抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

/pdf/xgboost-a-scalable-tree-boosting-system-48ocu6x4c7.pdf

XGBoost: A Scalable Tree Boosting System

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

/pdf/the-anatomy-of-a-large-scale-hypertextual-web-search-engine-496poj789d.pdf

The anatomy of a large-scale hypertextual Web search engine

The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

The PageRank Citation Ranking : Bringing Order to the Web

Regulation of transgene expression is becoming an integral component of gene therapies, cell therapies and biomanufacturing. However, transcription factor-based regulation upon which the majority of such applications are based suffers from complications such as epigenetic silencing, which limits the longevity and reliability of these efforts. Genetically engineered mammalian cells used for cell therapies and biomanufacturing as well as newer RNA-based gene therapies would benefit from post-transcriptional methods of gene regulation, but few such platforms exist that enable sophisticated programming of cell behavior. Here we engineer the 5’ and 3’ untranslated regions of transcripts to enable robust and composable RNA-level regulation through transcript cleavage and, in particular, create modular RNA-level OFF- and ON-switch motifs. We show that genomically introduced transgenes exhibit resistance to silencing when regulated using this platform compared to those that are transcriptionally-regulated. We adapt nine CRISPR-specific endoRNases as RNA-level “activators” and “repressors” and show that these can be easily layered and composed to reconstruct genetic programming topologies previously achieved with transcription factor-based regulation including cascades, all 16 two-input Boolean logic functions, positive feedback, a feed-forward loop and a putative bistable toggle switch. The orthogonal, modular and composable nature of this platform as well as the ease with which robust and predictable gene circuits are constructed holds promise for their application in gene and cell therapies.

https://biorxiv.org/content/biorxiv/early/2019/12/16/2019.12.15.867150.full-text.pdf

PERSIST: A programmable RNA regulation platform using CRISPR endoRNases

We describe a method of simultaneusly tracking noise and speech levels for signal-to-noise ratio adaptive speech endpoint detection. The method is based on the Kalman filter framework with switching observations and uses a dynamic distribution that 1) limits the rate of change of these levels 2) enforces a range on the values for the two levels and 3) enforces a ratio between the noise and the signal levels. We call this a Lombard dynamic distribution since it encodes the expectation that a speaker will increase his or her vocal intensity in noise. The method also employs a state transition matrix which encodes a prior on the states and provides a continuity constraint. The new method provides 46.1% relative improvement in WER over a baseline GMM based endpointer at 20 dB SNR.

https://www.ee.columbia.edu/%7Eronw/pubs/icslp2008-dysana.pdf

DySANA: dynamic speech and noise adaptation for voice activity detection.

Provided herein are high-input detector modules and multi-input biological classifier circuits and systems that integrate sophisticated sensing, information processing, and actuation in living cells and permit new directions in basic biology, biotechnology and medicine. The multi-input biological classifier circuits described herein comprise synthetic, scaleable transcriptional/post-transcriptional regulatory circuits that are designed to interrogate the status of a cell by simultaneously sensing expression levels of multiple endogenous inputs, such as microRNAs. The classifier circuits then compute whether to trigger a desired output or response if the expression levels match a pre-determined profile of interest.

Multiple input biologic classifier circuits for cells

We seek to couple protein-ligand interactions with synthetic gene networks in order to equip cells with the ability to process internal and environmental information in novel ways. In this paper, we propose and analyze a new genetic signal processing circuit that can be configured to detect various chemical concentration ranges of ligand molecules. These molecules freely diffuse from the environment into the cell. The circuit detects acyl-homoserine lactone ligand molecules, determines if the molecular concentration falls within two prespecified thresholds, and reports the outcome with a fluorescent protein. In the analysis of the circuit and the description of preliminary experimental results, we demonstrate how to adjust the concentration band thresholds by altering the kinetic properties of specific genetic elements, such as ribosome binding site efficiencies or dna-binding protein affinities to their operators.

/pdf/engineering-signal-processing-in-cells-towards-molecular-4gesbd4z0t.pdf

Engineering Signal Processing in Cells: Towards Molecular Concentration Band Detection

N-linked glycosylation affects the potency, safety, immunogenicity, and pharmacokinetic clearance of several therapeutic proteins including monoclonal antibodies. A robust control strategy is needed to dial in appropriate glycosylation profile during the course of cell culture processes accurately. However, N-glycosylation dynamics remains insufficiently understood owing to the lack of integrative analyses of factors that influence the dynamics, including sugar nucleotide donors, glycosyltransferases, and glycosidases. Here, an integrative approach involving multi-dimensional omics analyses was employed to dissect the temporal dynamics of glycoforms produced during fed-batch cultures of CHO cells. Several pathways including glycolysis, tricarboxylic citric acid cycle, and nucleotide biosynthesis exhibited temporal dynamics over the cell culture period. The steps involving galactose and sialic acid addition were determined as temporal bottlenecks. Our results show that galactose, and not manganese, is able to mitigate the temporal bottleneck, despite both being known effectors of galactosylation. Furthermore, sialylation is limited by the galactosylated precursors and autoregulation of cytidine monophosphate-sialic acid biosynthesis.

Ron Weiss

Papers

PERSIST: A programmable RNA regulation platform using CRISPR endoRNases

DySANA: dynamic speech and noise adaptation for voice activity detection.

Multiple input biologic classifier circuits for cells

Engineering Signal Processing in Cells: Towards Molecular Concentration Band Detection

Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses