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BackgroundSpatial transcriptomic technologies are powerful tools for resolving the spatial heterogeneity of gene expression in tissue samples. However, little evidence exists on relative strengths and weaknesses of the various available technologies for profiling human tumour tissue. In this study, we aimed to provide an objective assessment of two common spatial transcriptomics platforms, 10X Genomics’ Visium and Nanostring’s GeoMx DSP.MethodThe abilities of the DSP and Visium platforms to profile transcriptomic features were compared using matching cell line and primary breast cancer tissue samples. A head-to-head comparison was conducted using data generated from matching samples and synthetic tissue references. Platform specific features were also assessed according to manufacturers’ recommendations to evaluate the optimal usage of the two technologies.ResultsWe identified substantial variations in assay design between the DSP and Visium assays such as transcriptomic coverage and composition of the transcripts detected. When the data was standardised according to manufacturers’ recommendations, the DSP platform was more sensitive in gene expression detection. However, its specificity was diminished by the presence of non-specific detection. Our results also confirmed the strength and weakness of each platform in characterising spatial transcriptomic features of tissue samples, in particular their application to hypothesis generation versus hypothesis testing.ConclusionIn this study, we share our experience on both DSP and Visium technologies as end users. We hope this can guide future users to choose the most suitable platform for their research. In addition, this dataset can be used as an important resource for the development of new analysis tools.

A connection and a yearning towards a communal and primarily agrarian existence connects the Rastafari the world over. Many Rastafari yearn for a life in direct communication with the earth through agricultural labor in a space created for and by the community, a space “up in the hills” away from the pollutants of a contaminated, corrupting Babylonian society. This was no less the case amongst those in Saint Lucia with whom I conducted recent fieldwork. Different from previous conversations on this topic however, was a new context defined by a global pandemic and a subsequent widely mandated social withdrawal. This engendered a dual response of envisioning rural flight as now more urgent and, in some cases, as a necessary response to COVID-19.

Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).

Background Trigger finger and carpal tunnel syndrome are the two most common non-traumatic connective tissue disorders of the hand. Both of these conditions frequently co-occur, often in patients with rheumatoid arthritis. However, this phenotypic association is poorly understood. Hypothesising that the co-occurrence of trigger finger and carpal tunnel syndrome might be explained by shared germline predisposition, we aimed to identify a specific genetic locus associated with both diseases. Methods In this genome-wide association study (GWAS), we identified 2908 patients with trigger finger and 436 579 controls from the UK Biobank prospective cohort. We conducted a case-control GWAS for trigger finger, followed by co-localisation analyses with carpal tunnel syndrome summary statistics. To identify putative causal variants and establish their biological relevance, we did fine-mapping analyses and expression quantitative trait loci (eQTL) analyses, using fibroblasts from healthy donors (n=79) and tenosynovium samples from patients with carpal tunnel syndrome (n=77). We conducted a Cox regression for time to trigger finger and carpal tunnel syndrome diagnosis against plasma IGF-1 concentrations in the UK Biobank cohort. Findings Phenome-wide analyses confirmed a marked association between carpal tunnel syndrome and trigger finger in the participants from UK Biobank (odds ratio [OR] 11·97, 95% CI 11·1–13·0; p−300). GWAS for trigger finger identified five independent loci, including one locus, DIRC3, that was co-localised with carpal tunnel syndrome and could be fine-mapped to rs62175241 (0·76, 0·68–0·84; p=5·03 × 10−13). eQTL analyses found a fibroblast-specific association between the protective T allele of rs62175241 and increased DIRC3 and IGFBP5 expression. Increased plasma IGF-1 concentrations were associated with both carpal tunnel syndrome and trigger finger in participants from UK Biobank (hazard ratio >1·04, p Interpretation In this GWAS, the DIRC3 locus on chromosome 2 was significantly associated with both carpal tunnel syndrome and trigger finger, possibly explaining their co-occurrence. The disease-protective allele of rs62175241 was associated with increased expression of long non-coding RNA DIRC3 and its transcriptional target, IGBP5, an antagonist of IGF-1 signalling. These findings suggest a model in which IGF-1 is a driver of both carpal tunnel syndrome and trigger finger, and in which the DIRC3-IGFBP5 axis directly antagonises fibroblastic IGF-1 signalling.

