Structure and dynamics of aqueous NaCl solutions at high temperatures and pressures.

The structure of a concentrated solution of NaCl in D2O was investigated by in situ high-pressure neutron diffraction with chlorine isotope substitution to give site-specific information on the coordination environment of the chloride ion. A broad range of densities was explored by first increasing the temperature from 323 to 423 K at 0.1 kbar and then increasing the pressure from 0.1 to 33.8 kbar at 423 K, thus mapping a cyclic variation in the static dielectric constant of the pure solvent. The experimental work was complemented by molecular dynamics simulations using the TIP4P/2005 model for water, which were validated against the measured equation of state and diffraction results. Pressure-induced anion ordering is observed, which is accompanied by a dramatic increase in the Cl-O and O-O coordination numbers. With the aid of bond-distance resolved bond-angle maps, it is found that the increased coordination numbers do not originate from a sizable alteration to the number of either Cl⋯D-O or O⋯D-O hydrogen bonds but from the appearance of non-hydrogen-bonded configurations. Increased pressure leads to a marked decrease in the self-diffusion coefficients but has only a moderate effect on the ion-water residence times. Contact ion pairs are observed under all conditions, mostly in the form of charge-neutral NaCl0 units, and coexist with solvent-separated Na+-Na+ and Cl--Cl- ion pairs. The exchange of water molecules with Na+ adopts a concerted mechanism under ambient conditions but becomes non-concerted as the state conditions are changed. Our findings are important for understanding the role of extreme conditions in geochemical processes.

Structure of As-Se glasses by neutron diffraction with isotope substitution.

The method of neutron diffraction with selenium isotope substitution is used to measure the structure of glassy As0.30Se0.70, As0.35Se0.65, and As0.40Se0.60. The method delivers three difference functions for each sample in which the As-As, As-Se, or Se-Se correlations are eliminated. The measured coordination numbers are consistent with the "8-N" rule and show that the As0.30Se0.70 network is chemically ordered, a composition near to which there is a minimum in the fragility index and a boundary to the intermediate phase. Chemical ordering in glassy As0.35Se0.65 and As0.40Se0.60 is, however, broken by the appearance of As-As bonds, the fraction of which increases with the arsenic content of the glass. For the As0.40Se0.60 material, a substantial fraction of As-As and Se-Se defect pairs (∼11%) is frozen into the network structure on glass formation.

High-pressure transformation of SiO2 glass from a tetrahedral to an octahedral network: a joint approach using neutron diffraction and molecular dynamics.

A combination of in situ high-pressure neutron diffraction at pressures up to 17.5(5) GPa and molecular dynamics simulations employing a many-body interatomic potential model is used to investigate the structure of cold-compressed silica glass. The simulations give a good account of the neutron diffraction results and of existing x-ray diffraction results at pressures up to ~60 GPa. On the basis of the molecular dynamics results, an atomistic model for densification is proposed in which rings are "zipped" by a pairing of five- and/or sixfold coordinated Si sites. The model gives an accurate description for the dependence of the mean primitive ring size ⟨n⟩ on the mean Si-O coordination number, thereby linking a parameter that is sensitive to ordering on multiple length scales to a readily measurable parameter that describes the local coordination environment.

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.

BACKGROUND: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. METHODS: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. RESULTS: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2. CONCLUSIONS: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia.

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Predicting Outcome of Acquired Brain Injury by the Evolution of Paroxysmal Sympathetic Hyperactivity Signs.

In this multi-center study, we provide a systematic evaluation of the clinical variability associated with paroxysmal sympathetic hyperactivity (PSH) in patients with acquired brain injury (ABI) to determine how these signs can impact outcomes. A total of 156 ABI patients with a disorder of consciousness (DoC) were admitted to neurorehabilitation subacute units (intensive rehabilitation unit; IRU) and evaluated at baseline (T0), after 4 months from event (T1), and at discharge (T2). The outcome measure was the Glasgow Outcome Scale-Extended, whereas age, sex, etiology, Coma Recovery Scale-Revised (CRS-r), Rancho Los Amigos Scale (RLAS), Early Rehabilitation Barthel Index (ERBI), PSH-Assessment Measure (PSH-AM) scores and other clinical features were considered as predictive factors. A machine learning (ML) approach was used to identify the best predictive model of clinical outcomes. The etiology was predominantly vascular (50.8%), followed by traumatic (36.2%). At admission, prevalence of PSH was 31.3%, which decreased to 16.6% and 4.4% at T1 and T2, respectively. At T2, 2.8% were dead and 61.1% had a full recovery of consciousness, whereas 36.1% remained in VS or MCS. A support vector machine (SVM)-based ML approach provides the best model with 82% accuracy in predicting outcomes. Analysis of variable importance shows that the most important clinical factors influencing the outcome are the PSH-AM scores measured at T0 and T1, together with neurological diagnosis, CRS-r, and RLAS scores measured at T0. This joint multi-center effort provides a comprehensive picture of the clinical impact of PSH signs in ABI patients, demonstrating its predictive value in comparison with other well-known clinical measurements.