Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Large-Eddy simulations to design an adaptive sampling
strategy to assess cumulus cloud heterogeneities by Remotely
Piloted Aircraft

Abstract. Trade wind cumulus clouds have a significant impact on the earth's radiative balance, due to their ubiquitous presence and significant coverage in subtropical regions. Many numerical studies and field campaigns have focused on better understanding the thermodynamic and macroscopic properties of cumulus clouds with ground-based and satellite remote sensing as well as in-situ observations. Aircraft flights have provided a significant contribution, but their resolution remains limited by rectilinear transects and fragmented temporal data of individual clouds. To provide a higher spatial and temporal resolution, Remotely Piloted Aircraft (RPA) can now be employed for direct observations, using numerous technological advances, to map the microphysical cloud structure and to study entrainment mixing. In fact, the numerical representation of mixing processes between a cloud and the surrounding air has been a key issue in model parameterizations for decades. To better study these mixing processes as well as their impacts on cloud microphysical properties, the manuscript aims to improve exploration strategies that can be implemented by a fleet of RPAs. Here, we use a Large-Eddy simulation (LES) of oceanic cumulus clouds to design adaptive sampling strategies. An implementation of the RPA flight simulator within high-frequency LES outputs (every 5 s) allows to track individual clouds. A Rosette sampling strategy is used to explore clouds of different sizes, static in time and space. The adaptive sampling carried out by these explorations is optimized using one ors two RPAs and with or without Gaussian Process Regression (GPR) mapping, 1by comparing the results obtained with those of a reference simulation, in particular the total liquid water content (LWC) and the LWC distributions in a horizontal cross section. Also, a sensitivity test of lengthscale for GPR mapping is performed. The results of exploring a static cloud are then extended to a dynamic case of a cloud evolving with time, to assess the application of this exploration strategy to study the evolution of cloud heterogeneities.
Toward meaningful algorithmic music-making for non-programmers

Algorithmic composition typically involves manipulating structural elements such as indeterminism, parallelism, choice, multi-choice, recursion, weighting, sequencing, timing, and looping. There exist powerful tools for these purposes, however, many musicians who are not expert programmers find such tools inaccessible and difficult to understand and use. By analysing a representative selection of user interfaces for algorithmic composition, through the use of the Cognitive Dimensions of Notations (CDN) and other analytical tools, we identified candidate design principles, and applied these principles to create and implement a new visual formalism, programming abstraction and execution model. The resulting visual programming language, Choosers, is designed to allow ready visualisation and manipulation of structural elements of the kind involved in algorithmic music composition, while making minimal demand on programming ability. Programming walkthroughs with novice users were used iteratively to refine and validate diverse aspects of the design. Currently, workshops with musical experts and teachers are being conducted to explore the value of the language for varied pragmatic purposes by expressing, manipulating and reflecting on diverse musical examples.
International Society of Phonetic Sciences President :

Three different ways of representing vowels are discussed: the traditional view based on the highest point of the tongue as set down in the IPA vowel chart, the view of representing vowels in an F1/F2 plot, and the view based on the place of articulation, reconciling the representation of consonants and of vowels. It is argued that a representation of vowels should include the place of articulation, since the place of articulation of vowels motivates many phonological processes. Moreover, languages apply different modes of articulating vowels. Consequently, language specific representations of vowels should be given sincere consideration.
Progress in TDR probing

