In-Induced Electronic Structure Modulations of Bi─O Active Sites for Selective Carbon Dioxide Electroreduction to Liquid Fuel in Strong Acid.

The formation of carbonate in neutral/alkaline solutions leads to carbonate crossover, severely reducing carbon dioxide (CO2 ) single pass conversion efficiency (SPCE). Thus, CO2 electrolysis is a prospective route to achieve high CO2 utilization under acidic environment. Bimetallic Bi-based catalysts obtained utilizing metal doping strategies exhibit enhanced CO2 -to-formic acid (HCOOH) selectivity in alkaline/neutral media. However, achieving high HCOOH selectivity remains challenging in acidic media. To this end, Indium (In) doped Bi2O2CO3 via hydrothermal method is prepared for in-situ electroreduction to In-Bi/BiOx nanosheets for acidic CO2 reduction reaction (CO2RR). In doping strategy regulates the electronic structure of Bi, promoting the fast derivatization of Bi2O2CO3 into Bi-O active sites to enhance CO2RR catalytic activity. The optimized Bi2 O2 CO3 -derived catalyst achieves the maximum HCOOH faradaic efficiency (FE) of 96% at 200 mA cm-2 . The SPCE for HCOOH production in acid is up to 36.6%, 2.2-fold higher than the best reported catalysts in alkaline environment. Furthermore, in situ Raman and X-ray photoelectron spectroscopy demonstrate that In-induced electronic structure modulation promotes a rapid structural evolution from nanobulks to Bi/BiOx nanosheets with more active species under acidic CO2 RR, which is a major factor in performance improvement.

Effect of Charge on Carbon Support on the Catalytic Activity of Cu2O toward CO2 Reduction to C2 Products.

Electrochemical carbon dioxide (CO2) reduction for C2 products has been studied on a series of supported Cu-based catalysts; however, the charge-promotion effects from the substrates for the selectivity of CO2 reduction are still unclear. Here we localize nanosized Cu2O on three carbon-based substrates that provide different charge-promotion effects: positively charged boron-doped graphene (BG), negatively charged nitrogen-doped graphene (NG), and weak negatively charged reduced graphene oxide (rGO). We demonstrate that the charge-promotion effects lead to an increase in faradaic efficiency (FE) for C2 products with an order of rGO/Cu < BG/Cu < pure Cu < NG/Cu and an FEC2/FEC1 ratio from 0.2 to 7.1. By performing in situ characterization, electrokinetic investigations, and density functional theory (DFT) calculations, we reveal that the negatively charged NG is favorable for stabilizing Cu+ species under CO2 reduction, which strengthens CO* adsorption to further boost C-C coupling for C2 products. As a result, we achieve a high C2+ FE of ∼68% at high current densities of 100-250 mA cm-2.

Development of real-time PCR and droplet digital PCR based marker for the detection of Tilletia caries inciting common bunt of wheat.

This is the first study reporting droplet digital PCR and quantitative real time PCR for detection of Tilletia caries (syn. T. tritici), which causes common bunt of wheat and leads to yield losses of 80% in many wheat growing areas worldwide. To establish an accurate, rapid and quantifiable detection method, we tested 100 inter simple sequence repeats (ISSR) primers and obtained a species-specific fragment (515 bp) generated by ISSR 827. Then, a specific 266 bp band for the sequence characterized amplified region (SCAR) marker was produced from T. caries. The detection limit reached 50 pg/µL. Based on the SCAR marker, we further developed a higher sensitivity of quantitative real time-polymerase chain reaction (qRT-PCR) with a detection limit of 2.4 fg/µL, and droplet digital PCR (ddPCR) with a detection limit of 0.24 fg/µL. Both methods greatly improved the detection sensitivity of T. caries, which will be contribute a lot for quickly and accurately detection of T. caries, which causes wheat common bunt.

Super-Coordinated Nickel N4 Ni1 O2 Site Single-Atom Catalyst for Selective H2 O2 Electrosynthesis at High Current Densities.

Electrochemical production of hydrogen peroxide (H2 O2 ) from O2 on single-atom catalysts has attracted great attention, yet the quest for robust catalysts is driven by achieving >90 % Faradaic efficiency (FE) under industrial-relevant current densities (>100 mA cm-2 ). Herein we synthesize a catalyst that contains single nickel site coordinated by four nitrogen and two oxygen atoms (i.e., N4 Ni1 O2 ) via involving carboxyl functionalized multiwall carbon nanotubes as a substrate to provide extra O coordination to the regular NiN4 site. It has a cathodic energy efficiency of approximately 82 % and a H2 O2 FE of around 96 % at 200 mA cm-2 current density, outperforming the reported single-atom catalysts for H2 O2 electrosynthesis.

