An anatomically comprehensive atlas of the adult human brain transcriptome

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography-the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.

Accumulation of deficits as a proxy measure of aging

This paper develops a method for appraising health status in elderly people. A frailty index was defined as the proportion of accumulated deficits (symptoms, signs, functional impairments, and laboratory abnormalities). It serves as an individual state variable, reflecting severity of illness and proximity to death. In a representative database of elderly Canadians we found that deficits accumulated at 3% per year, and show a gamma distribution, typical for systems with redundant components that can be used in case of failure of a given subsystem. Of note, the slope of the index is insensitive to the individual nature of the deficits, and serves as an important prognostic factor for life expectancy. The formula for estimating an individual's life span given the frailty index value is presented. For different patterns of cognitive impairments the average within-group index value increases with the severity of the cognitive impairment, and the relative variability of the index is significantly reduced. Finally, the statistical distribution of the frailty index sharply differs between well groups (gamma distribution) and morbid groups (normal distribution). This pattern reflects an increase in uncompensated deficits in impaired organisms, which would lead to illness of various etiologies, and ultimately to increased mortality. The accumulation of deficits is as an example of a macroscopic variable, i.e., one that reflects general properties of aging at the level of the whole organism rather than any given functional deficiency. In consequence, we propose that it may be used as a proxy measure of aging.

Update of the LIPID MAPS comprehensive classification system for lipids

In 2005, the International Lipid Classification and Nomenclature Committee under the sponsorship of the LIPID MAPS Consortium developed and established a "Comprehensive Classification System for Lipids" based on well-defined chemical and biochemical principles and using an ontology that is extensible, flexible, and scalable. This classification system, which is compatible with contemporary databasing and informatics needs, has now been accepted internationally and widely adopted. In response to considerable attention and requests from lipid researchers from around the globe and in a variety of fields, the comprehensive classification system has undergone significant revisions over the last few years to more fully represent lipid structures from a wider variety of sources and to provide additional levels of detail as necessary. The details of this classification system are reviewed and updated and are presented here, along with revisions to its suggested nomenclature and structure-drawing recommendations for lipids.

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.

Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database

OBJECTIVE

We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms.

DESIGN

Data collection and retrospective analysis.

SETTING

All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital.

PATIENTS

Adult patients admitted to intensive care units between 2001 and 2007.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%).

CONCLUSIONS

MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.

A systematic review and quantitative appraisal of fMRI studies of verbal fluency: role of the left inferior frontal gyrus

The left inferior frontal gyrus (LIFG) has consistently been associated with both phonologic and semantic operations in functional neuroimaging studies. Two main theories have proposed a different functional organization in the LIFG for these processes. One theory suggests an anatomic parcellation of phonologic and semantic operations within the LIFG. An alternative theory proposes that both processes are encompassed within a supramodal executive function in a single region in the LIFG. To test these theories, we carried out a systematic review of functional magnetic resonance imaging studies employing phonologic and semantic verbal fluency tasks. Seventeen articles meeting our pre-established criteria were found, consisting of 22 relevant experiments with 197 healthy subjects and a total of 41 peak activations in the LIFG. We determined 95% confidence intervals of the mean location (x, y, and z coordinates) of peaks of blood oxygenation level-dependent (BOLD) responses from published phonologic and semantic verbal fluency studies using the nonparametric technique of bootstrap analysis. Significant differences were revealed in dorsal-ventral (z-coordinate) localizations of the peak BOLD response: phonologic verbal fluency peak BOLD response was significantly more dorsal to the peak associated with semantic verbal fluency (confidence interval of difference: 1.9-17.4 mm). No significant differences were evident in antero-posterior (x-coordinate) or medial-lateral (y-coordinate) positions. The results support distinct dorsal-ventral locations for phonologic and semantic processes within the LIFG. Current limitations to meta-analytic integration of published functional neuroimaging studies are discussed.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery

Ruminants provide essential nutrition for billions of people worldwide. The rumen is a specialized stomach that is adapted to the breakdown of plant-derived complex polysaccharides. The genomes of the rumen microbiota encode thousands of enzymes adapted to digestion of the plant matter that dominates the ruminant diet. We assembled 4,941 rumen microbial metagenome-assembled genomes (MAGs) using approximately 6.5 terabases of short- and long-read sequence data from 283 ruminant cattle. We present a genome-resolved metagenomics workflow that enabled assembly of bacterial and archaeal genomes that were at least 80% complete. Of note, we obtained three single-contig, whole-chromosome assemblies of rumen bacteria, two of which represent previously unknown rumen species, assembled from long-read data. Using our rumen genome collection we predicted and annotated a large set of rumen proteins. Our set of rumen MAGs increases the rate of mapping of rumen metagenomic sequencing reads from 15% to 50-70%. These genomic and protein resources will enable a better understanding of the structure and functions of the rumen microbiota.

A review of methods and databases for metagenomic classification and assembly

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.

Adherence to oral migraine-preventive medications among patients with chronic migraine

BACKGROUND

Chronic migraine (CM) is a disabling disorder characterized by ≥15 headache days per month that has been shown to significantly reduce quality of life. Migraine-prevention guidelines recommend preventive medications as the standard of care for patients with frequent migraine. The aim of this study was to assess adherence to 14 commonly prescribed oral migraine-preventive medications (OMPMs) among patients with CM.

METHODS

Retrospective claims analysis of a US claim database (Truven MarketScan® Databases) was queried to identify patients who were at least 18 years old, diagnosed with CM, and initiated an OMPM (antidepressants, beta blockers, or anticonvulsants) between January 1, 2008 and September 30, 2012. Medication possession ratios (MPR) and proportion of days covered (PDC) were calculated for each patient. A cutoff of ≥80% was used to classify adherence. The odds of adherence between OMPMs were compared using logistic regression models.

RESULTS

Of the 75,870 patients identified with CM, 8688 met the inclusion/exclusion criteria. Adherence ranged between 26% to 29% at six months and 17% to 20% at 12 months depending on the calculation used to classify adherence (PDC and MPR, respectively). Adherence among the 14 OMPMs was similar except for amitriptyline, nortriptyline, gabapentin, and divalproex, which had significantly lower odds of adherence when compared to topiramate.

CONCLUSION

Adherence to OMPMs is low among the US CM population at six months and worsens by 12 months.

A joint NCBI and EMBL-EBI transcript set for clinical genomics and research

Comprehensive genome annotation is essential to understand the impact of clinically relevant variants. However, the absence of a standard for clinical reporting and browser display complicates the process of consistent interpretation and reporting. To address these challenges, Ensembl/GENCODE1 and RefSeq2 launched a joint initiative, the Matched Annotation from NCBI and EMBL-EBI (MANE) collaboration, to converge on human gene and transcript annotation and to jointly define a high-value set of transcripts and corresponding proteins. Here, we describe the MANE transcript sets for use as universal standards for variant reporting and browser display. The MANE Select set identifies a representative transcript for each human protein-coding gene, whereas the MANE Plus Clinical set provides additional transcripts at loci where the Select transcripts alone are not sufficient to report all currently known clinical variants. Each MANE transcript represents an exact match between the exonic sequences of an Ensembl/GENCODE transcript and its counterpart in RefSeq such that the identifiers can be used synonymously. We have now released MANE Select transcripts for 97% of human protein-coding genes, including all American College of Medical Genetics and Genomics Secondary Findings list v3.0 (ref. 3) genes. MANE transcripts are accessible from major genome browsers and key resources. Widespread adoption of these transcript sets will increase the consistency of reporting, facilitate the exchange of data regardless of the annotation source and help to streamline clinical interpretation.

