Prognosis in malignant melanoma

Evapotranspiration is the most important variable next to rainfall in the context of irrigation to crops and it is a multivariate phenomenon as it is influenced by many hydrological variables. In this paper, the relative importance of different variables involved in evapotranspiration has been investigated through a multivariate technique, namely factor analysis. Monthly data pertaining to eight meteorological stations located in two tropical states of India are used in this analysis. Factor analysis was applied to determine the relative effect of rainfall, temperature (maximum and minimum), wind speed, relative humidity, and sunshine duration on evapotraspiration. The analysis was performed in two stages: in the first stage, the principal components were obtained and in the second stage, a set of factors was derived using Varimax factor rotation. Three factors were extracted which explained more than 85% of the total variation in the original data set. The identified factors were related to physical processes influencing the evapotranspiration. The results of the study reveal that the variables, relative humidity, temperature, and wind speed, are those with the most influence in the evapotranspiration process, in that order. The variables rainfall and sunshine duration have been found to have less influence on the ET process.

대부분의 사회과학분야 연구에서는 다중선형회귀모형에 포함되는 설명변수들의 상대적 중요도에 대해 많은 관심을 가지고 있다. SPSS를 비롯한 여러 상용 통계패키지에서는 표준화 회귀계수의 추정값을 계산해 주고 있으며 표준화 회귀계수의 추정값의 절대값이 큰 설명변수가 중요하다고 해석하기도 하지만 이러한 주장에는 많은 논란이 있다. 여러 회귀분석 교과서에서도 표준화 회귀계수의 맹목적인 사용에 유의할 필요가 있다고 강조하고 있지만 대안은 제시되고 있지않다. 본 연구에서는 설명변수의 상대적 중요도에 대한 구체적인 의미를 생각해보고 몇 가지 잔차산점도에서의 상관계수와 표준화 회귀계수의 관계를 알아보고 설명변수들의 상대적 중요도를 나타내는 적절한 척도를 제안한다. 추가변수그림의 상관계수는 추가적인 설명력을 검정하는 부분 검정통계량의 크기를 나타내는 척도로 사용될 수 있고, 추가결정계수그림의 상관계수는 설명변수의 추가를 통해서 발생하는 결정계수의 증가분을 나타내는 척도로 사용될 수 있다. 또한 보정계수를 이용하여 이러한 척도들과 표준화 회귀계수와의 관계를 구해본다. 제시된 척도들을 실제 자료에 적용시켜본다.In most social science research work there are some interests concerning the rank of relative importance of different variables in the regression model. Statistical packages such as SPSS provide the printouts on the standardized coefficient. But many people make cautionary remarks that the rankings of the standardized coefficients in terms of absolute magnitude does not necessarily reflect the importance of variables in explaining the variability of response variable. Many textbooks give warnings against the misuse of this automatic computer generated output. We consider the relationship between this standardized coefficient and the correlation coefficients of various residual plots. And this suggests that the appropriate correlation coefficients may be used in ranking the relative importance of variables. The correlation coefficient derived from added variable plot is a good measure in determining the ranks of variables according to the size of partial -values for regression coefficient. The correlation coefficient from the additional   plot compares each variable s contribution in terms of additional increase of coefficient of determination over the variability explained by other variables in the model.

Background: aCTLA-4/aPD-1 combination therapy has significantly improved clinical outcomes in patients with metastatic melanoma, with 50%-60% of patients responding to treatment, but predictors of response are poorly characterized. In this study, we set out to evaluate the hypothesis that circulating cytokines and peripheral white blood cell composition may predict response to therapy. Methods: We quantified cytokines and complete blood counts from 89 patients with advanced melanoma treated with combination therapy from three points in time: pre-treatment, one month and approximately three months after starting therapy. We evaluated the association between peripheral blood circulating features, cytokines and clinical labs values, and clinical outcome using predictive modeling, and investigated the prognostic values of these features using survival analysis. Results: Pre-treatment, patients with durable clinical benefit (DCB) had higher IL23, lower CXCL6, and lower IL10 levels. The ratio of neutrophils to lymphocytes (NLR) pre-treatment was not associated with response, but higher NLR one month after starting therapy predicted poorer progression free survival (PFS) and overall survival (OS), primarily driven by an increase in absolute lymphocytes. A multivariate model demonstrated that baseline CXCL6, IL10, IL23 were independent predictors of therapy response, and the combined model has reached an area under the curve (AUC) of 0.8 in prediction of response to combination therapy. Low IL10 and CXCL6 identified an excellent prognosis group, while high CXCL6 and low IL23 characterized a very poor prognosis group of patients. Conclusions: Baseline measurement of cytokines IL23, CXCL6, and IL10 can predict metastatic melanoma patients’ response to ipi/nivo combination treatment. Higher neutrophils-to-lymphocytes ratio approximately one month after therapy started associates with worse survival outcome. Our study presented circulating features from a relatively accessible and less costly avenue, peripheral blood, to be predictive of response to combination immune checkpoint blockade. Citation Format: Jiajia Chen, Giuseppe Tarantino, Mariano Severgnini, Joanna Baginska, Anita Giobbie-Hurder, Michael Manos, Janice D. Russell, Jason L. Weirather, Kathleen Pfaff, Amy Y. Huang, Scott J. Rodig, Srinika Ranashinge, David Liu, F.Stephen Hodi. Circulating cytokine associations with clinical outcomes in melanoma patients treated with checkpoint inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3249.

