In vitro and in vivo pharmacokinetics, disposition, and drug-drug interaction potential of tinengotinib (TT-00420), a promising investigational drug for treatment of cholangiocarcinoma and other solid tumors.

Early-stage clinical evaluation of tinengotinib (TT-00420) demonstrated encouraging preliminary efficacies in multiple types of refractory cancers, including fibroblast growth factor receptors (FGFR) inhibitors relapsed cholangiocarcinoma (CCA), castrate-resistant prostate cancer (CRPC), and HR+/HER2- breast cancer and triple negative breast cancer (TNBC). To further evaluate drug-like properties of the drug candidate, it is imperative to understand its metabolism and pharmacokinetic properties. This manuscript presented the investigation results of in vitro permeability, plasma protein binding, metabolic stability, metabolite identification, and drug-drug interaction of tinengotinib. Preclinical ADME (absorption, distribution, excretion, and metabolism) studies in rats and dogs was also conducted using a radioactive labeled tinengotinib, [14C]tinengotinib. Tinengotinib was found to have high permeability and high plasma protein binding and equally distributed between blood and plasma. There were no unique metabolites in human liver microsomes and tinengotinib showed moderate hepatic clearance. Tinengotinib is neither a potential inhibitor nor an inducer of P450 enzymes at clinically relevant concentrations, and unlikely to cause drug-drug interactions when used in combination with other drugs mediated by a key transporter, either as victim or perpetrator. Taken together, tinengotinib demonstrated a minimal risk of clinically relevant drug-drug interactions. Tinengotinib showed good oral bioavailability and dose-dependent exposures in both rat and dog after oral administration. The total radioactivity was largely distributed in the gastrointestinal system and liver, and tinengotinib could not easily pass through the blood-brain barrier. The major drug-related component in rat and dog plasma was unchanged drug (>89 %) with primary route of elimination via feces (>93 % of the dose) and minor via renal excretion (<4 % of the dose). Tinengotinib metabolism is mediated largely by CYP3A4, with minor contributions from CYP2D6 and CYP2C8. Major metabolic pathways include oxidation, oxidative cleavage of the morpholine ring, glucuronide and glutathione conjugations. The overall preclinical pharmacokinetics profile supported the selection and development of tinengotinib as a clinical candidate.

Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019.

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.

Epidemiological characteristics and spatiotemporal patterns of hand, foot, and mouth disease in Hubei, China from 2009 to 2019.

BACKGROUND: Hand, foot, and mouth disease (HFMD) is a public health issue in Hubei and studies of- spatiotemporal clustering at a fine scale are limited. The purpose of this research was to analyze the epidemiological characteristics, temporal variation characteristics, and spatiotemporal clustering of HFMD cases at the town level from 2009 to 2019 to improve public health outcomes. METHODS: Mathematical statistics, a seasonal index, wavelet analysis, and spatiotemporal scans were used to analyze epidemiological characteristics, time series trends, and spatiotemporal clusters of HFMD in Hubei. RESULTS: EV-A71 (Enterovirus A71) and CVA16 (Coxsackievirus A16) constitute the two primary pathogens of the HFMD epidemic in Hubei, among which EV-A71 is the dominant pathogen, especially in 2016. In terms of age distribution, a major peak occurred at 0-5 years and a very small increase appeared at 25-35 years, with the former having a higher incidence among males than females and the latter having the opposite difference between males and females. The number/rate of HFMD cases exhibited a considerable increase followed by a moderate decline from 2009 to 2019, with the first large peak in April-July and a smaller peak in November-December. HFMD in Hubei exhibited the characteristics of a 270-day cycle with multiscale nesting, which was similar to the periodicity of HFMD cases caused by EV-A71 (9 months). Cities with a higher incidence of HFMD formed a part of an "A-shaped urban skeleton". Subdistricts had the highest incidence of HFMD, followed by towns and villages. The spatiotemporal scan results showed one most likely cluster and 22 secondary clusters, which was consistent with the geographic location of railways and rivers in Hubei. CONCLUSIONS: These findings may be helpful in the prevention and control of HFMD transmission and in implementing effective measures in Hubei Province.

Learning Cross-Attention Discriminators via Alternating Time-Space Transformers for Visual Tracking.

In the past few years, visual tracking methods with convolution neural networks (CNNs) have gained great popularity and success. However, the convolution operation of CNNs struggles to relate spatially distant information, which limits the discriminative power of trackers. Very recently, several Transformer-assisted tracking approaches have emerged to alleviate the above issue by combining CNNs with Transformers to enhance the feature representation. In contrast to the methods mentioned above, this article explores a pure Transformer-based model with a novel semi-Siamese architecture. Both the time-space self-attention module used to construct the feature extraction backbone and the cross-attention discriminator used to estimate the response map solely leverage attention without convolution. Inspired by the recent vision transformers (ViTs), we propose the multistage alternating time-space Transformers (ATSTs) to learn robust feature representation. Specifically, temporal and spatial tokens at each stage are alternately extracted and encoded by separate Transformers. Subsequently, a cross-attention discriminator is proposed to directly generate response maps of the search region without additional prediction heads or correlation filters. Experimental results show that our ATST-based model attains favorable results against state-of-the-art convolutional trackers. Moreover, it shows comparable performance with recent "CNN + Transformer" trackers on various benchmarks while our ATST requires significantly less training data.

Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers.

Purpose: To establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers. Patients and methods: This study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria. Results: Five biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]). Conclusion: Our study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks.

During the process of drought and rehydration, dew can promote the rapid activation of photosynthetic activity and delay the wilting time of plant leaves and stems. It is clear that the amount of dew will affect the growth of plants. However, limited research is being done to detect and measure the amount of dew. Therefore, in this study, a statistical method for measuring the amount of dew based on computer vision processing was developed. In our framework, dewdrops can be accurately measured by isolating the background area based on color features and detecting the edge and statistical area. In this scheme, the multi-convolutional edge detection networks based on contour search loss function are proposed as the main implementation algorithm of edge detection. Through color feature background region segmentation and the proposed edge detection networks, our algorithm can detect dew in complex plant backgrounds. Experimental results showed that the proposed method gains a favorable detection accuracy compared with other edge detection methods. Moreover, we achieved the best Optimal Image Scale (OIS) and Optimal Dataset Scale (ODS) when testing with different pixel values, which illustrate the robustness of our method in dew detection.

A Loosely Coupled Extended Kalman Filter Algorithm for Agricultural Scene-Based Multi-Sensor Fusion.

With the arrival of aging society and the development of modern agriculture, the use of agricultural robots for large-scale agricultural production activities will become a major trend in the future. Therefore, it is necessary to develop suitable robots and autonomous navigation technology for agricultural production. However, there is still a problem of external noise and other factors causing the failure of the navigation system. To solve this problem, we propose an agricultural scene-based multi-sensor fusion method via a loosely coupled extended Kalman filter algorithm to reduce interference from external environment. Specifically, the proposed method fuses inertial measurement unit (IMU), robot odometer (ODOM), global navigation and positioning system (GPS), and visual inertial odometry (VIO), and uses visualization tools to simulate and analyze the robot trajectory and error. In experiments, we verify the high accuracy and the robustness of the proposed algorithm when sensors fail. The experimental results show that the proposed algorithm has better accuracy and robustness on the agricultural dataset than other algorithms.

Using Physical Organic Chemistry Knowledge to Predict Unusual Metabolites of Synthetic Phenolic Antioxidants by Cytochrome P450.

Biotransformation, especially by human CYP450 enzymes, plays a crucial role in regulating the toxicity of organic compounds in organisms, but is poorly understood for most emerging pollutants, as their numerous "unusual" biotransformation reactions cannot retrieve examples from the textbooks. Therefore, in order to predict the unknown metabolites with altering toxicological profiles, there is a realistic need to develop efficient methods to reveal the "unusual" metabolic mechanism of emerging pollutants. Combining experimental work with computational predictions has been widely accepted as an effective approach in studying complex metabolic reactions; however, the full quantum chemical computations may not be easily accessible for most environmentalists. Alternatively, this work practiced using the concepts from physical organic chemistry for studying the interrelationships between structure and reactivity of organic molecules, to reveal the "unusual" metabolic mechanism of synthetic phenolic antioxidants catalyzed by CYP450, for which the simple pencil-and-paper and property-computation methods based on physical organic chemistry were performed. The phenol-coupling product of butylated hydroxyanisole (BHA) (based on spin aromatic delocalization) and ipso-addition quinol metabolite of butylated hydroxytoluene (BHT) (based on hyperconjugative effect) were predicted as two "unusual" metabolites, which were further confirmed by our in vitro analysis. We hope this easily handled approach will promote environmentalists to attach importance to physical organic chemistry, with an eye to being able to use the knowledge gained to efficiently predict the fates of substantial unknown synthesized organic compounds in the future.

Mid-term outcomes of coronary artery bypass grafting in patients with mild left ventricular systolic dysfunction: a multicentre retrospective cohort study.

OBJECTIVES: Left ventricular systolic dysfunction (LVSD) is common and associated with adverse events in patients receiving coronary artery bypass grafting (CABG). However, the prognosis of mild LVSD has not been clearly described. We aimed to evaluate the mid-term outcomes of patients with mild LVSD following CABG. METHODS: This multicentre cohort study using propensity score matching took place from December 2012 to October 2019 in Jiangsu Province, China, with a mean and maximum follow-up of 3.2 and 7.2 years, respectively. Patients were classified to normal left ventricular systolic function (left ventricular ejection fraction ≥53%) and mild LVSD (left ventricular ejection fraction >40%/<53%). The primary outcomes were death from all causes and death from cardiovascular causes. The secondary outcomes were heart failure, myocardial infarction, repeat revascularization and a composite of all mentioned outcomes, including death from all causes (major adverse events). RESULTS: A total of 581 pairs were formed after matching. In-hospital death (1.5% vs 2.1%, P = 0.51) did not differ between 2 cohorts. Throughout 7 years, mild LVSD was associated with higher rates of death from all causes [hazard ratio (HR) 0.59, 95% confidence interval (CI) 0.39-0.89; P = 0.012], death from cardiovascular causes (HR 0.55, 95% CI 0.36-0.90; P = 0.017), heart failure (HR 0.60, 95% CI 0.37-0.93; P = 0.023) and major adverse events (HR 0.66, 95% CI 0.49-0.91; P = 0.009). There was no difference in the rates of myocardial infarction and repeat revascularization. CONCLUSIONS: Mild LVSD was associated with a worse mid-term prognosis in patients following CABG.

