A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example.

Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep-learning algorithm termed HAB-Ne was developed for the recognition of HABs in GF-1 Wide Field of View (WFV) images using Noctiluca scintillans algal bloom as an example. First, a pretrained image super-resolution model was integrated to improve the spatial resolution of the GF-1 WFV images and minimize the impact of mixed pixels caused by the strip distribution. Side-window convolution was also explored to enhance the edge features of HABs and minimize the effects of uneven biomass distribution. In addition, a convolutional encoder-decoder network was constructed for threshold-free HAB recognition to address the dependence on thresholds in existing methods. HAB-Net effectively recognized HABs from GF-1 WFV images, achieving an average precision of 90.1% and an F1-score of 0.86. HAB-Net showed more fine-grained recognition results than those of existing methods, with over 4% improvement in the F1-Score, especially in the marginal areas of HAB distribution. The algorithm demonstrated its effectiveness in recognizing HABs in different marine environments, such as the Yellow Sea, East China Sea, and northern Vietnam. Additionally, the algorithm was proven suitable for detecting the macroalga Sargassum. This study demonstrates the potential of deep-learning-based fine-grained recognition of HABs, which can be extended to the recognition of other fine-scale and strip-distributed objects, such as oil spills and Ulva prolifera.

Comparison of the clinical and radiographic outcomes of cortical bone trajectory and traditional trajectory pedicle screw fixation in transforaminal lumbar interbody fusion: a randomized controlled trial.

PURPOSE: To compare the clinical outcomes and radiographic outcomes of cortical bone trajectory (CBT) and traditional trajectory (TT) pedicle screw fixation in patients treated with single-level transforaminal lumbar interbody fusion (TLIF). METHODS: This trial included a total of 224 patients with lumbar spine disease who required single-level TLIF surgery. Patients were randomly assigned to the CBT and TT groups at a 1:1 ratio. Demographics and clinical and radiographic data were collected to evaluate the efficacy and safety of CBT and TT screw fixation in TLIF. RESULTS: The baseline characteristic data were similar between the CBT and TT groups. Back and leg pain for both the CBT and TT groups improved significantly from baseline to 24 months postoperatively. The CBT group experienced less pain than the TT group at one week postoperatively. The postoperative radiographic results showed that the accuracy of screw placement was significantly increased in the CBT group compared with the TT group (P < 0.05). The CBT group had a significantly lower rate of FJV than the TT group (P < 0.05). In addition, the rate of fusion and the rate of screw loosening were similar between the CBT and TT groups according to screw loosening criteria. CONCLUSION: This prospective, randomized controlled analysis suggests that clinical outcomes and radiographic characteristics, including fusion rates and caudal screw loosening rates, were comparable between CBT and TT screw fixation. Compared with the TT group, the CBT group showed advantages in the accuracy of screw placement and the FJV rate. CLINICAL TRIALS REGISTRATION: This trial has been registered at the US National Institutes of Health Clinical Trials Registry: NCT03105167.

Data-driven NODE based multirate sampled data state feedback control.

In control systems, multirate sampled data systems are widely used because they improve system performance and adaptability, especially when systems deal with both continuous and discrete signals or entirely asynchronous sampling signals. This paper addresses the challenges of system stability and optimization in these multirate systems, specifically for a certain class of nonlinear systems. Existing controllers, though capable in certain contexts, tend to be overly complex and often lack guidance on appropriate sampling interval selection for these intricate systems. Our approach takes into account both system stability and practical considerations, providing a criterion for selecting multiple sample periods that guarantees system stability, as well as an optimal choice of parameters by Neural Ordinary Differential Equation (NODE) for the linear practical controller that maximizes performance according to a predefined performance index. With the construction of a set of linear stabilizers that are implemented using multirate sampled data, the stability and controller design at three different sampling levels are studied. To demonstrate the effectiveness of our proposed strategy, the simulations and real world application of a single-link robot system are presented.

Deep Learning-Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver.

