High-density mapping of durable and broad-spectrum stripe rust resistance gene Yr30 in wheat.

KEY MESSAGE: The durable stripe rust resistance gene Yr30 was fine-mapped to a 610-kb region in which five candidate genes were identified by expression analysis and sequence polymorphisms. The emergence of genetically diverse and more aggressive races of Puccinia striiformis f. sp. tritici (Pst) in the past twenty years has resulted in global stripe rust outbreaks and the rapid breakdown of resistance genes. Yr30 is an adult plant resistance (APR) gene with broad-spectrum effectiveness and its durability. Here, we fine-mapped the YR30 locus to a 0.52-cM interval using 1629 individuals derived from residual heterozygous F5:6 plants in a Yaco"S"/Mingxian169 recombinant inbred line population. This interval corresponded to a 610-kb region in the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq version 2.1 on chromosome arm 3BS harboring 30 high-confidence genes. Five genes were identified as candidate genes based on functional annotation, expression analysis by RNA-seq and sequence polymorphisms between cultivars with and without Yr30 based on resequencing. Haplotype analysis of the target region identified six haplotypes (YR30_h1-YR30_h6) in a panel of 1215 wheat accessions based on the 660K feature genotyping array. Lines with YR30_h6 displayed more resistance to stripe rust than the other five haplotypes. Near-isogenic lines (NILs) with Yr30 showed a 32.94% higher grain yield than susceptible counterparts when grown in a stripe rust nursery, whereas there was no difference in grain yield under rust-free conditions. These results lay a foundation for map-based cloning Yr30.

Research on 3D Reconstruction of Binocular Vision Based on Thermal Infrared.

Thermal infrared imaging is less affected by lighting conditions and smoke compared to visible light imaging. However, thermal infrared images often have lower resolution and lack rich texture details, making them unsuitable for stereo matching and 3D reconstruction. To enhance the quality of infrared stereo imaging, we propose an advanced stereo matching algorithm. Firstly, the images undergo preprocessing using a non-local mean noise reduction algorithm to remove thermal noise and achieve a smoother result. Subsequently, we perform camera calibration using a custom-made chessboard calibration board and Zhang's camera calibration method to obtain accurate camera parameters. Finally, the disparity map is generated using the SGBM (semi-global block matching) algorithm based on the weighted least squares method, enabling the 3D point cloud reconstruction of the object. The experimental results demonstrate that the proposed algorithm performs well in objects with sufficient thermal contrast and relatively simple scenes. The proposed algorithm reduces the average error value by 10.9 mm and the absolute value of the average error by 1.07% when compared with the traditional SGBM algorithm, resulting in improved stereo matching accuracy for thermal infrared imaging. While ensuring accuracy, our proposed algorithm achieves the stereo reconstruction of the object with a good visual effect, thereby holding high practical value.

Head Gesture Recognition Combining Activity Detection and Dynamic Time Warping.

The recognition of head movements plays an important role in human-computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human-computer interfaces.

YOLOFM: an improved fire and smoke object detection algorithm based on YOLOv5n.

To address the current difficulties in fire detection algorithms, including inadequate feature extraction, excessive computational complexity, limited deployment on devices with limited resources, missed detections, inaccurate detections, and low accuracy, we developed a highly accurate algorithm named YOLOFM. We utilized LabelImg software to manually label a dataset containing 18644 images, named FM-VOC Dataset18644. In addition, we constructed a FocalNext network, which utilized the FocalNextBlock module from the CFnet network. This improves the integration of multi-scale information and reduces model parameters. We also proposed QAHARep-FPN, an FPN network that integrates the structure of quantization awareness and hardware awareness. This design effectively reduces redundant calculations of the model. A brand-new compression decoupled head, named NADH, was also created to enhance the correlation between the decoupling head structure and the calculation logic of the loss function. Instead of using the CIoU loss for bounding box regression, we proposed a Focal-SIoU loss. This promotes the swift convergence of the network and enhances the precision of the regression. The experimental results showed that YOLOFM improved the baseline network's accuracy, recall, F1, mAP50, and mAP50-95 by 3.1%, 3.9%, 3.0%, 2.2%, and 7.9%, respectively. It achieves an equilibrium that combines performance and speed, resulting in a more dependable and accurate solution for detection jobs.

The research on infrared radiation affected by smoke or fog in different environmental temperatures.

Infrared thermal imaging camera as a non-contact monitoring of the object to be measured is widely used in fire detection, driving assistance and so on. Although there are many related studies, there is a lack of research on the influence of fog or smoke on infrared imaging under different environmental temperatures. To address this shortcoming, The temperature of both the environment and the target in this experiment is controlled by PID technology. The smoke or fog environment is generated using a smoke cake or an ultrasonic fog machine. The temperature of the target was measured by infrared thermal imaging camera. It was observed that as the temperature of the environment increases, the measured temperature of the target also increases. However, the change in temperature is more pronounced in the fog environment compared to either the smoke environment or the normal environment. It has been found through research that environmental radiation causes temperature changes in fog droplets. Therefore, Infrared radiation is less affected in the smoke environment and more affected in the fog environment. Additionally, when the environmental temperature is close to the target's temperature, the infrared image becomes blurred.

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology.

Task and Resting-State fMRI Reveal Altered Salience Responses to Positive Stimuli in Patients with Major Depressive Disorder.

Altered brain function in patients with major depressive disorder (MDD) has been repeatedly demonstrated by task-based and resting-state studies, respectively. However, less is known concerning whether overlapped abnormalities in functional activities across modalities exist in MDD patients. To find out the answer, we implemented an fMRI experiment and collected both task and resting-state data from 19 MDD patients and 19 matched, healthy, controls. A distraction paradigm involving emotionally valenced pictures was applied to induce affective responses in subjects. As a result, concurrent deficits were found in arousing activation during a positive task in both the reward circuit and salience network (SN) that is composed of the dorsal part of anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in only the MDD group. Subsequent amplitude of low frequency fluctuations (ALFF) and functional connectivity analyses based on resting-state data exhibited consistent alterations in the bilateral AI of MDD patients, and indicated patients' difficulties in regulating the balance between central executive network (CEN) and default mode network (DMN) due to altered connectivity among the CEN, DMN, and SN. Our findings provide new evidence demonstrating impaired salience processing and resulting alterations in responses to positive stimuli in MDD patients. Furthermore, brain abnormalities synchronized across functional states in MDD patients can be evidenced by a combination of task and resting-state fMRI analyses.