Measurement of the electromagnetic characteristics of materials is of interest for several applications, ranging from geophysics [1] and quality control of alimentary products [2] to medical applications [3]. Recent studies have shown the possibility, at least theoretical, to measure both the equivalent relative dielectric permittivity and the equivalent magnetic permeability of the materials by means of time-domain reflectometer (TDR) probes [4]. It is well known that microwave circuital set-ups based on waveguides can be exploited in order to discriminate both the dielectric permittivity and the magnetic permeability of a material under test [5], however the use of TDR probes is an innovative method which isessentially based on the same concept in the framework of transmission lines. In [4], this has been shown using an open transmission line with respect to a lossless medium. In this paper, it will be shown that a short circuited line can also be exploited. An open ended line is suitable to describe a bifilar or trifilar probe driven into the soil, whereas a short-circuited line is suitable to describe a coaxial cable partially filled with the material under test and closed with a metallic termination. Additionally, when making use of an open ended termination the radiation losses are neglected, whereas a shorted coaxial cable does not require any approximation in this sense. This contribution describes work in progress that will be suplemented with numerical and experimental work scheduled in the near future. The research is funded by the project “TDRpro: Design of Time Domain Reflectometer (TDR) Probes” funded by Ministry for the Economy, Industry and Small Business (Malta)
Modeling the use of SARS-CoV-2 vaccination to safely relax non-pharmaceutical interventions

In response to the COVID-19 pandemic, widespread non-pharmaceutical interventions (NPIs), including physical distancing, mask wearing, and enhanced hygiene, have been implemented. As of March 2021, three effective vaccines have been approved for emergency use in the United States, with several other vaccines in the pipeline. We use a transmission model to study when and how NPIs could be relaxed in the United States with relative safety as vaccination becomes more widespread. We compare different relaxation scenarios where NPIs begin to relax 0-9 months after vaccination begins for both a one dose and two dose strategy, with historical levels of social interactions being reached within 1 month to 1 year. In our model, vaccination can allow widespread relaxation of NPIs to begin safely within 2 to 9 months, greatly reducing deaths and peak health system burden compared to relaxing NPIs without vaccination. Vaccinated individuals can safely begin to relax NPIs sooner than unvaccinated individuals. The extent of delay needed to safely reopen depends primarily on the rate of vaccine rollout, with the degree of protection against asymptomatic infection playing a secondary role. If a vaccination rate of 3 million doses/day can be achieved, similar to the typical rollout speed of seasonal influenza vaccination, NPIs could begin to be safely relaxed in 2-3 months. With a vaccination rate of 1 million doses/day, a 6-9-month delay is needed. A one dose strategy is preferred if relative efficacy is similar to a two-dose series, but the relative benefit of this strategy is minimal when vaccine rollout is fast. Due to the urgent need to pursue strategies that enable safe relaxation of NPIs, we recommend a two-dose strategy with an initial delay of at least 3 months in relaxing restrictions further, and that the speed of vaccine rollout be given immediate priority.
Clinical characteristics of COVID-19 in children and adolescents: a systematic review and meta-analysis

Background: Although the number of COVID-19 (coronavirus disease 2019) cases continues to increase globally, there are few studies on the clinical characteristics of children and adolescents with COVID-19. Objective: To conduct a comprehensive systematic evaluation and meta-analysis of the clinical characteristics of COVID-19 in children and adolescents to better guide the response to the current epidemic. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, CNKI (Chinese database), Clinical Trials.gov and chictr.org. cn (China). The methodological quality of the included literature was evaluated using the Quality Assessment Tool for Case Series Studies. Meta-analysis was performed using STATA 14.0. Heterogeneity was assessed by the Q statistic and quantified using I2. We used fixed-effects or random-effects models to pool clinical data in the meta-analysis. Publication bias was evaluated by the Begg's test. Results: We analyzed 49 studies involving 1627 patients. In the pooled data, the most common clinical symptoms were fever (56% [0.50-0.61]) and cough (45% [0.39-0.51]). The most common laboratory abnormalities were elevated procalcitonin (40% [0.23-0.57]), elevated lactate dehydrogenase (31% [0.19-0.43]), increased lymphocyte count (28% [0.17-0.42]), increased creatine kinase (28% [0.18- 0.40]), and elevated C-reactive protein (26% [0.17-0.36]). The most common abnormalities determined by computed tomography were lower-lobe involvement (56% [0.42- 0.70]), ground-glass opacities (33% [0.25-0.42]), bilateral pneumonia (32% [0.24- 0.40]), patchy shadowing (31% [0.18- 0.45]), and upper lobe involvement (30% [0.20- 0.41]). Conclusion: Disease severity among children and adolescents with COVID-19 was milder than that among adult patients, with a greater proportion of mild and asymptomatic cases, and thus, the diagnosis of COVID-19 and control of the infection source are more challenging.
Rapid Review of Social Contact Patterns During the COVID-19 Pandemic