BACKGROUND: An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of all deceased donors. Many potential candidates need to decide whether to accept an SDL offer or remain on the waitlist for a nonsteatotic donor liver (non-SDL). The objective of this study was to compare the survival of accepting an SDL vs using a non-SDL after waiting various times. METHODS: Using data from the United States' organ procurement and transplantation network, deep survival learning predictive models were built to compare post-decision survival after accepting an SDL vs waiting for a non-SDL. The comparison subjects contain simulated 20,000 different scenarios of a candidate either accepting an SDL immediately or receiving a non-SDL after waiting various times. The research variables were selected using the LASSO-Cox and Random Survival Forest (RSF) models. The Cox proportional hazards and RSF models were also comparatively included for survival prediction. In addition, personalized survival curves for randomly selected candidates were generated. RESULT: Deep survival learning outperformed Cox proportional hazards and RSF in predicting the survival of liver transplants. Among the simulations, 25% to 30% of scenarios demonstrated a higher 3-year survival post-decision for candidates accepting an SDL than waiting and receiving a non-SDL. The difference was only 1.43% in 3-year survival post-decision between accepting an SDL and waiting 260 days (mean waitlist time) for a non-SDL. As the number of days on the waitlist increases, the difference in survival between accepting SDLs and waiting for non-SDLs decreases. CONCLUSIONS: Appropriately used SDLs could expand the donor pool and relieve the candidates' unmet need for donor livers, which presents long-term survival benefits for recipients.

Quantifying the Growth of Glioblastoma Tumors Using Multimodal MRI Brain Images.

Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.

PPA: Principal parcellation analysis for brain connectomes and multiple traits.

Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.

Evaluating the effectiveness of coastal environmental management policies in China: The case of Bohai Sea.

With marine pollution issues becoming serious and widespread, a series of coastal environmental managemental policies are being carried out worldwide, the effectiveness of which requires comprehensive evaluation. Taking the Bohai Sea (BS) of China as an example, which has been plagued by serious ecological and environmental issues for decades due to terrestrial pollution discharge, this study explored and quantified, for the first time to our best knowledge, the variability of water quality after initiating a dedicated 3-year pollution control action (Uphill Battle for Integrated Bohai Sea Management, UBIBM, 2018-2020) implemented by China's central government, with two water quality indexes of water color (Forel-Ule index, FUI) and transparency (Secchi disk depth, ZSD, m) from satellite observations. During the UBIBM, a significant improvement in water quality was detected, characterized by a clearer and bluer BS, with ZSD and FUI improved by 14.1% and 3.2%, respectively, compared with the baseline period (2011-2017). In addition, an abrupt drop in the long-term record (2011-2022) of the coverage area of highly turbid waters (ZSD≤2 m or FUI≥8) was found in 2018, which coincided with the start of the UBIBM, indicating that the water quality improvement may be attributed to the pollution alleviation of the UBIBM. Independent data of land-based pollution statistics also supported this deduction. (3) Compared with the previous two pollution control actions in the first decade of 21st century, UBIBM was proved to be the most successful one in terms of the achieved highest transparency and lowest FUI during the past two decades. Reasons for the achievement and implications to future pollution control are discussed for a more sustainable and balanced improvement in the coastal environment. This research provides a valuable example that satellite remote sensing can play a vital role in the management of coastal ecosystems by providing effective evaluation of pollution control actions.

Trade-offs between fertilizer-N availability and Cd pollution potential under crop straw incorporation by 15 N stable isotopes in rice.

Application of crop residues and chemical nitrogen (N) fertilizer is a conventional practice for achieving high yield in a rice system. However, the fallacious combination of N fertilizers with crop straw not only significantly reduces the N use efficiencies (NUEs) but also leads to serious environmental problems. The present study employed five treatments including no N fertilization and no straw incorporation (ck), N fertilization incorporation only (S0), N fertilization with 40% straw (S40), N fertilization with 60% straw (S60), and N fertilization with 100% straw (S100) to improve N use efficiency as well as reduced Cd distribution in rice. The crop yields were largely enhanced by fertilization ranging from 13 to 52% over the straw addition treatments. Compared with ck, N fertilizer input significantly decreased soil pH, while DOC contents were raised in response to straw amendment, reaching the highest in S60 and S100 treatments, respectively. Moreover, straw addition substantially impacted the Cd accumulation and altered the bacterial community structure. The soil NH4+-N concentration under S0 performed the maximum in yellow soil, while the minimum in black soil compared to straw-incorporated pots. In addition, the soil NO3--N concentration in straw-incorporated plots tended to be higher than that in straw-removed plots in both soils, indicating that crop straw triggering the N mineralization was associated with native soil N condition. Furthermore, the NUE increased with 15 N uptake in the plant, and the residual 15 N in soil was increased by 26.8% with straw addition across four straw application rates. Overall, our study highlights the trade-offs between straw incorporation with N fertilizer in eliminating potential Cd toxicity, increasing fertilizer-N use efficiencies and help to provide a feasible agricultural management.

