Negative Binomial Distribution Model Research Articles

Inferring single- and multi-species distributional aggregation using quadrat sampling

Ecologists often employ cost-effective strategies such as quadrat sampling for field-based biodiversity assessments. While the negative binomial distribution (NBD) is widely used to characterize quadrat sampling-derived biodiversity data in single-species settings, an equivalent model for accurately modeling community-level distributional aggregation levels, especially for quadrat sampling data, is still lacking. Here, we recommend the application of symmetric Dirichlet-multinomial (SDM) distribution to characterize aggregation patterns for single- or multi-species. Theoretically, we proved that the likelihood formulae for the SDM and NBD models were nearly equivalent, and consequently, the shape parameters of both models were almost identical. Numerical simulations demonstrated that the SDM model consistently outperformed the NBD model except for cases with large numbers of small-sized quadrats. Empirical tests using the SDM model showed that trees of the forest plot on the Barro Colorado Island shifted from aggregated to even and then back to aggregated again from 1982 to 2015 because of stand competition dynamics driven by tree mortality and recruitment balance. Functionally, the aggregation parameter of the SDM model reflects more purely this biological mechanism-driven aggregation trend, whereas classical nonparametric aggregation metrics failed. In summary, the proposed SDM can be an indispensable tool for inferring species- and community-level distributional aggregation patterns.

Can job stress, health status and risky driving behaviours predict the crash risk level of taxi drivers? New evidence from China

Despite statistics indicating that China has the world’s largest taxi industry, there exists limited research about the relationship between workplace health hazards and taxi driver occupational crashes. In this paper, a cross-sectional survey of taxi drivers in four typical Chinese cities was conducted, and data on their self-reported job stress, health status, and daily risky driving behaviours, together with crash involvement experience in the two years before the survey was collected. Three hypotheses were then developed, and they were verified via multivariate analysis of variance (MANOVA) that the seriousness of drivers’ health problems and the frequency of their daily risky driving behaviours could be the accurate predictor of their crash risk of taxi drivers. These factors were subsequently substituted in a bivariate negative binomial (BNB) distribution model to determine the joint rate of at-fault taxi drivers’ involvement in property-damage-only (PDO) and personal-injury (PI) crashes. The results offer some useful advice for policy development to decrease and prevent professional taxi drivers from causing severe traffic crashes.

Exploring the relationship of green investment and green innovation: Evidence from Chinese corporate performance

Companies are increasingly seeking the route of sustainable growth via green innovation in the wake of environmental catastrophe and global warming. Studying its mechanism of action on green innovation has significant consequences for the green and low-carbon transformation of firms as a green development pointer in the capital market. Based on the panel data of Chinese heavy pollution listed enterprises, we use the negative binomial distribution model to conduct a theoretical mechanism analysis and empirical data validation on this issue. We find that green investments significantly contribute to businesses' green innovation, and this effect is influenced by company age heterogeneity and information disclosure heterogeneity. Further mechanism analysis reveals that this effect works mainly through alleviating corporate financing constraints and reducing corporate agency costs. We provide recommendations for the growth of green innovation and the green low-carbon transformation of firms based on the research results.

Modeling of Parking Violations Using Zero-Inflated Negative Binomial Regression: A Case Study for Berlin

Parking violations cause numerous problems, thus affecting daily mobility. Nevertheless, there are no extensive statistics on illegal parking in Germany, implying that the causes of this misconduct are still unexplored. The objective of this paper is to present a count data modeling approach for parking violations based on video footage taken from the windshields of driving vehicles, incorporating spatial data from OpenStreetMap (OSM). The main benefit of this data source is the transferability of the data collection procedure just by installing a recording device in vehicles of municipal services like waste collection. Moreover, the data from OSM are freely available for all cities. To account for excess zero counts in the street segments, a zero-inflated negative binomial distribution model was used to explain the number of violations per 100 m. “Excess” zeros were modeled using the logit part of the model, whereas the remaining counts of parking violations were fitted by the negative binomial model. Much effort was made to present the results of the count data models in an interpretable way. The most intuitive way seemed to be predictions of parking violations per 100 m (incidence rate) for different settings. Incidence rates were predicted for variations in explanatory variables, holding all other variables constant. We found parking violations per 100 m to be highest in main shopping streets. In addition, free parking spaces negatively- and the number of points of interest (such as buildings, craft stores, and shops) positively affected illegal parking.

MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments.

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

Abstract P2-11-06: Differential gene expression patterns in healthy breast tissue exhibiting epigenetic age acceleration

Abstract Introduction: Breast tissue age, measured with DNA methylation-based estimates, is accelerated in healthy women compared with matched peripheral blood samples. While we have shown that the degree of acceleration in breast epigenetic age is related to hormonal factors including elevated body mass index and earlier age at menarche, no prior work has examined alterations in gene expression that accompany breast epigenetic aging. In this study, we examine gene expression patterns associated with epigenetic age acceleration in healthy breast using six epigenetic clocks. Methods: Using the AllPrep (Qiagen) DNA/RNA Kit, we extracted DNA and RNA from the same breast tissue specimens from 192 healthy women aged 19-90 years who donated breast tissue to the Susan G. Komen Tissue Bank at the Indiana University Simon Comprehensive Cancer Center. Transcriptome analysis was performed using the QuantSeq 3’MRNA-SeqFWD kit to generate RNA sequencing libraries. DNA methylation was quantified using the Illumina EPIC 850K array platform, and methylation age was estimated using beta-values from these experiments. Age acceleration is defined using the residuals of a linear regression of methylation age on chronologic age, and samples with positive residuals were characterized as exhibiting epigenetic age acceleration. Differential gene expression analysis was performed using DESeq2 bioconductor software to estimate variance-mean dependence in count data and using a negative binomial distribution model to test for differential gene expression in samples with accelerated epigenetic age in 6 epigenetic clocks: the Horvath pan-tissue clock, Hannum Age, Phenotypic Age, Grim Age, Skin and Blood clock, and the Epigenetic Pacemaker clock (EPM) , a newly developed clock that accounts for curvilinearity in methylation-based estimates of age over time. Results: Principal components analysis revealed that a large proportion of the variance in gene expression was explained by the first two components (PC1 8.6%, PC2 8.2%). Using a false discovery rate cutoff of 0.05, we found 17 (pan-tissue), 909 (Hannum), 3573 (Phenotypic), 1459 (Grim), 3081 (Skin and Blood), and 227 (EPM) genes differentially expressed with the presence of epigenetic age acceleration for each clock. Examining overlap of these genes revealed distinct groups of genes differentially expressed with acceleration in each clock, with no genes in common for all clocks, Phenotypic and Skin and Blood clocks sharing 1137 genes; Phenotypic, Grim, and Skin and Blood clocks sharing 726 genes; and few genes shared by 5 out of 6 clocks (41 genes in all clocks except pan-tissue, 5 genes in all clocks except EPM). Gene set enrichment analysis revealed activation of pathways involved in cellular respiration, oxidative phosphorylation, energy derivation, organic acid metabolic processes, and immune response, and suppression of genes involved in embryonic epithelial morphogenesis, epidermal cell differentiation, chromatin organization, and lipoprotein transport. Conclusion: We identified significant alterations in gene expression patterns that accompany breast epigenetic acceleration in healthy female breast, with distinct patterns observed for six epigenetic clocks examined. Pathways involved include epithelial morphogenesis and differentiation, potentially representing a mechanistic link between breast accelerated aging and carcinogenesis. Citation Format: Mary Elizabeth Sehl, Wenbin Guo, Colin Farrell, Natascia Marino, Jill E. Henry, Anna Maria Storniolo, Jeannette Papp, Jingyi Jessica Li, Matteo Pellegrini, Steve Horvath, Patricia A. Ganz. Differential gene expression patterns in healthy breast tissue exhibiting epigenetic age acceleration [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-11-06.

