Transition-metal catalyzed redox triggered C-C bond forming reactions via carbonyl addition

Carbon-carbon (C–C) bonds construct the skeleton of all organic molecules. Hence, the development of new efficient methods of C–C bond formation is of great significance in organic chemistry. Since the discovery of the Grignard reaction, carbonyl addition has been an established method for C–C bond formation. In classical carbonyl additions, premetalated reagents or stoichiometric metallic reductants are required. By taking advantage of the native reducing capability of alcohols, our lab has developed methods that exploit alcohols and π-unsaturates to generate transient electrophile-nucleophile pairs to directly convert lower alcohols to higher alcohol. Efforts have been focused on the development of transition metal-catalyzed redox-triggered coupling reactions of primary alcohols and aldehydes to π-unsaturates as well as aryl iodides. Modern methods to construct new C-C bonds in highly selective manner via carbonyl addition were undertaken.Chemistr

Adaptive Dynamic Filtering Network for Image Denoising

In image denoising networks, feature scaling is widely used to enlarge the receptive field size and reduce computational costs. This practice, however, also leads to the loss of high-frequency information and fails to consider within-scale characteristics. Recently, dynamic convolution has exhibited powerful capabilities in processing high-frequency information (e.g., edges, corners, textures), but previous works lack sufficient spatial contextual information in filter generation. To alleviate these issues, we propose to employ dynamic convolution to improve the learning of high-frequency and multi-scale features. Specifically, we design a spatially enhanced kernel generation (SEKG) module to improve dynamic convolution, enabling the learning of spatial context information with a very low computational complexity. Based on the SEKG module, we propose a dynamic convolution block (DCB) and a multi-scale dynamic convolution block (MDCB). The former enhances the high-frequency information via dynamic convolution and preserves low-frequency information via skip connections. The latter utilizes shared adaptive dynamic kernels and the idea of dilated convolution to achieve efficient multi-scale feature extraction. The proposed multi-dimension feature integration (MFI) mechanism further fuses the multi-scale features, providing precise and contextually enriched feature representations. Finally, we build an efficient denoising network with the proposed DCB and MDCB, named ADFNet. It achieves better performance with low computational complexity on real-world and synthetic Gaussian noisy datasets. The source code is available at https://github.com/it-hao/ADFNet.Comment: 9 pages, Accepted in AAAI Conference on Artificial Intelligence (AAAI) 202

Connotation and Structure of University Students’ Marriage Values

Marriage values refer to various conditions of individual marriage in the field of selection and consideration, also the individual values reflected in the marriage. The results show that, the connotation of marriage values of college students relates to economy, material foundation, emotion, personality, character, appearance and other factors; college students marriage values has structure model of second order and 5 factors, including intrinsic spiritual needs, external reality requires two second order factors and the economic and material foundation, interest and personality, feelings and beliefs, aesthetic needs, personality and for the five first-order factors. Reliability and validity test and confirmatory factor analysis show that, college students’ marriage values scale has good reliability and validity, and can be used as the college students’ psychological measurement meter

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustworthy AI in global health, we convened a workshop on Fairness in Machine Intelligence for Global Health (FairMI4GH). The event brought together a global mix of experts from various disciplines, community health practitioners, policymakers, and more. Topics covered included managing AI bias in socio-technical systems, AI's potential impacts on global health, and balancing data privacy with transparency. Panel discussions examined the cultural, political, and ethical dimensions of AI in global health. FairMI4GH aimed to stimulate dialogue, facilitate knowledge transfer, and spark innovative solutions. Drawing from NIST's AI Risk Management Framework, it provided suggestions for handling AI risks and biases. The need to mitigate data biases from the research design stage, adopt a human-centered approach, and advocate for AI transparency was recognized. Challenges such as updating legal frameworks, managing cross-border data sharing, and motivating developers to reduce bias were acknowledged. The event emphasized the necessity of diverse viewpoints and multi-dimensional dialogue for creating a fair and ethical AI framework for equitable global health.Comment: 7 page

Control of ventricular excitability by neurons of the dorsal motor nucleus of the vagus nerve

