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Chinese font recognition (CFR) has gained significant attention in recent years. However, due to the sparsity of labeled font samples and the structural complexity of Chinese characters, CFR is still a challenging task. In this paper, a DropRegion method is proposed to generatea large number of stochastic variant font samples whose local regions are selectively disrupted and an inception font network (IFN) with two additional convolutional neural network (CNN) structure elements, i.e., a cascaded cross-channel parametric pooling (CCCP) and global average pooling, is designed. Because the distribution of strokes in a font image is non-stationary, an elastic meshing technique that adaptively constructs a set of local regions with equalized information is developed. Thus, DropRegion is seamlessly embedded in the IFN, which enables end-to-end training; the proposed DropRegion-IFN can be used for high performance CFR. Experimental results have confirmed the effectiveness of our new approach for CFR. [ABSTRACT FROM AUTHOR]

Rice panicle blast grading is very important in gauging cultivar resistance and in the precise control of a blast epidemic. However, the development of an automated, rapid, and accurate panicle blast grading system is a challenging task. This is mainly because of the complexity of the pathology, appearance, and definition of the blast disease level. In this study, a new method for grading panicle blast based on hyperspectral imaging technology is proposed. The method is based on the concept of the “bag of textons,” which is widely used in the document analysis field and which defines a “bag of spectra words” (BoSW) model for hyperspectral image data representation. Hyperspectral image data representation based on the BoSW model is used as the input to a chi-square kernel support vector machine (chi-SVM) classifier for predicting the rice panicle blast level. Specifically, the BoSW model jointly considers the image-spectrum information to attain improved classification accuracy. It reduces a high-dimension hyperspectral image into a compact, low-dimension representation. It also highlights the histogram statistics of a typical spectrum prototype to reflect the panicle blast severity level. In this way, it avoids the fine segmentation and morphological analysis of blast lesions. Experiments were conducted on a total of 312 fresh rice panicles covering more than 50 cultivars, which were collected from an experimental field under natural conditions. The results showed that the proposed method could effectively grade panicle blast with classification accuracies of up to 81.41% for six-class grading and 96.40% for two-class grading in the validation datasets. Comparison experiments were conducted on different data batches or combinations thereof. The results showed that the technique is viable for different types of rice cultivar and planting seasons, pointing to its widespread practical applicability. [ABSTRACT FROM AUTHOR]

Enantiomerically pure amines are crucial intermediates for a wide range of pharmaceuticals, natural products, and bioactive compounds. Asymmetric intermolecular hydroamination of alkenes is one of the most atom-economical methods for the synthesis of chiral amines. However, the direct stereoselective hydroamination of alkenes remains a significant challenge. Herein, we developed a novel fully biocatalytic system to enable the direct stereoselective hydroamination of aryl alkenes to produce chiral amines. The process involves sequential alkene asymmetric hydration catalyzed by a hydratase (HD), followed by in situ oxidation of alcohol into ketone by two enantiocomplementary alcohol dehydrogenases (ADHs), and finally, enantioselective transformation of ketone into enantiomerically pure amine by a ω-transaminase (TA). The addition of lactate dehydrogenase (LDH) facilitated the connection between alcohol oxidation and pyruvate reduction, making the cascade reaction redox self-sufficient and driving the process towards the desired target product. In vitro cascade biocatalysis for asymmetric hydroamination of 4-hydroxystyrenes 1a–f was first investigated by using the combined enzymes (HD/ADHs/LDH/TA) with trace amounts of NAD+ (0.05 mM) and pyridoxal-5′-phosphate (PLP) (0.1 mM), affording both enantiomers of amines 4a–f in 30–90% conversions and >99% ee. Furthermore, whole-cell-based cascade biocatalysis was achieved by using the constructed recombinant Escherichia coli cells co-expressing the five enzymes without additional NAD+ cofactor; (R)- and (S)-amines 4a–f could be obtained in 35–93% conversions and >99% ee. Finally, the preparative experiments were demonstrated by using the whole-cell biocatalyst E. coli (RFM-ERR-CB) and E. coli (RFC-ERR-CB) with substrate 1a, affording (S)-4a and (R)-4a in 54.8% and 59.2% yields and >99% ee. This research achievement provides a highly selective, highly atom-economical, green and sustainable method for synthesizing chiral amines from alkenes via formal alkene asymmetric hydroamination. [ABSTRACT FROM AUTHOR]

