High Spatial Resolution Research Articles

Nanoscale Mapping of Carrier Distribution Regulated by Polarization in 2D FeFETs.

The emergence of 2D ferroelectrics, sliding ferroelectrics, and 2D ferroelectric semiconductors has greatly expanded the potential applications of two-dimensional ferroelectric field-effect transistors (2D FeFETs) in nonvolatile memory, neuromorphic synapses, and negative capacitance. However, the interaction between ferroelectric and semiconductor layers remains not well understood, and characterization methods to correlate carriers and polarization dynamics at the nanoscale are still lacking. Utilizing in situ scanning microwave impedance microscopy and piezoresponse force microscopy measurements, we employed a Pb(Zr0.2Ti0.8)O3/MoS2-based 2D FeFET as an example to reveal, with high spatial resolution, the microscopic redistribution of carriers. This study uncovers the microscopic behavior of ferroelectric-semiconductor heterojunctions, paving the way for a deeper understanding of ferroelectric-gating effects and retention issues at the nanoscale in 2D FeFETs.

Rimota-Gd: Paramagnetic Probe for In Vivo MRI Studies of the Cannabinoid 1 Receptor Distribution in the Mouse Brain.

The cannabinoid 1 receptor (CB1) is highly expressed in the central nervous system, where its physiological functions include the regulation of energy balance, pain, and addiction. Herein, we develop and validate a technique to use magnetic resonance imaging (MRI) to investigate the distribution of CB1 across mouse brains with high spatial resolution, expanding previously described in vitro studies and in vivo studies with positron emission tomography (PET). To support the MRI investigations, we developed a ligand that is specific for in vivo neuroimaging of CB1. By chemically conjugating the CB1 antagonist rimonabant acid to a gadolinium chelator, we obtained the paramagnetic probe Rimota-Gd. The specificity of binding of rimonabant acid to CB1 and the relaxation enhancement by the paramagnetic gadolinium permit MRI-based localization of CB1. We used Rimota-Gd to investigate the spatial distribution of CB1 across the mouse brain and compared the results with an investigation using the PET radioligand [18F]MK-9470. Rimota-Gd opens the door for in vivo MRI imaging of CB1 and provides a roadmap for the study of other receptors by whole-brain images with high spatial and temporal resolution.

A 4 km daily gridded meteorological dataset for China from 2000 to 2020.

Multi-variate gridded meteorological data with high spatial resolution play a key role in studies related to climate change. This study constructed a 4 km daily gridded meteorological dataset for mainland of China (China Daily Meteorological Dataset; CDMet) from 2000 to 2020. The dataset includes nine meteorological variables: 2-meter air temperature (maximum, minimum, and mean temperatures), total precipitation, skin temperature, 10-meter wind speed, relative humidity, surface pressure, and sunshine duration. CDMet was generated using an adaptive interpolation scheme, which employed thin-plate spline and random forest methods to construct the interpolation model. Six combinations of location and terrain information were designed and used as covariates in the model together with reanalysis data. Validation with independent observation stations and existing datasets showed that CDMet has acceptable accuracy, reasonable seasonal variability, and precise spatial distribution, and its accuracy is comparable to that of other datasets. Due to its comprehensive variables and high resolution, CDMet can be used as input data for hydrological, agricultural, and ecological models.

Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved.

High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China

Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, APbox and APseg, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in APbox and APseg, respectively. Importantly, the APseg of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment.

Charge-coupled device readout by digital-correlated double sampling

Charge-coupled devices (CCDs) play crucial roles in astronomy owing to their widespread use in the optical band, offering high sensibility, low noise, high dynamic range, and high spatial resolution. Recent advancements in CCDs have focused on improving the sub-electron noise levels for particle detection and enhancing readout speeds through simulation studies. The main objective of this study is to replace the analog processing of CCD signals using the digital-correlated double sampling (DCDS) technique. DCDS allows post-acquisition noise correction through intermittent sampling of an internal reference voltage to provide compact and flexible performance. In this study, DCDS was evaluated using two digital processing systems, namely, a 2.5 mega-samples per second (MSPS) 24-bit analog-to-digital converter (ADC) board and a 250 MSPS 16-bit ADC field-programmable-gate-array( FPGA)-based buffer memory board. The readout noise value at 74 kpix/s (7.1 e) was a significant improvement over that obtained with analog processing (9.7 e). The DCDS implementation demonstrates optimal signal-to-noise ratio for a wide range of readout speeds. Parameters such as the sample positions per pixel, number of samples, and number of pixels were identified to be essential in achieving an accurate gain value. Finally, hardware implementation of the DCDS IP core algorithm on a Xilinx Zynq-7000 AP system-on-a-chip (SoC) showed a significant improvement in power dissipation (7.1 W) compared to the analog Monsoon correlated double sample (CDS) circuit (13.6 W). The DCDS IP core implementation also showed a background noise reduction of up to 34% compared to DCDS offline readout processing using a Python algorithm.