A Ganoderma lucidum polysaccharide F31 alleviates hyperglycemia through kidney protection and adipocyte apoptosis.

In this paper, we reported an excellent hypoglycemic effect of a Ganoderma lucidium polysaccharide F31 with efficacies between 45 and 54 %, approaching to that of liraglutide (52 %). Significantly, F31 reduced the body weight gains and food intakes. F31 decreased 4 key compounds, consisting of adenosine, adenosine, galactitol and glycerophosphocholine and elevated 8 key compounds, including arginine, proline, arachidonic acid, creatine, aspartic acid, leucine, phenylalanine and ornithine, which protected kidney function. Also, apoptosis was promoted by F31 in epididymal fat through increasing Caspase-3, Caspase-6 and Bax and decreasing Bcl-2. On 3 T3-L1 preadipocyte cells, F31 induced early apoptosis through reducing mitochondrial membrane potential. Finally, a molecular docking was performed to reveal a plausible cross-talk between kidney and epididymal fat through glycerophosphorylcholine-Bax axis. Overall, F31 alleviated hyperglycemia through kidney protection and adipocyte apoptosis in db/db mice. This work may provide novel insights into the hypoglycemic activity of polysaccharides.

LMFE: Learning-Based Multiscale Feature Engineering in Partial Discharge Detection.

The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.

Imaging Genetic Based Mediation Analysis for Human Cognition.

The brain connectome maps the structural and functional connectivity that forms an important neurobiological basis for the analysis of human cognitive traits while the genetic predisposition and our cognition ability are frequently found in close association. The issue of how genetic architecture and brain connectome causally affect human behaviors remains unknown. To seek for the potential causal relationship, in this paper, we carried out the causal pathway analysis from single nucleotide polymorphism (SNP) data to four common human cognitive traits, mediated by the brain connectome. Specifically, we selected 942 SNPs that are significantly associated with the brain connectome, and then estimated the direct and indirect effect on the human traits for each SNP. We found out that a majority of the selected SNPs have significant direct effects on human traits and discussed the trait-related brain regions and their implications.

A polysaccharide isolated from Ganoderma lucidum ameliorates hyperglycemia through modulating gut microbiota in type 2 diabetic mice.

In this study, we reported a thermal stable and non-toxic heteropolysaccharide F31, which decreased the blood glucose of diabetic mice (21.75 mmol/L) induced by high-fat diet (HFD) and streptozotocin (STZ) to 12.56 and 15.18 mmol/L (P < 0.01) at 180 and 60 mg/kg, depicting remarkable hypoglycemic effects of 42.25 and 30.21%. Moreover, F31 repaired islet cells and increased insulin secretion, promoted the synthesis and storage of glycogen in liver and improved activities of antioxidant enzymes and insulin resistances, declining HOMA-IR (43.77 mmol/mU) of diabetic mice (P < 0.01) to 17.32 and 20.96 mmol/mU at both doses. 16S rRNA gene sequencing revealed that F31 significantly decreased Firmicutes (44.92%, P < 0.01) and enhanced Bacteroidetes (33.73%, P < 0.01) and then increased B/F ratio of diabetic mice to 0.6969 (P < 0.01), even being close to normal control (P = 0.9579). F31 enriched Lactobacillus, Bacteroides and Ruminococcaceae, which may relieve glucose, insulin resistance and inflammation through decreasing the release of endotoxins into the circulation from intestine, carbohydrate fermentation in gut and activation of the intestine-brain axis. Functionally, F31 improved metabolism of gut microbiota to a normal state. These results may provide novel insights into the beneficial effect of F31 against hyperglycemia.

Super-resolution optical mapping of floating macroalgae from geostationary orbit.

