Impact of N-Linked Glycosylation on Therapeutic Proteins.

Therapeutic proteins have unique advantages over small-molecule drugs in the treatment of various diseases, such as higher target specificity, stronger pharmacological efficacy and relatively low side effects. These advantages make them increasingly valued in drug development and clinical practice. However, although highly valued, the intrinsic limitations in their physical, chemical and pharmacological properties often restrict their wider applications. As one of the most important post-translational modifications, glycosylation has been shown to exert positive effects on many properties of proteins, including molecular stability, and pharmacodynamic and pharmacokinetic characteristics. Glycoengineering, which involves changing the glycosylation patterns of proteins, is therefore expected to be an effective means of overcoming the problems of therapeutic proteins. In this review, we summarize recent efforts and advances in the glycoengineering of erythropoietin and IgG monoclonal antibodies, with the goals of illustrating the importance of this strategy in improving the performance of therapeutic proteins and providing a brief overview of how glycoengineering is applied to protein-based drugs.

Design, Synthesis and Antiproliferative Evaluation of Novel Disulfides Containing 1,3,4-Thiadiazole Moiety.

A series of novel disulfides containing 1,3,4-thiadiazole moiety were designed, synthesized, and the structures of all products were identified by spectral data (IR, NMR, and high resolution (HR)-MS). Their in vitro antiproliferative activities were evaluated using 2-(2-methoxy-4-nitro-phenyl)-3-(4-nitro-phenyl)-5-(2,4-disulfopheyl)-2H-tetrazolium monosodium salt (CCK-8) assay against human cancer cell lines, A549 (human lung cancer cell), HeLa (human cervical cancer cell), SMMC-7721 (human liver cancer cell) and normal cell lines L929. The bioassay results indicated that most of the tested compounds 6a-k, 7a-k and 8a-k exhibited antiproliferation with different degrees, and some compounds showed better effects than positive control 5-fluorouracil (5-FU) against various cancer cell lines. Among these compounds, compound 6e exhibited the most potent inhibitory activity against A549 cells with IC50 value of 3.62 µM. Compounds 6i, 7a, 7g, 8a and 8b showed significantly antiproliferative activities against HeLa cells with IC50 values of 3.88, 3.76, 3.59, 3.38 and 3.12 µM, respectively. Compounds 6a, 7a and 8a owned high antiproliferative activities against SMMC-7721 cells with IC50 values of 2.54, 2.69 and 2.31 µM, respectively. Furthermore, all of the tested compounds showed weak cytotoxic effect against the normal cell lines L929. Based on the preliminary results, the substituent groups are vital for improving the potency and selectivity of this class of compounds.

Synthesis and antiproliferative evaluation of novel 1,2,4-triazole derivatives incorporating benzisoselenazolone scaffold.

A series of novel 1,2,4-triazole derivatives incorporating benzisoselenazolone scaffold were designed, synthesized and evaluated for their in vitro antiproliferative activities against human cancer cell lines SMMC-7721, Hela, A549, and normal cell lines L929 by CCK-8 assay. The preliminary bioassay results demonstrated that most of the tested compounds 3a-3n exhibited antiproliferation with different degrees, and some compounds showed better effects than positive controls ethaselen and 5-fluorouracil (5-FU) against various cancer cell lines. Among these compounds, compounds 3b and 3c displayed highly effective biological activities against SMMC-7721cells with IC50 values of 6.02 and 6.01 µM, respectively. Compound 3n showed significant antiproliferative activities against Hela cells with IC50 value of 3.94 µM. Compound 3n exhibited the best inhibitory effect against A549 cells with IC50 value 9.14 µM. Furthermore, most of the tested compounds showed weak cytotoxic effect against the normal cell lines L929. The pharmacological results suggest that the substituent groups are vital for improving the potency and selectivity of this class of compounds.

Synthesis and in vitro antiproliferative evaluation of novel nonsymmetrical disulfides bearing 1,3,4-oxadiazole moiety.

A series of novel nonsymmetrical disulfides bearing 1,3,4-oxadiazole moiety were designed, synthesized and evaluated for their in vitro antiproliferative activities against SMMC-7721, Hela and A549 human cancer cell lines by CCK-8 assay. The preliminary bioassay results demonstrated that all tested compounds 7a-7o exhibited antiproliferation with different degrees, and some compounds showed better effects than positive control 5-fluorouracil against various cancer cell lines. Among these compounds, compound 7j showed significant antiproliferative activity against SMMC-7721 cells with IC50 value of 3.40µM. Compound 7a displayed highly effective biological activity against Hela cells with IC50 value of 4.26µM. Compound 7g exhibited the best inhibitory effect against A549 cells with IC50 value of 6.26µM.

