Physics-Driven ML-Based Modelling for Correcting Inverse Estimation

When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design. This work focuses on detecting and correcting failed state estimations before adopting them in SAE inverse problems, by utilizing simulations and performance metrics guided by physical laws. We suggest to flag a machine learning estimation when its physical model error exceeds a feasible threshold, and propose a novel approach, GEESE, to correct it through optimization, aiming at delivering both low error and high efficiency. The key designs of GEESE include (1) a hybrid surrogate error model to provide fast error estimations to reduce simulation cost and to enable gradient based backpropagation of error feedback, and (2) two generative models to approximate the probability distributions of the candidate states for simulating the exploitation and exploration behaviours. All three models are constructed as neural networks. GEESE is tested on three real-world SAE inverse problems and compared to a number of state-of-the-art optimization/search approaches. Results show that it fails the least number of times in terms of finding a feasible state correction, and requires physical evaluations less frequently in general.Comment: 19 pages, the paper is accepted by Neurips 2023 as a spotligh

Land-use changes from arable crop to kiwi-orchard increased nutrient surpluses and accumulation in soils

The potential environmental risk associated to nutrient surpluses after changing arable crops to kiwi-orchards was assessed in the Yujiahe catchment of Shaanxi, China. This was achieved by surveying 242 kiwi-orchards and 21 croplands and determining their nutrient inputs and outputs as well as the soil nutrient status for the over 2 years. The total inputs of nitrogen (N), phosphorus (P) and potassium (K) from fertilizers, manures, deposition, and irrigation in kiwi-orchards were 1201, 268 and 615 kg ha−1 yr−1, respectively, which were higher than the rates of 425, 59 and 109 kg ha−1 yr−1 in wheat-maize fields. The mean annual apparent nutrient surpluses in kiwi-orchards were 1081 kg N ha−1 yr−1, 237 kg P ha−1 yr−1 and 491 kg K ha−1 yr−1. Within comparison to the croplands, the soil organic matter (SOM) and total N (TN) in the topsoil (0–20 cm) increased in kiwi-orchards, and soil pH decreased. The average contents of Olsen-P, and available K in 0–20 cm soils of the orchards were 86 mg kg−1, and 360 mg kg−1, which were higher than recommended levels. The nitrate-N accumulation in the 0–100 cm and 0–200 cm soil layers in kiwi-orchards were 466 and 793 kg N ha−1, respectively. The high proportion of nitrate-N in deeper soil profiles of kiwi-orchards poses a great risk for nitrate leaching and subsequent ground water pollution. It is concluded that changing arable crops to kiwi-orchards increased the environmental burden of the catchment due to excessive fertilizer application in kiwi-orchards

Gas Flow Model of Adhesion Sand Casing Well First Interface Micro Clearance and Application

In order to accurately describe the flow characteristics of the gas channeling in the adhesion sand casing well first interface, this article assumes the first interface annulus composed by adhesion sand casing and cement mantle is a microscopic rough gas flow channel. Using the lattice Boltzmann method, the flow model of gas channeling in first interface rough microscopic channel is established, the fundamental relationship of gas flow rate, gas flow pressure distribution and gas annulus pressure differential are calculated and analyzed. And the flow characteristics of gas under different adhesion sand density is simulated. The results show that the theoretical calculation and the numerical simulation results are in good agreement, the new calculation model reveals the essential rule of cementing gas channeling flow in adhesion sand casing well, which provides a new method for the gas channeling problem in process of subsequent oil well cementing.Key words: Adhesion sand casing well; Rough surface; Cementing first interface; Lattice Boltzmann; Gas channelin

Performance of a new Candida anti-mannan IgM and IgG assays in the diagnosis of candidemia

Candida is one of the most frequent pathogens of bloodstream infections, which is associated with high morbidity and mortality rates. Rapid immunological detection methods are essential in the early diagnosis of candidemia. Anti-mannan is one of host-derived biomarkers against cell wall components of Candida. We conducted this study to evaluate the diagnostic performance of two anti-mannan assays (IgM, IgG) for candidemia through the analysis of 40 candidemia patients, 48 participants with Candida colonization and 213 participants with neither Candida colonization nor Candida infections (13 patients with other bloodstream infections, 145 hospitalized patients and 55 healthy controls). The performance of the two assays were evaluated by calculating their sensitivity and specificity. The sensitivity ranged from 0.78 to 0.80 for the IgM assay and 0.68 to 0.75 for the IgG assay. The specificity ranged from 0.97 to 0.98 for the IgM assay and 0.91 to 0.94 for the IgG assay. The diagnostic performance of the anti-mannan IgM assay was better than that of IgG, with higher sensitivity and specificity. Combining the two assays (positive results of single or both assays are both considered as positive) could improve the sensitivity up to 0.93 (0.79-0.98) and only slightly reduce the specificity (0.93(0.89-0.95)). The anti-mannan IgM, IgG assays are rapid and cost-effective assays that may be probably useful in the diagnosis of candidemia

A duplex real-time reverse transcriptase polymerase chain reaction assay for detecting western equine and eastern equine encephalitis viruses

In order to establish an accurate, ready-to-use assay for simultaneous detection of Eastern equine encephalitis virus (EEEV) and Western equine encephalitis virus (WEEV), we developed one duplex TaqMan real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay, which can be used in human and vector surveillance. First, we selected the primers and FAM-labeled TaqMan-probe specific for WEEV from the consensus sequence of NSP3 and the primers and HEX-labeled TaqMan-probe specific for EEEV from the consensus sequence of E3, respectively. Then we constructed and optimized the duplex real-time RT-PCR assay by adjusting the concentrations of primers and probes. Using a series of dilutions of transcripts containing target genes as template, we showed that the sensitivity of the assay reached 1 copy/reaction for EEEV and WEEV, and the performance was linear within the range of at least 10(6 )transcript copies. Moreover, we evaluated the specificity of the duplex system using other encephalitis virus RNA as template, and found no cross-reactivity. Compared with virus isolation, the gold standard, the duplex real time RT-PCR assay we developed was 10-fold more sensitive for both WEEV and EEEV detection

Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost

ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent

The macrophages regulate intestinal motility dysfunction through the PGE2 Ptger3 axis during Klebsiella pneumonia sepsis

IntroductionGut motility dysfunction, the most common complication of post-septic organ dysfunction, depends on immune and neuronal cells. This study aimed to investigate the mechanisms that activate these cells and the contribution of macrophages to the recovery of intestinal motility dysfunction after sepsis.Materials and methodsPostoperative gut motility dysfunction was induced by establishing Klebsiella pneumonia sepsis in mice with selective deletion of neutrophils and macrophages in the gut. The distribution of orally administered fluorescein isothiocyanate-dextran and carmine excretion time was used to determine the severity of small bowel disease. The effect of macrophages on intestinal motility was evaluated after prostaglandin E2 therapy.ResultsWe found that muscular neutrophil infiltration leading to neuronal loss in the intestine muscle triggered intestinal motility dysfunction after pneumonia sepsis; however, reduced neutrophil infiltration did not improve intestinal motility dysfunction. Moreover, macrophage depletion aggravated gut motility dysfunction. The addition of macrophages directly to a smooth muscle was responsible for the recovery of intestinal motility.ConclusionOur results suggest that a direct interaction between macrophages and smooth muscle is neurologically independent of the restoration of intestinal dysmotility