Multitask learning and benchmarking with clinical time series data

Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Fetal Bovine Serum (FBS): Past - Present - Future

The supplementation of culture medium with fetal bovine serum (FBS, also referred to as "fetal calf serum") is still common practice in cell culture applications. Due to a number of disadvantages in terms of quality and reproducibility of in vitro data, animal welfare concerns, and in light of recent cases of fraudulent marketing, the search for alternatives and the development of serum-free medium formulations has gained global attention. Here, we report on the 3rd Workshop on FBS, Serum Alternatives and Serum-free Media, where regulatory aspects, the serum dilemma, alternatives to FBS, case-studies of serum-free in vitro applications, and the establishment of serum-free databases were discussed. The whole process of obtaining blood from a living calf fetus to using the FBS produced from it for scientific purposes is de facto not yet legally regulated despite the existing EU-Directive 2010/63/EU on the use of animals for scientific purposes. Together with the above-mentioned challenges, several strategies have been developed to reduce or replace FBS in cell culture media in terms of the 3Rs (Refinement, Reduction, Replacement). Most recently, releasates of activated human donor thrombocytes (human platelet lysates) have been shown to be one of the most promising serum alternatives when chemically-defined media are not yet an option. Additionally, new developments in cell-based assay techniques, advanced organ-on-chip and microphysiological systems are covered in this report. Chemically-defined serum-free media are shown to be the ultimate goal for the majority of culture systems, and examples are discussed.

Positive predictive value of the diagnosis of acute myocardial infarction in an administrative database

OBJECTIVE

To determine the positive predictive value of ICD-9-CM coding of acute myocardial infarction and cardiac procedures.

METHODS

Using chart-abstracted data as the standard, we examined administrative data from the Veterans Health Administration for a national random sample of 5,151 discharges.

MAIN RESULTS

The positive predictive value of acute myocardial infarction coding in the primary position was 96.9%. The sensitivity and specificity of coding were, respectively, 96% and 99% for catheterization, 95.7% and 100% for coronary artery bypass graft surgery, and 90.3% and 99. 7% for percutaneous transluminal coronary angioplasty.

CONCLUSIONS

The positive predictive value of acute myocardial infarction and related procedure coding is comparable to or better than previously reported observations of administrative databases.

Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics

The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.

Autotaxin is released from adipocytes, catalyzes lysophosphatidic acid synthesis, and activates preadipocyte proliferation. Up-regulated expression with adipocyte differentiation and obesity

Our group has recently demonstrated (Gesta, S., Simon, M., Rey, A., Sibrac, D., Girard, A., Lafontan, M., Valet, P., and Saulnier-Blache, J. S. (2002) J. Lipid Res. 43, 904-910) the presence, in adipocyte conditioned-medium, of a soluble lysophospholipase d-activity (LPLDact) involved in synthesis of the bioactive phospholipid lysophosphatidic acid (LPA). In the present report, LPLDact was purified from 3T3F442A adipocyte-conditioned medium and identified as the type II ecto-nucleotide pyrophosphatase phosphodiesterase, autotaxin (ATX). A unique ATX cDNA was cloned from 3T3F442A adipocytes, and its recombinant expression in COS-7 cells led to extracellular release of LPLDact. ATX mRNA expression was highly up-regulated during adipocyte differentiation of 3T3F442A-preadipocytes. This up-regulation was paralleled by the ability of newly differentiated adipocytes to release LPLDact and LPA. Differentiation-dependent up-regulation of ATX expression was also observed in a primary culture of mouse preadipocytes. Treatment of 3T3F442A-preadipocytes with concentrated conditioned medium from ATX-expressing COS-7 cells led to an increase in cell number as compared with concentrated conditioned medium from ATX non-expressing COS-7 cells. The specific effect of ATX on preadipocyte proliferation was completely suppressed by co-treatment with a LPA-hydrolyzing phospholipase, phospholipase B. Finally, ATX expression was found in mature adipocytes isolated from mouse adipose tissue and was substantially increased in genetically obese-diabetic db/db mice when compared with their lean siblings. In conclusion, the present work shows that ATX is responsible for the LPLDact released by adipocytes and exerts a paracrine control on preadipocyte growth via an LPA-dependent mechanism. Up-regulations of ATX expression with adipocyte differentiation and genetic obesity suggest a possible involvement of this released protein in the development of adipose tissue and obesity-associated pathologies.

Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing

Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended.

IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.

Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

Data-Driven Materials Science: Status, Challenges, and Perspectives

Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

Falls among adult patients hospitalized in the United States: prevalence and trends

OBJECTIVES

The purpose of this study was to provide normative data on fall prevalence in U.S. hospitals by unit type and to determine the 27-month secular trend in falls before the implementation of the Centers for Medicare and Medicaid Service (CMS) rule, which does not reimburse hospitals for care related to injury resulting from hospital falls.

METHODS

We used data from the National Database of Nursing Quality Indicators (NDNQI) collected between July 1, 2006, and September 30, 2008, to estimate prevalence and secular trends of falls occurring in adult medical, medical-surgical, and surgical nursing units. More than 88 million patient days (pd) of observation were contributed from 6100 medical, surgical, and medical-surgical nursing units in 1263 hospitals across the United States.

RESULTS

A total of 315,817 falls occurred (rate = 3.56 falls/1000 pd) during the study period, of which, 82,332 (26.1%) resulted in an injury (rate = 0.93/1000 pd). Both total fall and injurious fall rates were highest in medical units (fall rate = 4.03/1000 pd; injurious fall rate = 1.08/1000 pd) and lowest in surgery units (fall rate = 2.76/1000 pd; injurious fall rate = 0.67/1000 pd). Falls (0.4% decrease per quarter, P < 0.0001) and injurious falls (1% decrease per quarter, P < 0.0001) both decreased over the 27-month study.

CONCLUSIONS

In this large sample, fall and injurious fall prevalence varied by nursing unit type in U.S. hospitals. Over the 27-month study, there was a small, but statistically significant, decrease in falls (P < 0.0001) and injurious falls (P < 0.0001).

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

abYsis: Integrated Antibody Sequence and Structure-Management, Analysis, and Prediction

abYsis is a web-based antibody research system that includes an integrated database of antibody sequence and structure data. The system can be interrogated in numerous ways-from simple text and sequence searches to sophisticated queries that apply 3D structural constraints. The publicly available version includes pre-analyzed sequence data from the European Molecular Biology Laboratory European Nucleotide Archive (EMBL-ENA) and Kabat as well as structure data from the Protein Data Bank. A researcher's own sequences can also be analyzed through the web interface. A defining characteristic of abYsis is that the sequences are automatically numbered with a series of popular schemes such as Kabat and Chothia and then annotated with key information such as complementarity-determining regions and potential post-translational modifications. A unique aspect of abYsis is a set of residue frequency tables for each position in an antibody, allowing "unusual residues" (those rarely seen at a particular position) to be highlighted and decisions to be made on which mutations may be acceptable. This is especially useful when comparing antibodies from different species. abYsis is useful for any researcher specializing in antibody engineering, especially those developing antibodies as drugs. abYsis is available at www.abysis.org.

How to prepare a systematic review of economic evaluations for informing evidence-based healthcare decisions: a five-step approach (part 1/3)

INTRODUCTION

Systematic reviews of economic evaluations are useful for synthesizing economic evidence about health interventions and for informing evidence-based decisions. Areas covered: As there is no detailed description of the methods for performing a systematic review of economic evidence, this paper aims to provide an overview of state-of-the-art methodology. This is laid out in a 5-step approach, as follows: step 1) initiating a systematic review; step 2) identifying (full) economic evaluations; step 3) data extraction, risk of bias and transferability assessment; step 4) reporting results; step 5) discussion and interpretation of findings. Expert commentary: The paper aims to help inexperienced reviewers and clinical practice guideline developers, but also to be a resource for experts in the field who want to check on current methodological developments.