Although perovskite X-ray detectors have revealed promising properties, their dark currents are usually hundreds of times larger than the practical requirements. Here, we report a detector architecture with a unique shunting electrode working as a blanking unit to suppress dark current, and it theoretically can be reduced to zero. We experimentally fabricate the dark-current-shunting X-ray detector, which exhibits a record-low dark current of 51.1 fA at 5 V mm-1, a detection limit of 7.84 nGyair s-1, and a sensitivity of 1.3 × 104 μC Gyair-1 cm-2. The signal-to-noise ratio of our polycrystalline perovskite-based detector is even outperforming many previously reported state-of-the-art single crystal-based X-ray detectors by serval orders of magnitude. Finally, the proof-of-concept X-ray imaging of a 64 × 64 pixels dark-current-shunting detector array is successfully demonstrated. This work provides a device strategy to fundamentally reduce dark current and enhance the signal-to-noise ratio of X-ray detectors and photodetectors in general.

The widespread Mn2+-sensing yybP-ykoY riboswitch controls the expression of bacterial Mn2+ homeostasis genes. Here, we first determine the crystal structure of the ligand-bound yybP-ykoY riboswitch aptamer from Xanthomonas oryzae at 2.96 Å resolution, revealing two conformations with docked four-way junction (4WJ) and incompletely coordinated metal ions. In >100 µs of MD simulations, we observe that loss of divalents from the core triggers local structural perturbations in the adjacent docking interface, laying the foundation for signal transduction to the regulatory switch helix. Using single-molecule FRET, we unveil a previously unobserved extended 4WJ conformation that samples transient docked states in the presence of Mg2+. Only upon adding sub-millimolar Mn2+, however, can the 4WJ dock stably, a feature lost upon mutation of an adenosine contacting Mn2+ in the core. These observations illuminate how subtly differing ligand preferences of competing metal ions become amplified by the coupling of local with global RNA dynamics.

Avalanche Photodiodes (APDs) are key semiconductor components that amplify weak optical signals via the impact ionization process, but this process' stochastic nature introduces 'excess' noise, limiting the useful signal to noise ratio (or sensitivity) that is practically achievable. The APD material's electron and hole ionization coefficients (α and β respectively) are critical parameters in this regard, with very disparate values of α and β necessary to minimize this excess noise. Here, the analysis of thirteen complementary p-i-n/n-i-p diodes shows that alloying GaAs with ≤ 5.1 % Bi dramatically reduces β while leaving α virtually unchanged-enabling a 2 to 100-fold enhancement of the GaAs α/β ratio while extending the wavelength beyond 1.1 µm. Such a dramatic change in only β is unseen in any other dilute alloy and is attributed to the Bi-induced increase of the spin-orbit splitting energy (∆so). Valence band engineering in this way offers an attractive route to enable low noise semiconductor APDs to be developed.

Absorption spectroscopy is one of the most widely used spectroscopic methods. The signal-to-noise ratio in conventional absorption spectroscopy is ultimately limited by the shot noise, which arises from the statistical property of the light used for the measurement. Here we show that the noise in absorption spectra can be suppressed below the shot-noise limit when entangled photon pairs are used for the light source. By combining broadband entangled photon pairs and multichannel detection, we realize the acquisition of sub-shot-noise absorption spectra in the entire visible wavelength. Furthermore, we demonstrate the strength of sub-shot-noise absorption spectroscopy for the identification and quantification of chemical species, which are two primary aims of absorption spectroscopy. For highly diluted binary mixture solutions, sub-shot-noise absorption spectroscopy enables us to determine the concentration of each chemical species with precision beyond the limit of conventional absorption spectroscopy. That is, sub-shot-noise absorption spectroscopy achieves superresolution in concentration measurements.

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.

The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments using more than 40 molecularly-specific channels. However, this modality has unique data processing requirements, particularly for patient tissue specimens where signal-to-noise ratios for markers can be low, despite optimization, and pixel intensity artifacts can deteriorate image quality and downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images deploying a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for shot noise image filtering (DeepSNiF). IMC-Denoise outperforms existing methods for adaptive hot pixel and background noise removal, with significant image quality improvement in modeled data and datasets from multiple pathologies. This includes in technically challenging human bone marrow; we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately 2 × improved F1 score. Our approach enhances manual gating and automated phenotyping with cell-scale downstream analyses. Verified by manual annotations, spatial and density analysis for targeted cell groups reveal subtle but significant differences of cell populations in diseased bone marrow. We anticipate that IMC-Denoise will provide similar benefits across mass cytometric applications to more deeply characterize complex tissue microenvironments.

Coupling myoelectric and mechanical signals during voluntary muscle contraction is paramount in human-machine interactions. Spatiotemporal differences in the two signals intrinsically arise from the muscular excitation-contraction process; however, current methods fail to deliver local electromechanical coupling of the process. Here we present the locally coupled electromechanical interface based on a quadra-layered ionotronic hybrid (named as CoupOn) that mimics the transmembrane cytoadhesion architecture. CoupOn simultaneously monitors mechanical strains with a gauge factor of ~34 and surface electromyogram with a signal-to-noise ratio of 32.2 dB. The resolved excitation-contraction signatures of forearm flexor muscles can recognize flexions of different fingers, hand grips of varying strength, and nervous and metabolic muscle fatigue. The orthogonal correlation of hand grip strength with speed is further exploited to manipulate robotic hands for recapitulating corresponding gesture dynamics. It can be envisioned that such locally coupled electromechanical interfaces would endow cyber-human interactions with unprecedented robustness and dexterity.

Osteoporosis is caused by increased bone resorption and decreased bone formation. Intermittent administration of a fragment of Parathyroid hormone (PTH) activates osteoblast-mediated bone formation and is used in patients with severe osteoporosis. However, the mechanisms by which PTH elicits its anabolic effect are not fully elucidated. Here we show that the absence of the homeodomain protein TG-interacting factor 1 (Tgif1) impairs osteoblast differentiation and activity, leading to a reduced bone formation. Deletion of Tgif1 in osteoblasts and osteocytes decreases bone resorption due to an increased secretion of Semaphorin 3E (Sema3E), an osteoclast-inhibiting factor. Tgif1 is a PTH target gene and PTH treatment failed to increase bone formation and bone mass in Tgif1-deficient mice. Thus, our study identifies Tgif1 as a novel regulator of bone remodeling and an essential component of the PTH anabolic action. These insights contribute to a better understanding of bone metabolism and the anabolic function of PTH.

During cell division, chromosome congression to the spindle center, their orientation along the spindle long axis and alignment at the metaphase plate depend on interactions between spindle microtubules and kinetochores, and are pre-requisite for chromosome bi-orientation and accurate segregation. How these successive phases are controlled during oocyte meiosis remains elusive. Here we provide 4D live imaging during the first meiotic division in C. elegans oocytes with wild-type or disrupted kinetochore protein function. We show that, unlike in monocentric organisms, holocentric chromosome bi-orientation is not strictly required for accurate chromosome segregation. Instead, we propose a model in which initial kinetochore-localized BHC module (comprised of BUB-1Bub1, HCP-1/2CENP-F and CLS-2CLASP)-dependent pushing acts redundantly with Ndc80 complex-mediated pulling for accurate chromosome segregation in meiosis. In absence of both mechanisms, homologous chromosomes tend to co-segregate in anaphase, especially when initially mis-oriented. Our results highlight how different kinetochore components cooperate to promote accurate holocentric chromosome segregation in oocytes of C. elegans.

The exact drivers for the end-Permian mass extinction (EPME) remain controversial. Here we focus on a ~10,000 yr record from the marine type section at Meishan, China, preceding and covering the onset of the EPME. Analyses of polyaromatic hydrocarbons at sampling intervals representing 1.5-6.3 yr reveal recurrent pulses of wildfires in the terrestrial realm. Massive input pulses of soil-derived organic matter and clastic materials into the oceans are indicated by patterns of C2-dibenzofuran, C30 hopane and aluminum. Importantly, in the ~2,000 years preceding the main phase of the EPME, we observe a clearly defined sequence of wildfires, soil weathering, and euxinia provoked by the fertilization of the marine environment with soil-derived nutrients. Euxinia is indicated by sulfur and iron concentrations. Our study suggests that, in South China, centennial scale processes led to a collapse of the terrestrial ecosystem ~300 yr (120-480 yr; ± 2 s.d.) before the onset of the EPME and that this collapse induced euxinic conditions in the ocean, ultimately resulting in the demise of marine ecosystems.

Protein complexes are highly dynamic entities that display substantial diversity in their assembly, post-translational modifications, and non-covalent interactions, allowing them to play critical roles in various biological processes. The heterogeneity, dynamic nature, and low abundance of protein complexes in their native states present challenges to study using conventional structural biology techniques. Here we develop a native nanoproteomics strategy for the enrichment and subsequent native top-down mass spectrometry (nTDMS) analysis of endogenous cardiac troponin (cTn) complex directly from human heart tissue. The cTn complex is enriched and purified using peptide-functionalized superparamagnetic nanoparticles under non-denaturing conditions to enable the isotopic resolution of cTn complex, revealing their complex structure and assembly. Moreover, nTDMS elucidates the stoichiometry and composition of the cTn complex, localizes Ca2+ binding domains, defines cTn-Ca2+ binding dynamics, and provides high-resolution mapping of the proteoform landscape. This native nanoproteomics strategy opens a paradigm for structural characterization of endogenous native protein complexes.

