Synthesis and volume phase transition of concanavalin A-based glucose-responsive nanogels

NSFC [21274118, 91227120, 20923004]; FRFCU [2012121016]; NFFTBS [J1210014]; NCETFJGlucose-responsive polymer nanogels that can undergo a reversible and rapid volume phase transition in response to the fluctuation in blood glucose concentration have the potential to regulate the delivery of insulin mimicking pancreatic activity. We report here such a glucose-responsive polymer nanogel, which is made of concanavalin A (ConA) interpenetrated in a chemically crosslinked network of poly(N-isopropylacrylamide) (poly(NIPAM)). The introduction of ConA, a plant lectin protein, into the poly(NIPAM) network makes the newly developed semi-interpenetrating-structured nanogels responsive to glucose over a glucose concentration range of 0-20 mM at a physiological pH of 7.4. While the nanogels can swell and become stable shortly (<1 s) after adding glucose over a concentration range of 50.0 mu M to 20.0 mM, the changes in the average hydrodynamic radius and the size distribution of the nanogels can be fully reversible within the experimental error even after ten cycles of adding/removing glucose. The association rate constant is determined to be ca. 1.8 mM(-1) s(-1), and the dissociation rate constant is ca. 7.5 s(-1), indicating a fast reversible time response to the glucose concentration change of the nanogels. Moreover, in vitro insulin release can be modulated in a pulsatile profile in response to glucose concentrations

Impact of Biochar Application on Ammonia Volatilization from Paddy Fields under Controlled Irrigation

Ammonia volatilization is an important nitrogen loss pathway in the paddy field ecosystem which leads to low nitrogen-utilization efficiency and severe atmospheric pollution. To reveal the influence and the mechanism of biochar application on ammonia volatilization from paddy fields under controlled irrigation, field experiments were conducted in the Taihu Lake Basin in China. The experiment consisted of three levels of biochar application (0, 20, and 40 t·ha−1) and two types of irrigation management (controlled irrigation and flood irrigation). Increasing ammonia volatilization occurred after fertilization. Biochar application reduced the cumulative ammonia volatilization from controlled-irrigation paddy fields, compared with non-biochar treatment. The cumulative ammonia volatilization in controlled-irrigation paddy fields with 40 t·ha−1 biochar application was reduced by 12.27%. The decrease in ammonia volatilization was related to the change in soil physical and soil physical–chemical properties and soil microbial activities. The high biochar application (40 t·ha−1) increased the NH4+-N content in soil (p p p −1) increased soil urease activity by 33.70%. Ammonia volatilization from paddy fields was significantly correlated with the nitrogen concentration (p p < 0.05). Meanwhile, the abundance of ammonia monooxygenase subunit A (AOA) and ammonia-oxidizing bacteria (AOB) with biochar application under controlled irrigation showed an increasing trend with rice growth. The long-term application of biochar may have a relatively strong potential to inhibit ammonia volatilization. In general, the combined application of controlled irrigation and biochar provides an eco-friendly strategy for reducing farmland N loss and improving paddy field productivity

A New Species of Rhodoleia (Hamamelidaceae) From the Upper Pliocene of West Yunnan, China and Comments on Phytogeography and Insect Herbivory

In Europe, fossil fruits and seeds of Rhodoleia (Hamamelidaceae) have been described from the Upper Cretaceous to the Miocene, whereas no fossil record of Rhodoleia has been reported in Asia, where the modern species occur. Herein, 21 fossil leaves identified as Rhodoleia tengchongensis sp. nov. are described from the Upper Pliocene of Tengchong County, Yunnan Province, Southwest China. The fossils exhibit elliptic lamina with entire margins, simple brochidodromous major secondary veins, mixed percurrent intercostal tertiary veins, and looped exterior tertiaries. The leaf cuticle is characterized by pentagonal or hexagonal cells, stellate multicellular trichomes, and paracytic stomata. The combination of leaf architecture and cuticular characteristics suggests that the fossil leaves should be classified into the genus Rhodoleia. The fossil distributions indicate that the genus Rhodoleia might originate from Central Europe, and that migrated to Asia prior to the Late Pliocene. Additionally, insect damage is investigated, and different types of damage, such as hole feeding, margin feeding, surface feeding, and galling, are observed on the thirteen fossil leaves. Based on the damage frequencies for the fossil and extant leaves, the specific feeding behavior of insects on Rhodoleia trees appears to have been established as early as the Late Pliocene. The high occurrence of Rhodoleia insect herbivory may attract the insect-foraging birds, thereby increasing the probability of pollination

Surface Engineering Strategy Enables 4.5 V Sulfide-Based All-Solid-State Batteries with High Cathode Loading and Long Cycle Life

Sulfide-based all-solid-state lithium batteries (ASSLBs) with LiCoO2 (LCO) operating at high voltage (≥4.5 V vs Li+/Li) hold promise in realizing high energy density while maintaining safety. Here, we propose a solid electrolyte coating strategy to stabilize the cathode electrolyte interface and demonstrate the benefit of lithium difluoro(oxalate)borate (LiDFOB) as coating layer on the surface of Li6PS5Cl (LPSCl) to improve the performance of LCO at 4.5 V. 89.3% of initial discharge capacity can be retained after 1500 cycles at 1C (1C = 150 mA g–1). ASSLBs with high cathode loading (35.7 mg cm–2) could deliver an areal capacity over 6 mAh cm–2 (167 mAh g–1) at 0.1C and keep 85% capacity retention after 200 cycles at 0.3C. The investigation of the improvement mechanism further verifies that in situ decomposition of LiDFOB would build an (electro)chemomechanically stable interface, which not only suppresses interfacial side reactions but also buffers the cathode cracking

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset