Partial Correlation Research Articles

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach

BackgroundDepressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression. MethodsCross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test. ResultsSymptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients. ConclusionThe network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Association between mammillary body atrophy and memory impairment in retired athletes with a history of repetitive mild traumatic brain injury

Cognitive dysfunction, especially memory impairment, is a typical clinical feature of long-term symptoms caused by repetitive mild traumatic brain injury (rmTBI). The current study aims to investigate the relationship between regional brain atrophy and cognitive impairments in retired athletes with a long history of rmTBI. Overall, 27 retired athletes with a history of rmTBI (18 boxers, 3 kickboxers, 2 wrestlers, and 4 others; rmTBI group) and 23 age/sex-matched healthy participants (control group) were enrolled. MPRAGE on 3 T MRI was acquired and segmented. The TBV and TBV–adjusted regional brain volumes were compared between groups, and the relationship between the neuropsychological test scores and the regional brain volumes were evaluated. Total brain volume (TBV) and regional brain volumes of the mammillary bodies (MBs), hippocampi, amygdalae, thalami, caudate nuclei, and corpus callosum (CC) were estimated using the SPM12 and ITK–SNAP tools. In the rmTBI group, the regional brain volume/TBV ratio (rmTBI vs. control group, Mann–Whitney U test, p < 0.05) underwent partial correlation analysis, adjusting for age and sex, to assess its connection with neuropsychological test results. Compared with the control group, the rmTBI group showed significantly lower the MBs volume/TBV ratio (0.13 ± 0.05 vs. 0.19 ± 0.03 × 10−3, p < 0.001). The MBs volume/TBV ratio correlated with visual memory, as assessed, respectively, by the Rey–Osterrieth Complex Figure test delayed recall (ρ = 0.62, p < 0.001). In conclusion, retired athletes with rmTBI have MB atrophy, potentially contributing to memory impairment linked to the Papez circuit disconnection.

Network Motif Detection in the Network of Inflammatory Markers and Depression Symptoms among Patients with Stable Coronary Heart Disease: Insights from the Heart and Soul Study

Background. Network motif analysis is a technique used to explore recurrent and statistically significant subgraphs within a network. Applying a motif analysis to the complex network of inflammation and depression may yield nuanced insight into the specific interaction mechanisms between inflammatory markers and individual depression symptoms, which is our aim. Methods. This cross-sectional study is based on patients with stable coronary heart disease (CHD). A partial correlation network was initially constructed to link inflammatory markers, including C-reactive protein (CRP), Interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein-1 (MCP-1), individual depression symptoms, and covariates. A network-centric approach searched all possible non-isomorphic subgraph patterns of size k = 4 in the network. Results. Although CRP, IL-6, and TNF-α displayed an insignificant association with specific depression symptoms, the motif analysis revealed various subgraph patterns of interactions between depression symptoms associated with MCP-1. Generally, MCP-1 formed a closed loop with psychomotor problems and sleep disturbances, and this configuration was connected in various forms with other symptoms, particularly cognitive (e.g., feelings of worthlessness, concentration difficulty, and suicidal ideation) and neurovegetative/somatic (e.g., appetite changes and fatigue) symptoms. Moreover, MCP-1 was frequently associated with a closed-loop triangle comprising cognitive and neurovegetative/somatic symptoms but not with mood symptoms (e.g., loss of interest and feelings of sadness). Conclusions. The findings provide insight into how MCP-1 may be involved in the pathology of depression among patients with stable CHD in a more precise manner. This study also proposes future directions for research on depression.

Relationships between rumination and different types of rapid eye movement sleep in patients with chronic insomnia disorder

Background and objectiveRumination, a common factor of chronic insomnia disorder (CID) caused by cognitive-emotional arousal, is associated with an increased amount of rapid eye movement (REM) sleep. However, the specific subtypes, such as phasic REM and tonic REM, that contribute to the increased REM sleep have not been reported. This study aimed to determine the association between rumination and different REM sleep subtypes in patients with CID. MethodsThis study enrolled 35 patients with CID and 27 age- and sex-matched healthy controls. The Immersion-Rumination Questionnaire evaluated participants' rumination, and the Insomnia Severity Index was used to assess insomnia severity. Finally, polysomnography was used to monitor objective sleep quality and quantification of different types of REM. ResultsThe CID patients had higher rumination scores than the healthy controls. They had a shorter REM sleep duration, less phasic REM, a lower percentage of phasic REM time, and a higher percentage of tonic REM time. Spectral analysis revealed that the patients affected by insomnia had higher β power during REM sleep, higher β and σ power during phasic REM sleep, and higher β, and γ power during tonic REM sleep. Partial correlation analysis showed that rumination in the CID patients correlated negatively with the duration of phasic REM sleep. Additionally, rumination correlated negatively with δ power in REM sleep and positively with β power in REM sleep, tonic REM sleep, phasic REM sleep, N3and N2 sleep in the patients with CID. ConclusionThe CID patients had stronger rumination, reduced total and phasic REM sleep, and the stronger rumination was, the shorter phasic REM was and the higher fast (β) wave power in REM sleep.

The Impact of the Measure Used to Calculate the Distance between Exchange Rate Time Series on the Topological Structure of the Currency Network.

Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson's linear correlation coefficient, Spearman's rank correlation coefficient, Kendall's coefficient, partial correlation, dynamic time warping measure, and Kullback-Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia's invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries' geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries.

Testing a Prioritization Tool to Identify Older Adults in Need of Home-Delivered Meals and Nutrition-Related Services

The Expanded Food Security Screener (FSS-Exp) determines food insecurity risk based on physical, economic, and social access to food. It categorizes individuals into 5 priorities based on need for a home-delivered meal. Concurrent and construct validity were applied to the tool. For concurrent validity, partial correlations between FSS-Exp and health- and food-related measures were significant and ranged between 0.20 and 0.33. For construct validity, analysis of variance was used to examine scores of the health- and food-related tools by priority level. The 2 highest priority levels had significantly lower mean scores for all the health variables except for dietary intake. The FSS-Exp tool effectively identifies those most in need of meal services.

Clinical prediction factors of nonchronic total occlusion lesion progression in patients with unstable angina receiving percutaneous coronary intervention for chronic total occlusion lesions

BackgroundIn this study, we investigated clinical prediction factors of nonchronic total occlusion lesion (NCTOL) progression in patients who underwent percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) lesions. MethodsIn total, 450 patients with unstable angina (mean age = 57.1 ± 9.2 years) who underwent PCI for CTO lesions between January 2016 and December 2018 at Beijing Anzhen Hospital were enrolled in this study. A clinical and angiographic follow-up examination was performed 12 months postoperatively. The patients were divided into NCTOL progression (145 cases) and control (305 cases) groups based on the outcome of the 12-month angiographic follow-up. The clinical and angiographic features of the participants were analyzed. ResultsThe adenosine diphosphate-induced platelet aggregation (ADP-IPA) rate and levels of lipoprotein (a) (Lp(a)) in the NCTOL progression group were significantly higher than those in the control group (51.89 ± 14.81 vs. 39.63 ± 17.12, P < 0.01; 0.22 ± 0.26 vs. 0.14 ± 0.18, P < 0.05, respectively). Logistic regression showed that the ADP-IPA rate (odds ratio = 1.047, 95 % confidence interval: 1.014–1.082, P = 0.005) and Lp(a) (odds ratio = 11.972, 95 % confidence interval: 1.230–116.570, P = 0.033) were independent predictors of NCTOL progression. Partial correlation analysis demonstrated that the ADP-IPA rate was positively correlated with NCTOL progression (r = 0. 351, P < 0.001). Receiver operating characteristic curve showed that the boundary point of the ADP-IPA rate to predict NCTOL progression was 30 % (sensitivity, 86.2 %; specificity, 68.9 %). ConclusionsNCTOL progression is an important cause of recurrent PCI in patients with coronary artery disease after PCI for CTO lesions. The ADP-IPA rate is a useful predictor for NCTOL progression in patients with unstable angina who undergo PCI for CTO lesions.

Language-Guided Transformer for Federated Multi-Label Classification

Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private data. Most existing approaches of FL only consider traditional single-label image classification, ignoring the impact when transferring the task to multi-label image classification. Nevertheless, it is still challenging for FL to deal with user heterogeneity in their local data distribution in the real-world FL scenario, and this issue becomes even more severe in multi-label image classification. Inspired by the recent success of Transformers in centralized settings, we propose a novel FL framework for multi-label classification. Since partial label correlation may be observed by local clients during training, direct aggregation of locally updated models would not produce satisfactory performances. Thus, we propose a novel FL framework of Language-Guided Transformer (FedLGT) to tackle this challenging task, which aims to exploit and transfer knowledge across different clients for learning a robust global model. Through extensive experiments on various multi-label datasets (e.g., FLAIR, MS-COCO, etc.), we show that our FedLGT is able to achieve satisfactory performance and outperforms standard FL techniques under multi-label FL scenarios. Code is available at https://github.com/Jack24658735/FedLGT.

