Maturity Grade Research Articles

Phenotypic-Based Maturity Detection and Oil Content Prediction in Xiangling Walnuts

The maturity grading of walnuts during harvesting relies on experience. In this paper, walnut images in a natural environment were collected to construct a dataset, and deep learning algorithms were utilized to combine walnut internal physical and chemical indicators to carry out research on walnut maturity detection methods and further research on walnut oil content prediction by combining walnut images with walnut oil content indicators. The main contents of this paper include collecting walnut images in a natural environment, constructing datasets, and using deep learning algorithms combined with internal physical and chemical indexes of walnuts to study walnut maturity detection and oil content prediction methods. First, two walnut image acquisition schemes were designed, and a total of 9504 images were collected from 23 August to 21 September 2021. The dataset was expanded to 18,504 images through data preprocessing and image enhancement. A self-supervised Gaussian attention network (GATCluster) walnut ripeness detection method based on image clustering is proposed to develop ripeness criteria through unsupervised clustering, and the accuracy of the criteria is verified by analysis of variance (ANOVA). The maturity detection accuracy of the test set of 1500 images is 88.33%. Secondly, a walnut oil content prediction method based on improved ResNet34 is proposed. The feature extraction capability is improved by introducing the Squeeze-and-Excitation Networks (SENet) channel attention mechanism and the convolutional self-attention module. The prediction results on 50 images show that the root mean square error, average absolute percentage error, and regression coefficient are 2.96, 0.103, and 0.8822, respectively. The experiments show that the method performs well in predicting the oil content of walnuts at different maturity levels.

Presepsin in Human Milk Is Delivery Mode and Gender Dependent.

Breast milk (BM) is a unique food due to its nutritional composition and anti-inflammatory characteristics. Evidence has emerged on the role of Presepsin (PSEP) as a reliable marker of early sepsis diagnosis. In the present study, we aimed to investigate the measurability of PSEP in BM according to different maturation stages (colostrum, C; transition, Tr; and mature milks, Mt) and corrected for delivery mode and gender. We conducted a multicenter prospective case-control study in women who had delivered 22 term (T) and 22 preterm (PT) infants. A total of 44 human milk samples were collected and stored at -80 °C. BM PSEP (pg/mL) levels were measured by using a rapid chemiluminescent enzyme immunoassay. PSEP was detected in all samples analyzed. Higher (p < 0.05) BM PSEP concentrations were observed in the PT compared to the T infants. According to the grade of maturation, higher (p < 0.05) levels of PSEP in C compared to Tr and Mt milks were observed in the whole study population. The BM subtypes' degrees of maturation were delivery mode and gender dependent. We found that PSEP at high concentrations supports its antimicrobial action both in PT and T infants. These results open the door to further studies investigating the role of PSEP.

Maturity grading of jujube for industrial applications harnessing deep learning

PurposeTo develop and examine an efficient and reliable jujube grading model with reduced computational time, which could be utilized in the food processing and packaging industries to perform quick grading and pricing of jujube as well as for the other similar types of fruits.Design/methodology/approachThe whole process begins with manual analysis and collection of four jujube grades from the jujube tree, in addition to this jujube image acquisition was performed utilizing MVS which is further followed by image pre-processing and augmentation tasks. Eventually, classification models (i.e. proposed model, from scratch and pre-trained VGG16 and AlexNet) were trained and validated over the original and augmented datasets to discriminate the jujube into maturity grades.FindingsThe highest success rates reported over the original and augmented datasets were 97.53% (i.e. error of 2.47%) and 99.44% (i.e. error of 0.56%) respectively using Adam optimizer and a learning rate of 0.003.Research limitations/implicationsThe investigation relies upon a single view of the jujube image and the outer appearance of the jujube. In the future, multi-view image capturing system could be employed for the model training/validation.Practical implicationsDue to the vast functional derivatives of jujube, the identification of maturity grades of jujube is paramount in the fruit industry, functional food production industries and pharmaceutical industry. Therefore, the proposed model which is practically feasible and easy to implement could be utilized in such industries.Originality/valueThis research examines the performance of proposed CNN models for selected optimizer and learning rates for the grading of jujube maturity into four classes and compares them with the classical models to depict the sublime model in terms of accuracy, the number of parameters, epochs and computational time. After a thorough investigation of the models, it was discovered that the proposed model transcends both classical models in all aspects for both the original and augmented datasets utilizing Adam optimizer with learning rate of 0.003.

