Background. Better understanding of complications and outcomes of adults hospitalized with respiratory syncytial virus (RSV) infection is necessary. Methods. A retrospective cohort study was ...conducted on all adults (≥18 years) admitted to 3 acute care general hospitals in Hong Kong with virologically confirmed RSV infection during 2009–2011 (N = 607). Adults hospitalized for seasonal influenza during the period were used for comparison (n = 547). Both infections were prospectively diagnosed following a standard protocol. Independent reviews of chest radiographs were performed by radiologists. Main outcome measures were all-cause death, respiratory failure requiring ventilatory support, and hospitalization duration. Cox proportional hazards models were used for analyses. Results. The mean age of RSV patients was 75 (SD, 16) years; 87% had underlying conditions. Lower respiratory and cardiovascular complications were diagnosed in 71.9% (pneumonia, 42.3%; acute bronchitis, 21.9%; chronic obstructive pulmonary disease/asthma exacerbation, 27.3%) and 14.3% of patients, respectively; 12.5% had bacterial superinfections. Supplemental oxygen and ventilatory support were required in 67.9% and 11.1%, respectively. Crude all-cause mortality was 9.1% and 11.9% within 30 days and 60 days, respectively; mean length of stay of survivors was 12 (SD, 13) days. Advanced age, radiographic pneumonia, requirement for ventilation, bacterial superinfection, and elevated urea level and white blood cell count were independently associated with poorer survival. Systemic corticosteroid use was associated with longer hospitalization and secondary infections. The overall outcomes of survival and length of stay were not significantly different from those in influenza. Conclusions. RSV can cause severe lower respiratory complications in older adults, resulting in respiratory failure, prolonged hospitalization, and high mortality similar to seasonal influenza. Corticosteroids did not seem to improve outcomes. The unmet need for antiviral therapy and vaccination against RSV in adults should be promptly addressed.
Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations ...of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor during the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.
Electronic nematic materials are characterized by a lowered symmetry of the electronic system compared to the underlying lattice, in analogy to the directional alignment without translational order ...in nematic liquid crystals. Such nematic phases appear in the copper- and iron-based high-temperature superconductors, and their role in establishing superconductivity remains an open question. Nematicity may take an active part, cooperating or competing with superconductivity, or may appear accidentally in such systems. Here we present experimental evidence for a phase of fluctuating nematic character in a heavy-fermion superconductor, CeRhIn5 (ref. 5). We observe a magnetic-field-induced state in the vicinity of a field-tuned antiferromagnetic quantum critical point at Hc ≈ 50 tesla. This phase appears above an out-of-plane critical field H* ≈ 28 tesla and is characterized by a substantial in-plane resistivity anisotropy in the presence of a small in-plane field component. The in-plane symmetry breaking has little apparent connection to the underlying lattice, as evidenced by the small magnitude of the magnetostriction anomaly at H*. Furthermore, no anomalies appear in the magnetic torque, suggesting the absence of metamagnetism in this field range. The appearance of nematic behaviour in a prototypical heavy-fermion superconductor highlights the interrelation of nematicity and unconventional superconductivity, suggesting nematicity to be common among correlated materials.
We introduce a bond order potential (BOP) for stanene based on an ab initio derived training data set. The potential is optimized to accurately describe the energetics, as well as thermal and ...mechanical properties of a free-standing sheet, and used to study diverse nanostructures of stanene, including tubes and ribbons. As a representative case study, using the potential, we perform molecular dynamics simulations to study stanene’s structure and temperature-dependent thermal conductivity. We find that the structure of stanene is highly rippled, far in excess of other 2-D materials (e.g., graphene), owing to its low in-plane stiffness (stanene: ∼ 25 N/m; graphene: ∼ 480 N/m). The extent of stanene’s rippling also shows stronger temperature dependence compared to that in graphene. Furthermore, we find that stanene based nanostructures have significantly lower thermal conductivity compared to graphene based structures owing to their softness (i.e., low phonon group velocities) and high anharmonic response. Our newly developed BOP will facilitate the exploration of stanene based low dimensional heterostructures for thermoelectric and thermal management applications.
An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained ...(CG) models (ML-BOP, ML-BOP
, and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10's of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model).
The Saccharomyces Genome Database (SGD, http://www.yeastgenome.org) is the community resource for the budding yeast Saccharomyces cerevisiae. The SGD project provides the highest-quality manually ...curated information from peer-reviewed literature. The experimental results reported in the literature are extracted and integrated within a well-developed database. These data are combined with quality high-throughput results and provided through Locus Summary pages, a powerful query engine and rich genome browser. The acquisition, integration and retrieval of these data allow SGD to facilitate experimental design and analysis by providing an encyclopedia of the yeast genome, its chromosomal features, their functions and interactions. Public access to these data is provided to researchers and educators via web pages designed for optimal ease of use.
The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In ...particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.
The aim of this study was to investigate factors affecting clinical outcomes of adults hospitalised with severe seasonal influenza.
