Count Variables Research Articles

Sub-tree counts on hyperbolic random geometric graphs

AbstractThe hyperbolic random geometric graph was introduced by Krioukov et al. (Phys. Rev. E82, 2010). Among many equivalent models for the hyperbolic space, we study the d-dimensional Poincaré ball ( $d\ge 2$ ), with a general connectivity radius. While many phase transitions are known for the expectation asymptotics of certain subgraph counts, very little is known about the second-order results. Two of the distinguishing characteristics of geometric graphs on the hyperbolic space are the presence of tree-like hierarchical structures and the power-law behaviour of the degree distribution. We aim to reveal such characteristics in detail by investigating the behaviour of sub-tree counts. We show multiple phase transitions for expectation and variance in the resulting hyperbolic geometric graph. In particular, the expectation and variance of the sub-tree counts exhibit an intricate dependence on the degree sequence of the tree under consideration. Additionally, unlike the thermodynamic regime of the Euclidean random geometric graph, the expectation and variance may exhibit different growth rates, which is indicative of power-law behaviour. Finally, we also prove a normal approximation for sub-tree counts using the Malliavin–Stein method of Last et al. (Prob. Theory Relat. Fields165, 2016), along with the Palm calculus for Poisson point processes.

A generalized stochastic condensation mechanism for inducing underdispersion in count models

It is quite easy to stochastically distort an original count variable to obtain a new count variable with relatively more variability than in the original variable. Many popular overdispersion models (variance greater than mean) can indeed be obtained by mixtures, compounding or randomly stopped sums. This work proposes a stochastic mechanism, termed generalized condensation, for the construction of underdispersed count variables (variance less than mean), starting from an original count distribution of interest. For illustrative purposes, we developed the generalized condensed Poisson distribution, which allows for both under- and equidispersion. An application on a dataset demonstrates the potential of the proposal to accommodate underdispersion in the analysis of real count data.

Haematological variables and risk of future venous thromboembolism in the British Regional Heart Study on men. Combined D-dimer and APTT as a predictive test for thromboembolism?

SummaryWe examined the associations between haematological and inflammatory variables with future venous thromboembolism (VTE), in 3494 men aged 60–79 years, with no previous history of VTE or myocardial infarction, who were not receiving oral anticoagulants. After a mean follow‐up period of 18 years, there were 149 confirmed cases of fatal or non‐fatal VTE (deep vein thrombosis and/or pulmonary embolism). Among classical cardiovascular risk factors, only obesity and cigarette smoking were associated with VTE risk. After adjustment for age, obesity and smoking, VTE risk was associated with coagulation factor VIII, factor IX, von Willebrand factor (VWF), activated partial thromboplastin time (APTT), and fibrin D‐dimer. Hazard ratios (95% CI) for top to bottom quarters (bottom to top for APTT), were respectively 2.17 (1.37, 3.44), 2.15 (1.30, 3.53), 2.02 (1.27, 3.22), 2.43 (1.47, 4.02) and 3.62 (2.18, 6.08). The 11% of men with both the shortest APTT and highest D‐dimer combined had a 5.02 (2.37, 10.62) higher risk of VTE. VTE risk was not associated with fibrinogen, factor VII or activated protein C resistance; full blood count variables or with inflammatory markers, plasma viscosity, C‐reactive protein or interleukin‐6. The combination of D‐dimer and APTT merits evaluation as an adjunct to VTE risk prediction scores.