Integrated, data-driven health management provides a roadmap to personalized and predictive healthcare. In this issue of Cell Systems, Marabita et al. showcase the application of data-driven, individualized lifestyle coaching to promote health and present an interpretable view of human health by integrating deep molecular, digital health, and clinical data.

Baranauskas, Marissa N., Joseph Powell, Alyce D. Fly, Bruce J. Martin, Timothy D. Mickleborough, Hunter L. Paris, and Robert F. Chapman. Influence of zinc on the acute changes in erythropoietin and proinflammatory cytokines with hypoxia.

The Rastafari movement stands as one of the most instantly recognizable and socially influential spiritual groups of the last century. Having successfully crossed into the realm of popular culture ...

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

Recent innovations in droplet-based single-cell RNA-sequencing (scRNA-seq) have provided the technology necessary to investigate biological questions at cellular resolution. With the ability to assay thousands of cells in a single capture, pooling cells from multiple individuals has become a common strategy. Droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences, and numerous computational methods have been developed to address this problem. However, another challenge implicit with droplet-based scRNA-seq is the occurrence of doublets - droplets containing two or more cells. The inaccurate assignment of cells to individuals or failure to remove doublets contribute unwanted noise to the data and result in erroneous scientific conclusions. Therefore, it is essential to assign cells to individuals and remove doublets accurately. We present a new framework to improve individual singlet classification and doublet removal through a multi-method intersectional approach.We developed a framework to evaluate the enhancement in donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. The accuracy was assessed using scRNA-seq data of ∼1.4 million peripheral blood mononucleated cells from 1,034 unrelated individuals and ∼90,000 fibroblast cells from 81 unrelated individuals. We show that our approach significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual compared to any single method. We show that the best combination of techniques varies under different biological and experimental conditions, and we present a framework to optimise cell assignment for a given experiment. We offer Demuxafy (https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html) - a framework built-in Singularity to provide clear, consistent documentation of each method and additional tools to simplify and improve demultiplexing and doublet removal. Our results indicate that leveraging multiple demultiplexing and doublet detecting methods improves accuracy and, consequently, downstream analyses in multiplexed scRNA-seq experiments.

Cross-domain data analysis is arguably the most important part of oilfield data analytics. While it enables holistic process optimization, it is also challenging to execute. Data are often scattered across different databases making it complex to join and may require multidomain expertise to properly analyze. Here, processing and analysis of data collected by three well construction business lines of the same service company were performed to establish a link between drilling fluid properties and drilling performance.The data engineering workflow starts by taking information from a single service company and combining that information about drilling operations, drill bits, and drilling fluids into a single dataset. Metadata including locations, operators, and wells are then mapped, and overlapping attributes are unified and reconciled. Data is further processed to extract relevant drilling performance metrics and drilling fluid properties and then labeled by well, section, and drilling run. The resultant data workflow enables detailed analysis, focusing on particular locations, drilling practices, hole conditions, and fluids.The joined, cleaned, and processed dataset includes information from thousands of wells drilled globally since 2016. The datasets from different sources differ in the level of detail, but are complementary to each other, providing a broader picture when merged. The data is organized and visualized on dashboards, enabling in-depth analysis through intuitive filtering on a variety of conditions. These conditions may include location, drilling run type, depth, used drill bits and tools, and drilling fluid type and properties. The main drilling performance metrics are distance drilled per run and run duration. These are used to calculate the run average rate of penetration (ROP). Reasons for pulling out of the hole (POOH) and risks for POOH are extracted from text comments of the daily drilling reports. This enables the tracking of abnormal run terminations due to drilling tool failures. It also enables tracking of wellbore integrity, and substandard drilling and hole conditioning practices, especially at section total depth (TD) or because of drilling fluid issues. Aggregated metrics of minimum, maximum, and median are used for high-level data evaluation. Statistical significance of effects and causality are analyzed in detail on selected cases. Based on the data, several examples of such analyses are created that focus on the effects of water-based fluid vs. oil-based fluid, on drilling performance in the major oil fields in the United States.Holistic analysis of the effects of drilling fluids on drilling performance becomes possible through the well construction cross-domain data fusion. The developed workflow enables analysis of drilling fluid-related big data, covering tens of thousands of wells globally. The analysis results are expected to improve drilling efficiency and reliability and ultimately reduce operators' total well expenditures.

