Real-time Realistic Volume Rendering of Consistently High Quality with Dynamic Illumination.

Direct Volume Rendering (DVR) plays an important role in scientific data visualization. To generate photo-realistic DVR results, the physical light transport throughout the volume is simulated by applying the Monte Carlo-based volumetric path tracing (VPT) approach. For real-time applications, due to the time constraint for rendering each frame, only a limited number of samples shall be taken for the computation per pixel. This can result in a significant amount of noise in the rendering results. This paper describes our optimized VPT sampling algorithm and a novel denoising technique to generate consistently high-quality realistic DVR results in real time. We develop a new shading model that can reduce estimation variance to enhance the quality of DVR results. Additionally, a hybrid acceleration structure is created by integrating both octree and macrocell to improve sampling efficiency. This allows the acquisition of sufficiently more shading samples while maintaining the desired interactive frame rate. To further eliminate remaining noise and improve temporal stability of DVR results, we develop a novel spatiotemporal denoising framework. Our denoiser decouples the estimated radiance into high-detail low-noise and low-detail high-noise components. Different denoising algorithms are separately applied to these components to reduce noise without introducing blurring artifacts. Our DVR system can consistently offer high rendering quality and good temporal stability across DVR result frames in real time. During fast user interactions and with rapid alterations of the illumination condition, our rendering method can still provide good visual comfort and representation accuracy without visible latency.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Regulating intermolecular hydrogen bonding to construct solvation-induced emission shift quinoline derivatives for real-time monitoring of water content in organic solvents.

Water constitutes the most prevalent impurity in organic solvents, exerting significant influence on chemical reactions and potentially leading to fires and explosions, even in minute quantities. Thus, the development of convenient, rapid, and cost effective fluorescent probes for real-time monitoring of water content in organic solvents is imperative. Although some fluorescent materials have been synthesized for this purpose, most suffer from laborious preparation processes and poor cycling performance, constraining their practical application. This study investigates the impact of hydrogen bonding on the aggregation-induced emission (AIE) properties of quinoline derivatives, leveraging quinoline as the foundational scaffold and its nitrogen atom as the hydrogen bond acceptor. Research findings elucidate that intermolecular hydrogen bonding of quinoline is the primary determinant of their AIE behaviors. By harnessing the phenomenon of water molecules forming intermolecular hydrogen bonds with quinoline nitrogen atoms, we devised a straightforward and rapid method to fabricate a fluorescent test paper for real-time monitoring of water content in organic solvents. Experimental results demonstrate that even minute changes in water content, down to concentrations as low as 0.5 % by volume in organic solvents, can induce fluorescence changes in the test paper, which also exhibits favorable cycling performance. This study not only explores the influence of hydrogen bonds on the AIE properties of quinoline derivatives but also pioneers the development of a cost-effective, rapid, and recyclable test paper for real-time monitoring of water content fluctuations in organic solvents.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.

Objective.Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors.Approach:We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively.Main results:Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.Significance:Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

Vertical directional coupling based grating emission engineering for optical phased arrays.

In this Letter, a novel, to the best of our knowledge, vertical directional coupling waveguide grating (VDCWG) architecture is proposed to increase the length of waveguide grating antennas for large aperture on-chip optical phased arrays (OPAs). In this new architecture, the grating emission strength is engineered by the vertical directional coupler, which provides additional degrees of design freedom. Theoretical analysis and numerical simulation show that the VDCWG can adjust the grating strength in the range of more than two orders of magnitude, corresponding to an effective grating length more than a centimeter. For proof-of-concept, a VDCWG antenna with a length of 1.5 mm is experimentally demonstrated. The grating strength is measured to be 0.17 mm-1, and the far-field divergence angle is 0.061°. A 16-channel OPA is also developed based on the proposed VDCWG, which proves the potential of the new architecture for large aperture OPAs.

Preparation and Performance Evaluation of Castor Oil-Based Asphalt Regeneration Agent.

