The Detection of Signals in Noise: A Comparison Between the Human Detector and an Electronic Detector

Control Systems Laboratory changed its name to Coordinated Science LaboratoryContract DA-36-039-SC-5669

Absence of N addition facilitates B cell development, but impairs immune responses

The programmed, stepwise acquisition of immunocompetence that marks the development of the fetal immune response proceeds during a period when both T cell receptor and immunoglobulin (Ig) repertoires exhibit reduced junctional diversity due to physiologic terminal deoxynucleotidyl transferase (TdT) insufficiency. To test the effect of N addition on humoral responses, we transplanted bone marrow from TdT-deficient (TdT−/−) and wild-type (TdT+/+) BALB/c mice into recombination activation gene 1-deficient BALB/c hosts. Mice transplanted with TdT−/− cells exhibited diminished humoral responses to the T-independent antigens α-1-dextran and (2,4,6-trinitrophenyl) hapten conjugated to AminoEthylCarboxymethyl-FICOLL, to the T-dependent antigens NP19CGG and hen egg lysozyme, and to Enterobacter cloacae, a commensal bacteria that can become an opportunistic pathogen in immature and immunocompromised hosts. An exception to this pattern of reduction was the T-independent anti-phosphorylcholine response to Streptococcus pneumoniae, which is normally dominated by the N-deficient T15 idiotype. Most of the humoral immune responses in the recipients of TdT−/− bone marrow were impaired, yet population of the blood with B and T cells occurred more rapidly. To further test the effect of N-deficiency on B cell and T cell population growth, transplanted TdT-sufficient and -deficient BALB/c IgMa and congenic TdT-sufficient CB17 IgMb bone marrow were placed in competition. TdT−/− cells demonstrated an advantage in populating the bone marrow, the spleen, and the peritoneal cavity. TdT deficiency, which characterizes fetal lymphocytes, thus appears to facilitate filling both central and peripheral lymphoid compartments, but at the cost of altered responses to a broad set of antigens

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Basic concepts of physics

xi, 410 p.1. Vectors; 2. Differential equations; 3. Classical mechanics; 4. Relativity; 5. Electricity; 6. Quantum mechanics; 7. Statistical mechanic

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press