Adipose stem cells-derived small extracellular vesicles transport Thrombospondin 1 cargo to promote insulin resistance in gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) is a highly prevalent disease and poses a significant risk to the health of pregnant women. Abdominal adipose tissue (AT) contributes to insulin resistance (IR) associated with GDM. However, the underlying mechanisms remain unclear. METHODS: In this study, we developed a mouse model of GDM by subjecting mice to a high-fat diet. We collected adipose-derived stem cells (ADSCs) from the abdominal and inguinal regions and examined their role in inducing IR in normal tissues through the secretion of small extracellular vesicles (sEVs). The sEVs derived from ADSCs isolated from GDM mice (ADSC/GDM) were found to inhibit cell viability and insulin sensitivity in AML12, a normal mouse liver cell line. RESULTS: Through proteomic analysis, we identified high levels of the thrombospondin 1 (Thbs1) protein in the sEVs derived from ADSC/GDM. Subsequent overexpression of Thbs1 protein in AML12 cells demonstrated similar IR as observed with ADSC/GDM-derived sEVs. Mechanistically, the Thbs1 protein within the sEVs interacted with CD36 and transforming growth factor (Tgf) ß receptors in AML12 cells, leading to the activation of Tgfß/Smad2 signaling. Furthermore, the administration of LSKL, an antagonistic peptide targeting Thbs1, suppressed Thbs1 expression in ADSC/GDM-derived sEVs, thereby restoring insulin sensitivity in AML12 cells and GDM mice in vivo. CONCLUSIONS: These findings shed light on the intercellular transmission mechanism through which ADSCs influence hepatic insulin sensitivity and underscore the therapeutic potential of targeting the Thbs1 protein within sEVs.

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ.

Background: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods: The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results: In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions: Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Identification of Burkholderia gladioli pv. cocovenenans in Black Fungus and Efficient Recognition of Bongkrekic Acid and Toxoflavin Producing Phenotype by Back Propagation Neural Network.

Burkholderia gladioli pv. cocovenenans is a serious safety issue in black fungus due to the deadly toxin, bongkrekic acid. This has triggered the demand for an efficient toxigenic phenotype recognition method. The objective of this study is to develop an efficient method for the recognition of toxin-producing B. gladioli strains. The potential of multilocus sequence typing and a back propagation neural network for the recognition of toxigenic B. cocovenenans was explored for the first time. The virulent strains were isolated from a black fungus cultivation environment in Qinba Mountain area, Shaanxi, China. A comprehensive evaluation of toxigenic capability of 26 isolates were conducted using Ultra Performance Liquid Chromatography for determination of bongkrekic acid and toxoflavin production in different culturing conditions and foods. The isolates produced bongkrekic acid in the range of 0.05-6.24 mg/L in black fungus and a highly toxin-producing strain generated 201.86 mg/L bongkrekic acid and 45.26 mg/L toxoflavin in co-cultivation with Rhizopus oryzae on PDA medium. Multilocus sequence typing phylogeny (MLST) analysis showed that housekeeping gene sequences have a certain relationship with a strain toxigenic phenotype. We developed a well-trained, back-propagation neutral network for prediction of toxigenic phenotype in B. gladioli based on MLST sequences with an accuracy of 100% in the training set and an accuracy of 86.7% in external test set strains. The BP neutral network offers a highly efficient approach to predict toxigenic phenotype of strains and contributes to hazard detection and safety surveillance.

Correlation between Baseline Conventional Ultrasounds, Shear-Wave Elastography Indicators, and Neoadjuvant Therapy Efficacy in Triple-Negative Breast Cancer.

