The human oral cavity harbors diverse communities of microbes that live as biofilms: highly ordered, surface-associated assemblages of microbes embedded in an extracellular matrix. Oral microbial ...communities contribute to human health by fine-tuning immune responses and reducing dietary nitrate. Dental caries and periodontal disease are together the most prevalent microbially mediated human diseases worldwide. Both of these oral diseases are known to be caused not by the introduction of exogenous pathogens to the oral environment, but rather by a homeostasis breakdown that leads to changes in the structure of the microbial communities present in states of health. Both dental caries and periodontal disease are mediated by synergistic interactions within communities, and both diseases are further driven by specific host inputs: diet and behavior in the case of dental caries and immune system interactions in the case of periodontal disease. Changes in community structure (taxonomic identity and abundance) are well documented during the transition from health to disease. In this review, changes in biofilm physical structure during the transition from oral health to disease and the concomitant relationship between structure and community function will be emphasized.
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•Dental caries and periodontal disease are the most prevalent microbially mediated diseases that afflict humans.•Dental plaque has a highly ordered structure mediated by intercellular interactions, and environmental and host inputs.•Periodontal disease is associated with shifts in microbial community structure, that is, taxonomic membership and abundance.•Periodontal disease is thought to be mediated by synergist interactions between subgingival microbial communities and host.•Spatial structure of intact supragingival and subgingival biofilms is equally important as taxonomic composition for understanding microbiome changes in health and disease.
In this paper, we propose a new metasurface that is able to reflect a known incoming electromagnetic wave into an arbitrary direction, with perfect power efficiency. This seemingly simple task, which ...we hereafter call perfect anomalous reflection, is actually highly nontrivial because of the differing wave impedances and complex interference between the incident and reflected waves. Heretofore, proposed metasurfaces that achieve perfect anomalous reflection require complicated, deeply subwavelength and/or multilayer element structures, which allow them to couple to and from leaky and/or evanescent waves. In contrast, we demonstrate that using a bipartite Huygens’ metasurface (BHM)—a passive and lossless metasurface with only two cells per period—perfect anomalous reflection can be achieved over a wide angular and frequency range. Through simulations and experiments at 24 GHz, we show that a properly designed BHM can anomalously reflect an incident electromagnetic wave fromθi=50°toθr=−22.5°, with perfect power efficiency to within experimental precision.
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing pipeline. Our model learns a mapping from the raw low-light mosaiced image to the final visually ...compelling image and encompasses low-level tasks, such as demosaicing and denoising, as well as higher-level tasks, such as color correction and image adjustment. The training and evaluation of the pipeline were performed on a dedicated data set containing pairs of low-light and well-lit images captured by a Samsung S7 smartphone camera in both raw and processed JPEG formats. The proposed solution achieves the state-of-the-art performance in objective evaluation of peak signal-to-noise ratio on the subtask of joint denoising and demosaicing. For the full end-to-end pipeline, it achieves better visual quality compared to the manufacturer ISP, in both a subjective human assessment and when rated by a deep model trained for assessing image quality.
Natural killer (NK) cells are the predominant innate lymphocyte subsets that mediate anti-tumor and anti-viral responses, and therefore possess promising clinical utilization. NK cells do not express ...polymorphic clonotypic receptors and utilize inhibitory receptors (killer immunoglobulin-like receptor and Ly49) to develop, mature, and recognize "
" from "
." The essential roles of common gamma cytokines such as interleukin (IL)-2, IL-7, and IL-15 in the commitment and development of NK cells are well established. However, the critical functions of pro-inflammatory cytokines IL-12, IL-18, IL-27, and IL-35 in the transcriptional-priming of NK cells are only starting to emerge. Recent studies have highlighted multiple shared characteristics between NK cells the adaptive immune lymphocytes. NK cells utilize unique signaling pathways that offer exclusive ways to genetically manipulate to improve their effector functions. Here, we summarize the recent advances made in the understanding of how NK cells develop, mature, and their potential translational use in the clinic.
