Quadruple bonding between iron and boron in the BFe(CO)3- complex.

While main group elements have four valence orbitals accessible for bonding, quadruple bonding to main group elements is extremely rare. Here we report that main group element boron is able to form quadruple bonding interactions with iron in the BFe(CO)3- anion complex, which has been revealed by quantum chemical investigation and identified by mass-selected infrared photodissociation spectroscopy in the gas phase. The complex is characterized to have a B-Fe(CO)3- structure of C3v symmetry and features a B-Fe bond distance that is much shorter than that expected for a triple bond. Various chemical bonding analyses indicate that the complex involves unprecedented B≣Fe quadruple bonding interactions. Besides the common one electron-sharing σ bond and two Fe→B dative π bonds, there is an additional weak B→Fe dative σ bonding interaction. This finding of the new quadruple bonding indicates that there might exist a wide range of boron-metal complexes that contain such high multiplicity of chemical bonds

Exploratory Pharmacokinetics of Geniposide in Rat Model of Cerebral Ischemia Orally Administered with or without Baicalin and/or Berberine

Huang-Lian-Jie-Du-Tang (HLJDT), a classical Chinese prescription, has been clinically employed to treat cerebral ischemia for thousands of years. Geniposide is the major active ingredient in HLJDT. The aim is to investigate the comparative evaluations on pharmacokinetics of geniposide in MCAO rats in pure geniposide, geniposide : berberine, and geniposide : berberine : baicalin. Obviously, the proportions of geniposide : berberine, geniposide : baicalin, and geniposide : berberine : baicalin were determined according to HLJDT. In our study, the cerebral ischemia model was reproduced by suture method in rats. The MCAO rats were randomly assigned to four therapy groups and orally administered with different prescription proportions of pure geniposide, geniposide : berberine, geniposide : baicalin, and geniposide : berberine : baicalin, respectively. The concentrations of geniposide in rat serum were determined using HPLC, and main pharmacokinetic parameters were investigated. The results indicated that the pharmacokinetics of geniposide in rat serum was nonlinear and there were significant differences between different groups. Berberine might hardly affect the absorption of geniposide, and baicalin could increase the absorption ability of geniposide. Meanwhile, berberine could decrease the absorption increase of baicalin on geniposide

CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System

Long-term time-series forecasting (LTSF) plays a crucial role in various practical applications. Transformer and its variants have become the de facto backbone for LTSF, offering exceptional capabilities in processing long sequence data. However, existing Transformer-based models, such as Fedformer and Informer, often achieve their best performances on validation sets after just a few epochs, indicating potential underutilization of the Transformer's capacity. One of the reasons that contribute to this overfitting is data redundancy arising from the rolling forecasting settings in the data augmentation process, particularly evident in longer sequences with highly similar adjacent data. In this paper, we propose a novel approach to address this issue by employing curriculum learning and introducing a memory-driven decoder. Specifically, we progressively introduce Bernoulli noise to the training samples, which effectively breaks the high similarity between adjacent data points. To further enhance forecasting accuracy, we introduce a memory-driven decoder. This component enables the model to capture seasonal tendencies and dependencies in the time-series data and leverages temporal relationships to facilitate the forecasting process. The experimental results on six real-life LTSF benchmarks demonstrate that our approach can be seamlessly plugged into varying Transformer-based models, with our approach enhancing the LTSF performances of various Transformer-based models by maximally 30%.Comment: Tech repor

Virus-induced gene complementation reveals a transcription factor network in modulation of tomato fruit ripening

Plant virus technology, in particular virus-induced gene silencing, is a widely used reverse- and forward-genetics tool in plant functional genomics. However the potential of virus technology to express genes to induce phenotypes or to complement mutants in order to understand the function of plant genes is not well documented. Here we exploit Potato virus X as a tool for virus-induced gene complementation (VIGC). Using VIGC in tomato, we demonstrated that ectopic viral expression of LeMADS-RIN, which encodes a MADS-box transcription factor (TF), resulted in functional complementation of the non-ripening rin mutant phenotype and caused fruits to ripen. Comparative gene expression analysis indicated that LeMADS-RIN up-regulated expression of the SBP-box (SQUAMOSA promoter binding protein-like) gene LeSPL-CNR, but down-regulated the expression of LeHB-1, an HD-Zip homeobox TF gene. Our data support the hypothesis that a transcriptional network may exist among key TFs in the modulation of fruit ripening in tomato

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.Comment: AAAI 202

Genome-wide identification and analysis of heterotic loci in three maize hybrids

Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large-scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield-related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete–incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow-up studies for 17 heterotic loci in an independent experiment using 2225 F2 individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants

Approaching isotropic transfer integrals in crystalline organic semiconductors

Dynamic disorders, which possess a finite charge delocalization, play a critical role in the charge transport properties of high-mobility molecular organic semiconductors. The use of two-dimensional (2D) charge transport in crystalline organic semiconductors can effectively facilitate reducing the sensitivity of charge carriers to thermal energetic disorders existing in even single crystals to enhance the carrier mobility. An isotropic transfer integral among adjacent molecules enables a dimensional transition from quasi-one-dimensional to 2D for charge transport among molecules. Herein, a tuned molecular packing, especially molecular rotation, was achieved in highly crystalline organic thin films via a brush-coating method. This tuned molecular packing was favorable for approaching isotropic transfer integrals. Consequently, high-performance organic transistors with a carrier mobility up to 21.5cm2V−1s−1 and low angle dependence were obtained. This work presents a unique modulation of molecular packing at the molecular scale to enable less sensitivity of the charge transport to dynamic disorders, providing an alternative route for enhancing the electrical performance of organic electronic devices

Global optimization of clustering problems

Clustering is a fundamental unsupervised machine learning task that aims to aggregate similar data into one cluster and separate those in diverse into different clusters. Cluster analysis can always be formulated as an optimization problem. Various objective functions may lead to different clustering problems. In this thesis, we concentrate on k-means and k-center problems. Each can be formulated as a mixed-integer nonlinear programming problem. The work about k-means clustering optimization has been published on ICML 2021 [30]. Moreover, we also submitted the work about global optimization of k-center clustering to ICML 2022 and the paper has been accepted in Phase 1 of reviewing. This thesis provides a practical global optimization algorithm for these two tasks based on a reduced-space spatial branch and bound (BB) scheme. This algorithm can guarantee convergence to the global optimum by only branching on the centers of clusters, which is independent of the dataset’s cardinality. We also design several methods to construct lower and upper bounds at each node in the BB scheme. In addition, for k-center problem, a set of feasibility-based bounds tightening techniques are proposed to narrow down the domain of centers and significantly accelerate the convergence. To demonstrate the capacity of this algorithm, we present computational results on UCI datasets and compare our proposed algorithms with the off-the-shelf global optimal solvers and classical local optimal algorithms. For k-means clustering, the numerical experiments demonstrated our algorithm’s ability to handle datasets with up to 200,000 samples. Besides, for k-center clustering, the serial implementation of the algorithm on the dataset with 14 million samples and 3 features can attain the global optimum to an optimality gap of 0.1% within 2 hours.Science, Faculty ofMathematics, Department ofGraduat