In patients with triple-negative breast cancer (TNBC)-the subtype with the poorest prognosis among breast cancers-it is crucial to assess the response to the currently widely employed neoadjuvant treatment (NAT) approaches. This study investigates the correlation between baseline conventional ultrasound (US) and shear-wave elastography (SWE) indicators and the pathological response of TNBC following NAT, with a specific focus on assessing predictive capability in the baseline state. This retrospective analysis was conducted by extracting baseline US features and SWE parameters, categorizing patients based on postoperative pathological grading. A univariate analysis was employed to determine the relationship between ultrasound indicators and pathological reactions. Additionally, we employed a receiver operating characteristic (ROC) curve analysis and multivariate logistic regression methods to evaluate the predictive potential of the baseline US indicators. This study comprised 106 TNBC patients, with 30 (28.30%) in a nonmajor histological response (NMHR) group and 76 (71.70%) in a major histological response (MHR) group. Following the univariate analysis, we found that T staging, dmax values, volumes, margin changes, skin alterations (i.e., thickening and invasion), retromammary space invasions, and supraclavicular lymph node abnormalities were significantly associated with pathological efficacy (p < 0.05). Combining clinical information with either US or SWE independently yielded baseline predictive abilities, with AUCs of 0.816 and 0.734, respectively. Notably, the combined model demonstrated an improved AUC of 0.827, with an accuracy of 76.41%, a sensitivity of 90.47%, a specificity of 55.81%, and statistical significance (p < 0.01). The baseline US and SWE indicators for TNBC exhibited a strong relationship with NAT response, offering predictive insights before treatment initiation, to a considerable extent.

Enhancing CO2 adsorption capacity of ZIF-8 by synergetic effect of high pressure and temperature.

Metal-organic frameworks (MOFs) and zeolitic imidazolate frameworks (ZIFs) are promising porous materials for adsorption and storage of greenhouse gases, especially CO2. In this study, guided by the CO2 phase diagram, we explore the adsorption behavior of solid CO2 loaded with ZIF-8 framework by heating the sample under high pressures, resulting in a drastic improvement in the CO2 uptake. The behavior of CO2 under simultaneous high temperature (T) and pressure (P) conditions is directly monitored by in situ FTIR spectroscopy. The remarkable enhancement in CO2 adsorption capability observed can be attributed to the synergetic effect of high T and P: high temperature greatly enhances the transport property of solid CO2 by facilitating its diffusion into the framework; high pressure effectively modifies the pore size and shape via changing the linker orientation and creating new adsorption sites within ZIF-8. Our study thus provides important new insights into the tunability and enhancement of CO2 adsorptive capability in MOFs/ZIFs using pressure and temperature combined as a synergetic approach.

Effect of WISP1 on paraquat-induced EMT.

BACKGROUND: Paraquat (PQ) can induce pulmonary fibrosis (PF) by modulating epithelial-mesenchymal transition (EMT) of alveolar epithelial cells, but the molecular mechanism is unknown. In this paper, the role of Wnt-inducible signaling protein-1 (WISP1) in PQ-induced EMT was inspected. METHODS: The morphology, apoptosis, and mortality of A549 cells were observed through a microscope. The mRNA and protein levels of WISP1, E-cadherin, and Vimentin were confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot. RESULTS: With the increase of PQ concentration, the morphology of A549 cells was apparently changed, cell apoptosis and mortality were enhanced. Besides, the E-cadherin abundance was reduced (p < 0.01), however, WISP1 and Vimentin contents were boosted after PQ treatment (p < 0.01). With the increase of PQ treatment time, the epithelial index of cells first increased and then decreased. The expression of WISP1 gene increased significantly with the increase of PQ treatment time (p < 0.01). Silence of WISP1 abolished the effect of PQ treatment on E-cadherin and Vimentin levels (p < 0.01). Downregulation of WISP1 curbed morphology change and PQ-induced EMT in A549 cells. CONCLUSION: Knockdown of WISP1 inhibited PQ-induced EMT in A549 cells. This conclusion might provide a new therapeutic target for PQ poisoning treatment.

Giant Piezoelectric Output and Stability Enhancement in Piezopolymer Composites with Liquid Metal Nanofillers.

Integrating nanomaterials into the polymer matrix is an effective strategy to optimize the performance of polymer-based piezoelectric devices. Nevertheless, the trade-off between the output enhancement and stability maintenance of piezoelectric composites usually leads to an unsatisfied overall performance for the high-strength operation of devices. Here, by setting liquid metal (LM) nanodroplets as the nanofillers in a poly(vinylidene difluoride) (PVDF) matrix, the as-formed liquid-solid/conductive-dielectric interfaces significantly promote the piezoelectric output and the reliability of this piezoelectric composite. A giant performance improvement featured is obtained with, nearly 1000% boosting on the output voltage (as high as 212 V), 270% increment on the piezoelectric coefficient (d33 ∼51.1 pC N-1 ) and long-term reliability on both structure and output (over 36 000 cycles). The design of a novel heterogenous interface with both mechanical matching and electric coupling can be the new orientation for developing high performance piezoelectric composite-based devices.

Novel Semiconductive Ternary Hybrid Heterostructures for Artificial Optoelectronic Synapses.

Synaptic devices that mimic biological synapses are considered as promising candidates for brain-inspired devices, offering the functionalities in neuromorphic computing. However, modulation of emerging optoelectronic synaptic devices has rarely been reported. Herein, a semiconductive ternary hybrid heterostructure is prepared with a D-D'-A configuration by introducing polyoxometalate (POM) as an additional electroactive donor (D') into a metalloviologen-based D-A framework. The obtained material features an unprecedented porous 8-connected bcu-net that accommodates nanoscale [α-SiW12 O40 ]4- counterions, displaying uncommon optoelectronic responses. Besides, the fabricated synaptic device based on this material can achieve dual-modulation of synaptic plasticity due to the synergetic effect of electron reservoir POM and photoinduced electron transfer. And it can successfully simulate learning and memory processes similar to those in biological systems. The result provides a facile and effective strategy to customize multi-modality artificial synapses in the field of crystal engineering, which opens a new direction for developing high-performance neuromorphic devices.

2,2,2-Trifluoroethanol-Assisted Construction of 2D/3D Perovskite Heterostructures for Efficient and Stable Perovskite Solar Cells Made in Ambient Air.

To achieve future commercialization of perovskite solar cells (PSCs), balancing the efficiency, stability, and manufacturing cost is required. In this study, we develop an air processing strategy for efficient and stable PSCs based on 2D/3D heterostructures. The organic halide salt phenethylammonium iodide is adopted to in situ construct a 2D/3D perovskite heterostructure, in which 2,2,2-trifluoroethanol as a precursor solvent is introduced to recrystallize 3D perovskite and form an intermixed 2D/3D perovskite phase. This strategy simultaneously passivates defects, reduces nonradiative recombination, prevents carrier quenching, and improves carrier transport. As a result, a champion power conversion efficiency of 20.86% is obtained for air-processed PSCs based on 2D/3D heterostructures. Moreover, the optimized devices exhibit superior stability, remaining more than 91 and 88% of their initial efficiencies after 1800 h of storage under dark condition and 24 h of continuous heating at 100 °C, respectively. Our study provides a convenient method to fabricate all-air-processed PSCs with high efficiency and stability.

Schottky Barrier Control of Self-Polarization for a Colossal Ferroelectric Resistive Switching.

Controlling the domain evolution is critical both for optimizing ferroelectric properties and for designing functional electronic devices. Here we report an approach of using the Schottky barrier formed at the metal/ferroelectric interface to tailor the self-polarization states of a model ferroelectric thin film heterostructure system SrRuO3/(Bi,Sm)FeO3. Upon complementary investigations of the piezoresponse force microscopy, electric transport measurements, X-ray photoelectron/absorption spectra, and theoretical studies, we demonstrate that Sm doping changes the concentration and spatial distribution of oxygen vacancies with the tunable host Fermi level which modulates the SrRuO3/(Bi,Sm)FeO3 Schottky barrier and the depolarization field, leading to the evolution of the system from a single domain of downward polarization to polydomain states. Accompanied by such modulation on self-polarization, we further tailor the symmetry of the resistive switching behaviors and achieve a colossal on/off ratio of ∼1.1 × 106 in the corresponding SrRuO3/BiFeO3/Pt ferroelectric diodes (FDs). In addition, the present FD also exhibits a fast operation speed of ∼30 ns with a potential for sub-nanosecond and an ultralow writing current density of ∼132 A/cm2. Our studies provide a way for engineering self-polarization and reveal its strong link to the device performance, facilitating FDs as a competitive memristor candidate used for neuromorphic computing.

Prediction of clinical response to neoadjuvant therapy in advanced breast cancer by baseline B-mode ultrasound, shear-wave elastography, and pathological information.

