Dispersion Metrics Research Articles

Integrating generative AI with ABCDE rule analysis for enhanced skin cancer diagnosis, dermatologist training and patient education.

The early detection and accurate monitoring of suspicious skin lesions are critical for effective dermatological diagnosis and treatment, particularly for reliable identification of the progression of nevi to melanoma. The traditional diagnostic framework, the ABCDE rule, provides a foundation for evaluating lesion characteristics by visual examination using dermoscopes. Simulations of skin lesion progression could improve the understanding of melanoma growth patterns. This study aims to enhance lesion analysis and understanding of lesion progression by providing a simulated potential progression of nevi into melanomas. The study generates a dataset of simulated lesion progressions, from nevi to simulated melanoma, based on a Cycle-Consistent Adversarial Network (Cycle-GAN) and frame interpolation. We apply an optical flow analysis to the generated dermoscopic image sequences, enabling the quantification of lesion transformation. In parallel, we evaluate changes in ABCDE rule metrics as example to assess the simulated evolution. We present the first simulation of nevi progressing into simulated melanoma counterparts, consisting of 152 detailed steps. The ABCDE rule metrics correlate with the simulation in a natural manner. For the seven samples studied, the asymmetry metric increased by an average of 19%, the border gradient metric increased by an average of 63%, the convexity metric decreased by an average of 3%, the diameter increased by an average of 2%, and the color dispersion metric increased by an average of 45%. The diagnostic value of the ABCDE rule is enhanced through the addition of insights based on optical flow. The outward expansion of lesions, as captured by optical flow vectors, correlates strongly with the expected increase in diameter, confirming the simulation's fidelity to known lesion growth patterns. The heatmap visualizations further illustrate the degree of change within lesions, offering an intuitive visual proxy for lesion evolution. The achieved simulations of potential lesion progressions could facilitate improved early detection and understanding of how lesions evolve. By combining the optical flow analysis with the established criteria of the ABCDE rule, this study presents a significant advancement in dermatoscopic diagnostics and patient education. Future research will focus on applying this integrated approach to real patient data, with the aim of enhancing the understanding of lesion progression and the personalization of dermatological care.

Latitudinal gradients in the phylogenetic assembly of angiosperms in Asia during the Holocene

Spatio-temporal assessment of phylogenetic diversity gradients during the Holocene (past 12,000 years) provides an opportunity for a deeper understanding of the dynamics of species co-occurrence patterns under environmental fluctuations. Using two robust metrics of phylogenetic dispersion (PD) and 99 fossil pollen sequences containing 6557 samples/assemblages, we analyse spatio-temporal variation in PD of angiosperms and its relationship with Holocene climate in central Asia. Overall, PD throughout the Holocene decreases linearly with increasing latitude, except for a rise in mean nearest taxon distance from ca. 25 to 35° N. This indicates that phylogenetically divergent taxa decrease progressively with increasing latitude, leaving more phylogenetically closely related taxa in the assemblages, thereby increasing phylogenetic relatedness among the co-occurring taxa. The latitudinal gradient of PD has not been consistent during the Holocene, and this temporal variation is concordant with the Holocene climate dynamics. In general, profound temporal changes in the latitudinal PD toward higher latitudes implies that the major environmental changes during the Holocene have driven considerable spatio-temporal changes in the phylogenetic assembly of high-latitude angiosperm assemblages. Our results suggest that environmental filtering and the tendency of taxa and lineages to retain ancestral ecological features and geographic distributions (phylogenetic niche conservatism) are the main mechanisms underlying the phylogenetic assembly of angiosperms along the climate-latitudinal gradient. Ongoing environmental changes may pose future profound phylogenetic changes in high-latitude plant assemblages, which are adapted to harsh environmental conditions, and therefore are phylogenetically less dispersed (more conservative or clustered).

CD8 radiomics signature to assess inter-lesion spatial heterogeneity and cold liver lesions in advanced non-small cell lung cancers treated with durvalumab.