New phenolic compounds from the seeds of Nigella glandulifera and their inhibitory activities against human cancer cells.

Four phenolic compounds, including two new ones, Nigephenol A and B (1-2), and a new natural product, Nigephenol C (3), were isolated from the seeds of Nigella glandulifera. Their structures were elucidated on the basis of spectroscopic analyses using HR-ESI-MS, 1D and 2D NMR spectra. All compounds were evaluated by MTT method for in vitro cytotoxicity against four human cancer cell lines (Bel7402, HepG2, HCT-8 and A549). The results revealed that Compounds 1-4 showed more selective activities against HepG2 cells, and that Compound 2 showed significant inhibitory effects against four human tumor cell lines with IC50 values comparable to those of 5-fluorouracil.

Synthesis and in vitro antiproliferative evaluation of novel hybrids from 1,3,4-thiadiazole and benzisoselenazolone.

Novel hybrids from 1,3,4-thiadiazole and benzisoselenazolone were designed, synthesized and evaluated for their in vitro antiproliferative activities by CCK-8 assay against three types of human cancer cell lines, SMMC-7721, MCF-7 and A549 cells. The preliminary bioassay results demonstrated that all tested compounds 4a-p showed potent antiproliferative activities, and some compounds exhibited better effects than positive control ethaselen and 5-fluorouracil (5-FU) against various cancer cell lines. Furthermore, compound 4g showed significant antiproliferative activities against SMMC-7721 cells with an IC50 value of 2.08 µM. Compounds 4b and 4m displayed highly effective biological activities against MCF-7 cells with an IC50 values of 2.03 and 2.06 µM, respectively. Compound 4i exhibited the best inhibitory effect against A549 cells with an IC50 value of 1.03 µM.

Synthesis and antitumor activities of novel hybrid molecules containing 1,3,4-oxadiazole and 1,3,4-thiadiazole bearing Schiff base moiety.

A series of novel hybrid molecules containing 1,3,4-oxadiazole and 1,3,4-thiadiazole bearing Schiff base moiety were designed, synthesized and evaluated for their in vitro antitumor activities against SMMC-7721, MCF-7 and A549 human tumor cell lines by CCK-8 assay. The bioassay results demonstrated that most of the tested compounds showed potent antitumor activities, and some compounds exhibited stronger effects than positive control 5-fluorouracil (5-FU) against various cell lines. Among these compounds, compound 8d showed the best inhibitory effect against SMMC-7721 cells, with IC50 value of 2.84 µM. Compounds 8k and 8 n displayed highly effective antitumor activities against MCF-7 cells, with IC50 values of 4.56 and 4.25 µM, respectively. Compounds 8a and 8 n exhibited significant antiproliferative activity against A549 cells, with IC50 values of 4.11 and 4.13 µM, respectively. The pharmacological results suggest that the substituents of phenyl ring on the 1,3,4-oxadiazole are vital for modulating antiproliferative activities against various tumor cell lines.

Silicon-organic hybrid (SOH) IQ modulator using the linear electro-optic effect for transmitting 16QAM at 112 Gbit/s.

Advanced modulation formats call for suitable IQ modulators. Using the silicon-on-insulator (SOI) platform we exploit the linear electro-optic effect by functionalizing a photonic integrated circuit with an organic χ(2)-nonlinear cladding. We demonstrate that this silicon-organic hybrid (SOH) technology allows the fabrication of IQ modulators for generating 16QAM signals with data rates up to 112 Gbit/s. To the best of our knowledge, this is the highest single-polarization data rate achieved so far with a silicon-integrated modulator. We found an energy consumption of 640 fJ/bit.

Synthesis and antitumor-evaluation of 1,3-selenazole-containing 1,3,4-thiadiazole derivatives.

A series of novel 1,3-selenazole-containing 1,3,4-thiadiazole derivatives bearing Schiff base moieties were synthesized and evaluated for their in vitro antiproliferative activities against human breast cancer cell MCF-7 and mouse lymphocyte leukemia cell L1210 by CCK-8 assay. The majority of the compounds showed better activity against MCF-7 cell, compared with lead compound PCS. In particular, compound 6c was the most potent compound with IC50 value of 4.02 µM.