Background: Neoadjuvant therapy (NAT) is the preferred treatment for advanced breast cancer nowadays. The early prediction of its responses is important for personalized treatment. This study aimed at using baseline shear wave elastography (SWE) ultrasound combined with clinical and pathological information to predict the clinical response to therapy in advanced breast cancer. Methods: This retrospective study included 217 patients with advanced breast cancer who were treated in West China Hospital of Sichuan University from April 2020 to June 2022. The features of ultrasonic images were collected according to the Breast imaging reporting and data system (BI-RADS), and the stiffness value was measured at the same time. The changes were measured according to the Response evaluation criteria in solid tumors (RECIST1.1) by MRI and clinical situation. The relevant indicators of clinical response were obtained through univariate analysis and incorporated into a logistic regression analysis to establish the prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the prediction models. Results: All patients were divided into a test set and a validation set in a 7:3 ratio. A total of 152 patients in the test set, with 41 patients (27.00%) in the non-responders group and 111 patients (73.00%) in the responders group, were finally included in this study. Among all unitary and combined mode models, the Pathology + B-mode + SWE model performed best, with the highest AUC of 0.808 (accuracy 72.37%, sensitivity 68.47%, specificity 82.93%, P<0.001). HER2+, Skin invasion, Post mammary space invasion, Myometrial invasion and Emax were the factors with a significant predictive value (P<0.05). 65 patients were used as an external validation set. There was no statistical difference in ROC between the test set and the validation set (P>0.05). Conclusion: As the non-invasive imaging biomarkers, baseline SWE ultrasound combined with clinical and pathological information can be used to predict the clinical response to therapy in advanced breast cancer.

Xanthine oxidase, a therapeutic target of realgar for non-small cell lung cancer.

Background: The effects of realgar against non-small cell lung cancer (NSCLC) have been massively studied, but the direct therapeutic targets of realgar remain unclear. This study aimed to identify the molecular targets of realgar against NSCLC and explore their therapeutic mechanisms based on a network pharmacology approach and experimental validations. Methods: The BATMAN-TCM and Digsee databases were used to predict realgar targets and NSCLC-related genes, respectively. A protein-protein interaction network was constructed for each gene set, and the overlapping genes were identified as potential targets of realgar against NSCLC. The correlation between potential targets and NSCLC was analyzed using The Cancer Genome Atlas and International Cancer Genome Consortium databases, and the key target was validated by in-silico and in-vitro experiments. Results: Twenty-three overlapping genes, including xanthine oxidase (XO), were identified as potential targets of realgar against NSCLC. XO was selected as the key target for validation, as it was found to be upregulated in NSCLC tumor tissue, which correlated with poor overall survival. A possible interaction between realgar and XO was revealed by molecular docking which was further validated experimentally. Realgar treatment suppressed the activity of XO in NSCLC cells, as demonstrated by the unchanged XO protein levels. Finally, the mechanism of action of XO as a target against NSCLC through the cell-cell junction organization pathway was investigated. Conclusions: Overall, this study proposes a potential molecular mechanism illustrating that XO is a target of realgar against NSCLC and highlights the usefulness of XO as a therapeutic target for NSCLC.

Spacer-Dependent and Pressure-Tuned Structures and Optoelectronic Properties of 2D Hybrid Halide Perovskites.

Compared with their 3D counterparts, 2D hybrid organic-inorganic halide perovskites (HOIPs) exhibit enhanced chemical stabilities and superior optoelectronic properties, which can be further tuned by the application of external pressure. Here, we report the first high-pressure study on CMA2PbI4 (CMA = cylcohexanemethylammonium), a 2D HOIP with a soft organic spacer cation containing a flexible cyclohexyl ring, using UV-visible absorption, photoluminescence (PL) and vibrational spectroscopy, and synchrotron X-ray microdiffraction, all aided with density functional theory (DFT) calculations. Substantial anisotropic compression behavior is observed, as characterized by unprecedented negative linear compressibility along the b axis. Moreover, the pressure dependence of optoelectronic properties is found to be in strong contrast with those of 2D HOIPs with rigid spacer cations. DFT calculations help to understand the compression mechanisms that lead to pressure-induced bandgap narrowing. These findings highlight the important role of soft spacer cations in the pressure-tuned optoelectronic properties and provide guidance to the design of new 2D HOIPs.

Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach.

Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive.

Digital Counting of Breaks Labeling In Situ: A Fast and Absolute Quantification Method for Measurement of DNA Double-Strand Breaks Based on Digital Polymerase Chain Reaction.