A prospective, observational cohort study was conducted over 24 ...months (2007-2008) in two acute, general hospitals. Consecutive, hospitalised adult patients were recruited and followed once their laboratory diagnosis of influenza A/B was established (based on viral antigen detection and virus isolation from nasopharyngeal aspirates collected per protocol). Outcomes studied included in-hospital death, length of stay and duration of oxygen therapy. Factors affecting outcomes were analysed using multivariate Cox proportional hazards models. Sequencing analysis on the neuraminidase gene was performed for available H1N1 isolates.
754 patients were studied (influenza A, n=539; >75% H3N2). Their mean age was 70+/-18 years; co-morbidities and serious complications were common (61-77%). Supplemental oxygen and ventilatory support was required in 401 (53.2%) and 41 (5.4%) patients, respectively. 39 (5.2%) patients died; pneumonia, respiratory failure and sepsis were the causes. 395 (52%) patients received antiviral (oseltamivir) treatment. Omission of antiviral treatment was associated with delayed presentation or negative antigen detection results. The mortality rate was 4.56 and 7.42 per 1000 patient-days in the treated and untreated patients, respectively; among those with co-morbidities, it was 5.62 and 11.64 per 1000 patient-days, respectively. In multivariate analysis, antiviral use was associated with reduced risk of death (adjusted HR (aHR) 0.27 (95% CI 0.13 to 0.55); p<0.001). Improved survival was observed with treatment started within 4 days from onset. Earlier hospital discharge (aHR 1.28 (95% CI 1.04 to 1.57); p=0.019) and faster discontinuation of oxygen therapy (aHR 1.30 (95% CI 1.01 to 1.69); p=0.043) was associated with early treatment within 2 days. Few (n=15) H1N1 isolates in this cohort had the H275Y mutation.
Antiviral treatment for severe influenza is associated with reduced mortality and improved clinical outcomes.
Early-stage nasopharyngeal carcinoma (NPC) evades detection when the primary tumor is hidden from view on endoscopic examination. Therefore, in a prospective study of subjects being screened for NPC ...using plasma Epstein–Barr virus (EBV) DNA, we conducted a study to investigate whether magnetic resonance imaging (MRI) could detect endoscopically occult NPC.
Participants with persistently positive EBV DNA underwent endoscopic examination and biopsy when suspicious for NPC, followed by MRI blinded to the endoscopic findings. Participants with a negative endoscopic examination and positive MRI were recalled for biopsy or surveillance. Diagnostic performance was assessed by calculating sensitivity, specificity and accuracy, based on the histologic confirmation of NPC in the initial study or in a follow-up period of at least two years.
Endoscopic examination and MRI were performed on 275 participants, 34 had NPC, 2 had other cancers and 239 without cancer were followed-up for a median of 36months (24–60months). Sensitivity, specificity and accuracy were 76.5%, 97.5% and 94.9%, respectively, for endoscopic examination and 91.2%, 97.5% and 96.7%, respectively, for MRI. NPC was detected only by endoscopic examination in 1/34 (2.9%) participants (a participant with stage I disease), and only by MRI in 6/34 (17.6%) participants (stage I=4, II = 1, III = 1), two of whom had stage I disease and follow-up showing slow growth on MRI but no change on endoscopic examination for 36months.
MRI has a complementary role to play in NPC detection and can enable the earlier detection of endoscopically occult NPC.
Nasopharyngeal carcinoma (NPC) is highly sensitive to both radiotherapy (RT) and chemotherapy. This randomized phase III trial compared concurrent cisplatin-RT (CRT) with RT alone in patients with ...locoregionally advanced NPC.
Patients with Ho's N2 or N3 stage or N1 stage with nodal size > or = 4 cm were randomized to receive cisplatin 40 mg/m(2) weekly up to 8 weeks concurrently with radical RT (CRT) or RT alone. The primary end point was progression-free survival (PFS).
Three hundred fifty eligible patients were randomized. Baseline patient characteristics were comparable in both arms. There were significantly more toxicities, including mucositis, myelosuppression, and weight loss in the CRT arm. There were no treatment-related deaths in the CRT arm, and one patient died during treatment in the RT-alone arm. At a median follow-up of 2.71 years, the 2-year PFS was 76% in the CRT arm and 69% in the RT-alone arm (P =.10) with a hazards ratio of 1.367 (95% confidence interval CI, 0.93 to 2.00). The treatment effect had a significant covariate interaction with tumor stage, and a subgroup analysis demonstrated a highly significant difference in favor of the CRT arm in Ho's stage T3 (P =.0075) with a hazards ratio of 2.328 (95% CI, 1.26 to 4.28). For T3 stage, the time to first distant failure was statistically significantly different in favor of the CRT arm (P =.016).
Concurrent CRT is well tolerated in patients with advanced NPC in endemic areas. Although PFS was not significantly different between the concurrent CRT arm and the RT-alone arm in the overall comparison, PFS was significantly prolonged in patients with advanced tumor and node stages.
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