The relationship between bone strain index, bone mass, microarchitecture and mechanical behavior in human vertebrae: an ex vivo study.

Bone Strain Index (BSI) is a new DXA-based bone index that represents the finite element analysis of the bone deformation under load. The current study aimed to assess whether the BSI is associated with 3D microarchitecture and the mechanical behavior of human lumbar vertebrae. Lumbar vertebrae (L3) were harvested fresh from 31 human donors. The anteroposterior BMC (g) and aBMD (g/cm2) of the vertebral body were measured using DXA, and then the BSI was automatically derived. The trabecular bone volume (Tb.BV/TV), trabecular thickness (Tb.Th), degree of anisotropy (DA), and structure model index (SMI) were measured using µCT with a 35-µm isotropic voxel size. Quasi-static uniaxial compressive testing was performed on L3 vertebral bodies under displacement control to assess failure load and stiffness. The BSI was significantly correlated with failure load and stiffness (r = -0.60 and -0.59; p < 0.0001), aBMD and BMC (r = -0.93 and -0.86; p < 0.0001); Tb.BV/TV and SMI (r = -0.58 and 0.51; p = 0.001 and 0.004 respectively). After adjustment for aBMD, the association between BSI and stiffness, BSI and SMI remained significant (r = -0.51; p = 0.004 and r = -0.39; p = 0.03 respectively, partial correlations) and the relation between BSI and failure load was close to significance (r = -0.35; p = 0.06). The BSI was significantly correlated with the microarchitecture and mechanical behavior of L3 vertebrae, and these associations remained statistically significant regardless of aBMD.

Estimating groundwater geogenic arsenic contamination and the affected population of river basins underlain mostly with crystalline rocks in Ghana

Arsenic is a human carcinogen and contributes to the incidences of cancer, diabetes, and skin diseases in the exposed populations. Therefore, its occurrence in drinking water is of public health concern worldwide, especially in the Tano, Pra, and Ankobra River Basins in Ghana. The metasediments and metavolcanic formations which underlie these basins (42,092.5 km2) host arsenic sulphide minerals that are a source of arsenic contamination of the weathered and fractured aquifers that serve as drinking water supply to most of the population (∼9.8 million), especially in the rural areas. A random forest machine learning method and partial dependence plots were applied to maps of spatially continuous environmental factors (fault density, surface bulk density, coarse fragment and soil pH; and sub-surface silt and soil pH) and hydrochemical variables, respectively, and arsenic measurements from wells (n = 229) which included 57 new datasets of which 7.86 % exceeded the 10 µg/L to develop arsenic prediction models for the basins at thresholds of 1 µg/L, 5 µg/L, and 10 µg/L. Fault density and coarse fragments were more reliable contributors to the models. A partial dependence plot and Spearman's rank correlation analysis showed a significant positive correlation (p < 0.01) between arsenic enrichment and HCO3−, pH, Fe, and Mn, except NO3− in groundwater. Arsenic hotspots occurred in urban districts in the Ashanti Region and known gold-mining areas in Nzema, Obuasi, Prestea, and Tarkwa. Spatially, arsenic contamination in groundwater covered 49.44 % of the land area of the basins. By cross-referencing population density estimates and the arsenic hazard map,15.8 % of the population is exposed to arsenic-contaminated groundwater in the basins. The hazard model will facilitate efforts to reduce rural poverty and protect public health by delivering potable water in Ghana. The hazard models apply to crystalline terrain in other sub-Saharan African countries.

Impact of semolina-barley mixture on the volatolomic profile of dough and pasta: characterization by a multivariate chemometric approach.