The influence of spheroid maturity on fusion dynamics and micro-tissue assembly in 3D tumor models

Three-dimensional (3D) cell culture has been used in many fields of biology because of its unique advantages. As a representative of the 3D systems, 3D spheroids are used as building blocks for tissue construction. Larger tumor aggregates can be assembled by manipulating or stacking the tumor spheroids. The motivation of this study is to investigate the behavior of the cells distributed at different locations of the spheroids in the fusion process and the mechanism behind it. To this aim, spheroids with varying grades of maturity or age were generated for fusion to assemble micro-tumor tissues. The dynamics of the fusion process, the motility of the cells distributed in different heterogeneous architecture sites, and their reactive oxygen species profiles were studied. We found that the larger the spheroid necrotic core, the slower the fusion rate of the spheroid. The cells that move were mainly distributed on the spheroid’s surface during fusion. In addition to dense microfilament distribution and low microtubule content, the reactive oxygen content was high in the fusion site, while the non-fusion site was the opposite. Last, multi-spheroids with different maturities were fused to complex micro-tissues to mimic solid tumors and evaluate Doxorubicin’s anti-tumor efficacy.

SEATANI: hazards from seamounts in Southeast Asia, Taiwan, and Andaman and Nicobar Islands (eastern India)

Abstract. Submarine volcanism makes up approximately 85 % of volcanism that occurs on Earth, and its eruptions have the potential to cause several hazards including ash dispersal, pumice rafts, pyroclastic density currents, sector collapses, and tsunamis. Recent examples include the eruptions in Japan and in the Kingdom of Tonga in 2021 and 2022 respectively, but there has been little to no study of submarine volcanism in Southeast Asia and surroundings. Here we provide a compilation of 466 seamounts from the region, from different published sources, through the SEATANI dataset (Southeast Asia, Taiwan, and the Andaman and Nicobar Islands). We use this newly compiled dataset to assess on a regional level the seamount hazard potential and exposure potential as a springboard for future more quantitative hazard studies of the region. The hazard potential was assessed through seamount morphological and structural analyses, to determine the seamount evolution stage and grade of maturity. The exposure potential was evaluated with two different approaches: an areal analysis of the number of assets within a 100 km radius of each seamount and the development of a hazard-weighted seamount density map to highlight potential areas of interest for future more-in-depth studies. Our results show that there are several potentially hazardous seamounts in this region. Taiwan has the highest hazard and exposure potential, for all assets considered, while the Philippines, Indonesia, and Vietnam have relatively high exposure potential for submarine communication cables and ship traffic density. The results from this work serve as a first step towards Southeast Asia and neighbouring countries becoming more resilient against and prepared for submarine volcanic eruptions in the region.

Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

Investigation of the crystal formation from calcium silicate in human dentinal tubules and the effect of phosphate buffer saline concentration

Background/purposeBased on hydrodynamic theory, blocking the dentinal tubules can reduce discomfort caused by dentin hypersensitivity. This study identified the crystals formed in dentinal tubules from tricalcium silicate (TCS) in phosphate-buffered saline (PBS) and evaluated the effect of PBS concentration on crystal formation. Materials and methodsSixty-nine specimens were made by isolating the cervical part of extracted premolars. TCS was applied by brushing for 10,000 strokes on dentin surface simulating sensitive dentin. Specimens were stored in PBS or solutions with concentrations 1/100, 1/10, 10, and 100 times that of PBS for 1, 30, 60, or 90 days (n = 3). Another nine specimens applied TCS, were immersed in PBS for 3 months, and divided into three subgroups: no treatment, sonication for 10 min, and 1M acetic acid treatment for 3 min. Crystal formation was examined using a scanning electron microscope, assigned five grade scores (0–4) according to maturation, and analyzed by a nonparametric two-way ANOVA (α = 0.05). Crystal components were analyzed using X-ray diffraction (XRD) and energy dispersion X-ray spectroscopy (EDS). ResultsThe maturation of intratubular crystals was dependent on time and PBS concentration (P < 0.05). In all periods, the high-concentration group showed a higher maturation grade than the low-concentration group. Intratubular crystals were similar to hydroxyapatite according to XRD and EDS, and they withstood sonication and acid application. ConclusionTCS with nanosized particles formed hydroxyapatite-like crystals in the dentinal tubules, which were dependent on time and concentration of PBS and withstood sonication and acid application.

