Knockout of miR396 genes increases seed size and yield in soybean.

Yield improvement has long been an important task for soybean breeding in the world in order to meet the increasing demand for food and animal feed. miR396 genes have been shown to negatively regulate grain size in rice, but whether miR396 family members may function in a similar manner in soybean is unknown. Here, we generated eight soybean mutants harboring different combinations of homozygous mutations in the six soybean miR396 genes through genome editing with clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated nuclease (Cas)12SF01 in the elite soybean cultivar Zhonghuang 302 (ZH302). Four triple mutants (mir396aci, mir396acd, mir396adf, and mir396cdf), two quadruple mutants (mir396abcd and mir396acfi), and two quintuple mutants (mir396abcdf and mir396bcdfi) were characterized. We found that plants of all the mir396 mutants produced larger seeds compared to ZH302 plants. Field tests showed that mir396adf and mir396cdf plants have significantly increased yield in growth zones with relatively high latitude which are suited for ZH302 and moderately increased yield in lower latitude. In contrast, mir396abcdf and mir396bcdfi plants have increased plant height and decreased yield in growth zones with relatively high latitude due to lodging issues, but they are suited for low latitude growth zones with increased yield without lodging problems. Taken together, our study demonstrated that loss-of-function of miR396 genes leads to significantly enlarged seed size and increased yield in soybean, providing valuable germplasms for breeding high-yield soybean.

Breeding exceptionally fragrant soybeans for soy milk with strong aroma.

Knockout of the soybean (Glycine max) betaine aldehyde dehydrogenase genes GmBADH1 and GmBADH2 using CRISPR/Cas12i3 enhances the aroma of soybeans. Soy milk made from the gmbadh1/2 double mutant seeds exhibits a much stronger aroma, which consumers prefer; this mutant has potential for enhancing quality in soy-based products.

An engineered Cas12i nuclease that is an efficient genome editing tool in animals and plants.

The type V-I CRISPR-Cas system is becoming increasingly more attractive for genome editing. However, natural nucleases of this system often exhibit low efficiency, limiting their application. Here, we used structure-guided rational design and protein engineering to optimize an uncharacterized Cas12i nuclease, Cas12i3. As a result, we developed Cas-SF01, a Cas12i3 variant that exhibits significantly improved gene editing activity in mammalian cells. Cas-SF01 shows comparable or superior editing performance compared to SpCas9 and other Cas12 nucleases. Compared to natural Cas12i3, Cas-SF01 has an expanded PAM range and effectively recognizes NTTN and noncanonical NATN and TTVN PAMs. In addition, we identified an amino acid substitution, D876R, that markedly reduced the off-target effect while maintaining high on-target activity, leading to the development of Cas-SF01HiFi (high-fidelity Cas-SF01). Finally, we show that Cas-SF01 has high gene editing activities in mice and plants. Our results suggest that Cas-SF01 can serve as a robust gene editing platform with high efficiency and specificity for genome editing applications in various organisms.

Simple method for transformation and gene editing in medicinal plants.

A sample delivery method, modified from cut-dip-budding, uses explants with robust shoot regeneration ability, enabling transformation and gene editing in medicinal plants, bypassing tissue culture and hairy root formation. This method has potential for applications across a wide range of plant species.

Enhanced CO2 reduction with hydrophobic cationic-ionomer layer-modified zero-gap MEA in acidic electrolyte.

A hydrophobic cationic-ionomer layer of quaternary ammonium poly(N-methyl-piperidine-co-p-terphenyl) and PTFE is presented to enhance the CO2 electroreduction in a zero-gap membrane electrode assembly (MEA) electrolyzer under acidic and low alkali ion concentration conditions. The modified MEA achieved a maximum CO faradaic efficiency of 95.6% at 100 mA cm-2.

The cognitive improvement in patients with schizophrenia following low-intensity repetitive transcranial magnetic stimulation could last for 6 months: A randomized controlled trial.

Cognitive impairments are a core symptom of schizophrenia. Although low-intensity repetitive transcranial magnetic stimulation (rTMS) also has cognitive improving effect like the commonly used high-intensity rTMS, it has not been applied in schizophrenia yet. To fill this gap, inpatients with schizophrenia were randomized to receive 20 sessions of daily adjunctive active low-intensity rTMS in 4 weeks, or sham treatment. At baseline, 4 weeks, and 6 months, the Positive and Negative Syndrome Scale (PANSS) was used to assess psychotic symptom severity, while the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Stroop Color and Word Test (SCWT) were used to assess cognitive functions. Compared to the sixty-nine patients receiving sham treatment, those fifty-nine patients receiving active rTMS performed better in all cognitive domains at post-treatment with small to large effect sizes. This superiority of active rTMS over sham treatment remained significant at 6-month follow-up, with small to large effect sizes, except for visuospatial function and delayed memory. The reduction in PANSS scores were not correlated with cognitive improvements. Our findings provide evidence for using low-intensity rTMS to ameliorate cognitive impairments in schizophrenia. More research are needed to determine the optimal intensity for each domain of cognitive functions.