Supplemental Digital Content is available in the text. Background: Physical distancing measures aim to reduce person-to-person contact, a key driver of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. In response to unprecedented restrictions on human contact during the coronavirus disease 2019 (COVID-19) pandemic, studies measured social contact patterns under the implementation of physical distancing measures. This rapid review synthesizes empirical data on the changing social contact patterns during the COVID-19 pandemic. Method: We conducted a systematic review using PubMed, Medline, Embase, and Google Scholar following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We descriptively compared the distribution of contacts observed during the pandemic to pre-COVID data across countries to explore changes in contact patterns during physical distancing measures. Results: We identified 12 studies reporting social contact patterns during the COVID-19 pandemic. Eight studies were conducted in European countries and eleven collected data during the initial mitigation period in the spring of 2020 marked by government-declared lockdowns. Some studies collected additional data after relaxation of initial mitigation. Most study settings reported a mean of between 2 and 5 contacts per person per day, a substantial reduction compared to pre-COVID rates, which ranged from 7 to 26 contacts per day. This reduction was pronounced for contacts outside of the home. Consequently, levels of assortative mixing by age substantially declined. After relaxation of initial mitigation, mean contact rates increased but did not return to pre-COVID levels. Increases in contacts post-relaxation were driven by working-age adults. Conclusion: Information on changes in contact patterns during physical distancing measures can guide more realistic representations of contact patterns in mathematical models for SARS-CoV-2 transmission.
Effects of stomach content on the breath alcohol concentration-transdermal alcohol concentration relationship.

INTRODUCTION Wearable devices that obtain transdermal alcohol concentration (TAC) could become valuable research tools for monitoring alcohol consumption levels in naturalistic environments if the TAC they produce could be converted into quantitatively-meaningful estimates of breath alcohol concentration (eBrAC). Our team has developed mathematical models to produce eBrAC from TAC, but it is not yet clear how a variety of factors affect the accuracy of the models. Stomach content is one factor that is known to affect breath alcohol concentration (BrAC), but its effect on the BrAC-TAC relationship has not yet been studied. METHODS We examine the BrAC-TAC relationship by having two investigators participate in four laboratory drinking sessions with varied stomach content conditions: (i) no meal, (ii) half and (iii) full meal before drinking, and (iv) full meal after drinking. BrAC and TAC were obtained every 10 min over the BrAC curve. RESULTS Eating before drinking lowered BrAC and TAC levels, with greater variability in TAC across person-device pairings, but the BrAC-TAC relationship was not consistently altered by stomach content. The mathematical model calibration parameters, fit indices, and eBrAC curves and summary score outputs did not consistently vary based on stomach content, indicating that our models were able to produce eBrAC from TAC with similar accuracy despite variations in the shape and magnitude of the BrAC curves under different conditions. DISCUSSION AND CONCLUSIONS This study represents the first examination of how stomach content affects our ability to model estimates of BrAC from TAC and indicates it is not a major factor.
Current and Emerging Clinical Applications of PSMA PET Diagnostic Imaging for Prostate Cancer