DNA double-strand break (DSB) is the most dangerous type of DNA damage. In addition, DSBs are also common consequences of various therapeutic and genetic modifications. Therefore, quantification of DSB is of great importance in many fields including DNA damage repair, cancer therapy, gene editing, and radiation biology. Current methods are either low-throughput, laborious, or high cost. Here, we developed dc-BLIS (digital counting of breaks labeling in situ), a new method that can rapidly and precisely quantify the number of intracellular DSBs at a low cost by digital polymerase chain reaction. Using dc-BLIS, we quantified and compared the amount of DSBs induced by anti-cancer drugs, Cas9 variants, and different radiation doses, proving the capacity of dc-BLIS to quantify DSBs. We propose that dc-BLIS is suitable for various application scenes that require rapid and precise quantification of DSBs, including drug screening, gene-editing tool modification, and radiation effect assessment.

Identification of six hub genes and two key pathways in two rat renal fibrosis models based on bioinformatics and RNA-seq transcriptome analyses.

Renal fibrosis (RF) is the common pathological manifestation and central treatment target of multiple chronic kidney diseases with high morbidity and mortality. Currently, the molecular mechanisms underlying RF remain poorly understood, and exploration of RF-related hub targets and pathways is urgently needed. In this study, two classical RF rat models (adenine and UUO) were established and evaluated by HE, Masson and immunohistochemical staining. To clear molecular mechanisms of RF, differentially expressed genes (DEGs) were identified using RNA-Seq analysis, hub targets and pathways were screened by bioinformatics (functional enrichment analyses, PPI network, and co-expression analysis), the screening results were verified by qRT-PCR, and potential drugs of RF were predicted by network pharmacology and molecular docking. The results illustrated that renal structures were severely damaged and fibrotic in adenine- and UUO-induced models, as evidenced by collagen deposition, enhanced expressions of biomarkers (TGF-ß1 and α-SMA), reduction of E-cadherin biomarker, and severe renal function changes (significantly decreased UTP, CREA, Ccr, and ALB levels and increased UUN and BUN levels), etc. 1189 and 1253 RF-related DEGs were screened in the adenine and UUO models, respectively. Two key pathways (AGE-RAGE and NOD-like receptor) and their hub targets (Tgfb1, Col1a1, Nlrc4, Casp4, Trpm2, and Il18) were identified by PPI networks, co-expressed relationships, and qRT-PCR verification. Furthermore, various reported herbal ingredients (curcumin, resveratrol, honokiol, etc.) were considered as important drug candidates due to the strong binding affinity with these hub targets. Overall, this study mainly identified two key RF-related pathways (AGE-RAGE and NOD-like receptor), screened hub targets (Tgfb1, Col1a1, Nlrc4, Casp4, Trpm2, and Il18) that involved inflammation, ECM formation, myofibroblasts generation, and pyroptosis, etc., and provided referable drug candidates (curcumin, resveratrol, honokiol, etc.) in basic research and clinical treatment of RF.

Natural polymorphism of ZmICE1 contributes to amino acid metabolism that impacts cold tolerance in maize.

Cold stress negatively affects maize (Zea mays L.) growth, development and yield. Metabolic adjustments contribute to the adaptation of maize under cold stress. We show here that the transcription factor INDUCER OF CBF EXPRESSION 1 (ZmICE1) plays a prominent role in reprogramming amino acid metabolome and COLD-RESPONSIVE (COR) genes during cold stress in maize. Derivatives of amino acids glutamate/asparagine (Glu/Asn) induce a burst of mitochondrial reactive oxygen species, which suppress the cold-mediated induction of DEHYDRATION RESPONSE ELEMENT-BINDING PROTEIN 1 (ZmDREB1) genes and impair cold tolerance. ZmICE1 blocks this negative regulation of cold tolerance by directly repressing the expression of the key Glu/Asn biosynthesis genes, ASPARAGINE SYNTHETASEs. Moreover, ZmICE1 directly regulates the expression of DREB1s. Natural variation at the ZmICE1 promoter determines the binding affinity of the transcriptional activator ZmMYB39, a positive regulator of cold tolerance in maize, resulting in different degrees of ZmICE1 transcription and cold tolerance across inbred lines. This study thus unravels a mechanism of cold tolerance in maize and provides potential targets for engineering cold-tolerant varieties.

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences.

