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Prior research has mainly examined the effect of social exclusion on individuals' interactions with other people or on their product choices as an instrument to facilitate interpersonal connection. The current research takes a novel perspective by proposing that socially excluded consumers would be more motivated to establish a relationship with a brand (rather than using the brand to socially connect with other people) when the brand exhibits human-like features. Based on this premise, we predict and find support in three studies that socially excluded consumers, compared with non-excluded consumers, exhibit greater preference for anthropomorphized brands (studies 1–3). This effect is mediated by consumers' need for social affiliation and is moderated by the opportunity for social connection with other people (study 2). Furthermore, socially excluded consumers differ in the types of relationships they would like to build with anthropomorphized brands, depending on their attributions about the exclusion. Specifically, consumers who blame themselves (others) for being socially excluded show greater preference for anthropomorphized partner (fling) brands (study 3).

Though usually construed in terms of defective or recalcitrant agency (not doing what should be done), Hamlet’s delay can be freshly illumined by considering it in terms of patiency: the liability to be affected in various ways. Like the Ghost, Hamlet’s patiency involves a process of purgation—not of sin, as with the Ghost, but of a way of thinking. The purgative process is challenged by the penetrative violence of a countervailing process—the speech of others, whose words, entering the ears of auditors like the poison poured into the ear of the sleeping King, profoundly influence and disrupt their thoughts. The purgation of Hamlet involves a multistage development whereby new cognitive characteristics displace or coexist alongside old ones, in one of the most subtle, elusive, and consequential mental evolutions depicted in drama. Highlights of this essay’s explication include (a) the motif of the secondary ghost, (b) the striking interrelationships between the scene in Ophelia’s closet and the narrated scene aboard the ship bound for England, (c) the removal of the arras that the revenge morality hangs between act and consequence, and (d) the recasting of the notion of agency such that inaction, not action, facilitates the achievement of the agent’s ends.

PREMISE OF THE STUDY: The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation. METHODS: Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated alíele frequencies with climate gradients using single-locus and multivariate models. RESULTS: The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%. CONCLUSIONS: Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change.

Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy. Human leukocyte antigen loss of heterozygosity (HLA LOH) is an important mechanism of immune escape in patients with cancer. Here the authors design and validate a machine learning algorithm with subclonal sensitivity for the identification of HLA LOH from paired tumor-normal sequencing data.

Kawasaki disease (KD) is the most common acquired pediatric heart disease. We analyzed Whole Genome Sequences (WGS) from a 6-member African American family in which KD affected two of four children. We sought rare, potentially causative genotypes by sequentially applying the following WGS filters: sequence quality scores, inheritance model (recessive homozygous and compound heterozygous), predicted deleteriousness, allele frequency, genes in KD-associated pathways or with significant associations in published KD genome-wide association studies (GWAS), and with differential expression in KD blood transcriptomes. Biologically plausible genotypes were identified in twelve variants in six genes in the two affected children. The affected siblings were compound heterozygous for the rare variants p.Leu194Pro and p.Arg247Lys in Toll-like receptor 6 (TLR6), which affect TLR6 signaling. The affected children were also homozygous for three common, linked (r2 = 1) intronic single nucleotide variants (SNVs) in TLR6 (rs56245262, rs56083757 and rs7669329), that have previously shown association with KD in cohorts of European descent. Using transcriptome data from pre-treatment whole blood of KD subjects (n = 146), expression quantitative trait loci (eQTL) analyses were performed. Subjects homozygous for the intronic risk allele (A allele of TLR6 rs56245262) had differential expression of Interleukin-6 (IL-6) as a function of genotype (p = 0.0007) and a higher erythrocyte sedimentation rate at diagnosis. TLR6 plays an important role in pathogen-associated molecular pattern recognition, and sequence variations may affect binding affinities that in turn influence KD susceptibility. This integrative genomic approach illustrates how the analysis of WGS in multiplex families with a complex genetic disease allows examination of both the common disease-common variant and common disease-rare variant hypotheses.

BackgroundComprehensive profiling of both the tumor and tumor microenvironment (TME) can help further our understanding of tumor progression and response to treatment. Many immune features can be extracted from transcriptomic data, including characterization of the immune infiltrate and profiling the diversity of immune receptors. To address this, we have developed multiple TME profiling features as part of the ImmunoID NeXT Platform®, an augmented, immuno-oncology-optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and TME from a single FFPE tumor sample. These features including quantification of immune cell infiltration and profiling of the T-cell receptor (TCR) and B-cell receptor (BCR).MethodsTo develop our immune infiltrate quantification method, we profiled the transcriptomes of eight purified immune cell types using ImmunoID NeXT™ to develop platform-specific gene sets, and compared our transcriptome quantification to immune cell quantification with IHC. For TCR and BCR methods, we analyzed the reproducibility of clone results, and compared top clones to standalone TCR and BCR sequencing approaches. In addition, we characterized the immune content of over 800 tumor samples across 14 cancer types. Finally, we analyzed the immune features in a cohort of melanoma patients who underwent PD-1 blockade.ResultsWe observe significant concordances between cell fractions by IHC and ImmunoID NeXT’s transcriptome-based scores in tumor FFPE samples for B cells, CD8+ T cells, and macrophages (R2>0.82, R2>0.75, and R2>0.52, respectively). For TCR and BCR methods, abundances of clones shared between subsequent curls of a tumor FFPE sample have very high concordances (R2>0.89, R2>0.92, and R2>0.76 for TRB, IgG, and IgA, respectively). Compared to the standalone approaches, we identify 100% of the top 500 TRB clones and 95% of the top 500 IgG clones, with highly concordant abundances (R2>0.94 and R2>0.82 for TRB and IgG, respectively) in a PBMC sample. We identify biologically-relevant immune signatures across tumor types by characterizing the immune features across over 800 tumor samples. Finally, in a melanoma cohort, TRB clonality and CD8+ T cell scores are significantly different in responders to checkpoint inhibition.ConclusionsRNA sequencing can be used as a scalable approach to profile the immune composition in tumors. Such analysis can add to our understanding of the tumor-immune interaction, including studies of response to immunotherapy. We show that immune infiltrate quantification and TCR and BCR profiling – all part of the ImmunoID NeXT Platform – are able to accurately and effectively evaluate the composition and diversity of tumor-infiltrating immune cells.