Predictors of adverse perinatal outcomes in women at 40 weeks or more of pregnancy

Introduction and aim. To evaluate the clinical features of women at ≥40 weeks of pregnancy and the utility of obstetric Doppler indices in predicting adverse perinatal outcomes in these pregnancies. Material and methods. This prospective study was conducted at a single academic medical center between 2020 and 2022. Women aged 18 years and older with no risk factors who were at ≥40 weeks of pregnancy and delivered their babies in our hospital were included in the study. The fetal biometry, placental maturity grading, and doppler velocytometry indices of the pregnant women were evaluated. The cases were divided into two groups according to the development of adverse perinatal outcomes. The relationship between clinical features and adverse perinatal outcomes was evaluated. Results. Adverse perinatal outcomes developed in 19.6% (42) of the 214 cases. The multiple logistic regression analysis was performed to identify factors affecting perinatal outcomes. Accordingly, a maternal age of ≥35 years (odds ratio [OR]: 1.74, 95% confidence interval [CI]: 1.29–3.96, p=0.038), nulliparity (OR: 1.42, 95% CI: 1.13–4.63, p=0.040), and grade 3 placental calcification (OR: 1.98, 95% CI: 1.11–4.53, p=0.029) were independent predictors of adverse perinatal outcomes. Conclusion. Care should be taken in terms of adverse perinatal outcomes in the presence of nulliparity, a maternal age of ≥35 years, and grade 3 placental calcification in ≥40 week pregnancies.

Maturity stages detection prototype device for classifying custard apple (Annona squamosa L) fruit using image processing approach

Custard apple (Annona squamosa L.) is commercially grown for its sweetness, delicacy, and nutraceutical benefits across the world. Fruit must be harvested from trees before the climacteric stage begins for stabilizing the maturation process and perishability, which otherwise restricts longer shelf–life. Therefore, maturity–based grading or classifying custard apple fruit in accordance with their ripening is required for commercial marketing and industrial storage purposes. This is a crucial task that agriculturists and the food processing industries must have to perform manually, which is ineffective and susceptible to human errors. In this research, we have proposed an image processing–based maturity stages detection prototype device for fruit classification of custard apple (Cv. Balanagar) based on changes in their physical, chemical and imaging features (skin color of fruit areoles). The newly developed handheld device comprises of a Raspberry Pi board, camera module, LCD touch screen, and image processing algorithms to identify five essential maturity stages i.e. 0 %, 25 %, 50 %, 75 % and 100 % areoles opening of custard apple. The images were pre–processed and segmented, after which imaging aspects of custard apple fruit including relative R, G and B components were extracted and their relationship with chemical properties such as TSS was established. For classification of captured images, K–means clustering and SVM algorithms were performed on Raspberry Pi Platform with model training and classifier codes in Python (3.9.6) to get desired results. The statistical analysis revealed a substantial difference between the maturity stages in terms of R, G and TSS. Overall, 100 % accuracy was accomplished with respect to the grading of custard apple fruit at varied maturity. This could be a low–cost device useful for farm–level custard apple fruit classification, minimising economic losses caused by faulty supply entering the marketing chain. With slight modifications in algorithms, this device could also be used to assess the maturity of other fruit crops. It could be also an alternative to several expensive approaches being used for automating the maturity grading process that is not feasible, particularly for farmers working in field conditions.

Identification of CNN hyper-parameters for tobacco leaf quality classification on Nvidia Jetson Nano

At the moment, there are some inaccuracies in manual classification for tobacco leaf quality selection, which are influenced by some factors, such as human fatigue or poor lighting. These cases lead to the need for another method that is more consistent, faster and reliable. This research is an implementation of CNN (Convolutional Neural Network) in the classification of fresh tobacco leaves in terms of maturity grades. The primary objective is to develop an efficient CNN model capable to automize the classification of tobacco leaves into three maturity criteria: immature, mature, and old. This methodology consists of some key factors, including color thresholding strategies to purge the noise from the background, Basic Image Manipulation approaches, the systemized screening of different input sizes, and CNN models to enhance the results. The result of this research proves that the developed CNN model has 97.9 % accuracy achieved following 200 training sessions. The model is trained on a dataset comprising 1,249 fresh leaf photos, with a balanced 80:10:10 for train, validation and test ratio. However, the study emphasizes that the CNN model has successfully supported the tobacco leaf discrimination on a Jetson Nano Single-Board Computer with a Graphic Processing Unit (GPU). The study extends beyond the mere theoretical contribution to practical applications in sorting “Gagang Rejeb Sidi” tobacco leaf, the highest quality tobacco variety in South Malang, East Java, Indonesia. Classification using a webcam as an input device shows the fastest processing time of 203.17 ms and the maximum is 1,363 ms. This CNN model algorithm will be applied to a tobacco leaf selector machine, which has a high-speed conveyor and a three-position selector arm. The machine will be operated close to the field in post-harvest time under uniform lighting conditions. Overall, the result of this research is highly relevant in terms of the short duration and accuracy for understanding the commodity classification. It provides a new angle toward speeding up the classification process and improving Indonesian tobacco quality

