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Background Current mortality data for pulmonary arterial hypertension (PAH) in the United States are based on registries that enrolled patients prior to 2010. We sought to determine mortality in PAH in the modern era using the PHAR (Pulmonary Hypertension Association Registry). Methods and Results We identified all adult patients with PAH enrolled in the PHAR between September 2015 and September 2020 (N=935). We used Kaplan‐Meier survival analysis and Cox proportional hazards models to assess mortality at 1, 2, and 3 years. Patients were stratified based on disease severity by 3 validated risk scores. In treatment‐naïve patients, we compared survival based on initial treatment strategy. The median age was 56 years (44–68 years), and 76% were women. Of the 935 patients, 483 (52%) were ≤6 months from PAH diagnosis. There were 121 deaths (12.9%) during a median follow‐up time of 489 days (281–812 days). The 1‐, 2‐, and 3‐year mortality was 8% (95% CI, 6%–10%), 16% (95% CI, 13%–19%), and 21% (95% CI, 17%–25%), respectively. When stratified into low‐, intermediate‐, and high‐risk PAH, the mortality at 1, 2, and 3 years was 1%, 4% to 6%, and 7% to 11% for low risk; 7% to 8%, 11% to 16%, and 18% to 20% for intermediate risk; and 12% to 19%, 22% to 38%, and 28% to 55% for high risk, respectively. In treatment‐naïve patients, initial combination therapy was associated with better 1‐year survival (adjusted hazard ratio, 0.43 [95% CI, 0.19–0.95]; P =0.037). Conclusions Mortality in the intermediate‐ and high‐risk patients with PAH remains unacceptably high in the PHAR, suggesting the importance for early diagnosis, aggressive use of available therapies, and the need for better therapeutics.

In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.

Electron paramagnetic resonance spectra of copper-doped potassium tetrachloropalladate and potassium tetrabromopalladate have been observed. Both hyperfine and superhyperfine structures are resolved at room temperature. The CuCl42− and CuBr42− units are in a square-planar configuration. The data can be fitted with an axial spin Hamiltonian. The parameters are as follows; g∥ = 2.232, g⊥=2.049, A∥Cu=163.6 × 10−4cm−1, A⊥Cu=34.5 × 10−4cm−1, A∥Cl=23.3 × 10−4cm−1, and A⊥Cl=5.3 × 10−4cm−1 for CuCl42− and g∥ = 2.143, g⊥=2.0438, A∥Cu=189.5 × 10−4cm−1, A⊥Cu=45.8 ×−4cm−1, A∥Br=123 × 10−4cm−1, and A⊥Br=27.9 × 10−4cm−1 for CuBr42−. The MO parameters are evaluated exactly. There are strong covalent in-plane σ and π bonds and out-of-plane π bonds. The covalency is larger for Br− than Cl− as a ligand. Data were also obtained on single crystal copper-doped tetraphenylarsonium trichloropalladate. The pure salt contains Pd2Cl62− dimers, and the EPR spectra are consistent with PdCuCl62− dimeric units in the doped species. The spin Hamiltonian parameters are essentially the same as in CuCl42−, with g∥ = 2.245, g⊥=2.045, A∥Cu=155 × 10−4cm−1, A⊥Cu=57 × 10−4cm−1, A∥Cl=23.8 × 10−4cm−1, and A⊥Cl=5.2 × 10−4cm−1. Dimerization does not change significantly the character of the Cu–Cl bonds.

Huffman, Park and Skoug obtained various results for the L p analytic Fourier-Feynman transform and the convolution of functionals in some Banach algebra S on classical Wiener space. Recently, Ahn studied L 1 analytic Fourier-Feynman transform theory for functionals in the Fresnel class F (B) of abstract Wiener space (B, ν).In this paper we first define an L p analytic Fourier-Feynman transform and a convolution of functionals on a product abstract Wiener space and establish various relationships between the Fourier-Feynman transform and convolution for functionals in the generalized Fresnel class F A 1 ,A 2 containing F (B). Also we obtain Parseval's relation for those functionals. Results of Huffman, Park, Skoug and Ahn are corollaries of our results.

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Dynamic optimization of a continuous polymer reactor aims to decide optimal trajectories of control input variables so that the transition time, required to reach the desired steady state from the initial state during startup or grade-change operation, is minimized. The problem is challenging because of its highly nonlinear dynamics and multimodal properties. The proposed modified differential evolution (MDE) algorithm is different from differential evolution algorithms in the sense that MDE employs a local search to enhance the computational efficiency and modified heuristic constraints to systematically reduce the size of the search space. The algorithm is illustrated by several case studies using the dynamic optimization problem of a continuous methyl methacrylate-vinyl acetate copolymerization reactor. The case studies have shown that the proposed algorithm offers faster speed, flexible implementation, and higher robustness to find the global optimum than differential evolution algorithms.

Systemic treatment with statins mitigates allergic airway inflammation, TH2 cytokine production, epithelial mucus production, and airway hyperreactivity (AHR) in murine models of asthma. We hypothesized that pravastatin delivered intratracheally would be quantifiable in lung tissues using mass spectrometry, achieve high drug concentrations in the lung with minimal systemic absorption, and mitigate airway inflammation and structural changes induced by ovalbumin. Male BALB/c mice were sensitized to ovalbumin (OVA) over 4 weeks, then exposed to 1% OVA aerosol or filtered air (FA) over 2 weeks. Mice received intratracheal instillations of pravastatin before and after each OVA exposure (30 mg/kg). Ultra performance liquid chromatography – mass spectrometry was used to quantify plasma, lung, and bronchoalveolar lavage fluid (BALF) pravastatin concentration. Pravastatin was quantifiable in mouse plasma, lung tissue, and BALF (BALF > lung > plasma for OVA and FA groups). At these concentrations pravastatin inhibited airway goblet cell hyperplasia/metaplasia, and reduced BALF levels of cytokines TNFα and KC, but did not reduce BALF total leukocyte or eosinophil cell counts. While pravastatin did not mitigate AHR, it did inhibit airway hypersensitivity (AHS). In this proof-of-principle study, using novel mass spectrometry methods we show that pravastatin is quantifiable in tissues, achieves high levels in mouse lungs with minimal systemic absorption, and mitigates some pathological features of allergic asthma. Inhaled pravastatin may be beneficial for the treatment of asthma by having direct airway effects independent of a potent anti-inflammatory effect. Statins with greater lipophilicity may achieve better anti-inflammatory effects warranting further research.