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Abstract Background The Covid Connected Care Center (C4), a low-barrier telephone nurse hotline, was developed at an academic medical center to increase access to healthcare information and services across the state of Oregon, including to those without a usual source of care. Other studies have demonstrated that telephone triage services can positively influence health behaviors, but it is not known how this effect is maintained across racial/ethnic groups. The objective of this study was to show that the C4 reached throughout the state of Oregon, was valuable to callers, and that recommendations given affected callers’ subsequent health-related behaviors. Methods This mixed-methods study, informed by the RE-AIM (Reach, Effectiveness, Addoption, Implementation and Maintenance) framework, assessed caller demographics and clinical care from March 30 2020 until September 8, 2021. Descriptive statistics, multivariable risk models and Zou’s modified Poisson modeling were applied to electronic health record and call system data; An inductive approach was used for patient and staff experience surveys and semi-structured interviews. Approval was obtained from the OHSU Institutional Review Board (Study 00021413). Results 145,537 telephone calls and 92,100 text-based contacts (61% and 39%, respectively) were included. Callers tended to not have a usual source of primary care and utilized recommended services. Emergency department utilization was minimal (1.5%). Racial or ethnic disparities were not detected in the recommendations, but Black (RR 0.92, CI 0.86–0.98) and Multiracial (RR 0.90 CI 0.81–0.99) callers were less likely than non-Hispanic white callers to receive a COVID-19 test. Participants in the post-call survey (n = 50) would recommend this service to friends or family. Interviews with callers (n = 9) revealed this was because they valued assistance translating general recommendations into a personalized care plan. C4 staff interviewed (n = 9) valued the opportunity to serve the public. The C4 was a trusted resource to the public and reached the intended audiences. However, disparities in access to COVID-19 testing persisted. Conclusions Nursing triage hotlines can guide caller behavior and be an effective part of a robust public health information infrastructure.

Objective: To describe telemedicine use patterns and understand clinic’s approaches to shifting care delivery during the COVID-19 pandemic. Methods: We used electronic health record data from 203 community health centers across 13 states between 01/01/2019 and 6/31/2021 to describe trends in telemedicine visit rates over time. Qualitative data were collected from 13 of those community health centers to understand factors influencing adoption and implementation of telemedicine. Results: Most clinics in our sample were in urban areas (n = 176) and served a majority of uninsured and publicly insured patients (12.8% and 44.4%, respectively) across racial and ethnic minority groups (16.6% Black and 29.3% Hispanic). During our analysis period there was a 791% increase in telemedicine visits from before the pandemic (.06% pre- vs 47.5% during). A latent class growth analysis was used to examine differences in patterns of adoption of telemedicine across the 203 CHCs. The model resulted in 6 clusters representing various levels of telemedicine adoption. A mixed methods approach streamlined these clusters into 4 final groups. Clinics that reported rapid adoption of telemedicine attributed this change to leadership prioritization of telemedicine, robust quality improvement processes (eg, using PDSA processes), and emphasis on training and technology support. Conclusions: In response to the COVID-19 pandemic, telemedicine adoption rates varied across clinics. Our study highlight that organizational factors contributed to the clinic’s ability to rapidly uptake and use telemedicine services throughout the pandemic. These approaches could inform future non-pandemic practice change and care delivery.

Abstract Background In addition to delivering vital health care to millions of patients in the United States, community health centers (CHCs) provide needed health insurance outreach and enrollment support to their communities. We developed a health insurance enrollment tracking tool integrated within the electronic health record (EHR) and conducted a hybrid implementation-effectiveness trial in a CHC-based research network to assess tool adoption using two implementation strategies. Methods CHCs were recruited from the OCHIN practice-based research network. Seven health center systems (23 CHC clinic sites) were recruited and randomized to receive basic educational materials alone (Arm 1), or these materials plus facilitation (Arm 2) during the 18-month study period, September 2016–April 2018. Facilitation consisted of monthly contacts with clinic staff and utilized audit and feedback and guided improvement cycles. We measured total and monthly tool utilization from the EHR. We conducted structured interviews of CHC staff to assess factors associated with tool utilization. Qualitative data were analyzed using an immersion-crystallization approach with barriers and facilitators identified using the Consolidated Framework for Implementation Research. Results The majority of CHCs in both study arms adopted the enrollment tool. The rate of tool utilization was, on average, higher in Arm 2 compared to Arm 1 (20.0% versus 4.7%, p

