An open relaxation-diffusion MRI dataset in neurosurgical studies.

Diffusion MRI (dMRI) is a safe and noninvasive technique that provides insight into the microarchitecture of brain tissue. Relaxation-diffusion MRI (rdMRI) is an extension of traditional dMRI that captures diffusion imaging data at multiple TEs to detect tissue heterogeneity between relaxation and diffusivity. rdMRI has great potential in neurosurgical research including brain tumor grading and treatment response evaluation. However, the lack of available data has limited the exploration of rdMRI in clinical settings. To address this, we are sharing a high-quality rdMRI dataset from 18 neurosurgical patients with different types of lesions, as well as two healthy individuals as controls. The rdMRI data was acquired using 7 TEs, where at each TE multi-shell dMRI with high spatial and angular resolutions is obtained at each TE. Each rdMRI scan underwent thorough artifact and distortion corrections using a specially designed processing pipeline. The dataset's quality was assessed using standard practices, including quality control and assurance. This resource is a valuable addition to neurosurgical studies, and all data are openly accessible.

Function of hormone signaling in regulating nitrogen-use efficiency in plants.

Nitrogen (N) is one of the most important nutrients for crop plant performance, however, the excessive application of nitrogenous fertilizers in agriculture significantly increases production costs and causes severe environmental problems. Therefore, comprehensively understanding the molecular mechanisms of N-use efficiency (NUE) with the aim of developing new crop varieties that combine high yields with improved NUE is an urgent goal for achieving more sustainable agriculture. Plant NUE is a complex trait that is affected by multiple factors, of which hormones are known to play pivotal roles. In this review, we focus on the interaction between the biosynthesis and signaling pathways of plant hormones with N metabolism, and summarize recent studies on the interplay between hormones and N, including how N regulates multiple hormone biosynthesis, transport and signaling and how hormones modulate root system architecture (RSA) in response to external N sources. Finally, we explore potential strategies for promoting crop NUE by modulating hormone synthesis, transport and signaling. This provides insights for future breeding of N-efficient crop varieties and the advancement of sustainable agriculture.

Self-poled piezoelectric polymer composites via melt-state energy implantation.

Lightweight flexible piezoelectric polymers are demanded for various applications. However, the low instinctively piezoelectric coefficient (i.e. d33) and complex poling process greatly resist their applications. Herein, we show that introducing dynamic pressure during fabrication is capable for poling polyvinylidene difluoride/barium titanate (PVDF/BTO) composites with d33 of ~51.20 pC/N at low density of ~0.64 g/cm3. The melt-state dynamic pressure driven energy implantation induces structure evolutions of both PVDF and BTO are demonstrated as reasons for self-poling. Then, the porous material is employed as pressure sensor with a high output of ~20.0 V and sensitivity of ~132.87 mV/kPa. Besides, the energy harvesting experiment suggests power density of ~58.7 mW/m2 can be achieved for 10 N pressure with a long-term durability. In summary, we not only provide a high performance lightweight, flexible piezoelectric polymer composite towards sustainable self-powered sensing and energy harvesting, but also pave an avenue for electrical-free fabrication of piezoelectric polymers.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI.

Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across sequences, which interferes with mining efficient representations by learning-based models. To handle various clinical scenarios, we propose a sequence-to-sequence generation framework (Seq2Seq) for imaging-differentiation representation learning. In this study, not only do we propose arbitrary 3D/4D sequence generation within one model to generate any specified target sequence, but also we are able to rank the importance of each sequence based on a new metric estimating the difficulty of a sequence being generated. Furthermore, we also exploit the generation inability of the model to extract regions that contain unique information for each sequence. We conduct extensive experiments using three datasets including a toy dataset of 20,000 simulated subjects, a brain MRI dataset of 1251 subjects, and a breast MRI dataset of 2101 subjects, to demonstrate that (1) top-ranking sequences can be used to replace complete sequences with non-inferior performance; (2) combining MRI with our imaging-differentiation map leads to better performance in clinical tasks such as glioblastoma MGMT promoter methylation status prediction and breast cancer pathological complete response status prediction. Our code is available at https://github.com/fiy2W/mri_seq2seq.

Corrigendum: Alterations via inter-regional connective relationships in Alzheimer's disease.

[This corrects the article DOI: 10.3389/fnhum.2023.1276994.].

Alterations via inter-regional connective relationships in Alzheimer's disease.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

Improving rice nitrogen-use efficiency by modulating a novel monouniquitination machinery for optimal root plasticity response to nitrogen.