Design and First Tests From Room Temperature to 200 mK of a 16-to-1 CMOS Multiplexing ASIC for High Impedance NbSi TESs

AbstractAchieving high spectral and spatial resolution of wide astrophysical objects in the X-ray band will be the main focus of the future X-ray space telescopes. We explore a new technological solution based on high impedance NbSi TES detectors ($$\sim $$ ∼ 2 M$$\Omega $$ Ω ) enabling the transfer of the pre-amplification stage to higher temperatures (4 K) and the use of a 50 mK CMOS time-division multiplexer to reduce power dissipation at 50 mK. We present the design and first tests, down to 200 mK, of this CMOS ASIC eventually able to work down to 50 mK and multiplexing 16 high impedance NbSi TES detectors to one 4 K amplifier with a total power budget under 2 $$\mu $$ μ W. In parallel of this development we fabricated 4-by-4 NbSi pixel matrices to build a complete demonstrator (comprising the detector array, presented in another paper to be published, the multiplexing ASIC and the 4 K amplification stage). We aim at a multiplexing frame time of 48 $$\mu $$ μ s leaving 3 $$\mu $$ μ s for reading-out each high impedance pixel. The multiplexing ASIC embeds parasitic capacity compensation techniques.

Bidirectional propagating brightenings in arch filament systems observed by Solar Orbiter/EUI

Arch filament systems (AFSs) are chromospheric and coronal manifestations of emerging magnetic flux. Using high spatial resolution observations taken at a high cadence by the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter, we identified small-scale elongated brightenings within the AFSs. These brightenings appear as bidirectional flows along the threads of AFSs. For our study, we investigated the coordinated observations of the AFSs acquired by the EUI instrument and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) on 2022 March 4 and 17. We analyzed 15 bidirectional propagating brightenings from EUI 174 AA images. These brightenings reached propagating speeds of 100--150 km s$^ $. The event observed on March 17 exhibits blob-like structures, which may be signatures of plasmoids and due to magnetic reconnection. In this case, we also observed counterparts in the running difference slit-jaw images in the 1400 AA passbands taken by the Interface Region Imaging Spectrograph (IRIS). Most events show co-temporal intensity variations in all AIA EUV passbands. Together, this implies that these brightenings in the AFSs are dominated by emission from cool plasma with temperatures well below 1 MK. The Polarimetric and Helioseismic Imager (PHI) on board Solar Orbiter provides photospheric magnetograms at a similar spatial resolution as EUI and from the same viewing angle. The magnetograms taken by PHI show signatures of flux emergence beneath the brightenings. This suggests that the events in the AFSs are triggered by magnetic reconnection that may occur between the newly emerging magnetic flux and the preexisting magnetic field structures in the middle of the AFSs. This would also give a natural explanation for the bidirectional propagation of the brightenings near the apex of the AFSs. The interaction of the preexisting field and the emerging flux may be important for mass and energy transfer within the AFSs.

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

Abstract 4139162: Optical coherence tomography measures mechanical contractile properties of the beating murine heart