We propose to use a systemic risk metric for an extended network which includes the inter-bank network, the banks-firms bipartite network, and the intrafirm exposures network in Uruguay. This is the first work, to the best of our knowledge, in which the intra-firm exposures network is estimated with good accuracy by using information from a firm survey. Given that the survey only includes the three most relevant debtors and creditors, we complete the full intra-firm exposures matrix by resorting to Maximum Entropy, Minimum Density and a new method which takes into account the known entries of the matrix obtained from the survey. We show that ignoring intra-firm exposures results in an important underestimation of systemic risk. Moreover, if the marginal liabilities are used as an indicator of the systemic relevance of firms, important network effects are ignored. To conclude, the paper contributes with a precise estimation of the impact of intra-firm exposures to overall systemic risk.

Abstract Background The essential oil is an important compound of the root and rhizome of medicinally used valerian (Valeriana officinalis L. s.l.), with a stated minimum content in the European pharmacopoeia. The essential oil is located in droplets, of which the position and distribution in the total root cross-section of different valerian varieties, root thicknesses and root horizons are determined in this study using an adapted fluorescence-microscopy and automatic imaging analysis method. The study was initiated by the following facts:A probable negative correlation between essential oil content and root thickness in selected single plants (elites), observed during the breeding of coarsely rooted valerian with high oil content.Higher essential oil content after careful hand-harvest and processing of the roots. Results In preliminary tests, the existence of oil containing droplets in the outer and inner regions of the valerian roots was confirmed by histological techniques and light-microscopy, as well as Fourier-transform infrared spectroscopy. Based on this, fluorescence-microscopy followed by image analysis of entire root cross-sections, showed that a large number of oil droplets (on average 43% of total oil droplets) are located close to the root surface. The remaining oil droplets are located in the inner regions (parenchyma) and showed varying density gradients from the inner to the outer regions depending on genotype, root thickness and harvesting depth. Conclusions Fluorescence-microscopy is suitable to evaluate prevalence and distribution of essential oil droplets of valerian in entire root cross-sections. The oil droplet density gradient varies among genotypes. Genotypes with a linear rather than an exponential increase of oil droplet density from the inner to the outer parenchyma can be chosen for better stability during post-harvest processing. The negative correlation of essential oil content and root thickness as observed in our breeding material can be counteracted through a selection towards generally high oil droplet density levels, and large oil droplet sizes independent of root thickness.

Abstract Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the “ideal memristor” which can deliver performance, energy efficiency, reliability and accuracy. However, designing RRAM-based systems is challenging. Engineering a new material stack, designing a device, and experimenting takes significant time for material and device researchers. Furthermore, the acceptability of the device is ultimately decided at the system level. We see a gap here where there is a need for facilitating material and device researchers with a “push button” modeling framework that allows to evaluate the efficacy of the device at system level during early device design stages. Speed, accuracy, and adaptability are the fundamental requirements of this modelling framework. In this paper, we propose a digital twin (DT)-like modeling framework that automatically creates RRAM device models from device measurement data. Furthermore, the model incorporates the peripheral circuit to ensure accurate energy and performance evaluations. We demonstrate the DT generation and DT usage for multiple RRAM technologies and applications and illustrate the achieved performance of our GPU implementation. We conclude with the application of our modeling approach to measurement data from two distinct fabricated devices, validating its effectiveness in a neural network processing an Electrocardiogram (ECG) dataset and incorporating Fault Aware Training (FAT).

Abstract We present a new prescription to account for heavy quark mass effects in the determination of parton distribution functions (PDFs) based on the FONLL scheme. Our prescription makes explicit use of the freedom to choose the number of active flavors at a given scale and, thus, use coexisting PDFs with different active flavor number. This new prescription is perturbatively equivalent to the former but improves the implementation in two ways. First, it can be naturally generalized to account simultaneously for multiple heavy quark effects, such as charm and bottom effects, which can both be relevant at the same scale due to the small mass difference. Second, it can be trivially generalized to use at any fixed-order or collinear resummed accuracy, while previous prescriptions required ad-hoc expansions of the DGLAP evolution kernels for each coefficient. We supplement the paper with codes for the computation of deep inelastic scattering observables in this new prescription.