Currently image classifiers based on multikernel learning (MKL) mostly use batch approach, which is slow and difficult to scale up for large datasets. In the meantime, standard MKL model neglects the correlations among examples associated with a specific kernel, which makes it infeasible to adjust the kernel combination coefficients. To address these issues, a new and efficient multikernel multiclass algorithm called TripleReg-MKL is proposed in this work. Taking the principle of strong convex optimization into consideration, we propose a new triple-norm regularizer (TripleReg) to constrain the empirical loss objective function, which exploits the correlations among examples to tune the kernel weights. It highlights the application of multivariate hinge loss and a conservative updating strategy to filter noisy samples, thereby reducing the model complexity. This novel MKL formulation is then solved in an online mode using a primal-dual framework. A theoretical analysis of the complexity and convergence of TripleReg-MKL is presented. It shows that the new algorithm has a complexity of OCMT and achieves a fast convergence rate of OlogT/T. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of this new approach. [ABSTRACT FROM AUTHOR]

An asymmetric total synthesis of (+)-7-epi-tarchonanthuslactone was achieved from commercially available methyl (R)-3-hydroxybutyrate. The key step employed a diastereoselective chelation-controlled Mukaiyama aldol reaction to construct the chiral hydroxyl group at the C-5 position. [ABSTRACT FROM AUTHOR]

Estimation of panicle blast level plays an important role in high-quality production of rice. It helps to quantitatively assess the level of blast resistance and severity in the field to make appropriate decisions in gauging cultivar resistance in rice breeding or precisely controlling blast epidemic. However, it is difficult to evaluate the blast disease degree automatically and accurately. In this study, a novel grading method for panicle blast severity based on hyperspectral imaging technology is proposed. The method defines a bag of spectrum words (BoSW) model for hyperspectral image data representation. The BoSW model based on hyperspectral image data representation is used as the input of a Chi-square kernel support vector machine (Chi-SVM) classifier for predicting the rice panicle blast level. More precisely, dense grids are firstly extracted over the spatial X- and Y-axes across the whole spectral Z-axis. The average spectrum curve of all the pixels within a grid cube is calculated. Then, K-Means clustering would be performed on the large collection of average spectrum curves from the training samples to form the dictionary of spectrum words. Next, each spectrum curve on the grid cube is quantized into one of spectrum words. Each hyperspectral image of rice panicle is transformed into a map of spectrum words. All the spectrum words are distributed evenly on the spatial XY-axis plane. BoSW model for each hyperspectral data cube is then formed by means of histogram statistics of spectrum word occurrences. Finally, a Chi-SVM classifier is trained using the BoSW representations of rice panicle hyperspectral images for predicting panicle blast infection levels. The proposed BoSW method uses both the image and full-spectrum information by means of regular grid cube extraction, which utilizes the full potential of the imaging sensing system. Meanwhile, the representation dimension for each hyperspectral image is significantly reduced, i.e. 100 here, and thus relieving modeling difficulty. The procedure of clustering helps to find the representative spectrum curves and quantizing helps to transform all the continuous-state spectrum curves into one of representative spectrum curve. Thus the proposed BoSW method is invariant to complicated noise and robust to rice cultivars. To verify the proposed BoSW method, a total of 170 fresh rice panicles covering more than 50 cultivars are collected from an experimental field for the performance evaluation. The experimental field is located in regional testing area for evaluating rice cultivars in Guangdong province. Therefore, all the rice plants in this area are naturally inoculated as the area is a typical source of rice blast fungus. The hyperspectral images of all the rice panicles are acquired using HyperSIS-VNIR-QE imaging spectrometer and then are transformed into 100-dimension BoSW representation for the construction of Chi-SVM classifier. Four-class label of hyperspectral image sample is determined by plant protection expert according to description of blast infection levels. Two thirds of the labeled BoSW representations are randomly selected for training and the rest for testing. Experimental results show that the proposed BoSW based method achieved high classification accuracy of 94.72%. This result is much better than traditional hyperspectral image analysis methods such as Principal component analysis (PCA), sensitive bands selection and etc. Moreover, the proposed BoSW demonstrates strong robustness to rice cultivars, which is vital for the wide and practical application. This research improves the classification accuracy of rice panicle blast grading and provides a reference to evaluate other disease level grading as well. [ABSTRACT FROM AUTHOR]