Background and ObjectivesNarrative-based evaluations are increasingly used to discriminate between levels of trainee performance, yet barriers to high-quality narratives remain. Prior evidence shows mixed results regarding the effectiveness of faculty development efforts on improving narrative evaluation quality.MethodsWe used a quasi-experimental study incorporating a historical control group to examine the effectiveness of a pragmatic, multipronged, 4-year faculty development initiative on narrative evaluation quality in a neurology clerkship. We evaluated narrative evaluation quality using the narrative evaluation quality instrument (NEQI) in random samples of narrative evaluations from a historical control and intervention group. We used multilevel modeling to compare NEQI scores (and subscale scores) across groups. Informed by the theory of deliberate practice, our faculty development initiative included (1) annual grand rounds sessions focused on developing high-quality narratives and reporting evaluation metrics, (2) restructuring the clerkship assessment form to simplify and prioritize narratives, (3) recruiting key faculty to rotate on the clerkship grading committee to gain experience with and practice developing quality narratives, and (4) instituting a narrative evaluation excellence award to faculty and residents.ResultsThe faculty development initiative was associated with improvements in the quality of students' narrative evaluations. Specifically, the intervention group was a significant predictor of NEQI score, with means of 6.4 (95% CI 5.9–6.9) and 7.6 (95% CI 7.2–8.1) for the historical control and intervention groups, respectively. In addition, the intervention group was associated with significant improvement in the specificity and usefulness NEQI subscale scores, but not the performance domain subscale score.DiscussionA long-term, multipronged faculty development initiative can facilitate improvements in narrative evaluation quality. We attribute these findings to 2 factors: (1) pragmatic, solution-oriented efforts that balance focused didactics with programmatic shifts that promote deliberate practice and skill improvement and (2) departmental resources that prioritize and convey a commitment to improving trainee assessment.

This paper presents attacks targeting the FPGAs of AWS F1 instances at the electrical level through power-hammering, where excessive dynamic power is used to crash FPGA instances. We demonstrate different power-hammering attacks that pass all AWS security fences implemented on F1 instances, including the FPGA vendor design rule checks. In addition, we fingerprint the FPGA instances to observe the responsiveness of the instances, which indicates a successful denial-of-service attack. Most importantly, we provide an FPGA virus scanner framework, which was improved to support large datacenter FPGAs for preventing such attacks, including virtually all currently demonstrated side-channel attacks. Our experiments showed that an AWS F1 instance crashes immediately by starting an FPGA design demanding 369W. By using FPGA-fingerprinting, we found that crashed instances are unavailable for about one to over 200 hours.

Background Colorectal cancer (CRC) is one of the leading causes of cancer-related death. Early detection is critical to reduce CRC morbidity and mortality. In order to meet this need, we developed a molecular clamping assay called the ColoScape TM assay for early colorectal cancer diagnostics. Methods Nineteen mutations in four genes (APC, KRAS, BRAF and CTNNB1) associated with early events in CRC pathogenesis are targeted in the ColoScapeTM assay. Xenonucleic Acid (XNA)-mediated qPCR clamping technology was applied to minimize the wild-type background amplification in order to improve assay sensitivity of CRC mutation detection. The assay analytical performance was verified and validated, cfDNA and FFPE CRC patient samples were evaluated, and an ROC curve was applied to evaluate its performance. Results The data showed that the assay analytical sensitivity was 0.5% Variant Allele Frequency, corresponding to ~7–8 copies of mutant DNA with 5 ng total DNA input per test. This assay is highly reproducible with intra-assay CV of Conclusions The XNA-mediated molecular clamping assay is a rapid, precise, and sensitive assay for the detection of precancerous lesions cfDNA and CRC cfDNA or FFPE samples.