Background The central nervous origins of functional parasympathetic innervation of cardiac ventricles remain controversial. Objective This study aimed to identify a population of vagal preganglionic neurons that contribute to the control of ventricular excitability. An animal model of synuclein pathology relevant to Parkinson’s disease was used to determine whether age-related loss of the activity of the identified group of neurons is associated with changes in ventricular electrophysiology. Methods In vivo cardiac electrophysiology was performed in anesthetized rats in conditions of selective inhibition of the dorsal vagal motor nucleus (DVMN) neurons by pharmacogenetic approach and in mice with global genetic deletion of all family members of the synuclein protein. Results In rats anesthetized with urethane (in conditions of systemic beta-adrenoceptor blockade), muscarinic and neuronal nitric oxide synthase blockade confirmed the existence of a tonic parasympathetic control of cardiac excitability mediated by the actions of acetylcholine and nitric oxide. Acute DVMN silencing led to shortening of the ventricular effective refractory period (vERP), a lowering of the threshold for triggered ventricular tachycardia, and prolongation of the corrected QT (QTc) interval. Lower resting activity of the DVMN neurons in aging synuclein-deficient mice was found to be associated with vERP shortening and QTc interval prolongation. Conclusion Activity of the DVMN vagal preganglionic neurons is responsible for tonic parasympathetic control of ventricular excitability, likely to be mediated by nitric oxide. These findings provide the first insight into the central nervous substrate that underlies functional parasympathetic innervation of the ventricles and highlight its vulnerability in neurodegenerative diseases

Dimebon Does Not Ameliorate Pathological Changes Caused by Expression of Truncated (1–120) Human Alpha-Synuclein in Dopaminergic Neurons of Transgenic Mice

Background: Recent clinical studies have demonstrated that dimebon, a drug originally designed and used as a non-selective antihistamine, ameliorates symptoms and delays progress of mild to moderate forms of Alzheimer’s and Huntington’s diseases. Although the mechanism of dimebon action on pathological processes in degenerating brain is elusive, results of studies carried out in cell cultures and animal models suggested that this drug might affect the process of pathological accumulation and aggregation of various proteins involved in the pathogenesis of proteinopathies. However, the effect of this drug on the pathology caused by overexpression and aggregation of alpha-synuclein, including Parkinson’s disease (PD), has not been assessed. Objective: To test if dimebon affected alpha-synuclein-induced pathology using a transgenic animal model. Methods: We studied the effects of chronic dimebon treatment on transgenic mice expressing the C-terminally truncated (1–120) form of human alpha-synuclein in dopaminergic neurons, a mouse model that recapitulates several biochemical, histopathological and behavioral characteristics of the early stage of PD. Results: Dimebon did not improve balance and coordination of aging transgenic animals or increase the level of striatal dopamine, nor did it prevent accumulation of alpha-synuclein in cell bodies of dopaminergic neurons. Conclusion: Our observations suggest that in the studied model of alpha-synucleinopathy dimebon has very limited effect on certain pathological alterations typical of PD and related diseases

A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Published in final edited form as: Nat Biotechnol. 2020 February ; 38(2): 199–209. doi:10.1038/s41587-019-0322-9.Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.P01 CA229092 - NCI NIH HHS; P50 CA101942 - NCI NIH HHS; T32 HG002295 - NHGRI NIH HHS; T32 CA009172 - NCI NIH HHS; U24 CA224331 - NCI NIH HHS; R21 CA216772 - NCI NIH HHS; R01 CA155010 - NCI NIH HHS; U01 CA214125 - NCI NIH HHS; T32 CA207021 - NCI NIH HHS; R01 HL103532 - NHLBI NIH HHS; U24 CA210986 - NCI NIH HHSAccepted manuscrip

Air-silica core microstructured optical fiber-based SPR sensor for temperature and refractive index measurement

We propose an air-silica core microstructured optical fiber-based surface plasmon resonance (SPR) sensor to simultaneously measure temperature and refractive index (RI). The sensing channel is formed by coating the outside of the fiber with a gold film and a polydimethylsiloxane (PDMS) layer as a temperature sensing medium, and then being immersed into the liquid analyte. The plasmon mode can penetrate through the PDMS layer and then into the analyte, therefore both the temperature and the analyte RI changings can lead to the variations of SPR spectra that will be measured. Our numerical results demonstrate that the proposed sensor can support two resonance peaks in the x-polarized core mode and one resonance peak in the y-polarized core mode, therefore providing two detection approaches, peak-based and polarization-based approaches. By measuring the two peaks in the x-polarized core mode, for the peak-based approach, the temperature coefficients are −2.077 nm/°C and −2.723 nm/°C, and the RI coefficients are 1252 nm/RIU and 1931 nm/RIU, respectively. While by measuring the second peak in x-polarized core mode and the peak in y-polarized core mode, for the polarization-based approach, the temperature coefficients are −2.723 nm/°C and −3.401 nm/°C, and the RI coefficients are 1931 nm/RIU and 2973 nm/RIU, respectively