The spatial resolution of an observation from a geostationary orbiting satellite is usually too coarse to track small scale macroalgae blooms. For macroalgae mapping to benefit from a geostationary orbit's staring monitoring and frequent revisit intervals, we introduced a super-resolution method that reconstructs a high-resolution (HR) image of a region from a sequence of raw geostationary low-resolution images of the same region. We tested our method with GF-4 images at 50 m spatial resolution and demonstrated that the spatial resolution increased to 25 m. In addition, the derived HR image had better image quality characterized by a higher signal-to-noise ratio, clarity, and contrast. The increased spatial resolution and improved image quality improved our ability to distinguish macroalgae patches from the surrounding waters, especially tiny patches of macroalgae, and to precisely delineate the patch boundaries. Lastly, we more accurately estimated the areal coverage of the patches by reducing underestimation of the coverage of tiny patches and overestimation of the coverage of large patches.

A predictive model of radiation-related fibrosis based on the radiomic features of magnetic resonance imaging and computed tomography.

BACKGROUND: To establish a predictive model for the fibrotic level of neck muscles after radiotherapy by using radiomic features extracted from the magnetic resonance imaging (MRI) before and after radiotherapy and planning computed tomography (CT) in nasopharyngeal carcinoma patients. METHODS: A total of one hundred and eighty-six patients were finally enrolled in this study. According to the specific standard, all patients were divided into three different fibrosis groups. Regions of interests (ROI), including sternocleidomastoids (SCMs), trapezius (T), levator scapulae (LS), and scalenus muscles (S), were delineated manually and used for features extraction on IBEX. XGBoost, a machine learning algorithm, was used for the establishment of the prediction model. First, the patients were divided into training cohort (80%) and testing cohort (20%) randomly. Then the image features of CT or delta changes calculated from pre- and post-radiotherapy MRI images on each cohort constituted training and testing datasets. Then, based on the training dataset, a well-trained prediction model was produced. We used five-fold cross-validation to validate the predictive models. Afterward, the model performance was assessed on the 'testing' set and reported in terms of area under the receiver operating characteristic curve (AUC) under five scenarios: (I) only T1 sequence, (II) only T2 sequence, (III) only T1 post-contrast (T1 + C) sequence, (IV) Combination of all MRI sequences, (V) only CT. RESULTS: Most of the patients enrolled are male (73.1%), mean age was 47 years, receiving concurrent chemo-radiotherapy as the primary treatment (90.9%). By the end of the final follow-up, most of the patients were rated as mild fibrosis (60.8%). We found the prediction model based on the CT image features outperform all MRI features with an AUC of 0.69 and accuracy of 0.65. Contrarily, the model based on features from all MRI sequence showed lower AUC less than 0.5 and lower accuracy less than 0.6. CONCLUSIONS: The prediction model based on CT radiomics features has better performance in the prediction of the grade of post-radiotherapy neck fibrosis. This might help guide radiotherapy treatment planning to achieve a better quality of life.

Stability analysis of CT radiomic features with respect to segmentation variation in oropharyngeal cancer.

INTRODUCTION: Accurate segmentation of tumors and quantification of tumor features are important for cancer detection, diagnosis, monitoring, and planning therapeutic intervention. Due to inherent noise components in multi-parametric imaging and inter-observer and intra-observer variations, it is common that various segmentation methods may produce large segmentation errors in tumor volumes and their associated radiomic features. The purpose of this study is to carry out the stability analysis for radiomic features with respect to segmentation variation in oropharyngeal cancer (OPC). METHODS: In this study, 436 contrast-enhanced computed tomography (CT) axial images were collected from patients with OPC. In order to derive various segmentations of tumor volumes, two additional segmentations were obtained via resizing the original segmented regions of interest (ROIs) based on their geometric information on the boundary. For three ROI image groups, we calculated 109 radiomic features. Then, a logistic regression model was built to investigate the correlation between the radiomic features extracted from GTVp and the response to chemotherapy and radiation in terms of overall survival (OS). Finally, in order to evaluate the stability of each feature with respect to segmentation results, based on the prediction probabilities, we assessed the inter-rater reliability and reproducibility by calculating the intra-class correlation coefficients (ICC) and concordance correlation coefficients (CCC). RESULTS: Most radiomic features in this study varied a lot when the ROIs were not well segmented. For both the representation agreement and predictive agreement, the ICC and CCC were below 0.5 for all the features. We still found some robust features with relatively high ICC and CCC compared to most features. For example, 25percentile (ICC = 0.38, CCC = 0.37 in representation agreement and ICC = CCC = 0.27 in predictive agreement) is a quantile based feature, which is robust to the extremely high or low values; and Hu_1_std (ICC = 0.31, CCC = 0.31 in representation agreement) is a feature calculated based on the first Hu moment, which is invariant to the transformation of ROIs. CONCLUSION: In OPC studies, the tumor segmentation variation affects the radiomic features from CT images in terms of both representation and prediction. Some features that are robust to the extreme values or invariant to the transformation of ROIs may be treated as radiomic markers to assist with OPC treatment monitoring and prognostic prediction.

