Predicting maximum temperatures over India 10-days ahead using machine learning models.

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models.

Subtropical Mode Water south of Japan impacts typhoon intensity.

Subtropical Mode Water (STMW), characterized by vertically uniform temperature of ~17°C, is distributed horizontally over 5000 kilometers at the 100- to 500-meter depths in the subtropical North Pacific Ocean. Its formation and spreading fluctuate in relation to the Pacific Decadal Oscillation and the Kuroshio path variation, but the feedback from STMW on the sea surface temperature (SST) and the overlying atmosphere remains unclear. Using Argo profiling float data, we show that STMW south of Japan, whose thickness varies decadally, modulates the overlying thermal structure throughout the year by increasing isotherm uplift with increasing thickness. The STMW-induced decadal temperature change has a magnitude of up to ~1°C and is large in the warm season in the presence of the seasonal thermocline. Furthermore, 50-year observations, together with numerical simulation, show that SST, upper ocean heat content, and typhoon intensification rate have been significantly lower in years with thicker STMW and higher in years with thinner STMW.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

Climate Precursors of Satellite Water Marker Index for Spring Cholera Outbreak in Northern Bay of Bengal Coastal Regions.

Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker; SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997-2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948-2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms.

The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El Niño (La Niña) Modoki (ENSO Modoki) index (EMI). We evaluated two widely used non-linear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. 6, 12, 18 and 24 months. The predictors for the EMI are identified using Kendall's tau correlation coefficient between the monthly EMI index and the monthly anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the EMI is underestimated for the strong events. The analysis also indicates the SVR to perform better than the RF method in forecasting the EMI.

Improving seasonal forecasts of air temperature using a genetic algorithm.

Seasonal forecasts of air-temperature generated by numerical models provide guidance to the planners and to the society as a whole. However, generating accurate seasonal forecasts is challenging mainly due to the stochastic nature of the atmospheric internal variability. Therefore, an array of ensemble members is often used to capture the prediction signals. With large spread in the prediction plumes, it becomes important to employ techniques to reduce the effects of unrealistic members. One such technique is to create a weighted average of the ensemble members of seasonal forecasts. In this study, we applied a machine learning technique, viz. a genetic algorithm, to derive optimum weights for the 24-ensemble members of the coupled general circulation model; the Scale Interaction Experiment-Frontier research center for global change version 2 (SINTEX-F2) boreal summer forecasts. Our analysis showed the technique to have significantly improved the 2m-air temperature anomalies over several regions of South America, North America, Australia and Russia compared to the unweighted ensemble mean. The spatial distribution of air temperature anomalies is improved by the GA technique leading to better representation of anomalies in the predictions. Hence, machine learning techniques could help in improving the regional air temperature forecasts over the mid- and high-latitude regions where the model skills are relatively modest.

Disposition and metabolism of the G protein-coupled receptor 40 agonist TAK-875 (fasiglifam) in rats, dogs, and humans.

The absorption, distribution, metabolism, and excretion of fasiglifam were investigated in rats, dogs, and humans. The absolute oral bioavailability of fasiglifam was high in all species (>76.0%). After oral administration of [14C]fasiglifam, the administered radioactivity was quantitatively recovered and the major route of excretion of radioactivity was via feces in all species. Fasiglifam was a major component in the plasma and feces in all species. Its oxidative metabolite (M-I) was observed as a minor metabolite in rat and human plasma (<10% of plasma radioactivity). In human plasma, hydroxylated fasiglifam (T-1676427), the glucuronide of fasiglifam (fasiglifam-G), and the glucuronide of M-I were detected as additional minor metabolites (<2% of plasma radioactivity). None of these metabolites were specific to humans. Fasiglifam-G was the major component in the rat and dog bile. In vitro cytochrome P450 (CYP) and uridine diphosphate glucuronosyltransferase (UGT) reaction phenotyping indicated that oxidation (to form M-I and T-1676427) and glucuronidation of fasiglifam are mainly mediated by CYP3A4/5 and UGT1A3, respectively. Fasiglifam and fasiglifam-G are substrates of BCRP and Mrp2/MRP2, respectively. Glucuronidation of fasiglifam-G was found to be the predominant elimination pathway of fasiglifam in all species tested, including humans.