As an important method for crop phenotype quantification, three-dimensional (3D) reconstruction is of critical importance for exploring the phenotypic characteristics of crops. In this study, maize seedlings were subjected to 3D reconstruction based on the imaging technology, and their phenotypic characters were analyzed. In the first stage, a multi-view image sequence was acquired via an RGB camera and video frame extraction method, followed by 3D reconstruction of maize based on structure from motion algorithm. Next, the original point cloud data of maize were preprocessed through Euclidean clustering algorithm, color filtering algorithm and point cloud voxel filtering algorithm to obtain a point cloud model of maize. In the second stage, the phenotypic parameters in the development process of maize seedlings were analyzed, and the maize plant height, leaf length, relative leaf area and leaf width measured through point cloud were compared with the corresponding manually measured values, and the two were highly correlated, with the coefficient of determination (R 2) of 0.991, 0.989, 0.926 and 0.963, respectively. In addition, the errors generated between the two were also analyzed, and results reflected that the proposed method was capable of rapid, accurate and nondestructive extraction. In the third stage, maize stem leaves were segmented and identified through the region growing segmentation algorithm, and the expected segmentation effect was achieved. In general, the proposed method could accurately construct the 3D morphology of maize plants, segment maize leaves, and nondestructively and accurately extract the phenotypic parameters of maize plants, thus providing a data support for the research on maize phenotypes.

aEYE: A deep learning system for video nystagmus detection.

Background: Nystagmus identification and interpretation is challenging for non-experts who lack specific training in neuro-ophthalmology or neuro-otology. This challenge is magnified when the task is performed via telemedicine. Deep learning models have not been heavily studied in video-based eye movement detection. Methods: We developed, trained, and validated a deep-learning system (aEYE) to classify video recordings as normal or bearing at least two consecutive beats of nystagmus. The videos were retrospectively collected from a subset of the monocular (right eye) video-oculography (VOG) recording used in the Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage (AVERT) clinical trial (#NCT02483429). Our model was derived from a preliminary dataset representing about 10% of the total AVERT videos (n = 435). The videos were trimmed into 10-sec clips sampled at 60 Hz with a resolution of 240 × 320 pixels. We then created 8 variations of the videos by altering the sampling rates (i.e., 30 Hz and 15 Hz) and image resolution (i.e., 60 × 80 pixels and 15 × 20 pixels). The dataset was labeled as "nystagmus" or "no nystagmus" by one expert provider. We then used a filtered image-based motion classification approach to develop aEYE. The model's performance at detecting nystagmus was calculated by using the area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: An ensemble between the ResNet-soft voting and the VGG-hard voting models had the best performing metrics. The AUROC, sensitivity, specificity, and accuracy were 0.86, 88.4, 74.2, and 82.7%, respectively. Our validated folds had an average AUROC, sensitivity, specificity, and accuracy of 0.86, 80.3, 80.9, and 80.4%, respectively. Models created from the compressed videos decreased in accuracy as image sampling rate decreased from 60 Hz to 15 Hz. There was only minimal change in the accuracy of nystagmus detection when decreasing image resolution and keeping sampling rate constant. Conclusion: Deep learning is useful in detecting nystagmus in 60 Hz video recordings as well as videos with lower image resolutions and sampling rates, making it a potentially useful tool to aid future automated eye-movement enabled neurologic diagnosis.

Understanding of Hormonal Regulation in Rice Seed Germination.

Seed germination is a critical stage during the life cycle of plants. It is well known that germination is regulated by a series of internal and external factors, especially plant hormones. In Arabidopsis, many germination-related factors have been identified, while in rice, the important crop and monocot model species and the further molecular mechanisms and regulatory networks controlling germination still need to be elucidated. Hormonal signals, especially those of abscisic acid (ABA) and gibberellin (GA), play a dominant role in determining whether a seed germinates or not. The balance between the content and sensitivity of these two hormones is the key to the regulation of germination. In this review, we present the foundational knowledge of ABA and GA pathways obtained from germination research in Arabidopsis. Then, we highlight the current advances in the identification of the regulatory genes involved in ABA- or GA-mediated germination in rice. Furthermore, other plant hormones regulate seed germination, most likely by participating in the ABA or GA pathways. Finally, the results from some regulatory layers, including transcription factors, post-transcriptional regulations, and reactive oxygen species, are also discussed. This review aims to summarize our current understanding of the complex molecular networks involving the key roles of plant hormones in regulating the seed germination of rice.