Plant nitrogen (N)-use efficiency (NUE) is largely determined by the ability of root to take up external N sources, whose availability and distribution in turn trigger the modification of root system architecture (RSA) for N foraging. Therefore, improving N-responsive reshaping of RSA for optimal N absorption is a major target for developing crops with high NUE. In this study, we identified RNR10 (REGULATOR OF N-RESPONSIVE RSA ON CHROMOSOME 10) as the causal gene that underlies the significantly different root developmental plasticity in response to changes in N level exhibited by the indica (Xian) and japonica (Geng) subspecies of rice. RNR10 encodes an F-box protein that interacts with a negative regulator of auxin biosynthesis, DNR1 (DULL NITROGEN RESPONSE1). Interestingly, RNR10 monoubiquitinates DNR1 and inhibits its degradation, thus antagonizing auxin accumulation, which results in reduced root responsivity to N and nitrate (NO3-) uptake. Therefore, modulating the RNR10-DNR1-auxin module provides a novel strategy for coordinating a desirable RSA and enhanced N acquisition for future sustainable agriculture.

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network.

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details.

Modified Dynamic Movement Primitives: Robot Trajectory Planning and Force Control Under Curved Surface Constraints.

Dynamic movement primitives (DMPs) have been widely applied in robot motion planning and control. However, in some special cases, original discrete DMP fails to generalize proper trajectories. Moreover, it is difficult to produce trajectories on the curved surface. To solve the above problems, a modified DMP method is proposed for robot control by adding the scaling factor and force coupling term. First, the adjusted cosine similarity is defined to assess the similarity of the generalized trajectory with respect to the demonstrated trajectory. By optimizing the similarity, the trajectories can be generated in all situations. Next, by adding the force coupling term derived from adaptive admittance control to the transformation system of the original DMP, the controller achieves the force control ability. Then, the modified DMP-based robot control system is developed. The stability and convergence of the system are proved. Finally, the high precisions of the proposed method are verified by simulations and experiments. The method is significant for trajectory learning and generalization on the curved surface.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Dietary Patterns and Nutrient Intake in University Students of Macao: A Cross-Sectional Study.

BACKGROUND: Nutritional status affects the health of the public and is one of the key factors influencing social-economic development. To date, little research on the nutritional status of the Macao university student population has been conducted. OBJECTIVES: To identify and evaluate the dietary pattern and the nutritional intake among Macao university students. METHODS: The Macao students were selected by the stratified cluster random sampling method. A semi-quantitative food frequency questionnaire was used to investigate food consumption. Data were analyzed through a t-test and factor analysis by using SPSS Version 24.0. RESULTS: A total of 1230 questionnaires were distributed. From the respondents, 1067 (86.7%) were valid. In general, we identified three major dietary patterns in this population: (1) fruit and vegetable dietary pattern, characterized by abundant consumption of fruits and vegetables; (2) grain and high fat dietary pattern, characterized as high intakes of grains and animal foods; (3) high sugar dietary pattern, characterized by a large quantity of daily sugary drinks. The average daily intake of vitamin A, thiamine, calcium, and iodine were significantly lower than the Chinese Recommended Nutrient Intake (RNI) in the subjects. Conclusions: The dietary pattern of Macao students is similar to that of other Asians. Surprisingly, the daily intake of vitamin A, thiamine, calcium, and iodine by Macao university students is significantly lower than the Chinese RNI.

Fabrication of triboelectric polymer films via repeated rheological forging for ultrahigh surface charge density.

Triboelectric polymer with high charge density is the foundation to promote the wide range of applications of triboelectric nanogenerators. This work develops a method to produce triboelectric polymer based on repeated rheological forging. The fluorinated ethylene propylene film fabricated by repeated forging method not only has excellent mechanical properties and good transmittance, but also can maintain an ultrahigh tribo-charge density. Based on the film with a thickness of 30 µm, the output charge density from contact-separation nanogenerator reaches 352 µC·m-2. Then, the same film is applied for the nanogenerator with air-breakdown mode and a charge density of 510 µC·m-2 is further achieved. The repeated forging method can effectively regulate the composition of surface functional groups, the crystallinity, and the dielectric constants of the fluorinated ethylene propylene, leading to the superior capability of triboelectrification. Finally, we summarize the key parameters for elevating the electrification performance on the basis of molecular structure and related fabrication crafts, which can guide the further development of triboelectric polymers.

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation.

BACKGROUND: It is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images' lack of spatial information. OBJECTIVE: The aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. METHODS: We proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. RESULTS: A total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. CONCLUSIONS: According to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.

Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images.

Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net.

Weakly Supervised MR-TRUS Image Synthesis for Brachytherapy of Prostate Cancer.