Barley flour, known to be rich in various phytochemicals, has been demonstrated to improve the technological and nutritional properties of pasta; however, its volatile profile, on which its aromatic properties depend, also plays an important role in the acceptance of barley-enriched pasta. In the present work, volatile organic compounds (VOCs) of semolina doughs enriched with different percentages of barley and of the related pasta were characterized by solid phase micro-extraction (HS-SPME) coupled to gas-chromatography/mass spectrometry (GC-MS), and evaluated using a multivariate statistical approach, including principal component analysis (PCA), cluster heatmaps, Pearson's and Spearman's correlations, and partial least squares correlation (PLSC). The effects of single raw materials, and their interactions, were studied to establish their importance in the volatile profile of the samples, and the correlation between the dough VOCs and the processed product VOCs was assessed. The presence of barley flour markedly affected the volatile profile in comparison with the dough obtained with only durum wheat. For alcohols, esters, terpenes, and some aldehydes there was a clear correlation with the percentage of barley. For some of the VOCs, on the other hand, a strong dependence on the ingredients interaction effect due to the mixing stage has been demonstrated. The heatmaps allowed a good graphical visualization of the relationship between molecules and barley percentage, offering the possibility to select the best one according to the desired volatolomic footprint. Pasta with 40% of barley was demonstrated to give pasta with the most complex volatile profile. © 2024 Society of Chemical Industry.

The use of Brix refractometer as a simple and economic device to estimate the protein content of sheep milk

In this study, 737 individual sheep milk samples were collected 2 times from morning milkings to evaluate the relationships between Brix refractometer measurements and milk constituents— protein and fat percentages—to verify its ability to predict milk constituents. The Pearson's simple (rSP) and partial (rPP) correlations between milk constituents were calculated, and several first- and second-order regressions were tested to predict the protein and fat percentages. The rSP coefficients between the Brix refractometer measurement and fat (rSP = 0.46) and protein (rSP = 0.87) contents were different from the rPP coefficients, particularly for fat percentage (rPP = 0.04 and rPP = 0.82, for fat and protein percentages, respectively). The results of the forecasts can be considered satisfactory only for the first order regression that predicted the percentage of milk protein, while the regression that predicted the percentage of fat + milk protein presented a weak forecasting capacity.

Abstract 5369: Mapping a T cell-cold phenotype to the colorectal cancer genome to identify POFUT1 as a driver of CD8+ T cell inhibition

Abstract CD8+ T cells are a critical anti-cancer immune subset; however, most colorectal cancer (CRC) patients have inadequate levels of CD8+ T cells in their tumor microenvironment (TME), contributing to poor survival and increased CRC aggression. While current immune checkpoint inhibitors are able to elicit anti-cancer CD8+ T cell responses in some patients, a majority of patients are still nonresponsive, and have minimal levels of tumor-infiltrating CD8+ T cells. This effect is potentially due to still-unrecognized cancer-driven mechanisms of immunosuppression. To identify candidate genes with roles in CD8+ T cell exclusion, we used a T cell gene signature (Tsig) to quantify CD8+ T cell infiltration in tumors profiled by RNA-seq. Partial Spearman correlation adjusted for tumor purity was performed for all annotated genes (N=20501) in the TCGA CRC RNA-seq data set (N=373) to identify genes most inversely correlated with Tsig. A locus at chromosome 20q was identified as the region of the genome most inversely correlated with Tsig - both by copy number analysis and by RNA expression. Interestingly, this region is the most copy number-amplified region in CRC tumors, and the genes at this locus are among the most highly expressed genes in CRC tumors. Within this region, the amplified and overexpressed gene Protein O-Fucosyltransferase 1 (POFUT1) had the strongest inverse correlation to Tsig. POFUT1 function is essential to canonical NOTCH signaling, and CRC-expressed NOTCH is implicated in increased CRC secretion of immunosuppressive factors IL-6, TGF-β and VEGF, and reduced CD8+ T cell secretion of IFN-γ and IL-2. POFUT1 overexpressing (OE) CT26 and MC38 cell lines were generated, and confirmed by Western Blot to have elevated levels of both cleaved NOTCH1 (NICD1), and NOTCH pathway transcriptional target, HES1. Murine CD8+ T cells were stimulated in vitro with CD3/CD28 microbeads and IL-2 in conditioned medium (CM) from vector or POFUT1-OE lines. CD8+ T cells cultured in CM from POFUT1-OE lines had significantly lower levels of activation markers (CD45 (p=0.0022), CD8 (p=0.0095), CD25 (p=0.0069, CD69 (p=0.0099)), reduced proliferation (CFSE, p=0.1281), and generated less ATP (Cell-Titer Glo, p=0.0003) than CD8+ T cells cultured in vector CM. In pan-cancer correlational analyses, POFUT1 is in the top 5% of genes most inversely correlated with Tsig in 12/24 solid tumor types annotated in TCGA (SKCM, THCA, COAD, READ, LUSC, CESC, KIRC, STAD, HNSC, KIRP, LIHC, UCEC p&amp;lt;0.0001). POFUT1 is robustly negatively correlated with T cell infiltration in many cancer types. We observed a positive association in CRC between POFUT1 and NOTCH signaling, and observed that POFUT1 expression reduced CD8+ T cell activity, potentially through upregulation of extracellular factors (in progress). Our findings fit a model where POFUT1 amplification in CRC promotes CD8+ T cell dysfunction as a mechanism of immune escape. Citation Format: Calvin J. Wagner, Eric D. Routh, Raven Rutledge, Lance D. Miller. Mapping a T cell-cold phenotype to the colorectal cancer genome to identify POFUT1 as a driver of CD8+ T cell inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5369.