Abstract We extend the existing leading (LO), next-to-leading (NLO), and next-to-next-to-leading order (NNLO) NNPDF4.0 sets of parton distribution functions (PDFs) to approximate next-to-next-to-next-to-leading order ( $$\hbox {aN}^3\hbox {LO}$$ aN 3 LO ). We construct an approximation to the $$\hbox {N}^3\hbox {LO}$$ N 3 LO splitting functions that includes all available partial information from both fixed-order computations and from small and large x resummation, and estimate the uncertainty on this approximation by varying the set of basis functions used to construct the approximation. We include known $$\hbox {N}^3\hbox {LO}$$ N 3 LO corrections to deep-inelastic scattering structure functions and extend the FONLL general-mass scheme to $$\mathcal {O}\left( \alpha _s^3\right) $$ O α s 3 accuracy. We determine a set of $$\hbox {aN}^3\hbox {LO}$$ aN 3 LO PDFs by accounting both for the uncertainty on splitting functions due to the incomplete knowledge of $$\hbox {N}^3\hbox {LO}$$ N 3 LO terms, and to the uncertainty related to missing higher corrections (MHOU), estimated by scale variation, through a theory covariance matrix formalism. We assess the perturbative stability of the resulting PDFs, we study the impact of MHOUs on them, and we compare our results to the $$\hbox {aN}^3\hbox {LO}$$ aN 3 LO PDFs from the MSHT group. We examine the phenomenological impact of $$\hbox {aN}^3\hbox {LO}$$ aN 3 LO corrections on parton luminosities at the LHC, and give a first assessment of the impact of $$\hbox {aN}^3\hbox {LO}$$ aN 3 LO PDFs on the Higgs and Drell–Yan total production cross-sections. We find that the $$\hbox {aN}^3\hbox {LO}$$ aN 3 LO NNPDF4.0 PDFs are consistent within uncertainties with their NNLO counterparts, that they improve the description of the global dataset and the perturbative convergence of Higgs and Drell–Yan cross-sections, and that MHOUs on PDFs decrease substantially with the increase of perturbative order.

Paired associative stimulation (PAS) is a non-invasive brain stimulation technique combining transcranial magnetic stimulation and peripheral nerve stimulation. PAS allows connections between cortical areas and peripheral nerves (C/P PAS) or between cortical regions (C/C PAS) to be strengthened or weakened by spike-timing-dependent neural plasticity mechanisms. Since PAS modulates both neurophysiological features and motor performance, there is growing interest in its application in neurorehabilitation. We aimed to synthesize evidence on the motor rehabilitation role of PAS in stroke patients. We performed a literature search following the PRISMA Extension for Scoping Reviews Framework. Eight studies were included: one investigated C/C PAS between the cerebellum and the affected primary motor area (M1), seven applied C/P PAS over the lesional, contralesional, or both M1. Seven studies evaluated the outcome on upper limb and one on lower limb motor recovery. Although several studies omit crucial methodological details, PAS highlighted effects mainly on corticospinal excitability, and, more rarely, an improvement in motor performance. However, most studies failed to prove a correlation between neurophysiological changes and motor improvement. Although current studies seem to suggest a role of PAS in post-stroke rehabilitation, their heterogeneity and limited number do not yet allow definitive conclusions to be drawn.

Abstract We construct a set of parton distribution functions (PDFs), based on the recent NNPDF4.0 PDF set, that also include a photon PDF. The photon PDF is constructed using the LuxQED formalism, while QED evolution accounting for $${\mathcal {O}}\left( \alpha \right) $$ O α , $${\mathcal {O}}\left( \alpha \alpha _s\right) $$ O α α s , and $${\mathcal {O}}\left( \alpha ^2\right) $$ O α 2 corrections is implemented and benchmarked by means of the EKO code. We investigate the impact of QED effects on NNPDF4.0, and compare our results both to our previous NNPDF3.1QED PDF set and to other recent PDF sets that include the photon. We assess the impact of photon-initiated processes and electroweak corrections on a variety of representative LHC processes, and find that they can reach the 5% level in vector boson pair production at large invariant mass.