P-097: Preclinical validation of Ecto-5’ Nucleotidase (NT5E/CD73) as a novel immunotherapeutic target in Multiple Myeloma

<h3>Background</h3> The interaction of plasmacytoid dendritic cells (pDCs) with multiple myeloma (MM) cells, and T- or NK-cells in the bone marrow (BM) microenvironment triggers enhanced tumor growth, as well as inhibits innate and adaptive immune responses. In the present study, we analyzed the genetic changes in MM cells triggered by co-culture with pDCs using next generation sequencing (NGS), and identified that pDC-MM interactions induce metabolic enzyme Ecto-5' Nucleotidase (NT5E/CD73) in both pDCs and MM cells. CD73 has been implicated in cancer metabolism and immunosuppression via nucleotide metabolism pathway. <h3>Methods</h3> Purified MM patient pDCs (n=3) were co-cultured with MM cells (1pDC/5MM; n=3) for 48h, followed by separation of MM cells from pDCs by flow. Total RNA from MM cells was subjected to RNAseq analysis using Illumina NGS. Data were analyzed by VIPER workflow to generate differential expression. The log2FC (fold change) values in co-cultures vs MM alone, with a False Discovery Rate adjusted p value of <0.05, was considered significant. Cytotoxic T lymphocyte (CTL) assay: MM patient BM CD8+ T-cells were co-cultured with autologous pDCs (pDC:T/1:10) in the presence or absence of anti-CD73 Ab (0.5 µg/ml) for 3 days; pre-stained MM cells were added for 24h (10T:1MM), followed by FACS quantification of viable MM cells. <h3>Results</h3> A total of 9200 and 9250 genes were differentially expressed based on negative binomial distribution (DEseq2) and linear (Limma) RNA-seq models, respectively (p<0.05). MM cells cultured with (n=3) or without pDCs (n=3) clustered into two distinct groups, indicating contact-dependent transcriptional changes in MM cells after co-culture. pDC-MM interaction regulates several pathways including DNA replication/repair, cell cycle, and nucleotide metabolism. We showed that: 1) pDC-MM interactions induce transcription of adenosine-signaling pathway enzyme NT5E/CD73 in MM cells (1.34-fold increase after co-culture; p=0.0002); 2) Both pDCs and MM cells express CD73, and pDC-MM interactions further increase CD73 levels in MM cells (p=0.008); 3) pDC-MM interactions increase adenosine generation (5.5-fold), and importantly, CD73 blockade by anti-CD73 Ab decreases adenosine production (p=0.0167); 4) CD73 blockade activates MM pDCs, evidenced by the increased expression of CD40/CD83/CD86/HLA-DR on pDCs, and also restores pDCs ability to activate autologous T cells; 5) Targeting CD73 induces MM-specific CD8+ CTL activity against autologous MM cells; and 6) a combination of anti-CD73 Ab and TLR7 agonist triggers more potent MM-specific CD8+ CTL activity against autologous MM cells than either agent alone (%viable MM: anti-CD73 Ab:70%; TLR7 agonist:60%; combo:30%; p=0.009;n=7). <h3>Conclusions</h3> Our preclinical data therefore suggest that the therapeutic targeting of CD73, alone or in combination with TLR7 agonist, represents a promising novel strategy to restore host anti-MM immunity.

The unbearable lightness of buying

ABSTRACT Although marketers are increasingly asked to manage brands for the long term, it is difficult to do so when no clear picture exists of long-term brand buying. This study reports cumulative behavioural loyalty outcomes for 200 UK consumer-goods brands when observed in a five-year household panel of continuous reporters. We examine these brands in intervals from one to five years against NBD (Negative Binomial Distribution) model projections. Stationary brands attract over twice as many buyers in five years as they do in one. Of these buyers, 80% purchase the brand at a rate of once a year or less, yet contribute 40% to total sales, a Pareto ratio of just 60:20. For managers, this light buying is broadly predictable from NBD fittings to annual data, and implies a renewed emphasis on nudging the brand buying propensities of the whole market.

Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity

The Poisson regression model is widely used for count data. This model assumes equidispersion. In practice, equidispersion is seldom reflected in data. However, in real-life data, the variance usually exceeds the mean. This situation is known as overdispersion. Negative binomial distribution and other Poisson mix models are often used to model overdispersion count data. Another extension of the negative binomial distribution in another model for count data is the univariate generalized Waring. In addition, the model developed by Famoye can be used in the analysis of count data. When the count data contains a large number of zeros, it is necessary to use zero-inflated models. In this study, different generalized regression models are emphasized for the analysis of excessive zeros count data. For this purpose, a real data set was analysed with the generalized Poisson model, generalized negative binomial model, generalized negative binomial Famoye, generalized Waring model, and the foregoing zero-inflated models. Log-likelihood, Akaike information criterion, Bayes information criterion, Vuong statistics were used for model comparisons.

Evaluation of 24-h screen deployments as a standardized platform to monitor Gambierdiscus populations in the Florida Keys and U.S. Virgin Islands

Anchored mesh screens have been suggested as a standardized approach to monitor the cell abundances of epiphytic dinoflagellates in benthic habitats, including toxigenic members of the Gambierdiscus genus responsible for ciguatera poisoning (CP). Here we deployed screens for 24h at eight sites in the Florida Keys and St. Thomas (US Virgin Islands) to evaluate their performance relative to the traditional method of assessing Gambierdiscus abundance in which cell counts are normalized to wet weight of host algae. The 30-month study (April 2013 – August 2015) involved monthly sampling at sites where screens were suspended at near-bottom locations for a 24h period and retrieved, with concurrent collections of macrophytes; including Halimeda, Laurencia, and Thalassia in the Florida Keys, and Dictyota in both regions. Gambierdiscus cells were identified and enumerated in the screen and macrophyte samples, and several regression techniques were evaluated (linear regression using untransformed and log-transformed data; negative binomial distribution (NBD) regression) to determine how well the screen-derived data could estimate algal cell concentrations on the host algae. In all cases, the NBD models performed the best based on Akaike Information Criteria values, although 38% of the regressions were not statistically-significant, including all of the St. Thomas sites. The r2 values were all < 0.75 and averaged 0.36, indicating relatively poor fit of the screen data. False negative results (regression models underestimating actual cell abundances) were common occurrences, ranging from 5 to 74% of the scenarios tested. In summary, these results indicate that 24h screen deployments do not appear to be consistent in all situations. Caution is therefore needed when considering 24h screens as a standardized monitoring approach for quantifying Gambierdiscus population dynamics across geography and ecosystems. Furthermore, neutral (artificial) substrates may not adequately capture either the host preference or palatability that likely influence the initial vector of toxin incorporation in the food web via herbivory on these macrophytes.

吉林蛟河阔叶红松林物种多度分布模型研究

以吉林蛟河阔叶红松林的木本植物为研究对象，将30hm²的样地面积划分为5m×5m，10m×10m，20m×20m，25m×25m的连续取样单元，在4个不同尺度下分别统计各物种在每个取样单元中的有无，得到每个物种在不同尺度下的取样单元数。利用随机分布模型和负二项分布模型分析物种的多度分布，对比预测多度与观测多度讨论两个模型的科学性与实用性。结果表明：对于阔叶红松林而言，负二项分布模型在所有研究尺度上的预测精度都要优于随机分布模型。随机分布和负二项分布的模型预测误差随着研究尺度的增大而增大，因此选取较小的取样单元可以切实提高物种多度的预测精度。利用随机分布和负二项分布模型对多度较小的物种进行预测的效果要优于多度较大的物种。负二项分布模型适合用来模拟阔叶红松林的物种多度分布格局，并且模型的拟合效果受取样单元大小影响。;Taking the broad-leaved pine forest in Jiaohe, Jilin as the research object, the sample plot area of 30 hm² was divided into continuous sampling units of 5 m×5 m, 10 m×10 m, 20 m×20 m and 25 m×25 m. The samples of each species in each sampling unit were counted under four different scales to obtain the number of sampling units of each species in different scales. The random distribution model and negative binomial distribution model were used to analyze the species abundance distribution, and the scientific and practical applicability of the two models were discussed by comparing the predicted abundance and observed abundance. The results showed that the prediction accuracy of the negative binomial distribution model was better than that of the random distribution model at all research scales for broad-leaved pine forests. The model prediction error of random distribution and negative binomial distribution increased with the increase of research scale, so choosing smaller sampling unit could improve the prediction accuracy of species abundance. The random distribution and negative binomial distribution models were more effective than the ones with greater abundance in predicting species. The negative binomial distribution model is suitable for simulating the species abundance distribution pattern of broad-leaved Korean pine forest and the fitting effect of the model is affected by the size of the sampling unit.

