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The manipulation of light via nanoengineered surfaces has excited the optical community in the past few decades. Among the many applications enabled by nanophotonic devices, sensing has stood out due to their capability of identifying miniscule refractive index changes. In particular, when free-space propagating light effectively couples into subwavelength volumes created by nanostructures, the strongly-localized near-fields can enhance light’s interaction with matter at the nanoscale. As a result, nanophotonic sensors can non-destructively detect chemical species in real-time without the need of exogenous labels. The impact of such nanophotonic devices on biochemical sensor development became evident as the ever-growing research efforts in the field started addressing many critical needs in biomedical sciences, such as low-cost analytical platforms, simple quantitative bioassays, time-resolved sensing, rapid and multiplexed detection, single-molecule analytics, among others. In this review, the optical transduction methods used to interrogate optical resonances of nanophotonic sensors will be highlighted. Specifically, the optical methodologies used thus far will be evaluated based on their capability of addressing key requirements of the future sensor technologies, including miniaturization, multiplexing, spatial and temporal resolution, cost and sensitivity.

In this research we will try to trace the mechanisms and methods (deceitful and pilgrims) that Bahlul used to achieve his goals, and to communicate his ideas to the surrounding and society, by researching the tagged (Argument deception and its persuasive effect, Anecdotes of Al-Bahlul, for example) and to find out what Al-Bahlul tried to obtain from his work and why. According to the requirements of the topic, the research came in the form of paragraphs that began with the pilgrim deception and its divisions, and then the study of the pilgrim deception through dialogue and controversy, and then the study of the pilgrim deception through exhortation and counseling, followed by ways to achieve this through encouragement and intimidation, and how Bahlul exploited this to achieve his goal and demand, and from Then the argumentative deception by using some linguistic elements, including the interrogation and metaphor. The first was achieved through multiple questioning, and the work of a kind of intensification and accumulation of interrogative methods, and then tracing the metaphor in which Bahlul coexisted from the first metaphor in which he had his own life above his reed, so he was the crazy sane who had solutions for everything that encountered His method of dilemmas, and then following up on what emerged from this method in his stories, in which the metaphor was broadcast through his discussions and controversies. Then a conclusion we showed the most important results that emerged through the research, then the research margins and a list of sources and references._

Phylogenomic studies using genome-wide datasets are quickly becoming the state of the art for systematics and comparative studies, but in many cases, they result in strongly supported incongruent results. The extent to which this conflict is real depends on different sources of error potentially affecting big datasets (assembly, stochastic, and systematic error). Here, we apply a recently developed methodology (GGI or gene genealogy interrogation) and data curation to new and published datasets with more than 1000 exons, 500 ultraconserved element (UCE) loci, and transcriptomic sequences that support incongruent hypotheses. The contentious non-monophyly of the order Characiformes proposed by two studies is shown to be a spurious outcome induced by sample contamination in the transcriptomic dataset and an ambiguous result due to poor taxonomic sampling in the UCE dataset. By exploring the effects of number of taxa and loci used for analysis, we show that the power of GGI to discriminate among competing hypotheses is diminished by limited taxonomic sampling, but not equally sensitive to gene sampling. Taken together, our results reinforce the notion that merely increasing the number of genetic loci for a few representative taxa is not a robust strategy to advance phylogenetic knowledge of recalcitrant groups. We leverage the expanded exon capture dataset generated here for Characiformes (206 species in 23 out of 24 families) to produce a comprehensive phylogeny and a revised classification of the order.

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, and implications). We analyze this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasize or prioritize some over others depending on the proactive or reactive use of accountability and the missions of AI governance.

Coherent control of high–quality factor optical transitions in atoms has revolutionized precision frequency metrology. Leading optical atomic clocks rely on the interrogation of such transitions in either single ions or ensembles of neutral atoms to stabilize a laser frequency at high precision and accuracy. We demonstrate a platform that combines the key strengths of these two approaches, based on arrays of individual strontium atoms held within optical tweezers. We report coherence times of 3.4 seconds, single-ensemble duty cycles up to 96% through repeated interrogation, and frequency stability of 4.7 × 10−16 (τ/s)–1/2. These results establish optical tweezer arrays as a powerful tool for coherent control of optical transitions for metrology and quantum information science.