Introduction: Optical imaging techniques can characterize properties of tissue at high temporal and spatial resolution, offering unique insights into electrically excitable tissues such as nerve and muscle. We hypothesized that the gated high-speed optical coherence tomography (OCT) could profile the contractile properties of mouse myocardial tissue and facilitate investigations of microscale mechanical function in the beating heart. Methods: C57BL/6 male mice (n=6) underwent a sternotomy while anesthetized and ventilated and were imaged at baseline with OCT. Another cohort of C57BL/6 male mice (n=6) were sacrificed and the hearts were ex vivo perfused with crystalloid using a Langendorff system. These hearts were imaged at baseline and after inhibition of contractile function with blebbistatin. OCT was performed using a custom tissue stabilizer and a Thorlabs TEL 321 OCT system, which was synchronized to physiologic signals using custom hardware and software and cardiac pacing protocols (Fig. 1A and 1B). OCT cross-sectional B-scan images (Fig. 1C) were obtained throughout multiple cardiac cycles in both in vivo and ex vivo preparations. Data were processed in MATLAB and ImageJ using the workflow in Fig. 1D. Results: Highly reproducible cross-sectional mean intensity changes were observed over the contractile cycle in vivo and ex vivo (Fig. 1E, left and middle). With mechanical contraction inhibited by blebbistatin, this cyclical intensity extinguished, linking the optical scattering change measured by OCT to myocardial contraction (Fig. 1E, right). B-scan curves averaged over multiple mice and temporally aligned with the ECG waveform (Fig. 1F) demonstrate a signal intensity waveform that reproducibly varies with the contractile cycle for both in vivo and ex vivo preparations. In vivo and ex vivo baseline waveforms both had peak intensity during systole and minimum in end-diastole (Fig. 1F). Reproducibility in ex vivo beating heart preparations without blood perfusion provides evidence that myocardial mechanical properties, not red blood cell flow, generate the intensity changes. Conclusion: Gated high-speed OCT imaging can quantify the mechanical contraction wave in the myocardium. Combined with gated OCT angiography, OCT techniques will provide high resolution imaging of both blood flow and mechanical contractility of the beating heart in small animal models of heart disease.

Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications

IntroductionColorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist’s expertise and laboratory equipment, and patient survival is influenced by the cancer’s stage at detection. Non-invasive spectroscopic techniques can aid early diagnosis, monitor disease progression, and assess changes in physiological parameters in both heterogeneous samples and advanced platforms like Organ-on-Chip (OoC).MethodsIn this study, Raman microspectroscopy combined with Machine Learning was used to analyse structural and biochemical changes in a Caco-2 cell-based intestinal epithelial model before and after treatment with a calcium chelating agent.ResultsThe Machine Learning (ML) algorithm successfully classified different epithelium damage conditions, achieving an accuracy of 91.9% using only 7 features. Two data-splitting approaches, “sample-based” and “spectra-based,” were also compared. Further, Raman microspectroscopy results were confirmed by TEER measurements and immunofluorescence staining.DiscussionExperimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis.

Highly Efficient Flexible Antimony Halide Scintillator Films with In Situ Preparation for High‐Resolution X‐Ray Imaging

AbstractFlexible scintillators with high light yield, spatial resolution and low light scattering are ideal for X‐ray imaging application. However, conventional scintillators are always prepared by crystallization of functional layer, grinding and mixing with polymers, resulting in serious light scattering. Herein, an in situ fabrication strategy is proposed to prepare a low light scattering flexible scintillator film based on 0D antimony halide C38H36P2SbCl5 (MTP2SbCl5). The prepared scintillator film exhibits bright yellow emission with an outstanding photoluminescence quantum yield (PLQY) of 99.69%, and it demonstrates linear responsiveness to X‐ray dose, achieving an impressive light yield of 39800 photons MeV−1 and a low detection limitation of 78.4 nGyair s−1. The scintillator film possesses strong radiation hardness and stability. In addition, low light scattering greatly inhibits optical crosstalk during X‐ray detection, effectively improving the spatial resolution of MTP2SbCl5 film from 4.5 to 10.2 lp mm−1. On account of the simple preparation method and high performance, this work provides guidance for the preparation of high‐efficiency, large‐area, low‐scattering and high‐resolution flexible scintillator in the future.

Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar

Abstract. We demonstrate how high spatial and temporal resolution spaceborne synthetic aperture radar (SAR) imagery can improve slope deformation monitoring. We process ICEYE data over the Brienz/Brinzauls slope instability in the Swiss Alps, where a catastrophic failure occurred on 15 June 2023. Image correlation applied to retrieve surface velocities shows results comparable to in situ measurements. Pre- and post-failure acquisitions used to map areas invaded by debris and to compute associated volumetric changes are in good agreement with photogrammetric data. This study sets the baseline for new-generation satellite SAR data aimed at providing timely information in landslide early warning scenarios.