A method for describing and analyzing selection decisions is applied to the problem of the acceptance and admission of patients referred to psychiatric hospitals. The model directs attention to the separate decision points that constitute the overall selection process and permits an examination of the relative importance of different variables at different points in this process. The total populations of referrals to two psychiatric hospitals over an elevenweek period were followed to determine those persons who were accepted, or not accepted; and those admitted or not admitted to the hospital. These sub-groups were compared with each other on a number of social characteristics. Differences and similarities among these groups reflect the selection criteria of the institutions and indicate the ways in which the populations of referred, accepted, and admitted patients are related to each other.

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85±18.67 [mean ± SE]) vs. BLM forests (398.87±18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2 ), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Combining crop plants with other plant species in agro-ecosystems is one way to enhance ecological pest and disease regulation mechanisms. Resource availability and microclimatic variation mechanisms affect processes related to pest and pathogen life cycles. These mechanisms are supported both by empirical research and by epidemiological models, yet their relative importance in a real complex agro-ecosystem is still not known. Our aim was thus to assess the independent effects and the relative importance of different variables related to resource availability and microclimatic variation that explain pest and disease occurrence at the plot scale in real complex agro-ecosystems. The study was conducted in cacao (Theobroma cacao) agroforests in Cameroon, where cocoa production is mainly impacted by the mirid bug, Sahlbergella singularis, and black pod disease, caused by Phytophthora megakarya. Vegetation composition and spatial structure, resource availability and pest and disease occurrence were characterized in 20 real agroforest plots. Hierarchical partitioning was used to identify the causal variables that explain mirid density and black pod prevalence. The results of this study show that cacao agroforests can be differentiated on the basis of vegetation composition and spatial structure. This original approach revealed that mirid density decreased when a minimum number of randomly distributed forest trees were present compared with the aggregated distribution of forest trees, or when forest tree density was low. Moreover, a decrease in mirid density was also related to decreased availability of sensitive tissue, independently of the effect of forest tree structure. Contrary to expectations, black pod prevalence decreased with increasing cacao tree abundance. By revealing the effects of vegetation composition and spatial structure on mirids and black pod, this study opens new perspectives for the joint agro-ecological management of cacao pests and diseases at the plot scale, through the optimization of the spatial structure and composition of the vegetation.

Prognostic personal credit risk model considering censored information

Many problems in mathematical finance can be formulated as high-dimensional integrals, where the large number of dimensions arises from small time steps in time discretization and/or a large number of state variables. Quasi--Monte Carlo (QMC) methods have been successfully used for approximating such integrals. To understand this success, this paper focuses on investigating the special features of some typical high-dimensional finance problems, namely, option pricing and bond valuation. We provide new insight into the connection between the effective dimension and the efficiency of QMC, and we present methods to analyze the dimension structure of a function. We confirm the observation of Caflisch, Morokoff, and Owen that functions from finance are often of low effective dimension, in the sense that they can be well approximated by their low-order ANOVA (analysis of variance) terms, usually just the order-1 and order-2 terms. We explore why the effective dimension is small for many integrals from finance. By deriving explicit forms of the ANOVA terms in simple cases, we find that the importance of each dimension is naturally weighted by certain hidden weights. These weights characterize the relative importance of different variables or groups of variables and limit the importance of the higher-order ANOVA terms. We study thevariance ratios captured by low-order ANOVA terms and their asymptotic properties as the dimension tends to infinity, and we show that with the increase of dimension the lower-order terms continue to play a significant role and the higher-order terms tend to be negligible. This provides some insight into high-dimensional problems from finance and explains why QMC algorithms are efficient for problems of this kind.

The Van Nuys Prognostic Index (VNPI), an algorithm based on tumour size, tumour grade, presence of necrosis and excision margin width, is claimed to predict local recurrence after breast-conserving surgery for ductal carcinoma in situ (DCIS). The aim of this study was to examine the validity of the VNPI in a UK population. Clinicopathological data, including VNPI subgroups, for 237 patients who had breast-conserving operations for DCIS were examined. Multivariate data analysis was performed using a Cox regression model to examine the independence and relative importance of different variables in predicting recurrence, and to compare the data with those used in derivation of the VNPI. The median follow-up was 47 months. There were 37 ipsilateral local recurrences. Excision margin width (P < 0.001) and tumour grade (by Van Nuys grading (P = 0.014) or simple nuclear grading (P = 0.004)) were the only independent risk factors for local recurrence. Excision margin width had three times more power than grade in predicting local recurrence. Subgrouping data by VNPI score predicted recurrence-free survival (P < 0.001), but stratified 78 per cent of patients into a group with a moderate risk of local recurrence. Excision margin width is the most important predictor of local recurrence after breast-conserving surgery for DCIS. The VNPI lacked discriminatory power for guiding further patient management.

