Lowest Akaike Information Criterion Value Research Articles

First Total-Body Kinetic Modeling and Parametric Imaging of Dynamic 68Ga-FAPI-04 PET in Pancreatic and Gastric Cancer.

Fibroblast activation protein inhibitor (FAPI) is an ideal diagnostic and therapeutic target in malignant tumors. However, the knowledge of kinetic modeling and parametric imaging of 68Ga-FAPI is limited. Purpose: The purpose of this study was to explore the pharmacokinetics of 68Ga-FAPI-04 PET/CT in pancreatic cancer and gastric cancer and to conduct parametric imaging of dynamic total-body data compared with SUV imaging. Methods: Dynamic total-body 68Ga-FAPI-04 PET/CT was performed on 13 patients. The lesion time-activity curves were fitted by 3-compartment models and multigraphical models. The kinetics parameters derived from the 2-tissue reversible compartment model (2T4K) and multigraphical models were analyzed. Parametric [Formula: see text] imaging was generated using the 2T4K and Logan models, and their performances were evaluated compared with SUV images. Results: 2T4K had the lowest Akaike information criterion value, and its fitting curves matched excellently with the origin time-activity curves. Visual assessment revealed that the [Formula: see text](2T4K) images and [Formula: see text](Logan with spatial constraint [SC]) images both showed less image noise and higher lesion conspicuity compared with SUV images. Objective image quality assessment demonstrated that parametric [Formula: see text](2T4K) images and parametric [Formula: see text](Logan with SC) images had a 5.0-fold and 5.0-fold higher average signal-to-noise ratio and 3.6-fold and 4.1-fold higher average contrast-to-noise ratio compared with conventional SUV images, respectively. In addition, no significant differences in signal-to-noise ratio and contrast-to-noise of pathologic lesions were observed between parametric [Formula: see text](2T4K) images and parametric [Formula: see text](Logan with SC) images (all P > 0.05). Conclusions: The 2T4K model was the preferred compartment model. Total-body parametric imaging of 68Ga-FAPI-04 PET yielded superior quantification beyond SUV with enhanced lesion contrast, which may serve as a promising imaging method to make an early diagnosis, to better reflect tumor characterization, or to allow evaluation of treatment response. [Formula: see text](2T4K) images are comparable in image quality and consistent to [Formula: see text](Logan with SC) images in lesions conspicuity; however, [Formula: see text](Logan with SC) images presented an appealing alternative to [Formula: see text](2T4K) images because of their simplicity.

Analysis of Blood Collection of National Blood Transfusion Service, Sri Lanka

Background and Objectives: Blood transfusion is a widely accepted treatment modality in modern medical practice and it has no substitute. Therefore, blood is a scarce resource, and proper management of bloodstock is essential. Transfusion service is responsible to maintain an adequate blood stock to ensure the supply of blood for hospitals while minimizing blood wastage due to postexpiry. To achieve efficient bloodstock management, the pattern of blood collection should be identified. This study was designed to establish a time series model for monthly blood collection of Sri Lanka. Methods: Data on monthly blood collection of Sri Lanka were collected from the year 2010 to 2020 and time series models were developed using “R” statistical software. Results: Time series data clearly exhibited an increasing trend with seasonality in blood collection. Therefore, seasonal time series models were fitted and the best seasonal autoregressive integrated moving average (ARIMA) model was selected as ARIMA (0, 1, 1) (0, 1, 2) (12) which showed the lowest Akaike information criteria value. Conclusion: It is suitable for forecasting the monthly blood collection.

A Model for Gastric Cancer Risk Prediction Based on MUC1 Polymorphisms and Health-risk Behaviors in a Vietnamese Population.