Effect of Leaf Grade on Taste and Aroma of Shaken Hunan Black Tea.

Shaken Hunan black tea is an innovative Hunan black tea processed by adding shaking to the traditional Hunan black tea. The quality of shaken black tea is influenced by leaf grades of different maturity. In this study, the taste and aroma quality of shaken Hunan black tea processed with different grades were analyzed by sensory evaluation (SP, HPLC, and HS-SPME/GC-MS). The results showed that shaken Hunan black tea processed with one bud and two leaves has the best quality, which has a sweet, mellow, and slightly floral taste, as well as a floral, honey, and sweet aroma. Moreover, caffeine and EGCG were identified as the most important bitter and astringent substances in shaken Hunan black. Combined with the analysis of GC-MS and OAV analysis, geraniol, jasmone, β-myrcene, citral, and trans-β-ocimene might be the most important components that affect the sweet aroma, while methyl jasmonate, indole, and nerolidol were the key components that affect the floral aroma of shaken Hunan black tea. This study lays a foundation for this study of the taste and aroma characteristics of shaken Hunan black tea and guides enterprises to improve shaken black tea processing technology.

Evaluation of placental oxygenation by near-infrared spectroscopy in relation to ultrasound maturation grade in physiological term pregnancies.

A prospective observational study (ClinicalTrial ID: NCT05771415) was conducted to compare placental oxygenation in low-risk, uncomplicated term pregnancies measured by near-infrared spectroscopy (NIRS) in relation to the placental maturity grade determined by ultrasound assessment according to the Grannum scale. We included 34 pregnancies divided into two groups according to placental maturation. For each pregnancy, measurements were taken at the site above the central part of the placenta (test) and at the site outside of the placenta on the lower abdomen (control). Student's t-test was used to compare tissue oxygenation index (TOI) values among the study groups. The normality of distribution was proven by the Kolmogorov‒Smirnov test. In women with low placental maturity grade, the mean TOI value above the placenta was 70.38 ± 3.72, which was lower than the respective value in women with high placental maturity grade (77.99 ± 3.71; p < 0.001). The TOI values above the placenta and the control site were significantly different in both groups (70.38 ± 3.72 vs 67.83 ± 3.21 and 77.99 ± 3.71 vs 69.41 ± 3.93; p < 0.001). The results offer a new perspective on placental function based on specific non-invasive real-time oxygenation measurements. Unfortunately, and because of technical limitations, NIRS cannot yet be implemented as a routine clinical tool.

Non-destructive fruit firmness evaluation using a soft gripper and vision-based tactile sensing

As fruit firmness is a crucial characteristic associated with the maturity level, its accurate estimation is of great importance to post-harvest processing and wholesale in the industry. Benefiting from the advances of soft robotics, a soft gripper with simultaneous compliant deformation and tactile sensing is proposed in this study for the fruit firmness classification. The gripper design inspired by the fin ray effect can achieve active deformation, which helps simplify the actuation system and improve the delicate manipulation capability. Finite element modelling, along with experimental tests, is first utilized to validate the gripper’s feasibility in compliant and safe fruit grasping, and respiratory tests are then conducted to further demonstrate the non-destructive nature. Moreover, fruit–gripper interaction is captured by visual sensors and then processed using an attention-based CNN–LSTM algorithm to predict firmness information. Tomatoes and nectarines are chosen as the sample fruit for experimental validation. R2 values of their firmness prediction are 0.795 and 0.753, and the accuracy of maturity grading is 84.6% and 81.5%, respectively. In general, the soft gripper provides a promising solution for both safe grasping and non-destructive firmness evaluation, and it is expected to be integrated into automated production lines to pack fruit based on different firmness levels.

DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound

Placental maturity grading (PMG) is often utilized for evaluating fetal growth and maternal health. Currently, PMG often relied on the subjective judgment of the clinician, which is time-consuming and tends to incur a wrong estimation due to redundancy and repeatability of the process. The existing methods often focus on designing diverse hand-crafted features or combining deep features and hand-crafted features to learn a hybrid feature with an SVM for grading the placental maturity of ultrasound images. Motivated by the dominated performance of end-to-end convolutional neural networks (CNNs) at diverse medical imaging tasks, we devise a dilated granularity transformer network for learning multi-scale global transformer features for boosting PMG. Our network first devises dilated transformer blocks to learn multi-scale transformer features at each convolutional layer and then integrates these obtained multi-scale transformer features for predicting the final result of PMG. We collect 500 ultrasound images to verify our network, and experimental results show that our network clearly outperforms state-of-the-art methods on PMG. In the future, we will strive to improve the computational complexity and generalization ability of deep neural networks for PMG.

Detection of Fundamental Quality Traits of Winter Jujube Based on Computer Vision and Deep Learning

Winter jujube (Ziziphus jujuba Mill. cv. Dongzao) has been cultivated in China for a long time and has a richly abundant history, whose maturity grade determined different postharvest qualities. Traditional methods for identifying the fundamental quality of winter jujube are known to be time-consuming and labor-intensive, resulting in significant difficulties for winter jujube resource management. The applications of deep learning in this regard will help manufacturers and orchard workers quickly identify fundamental quality information. In our study, the best fundamental quality of winter jujube from the correlation between maturity and fundamental quality was determined by testing three simple physicochemical indexes: total soluble solids (TSS), total acid (TA) and puncture force of fruit at five maturity stages which classified by the color and appearance. The results showed that the fully red fruits (the 4th grade) had the optimal eating quality parameter. Additionally, five different maturity grades of winter jujube were photographed as datasets and used the ResNet-50 model and the iResNet-50 model for training. And the iResNet-50 model was improved to overlap double residuals in the first Main Stage, with an accuracy of 98.35%, a precision of 98.40%, a recall of 98.35%, and a F1 score of 98.36%, which provided an important basis for automatic fundamental quality detection of winter jujube. This study provided ideas for fundamental quality classification of winter jujube during harvesting, fundamental quality screening of winter jujube in assembly line production, and real-time monitoring of winter jujube during transportation and storage.

Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies

Pineapple is mainly grown in tropical regions and consumed fresh worldwide due to its attractive flavor and health benefits. With increasing global production and trade volume, there is an urgent need for nondestructive techniques for accurate and efficient detection of the internal quality of pineapples. Therefore, this study is dedicated to developing a nondestructive method for real-time determining the internal quality of pineapples by using VIS/NIR transmittance spectroscopy technique and machine learning methodologies. The VIS/NIR transmittance spectrums ranging in 400–1100 nm of total 195 pineapples were collected from a dynamic experimental platform. The maturity grade and soluble solids content (SSC) of individual pineapples were then measured as indicators of internal quality. The qualitative model for discriminating maturity grades of pineapple achieved a high accuracy of 90.8% by the PLSDA model for unknown samples. Meanwhile, the quantitative model for determining SSC also reached a determination coefficient (RP2) of 0.7596 and a root mean square error of prediction (RMSEP) of 0.7879 °Brix by the ANN-PLS model. Overall, high model performance demonstrated that using VIS/NIR transmittance spectroscopy technique coupled with machine learning methodologies could be a feasible method for nondestructive and real-time detection of the internal quality of pineapples.

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8.

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.

Changing clinical significance of oocyte maturity grades with advancing female age advances precision medicine in IVF

In current IVF practice, metaphase-2 (M2) oocytes are considered most efficient in producing good quality embryos. Maximizing their number at all ages is standard clinical practice, while immature germinal vesicle (GV) oocytes are mostly automatically discarded. We present preliminary evidence that oocyte maturity grades with advancing age significantly change in their abilities to produce good quality embryos, with M2 oocytes significantly declining, GV oocytes improving, and M1 oocytes staying the same. These data contradict the over-40-year-old dogma that oocyte grades functionally do not change with advancing age, supporting potential changes to current IVF practice: (1) Stimulation protocols and timing of oocyte retrieval can be adjusted to a patient's age and ovarian function. (2) In older and younger women with prematurely aging ovaries, GV oocytes may no longer be automatically discarded. (3) In some infertile women, rescue invitro maturation of immature oocytes may delay the need for third-party egg donation.