Toward High-Performance CO2 -to-C2 Electroreduction via Linker Tuning on MOF-Derived Catalysts.

Copper (Cu)-based metal-organic frameworks (MOFs) and MOF-derived catalysts are well studied for electroreduction of carbon dioxide (CO2 ); however, the effects of organic linkers for the selectivity of CO2 reduction are still unrevealed. Here, a series of Cu-based MOF-derived catalysts is investigated with different organic linkers appended, named X-Cu-BDC (BDC = 1,4-benzenedicarboxylic acid, X = NH2 , OH, H, F, and 2F). It is found that the linkers affect the faradaic efficiency (FE) for C2 products with an order of NH2  < OH < bare Cu-BDC < F < 2F, thus tuning the FEC2 :FEC1 ratios from 0.6 to 3.8. As a result, the highest C2 FE of ≈63% at a current density of 150 mA cm-2 on 2F-Cu-BDC derived catalyst is achieved. Using operando Raman measurements, it is revealed that the MOF derives to Cu2 O during eCO2 RR but organic linkers are stable. The fluorine group in organic linker can promote the H2 O dissociation to *H species, further facilitating the hydrogenation of *CO to *CHO that helps CC coupling.

pH-Responsive Polyoxometalates that Achieve Efficient Wastewater Reclamation and Source Recovery via Forward Osmosis.

Forward osmosis (FO) has been increasingly used for water treatment. However, the lack of suitable draw solutes impedes its further development. Herein, we design pH-responsive polyoxometalates, that is, (NH4)6Mo7O24 and Na6Mo7O24, as draw solutes for simultaneous water reclamation and resource recovery from wastewater via FO. Both polyoxometalates have a cage-like configuration and release multiple ionic species in water. These characteristics allow them to generate high osmotic pressures to drive the FO separation efficiently with negligible reverse solute diffusion. (NH4)6Mo7O24 and Na6Mo7O24 at a dilute concentration (0.4 M) produce water fluxes of 16.4 LMH and 14.2 LMH, respectively, against DI water, outperforming the frequently used commercial NaCl and NH4HCO3 draw solutes, and other synthetic materials. With an average water flux of 10.0 LMH, (NH4)6Mo7O24 reclaims water from the simulated glutathione-containing wastewater more efficiently than Na6Mo7O24 (9.1 LMH), NaCl (3.3 LMH), and NH4HCO3 (5.6 LMH). The final glutathione treated with (NH4)6Mo7O24 and Na6Mo7O24 remains intact but that treated with NaCl and NH4HCO3 is either denatured or contaminated owing to their severe leakage in FO. Remarkably, both polyoxometalates are readily recycled by pH regulation and reused for FO. Polyoxometalate is thus proven to be an appropriate candidate for FO separation in wastewater reclamation and resource recovery.

DTBP-promoted site-selective α-alkoxyl C-H functionalization of alkyl esters: synthesis of 2-alkyl ester substituted chromanones.

The direct C-H functionalization of ethyl acetates was developed, delivering a variety of 1-(4-oxochroman-2-yl)ethyl acetate derivatives by reacting with chromones. This reaction has a wide substrate scope with excellent site-selective C-H activation at the inactive α-hydrogen of the alkoxyl group instead of the α-hydrogen of the carbonyl group under radical conditions. Compared with other protocols for the α-alkoxyl C-H functionalization of alkyl esters, a distinguishing feature of this reaction is that no metal catalyst was required, with DTBP as the sole oxidant.

LNDb challenge on automatic lung cancer patient management.

Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules. In spite of promising results, the application of automatic methods to clinical routine is not straightforward and only a limited number of studies have addressed the problem in a holistic way. With the goal of advancing the state of the art, the Lung Nodule Database (LNDb) Challenge on automatic lung cancer patient management was organized. The LNDb Challenge addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines. 294 CT scans were thus collected retrospectively at the Centro Hospitalar e Universitrio de So Joo in Porto, Portugal and each CT was annotated by at least one radiologist. Annotations comprised nodule centroids, segmentations and subjective characterization. 58 CTs and the corresponding annotations were withheld as a separate test set. A total of 947 users registered for the challenge and 11 successful submissions for at least one of the sub-challenges were received. For patient follow-up prediction, a maximum quadratic weighted Cohen's kappa of 0.580 was obtained. In terms of nodule detection, a sensitivity below 0.4 (and 0.7) at 1 false positive per scan was obtained for nodules identified by at least one (and two) radiologist(s). For nodule segmentation, a maximum Jaccard score of 0.567 was obtained, surpassing the interobserver variability. In terms of nodule texture characterization, a maximum quadratic weighted Cohen's kappa of 0.733 was obtained, with part solid nodules being particularly challenging to classify correctly. Detailed analysis of the proposed methods and the differences in performance allow to identify the major challenges remaining and future directions - data collection, augmentation/generation and evaluation of under-represented classes, the incorporation of scan-level information for better decision-making and the development of tools and challenges with clinical-oriented goals. The LNDb Challenge and associated data remain publicly available so that future methods can be tested and benchmarked, promoting the development of new algorithms in lung cancer medical image analysis and patient follow-up recommendation.

Evaluation of resistance to wheat stem rust and identification of resistance genes in wheat lines from Heilongjiang province.

Wheat stem rust, caused by Puccinia graminis f. sp. tritici, (Pgt) is a devastating disease in wheat production. The disease has been effectively controlled since the 1970s due to the widespread use of the Sr31 resistance gene. However, Sr31 has lost its effectiveness following the emergence and spread of the Ug99 race variants. Therefore, there is an urgent global effort to identify new germplasm resources effective against those races. In this study, the resistance to Pgt of 95 wheat advance lines from Heilongjiang Province was evaluated using three predominant races of Pgt, 21C3CTTTM, 34C0MKGSM, and 34C3MTGQM, in China at the seedling and adult plant stage. The presence of 6 Sr genes (Sr2, Sr24, Sr25, Sr26, Sr31, and Sr38) was evaluated using linked molecular markers. The results showed that 86 (90.5%) wheat lines had plant stage resistance to all three races. Molecular marker analysis showed that 24 wheat lines likely carried Sr38, 15 wheat lines likely carried Sr2, 11 wheat lines likely carried Sr31, while none of the wheat lines carried Sr24, Sr25, or Sr26. Furthermore, six out of the 95 wheat lines tested carried both Sr2 and Sr38, three contained both Sr31 and Sr38, and two wheat lines contained both Sr2 and Sr31. Wheat lines with known Sr genes may be used as donor parents for further breeding programs to provide resistance to stem rust.

A pH-responsive supramolecular draw solute that achieves high-performance in arsenic removal via forward osmosis.

A pH-responsive, charge switchable piperazine derivative, 1,4-bis(3-propane- sulphonate sodium)-piperazinediethanesulfonic acid disodium-sulfate (4), has been designed via a stepwise synthesis and proposed as a draw solute to remove arsenics (AsIII, AsV) from water through forward osmosis (FO). Having multiple sulfonic groups, 4 generates a high osmotic pressure and produces a water flux as high as 58.4 LMH at a dilute concentration (0.24 M), surpassing most of the existing draw solutes in water transfer rate under the similar experimental conditions. Compound 4 at 0.24 M yields a water flux of 52.9 LMH with a 100% AsV rejection, and 57.8 LMH with a 96.0% AsIII rejection when 50 ppm AsV or AsIII as the corresponding feed, manifesting the best performance in arsenic removal and concurrent water recovery efficiency. Remarkably, being a polymeric configuration in water, 4 has a negligible solute loss in the FO process. 4 can be readily regenerated for reuse in FO by precipitation from its solution through acidification. The abundance in ionic groups and the pH-responsive property coupled with a supramolecular configuration make 4 an ideal draw solute for FO wastewater treatment.

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging.

