The effect of fine grits and fine debris concentrations on the MLVSS/MLSS ratio of an activated sludge system.

This study embarks on an explorative investigation into the effects of typical concentrations and varying particle sizes of fine grits (FG, the involatile portion of suspended solids) and fine debris (FD, the volatile yet unbiodegradable fraction of suspended solids) within the influent on the mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio of an activated sludge system. Through meticulous experimentation, it was discerned that the addition of FG or FD, the particle size of FG, and the concentration of FD bore no substantial impact on the pollutant removal efficiency (denoted by the removal rate of COD and ammonia nitrogen) under constant operational conditions. However, a notable decrease in the MLVSS/MLSS ratio was observed with a typical FG concentration of 20 mg/L, with smaller FG particle sizes exacerbating this reduction. Additionally, variations in FD concentrations influenced both MLSS and MLVSS/MLSS ratios; a higher FD concentration led to an increased MLSS and a reduced MLVSS/MLSS ratio, indicating FD accumulation in the system. A predictive model for MLVSS/MLSS was constructed based on quality balance calculations, offering a tool for foreseeing the MLVSS/MLSS ratio under stable long-term influent conditions of FG and FD. This model, validated using data from the BXH wastewater treatment plant (WWTP), showcased remarkable accuracy.

Investigation of Novel Radiological Parameters to Predict Treatment Failure and Ideal Surgical Interventions in Young Femoral Neck Fractures Treated with Triple Cannulated Screw: A Retrospective Analysis.

Introduction: Treatment failure remains a challenge in young femoral neck fractures treated with triple cannulated screws (TCS). This study aims to identify novel radiological parameters that can predict treatment failure and propose surgical techniques to enhance the success of TCS or aid in selecting alternative methods. Patients and methods: We conducted a retrospective analysis of 87 patients who underwent surgery for femoral neck fractures between February 2014 and June 2022, meeting the inclusion criteria. Patients achieving union were categorized as Non-Fail (Group 1), while those experiencing nonunion were categorized as Fail (Group 2). Various demographic and clinical factors were assessed, including age, gender, fracture side, and fracture classification (Garden and Pauwels). Radiological parameters such as fragmentation in the medial cortex, postoperative fracture displacement in the calcar region, collo-diaphyseal angle (CDA) difference (varus/valgus alignment), and several newly defined parameters (modified tip apex distance (m-TAD), tip cortex distance (TCD), upper-lower screw-cortex distance/neck diameter, the calcar screw-cortex distance/neck diameter (Buyukdogan index), and sub-capital area/basocervical area (Dogan index) were evaluated. Patients developing nonunion were studied to establish potential cut-off values based on radiological parameters. Results: Of the patients, 61 were classified as Non-Fail (Group 1) and 26 as Fail (Group 2). Both groups exhibited similar distributions in terms of gender, fracture side, Pauwels classification, and follow-up times (p > 0.05). However, Group 2 had a higher mean age than Group 1 (p = 0.006). There was a significant difference between the two groups in terms of Garden classification (p = 0.0003). Furthermore, postoperative calcar displacement, varus alignment, m-TAD, TCD, upper-lower screw-cortex distance/neck diameter ratio, Buyukdogan index, and Dogan index showed significant differences between the groups (p < 0.05). Conversely, medial calcar fragmentation did not differ significantly between the groups (p > 0.05). Conclusions: The Dogan index (≤ 0.5) can serve as an independent preoperative predictor of treatment failure, aiding in the selection of more effective surgical interventions than TCS. Varus alignment (> 10 degrees), the upper-lower screw-cortex distance to the neck diameter (> 0.45) and Buyukdogan index (> 0.2) are influenced by the surgical technique of TCS application and should be considered to decrease the success of TCS.

An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study.

BACKGROUND: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs. OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities. METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model. RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the "NWO Navigate Stroke" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes. CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.

Predicting Postoperative Satisfaction with Breasts: How Important is the Preoperative BREAST-Q Score?