Goal Directed Perfusion Is Not Associated with a Decrease in Acute Kidney Injury in Patients Predicted to Be at High Risk for Acute Renal Failure after Cardiac Surgery

Small increases in serum creatinine postoperatively reflect an acute kidney injury (AKI) that likely occurred during cardiopulmonary bypass (CPB). Maintaining adequate oxygen delivery (DO2) during CPB, known as GDP (goal-directed perfusion), improves outcomes. Whether GDP improves outcomes of patients at high risk for acute renal failure (ARF) is unknown. Forty-seven adult patients undergoing cardiac surgery with CPB utilizing GDP with Cleveland Clinic Acute Renal Failure Score of 3 or greater were compared with a matched cohort of patients operated upon using a flow-directed strategy. CPB flow in the GDP cohort was based on a DO2 goal of 260 mL/min/m2. Serum creatinine values were used to determine whether postoperative AKI occurred according to AKIN (Acute Kidney Injury Network) guidelines. We examined the distribution of all variables using proportions for categorical variables and means (standard deviations) for continuous variables and compared treatment groups using t tests for categorical variables and tests for differences in distributions for continuous and count variables. We used inverse probability of treatment weighting to adjust for treatment selection bias. In adjusted models, GDP was not associated with a decrease in AKI (odds ratio [OR]: .97; confidence interval [CI]: .62, 1.52), but was associated with higher odds of ARF (OR: 3.13; CI: 1.26, 7.79), mortality (OR: 3.35; CI: 1.14, 9.89), intensive care unit readmission (OR: 2.59; CI: 1.31, 5.15), need for intraoperative red blood cell transfusion (OR: 2.02; CI: 1.26, 3.25), and postoperative platelet transfusion (OR: 1.78; CI: 1.05, 3.01) when compared with the historic cohort. In patients who are at high risk for postoperative renal failure, GDP was not associated with a decrease in AKI when compared to the historical cohort managed traditionally by determining CPB flows based on body surface area. Surprisingly, the GDP cohort performed significantly worse than the retrospective control group in terms of ARF, mortality, intensive care unit readmission, and RBC and platelet transfusions.

Goal Directed Perfusion Is Not Associated with a Decrease in Acute Kidney Injury in Patients Predicted to Be at High Risk for Acute Renal Failure after Cardiac Surgery.

Small increases in serum creatinine postoperatively reflect an acute kidney injury (AKI) that likely occurred during cardiopulmonary bypass (CPB). Maintaining adequate oxygen delivery (DO2) during CPB, known as GDP (goal-directed perfusion), improves outcomes. Whether GDP improves outcomes of patients at high risk for acute renal failure (ARF) is unknown. Forty-seven adult patients undergoing cardiac surgery with CPB utilizing GDP with Cleveland Clinic Acute Renal Failure Score of 3 or greater were compared with a matched cohort of patients operated upon using a flow-directed strategy. CPB flow in the GDP cohort was based on a DO2 goal of 260 mL/min/m2. Serum creatinine values were used to determine whether postoperative AKI occurred according to AKIN (Acute Kidney Injury Network) guidelines. We examined the distribution of all variables using proportions for categorical variables and means (standard deviations) for continuous variables and compared treatment groups using t tests for categorical variables and tests for differences in distributions for continuous and count variables. We used inverse probability of treatment weighting to adjust for treatment selection bias. In adjusted models, GDP was not associated with a decrease in AKI (odds ratio [OR]: .97; confidence interval [CI]: .62, 1.52), but was associated with higher odds of ARF (OR: 3.13; CI: 1.26, 7.79), mortality (OR: 3.35; CI: 1.14, 9.89), intensive care unit readmission (OR: 2.59; CI: 1.31, 5.15), need for intraoperative red blood cell transfusion (OR: 2.02; CI: 1.26, 3.25), and postoperative platelet transfusion (OR: 1.78; CI: 1.05, 3.01) when compared with the historic cohort. In patients who are at high risk for postoperative renal failure, GDP was not associated with a decrease in AKI when compared to the historical cohort managed traditionally by determining CPB flows based on body surface area. Surprisingly, the GDP cohort performed significantly worse than the retrospective control group in terms of ARF, mortality, intensive care unit readmission, and RBC and platelet transfusions.