Abstract We include uncertainties due to missing higher order corrections to QCD computations (MHOU) used in the determination of parton distributions (PDFs) in the recent NNPDF4.0 set of PDFs. We use our previously published methodology, based on the treatment of MHOUs and their full correlations through a theory covariance matrix determined by scale variation, now fully incorporated in the new NNPDF theory pipeline. We assess the impact of the inclusion of MHOUs on the NNPDF4.0 central values and uncertainties, and specifically show that they lead to improved consistency of the PDF determination. PDF uncertainties on physical predictions in the data region are consequently either unchanged or moderately reduced by the inclusion of MHOUs.

Abstract Blooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.

Historic volcanic activity in Tenerife was concentrated within two of the island’s three dorsals and on the Teide-Pico Viejo complex located inside Las Cañadas caldera. Eruptions on the island are primarily characterised by basaltic and trachybasaltic fissural eruptions. However, the Teide-Pico Viejo complex also hosted explosive and effusive phonolitic eruptions. Our study focused on the analysis of the 2004–2005 unrest in Tenerife, which was characterised by an onset of a ground deformation pattern, heightened on-land seismic activity, changes in the chemical composition of fumaroles of the Teide composite volcano, an increase in diffusive emissions of carbon dioxide along the NW rift, and, significant gravity changes. We used the Envisat-ASAR satellite images from 2003 to 2010 to generate the Line-Of-Sight SBAS-DInSAR deformation time series to investigate the source responsible for ground deformation. We applied the Independent Component Analysis (ICA) to separate distinct ground deformation patterns. Specifically, we selected four components for the Independent Component Analysis decomposition: the first one mainly affects the stratovolcano’s summit region of Teide and shows a circular symmetry; the second and third components are possibly related to the topography and atmospheric artifacts, while the fourth contains only a noisy signal. We employed a non-linear optimisation approach in a Finite Element modelling environment to determine the source geometry responsible for the first identified ICA pattern of ground deformation within Las Cañadas. Our results revealed the existence of an ellipsoidal ground deformation source oriented along the E-W axis, located beneath the Teide and Montaña Blanca volcanoes at 1,600 m a.s.l. This source became active during the seismic crisis of 2004–2005, which was associated with the degassing of a magma batch that intruded into the northwest rift of Tenerife island. We propose that the ground deformation observed at Teide volcano during the 2004–2005 crisis was related to hydrothermal activity within the volcano.

Abstract Dark web marketplaces have been a significant outlet for illicit trade, serving millions of users worldwide for over a decade. However, not all users are the same. This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets and assess their role by analysing a dataset of 40 million Bitcoin transactions involving the 31 major markets in the period 2011–2021. First, we propose an algorithm that categorizes users either as buyers or sellers, and show that a large fraction of the trading volume is concentrated in a small group of elite market participants. We find that the dominance of markets is reflected in trading properties of buyers and sellers. Then, we investigate both market star-graphs and user-to-user networks, and highlight the importance of a new class of users, namely ‘multihomers’, who operate on multiple marketplaces concurrently. Specifically, we show how the networks of multihomers and seller-to-seller interactions can shed light on the resilience of the dark market ecosystem against external shocks. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.

Gait disorders are the most frequent symptoms associated to multiple sclerosis (MS). Robot-assisted gait training (RAGT) in people with MS (PwMS) has been proposed as a possible effective treatment option for severe motor disability without significant superiority when compared to intensive overground gait training (OGT). Furthermore, RAGT at high intensity may enhance fatigue and spasticity. This study aims to evaluate the effects of a low-intensity RAGT at progressively increasing intensity compared to conventional RAGT and OGT in PwMS and moderate to severe walking impairment. 24 PwMS will be recruited and assigned to one of the three treatment groups: low-intensity RAGT at progressively increasing intensity, conventional RAGT and OGT. All participants will receive 3-weekly treatment sessions of 3 hours each for 4 weeks. In the first 2 hours of treatment, all participants will receive a rehabilitation programme based on stretching exercises, muscle strengthening and educational interventions. During the last hour, subjects will undergo specific gait training according to the assignment group. Outcomes will be assessed before and after treatment and at 3-month follow-up. The primary outcome is walking speed. Secondary outcomes include mobility and balance, psychological measures, muscle oxygen consumption, electrical and haemodynamic brain activity, urinary biomarkers, usability, and acceptability of robotic devices for motor rehabilitation. The results of this study will provide a safe, affordable and non-operator-dependent, intervention for PwMS. Results in terms of functional, psychological, neurophysiological and biological outcomes will confirm our hypothesis. The study’s trial registration number: NCT06381440.