Prototypical Matching Networks for Video Object Segmentation.

Semi-supervised video object segmentation is the task of segmenting the target in sequential frames given the ground truth mask in the first frame. The modern approaches usually utilize such a mask as pixel-level supervision and typically exploit pixel-to-pixel matching between the reference frame and current frame. However, the matching at pixel level, which overlooks the high-level information beyond local areas, often suffers from confusion caused by similar local appearances. In this paper, we present Prototypical Matching Networks (PMNet) - a novel architecture that integrates prototypes into matching-based video objection segmentation frameworks as high-level supervision. Specifically, PMNet first divides the foreground and background areas into several parts according to the similarity to the global prototypes. The part-level prototypes and instance-level prototypes are generated by encapsulating the semantic information of identical parts and identical instances, respectively. To model the correlation between prototypes, the prototype representations are propagated to each other by reasoning on a graph structure. Then, PMNet stores both the pixel-level features and prototypes in the memory bank as the target cues. Three affinities, i.e., pixel-to-pixel affinity, prototype-to-pixel affinity, and prototype-to-prototype affinity, are derived to measure the similarity between the query frame and the features in the memory bank. The features aggregated from the memory bank using these affinities provide powerful discrimination from both the pixel-level and prototype-level perspectives. Extensive experiments conducted on four benchmarks demonstrate superior results than the state-of-the-art video object segmentation techniques.

What is the Real Need for Scene Text Removal? Exploring the Background Integrity and Erasure Exhaustivity Properties.

As a crucial application in privacy protection, scene text removal (STR) has received amounts of attention in recent years. However, existing approaches coarsely erasing texts from images ignore two important properties: the background texture integrity (BI) and the text erasure exhaustivity (EE). These two properties directly determine the erasure performance, and how to maintain them in a single network is the core problem for STR task. In this paper, we attribute the lack of BI and EE properties to the implicit erasure guidance and imbalanced multi-stage erasure respectively. To improve these two properties, we propose a new ProgrEssively Region-based scene Text eraser (PERT). There are three key contributions in our study. First, a novel explicit erasure guidance is proposed to enhance the BI property. Different from implicit erasure guidance modifying all the pixels in the entire image, our explicit one accurately performs stroke-level modification with only bounding-box level annotations. Second, a new balanced multi-stage erasure is constructed to improve the EE property. By balancing the learning difficulty and network structure among progressive stages, each stage takes an equal step towards the text-erased image to ensure the erasure exhaustivity. Third, we propose two new evaluation metrics called BI-metric and EE-metric, which make up the shortcomings of current evaluation tools in analyzing BI and EE properties. Compared with previous methods, PERT outperforms them by a large margin in both BI-metric ( ↑ 6.13 %) and EE-metric ( ↑ 1.9 %), obtaining SOTA results with high speed (71 FPS) and at least 25% lower parameter complexity. Code will be available at https://github.com/wangyuxin87/PERT.

Case report: Cardiac metastatic uterine intravenous leiomyomatosis excision with extracorporeal venous shunt under the guidance of 3-dimensional printing.

Intravenous leiomyomatosis (IVL) is relatively rare, and the incidence of cardiac IVL is even lower. The case report introduces a 48-year-old woman with two episodes of syncope in 2021. Echocardiography showed a cord-like mass in the inferior vena cava (IVC), right atrium (RA), right ventricle (RV) and pulmonary artery. Computed tomography venography and magnetic resonance imaging showed strips in RA, RV, IVC, right common iliac vein, and internal iliac vein, as well as a round-like mass in the right uterine adnexa. Combined with the patient's prior surgical history and rare anatomical structures, surgeons used cardiovascular 3-dimensional (3D) printing technology to create patient-specific preoperative 3D printed model. The model could help surgeons to visually and accurately understand the size of IVL and its relationship to adjacent tissues. Finally, surgeons successfully performed a concurrent transabdominal resection of cardiac metastatic IVL and adnexal hysterectomy with off-cardiopulmonary bypass. Preoperative evaluation and guidance of 3D printing may play a critical role to ensure this surgery for the patient with rare anatomical structures and high surgical risk. Clinical Trial Registration: [ClinicalTrials.gov], Protocol Registration System [NCT02917980].

Meta semi-supervised medical image segmentation with label hierarchy.

