Large scale total synthesis of apoptolidinone and progress towards the total synthesis of ammocidin

Apoptolidin 1.1 was isolated in 1997 by Hayakawa and co-workers from a soil bacterium Nocardiopsis sp. during screening for specific apoptosis inducers. The primary biological test revealed that this polyketide macrolide induced apoptosis in cells transformed with the adenovirus type E1A oncognene, but not normal cells. This dissertation describes the latest studies in understanding of apoptolidin’s biological activity mechanism and previous contributions towards its total synthesis. Synthesizing apoptolidinone 1.26 by an intra-molecular Horner-Wadsworth-Emmons approach featuring a Suzuki coupling, cross metathesis and two diastereoselective aldol reactions is discussed. 15 mg apoptolidinone is prepared via our previously developed intramolecular Suzuking coupling approach. Ammocidin 3.1, which was found to induce apoptosis in Ba/F3-v12 cells in an IL- 3 free medium, is a specific apoptosis inducer discovered by Hayakawa and co-workers in 2001 from Saccharothrix sp. AJ9571. A strategy featuring Suzuki coupling, cross metathesis, Yamaguchi macrolactonization and three asymmetric aldol reactions was applied to the total synthesis of ammocidinone 3.6, the aglycone of ammocidin. The preparation of the key building blocks was discussed in the following chapter: aldehyde 3.8 (C14-C19) was synthesized via Sharpless asymmetric epoxidation; ethyl ketone 3.9’ (C20-C28) was prepared via Kobayashi and Crimmins’s asymmetric aldol methodologies; aldehyde 3.14 (C7-C13) was generated by Brown crotylation and cross metathesis

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

Time series forecasting is an important and forefront task in many real-world applications. However, most of time series forecasting techniques assume that the training data is clean without anomalies. This assumption is unrealistic since the collected time series data can be contaminated in practice. The forecasting model will be inferior if it is directly trained by time series with anomalies. Thus it is essential to develop methods to automatically learn a robust forecasting model from the contaminated data. In this paper, we first statistically define three types of anomalies, then theoretically and experimentally analyze the loss robustness and sample robustness when these anomalies exist. Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly robust and outperforms all existing approaches. The code is available at https://github.com/haochenglouis/RobustTSF.Comment: Accepted by the 12th International Conference on Learning Representations (ICLR 2024

Construction of CaF2-appended PVA nanofibre scaffold

In this work, a new material, calcium fluoride ( CaF2 )-appended poly(vinyl alcohol) (PVA) nanofibre scaffold, was prepared through electrospinning technique successfully. Scanning electron microscopy result showed that the morphology of the fibres was uniform and smooth, and the average diameter of the fibres was about 200 nm. Transmission electron microscopy results showed that many CaF2 nanoparticles were well dispersed in the PVA fibre matrix. The water-resistant ability of the scaffold was improved through intermolecular crosslinking of PVA by formaldehyde vapour. This novel material seems to be a promising scaffold for bone tissue engineering

Analysis of Spatial Travel Association Rules for Rail Transit Based on AFC and POI Data

In order to explore the spatial distribution rules and causes of urban rail transit passenger travel, this paper mines the spatial 1-frequent itemset and 2-frequent itemsets of weekdays and weekends metro passenger travel based on Apriori algorithm using the continuous week of Automatic Fare Collection System (AFC) swipe card. At the same time, the K-Means algorithm is used to cluster the subway stations and explore the causes of association rules by combining the Point of Interest (POI) data of the same period within the radiation range of the subway stations. The study shows that the spatial distribution pattern of inbound and outbound passenger flow of Shanghai rail transit is consistent between weekdays and weekends, and the outbound passenger flow is more concentrated than the inbound passenger flow, and the significance of weekends is higher; the spatial distribution of metro stations is "circled"; the analysis of the high-lift association rules show that a large passenger flow group centered on the type 3 station is formed in the spatial location, and the passenger flow within the group is mainly commuter flow with separation of employment and residence. The association rule mining of metro passenger travel data is beneficial to understanding the spatial distribution pattern and causes of metro ridership, which can provide reference for rail network planning and operation management

Partition of a Binary Matrix into k

A biclustering problem consists of objects and an attribute vector for each object. Biclustering aims at finding a bicluster—a subset of objects that exhibit similar behavior across a subset of attributes, or vice versa. Biclustering in matrices with binary entries (“0”/“1”) can be simplified into the problem of finding submatrices with entries of “1.” In this paper, we consider a variant of the biclustering problem: the k-submatrix partition of binary matrices problem. The input of the problem contains an n×m matrix with entries (“0”/“1”) and a constant positive integer k. The k-submatrix partition of binary matrices problem is to find exactly k submatrices with entries of “1” such that these k submatrices are pairwise row and column exclusive and each row (column) in the matrix occurs in exactly one of the k submatrices. We discuss the complexity of the k-submatrix partition of binary matrices problem and show that the problem is NP-hard for any k≥3 by reduction from a biclustering problem in bipartite graphs

Quantitative and functional post-translational modification proteomics reveals that TREPH1 plays a role in plant thigmomorphogenesis

Plants can sense both intracellular and extracellular mechanical forces and can respond through morphological changes. The signaling components responsible for mechanotransduction of the touch response are largely unknown. Here, we performed a high-throughput SILIA (stable isotope labeling in Arabidopsis)-based quantitative phosphoproteomics analysis to profile changes in protein phosphorylation resulting from 40 seconds of force stimulation in Arabidopsis thaliana. Of the 24 touch-responsive phosphopeptides identified, many were derived from kinases, phosphatases, cytoskeleton proteins, membrane proteins and ion transporters. TOUCH-REGULATED PHOSPHOPROTEIN1 (TREPH1) and MAP KINASE KINASE 2 (MKK2) and/or MKK1 became rapidly phosphorylated in touch-stimulated plants. Both TREPH1 and MKK2 are required for touch-induced delayed flowering, a major component of thigmomorphogenesis. The treph1-1 and mkk2 mutants also exhibited defects in touch-inducible gene expression. A non-phosphorylatable site-specific isoform of TREPH1 (S625A) failed to restore touch-induced flowering delay of treph1-1, indicating the necessity of S625 for TREPH1 function and providing evidence consistent with the possible functional relevance of the touch-regulated TREPH1 phosphorylation. Bioinformatic analysis and biochemical subcellular fractionation of TREPH1 protein indicate that it is a soluble protein. Altogether, these findings identify new protein players in Arabidopsis thigmomorphogenesis regulation, suggesting that protein phosphorylation may play a critical role in plant force responses