributions converge to the target distributions. This setting is legitimate as Markovian noise arises in many contexts including Bayesian inference, reinforcement learning, and stochastic optimization in high-dimensional or combinatorial spaces. We then design a stochastic algorithm named Markov chain stochastic DCA (MCSDCA) based on DCA (DC algorithm) - a well-known method for nonconvex optimization. We establish the convergence analysis in both asymptotic and nonasymptotic senses. The MCSDCA is then applied to deep learning via PDEs (partial differential equations) regularization, where two realizations of MCSDCA are constructed, namely MCSDCA-odLD and MCSDCA-udLD, based on overdamped and underdamped Langevin dynamics, respectively. Numerical experiments on time series prediction and image classification problems with a variety of neural network topologies show the merits of the proposed methods.

An Interpretable Classification Model Using Gluten-Specific TCR Sequences Shows Diagnostic Potential in Coeliac Disease.

Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4+ T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100% and testing accuracy of 80% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4+ TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.

Online Stochastic DCA With Applications to Principal Component Analysis.

Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular, machine learning. We propose new online stochastic algorithms based on the state-of-the-art DC Algorithm (DCA)-a powerful approach in nonconvex programming framework, in the online context of streaming data continuously generated by some (unknown) source distribution. The new schemes use the stochastic approximations (SAs) principle: deterministic quantities of the standard DCA are replaced by their noisy estimators constructed using newly arriving samples. The convergence analysis of the proposed algorithms is studied intensively with the help of tools from modern convex analysis and martingale theory. Finally, we study several aspects of the proposed algorithms on an important problem in machine learning: the expected problem in principal component analysis (PCA).

Hybrid evolution repeats itself across environmental contexts in Texas sunflowers (Helianthus).

To what extent is evolution repeatable? Little is known about whether the evolution of hybrids is more (or less) repeatable than that of nonhybrids. We used field experimental evolution in annual sunflowers (Helianthus) in Texas to ask the extent to which hybrid evolution is repeatable across environments compared to nonhybrid controls. We created hybrids between Helianthus annuus (L.) and H. debilis (Nutt.) and grew plots of both hybrids and nonhybrid controls through eight generations at three sites in Texas. We collected seeds from each generation and grew each generation × treatment × home site combination at two final common gardens. We estimated the strength and direction of evolution in terms of fitness and 24 traits, tested for repeated versus nonrepeated evolution, and assessed overall phenotypic evolution across lineages and in relation to a locally adapted phenotype. Hybrids consistently evolved higher fitness over time, while controls did not, although trait evolution varied in strength across home sites. Repeated evolution was more evident in hybrids versus nonhybrid controls, and hybrid evolution was often in the direction of the locally adapted phenotype. Our findings have implications for both the nature of repeatability in evolution and the contribution of hybridization to evolution across environmental contexts.

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19.

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

The Diverse Roles of Heme Oxygenase-1 in Tumor Progression.

Heme oxygenase-1 (HO-1) is an inducible intracellular enzyme that is expressed in response to a variety of stimuli to degrade heme, which generates the biologically active catabolites carbon monoxide (CO), biliverdin and ferrous iron (Fe2+). HO-1 is expressed across a range of cancers and has been demonstrated to promote tumor progression through a variety of mechanisms. HO-1 can be expressed in a variety of cells within the tumor microenvironment (TME), including both the malignant tumor cells as well as stromal cell populations such as macrophages, dendritic cells and regulatory T-cells. Intrinsically to the cell, HO-1 activity provides antioxidant, anti-apoptotic and cytoprotective effects via its catabolites as well as clearing toxic intracellular heme. However, the catabolites of heme degradation can also diffuse outside of the cell to extrinsically modulate the wider TME, influencing cellular functionality and biological processes which promote tumor progression, such as facilitating angiogenesis and metastasis, as well as promoting anti-inflammation and immune suppression. Pharmacological inhibition of HO-1 has been demonstrated to be a promising therapeutic approach to promote anti-tumor immune responses and inhibit metastasis. However, these biological functions might be context, TME and cell type-dependent as there is also conflicting reports for HO-1 activity facilitating anti-tumoral processes. This review will consider our current understanding of the role of HO-1 in cancer progression and as a therapeutic target in cancer.