Prostate magnetic resonance imaging (MRI) offers accurate details of structures and tumors for prostate cancer brachytherapy. However, it is unsuitable for routine treatment since MR images differ significantly from trans-rectal ultrasound (TRUS) images conventionally used for radioactive seed implants in brachytherapy. TRUS imaging is fast, convenient, and widely available in the operation room but is known for its low soft-tissue contrast and tumor visualization capability in the prostate area. Conventionally, practitioners usually rely on prostate segmentation to fuse the two imaging modalities with non-rigid registration. However, prostate delineation is often not available on diagnostic MR images. Besides, the high non-linear intensity relationship between two imaging modalities poses a challenge to non-rigid registration. Hence, we propose a method to generate a TRUS-styled image from a prostate MR image to replace the role of the TRUS image in radiation therapy dose pre-planning. We propose a structural constraint to handle non-linear projections of anatomical structures between MR and TRUS images. We further include an adversarial mechanism to enforce the model to preserve anatomical features in an MR image (such as prostate boundary and dominant intraprostatic lesion (DIL)) while synthesizing the TRUS-styled counterpart image. The proposed method is compared with other state-of-art methods with real TRUS images as the reference. The results demonstrate that the TRUS images synthesized by our method can be used for brachytherapy treatment planning for prostate cancer.

Genetic improvement of nitrogen use efficiency in crops.

Nitrogen (N) is an essential mineral nutrient for plant growth and development. N deficiency is the major factor limiting plant growth and crop production in most natural and agricultural soils. The green revolution of the 1960's boosted crop yields through cultivation of semi-dwarf plant varieties. However, green revolution wheat and rice varieties have relatively poor nitrogen use efficiency (NUE), require a high N fertilizer supply to achieve maximum yield potential, and this leads to an increase in production costs and environmental problem. Therefore, a major challenge for sustainable agriculture is whether improvement of NUE through the reduction of N fertilizer supply can be achieved without yield penalty. In this review, we summarize the recent advances in understanding of molecular mechanisms underlying the regulation of N-responsive plant growth, utilization and possibility for improvements of NUE in crops, and new breeding strategies through modulation of N-responsive growth-metabolism coordination for future sustainable agriculture.

Paternal gene pool of Malays in Southeast Asia and its applications for the early expansion of Austronesians.

OBJECTIVES: The origin and differentiation of Austronesian populations and their languages have long fascinated linguists, archeologists, and geneticists. However, the founding process of Austronesians and when they separated from their close relatives, such as the Daic and Austro-Asiatic populations in the mainland of Asia, remain unclear. In this study, we explored the paternal origin of Malays in Southeast Asia and the early differentiation of Austronesians. MATERIALS AND METHODS: We generated whole Y-chromosome sequences of 50 Malays and co-analyzed 200 sequences from other Austronesians and related populations. We generated a revised phylogenetic tree with time estimation. RESULTS: We identified six founding paternal lineages among the studied Malays samples. These founding lineages showed a surprisingly coincident expansion age at 5000 to 6000 years ago. We also found numerous mostly close related samples of the founding lineages of Malays among populations from Mainland of Asia. CONCLUSION: Our analyses provided a refined phylogenetic resolution for the dominant paternal lineages of Austronesians found by previous studies. We suggested that the co-expansion of numerous founding paternal lineages corresponds to the initial differentiation of the most recent common ancestor of modern Austronesians. The splitting time and divergence pattern in perspective of paternal Y-chromosome evidence are highly consistent with the previous theories of ethnologists, linguists, and archeologists.

Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images.

The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for initializing the subsequent sub-network. The next two sub-networks progressively optimize at the patch level with different resolutions to predict a fine-scale deformation field. Embedding difficulty-aware learning into the hierarchical neural network allows harder patches to be identified in the deeper sub-networks at higher resolutions for refining the deformation field. Experiments conducted on four public datasets validate that our method achieves promising registration accuracy with better preservation of topology, compared with state-of-the-art registration methods.

Y-chromosome evidence confirmed the Kerei-Abakh origin of Aksay Kazakhs.

Aksay Kazakhs are the easternmost branch of Kazakhs, residing in Jiuquan city, the forefront of the ancient Silk Road. However, the genetic diversity of Aksay Kazakhs and its relationships with other Kazakhs still lack attention. To clarify this issue, we analyzed the non-recombining portion of the Y-chromosome from 93 Aksay Kazakhs samples, using a high-resolution analysis of 106 biallelic markers and 17 STRs. The lowest haplogroup diversity (0.38) was observed in Aksay Kazakhs among all studied Kazakh populations. The social and cultural traditions of the Kazakhs shaped their current pattern of genetic variation. Aksay Kazakhs tended to migrate with clans and had limited paternal admixture with neighboring populations. Aksay Kazakhs had the highest frequency (80%) of haplogroup C2b1a3a1-F3796 (previous C3*-Star Cluster) among the investigated Eurasian steppe populations, which was now seen as the genetic marker of Kerei clan. Furthermore, NETWORK analysis indicated that Aksay Kazakhs originated from sub-clan Kerei-Abakh in Kazakhstan with DYS448 = 23. TMRCA estimates of three recent descent clusters detected in C2*-M217 (xM48) network, one of which incorporate nearly all of the C2b1a3a1-F3796 Aksay Kazakhs samples, gave the age range of 976-1405 YA for DC1, 1059-1314 YA for DC2, and 1139-1317 YA for DC3, respectively; this is coherent with the 7th to the 11th centuries Altaic-speaking pastoral nomadic population expansion.