CORRELATION OF CAROTID INTIMA-MEDIA THICKNESS AND PULSE WAVE VELOCITY WITH HEMATOCRIT, PLATELET COUNT AND FIBRINOGEN LEVELS IN DIPPER AND NON-DIPPER HYPERTENSIVE PATIENTS

Objective: Carotid intima-media thickness (IMT) and pulse wave velocity (PWV) are the two most widely recognized methods for the assessment of arterial stiffness. The aim of this study was to verify whether there is a correlation between them and some blood parameters in dipper and non-dipper hypertensive patients. Design and method: This was a cross-sectional study that included patients with arterial hypertension (AHT) who were greater than 18 years old. Demographic and epidemiological variables were collected. Blood pressure (BP) parameters were assessed by office BP measurements and 24-hour ambulatory BP monitoring (24 h ABPM). We collected blood samples for biochemistry, blood count and coagulation variables. Individuals were divided into two groups: dipper and non-dipper patients. Quantitative normally distributed variables were nalysed using the Student’s t-test while non-normally distributed variables were nalysed using the Mann–Whitney U-test. A Chi-square test was used for categorical variables to test differences between groups. This study was approved by the center Ethics Committee of Clinical Research, and it adhered to the ethical standards that were outlined in the Declaration of Helsinki. All subjects provided informed consent. Results: There were 563 patients (47% women) with a mean age of 54 years who were included in this study. One third of patients suffered from diabetes and two out of three had plasma lipid abnormalities. There were differences in age, frequency of diabetes, dyslipidemia and antihypertensive therapy between dipper and non-dipper patients. After an adjustment for those variables, we observed a relevant correlation of PTC (Pearson correlation coefficient [r] -0.215, P = 0.002) and hematocrit (r 0.215 P = 0.002) with IMT in patients with a dipper profile while individuals with a non-dipper BP pattern showed a correlation of PWV with PTC (r -0.149, P = 0.031) and fibrinogen levels (r 0.238, P = 0.031). Uric acid levels were correlated with IMT in both groups of patients. Conclusions: It seems that some blood parameters could be linked to the development of arterial stiffness in both dipper and non-dipper patients. This was a preliminary retrospective, single-center study that could open the door to the investigation of new analytical indices in arterial stiffness.

The relationship between state Medicaid and CHIP options and child health outcomes

AbstractMedicaid and the Children's Health Insurance Program (CHIP) both combine federal mandates to cover certain services and groups with state options in providing that coverage and in covering other services and groups. Using state‐level panel data, we investigated the relationships of 83 state Medicaid and CHIP options with six child health outcomes from 2001 to 2018 using structural equation modeling, controlling for 10 possible demographic and economic variables, state fixed effects, and a quadratic year count variable. Our results suggest that reducing copayments and providing express eligibility may be especially important in improving child health outcomes; eliminating or reducing waiting periods, eliminating face‐to‐face interview requirements, and eliminating documentation requirements may also be helpful. Other state options that have been found to be important in improving child insurance outcomes, such as eliminating asset tests and providing continuous eligibility, were not as helpful for improving child health outcomes in our results.

Sums of singular series and primes in short intervals in algebraic number fields

Gross and Smith have put forward generalizations of Hardy–Littlewood twin prime conjectures for algebraic number fields. We estimate the behaviour of sums of a singular series that arises in these conjectures, up to lower-order terms. More exactly, where \(\mathfrak {S}(\eta )\) is the singular series, we find asymptotic formulas for smoothed sums of \(\mathfrak {S}(\eta )-1\). Based upon Gross and Smith’s conjectures, we use our result to suggest that for large enough ‘short intervals’ in an algebraic number field K, the variance of counts of prime elements in a random short interval deviates from a Cramér model prediction by a universal factor, independent of K. The conjecture over number fields generalizes a classical conjecture of Goldston and Montgomery over the integers. Numerical data are provided supporting the conjecture.

Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019–2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.

Female directors and environmental innovation: is stakeholder orientation a missing link?