Using Non-parametric Count Model for Credit Scoring

The purpose of this paper is to apply count data models to predict the number of times a borrower pays late the amount of the credit. Poisson models and negative binomial distribution models, taking into account the observed heterogeneity, are generally used in situations where the dependent variable is discrete. Alternatively, we propose to use non parametric model where the relationship form between conditional mean and the explanatory variables is unknown. The empirical results found suggest that the nonparametric poisson model regression has the best prediction of the number of default payment.

Seasonal Variation in the Volume of Posterior Spinal Arthrodesis Procedures for Pediatric Scoliosis.

Retrospective review of the Healthcare Cost and Utilization Project's Kids' Inpatient Database. The aim of this study was to investigate variations in surgical case volume in the US for spinal arthrodesis for pediatric patients with scoliosis. Approximately 38,000 spinal arthrodesis procedures are performed each year in the United States for pediatric patients with scoliosis. Anecdotally, pediatric spine surgeons report performing more scoliosis procedures during summer months and before national holidays than during other periods, but this has not been reported in the literature. A thorough understanding of seasonal variations in surgical volume may guide the allocation of health care resources. Using the Healthcare Cost and Utilization Project's Kids' Inpatient Database, we identified 32,563 patients aged <21 years with adolescent idiopathic scoliosis (54%), neuromuscular scoliosis (19%), congenital scoliosis (12%), early-onset scoliosis (12%), or scoliosis from other causes (5%) who underwent primary spinal arthrodesis from 2000 through 2016. χ goodness-of-fit tests were used to evaluate seasonal differences in the number of spinal arthrodesis procedures performed. A negative binomial distribution model was used to compare surgical volume by month. Alpha = 0.05. We found significant seasonal variation in the volume of spinal arthrodesis procedures performed, which persisted after stratifying by type of scoliosis (all, P < 0.001). For all types of scoliosis, surgical volume was highest during the summer. The 3 months with the highest surgical volumes were June, July, and December. The number of spinal arthrodesis procedures performed in the United States for pediatric scoliosis peaked significantly during the summer, with the highest volume of surgeries performed during June, July, and December. An understanding of these variations can help hospitals and providers allocate resources appropriately throughout the year according to predictable changes in surgical volume. 4.

Trends in U.S. Burden of Clostridioides difficile Infection and Outcomes

BackgroundEfforts to prevent Clostridioides difficile infection continue to expand across the health care spectrum in the United States. Whether these efforts are reducing the national burden of C. difficile infection is unclear.MethodsThe Emerging Infections Program identified cases of C. difficile infection (stool specimens positive for C. difficile in a person ≥1 year of age with no positive test in the previous 8 weeks) in 10 U.S. sites. We used case and census sampling weights to estimate the national burden of C. difficile infection, first recurrences, hospitalizations, and in-hospital deaths from 2011 through 2017. Health care–associated infections were defined as those with onset in a health care facility or associated with recent admission to a health care facility; all others were classified as community-associated infections. For trend analyses, we used weighted random-intercept models with negative binomial distribution and logistic-regression models to adjust for the higher sensitivity of nucleic acid amplification tests (NAATs) as compared with other test types.ResultsThe number of cases of C. difficile infection in the 10 U.S. sites was 15,461 in 2011 (10,177 health care–associated and 5284 community-associated cases) and 15,512 in 2017 (7973 health care–associated and 7539 community-associated cases). The estimated national burden of C. difficile infection was 476,400 cases (95% confidence interval [CI], 419,900 to 532,900) in 2011 and 462,100 cases (95% CI, 428,600 to 495,600) in 2017. With accounting for NAAT use, the adjusted estimate of the total burden of C. difficile infection decreased by 24% (95% CI, 6 to 36) from 2011 through 2017; the adjusted estimate of the national burden of health care–associated C. difficile infection decreased by 36% (95% CI, 24 to 54), whereas the adjusted estimate of the national burden of community-associated C. difficile infection was unchanged. The adjusted estimate of the burden of hospitalizations for C. difficile infection decreased by 24% (95% CI, 0 to 48), whereas the adjusted estimates of the burden of first recurrences and in-hospital deaths did not change significantly.ConclusionsThe estimated national burden of C. difficile infection and associated hospitalizations decreased from 2011 through 2017, owing to a decline in health care–associated infections. (Funded by the Centers for Disease Control and Prevention.)