Prostate-specific membrane antigen (PSMA) is highly expressed on most prostate cancer (PCa) cells, and several PSMA ligands for PET imaging are now available worldwide. 68Ga-PSMA-11 has already received U.S. Food and Drug Administration and European Medicines Agency approval, and use of PSMA PET is currently suggested by several international guidelines for investigating PCa in different clinical settings. In primary PCa, PSMA PET has been shown to be superior to cross-sectional imaging for the detection of pelvic lymph nodes and distant metastases with subsequent clinical management changes. Additionally, it might also have a role in intraprostatic tumor localization, especially when combined with multiparametric MRI. In a setting of PCa recurrence, higher detection rates have been observed than for any other available imaging techniques, especially at low prostate-specific antigen values. Furthermore, PSMA PET consistently led to a shift in clinical management, thus increasing the proportion of radiotherapy, surgery, or other focal therapies at the expense of systemic options or no treatment. In oligometastatic disease after radical surgery, PSMA PET may be relevant in guiding a metastasis-directed therapy approach, as preliminary data seem to suggest a benefit in terms of progression-free survival after treatment of PSMA PET–positive lesions. As a staging and gatekeeping technique, PSMA PET represents a reliable whole-body imaging procedure in combination with second-line therapy of castration-resistant PCa, as well as being pivotal when assessing patients eligible for radioligand therapy such as 177Lu-PSMA. This critical review aims at providing a comprehensive overview of the latest literature on the current or emerging main indications, as well as a general outlook on the recommended interpretation criteria for PSMA PET imaging.
A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients*

Supplemental Digital Content is available in the text. OBJECTIVES: The National Early Warning Score, Modified Early Warning Score, and quick Sepsis-related Organ Failure Assessment can predict clinical deterioration. These scores exhibit only moderate performance and are often evaluated using aggregated measures over time. A simulated prospective validation strategy that assesses multiple predictions per patient-day would provide the best pragmatic evaluation. We developed a deep recurrent neural network deterioration model and conducted a simulated prospective evaluation. DESIGN: Retrospective cohort study. SETTING: Four hospitals in Pennsylvania. PATIENTS: Inpatient adults discharged between July 1, 2017, and June 30, 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We trained a deep recurrent neural network and logistic regression model using data from electronic health records to predict hourly the 24-hour composite outcome of transfer to ICU or death. We analyzed 146,446 hospitalizations with 16.75 million patient-hours. The hourly event rate was 1.6% (12,842 transfers or deaths, corresponding to 260,295 patient-hours within the predictive horizon). On a hold-out dataset, the deep recurrent neural network achieved an area under the precision-recall curve of 0.042 (95% CI, 0.04–0.043), comparable with logistic regression model (0.043; 95% CI 0.041 to 0.045), and outperformed National Early Warning Score (0.034; 95% CI, 0.032–0.035), Modified Early Warning Score (0.028; 95% CI, 0.027– 0.03), and quick Sepsis-related Organ Failure Assessment (0.021; 95% CI, 0.021–0.022). For a fixed sensitivity of 50%, the deep recurrent neural network achieved a positive predictive value of 3.4% (95% CI, 3.4–3.5) and outperformed logistic regression model (3.1%; 95% CI 3.1–3.2), National Early Warning Score (2.0%; 95% CI, 2.0–2.0), Modified Early Warning Score (1.5%; 95% CI, 1.5–1.5), and quick Sepsis-related Organ Failure Assessment (1.5%; 95% CI, 1.5–1.5). CONCLUSIONS: Commonly used early warning scores for clinical decompensation, along with a logistic regression model and a deep recurrent neural network model, show very poor performance characteristics when assessed using a simulated prospective validation. None of these models may be suitable for real-time deployment.
Contact block reduction method and its application to a 10 nm MOSFET device

With the scaling of semiconductor devices to nano-size dimensions, gate lengths reach their physical limits of 8–15 nm. In such devices, a variety of undesirable effects begin to be prominent, including gate leakage, discrete impurity effects and source-to-drain tunnelling. To address the issue of source-to-drain tunnelling we apply to a prototypical 10 nm MOSFET device a novel highly efficient method, termed contact block reduction (CBR) method. It has been reported recently [1] that the CBR method allows us to calculate the ballistic current (transmission function) through an arbitrarily shaped, multi-terminal two- or three-dimensional device. Here we show that in the ballistic case the density matrix of the open device can be computed using the same method. This opens a possibility for an efficient fully quantum-mechanical self-consistent calculation within the ballistic limit.
PenA, a penicillin-binding protein-type thioesterase specialized for small peptide cyclization