Regeneration agents play a critical role in modifying the mechanical properties and durability of RAP asphalt mixtures. This paper aimed to develop a castor oil-based asphalt regeneration agent. The effects of this regeneration agent on the pavement performance of laboratory-aged asphalt and an RAP asphalt mixture were comparatively studied by a series of laboratory tests. For the developed castor oil-based asphalt regeneration agent, the weight ratio of the castor oil to dibutyl phthalate was determined as 1:4. Moreover, the regeneration effectiveness of the castor oil-based regeneration agent was tested on three laboratory-aged asphalt binders and an RAP asphalt binder; the penetration, softening point and ductility of the RAP asphalt binder recovered to 83 dmm, 50.3 °C, and more than 100 cm, respectively. The optimum content of the regeneration agent was 5% by the weight of the aged asphalt binder. Furthermore, the castor oil-based regeneration agent could effectively restore the pavement performance of an RAP asphalt mixture. In this study, the RAP percentage can reach up to 60% by the weight of the HMA mixture using the castor oil-based asphalt regeneration agent according to the Chinese specification.

Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing.

Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

Circularly Polarized Luminescence Induced by Hydrogen-Bonding Networks in a One-Dimensional Hybrid Manganese(II) Chloride.

Chiral hybrid metal halides hold great potential as circularly polarized luminescence light sources. Herein, we have obtained two enantiomeric pairs of one-dimensional hybrid chiral manganese(II) chloride single crystals, R/S-(3-methyl piperidine)MnCl3 (R/S-1) and R/S-(3-hydroxy piperidine)MnCl3 (R/S-2), crystallizing in the non-centrosymmetric space group P212121. In comparison to R/S-1, R/S-2 single crystals not only show red emission with near-unity photoluminescence quantum yield (PLQY) and high resistance to thermal quenching but also exhibit circularly polarized luminescence with an asymmetry factor (glum) of 2.5×10-3, which can be attributed to the enhanced crystal rigidity resulting from the hydrogen bonding networks between R/S-(3-hydroxy piperidine) cations and [MnCl6]4- chains. The circularly polarized luminescence activities originate from the asymmetric [MnCl6]4- luminophores induced by N-H⋅⋅⋅Cl hydrogen bonding with R/S-(3-hydroxy piperidine). Moreover, these samples demonstrate great application potential in circularly polarized light-emitting diodes and X-ray scintillators. This work shows a highly efficient photoluminescent Mn-based halide and offers a strategy for designing multifunctional chiral metal halides.

pathCLIP: Detection of Genes and Gene Relations From Biological Pathway Figures Through Image-Text Contrastive Learning.

In biomedical literature, biological pathways are commonly described through a combination of images and text. These pathways contain valuable information, including genes and their relationships, which provide insight into biological mechanisms and precision medicine. Curating pathway information across the literature enables the integration of this information to build a comprehensive knowledge base. While some studies have extracted pathway information from images and text independently, they often overlook the correspondence between the two modalities. In this paper, we present a pathway figure curation system named pathCLIP for identifying genes and gene relations from pathway figures. Our key innovation is the use of an image-text contrastive learning model to learn coordinated embeddings of image snippets and text descriptions of genes and gene relations, thereby improving curation. Our validation results, using pathway figures from PubMed, showed that our multimodal model outperforms models using only a single modality. Additionally, our system effectively curates genes and gene relations from multiple literature sources. Two case studies on extracting pathway information from literature of non-small cell lung cancer and Alzheimer's disease further demonstrate the usefulness of our curated pathway information in enhancing related pathways in the KEGG database.

Improved bacterial composition and co-occurrence patterns of rhizosphere increased nutrient uptake and grain yield through cultivars mixtures in maize.

Crop diversification contributes to agricultural productivity and resources efficient utilization. However, whether cultivar mixtures in maize affects soil bacterial community, nutrient uptake, plant growth and yield remains unknown. A two-year lysimetric experiment was conducted using two maize cultivars (LY16 and JS501) with different root system architectures planted in monoculture or in mixture under normal fertilization (NF), reduced fertilization (RF) or no addition of fertilizer (CK) and was assessed at the silking stages. Cultivar mixtures and monoculture of LY16 had higher shoot biomass, nutrient uptake and total root length at silking stage, and grain yield than monoculture of JS501 under NF and RF conditions. Under NF and RF conditions, cultivar mixtures and monoculture of LY16 led to an increase in bacterial diversity, significant changes in community structure, and a high abundance of Bacteroidia and biomarkers of Chitinophagaceae and Saprospiraceae (Bacteroidia). Cultivar mixtures showed specific responses from modules of the rhizosphere bacterial community co-occurrence network, and the relative abundance of keystone taxa of cultivar mixtures was higher than that of monoculture of JS501. The keystone taxa had a broad and significant positive correlation with plant nutrient accumulation and grain yield. Cultivar mixtures showed similar assembly processes of Bacteroidia with monoculture of LY16, and the increased abundance of Chitinophagaceae may lead to a healthy rhizosphere bacterial community. Overall, our findings indicate that cultivar mixtures significantly affects the assembly and composition of the rhizosphere bacterial community, and thus benefits plant nutrient acquisition and plant growth. These findings could deepen our understanding of the facilitating effect of rhizosphere functional microbial community (e.g. plant nutrition uptake or immunity)of cultivar mixtures.