The clinicomolecular landscape of de novo versus relapsed stage IV metastatic breast cancer

Backgroundde novo metastatic breast cancer (dnMBC) is responsible for 6–10% of breast cancer presentations with increasing incidence and has remained resistant to detection by mammography screening. Recent publications hypothesized that in addition to poor screening uptake, the presentation of dnMBC may be due to its unfavourable biology which remains unknown at the molecular level. Here we investigated the tumour biology of dnMBC in the form of clinicopathology, genomic alterations and differential gene expression to create a comparative landscape of de novo versus relapsed metastatic breast cancer (rMBC). Additionally, to address the current screening limitations, we conducted a preliminary biomarker investigation for early dnMBC detection. MethodsIn this retrospective case-control study, gene expression and clinical data were accessed from the Cancer Genome Atlas (TCGA) for primary tumours of treatment-naïve patients with dnMBC (n = 17), rMBC (n = 49), and normal tissue (n = 113). The clinical and histological data were assessed categorically using Fisher's Exact-Test for significance (p < .05), or continuously using the Mann-Whitney Test (p < .05) where appropriate. The differential gene expression analysis was performed using EdgeR's negative binomial distribution model with a false discovery rate (FDR) <0.05. The resulting gene list was analysed manually for roles in metastasis as well as ontologically using STRING-DB with FDR <0.05. ResultsdnMBCs showed improved median survival vs rMBC (36 vs. 12 months). dnMBCs were more likely to be hormone receptor positive, less likely to be triple negative with lower histological lymphocytic infiltrate. In terms of genome alterations, dnMBCs had 4-fold increased PTEN mutations and poor survival with ABL2 and GATA3 alterations. Expression-wise, dnMBCs down-regulated TNFa, IL-17 signalling, and chemotaxis, while up-regulating steroid biosynthesis, cell migration, and cell adhesion. Biomarker analysis detected pre-existing and novel breast cancer biomarkers. ConclusionThe comparative tumour landscape revealed significant clinical, pathological and molecular differences between dnMBC and rMBC, indicating that dnMBC may be a separate biological entity to rMBC at the primary level with differing paths to metastasis. Additionally, we provided a list of potential serum biomarkers that may be useful in detecting dnMBC in its pre-metastatic window if such a window exists.

Interaction of Plasmacytoid Dendritic Cells and Myeloma Cells Trigger Tumor Promoting Transcriptional Changes in Multiple Myeloma Cells

