Evidence of Covid-19 lockdown effects on riverine dissolved organic matter dynamics provides a proof-of-concept for needed regulations of anthropogenic emissions.

The fast spread of SARS-CoV-2 virus in Italy resulted in a 3-months lockdown of the entire country. During this period, the effect of the relieved anthropogenic activities on the environment was plainly clear all over the country. Herein, we provide the first evidence of the lockdown effects on riverine dissolved organic matter (DOM) dynamics. The strong reduction in anthropogenic activities resulted in a marked decrease in dissolved organic carbon (DOC) concentration in the Arno River (-44%) and the coastal area affected by its input (-15%), compared to previous conditions. The DOM optical properties (absorption and fluorescence) showed a change in its quality, with a shift toward smaller and less aromatic molecules during the lockdown. The reduced human activity and the consequent change in DOM dynamics affected the abundance and annual dynamics of heterotrophic prokaryotes. The results of this study highlight the extent to which DOM dynamics in small rivers is affected by secondary and tertiary human activities as well as the quite short time scales to return to the impacted conditions. Our work also supports the importance of long-term research to disentangle the effects of casual events from the natural variability.

A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis.

Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms with variable risk of evolution into post-PV and post-ET myelofibrosis, from now on referred to as secondary myelofibrosis (SMF). No specific tools have been defined for risk stratification in SMF. To develop a prognostic model for predicting survival, we studied 685 JAK2, CALR, and MPL annotated patients with SMF. Median survival of the whole cohort was 9.3 years (95% CI: 8-not reached-NR-). Through penalized Cox regressions we identified negative predictors of survival and according to beta risk coefficients we assigned 2 points to hemoglobin level <11 g/dl, to circulating blasts ⩾3%, and to CALR-unmutated genotype, 1 point to platelet count <150 × 109/l and to constitutional symptoms, and 0.15 points to any year of age. Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) allocated SMF patients into four risk categories with different survival (P<0.0001): low (median survival NR; 133 patients), intermediate-1 (9.3 years, 95% CI: 8.1-NR; 245 patients), intermediate-2 (4.4 years, 95% CI: 3.2-7.9; 126 patients), and high risk (2 years, 95% CI: 1.7-3.9; 75 patients). Finally, we found that the MYSEC-PM represents the most appropriate tool for SMF decision-making to be used in clinical and trial settings.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Visualizing the Induced Binding of SH2-Phosphopeptide.

Approximately 100 proteins in the human genome contain an SH2 domain recognizing small flexible phosphopeptides. It is therefore important to understand in atomistic detail the way these peptides bind and the conformational changes that take place upon binding. Here, we obtained several spontaneous binding events between the p56 lck SH2 domain and the pYEEI peptide within 2 Å RMSD from the crystal structure and with kinetic rates compatible with experiments using high-throughput molecular dynamics simulations. Binding is achieved in two phases, fast contacts of the charged phospho-tyrosine and then rearrangement of the ligand involving the stabilization of two important loops in the SH2 domain. These observations provide insights into the binding pathways and induced conformations of the SH2-phosphopeptide complex which, due to the characteristics of SH2 domains, should be relevant for other SH2 recognition peptides.

High-throughput all-atom molecular dynamics simulations using distributed computing.

Although molecular dynamics simulation methods are useful in the modeling of macromolecular systems, they remain computationally expensive, with production work requiring costly high-performance computing (HPC) resources. We review recent innovations in accelerating molecular dynamics on graphics processing units (GPUs), and we describe GPUGRID, a volunteer computing project that uses the GPU resources of nondedicated desktop and workstation computers. In particular, we demonstrate the capability of simulating thousands of all-atom molecular trajectories generated at an average of 20 ns/day each (for systems of approximately 30 000-80 000 atoms). In conjunction with a potential of mean force (PMF) protocol for computing binding free energies, we demonstrate the use of GPUGRID in the computation of accurate binding affinities of the Src SH2 domain/pYEEI ligand complex by reconstructing the PMF over 373 umbrella sampling windows of 55 ns each (20.5 mus of total data). We obtain a standard free energy of binding of -8.7 +/- 0.4 kcal/mol within 0.7 kcal/mol from experimental results. This infrastructure will provide the basis for a robust system for high-throughput accurate binding affinity prediction.