2511 Background: The objective of this study was to assess whether a validated CD8 radiomics signature may help to evaluate patient inter-lesion heterogeneity and to predict the clinical outcome of advanced non-small cell lung cancers (NSCLC) patients treated with durvalumab in Study 1108 phase I/II trial (NCT01693562). Methods: Clinical data and imaging data from patients with naïve and pretreated advanced NSCLC who received durvalumab monotherapy were used. Radiomic features were extracted on contrast-enhanced CT scans and a validated CD8 radiomics signature was applied. A progressive lesion was defined by an increase in lesion longest diameter of 20% at 8 weeks. Dispersion metrics of the radiomics signature were estimated to evaluate the impact of inter-lesion heterogeneity on patient’s response. Results: A total of 188 patients were included in this study, accounting for a total of 1137 lesions (median [IQR] = 4 [3 - 9] lesions per patient) evaluated at baseline using a radiomics approach. A low CD8 radiomics score at baseline was associated with a significantly higher risk of progression at the lesion-level (AUC=0.59, P-value<0.0001), and was especially performant for liver lesions (AUC=0.66, P-value=0.0002). At the patient level, the least infiltrated lesion of a patient according to the radiomics score of CD8 T-cells was positively associated with OS (HR=0.70, P-value=0.029) and PFS (HR=0.68, P-value=0.014), the highest values being associated with the best outcomes. 55 patients had liver lesions, with worse prognosis than patients without liver lesion (HR=2 for OS and PFS, P-value = 0.00012 and 0.00022 respectively). In these patients, the CD8 radiomic score enabled the stratification of patients according to hot and cold liver metastasis (HR=0.6, P-value=0.072 and HR=0.54, P-value=0.038 for OS and PFS respectively). A 4-class stratification of the whole cohort based on the least infiltrated (cold/hot) and liver or non-liver lesion was independently associated with clinical outcomes. These radiomics approaches have shown independent prognostic values when adjusting for PD-L1 status in multivariate analyses (Table). Conclusions: These results confirm the predictive value at a lesion level and the patient level of the biologically inspired CD8 radiomics score for advanced NSCLC patients treated with durvalumab. It has shown interesting results in discriminating outcomes of patients with liver lesions by identifying hot and cold lesions. [Table: see text]

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks.

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.

Evolution in key indicators of maternal and child health across the wealth gradient in 41 sub-Saharan African countries, 1986–2019

BackgroundAggregate trends can be useful for summarizing large amounts of information, but this can obscure important distributional aspects. Some population subgroups can be worse off even as averages climb, for example. Distributional information can identify health inequalities, which is essential to understanding their drivers and possible remedies.MethodsUsing publicly available Demographic and Health Survey (DHS) data from 41 sub-Saharan African countries from 1986 to 2019, we analyzed changes in coverage for eight key maternal and child health indicators: first dose of measles vaccine (MCV1); Diphtheria-Pertussis-Tetanus (DPT) first dose (DPT1); DPT third dose (DPT3); care-seeking for diarrhea, acute respiratory infections (ARI), or fever; skilled birth attendance (SBA); and having four antenatal care (ANC) visits. To evaluate whether coverage diverged or converged over time across the wealth gradient, we computed several dispersion metrics including the coefficient of variation across wealth quintiles. Slopes and 5-year moving averages were computed to identify overall long-term trends.ResultsAverage coverage increased for all quintiles and indicators, although the range and the speed at which they increased varied widely. There were small changes in the wealth-related gap for SBA, ANC, and fever. The wealth-related gap of vaccination-related indicators (DPT1, DPT3, MCV1) decreased over time. Compared to 2017, the wealth-gap between richest and poorest quintiles in 1995 was 7 percentage points larger for ANC and 17 percentage points larger for measles vaccination.ConclusionsMaternal and child health indicators show progress, but the distributional effects show differential evolutions in inequalities. Several reasons may explain why countries had smaller wealth-related gap trends in vaccination-related indicators compared to others. In addition to service delivery differences, we hypothesize that the allocation of development assistance for health, the prioritization of vaccine-preventable diseases on the global agenda, and indirect effects of structural adjustment programs on health system-related indicators might have played a role.