Abstract: Heterogeneous Feature Fusion Machines (HFFM) is a kernel based logistic regression model that effectively fuses multiple features for visual recognition tasks. However, the batch mode solution for HFFM, ‘Block Coordinate Gradient Descent’ (BCGD) has the same low efficiency and poor scalability as the most batch algorithms do. In this paper, we describe a newly developed online learning algorithm in multiple Reproducing Kernel Hilbert Spaces for solving HFFM model. This new algorithm is called OLHFFM, i.e. Online HFFM. OLHFFM is novel combination of kernel-based learning technique with dual averaging gradient descent methods. In addition, group LASSO regularization technique is used in OLHFFM for finding important explanatory coefficients that are related to support samples in group manner. The effectiveness of OLHFFM has been demonstrated by a number of experiments that were conducted on public event, object dataset, as well as on large scale handwritten digital dataset. Using the OLHFFM approach, we have achieved almost equivalent recognition performance to that using batch-mode approach. Experiments conducted on both MIT Caltech-6 and challenging VOC2011 TrainVal object datasets show that OLHFFM is superior in performance to kernel based online learning approaches such as ILK or NORMA. In addition, the classification performance of OLHFFM approach as demonstrated by the experiments conducted on large scale MNIST dataset is comparable to or better than that of the current state-of-the-art online multiple kernel learning approaches such as OM-2, UFO-MKL, OMCL and OMKL. Extensive experiments on visual data classification demonstrate the effectiveness and robustness of the new OLHFFM approach. [Copyright &y& Elsevier]

Chiral cyclic β-amino alcohols are vital building blocks for the synthesis of numerous pharmaceuticals and bioactive molecules. Asymmetric ring-opening of cyclic meso-epoxides is a straightforward and appealing method for their synthesis. However, developing an environmentally friendly and cost-effective approach for this process remains elusive. Herein, the synthesis of enantiomerically pure cyclic β-amino alcohols through a fully biocatalytic asymmetric ring-opening of cyclic meso-epoxides is described. A novel amine transaminase (CepTA) from Capronia epimyces with comparable activity and excellent enantioselectivity toward small molecule cyclic α-hydroxy ketones was discovered and proved to be efficient on a wide range of substrates. Homology modelling, molecular docking, and molecular dynamics simulations were also performed to elucidate the highly efficient catalytic mechanisms of CepTA for small cyclic α-hydroxy ketones. The asymmetric ring-opening of cyclic meso-epoxides (4′j–k) was successfully achieved by using combined recombinant E. coli (SpEH-AnDDH-BsLDH) and E. coli (CepTA) resting cells, leading to the production of cyclic (1R,2R)-trans-β-amino alcohols (1′j–k) up to 81.8% conversions and >99% ee. This study marks the first instance of achieving fully biocatalytic asymmetric ring-opening of meso-epoxides to chiral cyclic β-amino alcohols, offering a novel pathway for the environmentally friendly synthesis of chiral cyclic β-amino alcohols from readily available, cost-effective meso-epoxides. [ABSTRACT FROM AUTHOR]

