A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored

Robust Outdoor Vehicle Visual Tracking Based on k-Sparse Stacked Denoising Auto-Encoder

Robust visual tracking for outdoor vehicle is still a challenging problem due to large object appearance variations caused by illumination variation, occlusion, and fast motion. In this chapter, k-sparse constraint is added to the encoder part of stacked auto-encoder network to learn more invariant feature of object appearance, and a stacked k-sparse-auto-encoder–based robust outdoor vehicle tracking method under particle filter inference is further proposed to solve the problem of appearance variance during the tracking. Firstly, a stacked denoising auto-encoder is pre-trained to learn the generic feature representation. Then, a k-sparse constraint is added to the stacked denoising auto-encoder, and the encoder of k-sparse stacked denoising auto-encoder is connected with a classification layer to construct a classification neural network. Finally, confidence of each particle is computed by the classification neural network and is used for online tracking under particle filter framework. Comprehensive tracking experiments are conducted on a challenging single-object tracking benchmark. Experimental results show that our tracker outperforms most state-of-the-art trackers

Double-charm and hidden-charm hexaquark states under the complex scaling method

We investigate the double-charm and hidden-charm hexaquarks as molecules in the framework of the one-boson-exchange potential model. The multichannel coupling and $S-D$ wave mixing are taken into account carefully. We adopt the complex scaling method to investigate the possible quasibound states, whose widths are from the three-body decay channel $\Lambda_c\Lambda_c\pi$ or $\Lambda_c\bar{\Lambda}_c\pi$. For the double-charm system of $I(J^P)=1(1^+)$, we obtain a quasibound state, whose width is 0.50 MeV if the binding energy is -14.27 MeV. And the $S$-wave $\Lambda_c\Sigma_c$ and $\Lambda_c\Sigma_c^*$ components give the dominant contributions. For the $1(0^+)$ double-charm hexaquark system, we do not find any pole. We find more poles in the hidden-charm hexaquark system. We obtain one pole as a quasibound state in the $I^G(J^{PC})=1^+(0^{--})$ system, which only has one channel $(\Lambda_c\bar{\Sigma}_c+\Sigma_c\bar{\Lambda}_c)/\sqrt{2}$. Its width is 1.72 MeV with a binding energy of -5.37 MeV. But, we do not find any pole for the scalar $1^-(0^{-+})$ system. For the vector $1^-(1^{-+})$ system, we find a quasibound state. Its energies, widths and constituents are very similar to those of the $1(1^+)$ double-charm case. In the vector $1^+(1^{--})$ system, we get two poles -- a quasibound state and a resonance. The quasibound state has a width of 0.6 MeV with a binding energy of -15.37 MeV. For the resonance, its width is 2.72 MeV with an energy of 63.55 MeV relative to the $\Lambda_c\bar{\Sigma}_c$ threshold. And its partial width from the two-body decay channel $(\Lambda_c\bar{\Sigma}_c-\Sigma_c\bar{\Lambda}_c)/\sqrt{2}$ is apparently larger than the partial width from the three-body decay channel $\Lambda_c\bar{\Lambda}_c\pi$

Functioning and mechanisms of PTMs in renal diseases

Post-translational modifications (PTMs) are crucial epigenetic mechanisms that regulate various cellular biological processes. The use of mass spectrometry (MS)-proteomics has led to the discovery of numerous novel types of protein PTMs, such as acetylation, crotonylation, 2-hydroxyisobutyrylation, β-hydroxybutyrylation, protein propionylation and butyrylation, succinylation, malonylation, lactylation, and histone methylation. In this review, we specifically highlight the molecular mechanisms and roles of various histone and some non-histone PTMs in renal diseases, including diabetic kidney disease. PTMs exhibit diverse effects on renal diseases, which can be either protective or detrimental, depending on the specific type of protein PTMs and their respective targets. Different PTMs activate various signaling pathways in diverse renal pathological conditions, which could provide novel insights for studying epigenetic mechanisms and developing potential therapeutic strategies for renal diseases

2-Methyl­sulfanyl-4-(3-pyrid­yl)pyrimidine

In the title compound, C10H9N3S, the dihedral angle between the aromatic rings is 8.09 (14)°. In the crystal, a C—H⋯N interaction links the molecules, forming chains

Economic Burden for Lung Cancer Survivors in Urban China.

BackgroundWith the rapid increase in the incidence and mortality of lung cancer, a growing number of lung cancer patients and their families are faced with a tremendous economic burden because of the high cost of treatment in China. This study was conducted to estimate the economic burden and patient responsibility of lung cancer patients and the impact of this burden on family income.MethodsThis study uses data from a retrospective questionnaire survey conducted in 10 communities in urban China and includes 195 surviving lung cancer patients diagnosed over the previous five years. The calculation of direct economic burden included both direct medical and direct nonmedical costs. Indirect costs were calculated using the human capital approach, which measures the productivity lost for both patients and family caregivers. The price index was applied for the cost calculation.ResultsThe average economic burden from lung cancer was $43,336 per patient, of which the direct cost per capita was$42,540 (98.16%) and the indirect cost per capita was $795 (1.84$30,277 per capita, which accounted for 171% of the household annual income, a percentage that fell to 107% after subtracting the compensation from medical insurance.ConclusionsThe economic burden for lung cancer patients is substantial in the urban areas of China, and an effective control strategy to lower the cost is urgently needed

Re-evaluating serum angiotensin-converting enzyme in sarcoidosis

Sarcoidosis is a systemic inflammatory disease of unknown etiology, which mainly affects the lungs and lymph nodes, as well as extrapulmonary organs. Its incidence, and prevalence rate, and disease course largely vary with regions and populations globally. The clinical manifestations of sarcoidosis depend on the affected organs and the degree of severity, and the diagnosis is mainly based on serum biomarkers, radiographic, magnetic resonance, or positron emission tomography imaging, and pathological biopsy. Noncaseating granulomas composing T cells, macrophages, epithelioid cells, and giant cells, were observed in a pathological biopsy, which was the characteristic pathological manifestation of sarcoidosis. Angiotensin-converting enzyme (ACE) was first found in the renin–angiotensin–aldosterone system. Its main function is to convert angiotensin I (Ang I) into Ang II, which plays an important role in regulating blood pressure. Also, an ACE insertion/deletion polymorphism exists in the human genome, which is involved in the occurrence and development of many diseases, including hypertension, heart failure, and sarcoidosis. The serum ACE level, most commonly used as a biomarker in diagnosing sarcoidosis, in patients with sarcoidosis increases. because of epithelioid cells and giant cells of sarcoid granuloma expressing ACE. Thus, it serves as the most commonly used biomarker in the diagnosis of sarcoidosis and also aids in analyzing its therapeutic effect and prognosis in patients with sarcoidosis