Investigating surrogate-based hybrid acquisition processes. Application to Covid-19 contact mitigation

Surrogate models are built to produce computationally efficient versions of time-complex simulation-based objective functions so as to address expensive optimization. In surrogate-assisted evolutionary computations, the surrogate model evaluates and/or filters candidate solutions produced by evolutionary operators. In surrogate-driven optimization, the surrogate is used to define the objective function of an auxiliary optimization problem whose resolution generates new candidates. In this paper, hybridization of these two types of acquisition processes is investigated with a focus on robustness with respect to the computational budget and parallel scalability. A new hybrid method based on the successive use of acquisition processes during the search outperforms competing approaches regarding these two aspects on the Covid-19 contact mitigation problem. To further improve the generalization to larger ranges of search landscapes, another new hybrid method based on the dispersion metric is proposed. The integration of landscape analysis tools in surrogate-based optimization seems promising according to the numerical results reported on the CEC2015 test suite.

23 The Utility of Global versus Domain-specific Neuropsychological Test Score Dispersion as Markers of Cognitive Decline

Objective:Higher baseline dispersion (intra-individual variability) across neuropsychological test scores at a single time-point has been associated with more rapid cognitive decline, onset of Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), faster rates of hippocampal and entorhinal atrophy, and increased AD neuropathology. Comparison between predictions made from test score dispersion within a cognitive domain versus global, cross-domain dispersion is understudied. Global dispersion may be influenced by ability-and test-specific characteristics. This study examined the performance of global versus domain-specific dispersion metrics to identify which is most predictive of cognitive decline over time.Participants and Methods:Data for baseline and five follow-up visits of 308 participants with normal cognition (Mage=73.90, SD=8.12) were selected from the National Alzheimer’s Coordinating Center (NACC) Dataset. Participants were required to have no focal neurological deficits, or history of depression, stroke, or heart attack. Diagnoses and progression to MCI and/or dementia were determined at each visit through consensus conferences. Raw neuropsychological scores were standardized using NACC norms. Global baseline dispersion was defined as the intraindividual standard deviation (ISD) across the 10 scores in the NACC battery. Domain-specific dispersions were calculated by constructing composites and ISD was computed across tests sampling their respective domains (executive functioning/attention/processing speed [EFAS], language, and memory; see Table 1 for details on these tests). Higher values on each of these metrics reflect greater dispersion. Multinomial logistic regression model fit statistics and parameter estimates were compared across four different models (global, EFAS, Language, and Memory dispersion) covarying for age, years of education, sex, race, ethnicity, and ApoE4 status. Models were compared using the Likelihood Ratio Test (LRT) and the Akaike Information Criteria (AIC) of Models statistics.Results:Of the 308 participants, 70 (22.7%) progressed to MCI, and 82 (26.6%) progressed to dementia. Tables 1 and 2 show the results of the logistic regressions for the four models. All models fit the data well, with statistically significant predictions of conversion. Model 1 (global dispersion) showed a better fit than domain-specific models of dispersion per LRT and AIC values. Consistent with the results from mean differences between groups, parameter estimates showed that only global dispersion and EFAS dispersion significantly predicted conversion to dementia (when included with other covariates in models), with higher dispersion reflecting a greater risk of conversion.Conclusions:In this sample, baseline global and EFAS dispersion measures significantly predicted conversion to dementia. Although global dispersion was a stronger predictor of dementia progression, findings suggest that executive functioning performance may be driving this relationship. A single index of global variability, from the calculation of standard deviation across test scores, may be supplementary for clinicians when distinguishing individuals at risk for dementia progression. None of the models were predictive of conversion to MCI. Further research is required to examine cognitive variability differences among patients who progress to MCI and patient-specific factors that may relate to test score dispersion and its utility in predicting the progression of symptoms.