• A concise total synthesis of (+)-brazilin in 7 steps. • Sharpless asymmetric dihydroxylation to introduce the chiral hydroxyl group. • Tandem Prins/Friedel-Crafts reaction to construct the cis -fused B and C rings. Described herein is a concise total synthesis of (+)-brazilin from readily available 4-bromo-1,2-dimethoxybenzene. In this synthetic route, a Sharpless asymmetric dihydroxylation was employed to introduce the chiral hydroxyl group, and trifluoroacetic acid (TFA) catalyzed one-pot intramolecular tandem Prins/Friedel-Crafts reaction was also involved as the key transformation in the construction of the hybrid chromane and indane framework. [ABSTRACT FROM AUTHOR]

• Propose a deep localization model for intra-row crop detection in paddy field. • Construct a large rice plant localization dataset. • Propose a task-oriented evaluation method for rice localization algorithm. • The proposed method achieved a high accuracy of 93.22% and a high speed of 15 fps. Automated and precise rice plant localization is crucial for the mechanization of rice production, which can facilitate targeted spraying, site-specific fertilization, and mechanized weeding etc. Existing approaches adopted thus far have mainly focused on inter-row weed detection or rice seedling row detection. Nevertheless, techniques for intra-row individual rice plant positioning continue to face major challenges induced by the specific paddy field environments or complex morphology of rice plant. This paper proposed a new deep localization network for intra-row rice detection at the single plant level in a paddy field. This method designed a two-stage model. The module in stage 1 identified potential locations containing rice plants in the entire image. The module in stage 2 predicted the confidence of rice plant identification and refined the corresponding box bounds. The two-stage processing modules shared a deep backbone network for learning full-image convolutional features and are combined into a unified framework to facilitate an end-to-end training. In addition, we constructed a rice plant detection dataset and proposed a task-oriented evaluation method for performance verification of the algorithm. Experiment results showed the proposed deep model achieved a high localization accuracy of 93.22% and a high testing speed of 15 fps, verifying the effectiveness and efficiency of the method. Using this method, we can develop techniques for finer-level agriculture production, such as spraying and weed control, to achieve healthy and economical rice yields. [ABSTRACT FROM AUTHOR]

Inspired by speech recognition, most of the recent state-of-the-art works convert scene text recognition into sequence prediction. Like most speech recognition problems, context modeling is considered as a critical component in these methods for achieving better performance. However, they usually only consider using a holistic or single-scale local sequence context, in a single dimension. Actually, scene texts or sequence contexts may span arbitrarily across a two-dimensional (2-D) space and in any style, not limited to only horizontal. Moreover, contexts of various scales may synthetically contribute to text recognition, in particular for irregular text recognition. In our method, we consider the context in a 2-D manner, and simultaneously consider context reasoning at various scales, from local to global. Based on this, we propose a new Two-Dimensional Multi-Scale Perceptive Context (TDMSPC) module, which performs multi-scale context learning, along both the horizontal and vertical directions, and then merges them. This can generate shape and layout-dependent feature maps for scene text recognition. This proposed module can be handily inserted into existing sequence-based frameworks to replace their context learning mechanism. Furthermore, a new scene text recognition network, called TDMSPC-Net, is built, by using the TDMSPC module as a building block for the encoder, and adopting an attention-based LSTM as the decoder. Experiments on benchmark datasets show that the TDMSPC module can substantially boost the performance of existing sequence-based scene text recognizers, irrespective of the decoder or backbone network being used. The proposed TDMSPC-Net achieves state-of-the-art accuracy on all the benchmark datasets. [ABSTRACT FROM AUTHOR]