Abstract 3421: Associations of circulating levels of tryptophan-kynurenine metabolites and inflammation biomarker neopterin with overall survival among patients with stage I-III colorectal cancer

Abstract Introduction: Dysregulation of the kynurenine pathway of tryptophan metabolism has been linked to the development of inflammation-related chronic diseases, such as colorectal cancer (CRC). Circulating concentrations of certain kynurenine metabolites, the kynurenine/tryptophan ratio (KTR), and the pro-inflammatory marker neopterin increase during inflammation, particularly in response to interferon-gamma. However, comprehensive data on the associations between inflammation markers and kynurenine metabolites and clinical outcomes in CRC patients are lacking. To address this gap in knowledge, we investigated associations of circulating kynurenine metabolites and neopterin with overall survival (OS) in prospectively followed non-metastatic CRC patients. Methods: Pre-surgery blood samples from 2,101 stage I-III CRC patients participating in the international FOCUS consortium were used to measure circulating levels of nine tryptophan-kynurenine pathway metabolites and neopterin using liquid chromatography-tandem mass spectrometry. Multivariable linear regression models and Spearman partial correlation analyses were conducted to quantify associations between kynurenine metabolites and neopterin. Based on the results from correlation analyses, a subset of patients (n=984) was categorized into different groups (‘kynurenine metabolite/neopterin‘: i.e. ‘low/high‘, ‘high/high‘) using the median biomarker concentration as cut-off. Associations of biomarkers with OS were assessed using Cox proportional hazards regression models. All models were adjusted for age at diagnosis, sex, tumor stage, tumor site, circulating creatine levels and cohort. Results: Tryptophan was inversely correlated with neopterin (r=−0.21, β=−0.48, Plinear&amp;lt;0.001). Neopterin was positively correlated with kynurenine (kyn) (r=0.25, β=0.57, Plinear&amp;lt;0.001), the KTR (r=0.42, β=0.87, Plinear&amp;lt;0.001), 3-hydroxykynurenine (r=0.26, β=0.32, Plinear&amp;lt;0.001), anthranilic acid (r=0.29, β=0.36, Plinear&amp;lt;0.001), and quinolinic acid (QA) (r=0.41, β=0.56, Plinear&amp;lt;0.001). After a median follow-up of 3.2 years for OS, 14% (141) patients deceased. While patients with ‘high neopterin’ levels had a 43% increased risk of death (HR, 1.43, 95%CI 0.95-2.16), the ‘high kyn/high neopterin‘ patient group had a 52% increased risk of death (HR, 1.52, 95%CI 1.04-2.22). Similarly, the ‘high QA/high neopterin‘ and ‘high KTR/high neopterin‘ patient groups had a 85% and 89% increased risk of death, respectively (HRQA/Neopt, 1.85, 95%CI 1.24-2.75, HRKTR/Neopt, 1.89, 95%CI 1.27-2.80). Conclusion: Tryptophan-kynurenine pathway activation correlates with inflammation marker neopterin in CRC. Combined associations of kynurenine metabolites and neopterin may be a promising predictor for OS among stage I-III CRC patients. Citation Format: Victoria Damerell, Eline H. van Roekel, Stefanie Brezina, Dieuwertje E. Kok, Tengda Lin, Daniëlle D. Holthuijsen, Jennifer Ose, Caroline Himbert, Simone J. P. Eussen, Arve Ulvik, Fränzel J. van Duijnhoven, Jane C. Figueiredo, Christopher I. Li, Erin M. Siegel, Martin Schneider, David Shibata, Adetunji A. Toriola, Alexis B. Ulrich, Per M. Ueland, Matty P. Weijenberg, Andrea Gsur, Ellen Kampman, Cornelia M. Ulrich, Biljana Gigic. Associations of circulating levels of tryptophan-kynurenine metabolites and inflammation biomarker neopterin with overall survival among patients with stage I-III colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3421.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

The white matter characteristic of the genu of corpus callosum coupled with pain intensity and negative emotion scores in patients with trigeminal neuralgia: a multivariate analysis.

Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder that not only causes intense pain but also affects the psychological health of patients. Since TN pain intensity and negative emotion may be grounded in our own pain experiences, they exhibit huge inter-individual differences. This study investigates the effect of inter-individual differences in pain intensity and negative emotion on brain structure in patients with TN and the possible pathophysiology mechanism underlying this disease. T1 weighted magnetic resonance imaging and diffusion tensor imaging scans were obtained in 46 patients with TN and 35 healthy controls. All patients with TN underwent pain-related and emotion-related questionnaires. Voxel-based morphometry and regional white matter diffusion property analysis were used to investigate whole brain grey and white matter quantitatively. Innovatively employing partial least squares correlation analysis to explore the relationship among pain intensity, negative emotion and brain microstructure in patients with TN. Significant difference in white matter integrity were identified in patients with TN compared to the healthy controls group; The most correlation brain region in the partial least squares correlation analysis was the genus of the corpus callosum, which was negatively associated with both pain intensity and negative emotion. The genu of corpus callosum plays an important role in the cognition of pain perception, the generation and conduction of negative emotions in patients with TN. These findings may deepen our understanding of the pathophysiology of TN.

Integrated microRNA and proteome analysis of cancer datasets with MoPC.

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases.

Dissociative Experiences, Borderline Personality Disorder Features, and Childhood Trauma: Generating Hypotheses from Data-Driven Network Analysis in an International Sample

ABSTRACT Dissociation is a multifaceted phenomenon that occurs in various mental disorders, including borderline personality disorder (BPD), but also in non-clinical populations. Severity of childhood trauma (abuse, neglect) plays an important role in the development of dissociation and BPD. However, the complex interplay of different dissociative symptoms, BPD features, and self-reported childhood trauma experiences is not yet fully understood. Graph-theoretical network analysis can help to better understand such multivariate interrelations. Objective: This study aimed to investigate associations between self-reported dissociation, BPD features, and childhood trauma experiences using a graph-theoretical approach. Data was collected online via international mental health platforms and research sites. N = 921 individuals (77.4% female) were included; 40% reported pathological levels of dissociation. Variables were assessed with established psychometric scales (Dissociative Experiences Scale; Personality Assessment Inventory Borderline Features Scale; Childhood Trauma Questionnaire) and analyzed within a partial correlation network. Positive bivariate correlations between all variables were found. When accounting for their mutual influence on each other, dissociation was predominantly connected to BPD features with effect sizes between rp = .028 and rp = .126, while still showing a slight unique relationship with physical neglect (rp = .044). Findings suggest close associations between dissociative experiences and BPD features. While childhood trauma plays an important role in the development of dissociation and BPD, its recall may not fully explain their current co-occurrence. Prospective studies are needed to shed more light on causal pathways to better understand which factors contribute to dissociation and its link to BPD (features).

Assessing the impact of information-induced self-protection on Zika transmission: A mathematical modeling approach

Abstract As per the World Health Organization’s (WHO’s) suggestions, personal protection via adopting precautionary measures is one of the most effective control aspects to avoid Zika infection in the absence of suitable medical treatment. This personal protection further can be enhanced and explored by propagating information about disease prevalence. Therefore, in this study, we wish to see the effect of information on Zika transmission by formulating a compartmental mathematical model that quantifies the effect of an individual’s behavioral response as self-protection due to information. Furthermore, the basic reproduction number was calculated using the next-generation matrix technique. The model analysis was carried out to determine the local and global stability properties of equilibrium points. In addition, the model shows the occurrence of forward bifurcation when the reproduction number crosses unity. To understand the impact of various model parameters, we conducted a sensitivity analysis using both the normalized sensitivity index and the partial rank correlation coefficient methods. Moreover, we performed numerical simulations to assess the influence of important parameters on the model’s behavior for Zika prevalence. Our study accentuates that as information-induced self-protection increases, the prevalence of Zika infection will be at a very minimum level, and this observation is in line with WHO suggestions.