An Empirical Investigation Into the Influence of Software Communities’ Cultural and Geographical Dispersion on Productivity

Estimating and understanding software development productivity represent crucial tasks for researchers and practitioners. Although different works focused on evaluating the impact of human factors on productivity, a few explored the influence of cultural/geographical diversity in software development communities. More particularly, all previous treatise addresses cultural aspects as abstract concepts without providing a quantitative representation. Improved knowledge of these matters might help project managers to assemble more productive teams and tool vendors to design software analytics toolkits that may better estimate productivity. This paper has the goal of enlarging the existing body of knowledge on the factors affecting productivity by focusing on cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it. To reach this goal, we performed a mixed-method empirical study. First, we built a statistical model relating dispersion metrics with the productivity of 25 open-source communities on Github. Then, we performed a confirmatory survey with 140 practitioners. The key results of our study indicate that cultural and geographical dispersion considerably impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle. We conclude our paper by elaborating on the main insights from our analyses and instilling implications that may drive further research.

Mesoscopic Assessment of Microstructure in Glioblastomas and Metastases by Merging Advanced Diffusion Imaging with Immunohistopathology.

Glioblastomas and metastases are the most common malignant intra-axial brain tumors in adults and can be difficult to distinguish on conventional MR imaging due to similar imaging features. We used advanced diffusion techniques and structural histopathology to distinguish these tumor entities on the basis of microstructural axonal and fibrillar signatures in the contrast-enhancing tumor component. Contrast-enhancing tumor components were analyzed in 22 glioblastomas and 21 brain metastases on 3T MR imaging using DTI-fractional anisotropy, neurite orientation dispersion and density imaging-orientation dispersion, and diffusion microstructural imaging-micro-fractional anisotropy. Available histopathologic specimens (10 glioblastomas and 9 metastases) were assessed for the presence of axonal structures and scored using 4-level scales for Bielschowsky staining (0: no axonal structures, 1: minimal axonal fragments preserved, 2: decreased axonal density, 3: no axonal loss) and glial fibrillary acid protein expression (0: no glial fibrillary acid protein positivity, 1: limited expression, 2: equivalent to surrounding parenchyma, 3: increased expression). When we compared glioblastomas and metastases, fractional anisotropy was significantly increased and orientation dispersion was decreased in glioblastomas (each P < .001), with a significant shift toward increased glial fibrillary acid protein and Bielschowsky scores. Positive associations of fractional anisotropy and negative associations of orientation dispersion with glial fibrillary acid protein and Bielschowsky scores were revealed, whereas no association between micro-fractional anisotropy with glial fibrillary acid protein and Bielschowsky scores was detected. Receiver operating characteristic curves revealed high predictive values of both fractional anisotropy (area under the curve = 0.8463) and orientation dispersion (area under the curve = 0.8398) regarding the presence of a glioblastoma. Diffusion imaging fractional anisotropy and orientation dispersion metrics correlated with histopathologic markers of directionality and may serve as imaging biomarkers in contrast-enhancing tumor components.

Identifying the influence of training policies on employee performance in the county government of Nyandarua

This study aimed to examine the relationship between training policies and the performance of employees in the County Government of Nyandarua. The study used a descriptive survey research design. The 66 county administrators at Nyandarua County Government were the survey's main target. The main tool for gathering data was a questionnaire. The metrics of central tendency and dispersion, the absolute and relative frequencies, and percentages of each were used in descriptive statistics of mean and standard deviation, respectively. The data was shown using frequency tables and graphs for simple comparison. Training policies and employee performance showed a coefficient of regression ????1 = 0.319, p-value = 0.022&lt; 0.05. This was clearly manifested in that training lowers staff turnover, absenteeism, dissatisfaction and discontent, as well as boosting morale and increasing productivity. This was shown by workers' ability to realise they all have an equal chance of succeeding; it is easier for them to give their all at work. In conclusion, through training, organisations can attain management development, boosting managerial capability, stimulating and helping retain competent managers and encouraging managers to pursue professional advancement. The study recommends that the county government should ensure they establish sound training policies so as to equip the employees with the adequate skill set and capacity to deliver quality services to the citizens.

Leaf functional diversity and environmental filtering in a tropical dry forest: Comparison between two geological substrates.