Evaluation of degradation phenomena in the electric potential distribution inside organic light-emitting diodes by electron holography

Understanding the intrinsic degradation processes of organic light-emitting diodes is necessary to improve their lifetimes. This intrinsic degradation is typically caused by carrier injection at the interface between the hole transport layer (HTL) and the emissive layer (EML). However, revealing the charge behavior in this local region with a high spatial resolution remains challenging. Thus, this study employed electron holography, a transmission electron microscopy (TEM) technique, to measure the nanometer scale potential distribution inside an OLED composed of N,N′-di-[(1-naphthyl)-N,N′-diphenyl]-(1,1′-biphenyl)-4,4′-diamine (α-NPD) and tris-(8-hydroxyquinoline)aluminum (Alq3) that was degraded via continuous voltage application. The α-NPD and Alq3 functioned as the HTL and EML, respectively. The degraded OLED was found to exhibit several potential distributions, depending on the local positions from which the TEM samples were lifted out of the same bulk sample. The distributions included (i) formation of a potential valley at the α-NPD/Alq3 interface, (ii) disappearance of electric fields within the organic layers, and (iii) similar distribution to original before degradation. We suggest that the degradation was caused by charge accumulation, cationization of Alq3, and local failures. Thus, this study revealed the influence of electric degradation at the nanometer scale because of charge injection to the α-NPD/Alq3 interface. Electron holographic degradation analysis near the HTL/EML interface is expected to aid in the development of design guidelines for preventing device degradation and thus extend device lifetime.

Classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals

Abstract Distributed acoustic sensing (DAS) is an optoelectronic technology that utilises fibre optic cables to detect disturbances caused by seismic waves. Using DAS, seismologists can monitor geophysical phenomena at high spatial and temporal resolutions over long distances in inhospitable environments. Field experiments using DAS, are typically associated with large volumes of observations, requiring algorithms for efficient processing and monitoring capabilities. In this study, we present a supervised classifier trained to recognise seismic activity from other sources of hydroacoustic energy. Our classifier is based on a 2D convolutional neural network architecture. The 55km-long ocean-bottom fibre optic cable, located off Cape Muroto in south-west of Japan, was interrogated using DAS. Data were collected during two different monitoring time-periods. Optimisation of the model’s hyperparameters using Gaussian Processes Regression was necessary to prevent issues associated with small sizes of training data. Using a test set of 100 annotated images, we have shown that the top performing model is around $92\%$ accurate in classifying geophysical data from other sources of hydroacoustic energy and ambient noise.

Evaluating the Diagnostic Potential of Four-Dimensional Flow Magnetic Resonance Imaging in Aortic Stenosis Diagnosis: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis evaluates the potential of four-dimensionalflow magnetic resonance imaging(4DFM) in assessing aortic stenosis (AS) compared to traditional imaging modalities like two-dimensional phase contrast MRI (2D MRI) and transthoracic echocardiography (TTE). AS is a common and severe valvular heart disease, particularly in older adults, requiring accurate diagnosis for proper clinical management. Conventional imaging methods have limitations in capturing complex flow dynamics, prompting the need for advanced modalities like 4DFM. The objectives of the review were to determine whether 4DFM offers superior diagnostic metrics, including peak aortic jet velocity, transvalvular pressure gradients, and aortic valve area (AVA), and to identify potential advantages of 4DFM in overcoming the limitations of traditional modalities. This review included six cohort studies with 285 participants, examining the diagnostic accuracy of 4DFM in terms of peak aortic jet velocity, transvalvular pressure gradients, and aortic valve area (AVA). Studies were selected from MEDLINE (PubMed), Cochrane Library, and Google Scholar databases between December 2010 and October 2024. The study pool was limited by stringent inclusion criteria focusing on cohort studies that directly compared 4DFM with TTE or 2D MRI for AS assessment. The National Institutes of Health Quality Assessment Tool and Cochrane ROBINS-I tool were used to assess bias. Quantitative results showed that 4DFM typically measured higher AVA values than TTE, with a mean difference of 0.48 cm² (95% CI: -0.16 to 1.12). For mean pressure gradients, 4DFM reported slightly higher measurements in individual studies, but pooled results showed no significant difference compared to TTE (mean difference: 3.32 mmHg, 95% CI: -2.30 to 8.93). In terms of peak aortic jet velocity, 4DFM demonstrated a pooled mean difference of -0.18 m/s (95% CI: -0.44 to 0.08) compared to TTE. High heterogeneity was observed across studies (e.g., I² = 97% for peak velocity, I² = 93% for AVA), likely due to differences in patient populations, imaging protocols, and software for data analysis.4DFM demonstrates potential as a complementary imaging tool, particularly in complex AS cases where conventional methods like TTE may provide inconclusive results. Its capacity to capture intricate flow dynamics and deliver high spatial resolution could inform clinical decision-making, potentially influencing practice guidelines to integrate 4DFM as a supplementary tool. Limitations such as high costs, specialized training requirements, and access challenges currently restrict widespread adoption. Limitations of this review include small sample sizes, high heterogeneity, and variability in patient populations and imaging protocols. Despite these challenges, 4DFMI demonstrated superior spatial resolution and complex cardiovascular flow assessment, suggesting it could serve as a valuable complement to TTE for more detailed AS evaluation, particularly in complex cases.Future studies should aim to standardize imaging protocols, incorporate larger and more diverse populations, and conduct cost-benefit analyses to support the integration of 4DFM into clinical practice, potentially shaping future diagnostic guidelines.