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

Pyrotinib and Trastuzumab Plus Chemotherapy Serve as an Acceptable Neoadjuvant Regimen Exhibiting Good Efficacy and Tolerance in HER2-Positive Breast Cancer Patients.

Objective: Pyrotinib, a new irreversible dual pan-human epidermal growth factor receptor 2 (HER2) receptor tyrosine kinase inhibitor blocking EGFR and HER2, has achieved a promising efficacy for advanced HER2-positive (HER2+) breast cancer. This study intended to further investigate the efficacy and safety of neoadjuvant pyrotinib and trastuzumab plus chemotherapy for HER2+ breast cancer treatment. Methods: Thirty-eight HER2+ breast cancer patients who received neoadjuvant pyrotinib and trastuzumab plus chemotherapy (docetaxel and carboplatin) were retrospectively reviewed. Clinical response by Response Evaluation Criteria in Solid Tumors (RECIST), pathological complete response (pCR), and adverse events data was retrieved. Results: According to the RECIST, the complete response rate was 0.0%, 10.5%, and 15.8% after second-cycle, fourth-cycle, and sixth-cycle therapy, respectively; whereas the objective response rate was 76.3%, 92.1%, and 100.0%, accordingly. The total pCR (tpCR) rate was 52.6%, the pCR rate of the breast was also 52.6%, and the pCR rate of lymph nodes was 86.8%. The tpCR rate was lower in patients with HER2 immunohistochemistry (IHC)++ and amplification by fluorescent in situ hybridization (FISH) than in those with HER2 IHC+++ (14.3% vs. 61.3%, p = 0.024), which was also lower in patients with Ki-67 expression ≥30% than in those with Ki-67 expression <30% (40.0% vs. 76.9%, p = 0.031). The common adverse events included diarrhea (84.2%), anemia (73.7%), nausea and vomiting (63.2%), fatigue (50.0%), hypomagnesemia (44.7%), leukopenia (42.1%), thrombocytopenia (39.5%), elevated transaminase (36.8%), and pruritus (31.6%). Conclusions: Pyrotinib and trastuzumab plus chemotherapy serve as an acceptable neoadjuvant regimen exhibiting good efficacy and tolerance in HER2+ breast cancer patients, while further large-scale validation is warranted.

Spontaneous crystallization of strongly confined CsSnxPb1-xI3 perovskite colloidal quantum dots at room temperature.

The scalable and low-cost room temperature (RT) synthesis for pure-iodine all-inorganic perovskite colloidal quantum dots (QDs) is a challenge due to the phase transition induced by thermal unequilibrium. Here, we introduce a direct RT strongly confined spontaneous crystallization strategy in a Cs-deficient reaction system without polar solvents for synthesizing stable pure-iodine all-inorganic tin-lead (Sn-Pb) alloyed perovskite colloidal QDs, which exhibit bright yellow luminescence. By tuning the ratio of Cs/Pb precursors, the size confinement effect and optical band gap of the resultant CsSnxPb1-xI3 perovskite QDs can be well controlled. This strongly confined RT approach is universal for wider bandgap bromine- and chlorine-based all-inorganic and iodine-based hybrid perovskite QDs. The alloyed CsSn0.09Pb0.91I3 QDs show superior yellow emission properties with prolonged carrier lifetime and significantly increased colloidal stability compared to the pristine CsPbI3 QDs, which is enabled by strong size confinement, Sn2+ passivation and enhanced formation energy. These findings provide a RT size-stabilized synthesis pathway to achieve high-performance pure-iodine all-inorganic Sn-Pb mixed perovskite colloidal QDs for optoelectronic applications.