Aim: Brain tumors are among the most fatal cancers worldwide. Diagnosing and manually segmenting tumors are time-consuming clinical tasks, and success strongly depends on the doctor's experience. Automatic quantitative analysis and accurate segmentation of brain tumors are greatly needed for cancer diagnosis. Methods:This paper presents an advanced three-dimensional multimodal segmentation algorithm called nested dilation networks (NDNs). It is inspired by the U-Net architecture, a convolutional neural network (CNN) developed for biomedical image segmentation and is modified to achieve better performance for brain tumor segmentation. Thus, we propose residual blocks nested with dilations (RnD) in the encoding part to enrich the low-level features and use squeeze-and-excitation (SE) blocks in both the encoding and decoding parts to boost significant features. To prove the reliability of the network structure, we compare our results with those of the standard U-Net and its transmutation networks. Different loss functions are considered to cope with class imbalance problems to maximize the brain tumor segmentation results. A cascade training strategy is employed to run NDNs for coarse-to-fine tumor segmentation. This strategy decomposes the multiclass segmentation problem into three binary segmentation problems and trains each task sequentially. Various augmentation techniques are utilized to increase the diversity of the data to avoid overfitting. Results: This approach achieves Dice similarity scores of 0.6652, 0.5880, and 0.6682 for edema, non-enhancing tumors, and enhancing tumors, respectively, in which the Dice loss is used for single-pass training. After cascade training, the Dice similarity scores rise to 0.7043, 0.5889, and 0.7206, respectively. Conclusion: Experiments show that the proposed deep learning algorithm outperforms other U-Net transmutation networks for brain tumor segmentation. Moreover, applying cascade training to NDNs facilitates better performance than other methods. The findings of this study provide considerable insight into the automatic and accurate segmentation of brain tumors.

Rhodium-Catalyzed Site-Selective ortho-C-H Activation: Enone Carbonyl Directed Hydroarylation of Maleimides.

Enone carbonyl directed 1,4-addition of ortho-C-H bond in chalcones to maleimides was developed under the catalysis of Rh(III). This reaction furnished a variety of chalcone-based pharmacologically useful 3-arylated succinimide derivatives in good yields with excellent selectivity.

Metal-free oxidative decarbonylative alkylation of chromones using aliphatic aldehydes.

A decarbonylative alkylation of chromones via radical conjugate addition under metal-free conditions was developed using aliphatic aldehydes as alkylating reagents. A series of 2-tertiary, secondary, and even primary alkylated chromanones were obtained in moderate to excellent yields.

Site-specific hydroxyalkylation of chromones via alcohol mediated Minisci-type radical conjugate addition.

A metal-free site-specific C2-hydroxyalkylation of chromones through the Minisci-type reaction using simple alcohols was developed. This transformation proceeds via radical sp3 C-H activation and subsequent conjugate addition, generating a series of C2-hydroxyalkylated chromanones in moderate to good yields. Besides, ethers were also compatible in this Minisci reaction, leading to corresponding C2 ether-substituted chromanones.

Direct arylation of inactivated benzene with aryl acyl peroxides toward biaryls.

A direct arylation of inactivated benzene with acyl peroxides was developed, affording biaryls in good to excellent yields. This transformation underwent a radical pathway under metal-free, base-free and additive-free conditions with good functional group compatibility.

1,2-Arylalkylation of N-(arylsulfonyl)acrylamides using aliphatic aldehydes as the alkyl source.

A metal-free decarbonylative arylalkylation of N-(arylsulfonyl)acrylamides using aliphatic aldehydes as the alkyl radical source was developed, providing a series of α-aryl-ß-alkylamides in moderate to good yields. In this reaction, concomitant alkylation, aryl migration and desulfonylation were involved.

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing 3 dimensional (3D) information, and classify the tooth by employing unsupervised learning i.e., k-means++ method. In order to evaluate the proposed method, the experiments are conducted on the sufficient and extensive datasets of mandibular molars. The experimental results show that our method can achieve higher accuracy and robustness compared to other three clustering methods.

Metal-free radical addition/cyclization of alkynoates with xanthates towards 3-(ß-carbonyl)coumarins.

The dilauroyl peroxide (DLP)-promoted carboannulation of alkynoates with xanthates was developed, affording a series of 4-aryl-3-(ß-carbonyl) coumarins in moderate to good yields. The difunctionalization of alkynoates was achieved via a radical addition/cyclization process under metal-free conditions.

An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods.

Expression of activated checkpoint kinase 2 and histone 2AX in exfoliative oral cells after exposure to ionizing radiation.

Gamma-H2AX (activated histone 2AX) and pChk2 (activated checkpoint kinase 2), which are DNA damage response molecules, are produced in irradiated cells and may be signature molecules of radiation exposure. We investigated their use as potential biomarkers to identify individuals exposed to ionizing radiation. We collected exfoliated oral epithelial cell samples from 100 healthy individuals undergoing routine dental radiographic examination (2.34 cGy) both before and after the radiograph using a non-invasive technique. The expression levels of pChk2 and gamma-H2AX in oral cells were assessed by immunohistochemical assay. Both biomarkers showed statistically significant increases in levels of expression after the radiation exposure (P < 0.001). This suggests that pChk2 and gamma-H2AX may serve as sensitive indicators of low-dose radiation exposure.