Landau-Zener-Stuckelberg-Majorana interference in a 3D transmon driven by a chirped microwave

By driving a 3D transmon with microwave fields, we generate an effective avoided energy-level crossing. Then we chirp microwave frequency, which is equivalent to driving the system through the avoided energy-level crossing by sweeping the avoided crossing. A double-passage chirp produces Landau-Zener-St\"uckelberg-Majorana interference that agree well with the numerical results. Our method is fully applicable to other quantum systems that contain no intrinsic avoided level crossing, providing an alternative approach for quantum control and quantum simulation

Perception of Official Corruption, Satisfaction With Government Performance, and Subjective Wellbeing—An Empirical Study From China

Both corruption and subjective wellbeing are of concern to academics and governments. Although some evidence suggests that corruption deteriorates subjective wellbeing, the relationship between perception of official corruption and subjective wellbeing is still unknown. This study aims to examine the link between perceived official corruption and subjective wellbeing in the context of China and whether satisfaction with government performance has a mediating effect in the process. Based on data from China General Social Survey, a structural equation model was used to test the hypotheses. The results of 3,033 Chinese respondents suggest that perception of official corruption is negatively related to subjective wellbeing, and satisfaction with government performance plays a mediating role in the relationship between perception of official corruption and subjective wellbeing

Electrostatic-responsive microdroplet lasers for ultrasensitive molecular detection

Electrostatics plays a critical function in most biomolecules, therefore monitoring subtle biomolecular bindings and dynamics via the electrostatic changes of biomolecules at biointerfaces has been an attractive topic recently and has provided the basis in diagnosis and biomedical science. Here we present a bioelectrostatic responsive microlaser based on liquid crystal (LC) droplet and explored its application for ultrasensitive detection of negatively charged biomolecules. Whispering gallery mode (WGM) lasing from positively charged LC microdroplets was applied as the optical resonator, where the lasing wavelength shift was employed as a sensing parameter. With the dual impacts from whispering-gallery mode and liquid crystal, molecular binding signals will be amplified in such LC droplet sensors. It is found that molecular electrostatic changes at the biointerface of droplet triggered wavelength shift in lasing spectra. The total wavelength shift increased proportionally with the adhering target concentrations. Compared to a conventional polarized optical microscope, significant improvements in sensitivity and dynamic range by four orders of magnitude were achieved. Our work indicated that the surface-to-volume ratio plays a critical role in the detection sensitivity in WGM laser-based microsensors. Finally, bovine serum albumin and specific biosensing using streptavidin and biotin were exploited to demonstrate the potential applications of microlasers with a detection limit on the order of 1 pM. We anticipate this approach will open new possibilities for the ultrasensitive label-free detection of charged biomolecules and molecular interactions by providing a lower detection limit than conventional methods

CogVLM: Visual Expert for Pretrained Language Models

We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B. Codes and checkpoints are available at https://github.com/THUDM/CogVLM

Effects of habitat usage on hypoxia avoidance behavior and exposure in reef-dependent marine coastal species

Reef habitat in coastal ecosystems is increasingly being augmented with artificial reefs (ARs) and is simultaneously experiencing increasing hypoxia due to eutrophication and climate change. Relatively little is known about the effects of hypoxia on organisms that use complex habitat arrangements and how the presence of highly preferred AR habitat can affect the exposure of organisms to low dissolved oxygen (DO). We performed two laboratory experiments that used video recording of behavioral movement to explore 1) habitat usage and staying duration of individuals continuously exposed to 3, 5, and 7 mg/L dissolved oxygen (DO) in a complex of multiple preferred and avoided habitat types, and 2) the impact of ARs on exposure to different DO concentrations under a series of two-way replicated choice experiments with or without AR placement on the low-oxygen side. Six common reef-dependent species found in the northeastern sea areas of China were used (i.e., rockfish Sebastes schlegelii and Hexagrammos otakii, filefish Thamnaconus modestus, flatfish Pseudopleuronectes yokohamae, sea cucumber Stichopus japonicus, and crab Charybdis japonica). Results showed that lower DO levels decreased the usage of preferred habitats of the sea cucumber and the habitat-generalist filefish but increased the habitat affinity to preferred habitat types for the two habitat-specific rockfishes. Low DO had no effect on the crab’s habitat usage. In the choice experiment, all three fish species avoided 1 mg/L, and the rockfish S. schlegelii continued to avoid the lower DO when given choices involving pairs of 3, 5, and 7 mg/L, while H. otakii and the flatfish showed less avoidance. The availability of ARs affected exposure to low DO for the habitat-preferring rockfishes but was not significant for the flatfish. This study provides information for assessing the ecological effects and potential for adaptation through behavioral movement for key reef-dependent species under the increasing overlap of ARs and hypoxia anticipated in the future

Isolation and characterization of Hc-targeting chimeric heavy chain antibodies neutralizing botulinum neurotoxin type B

BackgroundBotulinum neurotoxin (BoNT) produced by Clostridium botulinum is one of the most potent known toxins. Moreover, BoNT is classified as one of the most important biological warfare agents that threatens the biosafety of the world. Currently, the approved treatment for botulism in humans is the use of polyvalent horse serum antitoxins. However, they are greatly limited because of insufficient supply and adverse reactions. Thus, treatment of human botulism requires the development of effective toxin-neutralizing antibodies. Considering their advantages, neutralizing nanobodies will play an increasing role as BoNTs therapeutics.MethodsHerein, neutralizing nanobodies binding to the heavy chain (Hc) domain of BoNT/B (BHc) were screened from a phage display library. Then, BoNT/B-specific clones were identified and fused with the human Fc fragment (hFc) to form chimeric heavy chain antibodies. Finally, the affinity, specificity, and neutralizing activity of antibodies against BoNT/B in vivo were evaluated.ResultsThe B5-hFc, B9-hFc and B12-hFc antibodies demonstrated high affinity for BHc in the nanomolar range. The three antibodies were proven to have potent neutralizing activity against BoNT/B in vivo.ConclusionThe results demonstrate that inhibiting toxin binding to the host receptor is an efficient strategy and the three antibodies could be used as candidates for the further development of drugs to prevent and treat botulism

Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability; (b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p<0.001). In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.Comment: 23 pages, 16 figure

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

Treatment response assessment of breast masses on dynamic contrastâ enhanced magnetic resonance scans using fuzzy câ means clustering and level set segmentation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134958/1/mp8101.pd