Semi-supervised learning (SSL) has attracted increasing attention in medical image segmentation, where the mainstream usually explores perturbation-based consistency as a regularization to leverage unlabelled data. However, unlike directly optimizing segmentation task objectives, consistency regularization is a compromise by incorporating invariance towards perturbations, and inevitably suffers from noise in self-predicted targets. The above issues result in a knowledge gap between supervised guidance and unsupervised regularization. To bridge the knowledge gap, this work proposes a meta-based semi-supervised segmentation framework with the exploitation of label hierarchy. Two main prominent components named Divide and Generalize, and Label Hierarchy, are built in this work. Concretely, rather than merging all knowledge indiscriminately, we dynamically divide consistency regularization from supervised guidance as different domains. Then, a domain generalization technique is introduced with a meta-based optimization objective which ensures the update on supervised guidance should generalize to the consistency regularization, thereby bridging the knowledge gap. Furthermore, to alleviate the negative impact of noise in self-predicted targets, we propose to distill the noisy pixel-level consistency by exploiting label hierarchy and extracting hierarchical consistencies. Comprehensive experiments on two public medical segmentation benchmarks demonstrate the superiority of our framework to other semi-supervised segmentation methods, with new state-of-the-art results.

Multi-task hourglass network for online automatic diagnosis of developmental dysplasia of the hip.

Developmental dysplasia of the hip (DDH) is one of the most common diseases in children. Due to the experience-requiring medical image analysis work, online automatic diagnosis of DDH has intrigued the researchers. Traditional implementation of online diagnosis faces challenges with reliability and interpretability. In this paper, we establish an online diagnosis tool based on a multi-task hourglass network, which can accurately extract landmarks to detect the extent of hip dislocation and predict the age of the femoral head. Our method utilizes a multi-task hourglass network, which trains an encoder-decoder network to regress the landmarks and predict the developmental age for online DDH diagnosis. With the support of precise image analysis and fast GPU computing, our method can help overcome the shortage of medical resources and enable telehealth for DDH diagnosis. Applying this approach to a dataset of DDH X-ray images, we demonstrate 4.64 mean pixel error of landmark detection compared to the results of human experts. Moreover, we can improve the accuracy of the age prediction of femoral heads to 89%. Our online automatic diagnosis system has provided service to 112 patients, and the results demonstrate the effectiveness of our method.

ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting.

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. First, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Second, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Third, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Additionally, based on an ensemble of the iterative predictions, a self-training method is developed which can learn from unlabeled images effectively. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers. Code is available at https://github.com/FangShancheng/ABINet-PP.

Cut-dip-budding delivery system enables genetic modifications in plants without tissue culture.

Of the more than 370 000 species of higher plants in nature, fewer than 0.1% can be genetically modified due to limitations of the current gene delivery systems. Even for those that can be genetically modified, the modification involves a tedious and costly tissue culture process. Here, we describe an extremely simple cut-dip-budding (CDB) delivery system, which uses Agrobacterium rhizogene to inoculate explants, generating transformed roots that produce transformed buds due to root suckering. We have successfully used CDB to achieve the heritable transformation of plant species in multiple plant families, including two herbaceous plants (Taraxacum kok-saghyz and Coronilla varia), a tuberous root plant (sweet potato), and three woody plant species (Ailanthus altissima, Aralia elata, and Clerodendrum chinense). These plants have previously been difficult or impossible to transform, but the CDB method enabled efficient transformation or gene editing in them using a very simple explant dipping protocol, under non-sterile conditions and without the need for tissue culture. Our work suggests that large numbers of plants could be amenable to genetic modifications using the CDB method.

Deep Fourier Ranking Quantization for Semi-Supervised Image Retrieval.

To reduce the extreme label dependence of supervised product quantization methods, the semi-supervised paradigm usually employs massive unlabeled data to assist in regularizing deep networks, thereby improving model performance. However, the existing method focuses on the overall distribution consistency between unlabeled data and class prototypes, while ignoring subtle individual variances between unlabeled instances. Therefore, the local neighborhood structure is not fully explored, which will cause the model to easily overfit in the training set. In this paper, we introduce a new Fourier perspective to alleviate this issue by exploring the semantic relations between unlabeled instances in a self-supervised manner. Specifically, based on Fourier Transform, we first design a Phase Mixing (PM) strategy, which can manipulate the mixing area and values of the phase component between two images to control the proportion of semantic information. In this way, we can construct multi-level similarity neighbors naturally for unlabeled data. Then, a ranking quantization loss is formulated to perceive multi-level semantic variances in neighbor instances, which improves the robustness and generalization of the model. Extensive experiments in three different semi-supervised settings show that our method outperforms existing state-of-the-art methods by averaged 3.95% improvement on four datasets.

