scispace - formally typeset
Search or ask a question
Author

Neil R. Smalheiser

Bio: Neil R. Smalheiser is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Neurite & Laminin. The author has an hindex of 50, co-authored 179 publications receiving 8933 citations. Previous affiliations of Neil R. Smalheiser include Oregon Health & Science University & University of Illinois at Urbana–Champaign.


Papers
More filters
Journal ArticleDOI
TL;DR: In all of the brain areas studied, RELN and its mRNA were significantly reduced in patients with schizophrenia; this decrease was similar in patients affected by undifferentiated or paranoid schizophrenia and is interpreted within a neurodevelopmental/vulnerability "two-hit" model for the etiology of schizophrenia.
Abstract: Postmortem prefrontal cortices (PFC) (Brodmann’s areas 10 and 46), temporal cortices (Brodmann’s area 22), hippocampi, caudate nuclei, and cerebella of schizophrenia patients and their matched nonpsychiatric subjects were compared for reelin (RELN) mRNA and reelin (RELN) protein content. In all of the brain areas studied, RELN and its mRNA were significantly reduced (≈50%) in patients with schizophrenia; this decrease was similar in patients affected by undifferentiated or paranoid schizophrenia. To exclude possible artifacts caused by postmortem mRNA degradation, we measured the mRNAs in the same PFC extracts from γ-aminobutyric acid (GABA)A receptors α1 and α5 and nicotinic acetylcholine receptor α7 subunits. Whereas the expression of the α7 nicotinic acetylcholine receptor subunit was normal, that of the α1 and α5 receptor subunits of GABAA was increased when schizophrenia was present. RELN mRNA was preferentially expressed in GABAergic interneurons of PFC, temporal cortex, hippocampus, and glutamatergic granule cells of cerebellum. A protein putatively functioning as an intracellular target for the signal-transduction cascade triggered by RELN protein released into the extracellular matrix is termed mouse disabled-1 (DAB1) and is expressed at comparable levels in the neuroplasm of the PFC and hippocampal pyramidal neurons, cerebellar Purkinje neurons of schizophrenia patients, and nonpsychiatric subjects; these three types of neurons do not express RELN protein. In the same samples of temporal cortex, we found a decrease in RELN protein of ≈50% but no changes in DAB1 protein expression. We also observed a large (up to 70%) decrease of GAD67 but only a small decrease of GAD65 protein content. These findings are interpreted within a neurodevelopmental/vulnerability “two-hit” model for the etiology of schizophrenia.

744 citations

Journal ArticleDOI
TL;DR: Interactive software and database search strategies that facilitate the discovery of previously unknown cross specialty information of scientific interest are described and evaluated.

462 citations

Journal ArticleDOI
TL;DR: It is shown that a subset of conventional mammalian microRNAs is derived from LINE-2 transposable elements and other genome repeats, which are distinct from the rasiRNAs, which appear to be processed from long double-stranded RNA precursors.

319 citations

Journal ArticleDOI
01 Oct 2015-PLOS ONE
TL;DR: The findings warrant replication and follow-up with a larger cohort of patients and controls who have been carefully characterized in terms of cognitive and imaging data, other biomarkers and risk factors, and who are sampled repeatedly over time.
Abstract: To assess the value of exosomal miRNAs as biomarkers for Alzheimer disease (AD), the expression of microRNAs was measured in a plasma fraction enriched in exosomes by differential centrifugation, using Illumina deep sequencing. Samples from 35 persons with a clinical diagnosis of AD dementia were compared to 35 age and sex matched controls. Although these samples contained less than 0.1 microgram of total RNA, deep sequencing gave reliable and informative results. Twenty miRNAs showed significant differences in the AD group in initial screening (miR-23b-3p, miR-24-3p, miR-29b-3p, miR-125b-5p, miR-138-5p, miR-139-5p, miR-141-3p, miR-150-5p, miR-152-3p, miR-185-5p, miR-338-3p, miR-342-3p, miR-342-5p, miR-548at-5p, miR-659-5p, miR-3065-5p, miR-3613-3p, miR-3916, miR-4772-3p, miR-5001-3p), many of which satisfied additional biological and statistical criteria, and among which a panel of seven miRNAs were highly informative in a machine learning model for predicting AD status of individual samples with 83–89% accuracy. This performance is not due to over-fitting, because a) we used separate samples for training and testing, and b) similar performance was achieved when tested on technical replicate data. Perhaps the most interesting single miRNA was miR-342-3p, which was a) expressed in the AD group at about 60% of control levels, b) highly correlated with several of the other miRNAs that were significantly down-regulated in AD, and c) was also reported to be down-regulated in AD in two previous studies. The findings warrant replication and follow-up with a larger cohort of patients and controls who have been carefully characterized in terms of cognitive and imaging data, other biomarkers (e.g., CSF amyloid and tau levels) and risk factors (e.g., apoE4 status), and who are sampled repeatedly over time. Integrating miRNA expression data with other data is likely to provide informative and robust biomarkers in Alzheimer disease.

300 citations

Journal ArticleDOI
TL;DR: This work test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters.
Abstract: Background: We recently described “Author-ity,” a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. Methods: Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals. Results: Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ∼98.8p. Lumping (putting two different individuals into the same cluster) affects ∼0.5p of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ∼2p of articles. Impact: The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior. Availability: The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.

286 citations


Cited by
More filters
28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
Abstract: 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。

18,940 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: 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.).

13,246 citations

Journal ArticleDOI
TL;DR: The first direct evidence that miRNA genes are transcribed by RNA polymerase II (pol II) is presented and the detailed structure of a miRNA gene is described, for the first time, by determining the promoter and the terminator of mir‐23a∼27a‐24‐2.
Abstract: MicroRNAs (miRNAs) constitute a large family of noncoding RNAs that function as guide molecules in diverse gene silencing pathways. Current efforts are focused on the regulatory function of miRNAs, while little is known about how these unusual genes themselves are regulated. Here we present the first direct evidence that miRNA genes are transcribed by RNA polymerase II (pol II). The primary miRNA transcripts (pri‐miRNAs) contain cap structures as well as poly(A) tails, which are the unique properties of class II gene transcripts. The treatment of human cells with α‐amanitin decreased the level of pri‐miRNAs at a concentration that selectively inhibits pol II activity. Furthermore, chromatin immunoprecipitation analyses show that pol II is physically associated with a miRNA promoter. We also describe, for the first time, the detailed structure of a miRNA gene by determining the promoter and the terminator of mir‐23a∼27a∼24‐2 . These data indicate that pol II is the main, if not the only, RNA polymerase for miRNA gene transcription. Our study offers a basis for understanding the structure and regulation of miRNA genes.

4,304 citations

Journal ArticleDOI
TL;DR: This work has shown that the regulation of miRNA metabolism and function by a range of mechanisms involving numerous protein–protein and protein–RNA interactions has an important role in the context-specific functions of miRNAs.
Abstract: MicroRNAs (miRNAs) are a large family of post-transcriptional regulators of gene expression that are ~21 nucleotides in length and control many developmental and cellular processes in eukaryotic organisms. Research during the past decade has identified major factors participating in miRNA biogenesis and has established basic principles of miRNA function. More recently, it has become apparent that miRNA regulators themselves are subject to sophisticated control. Many reports over the past few years have reported the regulation of miRNA metabolism and function by a range of mechanisms involving numerous protein-protein and protein-RNA interactions. Such regulation has an important role in the context-specific functions of miRNAs.

4,123 citations