Analysis of changes in nutrient salts and other water quality indexes in the pond water for largemouth bass (micropterus salmoides) farming.

To explore the changes in nitrite nitrogen, ammoniacal nitrogen, nitrate nitrogen, phosphates, pH, dissolved oxygen, salinity, and water temperature over time and the correlations and mutual influences between these indexes in the traditional farming of largemouth bass, this study selected three ponds in Lizigu Farm in Baodi District of Tianjin, China as research objects. From May to October 2021, nutrient salts and other water quality indexes in the ponds were measured, and water samples were collected at different depths for repetition, Water is collected from the ponds using Plexiglas samplers and sent back to the lab for determination of water quality indexes using our national laboratory standards. According to the analysis of the measurement results, in traditional farming, nitrite nitrogen, ammoniacal nitrogen, nitrate nitrogen, phosphates, pH, dissolved oxygen, salinity, and water temperature in the ponds for largemouth bass all change significantly over time, with different changing trends and certain correlations with each other. In particular, nutrient salts indexes in ponds are influenced by other water quality indexes, human activities, and phytoplankton. During the breeding process, strengthening the dynamic monitoring of nutrient salts and other water quality indexes in the ponds and adjusting the nitrogen, phosphorus, and ammonia levels in the ponds artificially play an important role in preventing eutrophication in the water and promoting the green and sustainable production of pond ecosystems, in particular, allowing better quality growth of the largemouth bass, as well as ensuring the production and economic efficiency. This study provides a theoretical basis and data support for further optimization of traditional pond aquaculture in similar regions, in order to provide aquatic products with better quality and achieve higher economic benefits.

A Comprehensive Evaluation of Large Language Models in Mining Gene Interactions and Pathway Knowledge.

Background: Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways is useful but cannot keep up with the exponential growth of the literature. Large-scale language models (LLMs), notable for their vast parameter sizes and comprehensive training on extensive text corpora, have great potential in automated text mining of biological pathways. Method: This study assesses the effectiveness of 21 LLMs, including both API-based models and open-source models. The evaluation focused on two key aspects: gene regulatory relations (specifically, 'activation', 'inhibition', and 'phosphorylation') and KEGG pathway component recognition. The performance of these models was analyzed using statistical metrics such as precision, recall, F1 scores, and the Jaccard similarity index. Results: Our results indicated a significant disparity in model performance. Among the API-based models, ChatGPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged their API-based counterparts, where Falcon-180b-chat and llama1-7b led with the highest performance in gene regulatory relations (F1 of 0.2787 and 0.1923, respectively) and KEGG pathway recognition (Jaccard similarity index of 0.2237 and 0. 2207, respectively). Conclusion: LLMs are valuable in biomedical research, especially in gene network analysis and pathway mapping. However, their effectiveness varies, necessitating careful model selection. This work also provided a case study and insight into using LLMs as knowledge graphs.

Two-photon fluorescence-guided precise photothermal therapy located in a single cancer cell utilizing bifunctional N-doped carbon quantum dots.

The utilization of carbon quantum dots (CQDs) for photothermal therapy has emerged as a hot research topic. However, there has been limited research on killing one single cancer cell which is critical in reducing unnecessary damage to the surrounding healthy tissues. In this work, we developed a two-photon fluorescence-guided precise photothermal therapy in a single human malignant melanoma (A375) cancer cell utilizing bifunctional N-doped CQDs. Resulting from the two-photon fluorescence of the CQDs, one single cancer cell can be located and simultaneously destroyed by the photothermal effect of the same CQDs. Specifically, the balanced two-photon absorption cross-section (7000 GM) and photoluminescence quantum yield (8.4%) of the CQDs enable the fluorescence-guided photothermal treatment to be achieved in only 5 s under the irradiation of 800 nm laser of 27.5 mW, much faster than the control experiment without the guidance of fluorescence. The heat generated by the aggregated CQDs is in sufficient amounts while being confined in a small area, as evidenced by the numerical simulations and photothermal experiments, to limit the range of thermal treatment in the cells. This work provides a new approach for realizing photothermal therapy with minimal damage and establishes a new application scenario of CQDs for precise tumor ablation.