Abstract Decentralization is a pervasive concept found across disciplines, including Economics, Political Science, and Computer Science, where it is used in distinct yet interrelated ways. Here, we develop and publicly release a general pipeline to investigate the scholarly history of the term, analysing 425 , 144 $425{,}144$ academic publications that refer to (de)centralization. We find that the fraction of papers on the topic has been exponentially increasing since the 1950s. In 2021, 1 author in 154 mentioned (de)centralization in the title or abstract of an article. Using both semantic information and citation patterns, we cluster papers in fields and characterize the knowledge flows between them. Our analysis reveals that the topic has independently emerged in the different fields, with small cross-disciplinary contamination. Moreover, we show how Blockchain has become the most influential field about 10 years ago, while Governance dominated before the 1990s. In summary, our findings provide a quantitative assessment of the evolution of a key yet elusive concept, which has undergone cycles of rise and fall within different fields. Our pipeline offers a powerful tool to analyze the evolution of any scholarly term in the academic literature, providing insights into the interplay between collective and independent discoveries in science.

Background: Following the ACA, millions of people gained Medicaid insurance. Most electronic health record (EHR) tools to date provide clinical-decision support and tracking of clinical biomarkers, we developed an EHR tool to support community health center (CHC) staff in assisting patients with health insurance enrollment documents and tracking insurance application steps. The objective of this study was to test the effectiveness of the health insurance support tool in (1) assisting uninsured patients gaining insurance coverage, (2) ensuring insurance continuity for patients with Medicaid insurance (preventing coverage gaps between visits); and (3) improving receipt of cancer preventive care.Methods: In this quasi-experimental study, twenty-three clinics received the intervention (EHR-based insurance support tool) and were matched to 23 comparison clinics. CHCs were recruited from the OCHIN network. EHR data were linked to Medicaid enrollment data. The primary outcomes were rates of uninsured and Medicaid visits. The secondary outcomes were receipt of recommended breast, cervical, and colorectal cancer screenings. A comparative interrupted time-series using Poisson generalized estimated equation (GEE) modeling was performed to evaluate the effectiveness of the EHR-based tool on the primary and secondary outcomes.Results: Immediately following implementation of the enrollment tool, the uninsured visit rate decreased by 21.0% (Adjusted Rate Ratio [RR] = 0.790, 95% CI = 0.621-1.005, p = .055) while Medicaid-insured visits increased by 4.5% (ARR = 1.045, 95% CI = 1.013-1.079) in the intervention group relative to comparison group. Cervical cancer preventive ratio increased 5.0% (ARR = 1.050, 95% CI = 1.009-1.093) immediately following implementation of the enrollment tool in the intervention group relative to comparison group. Among patients with a tool use, 81% were enrolled in Medicaid 12 months after tool use. For the 19% who were never enrolled in Medicaid following tool use, most were uninsured (44%) at the time of tool use.Conclusions: A health insurance support tool embedded within the EHR can effectively support clinic staff in assisting patients in maintaining their Medicaid coverage. Such tools may also have an indirect impact on evidence-based practice interventions, such as cancer screening.Trial Registration: This study was retrospectively registered on February 4th, 2015 with Clinicaltrials.gov (#NCT02355262). The registry record can be found at https://www.clinicaltrials.gov/ct2/show/NCT02355262 . [ABSTRACT FROM AUTHOR]