BACKGROUND: The role that preoperative Satisfaction with Breast plays in a patient's postoperative course after postmastectomy breast reconstruction (PMBR) is not understood. The aim of this study is to understand the impact of the preoperative score on postoperative outcome as an independent variable. METHODS: We examined patients who underwent PMBR between 2017 and 2021 and who completed the BREAST-Q Satisfaction with Breasts at 1 year postoperatively. Two multiple linear regression models (Model 1 with the preoperative Satisfaction with Breasts score and Model 2 without the preoperative score), likelihood ratio tests, simple t-statistics, and sample patient dataset to predict the 1 year score were performed. Multiple imputation was used to account for missing preoperative scores. RESULTS: Overall, 2324 patients were included. Model 1 showed that the preoperative score is significantly associated with the postoperative score (ß = 0.09, 95% confidence interval 0.04-0.14; p < 0.001). Comparing Model 1 and Model 2 demonstrated that including preoperative Satisfaction with Breasts in a regression significantly improves model fit (test statistic = 10.04; p = 0.0021). Using the absolute value of the t-statistics as a measure of variable importance in linear regression, the importance of the preoperative score was quantified as 3.39-more important than neoadjuvant radiation, mastectomy weight, body mass index, bilateral prophylactic mastectomy, and race, but less than adjuvant radiation, reconstruction type, and psychiatric diagnoses. CONCLUSION: Preoperative Satisfaction with Breasts scores are an important independent predictor of postoperative satisfaction after PMBR. Just as vital sign and work-up are carefully documented before surgery, preoperative scores should be collected to pre-emptively gauge patients' satisfaction and optimize postoperative outcomes.

Personalized treatment using predictive biomarkers in solid organ malignancies: A review.

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Cortical-subcortical interactions underlie processing of auditory predictions measured with 7T fMRI.

Perception integrates both sensory inputs and internal models of the environment. In the auditory domain, predictions play a critical role because of the temporal nature of sounds. However, the precise contribution of cortical and subcortical structures in these processes and their interaction remain unclear. It is also unclear whether these brain interactions are specific to abstract rules or if they also underlie the predictive coding of local features. We used high-field 7T functional magnetic resonance imaging to investigate interactions between cortical and subcortical areas during auditory predictive processing. Volunteers listened to tone sequences in an oddball paradigm where the predictability of the deviant was manipulated. Perturbations in periodicity were also introduced to test the specificity of the response. Results indicate that both cortical and subcortical auditory structures encode high-order predictive dynamics, with the effect of predictability being strongest in the auditory cortex. These predictive dynamics were best explained by modeling a top-down information flow, in contrast to unpredicted responses. No error signals were observed to deviations of periodicity, suggesting that these responses are specific to abstract rule violations. Our results support the idea that the high-order predictive dynamics observed in subcortical areas propagate from the auditory cortex.

Comparison of clinical outcomes and perinatal outcomes between natural cycle and hormone replacement therapy of frozen-thawed embryo transfer in patients with regular menstruation: a propensity score-matched analysis.

Purpose: To investigate potential differences in pregnancy outcomes among patients with regular menstruation who underwent frozen-thawed embryo transfer using natural cycle (NC) or hormone replacement therapy (HRT). Methods: This study retrospectively analyzed 2672 patients with regular menstruation who underwent FET from November 2015 to June 2021 at the single reproductive medical center. A one-to-one match was performed applying a 0.02 caliper with propensity score matching. Independent factors influencing the live birth and clinical pregnancy rates were screened and developed in the nomogram by logistic regression analysis. The efficacy of live birth rate and clinical pregnancy rate prediction models was assessed with the area under the ROC curve, and the live birth rate prediction model was internally validated within the bootstrap method. Results: The NC protocol outperformed the HRT protocol in terms of clinical pregnancy and live birth rates. The stratified analysis revealed consistently higher live birth and clinical pregnancy rates with the NC protocol across different variable strata compared to the HRT protocol. However, compared to the HRT treatment, perinatal outcomes indicated that the NC protocol was related to a higher probability of gestational diabetes. Multifactorial logistic regression analysis demonstrated independent risk factors for live birth rate and clinical pregnancy rate. To predict the two rates, nomogram prediction models were constructed based on these influencing factors. The receiver operating characteristic curve demonstrated moderate predictive ability with an area under curve (AUC) of 0.646 and 0.656 respectively. The internal validation of the model for live birth rate yielded an average AUC of 0.646 implying the stability of the nomogram model. Conclusion: This study highlighted that NC yielded higher live birth and clinical pregnancy rates in comparison to HRT in women with regular menstruation who achieved successful pregnancies through frozen-thawed embryo transfer. However, it might incur a higher risk of developing gestational diabetes.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics.