Hierarchical Spatiotemporal Context-Aware Correlation Filters for Visual Tracking.

Discriminative correlation filters (DCF)-based trackers have been increasingly applied to visual tracking due to their high precision while running at high frame rates. However, most recent DCF-based methods solely concentrate on learning the correlation filter with spatial information and thus do not have sufficient descriptive power to discriminate the target from the background in the complex circumstances, such as full occlusion (OCC) and rapid target variation. In this article, we introduce a novel tracking framework that exploits the relationship between the target and its spatiotemporal context to improve tracking accuracy and robustness. Especially, we present our spatiotemporal context model in a hierarchical way, where each layer of the context pyramid is a spatial correlation filter learned from different temporal instances. For gaining an accurate spatiotemporal model, we propose an optimization fusion approach that can adaptively and efficiently learn the effect of each hierarchical layer and exploit these multiple temporal levels of correlation filters for visual tracking. Moreover, an adaptive model update strategy for correlation filters is introduced into the framework to dynamically select proper hierarchical layers, which boosts the temporal diversity of the target appearance, while radically reduces the number of model parameters and guarantees the real-time performance of the tracking method. The experimental results show that, with conventional handcrafted features, our tracker achieves the best success rates among available state-of-the-art trackers with handcrafted features, and provides state-of-the-art performance comparable to those of deep-learning-based trackers on OTB-2013, OTB-2015, VOT-2016, and UAV-20L benchmarks but runs significantly faster than deep trackers.

Microencapsulation of Ginger Volatile Oil Based on Gelatin/Sodium Alginate Polyelectrolyte Complex.

The coacervation between gelatin and sodium alginate for ginger volatile oil (GVO) microencapsulation as functions of mass ratio, pH and concentration of wall material and core material load was evaluated. The microencapsulation was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FT-IR), and thermal gravimetric analysis (TGA). SEM and FT-IR studies indicated the formation of polyelectrolyte complexation between gelatin and sodium alginate and successful encapsulation of GVO into the microcapsules. Thermal property study showed that the crosslinked microparticles exhibited higher thermal stability than the neat GVO, gelatin, and sodium alginate. The stability of microencapsulation of GVO in a simulated gastric and an intestinal situation in vitro was also studied. The stability results indicated that the release of GVO from microcapsules was much higher in simulated intestinal fluid, compared with that in simulated-gastric fluid.

Biphenyl-3,3',4,4'-tetra-carboxylic acid dihydrate.

The asymmetric unit of the title compound, C(16)H(10)O(8)·2H(2)O, contains one-half of the centrosymmetric organic mol-ecule and one water mol-ecule. The dihedral angles between the carboxyl-ate groups and the adjacent phenyl ring are 71.31 (3) and 16.67 (3)°, while the carboxyl-ate groups are oriented at a dihedral angle of 72.01 (3)°. In the crystal structure, inter-molecular O-H⋯O and bifurcated O-H⋯(O,O) hydrogen bonds link the mol-ecules to form a three-dimensional supra-molecular network.

APOA5 Ala315>Val, identified in patients with severe hypertriglyceridemia, is a common mutation with no major effects on plasma lipid levels.

BACKGROUND: The importance of the apolipoprotein A5 (APOA5) gene in determining plasma triglyceride (TG) levels has been demonstrated in transgenic and knockout mice and confirmed by human association studies in different ethnic groups. METHODS: We screened for nonsynonymous APOA5 mutations in patients with plasma TG levels >10 mmol/L. The coding sequence and promoter region of the APOA5 gene were sequenced in 95 individuals with severe hypertriglyceridemia (HTG). A large population sample of 3,202 individuals was screened by PCR and restriction analysis for presence of detected mutation. RESULTS: In total, three heterozygous carriers of 944C>T (Ala315>Val) were identified in the severe HTG patients, while 22 carriers were identified in the population sample. The rare allele frequency of the Val315 was significantly higher in the HTG sample than in controls (0.016 vs. 0.003, p<0.01, respectively). Most of the control Ala315Val carriers, however, had plasma lipid levels (TGs, total cholesterol and high-density lipoprotein cholesterol) within the usual range detected in the population. CONCLUSIONS: APOA5 Ala315>Val does not play any dominant/important role in the genetic determination of plasma TG levels, but the increased frequency in HTG patients compared to controls suggests that it might interact with other gene variants to cause HTG.

A new type of NADH model compound: synthesis and enantioselective reduction of benzoylformates to the corresponding mandelates.

A new type of NADH model compound with good reactivity and enantioselectivity has been synthesized in good yields by an efficient and convenient synthetic method. The structures of these model compounds were confirmed by 1H and 13C-NMR and MS.