In vivo electrophysiological recordings of neuronal circuits are necessary for diagnostic purposes and for brain-machine interfaces. Organic electronic devices constitute a promising candidate because of their mechanical flexibility and biocompatibility. Here we demonstrate the engineering of an organic electrochemical transistor embedded in an ultrathin organic film designed to record electrophysiological signals on the surface of the brain. The device, tested in vivo on epileptiform discharges, displayed superior signal-to-noise ratio due to local amplification compared with surface electrodes. The organic transistor was able to record on the surface low-amplitude brain activities, which were poorly resolved with surface electrodes. This study introduces a new class of biocompatible, highly flexible devices for recording brain activity with superior signal-to-noise ratio that hold great promise for medical applications.

On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an 'elastic-D1' regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion.

Optical microscopy improves in resolution and signal-to-noise ratio by correcting for the system's point spread function; a measure of how a point source is resolved, typically determined by imaging nanospheres. Optical-resolution optoacoustic (photoacoustic) microscopy could be similarly corrected, especially to account for the spatially-dependent signal distortions induced by the acoustic detection and the time-resolved and bi-polar nature of optoacoustic signals. Correction algorithms must therefore include the spatial dependence of signals' origins and profiles in time, i.e. the four-dimensional total impulse response (TIR). However, such corrections have been so far impeded by a lack of efficient TIR-characterization methods. We introduce high-quality TIR determination based on spatially-distributed optoacoustic point sources (SOAPs), produced by scanning an optical focus on an axially-translatable 250 nm gold layer. Using a spatially-dependent TIR-correction improves the signal-to-noise ratio by >10 dB and the axial resolution by ~30%. This accomplishment displays a new performance paradigm for optoacoustic microscopy.

Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.

IgG secreted by B cells carry asparagine N(297)-linked glycans in the fragment crystallizable (Fc) region. Changes in Fc glycosylation are related to health or disease and are functionally relevant, as IgG without Fc glycans cannot bind to Fcɣ receptors or complement factors. However, it is currently unknown whether ɣ-heavy chain (ɣHC) glycans also influence the function of membrane-bound IgG-B-cell receptors (BCR) and thus the outcome of the B-cell immune response. Here, we show in a germinal center (GC)-derived human B-cell line that ɣHC glycans do not affect membrane expression of IgG-BCRs. Furthermore, antigen binding or other BCR-facilitated mechanisms appear unaffected, including BCR downmodulation or BCR-mediated signaling. As expected, secreted IgG lacking Fc glycosylation is unable to carry out effector functions. Together, these observations indicate that IgG-Fc glycosylation serves as a mechanism to control the effector functions of antibodies, but does not regulate the activation of IgG-switched B cells, as its absence had no apparent impact on BCR function.

Microalgae are a renewable and promising biomass for large-scale biofuel, food and nutrient production. However, their efficient exploitation depends on our knowledge of the cell wall composition and organization as it can limit access to high-value molecules. Here we provide an atomic-level model of the non-crystalline and water-insoluble glycoprotein-rich cell wall of Chlamydomonas reinhardtii. Using in situ solid-state and sensitivity-enhanced nuclear magnetic resonance, we reveal unprecedented details on the protein and carbohydrate composition and their nanoscale heterogeneity, as well as the presence of spatially segregated protein- and glycan-rich regions with different dynamics and hydration levels. We show that mannose-rich lower-molecular-weight proteins likely contribute to the cell wall cohesion by binding to high-molecular weight protein components, and that water provides plasticity to the cell-wall architecture. The structural insight exemplifies strategies used by nature to form cell walls devoid of cellulose or other glycan polymers.

Recombinant proteins in complex solutions are typically detected with tag-specific antibodies in Western blots. Here we describe an antibody-free alternative in which tagged proteins are detected directly in polyacrylamide gels. For this, the highly specific protein ligase Connectase is used to selectively fuse fluorophores to target proteins carrying a recognition sequence, the CnTag. Compared to Western blots, this procedure is faster, more sensitive, offers a better signal-to-noise ratio, requires no optimization for different samples, allows more reproducible and accurate quantifications, and uses freely available reagents. With these advantages, this method represents a promising alternative to the state of the art and may facilitate studies on recombinant proteins.

We developed a family of genetically encoded serotonin (5-HT) sensors (sDarken) on the basis of the native 5-HT1A receptor and circularly permuted GFP. sDarken 5-HT sensors are bright in the unbound state and diminish their fluorescence upon binding of 5-HT. Sensor variants with different affinities for serotonin were engineered to increase the versatility in imaging of serotonin dynamics. Experiments in vitro and in vivo showed the feasibility of imaging serotonin dynamics with high temporal and spatial resolution. As demonstrated here, the designed sensors show excellent membrane expression, have high specificity and a superior signal-to-noise ratio, detect the endogenous release of serotonin and are suitable for two-photon in vivo imaging.