Preparing glycosyl benzothiazoles from 2-isocyanoaryl thioethers and glycosyl radicals under thermal conditions.

Herein, we report a method for preparing glycosyl benzothiazoles via radical cascade cyclization, in which glycosyl radicals are generated from readily available and bench-stable allyl glycosyl sulfones. This cascade reaction proceeds under simple conditions and tolerates a broad substrate scope in high yield with excellent stereoselectivity. Mechanistic studies support that the reactions proceed via the intermediacy of imidoyl radicals, which attack the appended sulfide unit by a SH2 process to forge the thiazole ring.

Postoperative morbidity and mortality of patients with COVID-19 undergoing cardiovascular surgery: an inverse propensity-weighted study : Postoperative outcomes of patients undergoing cardiovascular surgery during the COVID-19 pandemic.

BACKGROUND: To evaluate the postoperative morbidity and mortality of patients undergoing cardiovascular surgery during the 2022 nationwide Omicron variant infection wave in China. METHODS: This retrospective cohort study included 403 patients who underwent cardiovascular surgery for the first time during the 2022 wave of the pandemic within 1 month. Among them, 328 patients were preoperatively diagnosed with COVID-19 Omicron variant infection during the pandemic, and 75 patients were negative. The association between Omicron variant exposure and postoperative prognosis was explored by comparing patients with and without COVID-19 exposure. The primary outcome was in-hospital death after cardiovascular surgery. The secondary outcomes were major postoperative morbidity, including myocardial infarction (MI), acute kidney injury (AKI), postoperative mechanical ventilation hours, ICU stay hours, and postoperative length of stay. The data were analyzed using inverse probability of treatment weighting (IPTW) to minimize bias. RESULTS: We identified 403 patients who underwent cardiovascular surgery, 328 (81.39%) had Omicron variant infections. In total, 10 patients died in the hospital. Omicron variant infection was associated with a much greater risk of death during cardiovascular surgery after adjustment for IPTW (2.8% vs. 1.3%, adjusted OR 2.185, 95%CI = 1.193 to 10.251, P = 0.041). For major postoperative morbidity, there were no significant differences in terms of myocardial infarction between the two groups (adjusted OR = 0.861, 95%CI = 0.444 to 1.657, P = 0.653), acute kidney injury (adjusted OR = 1.157, 95%CI = 0.287 to 5.155, P = 0.820), postoperative mechanical ventilation hours (B -0.375, 95%CI=-8.438 to 7.808, P = 0.939), ICU stay hours (B 2.452, 95%CI=-13.269 to 8.419, P = 0.660) or postoperative stay (B -1.118, 95%CI=-2.237 to 1.154, P = 0.259) between the two groups. CONCLUSION: Perioperative COVID-19 infection was associated with an increased risk of in-hospital death among patients who underwent cardiovascular surgery during the Omicron variant wave of the pandemic.

Carbohydrate-DNA Conjugation Enabled by Glycosyl Radicals Generated from Glycosyl Sulfinates.

Herein, we report a method that enables the synthesis of carbohydrate-DNA conjugates by radical addition. Key to the success is the use of readily available, bench-stable, and unprotected glycosyl sulfinates as precursors to glycosyl radicals. The redox neutral reaction proceeds under mild and simple conditions and tolerates a broad substrate scope. A small library of carbohydrate-DNA conjugates was prepared.

Importance Weighted Structure Learning for Scene Graph Generation.

Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image. Currently, the message passing neural network based mean field variational Bayesian methodology is the ubiquitous solution for such a task, in which the variational inference objective is often assumed to be the classical evidence lower bound. However, the variational approximation inferred from such loose objective generally underestimates the underlying posterior, which often leads to inferior generation performance. In this paper, we propose a novel importance weighted structure learning method aiming to approximate the underlying log-partition function with a tighter importance weighted lower bound, which is computed from multiple samples drawn from a reparameterizable Gumbel-Softmax sampler. A generic entropic mirror descent algorithm is applied to solve the resulting constrained variational inference task. The proposed method achieves the state-of-the-art performance on various popular scene graph generation benchmarks.

Constrained Structure Learning for Scene Graph Generation.