PETR: Rethinking the Capability of Transformer-Based Language Model in Scene Text Recognition.

The exploration of linguistic information promotes the development of scene text recognition task. Benefiting from the significance in parallel reasoning and global relationship capture, transformer-based language model (TLM) has achieved dominant performance recently. As a decoupled structure from the recognition process, we argue that TLM's capability is limited by the input low-quality visual prediction. To be specific: 1) The visual prediction with low character-wise accuracy increases the correction burden of TLM. 2) The inconsistent word length between visual prediction and original image provides a wrong language modeling guidance in TLM. In this paper, we propose a Progressive scEne Text Recognizer (PETR) to improve the capability of transformer-based language model by handling above two problems. Firstly, a Destruction Learning Module (DLM) is proposed to consider the linguistic information in the visual context. DLM introduces the recognition of destructed images with disordered patches in the training stage. Through guiding the vision model to restore patch orders and make word-level prediction on the destructed images, visual prediction with high character-wise accuracy is obtained by exploring inner relationship between the local visual patches. Secondly, a new Language Rectification Module (LRM) is proposed to optimize the word length for language guidance rectification. Through progressively implementing LRM in different language modeling steps, a novel progressive rectification network is constructed to handle some extremely challenging cases (e.g. distortion, occlusion, etc.). By utilizing DLM and LRM, PETR enhances the capability of transformer-based language model from a more general aspect, that is, focusing on the reduction of correction burden and rectification of language modeling guidance. Compared with parallel transformer-based methods, PETR obtains 1.0% and 0.8% improvement on regular and irregular datasets respectively while introducing only 1.7M additional parameters. The extensive experiments on both English and Chinese benchmarks demonstrate that PETR achieves the state-of-the-art results.

Exchange Relationships and Helping Behavior: An Empirical Analysis of Data from CGSS2015.

Purpose: Helping others is a classic virtue and a positive behavior advocated by organizations and society at large in accordance with social norms. Based on social information processing theory, this study examines the mechanisms by which social exchange relationships influence individual helping behavior. Patients and methods: Chinese General Social Survey data from 2015 (CGSS 2015) is applied, and regression analysis and bootstrapping methods are adopted. Results: The findings indicate that leader-member exchange and team-member exchange are positively and significantly related to employees' helping behavior. Affective commitment and job satisfaction play mediating roles between both leader-member exchange and team-member exchange and helping behavior. Conclusion: Leader-member exchange and team-member exchange have different effects on helping behavior. Compared with team-member exchange, the effect of leader-member exchange on helping behavior is stronger via affective commitment and job satisfaction. These results serve as a starting point for boosting the proactive behaviors of employees, thereby establishing a harmonious organizational climate.

Multi-nutrient stoichiometry of Chinese hickory (Carya cathayensis) saplings: plant organs vary in their response to nitrogen fertilization.

Nitrogen (N) enrichment from excessive fertilization in managed forests affects biogeochemical cycles on multiple scales, but our knowledge of how N availability shifts multi-nutrient stoichiometries (including macronutrients: N, phosphorus, potassium, calcium, magnesium and micronutrients: manganese, iron and zinc) within and among organs (root, stem and leaf) remains limited. To understand the difference among organs in terms of multi-nutrient stoichiometric homeostasis responding to N fertilization, a six-level N supply experiment was conducted through a hydroponic system to examine stem growth, multi-nutrient concentrations and stoichiometric ratios in roots, stems and leaves of 2-year-old Chinese hickory (Carya cathayensis Sarg.) saplings. Results showed that N supply significantly enhanced leaf length, width, basal diameter and sapling height. Increasing the rates of N also significantly altered multi-nutrient concentrations in roots, stems and leaves. Macronutrients generally respond more positively than micronutrients within organs. Among organs, leaves and stems generally responded more actively to N supply than roots. The stoichiometric ratios of nutrients within different organs changed significantly with N supply, but their direction and degree of change varied by organ. Specifically, increased N supply reduced the ratios of both macronutrients and micronutrients to N in plant organs, while increased N supply elevated the ratios of P to other nutrients. With N fertilization, ratios of micronutrients decreased in leaves and stems and increased in roots. In particular, leaf N and stem Mn stoichiometries responded strongly to N availability, indicating stimulated N uptake but a decreased risk of Mn2+ accumulation to excessive N. Overall, Chinese hickory saplings responded positively to increasing N availability in terms of stem growth, but the multi-nutrient stoichiometric homeostasis was distinctively organ-dependent. These results are expected to enhance our understanding of N-induced changes in homeostasis of multiple nutrients at the organ level and may offer new insights into how plants adapt to increasing N fertilization.

Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions.

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model's receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.