Development of introgression lines and mapping of qGW2, a novel QTL that confers grain width, in rice (Oryza sativa L.).

Rice grain size is a key determinant of both grain yield and quality. Identification of favorable alleles for use in rice breeding may help to meet the demand for increased yield. In this study, we developed a set of 210 introgression lines (ILs) by using indica variety Huanghuazhan as the donor parent and erect-panicle japonica rice variety Wuyujing3R as the recurrent parent. A total of 133 ILs were selected for high-throughput sequencing. Using specific-locus amplified fragment (SLAF) sequencing technology, 10,103 high-quality SLAF labels evenly distributed on 12 chromosomes were obtained and selected for subsequent analysis. Using a high-density map, quantitative trait locus (QTL) mapping of grain size-related traits was performed, and a total of 38 QTLs were obtained in two environments. Furthermore, qGW2, a novel QTL that controls grain width on chromosome 2, was validated and delimited to a region of 309 kb via substitution mapping. These findings provide new genetic material and a basis for future fine mapping and cloning of favorable QTLs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01453-0.

Buoyancy-driven attraction of active droplets.

For dissolving active oil droplets in an ambient liquid, it is generally assumed that the Marangoni effect results in repulsive interactions, while the buoyancy effects caused by the density difference between the droplets, diffusing product and the ambient fluid are usually neglected. However, it has been observed in recent experiments that active droplets can form clusters due to buoyancy-driven convection (Krüger et al. Eur. Phys. J. E, vol. 39, 2016, pp. 1-9). In this study, we numerically analyze the buoyancy effect, in addition to the propulsion caused by Marangoni flow (with its strength characterized by Péclet number Pe). The buoyancy effects have their origin in (i) the density difference between the droplet and the ambient liquid, which is characterized by Galileo number Ga, and (ii) the density difference between the diffusing product (i.e. filled micelles) and the ambient liquid, which can be quantified by a solutal Rayleigh number Ra. We analyze how the attracting and repulsing behaviour of neighbouring droplets depends on the control parameters Pe, Ga, and Ra. We find that while the Marangoni effect leads to the well-known repulsion between the interacting droplets, the buoyancy effect of the reaction product leads to buoyancy-driven attraction. At sufficiently large Ra, even collisions between the droplets can take place. Our study on the effect of Ga further shows that with increasing Ga, the collision becomes delayed. Moreover, we derive that the attracting velocity of the droplets, which is characterized by a Reynolds number Red, is proportional to Ra1/4/(ℓ/R), where ℓ/R is the distance between the neighbouring droplets normalized by the droplet radius. Finally, we numerically obtain the repulsive velocity of the droplets, characterized by a Reynolds number Rerep, which is proportional to PeRa-0.38. The balance of attractive and repulsive effect leads to Pe ~ Ra0.63, which agrees well with the transition curve between the regimes with and without collision.

Designing Yolk-Shell Nanostructures for Reversible Water-Vapor-Responsive Dual-Mode Switching of Fluorescence and Structural Color.

Metal halide perovskites offer ample opportunities to develop advanced optoelectronic devices. This work showcases that the integration of metal halide perovskites into metal oxide nanoshells with controllable interior cavities can enable water-vapor-responsive dual-mode switching of fluorescence and structural color. Through a ship-in-a-bottle method to introduce a controlled amount of CsPbBr3 into MnO2 nanoshells, we have designed CsPbBr3@MnO2 yolk-shell nanostructures, which can uptake a defined amount of water to exhibit rapid (less than 1 s) and reversible (≥100 cycles) responses in both fluorescence on-off and color change when exposed to dynamic water vapor. These responses originate from the water-triggered phase transformation of CsPbBr3 to CsPb2Br5 and the structural color change of the MnO2 shell. The altered electronic and bonding structure at the oxide-halide interface, rapid water accumulation in the yolk-shell cavity, and protective effect of the oxide shell facilitate the reversible transformations. The response characteristics of the yolk-shell nanostructures have been further demonstrated in fabricating patterned films capable of multiple fluorescence/structural color responses, highlighting their potential for applications in advanced anticounterfeiting and encryption.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.

Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.