Gradient boosting machine learning to improve satellite-derived column water vapor measurement error

Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics

BACKGROUND AND HYPOTHESIS

Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals-potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis.

STUDY DESIGN

People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control.

STUDY RESULTS

PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits.

CONCLUSIONS

Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

Developing survival prediction models in colorectal cancer using epigenome-wide DNA methylation data from whole blood

While genome-wide association studies are valuable in identifying CRC survival predictors, the benefit of adding blood DNA methylation (blood-DNAm) to clinical features, including the TNM system, remains unclear. In a multi-site population-based patient cohort study of 2116 CRC patients with baseline blood-DNAm, we analyzed survival predictions using eXtreme Gradient Boosting with a 5-fold nested leave-sites-out cross-validation across four groups: traditional and comprehensive clinical features, blood-DNAm, and their combination. Model performance was assessed using time-dependent ROC curves and calibrations. During a median follow-up of 10.3 years, 1166 patients died. Although blood-DNAm-based predictive signatures achieved moderate performances, predictive signatures based on clinical features outperformed blood-DNAm signatures. The inclusion of blood-DNAm did not improve survival prediction over clinical features. M1 stage, age at blood collection, and N2 stage were the top contributors. Despite some prognostic value, incorporating blood DNA methylation did not enhance survival prediction of CRC patients beyond clinical features.

Breast cancer but not the menopausal status is associated with small changes of the gut microbiota

Background

Possible relationships between gut dysbiosis and breast cancer (BC) development and progression have been previously reported. However, the results of these metagenomics studies are inconsistent. Our study involved 88 patients diagnosed with breast cancer and 86 cancer-free control women. Participants were divided into groups based on their menopausal status. Fecal samples were collected from 47 and 41 pre- and postmenopausal newly diagnosed breast cancer patients and 51 and 35 pre- and postmenopausal controls, respectively. In this study, we performed shotgun metagenomic analyses to compare the gut microbial community between pre- and postmenopausal BC patients and the corresponding controls.

Results

Firstly, we identified 12, 64, 158, and 455 bacterial taxa on the taxonomy level of phyla, families, genera, and species, respectively. Insignificant differences of the Shannon index and β-diversity were found at the genus and species levels between pre- and postmenopausal controls; the differences concerned only the Chao index at the species level. No differences in α-diversity indexes were found between pre- and postmenopausal BC patients, although β-diversity differed these subgroups at the genus and species levels. Consistently, only the abundance of single taxa differed between pre- and postmenopausal controls and cases, while the abundances of 14 and 23 taxa differed or tended to differ between premenopausal cases and controls, and between postmenopausal cases and controls, respectively. There were similar differences in the distribution of enterotypes. Of 460 bacterial MetaCyc pathways discovered, no pathways differentiated pre- and postmenopausal controls or BC patients, while two and one pathways differentiated cases from controls in the pre- and postmenopausal subgroups, respectively.

Conclusion

While our findings did not reveal an association of changes in the overall microbiota composition and selected taxa with the menopausal status in cases and controls, they confirmed differences of the gut microbiota between pre- and postmenopausal BC patients and the corresponding controls. However, these differences were less extensive than those described previously.

Predicting geogenic groundwater arsenic contamination risk in floodplains using interpretable machine-learning model

Long-term exposure to geogenic arsenic (As)-contaminated groundwater poses a severe threat to public health problems. Generally, elevated As concentrations have been observed with high amounts of ammonium in groundwater of floodplains. An extreme gradient boosting algorithm was conducted to develop a probability model based on hydrogeochemical data, which predicted the occurrence rates of groundwater As on a regional scale. Results showed that concentrations of NH4+, Eh, K, Cl-, SO42-, and NO3- were powerful predictive variables of As exposure. The model revealed the co-enrichment of As with NH4+, suggesting that the mineralization of nitrogen-containing organic matter promoted the reduction of As-bearing iron-oxides. The predicted distribution of high-As groundwater showed high consistency with known spatial distribution of As contamination, and the model also accurately predicted As concentrations in Jiangbei Plain of China and typical As-affected floodplains of Southeast Asia. The model can serve as a low-cost and rapid virtual sensor for detecting As concentrations in private or newly drilled wells, thereby providing critical information for informed management decisions, environmental protection and public health safety.

Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling

Cognitive control deficits are consistently identified in individuals with schizophrenia and other psychotic psychopathologies. In this analysis, we delineated proactive and reactive control deficits in psychotic psychopathology via hierarchical Drift Diffusion Modeling (hDDM). People with psychosis (PwP; N=123), their first-degree relatives (N=79), and controls (N=51) completed the Dot Pattern Expectancy task, which allows differentiation between proactive and reactive control. PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information for proactive control. They also showed longer non-decision times than controls on infrequent stimuli sequences suggesting slower perceptual processing. An explainable machine learning analysis indicated that the hDDM parameters were able to differentiate between the groups better than conventional measures. Through DDM, we found that cognitive control deficits in psychosis are characterized by slower motor/perceptual time and slower evidence-integration primarily in proactive control.

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

Karst watersheds accommodate high landscape complexity and are influenced by both human-induced and natural activity, which affects the formation and process of runoff, sediment connectivity and contaminant transport and alters natural hydrological and nutrient cycling. However, physical monitoring stations are costly and labor-intensive, which has confined the assessment of water quality impairments on spatial scale. The geographical characteristics of catchments are potential influencing factors of water quality, often overlooked in previous studies of highly heterogeneous karst landscape. To solve this problem, we developed a machining learning method and applied Extreme Gradient Boosting (XGBoost) to predict the spatial distribution of water quality in the world's most ecologically fragile karst watershed. We used the Shapley Addition interpretation (SHAP) to explain the potential determinants. Before this process, we first used the water quality damage index (WQI-DET) to evaluate the water quality impairment status and determined that COD_Mn, TN and TP were causing river water quality impairments in the WRB. Second, we selected 46 watershed features based on the three key processes (sources-mobilization-transport) which affect the temporal and spatial variation of river pollutants to predict water quality in unmonitored reaches and decipher the potential determinants of river impairments. The predicting range of COD_Mn spanned from 1.39 mg/L to 17.40 mg/L. The predictions of TP and TN ranged from 0.02 to 1.31 mg/L and 0.25-5.72 mg/L, respectively. In general, the XGBoost model performs well in predicting the concentration of water quality in the WRB. SHAP explained that pollutant levels may be driven by three factors: anthropogenic sources (agricultural pollution inputs), fragile soils (low organic carbon content and high soil permeability to water flow), and pollutant transport mechanisms (TWI, carbonate rocks). Our study provides key data to support decision-making for water quality restoration projects in the WRB and information to help bridge the science:policy gap.

Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls

BACKGROUND AND OBJECTIVES

As the worldwide secular trends are toward earlier puberty, identification of contributing factors for precocious puberty is critical. We aimed to identify and optimize contributing factors responsible for onset of precocious puberty via machine learning/deep learning algorithms in girls.

METHODS

A cross-sectional study was performed among girls aged 6-16 years from 26 schools in Beijing based on a cluster sampling method. Information was gleaned online via questionnaires. Machine/deep learning algorithms were performed using Python language (v3.7.6) on PyCharm platform.

RESULTS

Of 11308 students enrolled, there are 5527 girls, and 408 of them had experienced precocious puberty. Training 13 machine learning algorithms revealed that gradient boosting machine (GBM) performed best in predicting precocious puberty. By comparison, six top factors including maternal age at menarche, paternal body mass index (BMI), waist-to-height ratio, maternal BMI, screen time, and physical activity were sufficient in prediction performance, with accuracy of 0.9530, precision of 0.9818, and area under the receiver operating characteristic curve (AUROC) of 0.7861. The performance of the top six factors was further validated by deep learning sequential model, with accuracy reaching 92.9%.

CONCLUSIONS

We identified six important factors from both parents and girls that can help predict the onset of precocious puberty among Chinese girls.

A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements

BACKGROUND

Accurate and precise estimates of ambient air temperatures that can capture fine-scale within-day variability are necessary for studies of air temperature and health.

METHOD

We developed statistical models to predict temperature at each hour in each cell of a 927-m square grid across the Northeast and Mid-Atlantic United States from 2003 to 2019, across ~4000 meteorological stations from the Integrated Mesonet, using inputs such as elevation, an inverse-distance-weighted interpolation of temperature, and satellite-based vegetation and land surface temperature. We used a rigorous spatial cross-validation scheme and spatially weighted the errors to estimate how well model predictions would generalize to new cell-days. We assess the within-county association of temperature and social vulnerability in a heat wave as an example application.

RESULTS

We found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association.

CONCLUSION

Our high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.