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors.

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person and further animate them with the user's pose control. While most existing methods can either generalize to new persons or synthesize animations with user control, none of them can achieve both at the same time. We attribute this accomplishment to the employment of a 3D proxy for a shared multi-person human model, and further the warping of the spaces of different poses to a shared canonical pose space, in which we learn a neural field and predict the person- and pose-dependent deformations, as well as appearance with the features extracted from input images. To cope with the complexity of the large variations in body shapes, poses, and clothing deformations, we design our neural human model with disentangled geometry and appearance. Furthermore, we utilize the image features both at the spatial point and on the surface points of the 3D proxy for predicting person- and pose-dependent properties. Experiments show that our method significantly outperforms the state-of-the-arts on both tasks.

A nomogram for predicting the risk of intra-abdominal hypertension in critically ill patients based on ultrasound and clinical data.

Background: Intra-abdominal hypertension (IAH) is a common complication in critically ill patients. This study aimed to identify independent risk factors for IAH and generate a nomogram to distinguish IAH from non-IAH in these patients. Methods: We retrospectively analyzed 89 critically ill patients and divided them into an IAH group [intra-abdominal pressure (IAP) ≥12 mmHg] and a non-IAH group (IAP <12 mmHg) based on the IAP measured from their bladders. Ultrasound and clinical data were also measured. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for IAH. The correlation between IAP and independent risk factors was also assessed. Results: Of these 89 patients, 45 (51%) were diagnosed with IAH. Univariate analysis showed there were significant differences in the right renal resistance index (RRRI) of the interlobar artery, the right diaphragm thickening rate (RDTR), and lactic acid (Lac) between IAH and non-IAH groups (P<0.001). Multivariate logistic regression analysis revealed that increasing RRRI, RDTR, and Lactic acid (Lac) were independent risk factors for IAH (P=0.001, P=0.001, and P=0.039, respectively). IAP was significantly correlated with RRRI, RDTR, and Lac (r=0.741, r=-0.774, and r=0.396, respectively; P<0.001). The prediction model based on regression analysis results was expressed as follows: predictive score = -17.274 + 31.125 × RRRI - 29.074 × RDTR + 0.621 × Lac. Meanwhile, the IAH nomogram prediction model was established with an area under the receiver operating characteristic (ROC) curve of 0.956 (95% confidence interval: 0.909-1.000). The nomogram showed good calibration for IAH with the Hosmer-Lemeshow test (P=0.864) and was found to be applicable within a wide threshold probability range, especially that higher than 0.40. Conclusions: The noninvasive nomogram based on ultrasound and clinical data has good diagnostic efficiency and can predict the risk of IAH. This nomogram may provide valuable guidance for clinical interventions to reduce IAH morbidity and mortality in critically ill patients.

MILO: Multi-Bounce Inverse Rendering for Indoor Scene With Light-Emitting Objects.

Recently, many advances in inverse rendering are achieved by high-dimensional lighting representations and differentiable rendering. However, multi-bounce lighting effects can hardly be handled correctly in scene editing using high-dimensional lighting representations, and light source model deviation and ambiguities exist in differentiable rendering methods. These problems limit the applications of inverse rendering. In this paper, we present a multi-bounce inverse rendering method based on Monte Carlo path tracing, to enable correct complex multi-bounce lighting effects rendering in scene editing. We propose a novel light source model that is more suitable for light source editing in indoor scenes, and design a specific neural network with corresponding disambiguation constraints to alleviate ambiguities during the inverse rendering. We evaluate our method on both synthetic and real indoor scenes through virtual object insertion, material editing, relighting tasks, and so on. The results demonstrate that our method achieves better photo-realistic quality.

Using Multi-Level Consistency Learning for Partial-to-Partial Point Cloud Registration.

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

Insights into the effect of protein glycosylation on carbohydrate substrate binding.