The role of geological substrate in shaping plant community functional diversity remains poorly understood. Considering the involvement of leaves in the energy, water, and nutrient economics of plants, we hypothesized that leaves experience geology-related filtering, which in turn shapes their functional attributes and community leaf functional diversity on different substrates. We studied tropical dry forest communities on limestone and siliciclastic phyllite-derived soils, comparing their functional diversity and soil physico-chemical properties. We predicted the most benign habitat (less severe filter) to be associated with higher leaf functional diversity and an acquisitive strategy prevalence, while the more stressful habitat should show conservative leaf traits and lower leaf functional diversity. We measured six traits in 31 common tree species (representing ~80% of community crown cover): leaf area, specific leaf area, leaf thickness, leaf dry matter content, petiole length, and leaf blade narrowness. Leaf functional diversity was assessed through the functional trait dispersion metric. Intraspecific functional variation was examined in 25 species shared between substrates. The limestone substrate was more fertile (higher phosphorous) with higher water retention, while phyllite had higher nitrogen and lower humidity. Principal component analysis segregated plots by substrate, with limestone plots being more clustered. Community leaf functional diversity was higher in the limestone forest. Most species examined showed inter-substrate trait differences in at least one leaf functional trait. The two substrates constituted distinct growth environments, with the more benign substrate associated with higher community leaf functional diversity. The intraspecific analysis revealed the prevalence of acquisitive traits in the more benign and more conservative traits in the more stressful habitat. This study advances our understanding of the role of geological substrate as an environmental filter in tropical dry forests, influencing leaf functional responses and emphasizing the importance of intraspecific functional variation.

Design of a rainfall simulator for classroom demonstration and field research

AbstractRainfall is a key abiotic factor of ecosystems that can both provide benefits and contribute to negative environmental impacts. Due to spring precipitation levels increasing in the Midwest, there has been increased interest in developing and evaluating practices that can mitigate the effects of heavy rainfall. As this is an area of interest for both agriculture and environmental sciences, demonstrating the actions of severe rainfall can be a powerful tool in the natural sciences classroom as well as in the field. Rainfall simulators are ideal for this demonstration as they can create repeated, uniform rainfall events while introducing limited variability between events. Building on previous literature, we constructed a cost‐effective, easily transportable design that can be utilized in the classroom, the lab, and the field. Here, tests were conducted to measure flow rate, rain droplet size, and uniformity of water dispersion on the cost‐effective and easily obtained solid cone‐style herbicide nozzle. We found no statistically significant differences among rainfall dispersion metrics or in raindrop uniformity analyses. This demonstrates the capability of constructing this type of teaching and research equipment at little cost while producing highly consistent and size‐relevant artificial rainfall.

Self-Organized Aggregation Behavior Based on Virtual Expectation of Individuals with Wave-Based Communication

In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the swarm. During the whole process, a robot’s expectation constantly changes based on context awareness. This awareness is obtained by employing a low-cost communication system commonly found in swarm robot studies: infrared-based communication. Robots can make their own decisions by comparing their expected and estimated observed cluster sizes, which leads to macroscopic swarm aggregation. However, due to the limitations of local communication and mobility, robots are restricted in their ability to perceive global information, particularly regarding cluster size. Inspired by the slime mold aggregation process, a wave-based communication mechanism is implemented to help robots estimate a cluster size. Moreover, each transmission includes a modulated message that allows robots to explicitly share their knowledge with others. In this way, despite the fact that the robot may not belong to that cluster due to its perception range, it can easily grasp the cluster size when passing the cluster. Once a robot detects a desired cluster, it can approach this cluster with its direction determined by using average origin of wave (AOW) method. The performance of the aggregation algorithm was tested both in simulation and with a real swarm robot. Dispersion metrics and cluster metrics were employed to evaluate the proposed algorithm’s performance. The results show that the proposed microscopic model utilizes collective behavior which aggregates all randomly distributed robots into a single aggregate cluster with a reasonable swarm density and evaluation time.

A New Electromechanical Wave Imaging Dispersion Metric for the Characterization of Ventricular Activation in Different Cardiac Resynchronization Therapy Pacing Schemes.