Hollow ZnO nanorices prepared by a simple hydrothermal method for NO2 and SO2 gas sensors.

Chemoresistive gas sensors play an important role in detecting toxic gases for air pollution monitoring. However, the demand for suitable nanostructures that could process high sensing performance remains high. In this study, hollow ZnO nanorices were synthesized by a simple hydrothermal method to detect NO2 and SO2 toxic gases efficiently. Material characterization by some advanced techniques, such as scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and Raman spectroscopy, demonstrated that the hollow ZnO nanorices had a length and diameter size of less than 500 and 160 nm, respectively. In addition, they had a thin shell thickness of less than 30 nm, formed by an assembly of tiny nanoparticles. The sensor based on the hollow ZnO nanorices could detect low concentration of NO2 and SO2 gasses at sub-ppm level. At an optimum operating temperature of 200 °C, the sensor had response values of approximately 15.3 and 4.8 for 1 ppm NO2 and 1 ppm SO2, respectively. The sensor also exhibited good stability and selectivity, suggesting that the sensor can be applied to NO2 and SO2 toxic gas detection in ambient air.

TGF-ß1 potentiates Vγ9Vδ2 T cell adoptive immunotherapy of cancer.

Despite its role in cancer surveillance, adoptive immunotherapy using γδ T cells has achieved limited efficacy. To enhance trafficking to bone marrow, circulating Vγ9Vδ2 T cells are expanded in serum-free medium containing TGF-ß1 and IL-2 (γδ[T2] cells) or medium containing IL-2 alone (γδ[2] cells, as the control). Unexpectedly, the yield and viability of γδ[T2] cells are also increased by TGF-ß1, when compared to γδ[2] controls. γδ[T2] cells are less differentiated and yet display increased cytolytic activity, cytokine release, and antitumor activity in several leukemic and solid tumor models. Efficacy is further enhanced by cancer cell sensitization using aminobisphosphonates or Ara-C. A number of contributory effects of TGF-ß are described, including prostaglandin E2 receptor downmodulation, TGF-ß insensitivity, and upregulated integrin activity. Biological relevance is supported by the identification of a favorable γδ[T2] signature in acute myeloid leukemia (AML). Given their enhanced therapeutic activity and compatibility with allogeneic use, γδ[T2] cells warrant evaluation in cancer immunotherapy.

RIPK1 Promotes Energy Sensing by the mTORC1 Pathway.

The mechanisms of cellular energy sensing and AMPK-mediated mTORC1 inhibition are not fully delineated. Here, we discover that RIPK1 promotes mTORC1 inhibition during energetic stress. RIPK1 is involved in mediating the interaction between AMPK and TSC2 and facilitate TSC2 phosphorylation at Ser1387. RIPK1 loss results in a high basal mTORC1 activity that drives defective lysosomes in cells and mice, leading to accumulation of RIPK3 and CASP8 and sensitization to cell death. RIPK1-deficient cells are unable to cope with energetic stress and are vulnerable to low glucose levels and metformin. Inhibition of mTORC1 rescues the lysosomal defects and vulnerability to energetic stress and prolongs the survival of RIPK1-deficient neonatal mice. Thus, RIPK1 plays an important role in the cellular response to low energy levels and mediates AMPK-mTORC1 signaling. These findings shed light on the regulation of mTORC1 during energetic stress and unveil a point of crosstalk between pro-survival and pro-death pathways.

Molecular dynamics simulations of the mechanical behavior of alumina coated aluminum nanowires under tension and compression.

For materials with high oxygen affinity, oxide layers will significantly change the material properties. This is of particular importance for aluminum nanowires which have many applications because of their ultrahigh strengths. Recent studies show that thin amorphous oxide shell layers on aluminum surfaces significantly change the responses of the material. However, the relations between the thickness of the oxidized layer, the strain rate and the mechanical response of nanowires to compression and tension have not been investigated intensively. In this study, we use a ReaxFF potential to analyze the influences of oxide shell layers on the material responses of the nanowires under uniaxial tension and compression at different strain rates. The Al-O interface leads to an increased defect nucleation rate at the oxide interface preventing localized deformation. During tension, we observe a reorganization of the structure of the oxide layer leading to bond healing and preventing fracture. While ductility is increasing with coating thickness during tension, the thickness of the coating is less decisive during compression.