Although the expression of mucin 1(MUC1) and prostate stem cell antigen (PSCA) genes is correlated with gastric cancer development and progression, the utility of these two genes as biomarkers of gastric cancer prognosis still needs to be confirmed in clinical practice. This study aimed to develop a model predictive of gastric cancer that integrates several significant single nucleotide polymorphisms (SNPs) of MUC1 and PSCA genes, and some health-risk behavior factors in a Vietnamese population. A total of 302 patients with primary gastric carcinoma and 304 healthy persons were included in a case-control study. The generalized linear model was used with the profile of age, sex, history of smoking and using alcohol, personal and family medical history of stomach diseases, and the SNPs of MUC1 and PSCA. The prognostic value of the model was assessed by the area under a receiver operating characteristic curve (AUC) and Akaike Information Criterion (AIC) values. In male participants, the final model, consisting of age, sex, history of smoking and using alcohol, personal and family medical history of stomach diseases and SNP MUC1 rs4072037, provided acceptable discrimination, with an AUC of 0.6374 and the lowest AIC value (539.53). In female participants, the predictive model including age, sex, history of smoking and using alcohol, personal and family medical history of stomach diseases, SNPs MUC1 rs4072037 and rs2070803 had an AUC of 0.6937 and AIC of 266.80. The calibration plots of the male model approximately fitted the ideal calibration line. The predictive model based on age, sex, medical history, and genetic and health-risk behavior factors has a high potential in determining gastric cancer. Further studies that elucidate other genetic variants should be carried out to define high-risk gastric cancer groups and propose appropriate personalized prevention.

Predição do peso vivo em novilhas de corte usando a fórmula do volume corporal

ABSTRACT The objective of this study was to develop and evaluate linear, quadratic, and allometric models to predict live weight (LW) using the body volume formula (BV) in crossbred heifers raised in southeastern Mexico. The LW (426.25±117.49kg) and BV (338.05±95.38 dm3) were measured in 360 heifers aged between 3 and 30 months. Linear and non-linear regression were used to construct prediction models. The goodness-of-fit of the models was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through cross-validation (k-folds). The ability of the fitted models to predict the observed values was evaluated based on the RMSEP, R2, and mean absolute error (MAE). The quadratic model had the lowest values of AIC (2688.39) and BIC (2700.05). On the other hand, the linear model showed the lowest values of MSE (7954.74) and RMSE (89.19), and the highest values of AIC (2709.70) and BIC (2717.51). Despite this, all models presented the same R2 value (0.87). The cross-validation (k-folds) evaluation of fit showed that the quadratic model had better values of MSEP (41.49), R2 (0.85), and MAE (31.95). We recommend the quadratic model to predictive of the crossbred beef heifers' live weight using the body volume as the predictor.

Feasibility and implications of using subject-specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data.

Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (total creatine (tCr])and total choline (tCho)) for all MM strategies; however, findings for metabolites that are less distinguishable from the background signals associations depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal-to-noise ratio MM basis function (cohort-mean) in the short-TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially allows to identify the robustness in the detection of underlying effects (e.g., metabolite vs. age), reduces algorithm-based bias, and estimates algorithm-related variance.

Ten-Item Lower Extremity Functional Scale (LEFS-10): Instrument Reduction Based on Brazilian Patients With Lower Limb Dysfunction

ObjectiveTo propose a short version of the lower extremity functional scale (LEFS), to compare our short version with the different structures proposed for the LEFS by the specialized literature, and to verify the criterion validity of the best structure of the LEFS identified in our study. DesignCross-sectional observational study. SettingPhysiotherapy clinics. ParticipantsWe included 140 patients with lower limb dysfunction (N=140). InterventionsNone. Main Outcome MeasuresWe used confirmatory factor analysis (CFA), χ2/degree of freedom (DF), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), standardized root mean squared residual (SRMR), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The method of reducing the number of items of the LEFS considered the modification indices and factor loadings. ResultsThe body site most affected by pain was the knee (71.4%), and the most common diagnoses were knee osteoarthritis (55%) and anterior knee pain (11.4%). LEFS reduction generated a 1-dimensional structure of the LEFS with 10 items (LEFS-10). When compared with other structures, the LEFS-10 presented the best fit indices (χ2/DF=1.88, CFI=0.975, TLI=0.968, RMSEA=0.079, and SRMR=0.058) and the lowest values of AIC (3287.063) and BIC (3345.896). LEFS-10 presents a high correlation (Spearman's correlation coefficient [ρ]=0.911, P<.001) with the 20-item LEFS. ConclusionLEFS-10 is the proposal for a short version of the instrument with the most adequate internal structure, in addition to being satisfactorily correlated with the longer version of the instrument.