Remote sensing estimation of the biomass of floating Ulva prolifera and analysis of the main factors driving the interannual variability of the biomass in the Yellow Sea.

Since 2007, green tide blooms with Ulva prolifera as the dominant species have occurred every summer in the Yellow Sea. Biomass is a critical parameter used to describe the severity of green tide blooms. In this study, we analyzed the relationships between several indices (normalized difference vegetation index (NDVI), floating algae index (FAI), ratio vegetation index (RVI), enhanced vegetation index (EVI), ocean surface algal bloom index (OSABI), Korea Ocean Satellite Center (KOSC) approach) and the biomass per unit area of Ulva prolifera by using the in situ measurements from a water tank experiment. EVI, NDVI, and FAI showed strong exponential relationships with Ulva prolifera biomass per unit area. In order to apply the relationships to satellite remote sensing data, the impacts of the atmosphere (different aerosol optical depth at 550 nm) and mixed pixels to the relationships were analyzed. The results show that atmosphere has little effect on the relationship between EVI and Ulva prolifera biomass per unit area with R2 = 0.94 and APD (the average percentage deviation) = 19.55% when EVI is calculated from Rrc (Rayleigh-corrected reflectance), and R2 = 0.95 and APD = 17.53% when EVI is calculated from Rtoa (top-of-atmosphere reflectance). Due to the low sensitivity to the atmosphere, the EVI relationship can be directly utilized in the top-of-atmosphere (TOA) reflectance without atmospheric correction. In addition, the EVI was slightly affected by mixed pixels with the APD only increased by ~10%. The EVI relationship was then applied to a long MODIS image time series to obtain the maximal total biomass of floating Ulva prolifera in the Yellow Sea from 2007 to 2016. The results showed that the maximum and minimum total biomass occurred in 2016 (~1.17 million tons) and 2012 (~0.074 million tons), respectively. The main factors that caused the inter-annual biomass variability were analyzed. The total amount of nutrients from Sheyang River which was the largest river on the northern coast of Jiangsu Province, and Porphyra cultivation in the Radial Sand Ridges of Jiangsu Province had both strong correlation with Ulva prolifera total biomass.

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.

Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials.

Out-of-Band Response for the Coastal Zone Imager (CZI) Onboard China's Ocean Color Satellite HY-1C: Effect on the Observation Just above the Sea Surface.

The out-of-band (OOB) response is one of the key specifications for satellite optical sensors, which has important influences on quantitative remote sensing retrieval. In this paper, the effect of OOB response on the radiometric measurements made just above the sea surface is evaluated for the three broad visible bands (i.e., blue, green, and red) of the Coastal Zone Imager (CZI) onboard China's ocean satellite HY-1C to be launched in September 2018. For the turbid coastal (Case 2) waters whose optical properties are mainly dominated by suspended sediment and colored dissolved organic material, the OOB effect can be neglected (<2%) for all three CZI visible bands. For the phytoplankton-dominated (Case 1) waters which are mainly distributed in the clear open ocean, a significant (>2%) OOB effect was found in the green band over oligotrophic waters (chlorophyll a concentration ≤~0.1 mg/m³), and accordingly a model based on the CZI blue-green band ratio is proposed to correct this effect. The OOB influence on the CZI ocean color retrieval is discussed. This research highlights the importance of the comprehensive pre-launch radiometric characterization and the OOB effect correction for the broad band space-borne sensor, in order to achieve a high-quality quantitative ocean product.