Autophagy is markedly impaired in Alzheimer’s disease (AD). Here we reveal unique autophagy dysregulation within neurons in five AD mouse models in vivo and identify its basis using a neuron-specific transgenic mRFP-eGFP-LC3 probe of autophagy and pH, multiplex confocal imaging and correlative light electron microscopy. Autolysosome acidification declines in neurons well before extracellular amyloid deposition, associated with markedly lowered vATPase activity and build-up of Aβ/APP-βCTF selectively within enlarged de-acidified autolysosomes. In more compromised yet still intact neurons, profuse Aβ-positive autophagic vacuoles (AVs) pack into large membrane blebs forming flower-like perikaryal rosettes. This unique pattern, termed PANTHOS (poisonous anthos (flower)), is also present in AD brains. Additional AVs coalesce into peri-nuclear networks of membrane tubules where fibrillar β-amyloid accumulates intraluminally. Lysosomal membrane permeabilization, cathepsin release and lysosomal cell death ensue, accompanied by microglial invasion. Quantitative analyses confirm that individual neurons exhibiting PANTHOS are the principal source of senile plaques in amyloid precursor protein AD models. Interrogation of neuronal autophagy in vivo in Alzheimerʼs disease mouse models identified deficient autolysosome acidification as the basis for extreme autophagic stress, yielding β-amyloid accumulation within intact neurons, which are the main source of senile plaques.

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues. The integration of spatial, imaging, and sequencing information enables the mapping of cellular dynamics within a tissue. Here, authors show three algorithms in stLearn software to accurately reveal spatial trajectory, detect cell-cell interactions, and impute missing data.

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene–gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments. GEARS predicts transcriptional changes of multigene perturbations even for genes that were never experimentally perturbed.

The particle image velocimetry (PIV) is a well-established non-invasive optical technique for measuring the velocity field in fluids. Recently, the PIV approach has been extended to granular flows, where the medium under investigation is composed of a discrete number of grains that are typically non-transparent and of super-millimetric size. Granular PIV (g-PIV) still represents a non-standard application, as some accuracy concerns arise. In particular, since granular flows can be highly sheared, the choice of appropriate interrogation windows for the PIV analysis is not trivial. As well, owing to the spatially-averaged nature of the PIV approach, the estimation of second-order statistics remains a very challenging task. Here, we report a laboratory investigation on dry granular flows composed of glass spheres in a rotating drum. The velocity measurements at the sidewall are obtained by using a window deformation multi-pass PIV approach, where the open-source code PIVlab has been specifically used. Different combinations of the number of PIV passes and of interrogation windows are investigated. A slightly modified version of PIVlab allowed us to carry out g-PIV calculations with an arbitrary number of passes (i.e. greater than 4). Comparisons among different analyses helped us to identify reliable settings for g-PIV applications.

The current study assessed the relative propensity of a persuasion-based and a dialogue-based interrogation approach to generate true and false confessions (i.e., their diagnosticity). Following the Russano cheating paradigm, participants were first either induced or not induced to cheat on an experimental task to create innocent and guilty conditions. An experimenter, blind to the participants’ guilt or innocence, then attempted to generate a confession using a persuasion-based or a dialogue-based interrogation approach. Chi-square analyses showed that both interrogation approaches generated an equally high proportion of true confessions from guilty participants (95% for both approaches; p  = 0.942) but that the persuasion-based approach generated substantially more false confessions from innocent participants than the dialogue-based approach (45% vs. 0%, respectively; p  = 0.001). The dialogue-based approach’s clear advantage over the persuasion-based approach in this study adds to the call for law enforcement organizations to utilize dialogue-based approaches within real-world interrogations.