The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

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Initial sequencing and analysis of the human genome.

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

1. Introduction Designing PCR and sequencing primers are essential activities for molecular biologists around the world. This chapter assumes acquaintance with the principles and practice of PCR, as outlined in, for example, refs. 1–4. Primer3 is a computer program that suggests PCR primers for a variety of applications, for example to create STSs (sequence tagged sites) for radiation hybrid mapping (5), or to amplify sequences for single nucleotide polymor-phism discovery (6). Primer3 can also select single primers for sequencing reactions and can design oligonucleotide hybridization probes. In selecting oligos for primers or hybridization probes, Primer3 can consider many factors. These include oligo melting temperature, length, GC content , 3′ stability, estimated secondary structure, the likelihood of annealing to or amplifying undesirable sequences (for example interspersed repeats), the likelihood of primer–dimer formation between two copies of the same primer, and the accuracy of the source sequence. In the design of primer pairs Primer3 can consider product size and melting temperature, the likelihood of primer– dimer formation between the two primers in the pair, the difference between primer melting temperatures, and primer location relative to particular regions of interest or to be avoided.

/pdf/primer3-on-the-www-for-general-users-and-for-biologist-pgzaqxkztz.pdf

Primer3 on the WWW for general users and for biologist programmers.

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.

http://science.sciencemag.org/content/sci/291/5507/1304.full.pdf

The sequence of the human genome.

A system and method for forming microlattice structures of large thickness. In one embodiment, a photomonomer resin is secured in a mold having a transparent bottom, the interior surface of which is coated with a mold-release agent. A substrate is placed in contact with the top surface of the photomonomer resin. The photomonomer resin is illuminated from below by one or more sources of collimated light, through a photomask, causing polymer waveguides to form, extending up to the substrate, forming a microlattice structure connected with the substrate. After a layer of microlattice structure has formed, the substrate is raised using a translation-rotation system, additional photomonomer resin is added to the mold, and the photomonomer resin is again illuminated through the photomask, to form an additional layer of microlattice structure. The process is repeated multiple times to form a stacked microlattice structure.

Stacked microlattice materials and fabrication processes

Background: The rapid spread of illness and death caused by the severe respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated coronavirus disease 2019 (COVID-19) demands a rapid response in treatment development. Limitations of de novo drug development, however, suggest that drug repurposing is best suited to meet this demand. Methods: Due to the difficulty of accessing electronic health record data in general and in the midst of a global pandemic, and due to the similarity between SARS-CoV-2 and SARS-CoV, we propose mining the extensive biomedical literature for treatments to SARS that may also then be appropriate for COVID-19. In particular, we propose a method of mining a large biomedical word embedding for FDA approved drugs based on drug-disease treatment analogies. Results: We first validate that our method correctly identifies ground truth treatments for well-known diseases. We then use our method to find several approved drugs that have been suggested or are currently in clinical trials for COVID-19 in our top hits and present the rest as promising leads for further experimental investigation. Conclusions: We find our approach promising and present it, along with suggestions for future work, to the computational drug repurposing community at large as another tool to help fight the pandemic. Code and data for our methods can be found at https://github.com/finnkuusisto/covid19_word_embedding.

/pdf/word-embedding-mining-for-sars-cov-2-and-covid-19-drug-567qb45myj.pdf

Word embedding mining for SARS-CoV-2 and COVID-19 drug repurposing

This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary variables. In this paper, we extend skewing to directly handle functions of continuous and nominal variables. We present experiments with randomly generated functions and a number of real world datasets to evaluate the algorithm's accuracy. Our results indicate that our algorithm almost always outperforms an Information Gain-based decision tree learner.

/pdf/generalized-skewing-for-functions-with-continuous-and-szvkzfcloa.pdf

Generalized skewing for functions with continuous and nominal attributes

Recent statistical performance surveys of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution (SCD) of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a heavy tail), restarts can reduce the search cost expectation. Recently, this heavy tail phenomenon was observed in the SCD's of benchmark ILP problems. Following on this work, we report on an empirical study of randomised restarted search in ILP. Our experiments, conducted over a cluster of a few hundred computers, provide an extensive statistical performance sample of five search algorithms operating on two principally different ILP problems (artificially generated graph data and the well-known mutagenesis problem). The sample allows us to (1) estimate the conditional expected value of the search cost (measured by the total number of clauses explored) given the minimum clause score required and a cutoff value (the number of clauses examined before the search is restarted); and (2) compare the performance of randomised restarted search strategies to a deterministic non-restarted search. Our findings indicate that the cutoff value is significantly more important than the choice of (a) the specific refinement strategy; (b) the starting element of the search; and (c) the specific data domain. We find that the optimal value for the cutoff parameter remains roughly stable across variations of these three factors and that the mean search cost using this value in a randomised restarted search is up to three orders of magnitude (i.e. 1000 times) lower than that obtained with a deterministic non-restarted search.

A Monte Carlo study of randomised restarted search in ILP

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and Lasso-LR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

/pdf/quantifying-predictive-capability-of-electronic-health-1lshcuy5r1.pdf

David C. Page

Papers

Stacked microlattice materials and fabrication processes

Word embedding mining for SARS-CoV-2 and COVID-19 drug repurposing

Generalized skewing for functions with continuous and nominal attributes

A Monte Carlo study of randomised restarted search in ILP

Quantifying predictive capability of electronic health records for the most harmful breast cancer.