The best structure of the Tampa Scale for Kinesiophobia for patients with chronic low back pain has two domains and nine items

Purpose To identify the best internal structure of the Tampa Scale for Kinesiophobia in chronic low back pain patients. Design Questionnaire validation study was designed for this study. Setting This study was conducted in physical therapy facility. Subjects Respondents reporting chronic low back pain (≥3 points on the 11-point Numerical Pain Rating Scale). Main measures We included participants of both sexes, with a self-report of low back pain ≥3 months and with pain intensity ≥3 on the 11-point Numerical Pain Rating Scale; participants also answered the Roland–Morris Disability Questionnaire and the Pain-Related Catastrophizing Thoughts Scale for low back pain disability and catastrophizing, respectively. The dimensionality and number of items of the Tampa Scale for Kinesiophobia were evaluated using the confirmatory factor analysis. Criterion validity was assessed using Spearman’s correlation coefficient using the original version of the 17-item Tampa Scale for Kinesiophobia as the gold standard. Results A total of 122 participants were included, with mean values of low back pain duration ≥48 months, pain intensity >5 and disability >8. Tampa Scale for Kinesiophobia structure with two domains and nine items was the most suitable, with adequate values in all fit indices (Chi-square/degree of freedom <3, Comparative Fit Index and Tucker–Lewis Index >0.90, and root mean square error of approximation <0.08) and lower Akaike information criterion and Bayesian information criterion values. We observed a high correlation between the 17-item Tampa Scale for Kinesiophobia and the activity avoidance domain (rho = 0.850, P < 0.001) and somatic focus domain (rho = 0.792, P < 0.001) of the nine-item Tampa Scale for Kinesiophobia. Conclusion Tampa Scale for Kinesiophobia structure with two domains (activity avoidance and somatic focus) and nine items is the most suitable for patients with chronic low back pain.

O-025 Factors affecting biochemical pregnancy: Machine learning-assisted identification

Abstract Study question What variables in a PGT-A cycle can influence biochemical pregnancy loss? Summary answer There are paternal, maternal, ovarian stimulation, endometrial and embryo biopsy factors that are associated with the rate of biochemical pregnancy after the euploid embryo transfer. What is known already Biochemical miscarriage is an early termination in the development of pregnancy. Embryonic chromosomal alterations have been proposed to cause biochemical pregnancy. However, even though euploid embryos are transferred in PGT-A cycles, biochemical pregnancy rates do not seem to be modified, so there must be other factors associated with this type of miscarriage. The classical-statistical methods used to establish the factors related to biochemical miscarriage have produced contradictory results and there is no unanimity in the literature. As an alternative to traditional methods, different artificial intelligence algorithms are being used for the analysis of biological data. Study design, size, duration The study design is observational and retrospective. A total of 5892 embryos from 1919 PGT-A cycles were considered (January-2017 to October-2021). Only transferred embryos were included in the study (n = 1161). The trophoectoderm biopsies on D5, D6 or D7 blastocysts were analysed by NGS using the Illumina platform (VeriSeq Illumina®, San Diego, CA, USA). The biopsied embryos were vitrified and transferred in a subsequent cycle. Participants/materials, setting, methods Indications for PGT-A were advanced maternal age, altered karyotype or sperm FISH, history of chromosomal abnormalities in the offspring, repeated miscarriages and recurrent implantation failures. Clinical outcomes were recorded in a database including additional potential factors (n = 48) associated with biochemical pregnancy and related to progenitors, embryos and their biopsy, ovarian stimulation and adjuvant treatments. The association between the different variables and biochemical pregnancy was analysed using SPSS (v20.0) and R (v. 4.0.5) statistical software. Main results and the role of chance In order to determine which factors might increase biochemical pregnancy rates in euploid embryos, a multivariate analysis using logistic regression was initially performed. In the best predictive model (AUC=0.659) with a lower AIC (Akaike information criterion) value, only 3 factors showed a statistically significant association: uterine alterations (OR = 4.88, 95% CI [1.65-12.64]), day of embryo biopsy (OR = 2.19, 95% CI [1.46-3.31]) and mosaicism (number of altered chromosomes: OR = 1.59, 95% CI [1.10-2.22]) which significantly increased the risk of biochemical pregnancy. To identify other variables that might modify biochemical pregnancy rates and could be missed by classic statistical methods, different types of machine learning algorithms were used: unsupervised model (cluster analysis) and supervised predictive models (support vector machines (AUC=0.845), k-nearest neighbors (AUC=0.858), random forest (AUC=0.853), neural networks multilayer (AUC=0.719) and gradient boosting (AUC=0.825). The variables that had the greatest predictive power in the different machine learning algorithms were the variables associated with the embryo biopsy (day, number of laser pulses and biopsied cells), endometrial thickness and variables related to the male factor (sperm aneuploidy and DNA fragmentation). These algorithms apply different methodologies, but all agree on the fundamental role of these variables. Limitations, reasons for caution To confirm that the new identified variables are associated with biochemical pregnancy, it would be necessary to carry out prospective studies. Wider implications of the findings Biochemical pregnancy is the least studied clinical outcome in IVF. Knowledge of the variables that could affect biochemical pregnancy may be relevant as it may be a target for new therapies to reduce biochemical pregnancy rates and thus increase success rates. Trial registration number Not Applicable