Introduction We have previously shown that increased numbers of plasmacytoid dendritic cells (pDCs) in bone marrow (BM) of multiple myeloma (MM) patients promote tumor cell growth, survival, and drug resistance; as well as suppress T and NK cell mediated anti-MM immunity (Chauhan et al, Cancer Cell 2009, 16:309-323; Ray et al, Leukemia 2015, 29:1441-1444). Here, we analyzed genetic changes in MM cells triggered by coculture with pDCs using next generation sequencing (NGS). Functional validation of NGS data was performed in our coculture models of pDC-T-MM cells to assess impact on tumor cell surface phenotype, growth, survival, and drug resistance; as well as cytotoxic T lymphocyte (CTL) activity against MM. We identified and validated the metabolic ectoenzyme CD73/NT5E, implicated in cancer metabolism and immunosuppression via nucleotide degradation pathway, as a novel therapeutic target in MM. Methods Purified MM patient pDCs were cocultured with autologous MM cells or allogeneic MM cell lines (1pDC/5MM) for 48h, followed by separation of MM cells from pDCs using flow cytometry. Total RNA from MM cells was subjected to RNAseq analysis using Illumina Next Generation Sequencing (NGS). Raw sequence data were analyzed using VIPER workflow generating differential expression (DEseq2) and KEGG pathway. Statistical significance: log2FC (fold change) values in coculture vs control, with an FDR (False Discovery Rate) value of &amp;lt;0.05, was considered significant (CI &amp;gt; 95). Linear model for RNAseq analysis (Limma) and its GUI (Glimma) were also utilized for the visualization of data. Results RNA-seq data was analyzed using negative binomial distribution (DEseq2) and linear (Limma) models. Results showed 9200 and 9250 genes were differentially expressed (p &amp;lt; 0.05). MM cells cultured with or without pDCs clustered into two distinct groups, based upon pDC-MM contact-dependent transcriptional changes. Pathway enrichment analysis showed that pDCs interaction with MM cells regulates multiple physiological processes in MM cells including DNA replication/repair, purine/pyrimidine metabolism, and cell cycle. Hierarchical clustering showed increased expression of genes in MM cells after coculture with pDCs (log2FC range: ± 6.0): TLR7/9 (0.5; p=0.02), HDAC6 (0.65; p=0.00002), CD274 (0.6; p=0.02), or IL3Rα/CD123(0.1; p&amp;lt;0.05). On the other hand, pDCs reduces expression of CASP3 (-1.049; p= 1.1e-7), BAK1 (-0.5; p=0.000043), ADAM33 (-1.36; p=0.004), and BAD (-0.14; p = 0.0048) in MM cells. We validated the functional significance of pDC-induced gene alterations in MM cells using coculture model of patient MM-pDCs and autologous tumor cells. For example CD73 levels further increases in MM cells after coculture with pDCs (MFI: 1.2-fold vs MM; p = 0.008; CD73+ cells: 1.15-fold vs MM; n = 5; p = 0.005); and anti-CD73 Ab (1.0 µg/ml) treatment of autologous pDC-T cell cocultures (1pDC/10T cells) induces MM-specific CD8+ CTL activity against both autologous and allogeneic tumor cells. Furthermore, combining anti-CD73 Ab and TLR7 agonist triggers more robust MM-specific CD8+ CTL activity than either agent alone (% MM lysis: anti-CD73 Ab plus TLR7 agonist: 60-70%; TLR7 agonist: 40%; and anti-CD73 Ab: 30%; p = 0.009; n = 5). Conclusions Our RNA-seq and NGS analysis of pDCs-triggered transcriptome changes in MM cells identifies genes and pathways mediating tumor growth and immunosuppression, which therefore represent targets for novel therapeutics to improve patient outcome. Disclosures Chauhan: C4 Therapeutics.: Equity Ownership; Stemline Therapeutics: Consultancy. Anderson:Sanofi-Aventis: Other: Advisory Board; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau.

Comparison of distance-based and model-based ordinations.