To determine if adrenocortical gene expression is associated with clinical outcomes or response to corticosteroid treatment in septic shock.A pre-specified nested cohort study of a randomised controlled trial of hydrocortisone compared to placebo in septic shock. Blood was collected for RNAseq analysis prior to treatment with hydrocortisone or placebo. The expression of adrenocortical candidate genes related to pituitary releasing hormones, mineralocorticoid and glucocorticoid receptors, intracellular glucocorticoid metabolism and transport proteins was measured.From May 2014 to April 2017, 671 patients were enrolled in the nested cohort study, from which 494 samples were available for analysis. We found no evidence of an association between candidate gene expression levels and either 90-day mortality, 28-day mortality or time to shock reversal. We observed evidence of a significant interaction between expression and treatment group for time to shock reversal in two genes; GLCCI1 (HR 3.81, 95%CI 0.57-25.47 vs. HR 0.64, 95%CI 0.13-3.07 for hydrocortisone and placebo respectively, p for interaction 0.008) and BHSD1 (HR 0.55, 95%CI 0.28-1.09 vs. HR 1.32 95%CI 0.67-2.60, p for interaction 0.01).In patients with septic shock, there is no association between adrenocortical candidate gene expression and mortality. Patients with higher expression of GLCCI1 who received hydrocortisone achieved shock resolution faster than those receiving placebo; conversely, patients who had higher expression of BHSD1 who received hydrocortisone achieved shock resolution slower than those who received placebo. Variation in gene expression may be a mechanism for heterogeneity of treatment response to corticosteroids in septic shock.

With FPGAs now being deployed in the cloud and at the edge, there is a need for scalable design methods that can incorporate the heterogeneity present in the hardware and software components of FPGA systems. Moreover, these FPGA systems need to be maintainable and adaptable to changing workloads while improving accessibility for the application developers. However, current FPGA systems fail to achieve modularity and support for multi-tenancy due to dependencies between system components and the lack of standardised abstraction layers. To solve this, we introduce a modular FPGA operating system – FOS, which adopts a modular FPGA development flow to allow each system component to be changed and be agnostic to the heterogeneity of EDA tool versions, hardware and software layers. Further, to dynamically maximise the utilisation transparently from the users, FOS employs resource-elastic scheduling to arbitrate the FPGA resources in both time and spatial domain for any type of accelerators. Our evaluation on different FPGA boards shows that FOS can provide performance improvements in both single-tenant and multi-tenant environments while substantially reducing the development time and, at the same time, improving flexibility.

BACKGROUND: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.METHODS: We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.RESULTS: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.CONCLUSIONS: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

As FPGAs are now offered on the cloud and widely used in critical applications (infrastructure, military, medical), this exposes many potential security issues in which attackers can deploy attacks remotely. This demo will take a closer look at malicious bitstreams and demo our FPGA bitstream virus scanner FPGADefender that can scan for signatures relating to malicious circuits and many forms of bitstream manipulations. Additionally, we show a deny-of-service power-hammering attack, which can serve as a template for hardware Trojans.

With the introduction of Zynq UltraScale+ MPSoCs equipped with powerful 64-bit ARM CPUs along with a 16nm UltraScale+ FPGA fabric in the same die, we can now build full hardware-software programmable systems and configure the FPGA with accelerators, as needed. Traditionally, accelerators had been developed offline using powerful servers running heavy lifting CAD toolchains. In this demo, we show a self-compilation system supporting a user-friendly Jupyter Notebook GUI and multi-tenancy use of the FPGA for educational purposes. From a user perspective, this system compiles accelerators at run-time directly on the ARM CPU without any involvement of the vendor tools. The final bitstreams then execute on the FPGA fabric using PR.