A method for the inference of cytokine interaction networks.

Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.

Tumor regression grade combined with lymph node status in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy

IntroductionWe aimed to elucidate the prognostic value of tumor regression grade (TRG) combined with lymph node status compared with the 8th edition of the ypTNM staging system in patients with advanced esophageal squamous cell cancer (ESCC) after neoadjuvant chemoradiotherapy (nCRT).MethodsWe enrolled 325 patients with ESCC who underwent nCRT followed by complete resection. We adopted the modified Schneider TRG system, with high (ypT0N0), mid (ypT0N+ or ypT + N0), and low (ypT + N+). After developing a multivariable Cox model, the discrimination ability of the ypStage and TRG systems was evaluated using the Akaike Information Criterion (AIC) and R 2 measure.ResultsThe mean duration of follow‐up was 56.7 ± 43.3 months. The survival curves between the adjacent groups of TRG were significantly different for both overall survival (OS) and recurrence‐free survival (RFS). However, there were no significant differences between ypStages II and III for OS (p = 0.683) or RFS (p = 0.760). The TRG system also had a discrimination ability in patients with ypStage I (p < 0.001 for both OS and RFS) and ypStage III (p = 0.045 for OS and 0.042 for RFS). Compared with the ypTNM staging system, the modified TRG had a lower AIC value (1835.99 vs. 1852.02) and a higher R 2 (0.256 vs. 0.177), indicating better discrimination ability and prediction accuracy.ConclusionsFor patients with ESCC who underwent esophagectomy following nCRT, the modified Schneider TRG system may complement the ypStage and help clinicians select the most appropriate postoperative treatment and surveillance.

The Utility of the Modified Fels Knee Skeletal Maturity System in Limb Length Prediction.

Predicting ultimate lower extremity length is important in the treatment of lower limb length discrepancy (LLD), congenital limb deficiency, and other etiologies. Utilizing skeletal age over chronological age improves the prediction of ultimate lower extremity length. The recently described modified Fels knee skeletal maturity system allows for skeletal age estimation via imaging always available in LLD patients. We sought to compare the accuracy of the modified Fels knee skeletal maturity system versus chronological age in ultimate limb length prediction of a modern adolescent clinical population. The medical records of all patients treated at our institution over a 20-year period with unilateral lower extremity pathology and available lower extremity imaging before and after reaching skeletal maturity were reviewed. Skeletal maturity was defined radiographically by closed distal femoral, proximal tibial, and proximal fibular physes. The femoral, tibial, and lower extremity length was measured in all radiographs. The modified Fels knee skeletal maturity system was applied to all radiographs obtained before maturity to estimate skeletal age. The accuracy of 3 widely utilized lower extremity length prediction systems was compared when utilizing estimated Fels skeletal age versus chronological age inputs. A total of 245 radiographs (109 before maturity) from 43 patients were eligible for inclusion. On cross-sectional analysis, linear modeling using Fels skeletal ages was uniformly associated with higher (improved) R2 values than chronological age-based models. On longitudinal analysis, skeletal age mixed-effects models had significantly lower (improved) Akaike information criterion and Bayesian information criterion values than chronological age models in all cases. Cohen d values were also significantly different (P<0.05) for the skeletal age models compared with chronological age models in all cases. In the treatment of LLD, the modified Fels knee skeletal maturity system can be readily applied to available imaging to improve the prediction of ultimate femoral, tibial, and lower extremity length. This skeletal maturity system may have significant utility in the estimation of ultimate LLD and determination of appropriate timing of epiphysiodesis. Level III.

Adapting the 5-factor modified frailty index for prediction of postprocedural outcome in patients with unruptured aneurysms.

The 5-factor modified frailty index (mFI-5) is a practical tool that can be used to estimate frailty by measuring five accessible factors: functional status, history of diabetes, chronic obstructive pulmonary disease, congestive heart failure, and hypertension. The authors aimed to validate the utility of mFI-5 for predicting endovascular and microsurgical treatment outcomes in patients with unruptured aneurysms. A prospectively maintained database of consecutive patients with unruptured aneurysm who were treated with clip placement or endovascular therapy was used. Because patient age is an important predictor of treatment outcomes in patients with unruptured aneurysm, mFI-5 was supplemented with age to create the age-supplemented mFI-5 (AmFI-5). Associations of scores on these indices with major complications (symptomatic ischemic or hemorrhagic stroke, pulmonary embolism, pneumonia, or surgical site infection requiring reoperation) were evaluated. Validation was carried out with the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database (2006-2017). The institutional database included 275 patients (88 underwent clip placement, and 187 underwent endovascular treatment). Multivariable analysis of the surgical cohort showed that major complication was significantly associated with mFI-5 (OR 2.0, p = 0.046) and AmFI-5 (OR 1.9, p = 0.028) scores. Significant predictive accuracy for major complications was provided by mFI-5 (c-statistic = 0.709, p = 0.011) and AmFI-5 (c-statistic = 0.720, p = 0.008). The American Society of Anesthesiologists Physical Status Classification System (ASA) provided poor discrimination (area under the curve = 0.541, p = 0.618) that was significantly less than that of mFI-5 (p = 0.023) and AmFI-5 (p = 0.014). Optimal relative fit was achieved with AmFI-5, which had the lowest Akaike information criterion value. Similar results were obtained after equivalent analysis of the endovascular cohort, with additional significant associations between index scores and length of stay (β = 0.6 and p = 0.009 for mFI-5; β = 0.5 and p = 0.003 for AmFI-5). In 1047 patients who underwent clip placement and were included in the NSQIP database, mFI-5 (p = 0.001) and AmFI-5 (p < 0.001) scores were significantly associated with severe postoperative adverse events and provided greater discrimination (c-statistic = 0.600 and p < 0.001 for mFI-5; c-statistic = 0.610 and p < 0.001 for AmFI-5) than ASA score (c-statistic = 0.580 and p = 0.003). mFI-5 and AmFI-5 represent potential predictors of procedure-related complications in unruptured aneurysm patients. After further validation, integration of these tools into clinical workflows may optimize patients for intervention.

Loneliness and functional limitations among older adults with diabetes: Comparing directional models

ObjectiveMiddle-aged and older adults with diabetes are at increased risk for loneliness and functional limitations. Cross-sectional and longitudinal associations between loneliness and functional limitations have been demonstrated among the general population, but have not been established among those with diabetes. The purpose of this study was to directly compare the following models describing the direction of the association between loneliness and functional limitations among people with diabetes: (1) loneliness leads to functional limitations, (2) functional limitations lead to loneliness, and (3) a bidirectional association between loneliness and functional limitations. MethodsData came from the Health and Retirement Study. Participants were middle-aged and older individuals with diabetes in the United States (n = 2934). Loneliness and functional limitations were measured at baseline, 4-year follow-up, and 8-year follow-up. Path models for each of the three models, as well as a stability model, were created. Model fit was compared using Akaike's Information Criteria (AIC). ResultsParticipants were 54.6% female, 74.98% White, had a mean age of 69.66 years, had an average of 1.48 comorbid chronic conditions, and had diabetes for an average of 10.40 years. The bidirectional model best fit the data as evidenced by the lowest AIC value (AIC = 171,162.81). ∆AIC between the bidirectional model and the next best fitting model was 16.19, indicating strong support for selecting the bidirectional model. Higher levels of loneliness were associated with subsequent higher levels of functional limitations at some time points (βs = 0.07, 0.02) and higher levels of functional limitations were associated with subsequent higher levels of loneliness (βs = 0.13, 0.06) at all time points. ConclusionResults suggest that the association between loneliness and functional limitations among individuals with diabetes is bidirectional. This study demonstrates the value of directly comparing directional models.