Three important properties of a classification machinery are i) the system preserves the core information of the input data; ii) the training examples convey information about unseen data; and iii) ...the system is able to treat differently points from different classes. In this paper, we show that these fundamental properties are satisfied by the architecture of deep neural networks. We formally prove that these networks with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data. Similar points at the input of the network are likely to have a similar output. The theoretical analysis of deep networks here presented exploits tools used in the compressed sensing and dictionary learning literature, thereby making a formal connection between these important topics. The derived results allow drawing conclusions on the metric learning properties of the network and their relation to its structure, as well as providing bounds on the required size of the training set such that the training examples would represent faithfully the unseen data. The results are validated with state-of-the-art trained networks.
The electronic transport behaviour of materials determines their suitability for technological applications. We develop a computationally efficient method for calculating carrier scattering rates of ...solid-state semiconductors and insulators from first principles inputs. The present method extends existing polar and non-polar electron-phonon coupling, ionized impurity, and piezoelectric scattering mechanisms formulated for isotropic band structures to support highly anisotropic materials. We test the formalism by calculating the electronic transport properties of 23 semiconductors, including the large 48 atom CH
NH
PbI
hybrid perovskite, and comparing the results against experimental measurements and more detailed scattering simulations. The Spearman rank coefficient of mobility against experiment (r
= 0.93) improves significantly on results obtained using a constant relaxation time approximation (r
= 0.52). We find our approach offers similar accuracy to state-of-the art methods at approximately 1/500th the computational cost, thus enabling its use in high-throughput computational workflows for the accurate screening of carrier mobilities, lifetimes, and thermoelectric power.
Living in a Toxic World Nading, Alex M
Annual review of anthropology,
10/2020, Letnik:
49, Številka:
1
Journal Article
Recenzirano
Odprti dostop
While the proliferation of industrial toxic substances over the past century has had drastic environmental and bodily effects, conventional methods of measuring and mitigating those effects continue ...to produce uncertainty. The project of living in a toxic world entails ethical, technical, and aesthetic efforts to understand toxicity as a contingent encounter among beings, systems, and things, rather than as a fundamental characteristic of particular substances. Anthropologists do not just observe such encounters; they live and work within them. This review examines recent anthropological research on toxicity, proposing that responses to toxic disaster and occupational exposure, as well as acts of familial, state, or corporate care, are all modes of "toxic worlding." The review concludes with a summary of recent research in collaborative and engaged anthropology, suggesting that such approaches are essential not so much for purifying or detoxifying the world as for making it otherwise.
Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a ...subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information. We outline core standards and principles and describe commonly encountered problems. Although this guide targets psychological scientists, its high level of abstraction makes it potentially relevant to any subject area or discipline. We argue that systematic reviews are a key methodology for clarifying whether and how research findings replicate and for explaining possible inconsistencies, and we call for researchers to conduct systematic reviews to help elucidate whether there is a replication crisis.
Islet gene expression has been widely studied to better understand the transcriptional features that define a healthy β-cell. Transcriptomes of FACS-purified α-, β-, and δ-cells using bulk ...RNA-sequencing have facilitated our understanding of the complex network of cross talk between islet cells and its effects on β-cell function. However, these approaches were by design not intended to resolve heterogeneity between individual cells. Several recent studies used single-cell RNA sequencing (scRNA-Seq) to report considerable heterogeneity within mouse and human β-cells. In this Perspective, we assess how this newfound ability to assess gene expression at single-cell resolution has enhanced our understanding of β-cell heterogeneity. We conduct a comprehensive assessment of several single human β-cell transcriptome data sets and ask if the heterogeneity reported by these studies showed overlap and concurred with previously known examples of β-cell heterogeneity. We also illustrate the impact of the inevitable limitations of working at or below the limit of detection of gene expression at single cell resolution and their consequences for the quality of single-islet cell transcriptome data. Finally, we offer some guidance on when to opt for scRNA-Seq and when bulk sequencing approaches may be better suited.
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These ...leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.
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