Neighboring Matrix Research Articles

Collaborative filtering recommendation based on K-nearest neighbor and non-negative matrix factorization algorithm

Collaborative filtering recommendation based on K-nearest neighbor and non-negative matrix factorization algorithm

Odonata in the brazilian Cerrado: Influence of environmental and spatial factors on dragonfly and damselfly assemblages

Environmental and spatial variables can act as filters in species distribution, thereby structuring biological communities. Our study aims to analyze the effects of environmental and spatial factors on the structuring of Odonata assemblages in Cerrado. Testing the hypotheses: (1) the structuring of Odonata larvae and adult assemblages is more influenced by the environment than by space; and (2) the structuring of larvae and adult assemblages of the suborder Anisoptera is more influenced by the environment, while Zygoptera is more influenced by space. We measured physicochemical variables (dissolved oxygen, pH, conductivity and temperature) and Habitat Integrity Index. Data analysis involved the coverage-based rarefaction and extrapolation method, Partial Redundancy Analysis (RDAp), and Principal Coordinates of Neighbor Matrices. Thirteen streams were sampled, collecting 1204 specimens. The sampling effort for larvae showed an efficiency of 98 %, and for adults, 85 %. According to RDAp, the composition for larvae was explained solely by environmental factors, while for adults, there was an explanation by the environment for Anisoptera and by both environmental and spatial factors for the order and Zygoptera. The results highlight the joint influence of environmental and spatial factors on the configuration of adult and larval Odonata assemblages in Cerrado streams. A variety of responses, each corresponding to the specific object of analysis, is evident, driven by divergent eco-physiological characteristics among suborders. These nuances reflect the inherent complexity of the ecology of these aquatic insects, emphasizing the importance of considering both environmental and spatial factors when investigating the dynamics of Odonata communities at different developmental stages.

A Decomposition-Based Multi-Objective Flying Foxes Optimization Algorithm and Its Applications.

The flying foxes optimization (FFO) algorithm stimulated by the strategy used by flying foxes for subsistence in heat wave environments has shown good performance in the single-objective domain. Aiming to explore the effectiveness and benefits of the subsistence strategy used by flying foxes in solving optimization challenges involving multiple objectives, this research proposes a decomposition-based multi-objective flying foxes optimization algorithm (MOEA/D-FFO). It exhibits a great population management strategy, which mainly includes the following features. (1) In order to improve the exploration effectiveness of the flying fox population, a new offspring generation mechanism is introduced to improve the efficiency of exploration of peripheral space by flying fox populations. (2) A new population updating approach is proposed to adjust the neighbor matrices to the corresponding flying fox individuals using the new offspring, with the aim of enhancing the rate of convergence in the population. Through comparison experiments with classical algorithms (MOEA/D, NSGA-II, IBEA) and cutting-edge algorithms (MOEA/D-DYTS, MOEA/D-UR), MOEA/D-FFO achieves more than 11 best results. In addition, the experimental results under different population sizes show that the proposed algorithm is highly adaptable and has good application prospects in optimization problems for engineering applications.

Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility.

Structure, floristic composition, and distribution of swamp forests across a white‐water flood‐plain in the Colombian Amazon

