Excessive cadmium (Cd) accumulation in rice poses a risk to food safety. OsHMA3 plays an important role in restricting Cd translocation from roots to shoots. A non-functional allele of OsHMA3 has been reported in some Indica rice cultivars with high Cd accumulation, but it is not known if OsHMA3 allelic variation is associated with Cd accumulation in Japonica cultivars. In this study, we identified a Japonica cultivar with consistently high Cd accumulation in shoots and grain in both field and greenhouse experiments. The cultivar possesses an OsHMA3 allele with a predicted amino acid mutation at the 380(th) position from Ser to Arg. The haplotype had no Cd transport activity when the gene was expressed in yeast, and the allele did not complement a known nonfunctional allele of OsHMA3 in F1 test. The allele is present only in temperate Japonica cultivars among diversity panels of 1483 rice cultivars. Different cultivars possessing this allele showed greatly increased root-to-shoot Cd translocation and a shift in root Cd speciation from Cd-S to Cd-O bonding determined by synchrotron X-ray absorption spectroscopy. Our study has identified a new loss-of-function allele of OsHMA3 in Japonica rice cultivars leading to high Cd accumulation in shoots and grain.

Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.

Visual CAPTCHAs have been widely used across the Internet to defend against undesirable or malicious bot programs. In this paper, we document how we have broken most such visual schemes provided at Captchaservice.org, a publicly available web service for CAPTCHA generation. These schemes were effectively resistant to attacks conducted using a high-quality Optical Character Recognition program, but were broken with a near 100% success rate by our novel attacks. In contrast to early work that relied on sophisticated computer vision or machine learning algorithms, we used simple pattern recognition algorithms but exploited fatal design errors that we discovered in each scheme. Surprisingly, our simple attacks can also break many other schemes deployed on the Internet at the time of writing: their design had similar errors. We also discuss defence against our attacks and new insights on the design of visual CAPTCHA schemes. 1 The term of "Breaking CAPTCHA" is ambiguous. For example, when CAPTCHA is interpreted as a simple challenge-response protocol, "breaking CAPTCHA" can mean breaking the protocol, e.g. via a man-in-the middle or an oracle attack. In this paper, "breaking CAPTCHA" means to write a computer program that automatically solves CAPTCHA challenges-ideally, this task should be as hard as solving the underlying AI problem. ("Breaking CAPCHA", "breaking a CAPTCHA protocol", and "Defeating CAPTCHA based bot defence" are three different but related notions, as clarified in [ 15].

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