An Improved Algorithm for Incremental DFS Tree in Undirected Graphs

Depth first search (DFS) tree is one of the most well-known data structures for designing efficient graph algorithms. Given an undirected graph $G=(V,E)$ with $n$ vertices and $m$ edges, the textbook algorithm takes $O(n+m)$ time to construct a DFS tree. In this paper, we study the problem of maintaining a DFS tree when the graph is undergoing incremental updates. Formally, we show: Given an arbitrary online sequence of edge or vertex insertions, there is an algorithm that reports a DFS tree in $O(n)$ worst case time per operation, and requires $O\left(\min\{m \log n, n^2\}\right)$ preprocessing time. Our result improves the previous $O(n \log^3 n)$ worst case update time algorithm by Baswana et al. and the $O(n \log n)$ time by Nakamura and Sadakane, and matches the trivial $\Omega(n)$ lower bound when it is required to explicitly output a DFS tree. Our result builds on the framework introduced in the breakthrough work by Baswana et al., together with a novel use of a tree-partition lemma by Duan and Zhan, and the celebrated fractional cascading technique by Chazelle and Guibas

GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6 pages, 8 figure

E3 ligase ligand optimization of Clinical PROTACs

Proteolysis targeting chimeras (PROTACs) technology can realize the development of drugs for non-druggable targets that are difficult to achieve with traditional small molecules, and therefore has attracted extensive attention from both academia and industry. Up to now, there are more than 600 known E3 ubiquitin ligases with different structures and functions, but only a few have developed corresponding E3 ubiquitin ligase ligands, and the ligands used to design PROTAC molecules are limited to a few types such as VHL (Von-Hippel-Lindau), CRBN (Cereblon), MDM2 (Mouse Doubleminute 2 homolog), IAP (Inhibitor of apoptosis proteins), etc. Most of the PROTAC molecules that have entered clinical trials were developed based on CRBN ligands, and only DT2216 was based on VHL ligand. Obviously, the structural optimization of E3 ubiquitin ligase ligands plays an instrumental role in PROTAC technology from bench to bedside. In this review, we review the structure optimization process of E3 ubiquitin ligase ligands currently entering clinical trials on PROTAC molecules, summarize some characteristics of these ligands in terms of druggability, and provide some preliminary insights into their structural optimization. We hope that this review will help medicinal chemists to develop more druggable molecules into clinical studies and to realize the greater therapeutic potential of PROTAC technology

Harmonizing across datasets to improve the transferability of drug combination prediction

Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose-response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.A machine learning-based method improves the transferability of drug combination predictions across datasets from studies with variable experimental settings, such as the number of doses and dose ranges tested.Peer reviewe

Congenital insensitivity to pain associated with PRDM12 mutation: Two case reports and a literature review

Background:PRDM12 is a newly discovered gene responsible for congenital insensitivity to pain (CIP). Its clinical manifestations are various and not widely known.Methods: The clinical data of two infants diagnosed with CIP associated with PRDM12 mutation were collected. A literature review was performed, and the clinical characteristics of 20 cases diagnosed with a mutation of PRDM12 were summarized and analyzed.Results: Two patients had pain insensitivity, tongue and lip defects, and corneal ulcers. The genomic analysis results showed that variants of PRDM12 were detected in the two families. The case 1 patient carried heterozygous variations of c.682+1G > A and c.502C > T (p.R168C), which were inherited from her father and mother, respectively. We enrolled 22 patients diagnosed with CIP through a literature review together with our cases. There were 16 male (72.7%) and 6 female (27.3%) patients. The age of onset ranged from 6 months to 57 years. The prevalence of clinic manifestation was 14 cases with insensitivity to pain (63.6%), 19 cases with self-mutilation behaviors (86.4%), 11 cases with tongue and lip defects (50%), 5 cases with mid-facial lesions (22.7%), 6 cases with distal phalanx injury (27.3%), 11 cases of recurrent infection (50%), 3 cases (13.6%) with anhidrosis, and 5 cases (22.7%) with global developmental delay. The prevalence of ocular symptoms was 11 cases (50%) with reduced tear secretion, 6 cases (27.3%) with decreased corneal sensitivity, 7 cases (31.8%) with disappeared corneal reflexes, 5.5 cases (25%, 0.5 indicated a single eye) with corneal opacity, 5 cases (22.7%) with corneal ulceration, and 1 case (4.5%) with a corneal scar.Conclusion: The syndrome caused by PRDM12 mutation is a clinically distinct and diagnosable disease that requires joint multidisciplinary management to control the development of the disease and minimize the occurrence of complications