AbstractQuestionsThe fluvial dynamics of meandering white‐water rivers of Amazonia drive vegetation primary succession. Directional successional processes have been recorded for the seasonal várzea forests that occupy well‐drained soils on levees and point bars across the spatial gradient of the flood‐plains. However, the types of forests occupying the swampy depressions interspersed between the point bars and their distribution along the flood‐plain's spatial gradient are poorly understood. Here, we aimed to unravel the spatial patterns of swamp forests along the sequence defined by an axis perpendicular to the river and the relationship with edaphic and spatial factors.LocationThe flood‐plains of the middle Caquetá River basin, Colombian Amazonia.MethodsForest types were identified by cluster analysis performed on 42 square plots (33 m × 33 m) set in poorly drained depressions of the flood‐plain. Floristic composition and vegetation structure as response data, and edaphic and spatial variables as predictors, were analyzed through partial redundancy analysis (pRDA). The effect of geographic position was included by using the first two axes of a principal coordinates of neighbor matrix analysis as conditional factors in the pRDA.ResultsThe three identified swamp forest types were not arranged along a directional spatial pattern. Permanent várzea forests, closest to the river, showed the greatest diversity and alluvial sediment input. Oxandrales, furthest from the river, dominated by Oxandra polyantha, showed the greatest tree density, basal area and soil sand content, and received additional flooding from black‐water streams. Cananguchales, dominated by Mauritia flexuosa, exhibited the highest dominance and soil organic matter layer thickness. Distribution of the permanent várzea and oxandrales was relatively constrained by the distance to the river, whereas that of the cananguchales was not.ConclusionsFlooding dynamics delay vegetation development of the swamp forests in permanent várzea and oxandrales. Cananguchales keep accumulating organic matter, becoming ombrotrophic peats after isolation from the river flooding influence. The swamp forests across these flood‐plains are far from being arranged along a linear sequence.

Traffic Flow Prediction Based on Interactive Dynamic Spatio-Temporal Graph Convolution with a Probabilistic Sparse Attention Mechanism

Accurate traffic flow prediction is of great practical significance to alleviate road congestion. Existing methods ignore the hidden dynamic associations between road nodes, and for the problem of difficulty capturing the dynamic spatio-temporal features of traffic flow in the prediction process, a novel model based on the interactive dynamic spatio-temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed, which consists of an interactive dynamic graph convolutional network (IDGCN) structure with a spatio-temporal convolutional block (ST-Conv block) and a probabilistic sparse self-attention mechanism block (ProbSSAtt block). Among them, the IDGCN synchronizes the dynamic spatio-temporal features captured by interaction sharing, and the ST-Conv block is combined with the ProbSSAtt block to effectively capture the long short-term temporal features of the traffic flow. In addition, to effectively find the hidden dynamic associations between road network nodes, a dynamic graph convolutional network generated by the fusion of an adaptive neighbor matrix and a learnable neighbor matrix was constructed. Experimental results demonstrate that the prediction performance of the IDG-PSAtt model outperforms the baseline model under the evaluation criteria and experimental settings given in this paper. In the PEMS-BAY dataset, the mean absolute error and root mean square error of the IDG-PSAtt to 60 min are improved by 15.49% and 12.10%, compared with the state-of-the-art model, respectively.

Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer.

To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5-103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.

Research on the Construction of Campus Security Prevention and Control System Based on Internet of Things Technology

Abstract Campus safety is related to the healthy growth of students, and the research focus of this paper is to explore the impact of Internet of Things (IoT) technology on campus safety prevention and control. Based on the correlation between ISM and DEMATEL methods, this paper constitutes an integrated DEMATEL-ISM model by using neighbor matrix and reachable matrix to transform each other. It constructs a risk factor model for campus security prevention and control evaluation based on Internet of Things technology according to the modeling steps after soliciting experts’ suggestions through questionnaires. Combined with the SEM model to estimate students’ campus risk identification ability, the path index was used to construct the college security awareness score index to explore the relationship between latent variables (influencing factors) and observed variables (risk identification ability). The results show that every 1% increase in the safety climate of the social environment will lead to the rise of 0.58%, 0.20%, and 0.52% in the on-campus environment factor, the campus safety management team building factor, and the students’ literacy factor, respectively. A 0.17% increase in the on-campus environment will occur with every 1% increase in students’ literacy in higher education. This study contributes to a deeper understanding of the role of IoT technology in enhancing campus security prevention and control. It can provide deep research references for subsequent risk grading evaluation, risk warning prevention and control, and supervision in campus risk management.

Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features.

Telomerase reverse transcriptase (TERT) promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas. This was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3,948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 gray level run length matrix, 14 gray level dependence matrix, 16 gray level run length matrix, 16 gray level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis. Among the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status. The LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models.