Igneous records of transformation from ocean to continent of Mongolia–Okhotsk Ocean: Evidence from granitoids of Woduhe area, Heilongjiang Province, China

The Woduhe area of Heilongjiang Province is located in the eastern side of the Greater Xing'an Range. This area has experienced the evolution of the Palaeo‐Asian Ocean, the Palaeo‐Pacific, and the Mongolia–Okhotsk Ocean, and it can be used to discuss the transformation process of these palaeo‐oceanic plates to continental plates. However, controversies exist over its time period and the process of transformation. The 1:250000 Woduhe Geological Map shows that Early Cretaceous granitic batholith developed near the Dajinshan area. However, the LA‐ICP‐MS zircon U–Pb dating presented in this paper shows that the trondhjemites from the southern part of the Dajinshan area were formed at 156.9 ± 2.2 Ma, syenogranites were formed at 131.7 ± 1.6 Ma, and diorite porphyrite dikes were formed at 126.0 ± 3.1 Ma. Therefore, Early Cretaceous batholiths should be reclassified as different, separate units. The Late Jurassic intrusive rocks belong to TTGG assemblages (tonalite–trondhjemite–granodiorite‐granite). Trace element spider diagrams show that the rocks are enriched in K, Pb, Zr, and Hf; depleted in Nb, Ta, and Eu; with higher Sr; and lower Y content. The rare earth element pattern is right‐sloping (La/Yb &gt; 12). The Early Cretaceous intrusive rocks belong to G assemblages (granodiorite). Combined with the tectonic settings, we determined that the Late Jurassic and the Early Cretaceous granites were formed in a magmatic arc setting related to subduction. Late Jurassic intrusive magma mainly originated from the partial melting of the thickened lower crust under the Xing'an arc, and was likely contaminated by mantle material. Early Cretaceous intrusive magma originated from the partial melting of the Xing'an sub‐arc crust. Based on the distribution of the Late Jurassic TTG (N and NE) and G (S and SW) assemblages, intrusive records of the southward and southwestward subduction of the Mongolia–Okhotsk Ocean can be observed. In addition, the grade of maturity of the Xing'an arc is increasing during Late Jurassic to Early Cretaceous.

Preserving the Semitendinosus Distal Attachment Is Associated With Improved Graft Remodeling After ACL Reconstruction

Background: The semitendinosus (ST) tendon can be used by itself as a graft for anterior cruciate ligament (ACL) reconstruction. An increasing number of these procedures are being done while preserving the ST's tibial attachment, but there are no data on the remodeling of an attached ST (aST) graft. Purpose: To compare graft remodeling on magnetic resonance imaging (MRI) scans at 1 year after ACL reconstruction between standard free ST graft and aST graft. Study design: Cohort study; Level of evidence, 3. Methods: This prospective study enrolled 180 patients who were undergoing ACL reconstruction: 90 with the ST graft and 90 with the aST graft. The analysis was performed 1 year after the surgery. The main endpoint was the signal-to-noise quotient (SNQ) on MRI scans (T1-weighted sequence). The secondary endpoints were tibial tunnel widening (TTW), graft maturation (Howell classification), retear rate, new surgery rate, Simple Knee Value, Lysholm score, International Knee Documentation Committee (IKDC) score, postoperative Tegner score, difference between pre- and postoperative Tegner scores, ACL–Return to Sport after Injury (ACL-RSI), return-to-sports rate, and time to return to sports. Results: The mean adjusted SNQ was 1.18 (95% CI, 0.72-1.65) in the aST group and 3.88 (95% CI, 3.42-4.34) in the ST group (P < .001). The new surgery rate was 2.2% in the aST group and 10% in the ST group (P = .029). The median Lysholm score was significantly higher in the aST group (99; interquartile range [IQR], 95-100) than in the ST group (95; IQR, 91-99) (P = .004). The mean time to return to sports was significantly shorter in the aST group (248.73 ± 141.62 days) than the ST group (317.23 ± 144.69 days) (P = .002). No statistically significant difference was found between groups in the TTW (P = .503), Howell graft maturity grade (P = .149), retear rate (P > .999), Simple Knee Value (P = .061), postoperative Tegner score (P = .320), pre- to postoperative difference in Tegner score (P = .317), ACL-RSI (P = .097), IKDC score (P = .621), and return-to-sports rate (P > .999). Conclusion: At 1 year postoperatively, remodeling of an ST graft assessed using MRI is better when its distal attachment is left intact.