In several studies, the authors examine the potential to leverage a consumer's moral identity to enhance brand and company identification and promote goodwill through community relations. Studies 1a and 1b show that even when opportunity costs are equivalent (subjectively or economically), consumers who also have a highly self-important moral identity perceive the act of giving time versus money as more moral and self-expressive. The authors extend these findings to self-reported preferences and establish boundary conditions in two additional studies. Consumers with higher organizational status prefer to give money versus time, but this preference is weaker for those with a highly self-important moral identity (Study 2), and the preference for giving time versus money is more likely to emerge when the moral self is primed and the time given has a moral purpose (Study 3).

We have previously reported an association between ABCB1 C3435T polymorphism and docetaxel pharmacokinetics in breast cancer patients. We therefore investigated whether these parameters could account for variations in pathological response. Five ABCB1 polymorphisms including C3435T polymorphism were analyzed in breast cancer patients receiving neoadjuvant chemotherapy with doxorubicin and docetaxel ( n  = 101). Pathological response was assessed using the Sataloff classification. Pharmacokinetic analysis was performed for the first course of docetaxel ( n  = 84). No significant association was found between ABCB1 polymorphisms or docetaxel pharmacokinetics and pathological complete response. C3435T genotype was an independent predictive factor of good response in breast (response >50 %, i.e., Sataloff T-A and T-B): OR: 4.6 (95 % CI: 1.3–16.1), p  = 0.015, for TT patients versus CT and CC patients. Area under the plasma concentration–time curve (AUC) of docetaxel was the only independent predictive factor of the total absence of response in breast (Sataloff T-D): OR: 14.3, (95 % CI: 1.7–118), p  = 0.015, for AUC of docetaxel <3,500 μg h/L versus ≥3,500 μg h/L. These results suggest that C3435T polymorphism and docetaxel exposure are involved in the response to neoadjuvant chemotherapy in breast cancer patients and may be useful to optimize individualized therapy.

BackgroundHuman leukocyte antigen (HLA) genes facilitate communication between tumor cells and the immune system through the cell surface presentation of a diverse set of peptides. HLA loss of heterozygosity (LOH) has been associated with reduced immune pressure on neoantigens and impaired response to checkpoint blockade immunotherapy. Although HLA LOH is emerging as a key biomarker for response to immunotherapy, few tools exist to detect HLA LOH. Moreover, the accuracy of these tools is not well understood due to lack of orthogonal validation approaches. Here, we briefly describe DASH (Deletion of Allele-Specific HLAs), an algorithm to detect HLA LOH from exome sequencing data, and present a three-pronged validation approach to assess its performance.MethodsIn-silico evaluation of the limit of detection (LOD) of DASH was performed by deeply sequencing a tumor-normal paired cell line with HLA LOH and mixing reads at different proportions to simulate variable tumor purity and clonality. Direct genomic validation was performed using digital PCR (dPCR) with allele-specific primers targeting both predicted kept and lost alleles in ten patient samples and one cell line. Quantitative immunopeptidomics was performed to compare peptides presented by HLA alleles in tumor cells and adjacent normal cells. The relative increase or decrease of peptide presentation per allele was estimated by predicting the binding of each peptide to the patient-specific alleles.ResultsDASH is a machine learning model built upon the HLA-enhanced ImmunoID NeXT Platform®. We validated the performance of DASH using three orthogonal approaches to better understand the factors driving sensitivity and specificity of the algorithm. Evaluation using cell line mixtures that simulate LOH at various dilutions helped establish the LOD of DASH. For fully clonal tumors, DASH had 100% sensitivity at all tumor purity levels above 8% and 100% specificity at tumor purity levels higher than 24%. Patient-specific and allele-specific dPCR assays provided sensitive, direct evidence of HLA LOH. All samples predicted to have HLA LOH by DASH with high confidence were confirmed by dPCR. Finally, a quantitative immunopeptidomics experiment in one patient with HLA LOH revealed a large decrease in the peptides presented by deleted alleles, revealing the functional implications of HLA LOH.ConclusionsHLA LOH detection methods need to be rigorously validated in order to be used as a clinical biomarker. Here, we introduced three methods to assess performance, demonstrated the strong predictive power of DASH, and highlighted the need to consider tumor purity in such assessments.