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

Circulating brain injury biomarkers for prediction of outcome after elective neurosurgery - a scoping review.

OBJECTIVE: There is a need for refined methods to detect and quantify brain injuries that may be undetectable by magnetic resonance imaging and neurologic examination. This review evaluates the potential efficacy of circulating brain injury biomarkers for predicting outcomes following elective neurosurgical procedures. METHODS: A comprehensive search was conducted using the Cochrane, PubMed, and Scopus databases. RESULTS: Analysis of 23 relevant studies revealed that specific biomarkers, including glial fibrillary acidic protein, neurofilament light chain, neuron-specific enolase, S100B, and tau, are significantly associated with the extent of brain injury and could potentially predict postsurgical outcomes. The evaluated studies described intracranial tumor surgeries and miscellaneous neurosurgical interventions, and demonstrated the complex relationship between biomarker levels and patient outcomes. CONCLUSIONS: Circulating brain injury biomarkers show promise for providing objective insights into the extent of perioperative brain injury and improving prognostication of postsurgical outcomes. However, the heterogeneity in study designs and outcomes along with the lack of standardized biomarker thresholds underscore the need for further research.

Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review.

OBJECTIVE: To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs). DESIGN: A systematic review of the literature was conducted using a search algorithm in October 2022. The following databases were searched: PubMed, Scopus, Virtual Health Library (BVS), and Medrxiv. The risk of bias was assessed using the QUADAS-2 scale. SETTING: ICUs admitting cancer patients. PARTICIPANTS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU. INTERVENTIONS: Integrative study without interventions. MAIN VARIABLES OF INTEREST: Mortality prediction, standardized mortality, discrimination, and calibration. RESULTS: Seven mortality risk prediction models were analyzed in cancer patients in the ICU. Most models (APACHE II, APACHE IV, SOFA, SAPS-II, SAPS-III, and MPM II) underestimated mortality, while the ICMM overestimated it. The APACHE II had the SMR (Standardized Mortality Ratio) value closest to 1, suggesting a better prognostic ability compared to the other models. CONCLUSIONS: Predicting mortality in ICU cancer patients remains an intricate challenge due to the lack of a definitive superior model and the inherent limitations of available prediction tools. For evidence-based informed clinical decision-making, it is crucial to consider the healthcare team's familiarity with each tool and its inherent limitations. Developing novel instruments or conducting large-scale validation studies is essential to enhance prediction accuracy and optimize patient care in this population.

Predictive value of POSSUM scoring system for postoperative complications and mortality in elderly patients with colorectal cancer.

BACKGROUND: The POSSUM scoring system, widely employed in assessing surgical risks, offers a simplified and objective approach for the prediction of complications and mortality in patient. Despite its effectiveness in various surgical fields, including orthopedics and cardiovascular surgery, yet its utilization in elderly patients undergoing colorectal cancer surgery is infrequent. OBJECTIVE: To analyze the predictive value of POSSUM scoring system for postoperative complications and mortality in elderly with colorectal cancer. METHODS: 306 elderly colorectal cancer patients were grouped according to the complications and death within 30 days after surgery. Among them, 108 cases in complication group, 198 cases in non-complication group, 16 cases in death group and 290 cases in survival group. POSSUM scores of all subjects were obtained and its predictive value for postoperative complications and mortality of elderly was conducted by ROC curve. RESULTS: No apparent difference were observed in complications and mortality among patients with different disease types, operation types and operation timing (P> 0.05). The R2 in complication group was higher than non-complication group (P< 0.05). The R1 in death group were higher than survival group (P< 0.05). The AUC of R2 for predicting postoperative complications was 0.955 with a sensitivity of 88.89% and a specificity of 94.44% and the AUC of R1 for evaluating postoperative mortality of elderly with colorectal cancer was 0.783 with a sensitivity of 56.25% and a specificity of 82.93%. CONCLUSION: POSSUM score system has a certain predictive value for postoperative complications and mortality in elderly with colorectal cancer. However, the predicted mortality rate is higher than actual mortality rate.

Calibration transfer between NIR instruments using optimally predictive calibration subsets.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction.

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Evaluating predictive equations for energy requirements throughout breast cancer trajectory: A comparative study.