Species Abundance Distributions Patterns between Tiankeng Forests and Nearby Non-Tiankeng Forests in Southwest China

Identifying the species abundance distributions (SADs) in Tiankeng forests is crucial for restoring and managing degraded karst ecosystem, whereas previous studies rarely explored the differences and response of vegetation dynamics to environmental variations. The species composition and SADs of the inner and outer fringe areas of Tiankeng forest and nearby non-Tiankeng forest were compared in Southwest China. Six models were adopted to compare SADs of three habitats. Kolmogrov–Smirnov (K–S) test was selected to compare the discrepancy between the simulated and observed SAD patterns. The Akaike Information Criterion (AIC) test was adopted to compare the models, and the best model was indicated by the lowest AIC value. The results showed that (1) the species dispersal from the inside of Tiankeng forests to the nearby non-Tiankeng forests is limited, while species have unlimited dispersal from nearby non-Tiankeng forests to the inside of Tiankeng forests via the fringe of Tiankeng forests. (2) Species abundance, species rarity, richness, and species accumulation rate in the Tiankeng forests were significant in non-Tiankeng forests (p &lt; 0.05), and most species in inner Tiankeng forests originated from nearby non-Tiankeng forests. (3) Based on the criterion of K-S values, all models have passed the K–S test (p &gt; 0.05), which indicated that niche processes and neutral process worked together in the maintenance of community species diversity, the community in study area is a niche-neutral continuum. (4) Considered the lowest AIC value, the neutral (△mean AIC = 1.3) models performed better than the niche (△mean AIC = 22.7) models and statistical (△mean AIC = 2.7) in the Tiankeng forest, while the statistical models performed better than the niche and neutral models in the non-Tiankeng forests. The results suggested that the main driving force of Tiankeng forests is the neutral process. The negative terrain in Tiankeng restricted the species dispersal due to topographic constraints. However, the species dispersal from the nearby non-Tiankeng forests could promote the species succession in the inner Tiankeng. Therefore, we propose that nearby non-Tiankeng forests should be emphasized for protecting the biodiversity of Tiankeng forests.

Remotely sensed vegetation productivity predicts breeding activity and drought refuges for a threatened bird in semi‐arid Australia

AbstractRefuges play a critical role protecting species against the effects of climate change. Managing high priority refuges could improve species resilience and facilitate dispersal during periods of environmental change. In this study, we identified drought refuges in semi‐arid Australia for a threatened bird, the malleefowl Leipoa ocellata. Using a Poisson regression model, we quantified the effect of remotely sensed vegetation indices, rainfall, soil moisture and site characteristics on malleefowl breeding activity at 144 sites surveyed from 2000 to 2017 during and after drought. We tested the effect of two vegetation productivity indices on malleefowl breeding activity – the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) – averaged across three temporal and three spatial scales during the mound building and incubation stage of the breeding cycle. We found that NDVI and EVI were better predictors of malleefowl breeding activity than soil moisture and winter rainfall. The model with the lowest Akaike information criterion value contained NDVI averaged over 3 months (June–August) and a 1‐km radius. Malleefowl breeding activity had a strong positive association with NDVI (0.42 ± se 0.03) and a negative association with slope (−0.34 ± se 0.03) and vegetation patch size (−0.23 ± se 0.02). We found the proportion of refugia (top 20% of predicted breeding activity) in protected areas was highly variable, decreasing from 42% in an extreme wet year (2011) to 14% in an extreme drought year (2007). Expanding the reserve network to include refugia predicted to occur in the south of semi‐arid Victoria could improve resilience of malleefowl to climate change. We demonstrate how remotely sensed vegetation indices combined with citizen science data can identify where to protect native vegetation with high, stable productivity. Our approach could be applied to a broad range of species in semi‐arid regions vulnerable to climate change.

Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya

Avocado (<i>Persea americana</i>) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on <i>Bactrocera dorsalis</i> and <i>Ceratitis</i> spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both <i>B. dorsalis</i> and <i>Ceratitis</i> spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.

Modeling and Forecasting of SARS CoV-2 Cases in Sierra Leone

Severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) has been a global threat spreading in Sierra Leone, and many studies are being conducted using various Statistical models to predict the probable evolution of this pandemic. In this paper, we use the autoregressive integrated moving average (ARIMA) model with the aim of forecasting the cumulative confirmed cases of SARS CoV-2 in Sierra Leone. The Akaike Information Criterion (AIC) was applied to the training data as a criterion method to select the best model. In addition, the statistical measure RMSE and MAPE were utilized for testing this data, and the model with the minimum RMSE and MAPE was selected for future forecasting. ARIMA (3, 2, 1) was confirmed to be the optimal model based on the lowest AIC value. This model was then applied to study the trend of SARS CoV-2 from 1st February 2022 to 30th February 2022. The result shows that incidence of SARS CoV-2 from 1st February 2022 to 30th February 2022, increasing growth steep in Sierra Leone (7718.629, 95% confidence limit of 6785.985 - 8651.274).

Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

Comparison of the sixth, seventh, and eighth editions of the American Joint Committee on Cancer Tumor-Node-Metastasis staging system for gastric cancer: A single institution experience.

In 2018, the eighth edition of the American Joint Committee on Cancer Tumor-Node-Metastasis classification and staging system was implemented. Few reports were made comparing the performance of different editions of the American Joint Committee on Cancer (AJCC) system. Therefore, this study aimed to examine the prognostic predictability from the sixth to the eighth editions of the AJCC staging system for gastric cancer.A total of 414 patients with gastric cancer who underwent surgery at Changhua Christian Hospital from January 2007 to December 2017 were enrolled in the study. To identify the prognostic factors for gastric cancer death, univariate and multivariate analyses were performed. The homogeneity and discrimination abilities of the sixth to eighth editions of the staging system were compared using the likelihood ratio chi-square test, linear trend chi-square test, and Akaike information criterion.The sixth edition of the staging system had the lowest Akaike information criterion value, suggesting a better prognostic stratification than other editions. From the result of the likelihood ratio chi-square test, the T and N staging systems of the seventh and eighth editions had better homogeneity and discriminatory ability than the sixth edition. The eighth edition had better prognostic performance in patients at stage III compared with the seventh edition.The AJCC seventh and eighth editions had improved prognostic predictability of the T and N factors compared with the sixth edition. However, the overall staging performance of the eighth edition is not superior compared to the sixth edition. Further studies with larger sample size should be conducted to compare the performance of different editions of the AJCC staging system for different ethnic populations.

Mycoplasma pneumoniae infection prediction model for hospitalized community-acquired pneumonia children.

We sought to develop a nomogram to predict Mycoplasma pneumoniae (Mp) infection among hospitalized children with community-acquired pneumonia (CAP) and compare it with another model developed from age and duration of fever. Data on 5904 CAP children who were enrolled at Shanghai Children's Medical Center were retrospectively collected and divided into a training set (n = 4133) and a validation set (n = 1771). The model's performance was determined by concordance index (C-index), calibration curves, Brier scores, and decision curve analyses (DCAs). Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparisons. Incorporating five factors (age, duration of fever, erythrocyte sedimentation rate, leukocyte count, and neutrophil proportion), the nomogram achieved good C-index values of 0.74 (95% confidence interval [CI]: 0.72-0.76) and 0.75 (95% CI: 0.73-0.78) and good Brier scores of 0.14 (95% CI: 0.13-0.15) and 0.17 (95% CI: 0.15-0.18) in predicting Mp infection in the training and validation cohorts, respectively, and had moderate fitted calibration plots. The DCAs showed good clinical usefulness of the nomogram. Patients were effectively divided into low, medium, and high risk groups by two cut-off score points of the nomogram, 210 and 300. With the lower AIC (3673.5) and BIC (3774.7) value, the model of five predictors is the better model. By using five predictor variables, a simple nomogram of good predictive accuracy for Mp infection and moderate agreements between the actual outcome and the predicted probability was constructed. It could serve as a tool to aid physicians in clinical decision-making processes.