Spatial effects and heterogeneity analysis of the impact of environmental taxes on carbon emissions in China

Environmental taxes are important means by which governments can address environmental pollution problems. Amid increasingly severe global warming, how should environmental taxes be used to better combat pollution and reduce emissions to promote sustainable socioeconomic development? This empirical analysis explores the influence of environmental taxes on CO2 emissions by utilizing a spatial Durbin model constructed with panel data from 2006 to 2020 encompassing 30 provinces, autonomous regions, and municipalities under the direct jurisdiction of China's central government. First, we found that a strong spatial auto-correlation exists between carbon emission intensity and environmental taxes at the geographic and economic levels in each province. The characteristics of high–high and low–low agglomeration are consistent with the actual situation where each province has a strong regional correlation. Second, the estimation results of environmental taxes' spatial effect on carbon emissions show that under the neighboring space weight matrix, environmental taxes and fees can not only better promote regional carbon emission reduction but also reduce the carbon emissions of neighboring regions. Under the economic distance weight matrix, environmental taxes' impact on reducing carbon emission intensity in the province is not significant, but it can promote the reduction of carbon emissions in the economically neighboring provinces. Additionally, the results of the sub-tax estimation of environmental taxes and carbon emission intensity show that differences exist in the impacts of different environmental taxes on carbon emission intensity under different weight matrices. Among them, environmental protection, resource, vehicle and vessel, and urban land use taxes are basically unfavorable in reducing carbon emission intensity in a region and its neighboring regions, while urban maintenance and construction and cultivated land occupation taxes enhance carbon emission reduction. Our findings suggest that efficiently promoting carbon emissions reduction requires effectively utilizing the spatial effects of environmental taxes and carbon emissions, establishing and improving the regional carbon emissions reduction linkage mechanism, including carbon dioxide in the scope of taxation to further strengthen environmental taxes' positive impact on carbon emission reduction, and focusing on the heterogeneity of environmental tax implementation to achieve emission reduction goals.

Magnetic susceptibility and R2*-based texture analysis for evaluating liver fibrosis in chronic liver disease

PurposeTo explore potential feasibility of texture features in magnetic susceptibility and R2* maps for evaluating liver fibrosis. MethodsThirty-one patients (median age 46 years; 22 male) with chronic liver disease were prospectively recruited and underwent magnetic resonance imaging (MRI), blood tests, and liver biopsy. Susceptibility and R2* maps were obtained using a 3-dimensional volumetric interpolated breath-hold examination sequence with a 3T MRI scanner. Texture features, including histogram, gray-level co-occurrence matrix (GLCM), gray-level dependence matrix (GLDM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighboring gray tone difference matrix (NGTDM) features, were extracted. Texture features and blood test results of non-significant (Ishak-F < 3) and significant fibrosis patients (Ishak-F ≥ 3) were compared, and correlations with Ishak-F stages were analyzed. Areas under the curve (AUCs) were calculated to determine the efficacy for evaluating liver fibrosis. ResultsNine texture features of susceptibility maps and 19 features of R2* maps were significantly different between non-significant and significant fibrosis groups (all P < 0.05). Large dependence high gray-level emphasis (LDHGLE) of GLDM and long run high gray-level emphasis (LRHGLE) of GLRLM in R2* maps showed significantly negative and good correlations with Ishak-F stages (r = –0.616, P < 0.001; r = –0.637, P < 0.001). Busyness (NGTDM) in susceptibility maps, LDHGLE of GLDM and LRHGLE of GLRLM in R2* maps yield the highest AUCs (AUC = 0.786, P = 0.007; AUC = 0.807, P = 0.004; AUC = 0.819, P = 0.003). ConclusionTexture characteristics of susceptibility and R2* maps revealed possible staging values for liver fibrosis. Susceptibility and R2*-based texture analysis may be a useful and noninvasive method for staging liver fibrosis.

Development of a Radiomic Signature from Pre-Treatment FDG-PET for Recurrence Prediction in Cervical Cancer.