Conventional biventricular (BiV) pacing cardiac resynchronization therapy (CRT) is an established treatment for heart failure patients. Recently, multiple novel CRT delivering technologies such as His-Bundle pacing have been investigated as alternative pacing strategies for optimal treatment benefit. Electromechanical Wave Imaging (EWI), a high frame-rate echocardiography-based modality, is capable of visualizing the change from dyssynchronous activation to resynchronized BiV-paced ventricles in 3D. This proof-of-concept study introduces a new EWI-based dispersion metric to further characterize ventricular activation. Patients with His-Bundle device implantation (n = 4), left-bundle branch block (n = 10), right-ventricular (RV) pacing (n = 10), or BiV pacing (n = 15) were imaged, as well as four volunteers in normal sinus rhythm (NSR). EWI successfully mapped the ventricular activation resulting from His-Bundle pacing. Additionally, very similar activation patterns were obtained in the NSR subjects, confirming recovery of physiological activation with His pacing. The dispersion metric was the most sensitive EWI-based metric that identified His pacing as the most efficient treatment (lowest activation time spread), followed by BiV and RV pacing. More specifically, the dispersion metric significantly (p < 0.005) distinguished His pacing from the other two pacing schemes as well as LBBB. The initial findings presented herein indicate that EWI and its new dispersion metric may provide a useful resynchronization evaluation clinical tool in CRT patients under both novel His-Bundle pacing and more conventional BiV pacing strategies.

Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning

Data-driven forecasting techniques have been widely used for building load forecasting due to their accuracy and wide availability of operational data. Recent advances have been underpinned by the increased capability of machine learning (ML) algorithms; however, most studies only tested ML techniques on a single or a small number of buildings over short periods, lacking reliable tests. Moreover, few studies focused on the effects of characteristics of building load profiles on forecast accuracy, lacking the interpretation of ML-based prediction results. In this study, we investigate the impacts of building load dispersion level on its best load forecasting accuracy, which is obtained by comparing the forecasting performances of 11 prediction models over 9 weeks for 56 British non-domestic buildings. We find that conventional shallow ML models still outperform the increasingly popular deep learning models for time-series load forecasting, and ensemble learning can help improve forecast accuracy by integrating diverse individual models. We demonstrate that each building’s best forecasting performance is largely influenced by the load dispersion level. In practice, the proposed dispersion metrics are recommended to quantify load dispersion levels before model development. For a building with a low dispersion level, the simple persistence model has satisfactory performance and could be directly used for design, control, and fault diagnosis of building energy systems for energy efficiency and energy flexibility.

Design and Implementation of the Development of a Corpus-based Loose-leaf Textbook for Higher Vocational English Learners: Using the Cross-border E-Commerce Operations English as an Example

Current higher vocational English textbooks have limitations in providing timely work-related information, representing the nature of vocational education, preserving authenticity of text materials, and using technology to enhance textbook interactivity. To address the limitations of current higher vocational English textbooks, this study adopts corpus-based approaches to developing a loose-leaf textbook for higher vocational English, a new form of textbook that delivers timely, authentic, job-oriented content enhanced by digitalization. Upon an on-the-job investigation, this study builds CECLOTED, a corpus congruent with the typical job tasks for cross-border E-commerce operations. Based on corpus data analysis upon metrics of frequency, keyness, dispersion, collocation and colligation, this study strikes a balance between authentic text feature and linguistic complexity appropriate for higher vocational English learners. This study also develops an online learning platform coherent with the printed textbook with B/S architecture so as to present information in a ubiquitous, dynamic and interactive manner. Compared with the conventional textbook, the loose-leaf textbook which comprises learning texts, vocabulary lists, learning tasks and an online learning platform is more conducive to enhancing language proficiency and job-related skills.