TAM Kinases Promote Necroptosis by Regulating Oligomerization of MLKL.

Necroptosis, a cell death pathway mediated by the RIPK1-RIPK3-MLKL signaling cascade downstream of tumor necrosis factor α (TNF-α), has been implicated in many inflammatory diseases. Members of the TAM (Tyro3, Axl, and Mer) family of receptor tyrosine kinases are known for their anti-apoptotic, oncogenic, and anti-inflammatory roles. Here, we identify an unexpected role of TAM kinases as promoters of necroptosis, a pro-inflammatory necrotic cell death. Pharmacologic or genetic targeting of TAM kinases results in a potent inhibition of necroptotic death in various cellular models. We identify phosphorylation of MLKL Tyr376 as a direct point of input from TAM kinases into the necroptosis signaling. The oligomerization of MLKL, but not its membranal translocation or phosphorylation by RIPK3, is controlled by TAM kinases. Importantly, both knockout and inhibition of TAM kinases protect mice from systemic inflammatory response syndrome. In conclusion, this study discovers that immunosuppressant TAM kinases are promoters of pro-inflammatory necroptosis, shedding light on the biological complexity of the regulation of inflammation.

The stochastic Fitzhugh-Nagumo neuron model in the excitable regime embeds a leaky integrate-and-fire model.

In this paper, we provide a complete mathematical construction for a stochastic leaky-integrate-and-fire model (LIF) mimicking the interspike interval (ISI) statistics of a stochastic FitzHugh-Nagumo neuron model (FHN) in the excitable regime, where the unique fixed point is stable. Under specific types of noises, we prove that there exists a global random attractor for the stochastic FHN system. The linearization method is then applied to estimate the firing time and to derive the associated radial equation representing a LIF equation. This result confirms the previous prediction in Ditlevsen and Greenwood (J Math Biol 67(2):239-259, 2013) for the Morris-Lecar neuron model in the bistability regime consisting of a stable fixed point and a stable limit cycle.

Nonautonomous Young Differential Equations Revisited.

In this paper we prove that under mild conditions a nonautonomous Young differential equation possesses a unique solution which depends continuously on initial conditions. The proofs use estimates in p-variation norms, the construction of greedy sequence of times, and Gronwall-type lemma with the help of Shauder theorem of fixed points.

BRAF and AXL oncogenes drive RIPK3 expression loss in cancer.

Necroptosis is a lytic programmed cell death mediated by the RIPK1-RIPK3-MLKL pathway. The loss of Receptor-interacting serine/threonine-protein kinase 3 (RIPK3) expression and necroptotic potential have been previously reported in several cancer cell lines; however, the extent of this loss across cancer types, as well as its mutational drivers, were unknown. Here, we show that RIPK3 expression loss occurs progressively during tumor growth both in patient tumor biopsies and tumor xenograft models. Using a cell-based necroptosis sensitivity screen of 941 cancer cell lines, we find that escape from necroptosis is prevalent across cancer types, with an incidence rate of 83%. Genome-wide bioinformatics analysis of this differential necroptosis sensitivity data in the context of differential gene expression and mutation data across the cell lines identified various factors that correlate with resistance to necroptosis and loss of RIPK3 expression, including oncogenes BRAF and AXL. Inhibition of these oncogenes can rescue the RIPK3 expression loss and regain of necroptosis sensitivity. This genome-wide analysis also identifies that the loss of RIPK3 expression is the primary factor correlating with escape from necroptosis. Thus, we conclude that necroptosis resistance of cancer cells is common and is oncogene driven, suggesting that escape from necroptosis could be a potential hallmark of cancer, similar to escape from apoptosis.

Three-dimensional reconstruction of a random fibrous medium: Geometry, transport, and sound absorbing properties.