Obtaining 20 μm Spatial Resolution with a 2940 nm Laser by IR-MALDESI Mass Spectrometry Imaging.

High spatial resolution is a key parameter in mass spectrometry imaging (MSI), enabling a greater understanding of system biology and cellular processes. Using a novel IR laser with good Gaussian beam quality (M2 = 4) coupled with spatial filtering and a reflective objective, 20 μm spatial resolution was obtained by IR-MALDESI. The optical train was optimized on burn paper before demonstrating feasibility for imaging of liver tissue. Finally, a mouse brain was analyzed using nested regions of interest at 20 and 140 μm spatial resolution, detecting neurotransmitters and lipids with high spatial resolution on the corpus callosum and surrounding brain tissue.

Potential, economic and ecological benefits of sweet sorghum bio-industry in China

BackgroundSweet sorghum (Sorghum bicolor) displays an excellent potential to serve as a non-food bioenergy feedstock for bioethanol production in China due to its high potential yield on marginal lands. However, few studies have been conducted on the potential of sweet sorghum yield and appropriate industrial models in different marginal regions in China. This study explored the spatial distribution of potential sweet sorghum production using the Decision Support System for Agrotechnology Transfer (DSSAT) model and proposed three typical industrial models of sweet sorghum industry to calculate their economic and ecological benefits.ResultsThe results indicate that considering the factors of land use, annual precipitation, soil salinity, soil pH, and accumulated temperature, approximately 32.23 million ha of marginal land are suitable for sweet sorghum cultivation in China, and 130 million tonnes (t) of ethanol can be produced. Further, the development of the sweet sorghum industry under the three models can generate 1425.49 billion CNY potential, approximately accounting for 3.57% of industrial added value in China if measured against 2023 levels, and reduce CO2 emissions by 4.68 million t.ConclusionsThis study provides an innovative perspective for the multi-industry large-scale promotion of sweet sorghum in different marginal lands based on the high spatial resolution Geographic Information System (GIS) data by the DSSAT model with a Life Cycle Assessment (LCA) method, and this applies not only to China but also to the worldwide and other types of energy plants.

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning

Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.

Transport-of-intensity phase imaging using commercially available confocal microscope.

Confocal microscopy is an indispensable tool for biologists to observe samples and is useful for fluorescence imaging of living cells with high spatial resolution. Recently, phase information induced by the sample has been attracting attention because of its applicability such as the measurability of physical parameters and wavefront compensation. However, commercially available confocal microscopy has no phase imaging function. We reborn an off-the-shelf confocal microscope as a phase measurement microscope. This is a milestone in changing the perspective of researchers in this field. We would meet the demand of biologists if only they had measured the phase with their handheld microscopes. We proposed phase imaging based on the transport of intensity equation (TIE) in commercially available confocal microscopy. The proposed method requires no modification using a bright field imaging module of a commercially available confocal microscope. The feasibility of the proposed method is confirmed by evaluating the phase difference of a microlens array and living cells of the moss Physcomitrium patens and living mammalian cultured cells. In addition, multi-modal imaging of fluorescence and phase information is demonstrated. TIE-based quantitative phase imaging (QPI) using commercially available confocal microscopy is proposed. We evaluated the feasibility of the proposed method by measuring the microlens array, plant, and mammalian cultured cells. The experimental result indicates that QPI can be realized in commercially available confocal microscopy using the TIE technique. This method will be useful for measuring dry mass, viscosity, and temperature of cells and for correcting phase fluctuation to cancel aberration and scattering caused by an object in the future.