As many as 30-50% of patients with locally advanced cervical cancer (LACC) experience recurrence after standard-of-care chemoradiation therapy (CRT), creating a critical need to identify pre-treatment biomarkers of treatment failure. The purpose of this study is to identify whether radiomic features derived from pre-treatment FDG-PET imaging can be used to construct a predictive signature useful for assessing risk of recurrence during treatment planning. Standardized uptake values (SUV) were obtained from within the physician-defined metabolic tumor volumes (MTV) delineated on FDG-PET scans acquired for 90 LACC patients at our institution prior to standard of care curative-intent CRT. Clinical outcome data of these patients has a median follow-up time of 85 months. The clinical endpoint was local recurrence within 3 years of treatment. 851 quantitative radiomic features describing intensity, shape, texture and high and low frequency spatial filters of the MTV were extracted for each patient. Low information features, defined by pairwise correlation > 0.85 with another feature or a maximum deviation within 20% of the mean, were discarded, leaving 146 features. Predictive signatures were constructed from features using multiple techniques, including multivariate Cox modelling, a set of machine learning models (random forest (RF), support vector classifier (SVC), ridge regression, LASSO regression, and elastic net regression), and a deep neural network (DNN) classifier. The DNN classifier had the best overall performance, predicting a patient's recurrence group with an F1 score of 0.917 ± 0.028 under 5-fold cross-validation. By contrast, the Cox model classifier yielded an F1 score of 0.604 ± 0.085 and the best performing of the alternative machine learning models, elastic net, yielded F1 score of 0.868 ± 0.018. A set of textural features contributed the most to the output of the DNN classifier, including Large Area Low Gray Level Emphasis of the gray level size zone matrix (GLSZM) and coarseness and busyness of the neighboring gray tone difference matrix (NGTDM), reflecting the important role patterns of tumor heterogeneity play in post-treatment recurrence. In this pilot study, we investigated multiple techniques to construct predictive radiomic signatures for local recurrence in LACC, determining that a DNN classifier is most capable of stratifying patients by risk of early recurrence. Future work will seek to validate this result on additional PET imaging data sets and to integrate radiomic features with gene expression data from matched tumor samples to establish radiogenomic biomarkers for recurrence.

Study of the effect of heat treatment on corrosion property of the AlxCoCrFeNi high-entropy alloys (x = 0.3, 0.7, and 1) prepared by spark plasma sintering

The effect of spark plasma assisted heat treatment (SPHT) on the corrosion property of Al0.3CoCrFeNi, Al0.7CoCrFeNi, and AlCoCrFeNi HEAs is studied in this paper. The Tafel and EIS measurement is adopted to determine how the corrosion resistance varies before and after SPHT. The Al0.3CoCrFeNi and Al0.7CoCrFeNi HEAs show an enhancement in corrosion resistance after SPHT as their corrosion current density (icorr) decreases and polarization resistance. The SPHTed Al0.7CoCrFeNi HEA features the best corrosion resistance as it has the lowest icorr of 7.1 × 10−10 A/cm2 and the highest polarization resistance of 139.89 kΩ·cm2. Nevertheless, SPHT damages the corrosion resistance of the AlCoCrFeNi HEA. The phase transition of FCC-B2 and BCC-FCC is revealed to benefit the corrosion resistance but the precipitation of the σ phase is found to damage corrosion resistance after SPHT. Cr is proven to be the most corrosion-resistant content while Al and Ni contents are easy to be corroded. The different particle size of the transformed phase decides their disparate effects on corrosion resistance as the micro-sized Cr-enriched phase increases the corrosion resistance by effectively covering the corrosion surface but the nano-sized Cr-enriched phase decreases the corrosion resistance by reducing the Cr contents in the neighboring matrix.