PurposeThe purpose of this study is to examine the effect of stakeholder orientation in mediating the impact of female directors on environmental innovation. The authors argue that female directors are eco-friendly and more concerned with multi-stakeholder interests and demands. Thus, they promote environmental innovation by including more stakeholder-oriented values and beliefs in firm decision-making.Design/methodology/approachAs the dependent variable in this study is a nonnegative count variable, the authors use a count data model based on Poisson regression. A sample of Chinese listed firms between 2010 and 2020 is used to test the hypotheses.FindingsThe results of this study show that female directors can enhance environmental innovation. Further, stakeholder orientation represents an intermediate channel that accounts for the effects of female directors on environmental innovation. This suggests that having women on a board can lead to better stakeholder management, which, in turn, positively affects environmental innovation. The authors also reveal that female directors contribute more to stakeholder orientation with the presence of female chairpersons.Originality/valueA significant limitation in the literature is that little attention has been paid to the mechanisms linking female directors to firm outcomes. In the context of environmental innovation, while previous studies have investigated the influence of female directors on environmental innovation, the underlying channels of that influence remain largely unexplored. Therefore, the findings of this study advance the understanding of the effects of female directors on environmental innovation by revealing an important underlying channel – stakeholder orientation.

INVESTIGATION OF THE RELATIONSHIP BETWEEN NEUTROPHIL/LYMPHOCYTE RATIO AND ANALGESIC CONSUMPTION IN BIMAXILLARY ORTHOGNATHIC SURGERY PATIENTS

Objectives Neutrophil to lymphocyte ratio; as an inexpensive, easily measurable, and repeatable laboratory parameter is an indicator of subclinical inflammation, and is also a parameter that gives an idea about the systemic inflammatory response and cellular immune response. In general, lymphopenia reflects the weakness of cellular immunity, while neutrophilia is indicative of the response to systemic inflammation. The ratio of these two values to each other seems to be a marker showing the adequacy of the cellular immune response against this situation, despite the magnitude of systemic inflammation. In this study, we aimed to investigate the relationship between preoperative neutrophil-lymphocyte ratio (NLR) and postoperative analgesic drug consumption. Materials and Method Within the scope of the study, the files of 50 patients who underwent bimaxillary orthognathic surgery were retrospectively analyzed. The preoperative NLR values of the patients and the need for the analgesic drugs in the first 24 hours and after 24 hours were recorded and correlated. Results 24% of the patients were girls and 76% were boys. The average age was 20.54, and the average weight was 65.28 kg. There was a positive, strong, and significant correlation between the preoperative neutrophil count and preoperative NLR value and variables of analgesic drug consumption in the first 24 hours (r = 0.6814, p <0.001, and r = 0.6148, p <0.001, respectively). There was a negative, strong and significant correlation between the preoperative lymphocyte count and the first 24-hour analgesic drug consumption variables (r = -0.6773, p <0.001). Conclusion It was concluded that patients with a preoperative NLR value of 2.11 and above may require 4 or more analgesics in the first 24 hours, therefore, NLR value can give preliminary information about the need for postoperative analgesia, and these values could be taken into account when developing a strategy for postoperative analgesia.

Analysis of Over-Dispersed Count Data: Application to Obligate Parasite Pasteuria Penetrans

In this article we present with STATA regression models suitable for analyzing over-dispersed count outcomes. Specifically, the Negative Binomial regression can be an appropriate choice for modeling count variables, usually for over-dispersed count outcome variables. The common problem with count data with zeroes is that the empirical data often show more zeroes than would be expected under either Poisson or the Negative Binomial model. We concluded, this publications showcases that Zero-inflated models can be used to model count data that has excessive zero counts.

Mid-quantile regression for discrete responses.

We develop quantile regression methods for discrete responses by extending Parzen's definition of marginal mid-quantiles. As opposed to existing approaches, which are based on either jittering or latent constructs, we use interpolation and define the conditional mid-quantile function as the inverse of the conditional mid-distribution function. We propose a two-step estimator whereby, in the first step, conditional mid-probabilities are obtained nonparametrically and, in the second step, regression coefficients are estimated by solving an implicit equation. When constraining the quantile index to a data-driven admissible range, the second-step estimating equation has a least-squares type, closed-form solution. The proposed estimator is shown to be strongly consistent and asymptotically normal. A simulation study shows that our estimator performs satisfactorily and has an advantage over a competing alternative based on jittering. Our methods can be applied to a large variety of discrete responses, including binary, ordinal, and count variables. We show an application using data on prescription drugs in the United States and discuss two key findings. First, our analysis suggests a possible differential medical treatment that worsens the gender inequality among the most fragile segment of the population. Second, obesity is a strong driver of the number of prescription drugs and is stronger for more frequent medications users. The proposed methods are implemented in the R package Qtools. Supplemental materials for this article, including a brief R tutorial, are available as an online supplement.