As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image. Currently, the mean field variational Bayesian framework is the de facto methodology used by the existing methods, in which the unconstrained inference step is often implemented by a message passing neural network. However, such formulation fails to explore other inference strategies, and largely ignores the more general constrained optimization models. In this paper, we present a constrained structure learning method, for which an explicit constrained variational inference objective is proposed. Instead of applying the ubiquitous message-passing strategy, a generic constrained optimization method - entropic mirror descent - is utilized to solve the constrained variational inference step. We validate the proposed generic model on various popular scene graph generation benchmarks and show that it outperforms the state-of-the-art methods.

Neural Belief Propagation for Scene Graph Generation.

Scene graph generation aims to interpret an input image by explicitly modelling the objects contained therein and their relationships. In existing methods the problem is predominantly solved by message passing neural network models. Unfortunately, in such models, the variational distributions generally ignore the structural dependencies among the output variables, and most of the scoring functions only consider pairwise dependencies. This can lead to inconsistent interpretations. In this article, we propose a novel neural belief propagation method seeking to replace the traditional mean field approximation with a structural Bethe approximation. To find a better bias-variance trade-off, higher-order dependencies among three or more output variables are also incorporated into the relevant scoring function. The proposed method achieves the state-of-the-art performance on various popular scene graph generation benchmarks.

Synthesis of Polydiynes via an Unexpected Dimerization/Polymerization Sequence of C3 Propargylic Electrophiles.

Here, we describe the unexpected discovery of a Cu-catalyzed condensation polymerization reaction of propargylic electrophiles (CPPE) that transforms simple C3 building blocks into polydiynes of C6 repeating units. This reaction was achieved by a simple system composed of a copper acetylide initiator and an electron-rich phosphine ligand. Alkyne polymers (up to 33.8 kg/mol) were produced in good yields and exclusive regioselectivity with high functional group compatibility. Hydrogenation of the product afforded a new polyolefin-type backbone, while base-mediated isomerization led to a new type of dienyne-based electron-deficient conjugated polymer. Mechanistic studies revealed a new α-α selective Cu-catalyzed dimerization pathway of the C3 unit, followed by in situ organocopper-mediated chain-growth propagation. These insights not only provide an important understanding of the Cu-catalyzed CPPE of C3, C4, and C6 monomers in general but also lead to a significantly improved synthesis of polydiynes from simpler starting materials with handles for the incorporation of an α-end functional group.

Visual Semantic Information Pursuit: A Survey.

Visual semantic information comprises two important parts: the meaning of each visual semantic unit and the coherent visual semantic relation conveyed by these visual semantic units. Essentially, the former one is a visual perception task while the latter corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual semantic information pursuit, a visual scene semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual semantic segmentation, visual relationship detection, or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods. Surprisingly, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.

Deep Spiking Neural Network for Video-Based Disguise Face Recognition Based on Dynamic Facial Movements.

With the increasing popularity of social media and smart devices, the face as one of the key biometrics becomes vital for person identification. Among those face recognition algorithms, video-based face recognition methods could make use of both temporal and spatial information just as humans do to achieve better classification performance. However, they cannot identify individuals when certain key facial areas, such as eyes or nose, are disguised by heavy makeup or rubber/digital masks. To this end, we propose a novel deep spiking neural network architecture in this paper. It takes dynamic facial movements, the facial muscle changes induced by speaking or other activities, as the sole input. An event-driven continuous spike-timing-dependent plasticity learning rule with adaptive thresholding is applied to train the synaptic weights. The experiments on our proposed video-based disguise face database (MakeFace DB) demonstrate that the proposed learning method performs very well, i.e., it achieves from 95% to 100% correct classification rates under various realistic experimental scenarios.

Event-Driven Continuous STDP Learning With Deep Structure for Visual Pattern Recognition.

Human beings can achieve reliable and fast visual pattern recognition with limited time and learning samples. Underlying this capability, ventral stream plays an important role in object representation and form recognition. Modeling the ventral steam may shed light on further understanding the visual brain in humans and building artificial vision systems for pattern recognition. The current methods to model the mechanism of ventral stream are far from exhibiting fast, continuous, and event-driven learning like the human brain. To create a visual system similar to ventral stream in human with fast learning capability, in this paper, we propose a new spiking neural system with an event-driven continuous spike timing dependent plasticity (STDP) learning method using specific spiking timing sequences. Two novel continuous input mechanisms have been used to obtain the continuous input spiking pattern sequence. With the event-driven STDP learning rule, the proposed learning procedure will be activated if the neuron receive one pre- or post-synaptic spike event. The experimental results on MNIST database show that the proposed method outperforms all other methods in fast learning scenarios and most of the current models in exhaustive learning experiments.