Brazilin, structurally composed of a chroman skeleton cis-fused with a 2,3-dihydro-1H-indene unit, is an important active component of the famous traditional Chinese medicine, Caesalpinia sappan L., which belongs to homoisoflavonoid natural products. Brazilin possesses a series of outstanding bioactivities, such as antitumor, anti-inflammatory, hypoglycemic, antibacterial, hepatoprotective activities, which attracts the great interest of synthetic chemists and medicinal chemists. In almost all of the recently published approaches to brazilin, dihydroxylation using highly toxic osmium reagents was employed to construct chiral hydroxyl at C-3 position. Herein, we described a total synthesis of (+)-brazilin from 3,4-dimethoxybenzyl alcohol via overall nine steps, including oxidation using air to prepare hydroxyl at C-3 position, a lipase-catalyzed desymmetrization to establish chiral hydroxyl at the C-3 position, and a trifluoroacetic acid-catalyzed one-pot intramolecular tandem Prins/Friedel-Crafts reaction to construct the cis-fused chromane and indane framework. [ABSTRACT FROM AUTHOR]

A concise formal synthesis of (±)-aplykurodinone-1 starting from a commercially available material and based on the indium-catalyzed Conia-ene reaction has been accomplished. The synthesis features a Riley selenium dioxide oxidation, a Krapcho dealkoxycarbonylation, and a ring-closing metathesis approach. The synthetic strategy was also supported by a Saegusa oxidation and a classic Michael 1,4-conjugate addition. [ABSTRACT FROM AUTHOR]

We describe a concise and straightforward approach to the total syntheses of (+)-Strictifolione and Cryptofolione in the longest linear sequences of four steps and six steps from 3-phenyl propanal and trans-cinnamaldehyde, respectively. The route utilized a titanium tetraisopropoxide/(R)-[1,1'-binaphthalene]-2,2'-diol catalyzed Mukaiyama aldol reaction, indium(0)-promoted Barbier reaction, and olefin cross-metathesis as the key reactions. [ABSTRACT FROM AUTHOR]

We describe a new and straightforward approach to the formal synthesis of the hepatitis B virus inhibitor entecavir, an important hepatitis B drug, in ten steps overall. Key features of the route are a Morita–Baylis–Hillman reaction, a Sharpless asymmetric epoxidation, a reductive epoxide opening of an α,β-epoxy ketone, and a Riley selenium dioxide oxidation. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Formal synthesis of (±)-aplykurodinone-1 was achieved in 6 steps. • An intramolecular Michael addition was employed as the key to generate ring B. • Key intermediates and final product were determined by single-crystal X-ray. Abstract A concise and straightforward formal synthesis of (±)-aplykurodinone-1, a degraded steroid natural product isolated from sea hare Syphonota geographica is described, based on an intramolecular Michael addition. The route also includes an intermolecular Michael addition, a Riley selenium dioxide oxidation, and a Krapcho dealkoxycarbonylation. The relative stereochemistries of key intermediates have been determined through single-crystal X-ray diffraction analysis. [ABSTRACT FROM AUTHOR]

A series of non-conjugated methylene-anthracene Schiff base derivatives (DNCAs) were designed and synthesized. Photoluminescence emission spectra indicated that DNCA-4 and DNCA-12 showed obviously mechanofluorochromic properties, and distinctive 26- and 37-nm hypochromic shifts were observed, respectively. The experiment results revealed that there is no specific relationship between alkoxy chain lengths and their stimuli-response behavior. The PXRD profiles demonstrated a transformation from crystalline to amorphous state upon grinding. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of script and handwritten/machine-printed identification of texts. We simultaneously identify the script (Chinese, English, Japanese, Korean, or Russian) and whether it is handwritten or machine-printed text by designing a dual-branch structured deep convolutional neural network (CNN). For the training stage, we propose a two-stage multi-task learning strategy to learn robust shared features for script and handwritten/machine-printed identification. Accordingly, we can implement two identification tasks using the proposed single CNN model. We compare the effects of using different length of input to train CNN. The experimental results show that text-line input is a suitable choice for the two identification tasks, as it can effectively capture more discriminative content for both script and handwritten/machine-printed identification. Furthermore, we evaluate three CNN networks of different scales (small, medium, and large) to determine the best CNN architecture for script and handwritten/machine-printed identification. As shown by our experimental validation, integrating the text-line input with larger architecture significantly improves performance. The accuracies achieved by the two-stage multi-task CNN for handwritten/machine-printed and script identification are 99% and 95%, respectively. [ABSTRACT FROM AUTHOR]