Efficient Estimation of the Efficacy and Safety Endpoints for Clinical Trials with Preplanned Dose Titrations

Preplanned dose titrations are sometimes used in clinical trials to improve patient tolerability to treatment. Such titrations often start from a low dose and gradually increase to the targeted dose, and therefore, the low-dose and high-dose arms may share the same dose for a certain period of time. Traditionally, the analysis has been performed according to the treatment arm, ignoring the fact that there are overlapping initial doses between arms. This article describes an alternative method of analysis that exploits the design feature on titrations to produce a more efficient estimation for the mean response over time for each treatment regimen as well as the comparison between treatment regimens for continuous, time-to-event, binary, and count variables. For continuous and count data, we used the (generalized) linear mixed model framework to construct the new estimators; for time-to-event data, we developed a novel logrank test; and for the binary variable, we derived the point and variance estimators based on the events that occurred in each study period. Simulation studies and the application to a real clinical dataset demonstrate the newly proposed approach is advantageous over the traditional approach.

Semiparametric count data regression for self-reported mental health.

''For how many days during the past 30 days was your mental health not good?" The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: The data are overdispersed, zero-inflated, bounded by 30, and heaped in 5- and 7-day increments. To address these challenges-which are especially common for health questionnaire data-we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an expectation-maximization (EM) algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.

Pre-Statistical Harmonization of Behavioral Instruments Across Eight Surveys and Trials

Data harmonization methods facilitate further use of existing studies and research resources. Most statistical harmonization methods require pooling data across studies, which is complex and requires careful scrutiny of source data. Most methods (e.g., item response theory) require datasets to have common items for linking a common construct across studies: this necessitates the qualitative process of pre-statistical harmonization. Here, we document pre-statistical harmonization of items measuring behavioral and psychological symptoms (e.g., agitation, wandering, etc.) which represent problematic behaviors among people with dementia administered in a national survey (ADAMS), evaluations conducted at Alzheimer’s Disease Research Centers (NACC), and in six randomized trials (COPE, TAP, ALZQOL, ACT, REACH, ADSPlus). We describe our approach to review question content and scoring procedures to establish comparability across items prior to data pooling. We identified 327 items from 15 instruments across these eight studies. We found considerable cross-study heterogeneity in administration and coding procedures for items that measure the same domain. For example, eight items were coded as count variables in some studies but as categorical variables in others. Moreover, of the 359 items, 191 are conditionally dependent on values of another item. These issues around item response heterogeneity and conditional dependency needed to be resolved prior to estimation of item response theory models for statistical co-calibration. We leveraged several rigorous data transformation procedures to address these issues, including re-coding and winsorization. This study provides guidelines for how future research may acknowledge and address similar issues in pooling behavioral and related instruments.