Distance-based ordinations have played a critical role in community ecology for more than half a century, but are still under active development. These methods employ a matrix of pairwise distances or dissimilarities between sample units, and map sample units from the high-dimensional distance or dissimilarity space to a low-dimensional representation for analysis. Distance- or dissimilarity-based methods employ continuum or gradient ecological theory and a variety of statistical models to achieve the mapping. Recently, ecologists have developed model-based ordinations based on latent vectors and individual species response models. These methods employ the individualistic perspective of Gleason as the ecological model, and Bayesian or maximum-likelihood methods to estimate the parameters for the low dimensional space represented by the latent vectors. In this research I compared two distance-based methods (NMDS and t-SNE) with two model-based methods (BORAL and REO) on five data sets to determine which methods are superior for (1) extracting meaningful ecological drivers of variability in community composition, and (2) estimating sample unit locations in ordination space that maximize the goodness-of-fit of individual species response models to the estimated sample unit locations. Environmental variables and species were fitted to the ordinations by generalized additive models (GAMs) with Gaussian, negative binomial, or Poisson distribution models as appropriate. Across the five data sets, 22 models of environmental variability and 449 models of species distributions were calculated for each of the ordination methods. To minimize the effects of stochasticity the entire analysis was replicated three times and results averaged across the replicates. Results were evaluated by deviance explained and AIC for environmental variables and species distributions, averaged by ordination method for each data set, and ranked from best to worst. For the four assessments distance-based methods ranked 1 and 2 in three cases, and 1 and 3 in one case, significantly out performing the model-based methods. t-SNE was the top-performing method, out performing NMDS especially on the more complex data sets. In general the gradient-based theoretical basis and data sufficiency of distance-based methods allowed distance-based methods to outperform model-based methods in every assessment.

Using Non-Parametric Count Model for Credit Scoring

The purpose of this paper is to apply count data models to predict the number of times a credit applicant will not pay the amount awarded to repay the credit. Poisson models and negative binomial distribution models, taking into account the observed heterogeneity, are generally used in situations where the dependent variable is discrete. Alternatively, we propose to use non parametric model where the relationship form between conditional mean and the explanatory variables is unknown. The empirical results found suggest that the nonparametric poisson model regression has the best prediction of the number of default payment.

From clicks to bricks: The impact of product launches in offline stores for digital retailers

More digital retailers are expanding into offline channels by launching products in partners' stores, despite the mature offline shopping. Despite this extensive phenomenon, little attention is focused on the impact of offline product launches on sales. This paper examines how offline product launches via third-party offline stores affects online and mobile purchases, and extends understanding by investigating the moderating effect of offline store intensity. We analyze data obtained from a digital-first beauty retailer through a difference-in-differences analysis using a bivariate negative binomial distribution model. Empirical results show offline product launches are synergistic with online and mobile purchases, especially with respect to the latter. In addition, offline store intensity weakens the positive effect of offline product launches on online purchases, whereas it does not moderate the effect on mobile purchases. Moreover, we provide insights into developing geo-targeting launch strategies through what-if analyses. Our findings provide critical managerial implications for digital retailing.

Association Between Muscle Strength and Modeling Estimates of Muscle Tissue Heterogeneity in Young and Old Adults.

Assessing aging muscle through estimates of muscle heterogeneity may overcome some of the limitations of grayscale analyses. The objectives of this study included determining statistical model parameters that characterize muscle echogenicity and are associated with strength in younger and older participants. Thirty-three community-dwelling participants were assigned to younger and older groups. Quantitative B-mode ultrasound scanning of the rectus femoris and isometric grip strength testing were completed. Shape or dispersion parameters from negative binomial distribution, Nakagami, gamma, and gamma mixture models were fitted to the grayscale histograms. The mean ages ± SDs of the younger and older groups were 24.0 ± 2.3 and 65.1 ± 6.5 years, respectively. Statistical model shape and dispersion parameters for the grayscale histograms significantly differed between the younger and older participants (P = .002-.006). Among all of the statistical models considered, the gamma mixture model showed the best fit with the grayscale histograms (χ2 goodness of fit = 62), whereas the Nakagami distribution displayed the poorest fit (χ2 goodness of fit = 2595). Grayscale values were significantly associated with peak grip strength force in younger adult participants (R2 = 0.36; P < .008). However, the negative binomial dispersion parameter k (adjusted R2 = 0.70; P < .001) and gamma shape parameter α (adjusted R2 = 0.68; P < .01) showed the highest associations with peak grip strength force in older adult participants. The negative binomial dispersion parameter k and the gamma shape parameter α have clinical relevance for the assessment of age-related muscle changes. Statistical models of muscle heterogeneity may characterize the association between muscle tissue composition estimates and strength better than grayscale measures in samples of community-dwelling older adults.