Microhabitat variation affects the understory community assembly of a neotropical rainforest

Deterministic and stochastic processes combine with biotic interactions in community assembly. Stochasticity and biotic interactions are thought to operate on fine scales, and determinism on coarse scales. Would determinism be detected on a fine scale in an understory community of a tropical rainforest? We measured the altitude, index of convexity (IC), slope, and canopy openness, counted the individuals and species and calculated Simpson's dominance index of plants with height ≥ 30 cm and DBH < 5 cm for each of 50 randomly drawn 3 × 3 m plots from a one-hectare grid. We used redundancy analysis and spatial modeling with principal coordinates of neighbor matrices to assess how much of the community variation each variable explained. The same was done for each set of plots with 20%, 60%, and 20% respectively of the lower, intermediate, and higher values of the microrelief variables. Most of the community variation remained unexplained, but the effect of the microrelief variables was significant. IC had the highest contribution and was four times higher in the plots with 20% higher values, stressing the increasing importance of determinism towards the higher extremes of the variable's local range. Species richness decreased and dominance increased in convex plots (high IC), and the most abundant species differed between concave and convex plots. Microrelief variation can act as a local filter with a growing influence towards the extremes of the local range of microenvironmental variables in the community space. Niche-based processes rely on the strength of the environmental filter.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Contrasting spatial distribution of pioneer versus non‐pioneer saplings in a Taiwanese forest: A multiple scale approach

AbstractDispersal limitation and environmental filtering were recognized as the main dispersal‐based and niche‐based processes responsible for community assembly. These interactions usually leave more spatial signatures in sapling communities and create detectable spatial patterns in different functional types of species. We investigated saplings of pioneer and non‐pioneer species, which facilitated an understanding of species co‐existence and biodiversity recovery. Based upon mapped tree data in the subtropical evergreen broad‐leaved forest dynamics plot in Taiwan, we endeavored to: (1) determine the different spatial scales contributing to distribution patterns of pioneer and non‐pioneer saplings; (2) understand how dispersal limitation affects distribution of pioneer and non‐pioneer saplings; and (3) evaluate the degree to which the different environmental factors explain variation in species composition in pioneer and non‐pioneer saplings. We used the principal coordinates of neighbor matrices (PCNM) method, and variation partitioning to disentangle the contributions of these processes. Fine‐scale spatial patterns were more important for pioneer than for non‐pioneer saplings. Dispersal limitation was the main process influencing the spatial distribution of the non‐pioneer saplings. Environmental filtering was also an important process regulating the spatial distribution of saplings for both functional types, especially for pioneer saplings. Disturbance effects contributed to fine‐scale spatial variation in pioneer saplings, whereas topographic and soil effects contributed to broad and medium scale spatial variation in non‐pioneer saplings. It was evident that different spatial patterns of saplings of different functional types served to facilitate tree regeneration and maintain species co‐existence in this subtropical broad‐leaved forest.

Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL.

Emerging evidence indicates that chemoresistance is closely related to altered metabolism in cancer. Here, we hypothesized that distinct metabolic gene expression profiling (GEP) signatures might be correlated with outcome and with specific fluorodeoxyglucose positron emission tomography (FDG-PET) radiomic profiles in diffuse large B-cell lymphoma (DLBCL). We retrospectively analyzed a discovery cohort of 48 consecutive patients with DLBCL treated at our center with standard first-line chemoimmunotherapy by performing targeted GEP (T-GEP)- and FDG-PET radiomic analyses on the same target lesions at baseline. T-GEP-based metabolic profiling identified a 6-gene signature independently associated with outcomes in univariate and multivariate analyses. This signature included genes regulating mitochondrial oxidative metabolism (SCL25A1, PDK4, PDPR) that were upregulated and was inversely associated with genes involved in hypoxia and glycolysis (MAP2K1, HIF1A, GBE1) that were downregulated. These data were validated in 2 large publicly available cohorts. By integrating FDG-PET radiomics and T-GEP, we identified a radiometabolic signature (RadSig) including 4 radiomic features (histo kurtosis, histo energy, shape sphericity, and neighboring gray level dependence matrix contrast), significantly associated with the metabolic GEP-based signature (r= 0.43, P= .0027) and with progression-free survival (P= .028). These results were confirmed using different target lesions, an alternative segmentation method, and were validated in an independent cohort of 64 patients. RadSig retained independent prognostic value in relation to the International Prognostic Index score and metabolic tumor volume (MTV). Integration of RadSig and MTV further refined prognostic stratification. This study provides the proof of principle for the use of FDG-PET radiomics as a tool for noninvasive assessment of cancer metabolism and prognostic stratification in DLBCL.