Disposition and metabolism of TAK-438 (vonoprazan fumarate), a novel potassium-competitive acid blocker, in rats and dogs.

1. Following oral administration of [14C]TAK-438, the radioactivity was rapidly absorbed in rats and dogs. The apparent absorption of the radioactivity was high in both species. 2. After oral administration of [14C]TAK-438 to rats, the radioactivity in most tissues reached the maximum at 1-hour post-dose. By 168-hour post-dose, the concentrations of the radioactivity were at very low levels in nearly all the tissues. In addition, TAK-438F was the major component in the stomach, whereas TAK-438F was the minor component in the plasma and other tissues. High accumulation of TAK-438F in the stomach was observed after oral and intravenous administration. 3. TAK-438F was a minor component in the plasma and excreta in both species. Its oxidative metabolite (M-I) and the glucuronide of a secondary metabolite formed by non-oxidative metabolism of M-I (M-II-G) were the major components in the rat and dog plasma, respectively. The glucuronide of M-I (M-I-G) and M-II-G were the major components in the rat bile and dog urine, respectively, and most components in feces were other unidentified metabolites. 4. The administered radioactive dose was almost completely recovered. The major route of excretion of the drug-derived radioactivity was via the feces in rats and urine in dogs.

In vitro metabolism of TAK-438, vonoprazan fumarate, a novel potassium-competitive acid blocker.

1. TAK-438, vonoprazan fumarate, is a novel orally active potassium-competitive acid blocker, developed as an antisecretory drug. In this study, we investigated the in vitro metabolism of 14C-labeled TAK-438. In human hepatocytes, M-I, M-II, M-III and M-IV-Sul were mainly formed, and these were also detected in clinical studies. N-demethylated TAK-438 was also formed as an in vitro specific metabolite. Furthermore, CYP3A4 mainly contributed to the metabolism of TAK-438 to M-I, M-III, and N-demethylated TAK-438, and CYP2B6, CYP2C19 and CYP2D6 partly catalyzed the metabolism of TAK-438. The sulfate conjugation by SULT2A1 also contributed to the metabolism of TAK-438 to form TAK-438 N-sulfate, and CYP2C9 mediated the formation of M-IV-Sul from TAK-438 N-sulfate. The metabolite M-IV, which could be another possible intermediate in the formation of M-IV-Sul, was not observed as a primary metabolite of TAK-438 in any of the in vitro studies. 2. In conclusion, TAK-438 was primarily metabolized by multiple metabolizing enzymes including CYP3A4, CYP2B6, CYP2C19, CYP2D6, and a non-CYP enzyme SULT2A1, and the influence of the CYP2C19 genotype status on gastric acid suppression post TAK-438 dosing could be small. The multiple metabolic pathways could also minimize the effects of co-administrated CYP inhibitors or inducers on the pharmacokinetics of TAK-438.

How potentially predictable are midlatitude ocean currents?

Predictability of atmospheric variability is known to be limited owing to significant uncertainty that arises from intrinsic variability generated independently of external forcing and/or boundary conditions. Observed atmospheric variability is therefore regarded as just a single realization among different dynamical states that could occur. In contrast, subject to wind, thermal and fresh-water forcing at the surface, the ocean circulation has been considered to be rather deterministic under the prescribed atmospheric forcing, and it still remains unknown how uncertain the upper-ocean circulation variability is. This study evaluates how much uncertainty the oceanic interannual variability can potentially have, through multiple simulations with an eddy-resolving ocean general circulation model driven by the observed interannually-varying atmospheric forcing under slightly different conditions. These ensemble "hindcast" experiments have revealed substantial uncertainty due to intrinsic variability in the extratropical ocean circulation that limits potential predictability of its interannual variability, especially along the strong western boundary currents (WBCs) in mid-latitudes, including the Kuroshio and its eastward extention. The intrinsic variability also greatly limits potential predictability of meso-scale oceanic eddy activity. These findings suggest that multi-member ensemble simulations are essential for understanding and predicting variability in the WBCs, which are important for weather and climate variability and marine ecosystems.

Optimization of (2,3-dihydro-1-benzofuran-3-yl)acetic acids: discovery of a non-free fatty acid-like, highly bioavailable G protein-coupled receptor 40/free fatty acid receptor 1 agonist as a glucose-dependent insulinotropic agent.

G protein-coupled receptor 40 (GPR40)/free fatty acid receptor 1 (FFA1) is a free fatty acid (FFA) receptor that mediates FFA-amplified glucose-stimulated insulin secretion in pancreatic ß-cells. We previously identified (2,3-dihydro-1-benzofuran-3-yl)acetic acid derivative 2 as a candidate, but it had relatively high lipophilicity. Adding a polar functional group on 2 yielded several compounds with lower lipophilicity and little effect on caspase-3/7 activity at 30 µM (a marker of toxicity in human HepG2 hepatocytes). Three optimized compounds showed promising pharmacokinetic profiles with good in vivo effects. Of these, compound 16 had the lowest lipophilicity. Metabolic analysis of 16 showed a long-acting PK profile due to high resistance to ß-oxidation. Oral administration of 16 significantly reduced plasma glucose excursion and increased insulin secretion during an OGTT in type 2 diabetic rats. Compound 16 (TAK-875) is being evaluated in human clinical trials for the treatment of type 2 diabetes.

Marked impact of P-glycoprotein on the absorption of TAK-427 in rats.

The role of P-glycoprotein (P-gp, ABCB1) on the absorption process was investigated by drug-drug interaction studies of TAK-427 with P-gp inhibitors (erythromycin, ketoconazole or quinidine) in rats and by transport studies using rat multidrug resistance (MDR1) stably expressing cells and rat small intestine mounted in a Ussing-type chamber. TAK-427 showed high efflux activity with low permeability in rat MDR1a and MDR1b stably expressing cells and was revealed to be a typical substrate for P-gps. Although TAK-427 was mainly absorbed from the small intestine in rats, a large part of the dosed compound remained in the gastrointestinal tract. Orally co-administered P-gp inhibitors (50 mg/kg) increased the AUC of TAK-427 after a 5 mg/kg oral dose 5.4- to 18.3-fold, whereas orally administered P-gp inhibitors had a minor effect on the increase in the AUC of TAK-427 (1.3- to 2.2-fold) after a 0.5 mg/kg intravenous dose. Thus, the bioavailability of TAK-427 after oral administration in rats (7.3%) markedly increased when co-administered with P-gp inhibitors (28.6-57.6%). Moreover, the transport of TAK-427 was predominantly secretory throughout the rat small intestine and was inhibited by P-gp inhibitors. In conclusion, P-gp can markedly reduce the absorption of a typical P-gp substrate by its efflux activity throughout the absorption site.

Studies on non-thiazolidinedione antidiabetic agents. 3. Preparation and biological activity of the metabolites of TAK-559.

Preparation and biological activity of the metabolites of the potent antihyperglycemic and antihyperlipidemic agent, (E)-4-(4-[(5-methyl-2-phenyl-1,3-oxazol-4-yl)methoxy]benzyloxyimino)-4-phenylbutyric acid (TAK-559) (1), were investigated. Metabolites M-I (2), M-II (3), M-III (4) and M-IV (5) were synthesized and their biological activities were evaluated by in vitro and in vivo experiments. Compounds 2-4 activate human peroxisome proliferator-activated receptor gamma one (hPPARgamma1) and hPPARalpha, but their activities are weaker than those of TAK-559 (1). Compound 5 only activates hPPARgamma1 weakly. TAK-559 (1) showed potent in vivo plasma glucose and triglyceride lowering activities in Wistar fatty rats after intraperitoneal administration, while its metabolites (2-5) showed comparatively weak activities.