Assessment of sensorized garments as a flexible support to self-administered post-stroke physical rehabilitation.

The aim of this study was to provide a description of a newly-developed remote rehabilitation system that can be employed both at home and in the hospital, supporting motor rehabilitation for post-stroke patients with upper-limb impairment. A garment, which embeds kinesthetic sensors made of a piezoresitive polymer, is provided with a wireless connection to a computer (the patient station). The station detects in real time whether the patient is performing the exercises correctly or not, and provides feedback through an easy visual representation on the screen. Movement recognition is performed using a template matching approach, which allows exercises to be defined during each session as required without additional configuration. In this study an healthy volunteer used the garment to record 840 exercises, mimicking both correct and incorrect compensatory movements under expert supervision. The sensitivity and specificity of the recognition system were measured through its ability to correctly identify the pre-labelled exercises. A pilot set of 13 post-stroke subjects (mean age of 50) was then offered to use the rehabilitation system while in the neuro-rehabilitation ward; the acceptability was assessed through a 10-question subjective evaluation questionnaire. The wearable system tested provided a raw recognition performance (correct-versus-incorrect exercise detection) above 90%. The majority of the patients were satisfied with the system, considered it useful, and would use it at home. In conclusion, computer-based interventions can support widespread, earlier and more intense physical therapy after a neurological event, provided they are easy to use and blend well with the existing rehabilitation workflow. Wearable sensors are promising candidates to realize unobtrusive devices to support the rehabilitation process and its continuity after discharge from the Rehabilitation Unit.

Adaptable dialog architecture and runtime engine (AdaRTE): a framework for rapid prototyping of health dialog systems.

Spoken dialog systems have been increasingly employed to provide ubiquitous access via telephone to information and services for the non-Internet-connected public. They have been successfully applied in the health care context; however, speech technology requires a considerable development investment. The advent of VoiceXML reduced the proliferation of incompatible dialog formalisms, at the expense of adding even more complexity. This paper introduces a novel architecture for dialogue representation and interpretation, AdaRTE, which allows developers to lay out dialog interactions through a high-level formalism, offering both declarative and procedural features. AdaRTE's aim is to provide a ground for deploying complex and adaptable dialogs whilst allowing experimentation and incremental adoption of innovative speech technologies. It enhances augmented transition networks with dynamic behavior, and drives multiple back-end realizers, including VoiceXML. It has been especially targeted to the health care context, because of the great scale and the need for reducing the barrier to a widespread adoption of dialog systems.

AdaRTE: adaptable dialogue architecture and runtime engine. A new architecture for health-care dialogue systems.

Spoken dialogue systems have been increasingly employed to provide ubiquitous automated access via telephone to information and services for the non-Internet-connected public. In the health care context, dialogue systems have been successfully applied. Nevertheless, speech-based technology is not easy to implement because it requires a considerable development investment. The advent of VoiceXML for voice applications contributed to reduce the proliferation of incompatible dialogue interpreters, but introduced new complexity. As a response to these issues, we designed an architecture for dialogue representation and interpretation, AdaRTE, which allows developers to layout dialogue interactions through a high level formalism that offers both declarative and procedural features. AdaRTE aim is to provide a ground for deploying complex and adaptable dialogues whilst allows the experimentation and incremental adoption of innovative speech technologies. It provides the dynamic behavior of Augmented Transition Networks and enables the generation of different backends formats such as VoiceXML. It is especially targeted to the health care context, where a framework for easy dialogue deployment could reduce the barrier for a more widespread adoption of dialogue systems.