Introduction: Type 2 diabetes mellitus (T2DM) and hypertension (HTN) are considered strong risk factors for developing chronic kidney disease (CKD). Increased cardiorespiratory fitness (CRF) is associated with lower CKD risk. However, the CRF-CKD association in patients with T2DM and/or HTN has not been assessed.Methods: We identified 9,751 patients (age 58.6 + 10.1 years) with T2DM (N=1,444) or HTN (n=5,031) or both (n=3,276) prior to a maximal standardized exercise treadmill test (ETT) and no evidence of ischemia as indicated by the ETT. We established four CRF categories based on age-adjusted peak metabolic equivalents (METs) achieved: Least-Fit (4.6±1.2 METs; n=2,231); Low-Fit Fit (6.4±1.1 METs; n=2,693); Moderate-Fit (8.0±1.0 METs; n=2,432); and High-Fit (10.8±2.1 METs; n=2,395). We performed multivariable Cox Regression analyses to access the risk of CKD according to fitness. The models were adjusted for age, body mass index (BMI), traditional risk factors and medications. Results: During the median follow-up of 12.4 years, 1,118 patients developed CKD, accounting for 9.1 events/ 1,000 person-years of observation. The association between CRF and CKD was inverse and graded. The risk of CKD was 21% lower (Hazard Ratio [HR] 0.79; 95% confidence interval [CI] 0.77-0.81). When CRF categories were considered, the CKD risk was 44% lower for Moderate-Fit patients (HR 0.56; 95% CI 0.48-0.67) and 80% lower for High-Fit (HR 0.20; 95% CI 0.15-0.25). Similar findings were noted in patients with both T2DM and HTN. Conclusions: We noted an inverse and dose-response association between CRF and CKD incidence. The risk was attenuated significantly beyond a mean peak MET level of 8.0±1.0, suggesting that moderate increases in exercise capacity confers favorable health benefits in patients at high risk of developing CKD.

A variety of experimental and computational methods have been developed to demultiplex samples from pooled individuals in a single-cell RNA sequencing (scRNA-Seq) experiment which either require adding information (such as hashtag barcodes) or measuring information (such as genotypes) prior to pooling. We introduce scSplit which utilises genetic differences inferred from scRNA-Seq data alone to demultiplex pooled samples. scSplit also extracts a minimal set of high confidence presence/absence genotypes in each cluster which can be used to map clusters to original samples. Using a range of simulated, merged individual-sample as well as pooled multi-individual scRNA-Seq datasets, we show that scSplit is highly accurate and concordant with demuxlet predictions. Furthermore, scSplit predictions are highly consistent with the known truth in cell-hashing dataset. We also show that multiplexed-scRNA-Seq can be used to reduce batch effects caused by technical biases. scSplit is ideally suited to samples for which external genome-wide genotype data cannot be obtained (for example non-model organisms), or for which it is impossible to obtain unmixed samples directly, such as mixtures of genetically distinct tumour cells, or mixed infections. scSplit is available at: https://github.com/jon-xu/scSplit

Elevated C-reactive protein (CRP) concentrations in the blood are associated with acute and chronic infections and inflammation. Nevertheless, the functional role of increased CRP in multiple bacterial and viral infections as well as in chronic inflammatory diseases remains unclear. Here, we studied the relationship between CRP and gene expression levels in the blood in 491 individuals from the Estonian Biobank cohort, to elucidate the role of CRP in these inflammatory mechanisms. As a result, we identified a set of 1,614 genes associated with changes in CRP levels with a high proportion of interferon-stimulated genes. Further, we performed likelihood-based causality model selection and Mendelian randomization analysis to discover causal links between CRP and the expression of CRP-associated genes. Strikingly, our computational analysis and cell culture stimulation assays revealed increased CRP levels to drive the expression of complement regulatory protein CD59, suggesting CRP to have a critical role in protecting blood cells from the adverse effects of the immune defence system. Our results show the benefit of integrative analysis approaches in hypothesis-free uncovering of causal relationships between traits., Author summary Chronic inflammation is associated with chronic diseases, morbidity and mortality while lower base inflammation levels are thought to be predictive of healthy aging. Thus, to pursue a long and healthy lifespan, it is essential to understand the inflammatory regulatory mechanisms. To that end, we studied the functional role of C-reactive protein (CRP)–an inflammatory biomarker that is used to measure cardiovascular risk in clinical practice. There is evidence for a strong genetic component of elevated CRP levels but it is still unclear if it has a direct impact on the processes that lead to inflammatory diseases. In order to elucidate the function of CRP in the blood, we used statistical methods for causal inference to infer causal relationships between changes in CRP and gene expression levels. Our statistical analysis and cell culture experiments suggest that CRP drives the expression of complement regulatory protein CD59. Thus, CRP can have a functional role in protecting human blood cells from the adverse effects of the immune defence system.

A training program to teach counseling and interviewing skills to paraprofessionals is described. The content includes conveying an open invitation to talk, reflective responding, problem solving, giving feedback, and setting goals. The training model includes five stages. Four factors that probably contributed to the effectiveness of training are discussed. (Author)