Abstract Penicillin-binding protein-type thioesterases (PBP-type TEs) are a recently identified group of peptide cyclases that catalyze head-to-tail macrolactamization of nonribosomal peptides. PenA, a new member of this group, is involved in the biosyntheses of cyclic pentapeptides. In this study, we demonstrated the enzymatic activity of PenA in vitro, and analyzed its substrate scope with a series of synthetic substrates. A comparison of the reaction profiles between PenA and SurE, a representative PBP-type TE, showed that PenA is more specialized for small peptide cyclization. A computational model provided a possible structural rationale for the altered specificity for substrate chain lengths.
The Effects of Sodium Ions on Ligand Binding and Conformational States of G Protein-Coupled Receptors—Insights from Mass Spectrometry

The use of mass spectrometry to investigate proteins is now well established and provides invaluable information for both soluble and membrane protein assemblies. Maintaining transient noncovalent interactions under physiological conditions, however, remains challenging. Here, using nanoscale electrospray ionization emitters, we establish conditions that enable mass spectrometry of two G protein-coupled receptors (GPCR) from buffers containing high concentrations of sodium ions. For the Class A GPCR, the adenosine 2A receptor, we observe ligand-induced changes to sodium binding of the receptor at the level of individual sodium ions. We find that antagonists promote sodium binding while agonists attenuate sodium binding. These findings are in line with high-resolution X-ray crystallography wherein only inactive conformations retain sodium ions in allosteric binding pockets. For the glucagon receptor (a Class B GPCR) we observed enhanced ligand binding in electrospray buffers containing high concentrations of sodium, as opposed to ammonium acetate buffers. A combination of native and -omics mass spectrometry revealed the presence of a lipophilic negative allosteric modulator. These experiments highlight the advantages of implementing native mass spectrometry, from electrospray buffers containing high concentrations of physiologically relevant salts, to inform on allosteric ions or ligands with the potential to define their roles on GPCR function.
Depression and anxiety symptoms at TNF inhibitor initiation are associated with impaired treatment response in axial spondyloarthritis

Abstract Objectives Depression and anxiety are associated with more severe disease in cross-sectional studies of axial spondyloarthritis (axSpA). We examined the association between baseline symptoms of depression or anxiety and response to TNF inhibitors (TNFi) in axSpA. Methods Biologic naïve participants from a national axSpA register completed the Hospital Anxiety and Depression Scale (HADS) before initiating TNFi. Symptoms of anxiety and depression were each categorized as moderate–severe (≥11), mild (8–10) and ‘none’ (≤7), and compared against change in disease indices [BASDAI and AS Disease Activity Score (ASDAS)] over time and time to treatment discontinuation using marginal structural models. Inverse-probability weights balanced baseline age, gender, BMI, deprivation, education and baseline values of respective disease indices. Results Of the 742 participants (67% male, mean age 45 years), 176 (24%) had moderate–severe and 26% mild depression; 295 (40%) had moderate–severe and 23% mild anxiety. Baseline disease activity was higher in higher HADS symptom categories for both depression and anxiety. Participants with moderate–severe depression had significantly poorer response compared with those with ‘none’ throughout follow-up. At 6 months, the difference was approximately 2.2 BASDAI and 0.8 ASDAS units after balancing their baseline values. Equivalent comparisons for anxiety were 1.7 BASDAI and 0.7 ASDAS units. Treatment discontinuation was 1.59-fold higher (hazard ratio 95% CI: 1.12, 2.26) in participants with moderate–severe anxiety compared with ‘none’. Conclusions Symptoms of depression and anxiety at TNFi initiation are associated with poorer treatment outcomes. Targeted interventions to optimize mental health have potential to substantially improve treatment response and persistence.
A structural consideration of kanemite, octosilicate, magadiite and kenyaite

By comparing data from 29 Si, 1 H and 23 Na NMR studies, new model structures have been proposed for the layered sodium polysilicate hydrates, kanemite, octosilicate, magadiite and kenyaite. These are based on the known structures of the anhydrous silicate, KHSi 2 O 5 , and piperazine silicate (EU19), rather than the known structure of the layered sodium polysilicate hydrate, makatite, which previous authors have used as a basis for their models.