The role of glycosylation in the binding of glycoproteins to carbohydrate substrates has not been well understood. The present study addresses this knowledge gap by elucidating the links between the glycosylation patterns of a model glycoprotein, a Family 1 carbohydrate-binding module (TrCBM1), and the thermodynamic and structural properties of its binding to different carbohydrate substrates using isothermal titration calorimetry and computational simulation. The variations in glycosylation patterns cause a gradual transition of the binding to soluble cellohexaose from an entropy-driven process to an enthalpy-driven one, a trend closely correlated with the glycan-induced shift of the predominant binding force from hydrophobic interactions to hydrogen bonding. However, when binding to a large surface of solid cellulose, glycans on TrCBM1 have a more dispersed distribution and thus have less adverse impact on the hydrophobic interaction forces, leading to overall improved binding. Unexpectedly, our simulation results also suggest an evolutionary role of O-mannosylation in transforming the substrate binding features of TrCBM1 from those of type A CBMs to those of type B CBMs. Taken together, these findings provide new fundamental insights into the molecular basis of the role of glycosylation in protein-carbohydrate interactions and are expected to better facilitate further studies in this area.

Structural insight into why S-linked glycosylation cannot adequately mimic the role of natural O-glycosylation.

There is an increasing interest in using S-glycosylation as a replacement for the more commonly occurring O-glycosylation, aiming to enhance the resistance of glycans against chemical hydrolysis and enzymatic degradation. However, previous studies have demonstrated that these two types of glycosylation exert distinct effects on protein properties and functions. In order to elucidate the structural basis behind the observed differences, we conducted a systematic and comparative analysis of 6 differently glycosylated forms of a model glycoprotein, CBM, using NMR spectroscopy and molecular dynamic simulations. Our findings revealed that the different stabilizing effects of S- and O-glycosylation could be attributed to altered hydrogen-bonding capability between the glycan and the polypeptide chain, and their diverse impacts on binding affinity could be elucidated by examining the interactions and motion dynamics of glycans in substrate-bound states. Overall, this study underscores the pivotal role of the glycosidic linkage in shaping the function of glycosylation and advises caution when switching glycosylation types in protein glycoengineering.

Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis.

We propose to use optimally ordered orthogonal neighbor-joining (O 3 NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.

Synthesis and antitumor-evaluation of 1,3,4-thiadiazole-containing benzisoselenazolone derivatives.

A series of novel 1,3,4-thiadiazole-containing benzisoselenazolone derivatives were prepared by the condensation of 2-chloroselenobenzoyl chloride and 2-amino-5-substituted-1,3,4-thiadiazole. Their in vitro antiproliferative activities were evaluated in SSMC-7721, MCF-7 and A-549 cells. The results suggest that, in different tumor cells, some compounds have good antiproliferative activity, certain selectivity and potential value of further research.

Synthesis and in vitro antitumor activity of 1,3,4-oxadiazole derivatives based on benzisoselenazolone.

A series of novel 1,3,4-oxadiazole derivatives based on benzisoselenazolone has been prepared and tested for antiproliferative activity in vitro against the cells of human cancer cell lines: SSMC-7721 (human liver cancer cell), MCF-7 (human breast cancer cell) and A549 (human lung cancer cell). All the compounds obtained exhibited antiproliferative activity and showed selective cytotoxicity against different cancer cells. Compounds 7d and 7i showed significant antiproliferative activities against MCF-7 cells, with IC50 values of 1.07 and 1.76 µM respectively. Compound 7d were found to be the most potent compound against SSMC-7721 cells, with IC50 values 4.46 µM.

DO-Conv: Depthwise Over-Parameterized Convolutional Layer.

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. The composition of the two convolutions constitutes an over-parameterization, since it adds learnable parameters, while the resulting linear operation can be expressed by a single convolution layer. We refer to this depthwise over-parameterized convolutional layer as DO-Conv, which is a novel way of over-parameterization. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation. Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization. As DO-Conv introduces performance gains without incurring any computational complexity increase for inference, we advocate it as an alternative to the conventional convolutional layer. We open sourced an implementation of DO-Conv in Tensorflow, PyTorch and GluonCV at https://github.com/yangyanli/DO-Conv.

A General Decoupled Learning Framework for Parameterized Image Operators.

Many different deep networks have been used to approximate, accelerate or improve traditional image operators. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as "parameterized image operators". However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. To accelerate the parameter tuning for practical scenarios, the proposed framework can be further extended to dynamically change the weights of only one single layer of the base network while sharing most computation cost. We demonstrate that this cheap parameter-tuning extension of the proposed decoupled learning framework even outperforms the state-of-the-art alternative approaches.