Clustering of unhealthy lifestyle behaviors, self-rated health and disability

The main objective was to identify sociodemographic characteristics of the population at risk for a greater clustering of unhealthy behaviors and to evaluate the association of such clustering with self-rated health status and disability. Data come from the 2017 Spanish National Health Survey with a sample of 21,947 participants of 15 years of age or older. Based on tobacco consumption, risk drinking, unbalanced diet, sedentarism, and body mass index <18.5/≥25 we created two indicators of risk factor clustering: 1) Number of unhealthy behaviors (0–5); and 2) Unhealthy lifestyle index (score: 0–15). Self-rated health was dichotomized into “optimal” and “suboptimal,” and disability was classified as “no disability,” “mild,” and “severe” based on the Global Activity Limitation Index (GALI). We estimated prevalence ratios (PR) adjusted for covariates using generalized linear models using the clustering count variable, and dose-response curves using the unhealthy lifestyle index. Most participants (77.4%) reported 2 or more risk factors, with men, middle-age individuals, and those with low socioeconomic status being more likely to do so. Compared to those with 0–1 risk factors, the PR for suboptimal health was 1.26 (95% CI:1.18–1.34) for those reporting 2–3 factors, reaching 1.43 (95% CI:1.31–1.55) for 4–5 factors. The PR for severe activity limitation was 1.66 (95% CI:1.35–2.03) for those reporting 2–3 factors and 2.06 (95% CI:1.59–2.67) for 4–5 factors. The prevalence of both health indicators increased in a non-linear fashion as the unhealthy lifestyle index score increased, increasing rapidly up to 5 points, slowing down between 5 and 10 points, and plateauing afterwards.

Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.

Somatic Cell and Cheesemaking Variables of WAD Goat Milk: Influence of Parity and Lactation Stage

The suitability of any goat breed for dairy purposes depends on its assessment of milk-related traits. This study evaluates the influence of parity and lactation stage on somatic cell count (SCC) and cheese-making variables of West African Dwarf (WAD) goat milk. A total of 48 lactating WAD does of three different parities (the 1st, 2nd, and 3rd) were milked over a period of time-phased into three stages of lactation (early, mid, and late). The milk was subjected to SCC and cheese-making during which coagulation time, cheese yield (CY), whey volume, and whey pH were recorded. Collected data were subjected to analysis of variance and Pearsons’ correlation. The result revealed that the milk SCC of the does in the 3rd parity (2.16 x 105 cells) was significantly (p<0.05) higher than 1.89 x 105 and 1.87 x 105 cells in the milk of the 1st and the 2nd parity does respectively. There was a similar variation trend of SCC as lactation progresses in each of the parities. The CY significantly increased (p<0.05) as lactation progresses in the 1st parity (early – 16.95%; mid – 17.21%; late – 20.85%) while, the milk of the 2nd and the 3rd parity from mid to late lactation stage yielded statistically similar cheese that was comparable with late lactation CY of the 1st parity does. The lactation stage and SCC were positively correlated with CY. The volume and pH of the whey were neither affected by the lactation stage nor parity. The study concludes that parity uncovers the effect of lactation stage on the milk SCC of WAD does while the milk obtained from mid to late lactation stage yields more cheese.

Genome-wide by Environment Interaction Study of Stressful Life Events and Hospital-Treated Depression in the iPSYCH2012 Sample

BackgroundResearchers have long investigated a hypothesized interaction between genetic risk and stressful life events in the etiology of depression, but studies on the topic have yielded inconsistent results. MethodsWe conducted a genome-wide by environment interaction study (GWEIS) in 18,532 patients with depression from hospital-based settings and 20,184 population controls. All individuals were drawn from the iPSYCH2012 case-cohort study, a nationally representative sample identified from Danish national registers. Information on stressful life events including family disruption, serious medical illness, death of a first-degree relative, parental disability, and child maltreatment was identified from the registers and operationalized as a time-varying count variable. Hazard ratios for main and interaction effects were estimated using Cox regressions weighted to accommodate the case-cohort design. Our replication sample included 22,880 depression cases and 50,378 controls from the UK Biobank. ResultsThe GWEIS in the iPSYCH2012 sample yielded three novel, genome-wide–significant (p < 5 × 10−8) loci located in the ABCC1 gene (rs56076205, p = 3.7 × 10−10), the AKAP6 gene (rs3784187, p = 1.2 × 10−8), and near the MFSD1 gene (rs340315, p = 4.5 × 10−8). No hits replicated in the UK Biobank (rs56076205: p = .87; rs3784187: p = .93; rs340315: p = .71). ConclusionsIn this large, population-based GWEIS, we did not find any replicable hits for interaction. Future gene-by-stress research in depression should focus on establishing even larger collaborative GWEISs to attain sufficient power.