BACKGROUND & AIMS: Accurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors). METHODS: Data from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N = 46) or a whole-room indirect calorimeter (WRIC-REE N = 44). Agreement between methods was evaluated using Bland-Altman and Lin's concordance coefficient correlation (Lin's CCC). RESULTS: Ninety participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼300 kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (<0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer. CONCLUSION: Most equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

A Quantitative Adverse Outcome Pathway for Embryonic Activation of the Aryl Hydrocarbon Receptor of Fishes by Polycyclic Aromatic Hydrocarbons Leading to Decreased Fecundity at Adulthood.

Quantitative adverse outcome pathways (qAOPs) describe the response-response relationships that link the magnitude and/or duration of chemical interaction with a specific molecular target to the probability and/or severity of the resulting apical-level toxicity of regulatory relevance. The present study developed the first qAOP for latent toxicities showing that early life exposure adversely affects health at adulthood. Specifically, a qAOP for embryonic activation of the aryl hydrocarbon receptor 2 (AHR2) of fishes by polycyclic aromatic hydrocarbons (PAHs) leading to decreased fecundity of females at adulthood was developed by building on existing qAOPs for (1) activation of the AHR leading to early life mortality in birds and fishes, and (2) inhibition of cytochrome P450 aromatase activity leading to decreased fecundity in fishes. Using zebrafish (Danio rerio) as a model species and benzo[a]pyrene as a model PAH, three linked quantitative relationships were developed: (1) plasma estrogen in adult females as a function of embryonic exposure, (2) plasma vitellogenin in adult females as a function of plasma estrogen, and (3) fecundity of adult females as a function of plasma vitellogenin. A fourth quantitative relationship was developed for early life mortality as a function of sensitivity to activation of the AHR2 in a standardized in vitro AHR transactivation assay to integrate toxic equivalence calculations that would allow prediction of effects of exposure to untested PAHs. The accuracy of the predictions from the resulting qAOP were evaluated using experimental data from zebrafish exposed as embryos to another PAH, benzo[k]fluoranthene. The qAOP developed in the present study demonstrates the potential of the AOP framework in enabling consideration of latent toxicities in quantitative ecological risk assessments and regulatory decision-making. Environ Toxicol Chem 2024;00:1-12. © 2024 SETAC.

The evaluation value of atherogenic index of plasma and high-sensitivity C-reactive protein for the degree of coronary artery lesion in premature coronary artery disease.

BACKGROUND: Premature coronary artery disease (PCAD) is prevailing. We aimed to investigate the evaluation value of atherogenic index of plasma (AIP) and high-sensitivity C-reactive protein (hs-CRP) for the occurrence and severity of coronary artery lesion in PCAD patients. METHODS: PCAD (PACD group)/non-PCAD (control group) patients were enrolled. The coronary artery lesion degree was evaluated using Gensini score (GS). PCAD patients were allocated into the low/medium/high GS groups, with general clinical baseline data analyzed. Plasma hs-CRP/AIP levels were compared in PCAD patients with different disease degree. Correlations between plasma hs-CRP/AIP with Gensini score, independent risk factors affecting the occurrence of PCAD, and the predictive value of hs-CRP/AIP/their combination for the occurrence and degree of PCAD were evaluated by Spearman correlation analysis/Logistic multivariate regression/receiver operating characteristic (ROC) curve. The differences in the area under the curve (AUC) were compared using MedCalc-Comparison of ROC curves. RESULTS: Plasma hs-CRP/AIP levels in the PCAD group were increased. Plasma hs-CRP/AIP levels varied significantly among PCAD patients with different disease degree. Plasma hs-CRP/AIP levels were markedly positively correlated with the Gensini score. Smoking history/homocysteine/fasting blood-glucose/hs-CRP/AIP were all independent risk factors affecting PCAD occurrence. The AUC of hs-CRP and AIP combination predicting the occurrence of PCAD was 0.950 (90.80% sensitivity/93.33% specificity). hs-CRP/AIP combination assisted in predicting the disease degree in PCAD patients. CONCLUSIONS: AIP and hs-CRP are independent risk factors for the occurrence of PCAD, and their combination has high predictive value for PCAD occurrence and disease degree, which are both positively correlated with coronary artery lesion degree.

Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference.

Understanding the emergence of symbol systems, especially language, requires the construction of a computational model that reproduces both the developmental learning process in everyday life and the evolutionary dynamics of symbol emergence throughout history. This study introduces the collective predictive coding (CPC) hypothesis, which emphasizes and models the interdependence between forming internal representations through physical interactions with the environment and sharing and utilizing meanings through social semiotic interactions within a symbol emergence system. The total system dynamics is theorized from the perspective of predictive coding. The hypothesis draws inspiration from computational studies grounded in probabilistic generative models and language games, including the Metropolis-Hastings naming game. Thus, playing such games among agents in a distributed manner can be interpreted as a decentralized Bayesian inference of representations shared by a multi-agent system. Moreover, this study explores the potential link between the CPC hypothesis and the free-energy principle, positing that symbol emergence adheres to the society-wide free-energy principle. Furthermore, this paper provides a new explanation for why large language models appear to possess knowledge about the world based on experience, even though they have neither sensory organs nor bodies. This paper reviews past approaches to symbol emergence systems, offers a comprehensive survey of related prior studies, and presents a discussion on CPC-based generalizations. Future challenges and potential cross-disciplinary research avenues are highlighted.

Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir.

Cyanobacterial harmful algal blooms can pose risks to ecosystems and human health worldwide due to their capacity to produce natural toxins. The potential dangers associated with numerous metabolites produced by cyanobacteria remain unknown. Only select classes of cyanopeptides have been extensively studied with the aim of yielding substantial evidence regarding their toxicity, resulting in their inclusion in risk management and water quality regulations. Information about exposure concentrations, co-occurrence, and toxic impacts of several cyanopeptides remains largely unexplored. We used liquid chromatography-mass spectrometry (LC-MS)-based metabolomic methods associated with chemometric tools (NP Analyst and Data Fusion-based Discovery), as well as an acute toxicity essay, in an innovative approach to evaluate the association of spectral signatures and biological activity from natural cyanobacterial biomass collected in a eutrophic reservoir in southeastern Brazil. Four classes of cyanopeptides were revealed through metabolomics: microcystins, microginins, aeruginosins, and cyanopeptolins. The bioinformatics tools showed high bioactivity correlation scores for compounds of the cyanopeptolin class (0.54), in addition to microcystins (0.54-0.58). These results emphasize the pressing need for a comprehensive evaluation of the (eco)toxicological risks associated with different cyanopeptides, considering their potential for exposure. Our study also demonstrated that the combined use of LC-MS/MS-based metabolomics and chemometric techniques for ecotoxicological research can offer a time-efficient strategy for mapping compounds with potential toxicological risk. Environ Toxicol Chem 2024;00:1-10. © 2024 SETAC.

Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus.

The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902-0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.

Establishment of a nomogram model for predicting distant metastasis in pancreatic ductal adenocarcinoma: a comparative analysis of different lymph node staging systems based on the SEER database.

The purpose of this study was to compare the predictive value of different lymph node staging systems and to develop an optimal prognostic nomogram for predicting distant metastasis in pancreatic ductal adenocarcinoma (PDAC). Our study involved 6364 patients selected from the Surveillance, Epidemiology, and End Results (SEER) database and 126 patients from China. Independent risk factors for distant metastasis were screened by univariate and multivariate logistic regression analyses, and a model-based comparison of different lymph node staging systems was conducted. Furthermore, we developed a nomogram for predicting distant metastasis using the optimal performance lymph node staging system. The lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), age, primary site, grade, tumor size, American Joint Committee on Cancer (AJCC) 7th Edition T stage, and radiotherapy recipient status were significant predictors of distant metastasis in PDAC patients. The model with the LODDS was a better fit than the model with the LNR. We developed a nomogram model based on LODDS and six clinical parameters. The area under the curve (AUC) and concordance index (C-index) of 0.753 indicated that this model satisfied the discrimination criteria. Kaplan-Meier curves indicate a significant difference in OS among patients with different metastasis risks. LODDS seems to have a superior ability to predict distant metastasis in PDAC patients compared with the AJCC 8th Edition N stage, PLN and LNR staging systems. Moreover, we developed a nomogram model for predicting distant metastasis. Clinicians can use the model to detect patients at high risk of distant metastasis and to make further clinical decisions.