The main purpose of this article is to present, within a unified framework, a technique based on numerical homogenization, to model the acoustical properties of real fibrous media from their geometrical characteristics and to compare numerical results with experimental data. The authors introduce a reconstruction procedure for a random fibrous medium and use it as a basis for the computation of its geometrical, transport, and sound absorbing properties. The previously ad hoc "fiber anisotropies" and "volume weighted average radii," used to describe the experimental data on microstructure, are here measured using scanning electron microscopy. The authors show that these parameters, in conjunction with the bulk porosity, contribute to a precise description of the acoustical characteristics of fibrous absorbents. They also lead to an accurate prediction of transport parameters which can be used to predict acoustical properties. The computed values of the permeability and frequency-dependent sound absorption coefficient are successfully compared with permeability and impedance-tube measurements. The authors' results indicate the important effect of fiber orientation on flow properties associated with the different physical properties of fibrous materials. A direct link is provided between three-dimensional microstructure and the sound absorbing properties of non-woven fibrous materials, without the need for any empirical formulae or fitting parameters.

The house fly Y Chromosome is young and minimally differentiated from its ancient X Chromosome partner.

Canonical ancient sex chromosome pairs consist of a gene rich X (or Z) Chromosome and a male-limited (or female-limited) Y (or W) Chromosome that is gene poor. In contrast to highly differentiated sex chromosomes, nascent sex chromosome pairs are homomorphic or very similar in sequence content. Nascent sex chromosomes can arise if an existing sex chromosome fuses to an autosome or an autosome acquires a new sex-determining locus/allele. Sex chromosomes often differ between closely related species and can even be polymorphic within species, suggesting that nascent sex chromosomes arise frequently over the course of evolution. Previously documented sex chromosome transitions involve changes to both members of the sex chromosome pair (X and Y, or Z and W). The house fly has sex chromosomes that resemble the ancestral fly karyotype that originated ∼100 million yr ago; therefore, the house fly is expected to have X and Y Chromosomes with different gene content. We tested this hypothesis using whole-genome sequencing and transcriptomic data, and we discovered little evidence for genetic differentiation between the X and Y in house fly. We propose that the house fly has retained the ancient X Chromosome, but the ancestral Y was replaced by an X Chromosome carrying a new male determining gene. Our proposed hypothesis provides a mechanism for how one member of a sex chromosome pair can experience evolutionary turnover while the other member remains unaffected.

Effective fiber diameter for modeling the acoustic properties of polydisperse fiber networks.

The purpose of this research is to determine whether the acoustic properties of polydisperse fibrous medium (PDFM) and bidisperse fibrous medium (BDFM) can be modeled by monodisperse fiber media (MDFM) with an effective fiber diameter. Multi-scale numerical simulations on representative elementary volumes of these media are performed to retrieve the transport and geometrical properties governing their acoustic properties. Results show no significant difference between predictions obtained by PDFM or BDFM, and their corresponding effective MDFM.

Finite-time Lyapunov exponents and metabolic control coefficients for threshold detection of stimulus-response curves.

In biochemical networks transient dynamics plays a fundamental role, since the activation of signalling pathways is determined by thresholds encountered during the transition from an initial state (e.g. an initial concentration of a certain protein) to a steady-state. These thresholds can be defined in terms of the inflection points of the stimulus-response curves associated to the activation processes in the biochemical network. In the present work, we present a rigorous discussion as to the suitability of finite-time Lyapunov exponents and metabolic control coefficients for the detection of inflection points of stimulus-response curves with sigmoidal shape.

3-Methyl-4,5-dihydro-oxepine, polyoxygenated seco-cyclohexenes and cyclohexenes from Uvaria flexuosa and their anti-inflammatory activity.

The phytochemical investigation of the methanolic extract of Uvaria flexuosa (Annonaceae) leaves led to the isolation of seven compounds including, 3-methyl-4,5-dihydro-oxepine (flexuvaroxepine A), four polyoxygenated seco-cyclohexene (flexuvarin A-D) and two polyoxygenated cyclohexene (flexuvarol A-B) derivatives, together with four known flavones. The structures of the isolated compounds were elucidated using different spectroscopic techniques. A plausible biogenetic route of the new compounds was discussed. The anti-inflammatory activity of the isolated compounds was evaluated by superoxide anion generation and elastase release assays. Among the tested compounds, flexuvarol B and chrysin showed the most potent anti-inflammatory effect by inhibiting superoxide anion generation and elastase release from human neutrophils in response to fMLP with IC50 2.25-5.55µM. Moreover, 5-hydroxy-6,7-dimethoxy-flavone showed selective inhibitory activity on superoxide anion generation (IC50=1.19±0.34µM).