A concise total synthesis of ieodomycin A and B was achieved in six and seven steps respectively from commercially available geraniol, which involved the construction of the C-5 hydroxyl via an Ti(OiPr)4/(S)-BINOL-mediated asymmetric aldol reaction as the key step. [ABSTRACT FROM PUBLISHER]

This paper presents Discriminative Locality Alignment Network (DLANet), a novel manifold-learning-based discriminative learnable feature, for wild scene classification. Based on a convolutional structure, DLANet learns the filters of multiple layers by applying DLA and exploits the block-wise histograms of the binary codes of feature maps to generate the local descriptors. A DLA layer maximizes the margin between the inter-class patches and minimizes the distance of the intra-class patches in the local region. In particular, we construct a two-layer DLANet by stacking two DLA layers and a feature layer. It is followed by a popular framework of scene classification, which combines Locality-constrained Linear Coding–Spatial Pyramid Matching (LLC–SPM) and linear Support Vector Machine (SVM). We evaluate DLANet on NYU Depth V1, Scene-15 and MIT Indoor-67. Experiments show that DLANet performs well on depth image. It outperforms the carefully tuned features, including SIFT and is also competitive to the other reported methods. [ABSTRACT FROM AUTHOR]

NiO-InO composite nanofibers are synthesized via electrospinning and calcining techniques. Micro-sensors are fabricated by sputtering Pt electrodes on Si chips to form sensor substrates, and then spinning the NiO-InO composite nanofibers onto the sensor substrate surface. The as-fabricated micro-sensors exhibit excellent HS sensing properties at room temperature. The sensitivity of the micro-sensors is up to 6 when the sensors are exposed to 3 μL/L HS, and the corresponding response and recovery times are 14 and 22 s, respectively. The micro-sensors also exhibit high selectivity and good stability. Especially, the micro-sensors can operate at various humidity conditions. The sensitivity of the micro-sensors is 3.8 to 3 μL/L HS at 75% relative humidity. These characteristics make the micro-sensors good candidates for practical HS sensors with high performance. [ABSTRACT FROM AUTHOR]

• A concise total synthesis of (−)-γ-lycorane. • The shortest synthetic approach in only 7 steps. • Palladium-catalyzed aromatic C H alkylation to generate ring B. • Intramolecular iodocyclization to construct the cis -5,6-ring. A concise and straightforward total synthetic approach towards lycorine-type alkaloid (−)-γ-lycorane has been achieved in 7 steps. The key feature of the route is the employment of a palladium-catalyzed aromatic C H alkylation of unactivated secondary alkyl iodide. The synthetic protocols also involve a palladium-catalyzed deracemization of allylic carbonate, a Johnson-Claisen rearrangement and an iodocyclization. [ABSTRACT FROM AUTHOR]

Herein, a convergent, practicable and first total synthesis of the natural product, (±)-methyl salvianolate A, is reported. The key features of the approach are the use of a Horner–Wadsworth–Emmons reaction and the protection of multiple hydroxyls using silyl protecting groups. The employment of the readily removable silyl protecting groups allows the synthesis of (±)-methyl salvianolate A and its derivatives on a reasonably large scale. [ABSTRACT FROM AUTHOR]

The article presents chemical equations related to article "Enantioselective Synthesis of the ABC-Tricyclic Core of Phomactin A by a ˠ-Hydroxylation Strategy" by G. Du, published in the periodical "Organic Letters" in 2015.

The total synthesis of ieodomycin A (I) and B (II) is accomplished in six and seven steps respectively from commercially available geraniol. [ABSTRACT FROM AUTHOR]