Artificial bee colony algorithm based on online fitness landscape analysis

As an effective optimization tool, artificial bee colony (ABC) algorithm has attracted increasing attention in recent years. However, ABC still shows unsatisfactory performance in solving complex optimization problems. Although many ABC variants have been developed, the structural features of problems have been rarely considered, which have a significant effect on algorithm performance. Therefore, a novel ABC variant, called FLABC, is proposed by designing an online fitness landscape analysis technique. In this technique, the population is considered as a sample of the fitness landscape, and the idea of dispersion metric is used to identify the landscape features. According to the identified features, the solution search equations with distinct characteristics can be adaptively used, which helps adapt the search to the fitness landscape. In addition, FLABC has another two modifications, i.e., the dynamic multiple subpopulations and modified scout bee phase. In the experiments, FLABC is verified on two well-known test suites (CEC2013 and CEC2015) and two real-world optimization problems. Seven well-established ABC variants and five non-ABC variants are included in the performance comparison, and the results verify that FLABC has very competitive performance, especially on the functions with rugged fitness landscapes.

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

PurposeWhile there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results....

Climate change linked to functional homogenization of a subtropical estuarine system.

Climate change causes marine species to shift and expand their distributions, often leading to changes in species diversity. While increased biodiversity is often assumed to confer positive benefits on ecosystem functioning, many examples have shown that the relationship is specific to the ecosystem and function studied and is often driven by functional composition and diversity. In the northwestern Gulf of Mexico, tropical species expansion was shown to have increased estuarine fish and invertebrate diversity; however, it is not yet known how those increases have affected functional diversity. To address this knowledge gap, two metrics of functional diversity, functional richness (FRic) and functional dispersion (FDis), were estimated in each year for a 38‐year study period, for each of the eight major bays along the Texas coast. Then, the community‐weighted mean (CWM) trait values for each of the functional traits are calculated to assess how functional composition has changed through time. Finally, principal component analysis (PCA) was used to identify species contributing most to changing functional diversity. We found significant increases in log‐functional richness in both spring and fall, and significant decreases in functional dispersion in spring, suggesting that although new functional types are entering the bays, assemblages are becoming more dominated by similar functional types. Community‐weighted trait means showed significant increases in the relative abundance of traits associated with large, long‐lived, higher trophic level species, suggesting an increase in periodic and equilibrium life‐history strategists within the bays. PCA identified mainly native sciaenid species as contributing most to functional diversity trends although several tropical species also show increasing trends through time. We conclude that the climate‐driven species expansion in the northwestern Gulf of Mexico led to a decrease in functional dispersion due to increasing relative abundance of species with similar life‐history characteristics, and thus the communities have become more functionally homogeneous.

Phylogenetic diversity and community assembly in a naturally fragmented system.

We sought to assess effects of fragmentation and quantify the contribution of ecological processes to community assembly by measuring species richness, phylogenetic, and phenotypic diversity of species found in local and regional plant communities. Specifically, our fragmented system is Craters of the Moon National Monument and Preserve, Idaho, USA. CRMO is characterized by vegetated islands, kipukas, that are isolated in a matrix of lava. We used floristic surveys of vascular plants in 19 kipukas to create a local species list to compare traditional dispersion metrics, mean pairwise distance, and mean nearest taxon distance (MPD and MNTD), to a regional species list with phenotypic and phylogenetic data. We combined phylogenetic and functional trait data in a novel machine‐learning model selection approach, Community Assembly Model Inference (CAMI), to infer probability associated with different models of community assembly given the data. Finally, we used linear regression to explore whether the geography of kipukas explained estimated support for community assembly models. Using traditional metrics of MPD and MNTD neutral processes received the most support when comparing kipuka species to regional species. Individually no kipukas showed significant support for overdispersion. Rather, five kipukas showed significant support for phylogenetic clustering using MPD and two kipukas using MNTD. Using CAMI, we inferred neutral and filtering models structured the kipuka plant community for our trait of interest. Finally, we found as species richness in kipukas increases, model support for competition decreases and lower elevation kipukas show more support for habitat filtering models. While traditional phylogenetic community approaches suggest neutral assembly dynamics, recently developed approaches utilizing machine learning and model choice revealed joint influences of assembly processes to form the kipuka plant communities. Understanding ecological processes at play in naturally fragmented systems will aid in guiding our understanding of how fragmentation impacts future changes in landscapes.