Diversidade beta de Ephemeroptera, Plecoptera e Trichoptera ao longo de múltiplas escalas espaciais nas corredeiras do rio Xingu

Abstract: Aim Additive diversity partitioning has been used to explain the accumulation of diversity at different spatial scales with relative success. In lotic ecosystems, the spatial extent is extremely relevant in studies of diversity accumulation, because it encompasses environmental variation that causes changes in the observed communities. Despite of previous knowledge on the effect of extent on biological communities and diversity accumulation, little is known about the topic in aquatic insect communities in large rivers. In this context, we studied the effect of spatial extent and environmental variation on diversity components, alpha and beta, in Ephemeroptera, Plecoptera and Trichoptera (EPT) groups in Xingu River rapids. Methods The sampling was carried out in October 2015 in the dry period of the region, in nine rapids in the Xingu, Bacajá and Iriri rivers. At each collection site, five Surber samples were taken. We also recorded pH, dissolved oxygen, electrical conductivity, water temperature, and geographic coordinates. We used additive diversity partitioning to separate the diversity components α and β. For the spatial component, we generated the spatial filters using PCNM (Principal Coordinates of Neighbour Matrices) and partitioned the variance between space and environment using partial Redundancy Analysis (pRDA). Results We collected 12,249 individuals in 27 genera within 11 families in the EPT orders. The greatest accumulation of diversity was observed among rapids of the river, when the β diversity in this spatial extent was greater than the expected. The spatial structure was an indirect effect at this extent, since it is a relevant drive to environmental variables. Conclusions The results indicate that the effect of spatial extent on rapids is a contributing factor in the diversity components of aquatic insect communities in large river rapids. To the conservation and management of this environment is necessary cover as many rapids as possible, since the preservation of only a few rapids can mean a substantial loss of regional diversity.

Differences in bird community structure between riparian and upland zones in a New Guinean rainforest

Riparian forests serve as important habitats that enhance diversity of bird species and perform crucial ecosystem functions in tropical forest landscapes. We compared the community structure (represented by functional trait composition) and diversity (represented by Shannon index) of birds in riparian and upland zones in a semi-continuous primary rainforest in Papua New Guinea. Distance sampling was applied to provide unbiased abundance estimates. Bird community structure was examined by principal coordinate analysis of neighbour matrices (PCNM) in the Canoco 5 program. Analysis of diversity was performed using mixed-effects linear models in R 3.5.1. Species diversity was marginally higher in riparian zones (P = 0.065). Community structure differed significantly in riparian and upland zones (P = 0.008). Invertivorous and omnivorous birds preferred riparian habitats but frugivorous and nectivorous birds avoided riparian zones. Birds in riparian zones were characterised by preferences for high-density forest interior but birds in upland areas mostly occupied exposed canopy layers. Beak dimensions were greater in riparian zones but body mass, and lengths of wing and tarsus were greater in upland zones. Species found in riparian zones had greater geographical ranges than those in upland zones. Our results highlight the adaptability of a rainforest bird community to natural disturbances characterised by localised river flooding and riverbank erosion. For example, small invertivorous and omnivorous birds can navigate disturbed vegetation and exploit river-associated invertebrate food supply while moving to alternative food sources during intensive flooding. On the other hand, large frugivorous and nectarivorous birds are mostly sedentary and require an intact canopy layer for foraging, responding poorly to changes in vegetation structure. By moving beyond species-level relationships when examining bird community structure, we were able to better interpret and connect our findings with other studies on bird communities in tropical rainforest.

LncRNA–disease association prediction method based on the nearest neighbor matrix completion model

State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA–disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model’s reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.