&lt;div&gt;Abstract&lt;p&gt;Superficial spreading melanoma (SSM) and nodular melanoma (NM) are believed to represent sequential phases of linear progression from radial to vertical growth. Several lines of clinical, pathologic, and epidemiologic evidence suggest, however, that SSM and NM might be the result of independent pathways of tumor development. We utilized an integrative genomic approach that combines single nucleotide polymorphism array (6.0; Affymetrix) with gene expression array (U133A 2.0; Affymetrix) to examine molecular differences between SSM and NM. Pathway analysis of the most differentially expressed genes between SSM and NM (&lt;i&gt;N&lt;/i&gt; = 114) revealed significant differences related to metabolic processes. We identified 8 genes (&lt;i&gt;DIS3&lt;/i&gt;, &lt;i&gt;FGFR1OP&lt;/i&gt;, &lt;i&gt;G3BP2&lt;/i&gt;, &lt;i&gt;GALNT7&lt;/i&gt;, &lt;i&gt;MTAP&lt;/i&gt;, &lt;i&gt;SEC23IP&lt;/i&gt;, &lt;i&gt;USO1&lt;/i&gt;, and &lt;i&gt;ZNF668&lt;/i&gt;) in which NM/SSM-specific copy number alterations correlated with differential gene expression (&lt;i&gt;P&lt;/i&gt; &lt; 0.05; Spearman's rank). SSM-specific genomic deletions in &lt;i&gt;G3BP2&lt;/i&gt;, &lt;i&gt;MTAP&lt;/i&gt;, and &lt;i&gt;SEC23IP&lt;/i&gt; were independently verified in two external data sets. Forced overexpression of metabolism-related gene &lt;i&gt;MTAP&lt;/i&gt; (methylthioadenosine phosphorylase) in SSM resulted in reduced cell growth. The differential expression of another metabolic-related gene, aldehyde dehydrogenase 7A1 (&lt;i&gt;ALDH7A1&lt;/i&gt;), was validated at the protein level by using tissue microarrays of human melanoma. In addition, we show that the decreased ALDH7A1 expression in SSM may be the result of epigenetic modifications. Our data reveal recurrent genomic deletions in SSM not present in NM, which challenge the linear model of melanoma progression. Furthermore, our data suggest a role for altered regulation of metabolism-related genes as a possible cause of the different clinical behavior of SSM and NM. &lt;i&gt;Cancer Res; 71(7); 2561–71. ©2011 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;

RSK1 Activation Promotes Invasion in Nodular Melanoma

Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy.

The aberrant DNA methylation plays a critical role in a number of different malignancies, including melanoma. DNA methylation is catalyzed by DNA methyltransferases (DNMTs), involved in methylation maintenance (DNMT1) and de novo DNA methylation (DNMT3A and DNMT3B). The current study investigated the association of genetic variants in the DNMT1 and DNMT3B with the clinicopathologic features and the clinical course of melanoma patients. In the present study, DNMT1 (rs2228612, rs2228611, and rs2114724) and DNMT3B (rs406193 and rs2424932) polymorphisms were examined in 123 melanoma patients. Single nucleotide polymorphisms were assessed using TaqMan SNPs Genotyping Assays according to the manufacturer's protocols. The carriers of the variant genotype of DNMT1 rs2228612 had poorer overall survival and recurrence-free survival, (P = 0.000 and 0.000, respectively), and an increased risk for adverse outcome [hazard ratio (HR) = 6.620, 95% confidence interval (CI): 2.214-19.791, P = 0.001]. DNMT1 rs2228612 was also associated with ulceration (P = 0.045), nodal status (P = 0.030), progression (P = 0. 007), and stage of disease (P = 0.003). Univariate analysis indicated that tumor-infiltrating lymphocytes could be a marker of good prognosis in melanoma patients (HR = 0.323, 95% CI: 0.127-0.855, P = 0.025), whereas the genotype distribution of the DNMT3B rs406193 polymorphism correlated significantly with the presence of tumor-infiltrating lymphocytes (P = 0.012). The multivariate analysis showed that the DNMT1 rs2228612 polymorphism (HR = 12.126, 95% CI: 2.345-62.715, P = 0.003) is an independent predictor of poor overall survival in melanoma patients. As expected, disease progression was also found to be an independent prognostic factor in melanoma patients (HR = 37.888, 95% CI: 3.615-397.062, P = 0.002). DNMT1 rs2228612 was found to be an independent predictor of poor overall survival in melanoma patients. DNMTs polymorphisms could serve as a potential target for novel therapeutic approaches.

A study of 361 female melanoma patients and age matched controls was conducted in the four western provinces of Canada. Analysis of reproductive factors showed a significant negative association between number of livebirths and risk of melanoma. The relationship persisted for superficial spreading melanomas after adjustment for host pigmentation factors, freckling, and educational status. An inverse association between bilateral oophorectomy and risk of superficial spreading melanoma was also seen. No association was found between risk of melanoma and age at first birth, age at menarche and age at natural menopause. No association was found between risk of superficial spreading or nodular melanoma and use of either oral contraceptives or menopausal oestrogens.