Integrated quantum photonics leverages the on-chip generation of nonclassical states of light to realize key functionalities of quantum devices. Typically, the generation of such nonclassical states relies on whispering gallery mode resonators, such as integrated optical micro-rings, which enhance the efficiency of the underlying spontaneous nonlinear processes. While these kinds of resonators excel in maximizing either the temporal confinement or the spatial overlap between different resonant modes, they are usually associated with large mode volumes, imposing an intrinsic limitation on the efficiency and footprint of the device. Here, we engineer a source of time-energy entangled photon pairs based on a silicon photonic crystal cavity, implemented in a fully CMOS-compatible platform. In this device, resonantly enhanced spontaneous four-wave mixing converts pump photon pairs into signal/idler photon pairs under the energy-conserving condition in the telecommunication C-band. The design of the resonator is based on an effective bichromatic confinement potential, allowing it to achieve up to nine close-to-equally spaced modes in frequency, while preserving small mode volumes, and the whole chip, including grating couplers and access waveguides, is fabricated in a single run on a silicon-on-insulator platform. Besides demonstrating efficient photon pair generation, we also implement a Franson-type interference experiment, demonstrating entanglement between signal and idler photons with a Bell inequality violation exceeding five standard deviations. The high generation efficiency combined with the small device footprint in a CMOS-compatible integrated structure opens a pathway toward the implementation of compact quantum light sources in all-silicon photonic platforms.

Abstract The typical approaches to generate heralded single photons rely on parametric processes, with the advantage of generating highly entangled states at the price of a random pair emission. To overcome this limit, degenerate spontaneous Four-Wave-Mixing is a reliable technique which combines two pump photons into a pair of signal and idler photons via Kerr nonlinear optical effect. By exploiting the intrinsic small confinement volume and thermally tuning the resonances of a 20 μm-long Photonic Crystal cavity, we efficiently generate time-energy entangled photon pairs and heralded single photons at a large maximum on-chip rate of 22 MHz, using 36 μW of pump power. We measure time-energy entanglement with net visibility up to 96.6 % using 1 second integration time constant. Our measurements demonstrate the viability of Photonic Crystal cavities to act as an alternative and efficient photon pair source for quantum photonics.

This study investigated the associations among conduct problems, callous–unemotional (CU) traits, and indices of emotion recognition accuracy and emotion recognition bias obtained from human faces. Impairments in emotion recognition were considered within broader, impaired emotional and social functioning. The sample consisted of 293 middle-school students (51.19% girls; M age = 12.97 years, SD = 0.88 years). In general, CU traits were associated with less accuracy in recognizing emotions, especially fearful and angry faces, and such deficits in emotional recognition were not associated with conduct problems independent of CU traits. These results support the importance of studying potential deficits in the recognition of emotions other than fear. Furthermore, our results support the importance of considering the role of CU traits when studying emotional correlates of conduct problems. For children scoring high on CU traits, the emotion recognition accuracy of anger was low irrespective of the level of conduct problems, whereas in children scoring low on CU traits, less accuracy in recognizing emotions was related to increases in conduct problems. Finally, our results support the need for research to not only focus on accuracy of emotional recognition but also test whether there are specific biases leading to these inaccuracies. Specifically, CU traits were associated not only with lower accuracy in recognizing fearful faces but also with a tendency to interpret fearful faces as angry. This suggests that the emotional deficit associated with CU traits is not just a deficit in empathic concern toward others distress but also includes a tendency to overinterpret emotions as potential threats to oneself.

Abstract We quantify Non Fungible Token (NFT) rarity and investigate how it impacts market behaviour by analysing a dataset of 3.7M transactions collected between January 2018 and June 2022, involving 1.4M NFTs distributed across 410 collections. First, we consider the rarity of an NFT based on the set of human-readable attributes it possesses and show that most collections present heterogeneous rarity patterns, with few rare NFTs and a large number of more common ones. Then, we analyze market performance and show that, on average, rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii) guarantee higher returns on investment, and (iv) are less risky, i.e., less prone to yield negative returns. We anticipate that these findings will be of interest to researchers as well as NFT creators, collectors, and traders.

In subacute stroke patients we studied cortical oxygenation changes by near-infrared spectroscopy (NIRS) during a motor task performed with the hemiparetic arm (15 s of reaching and grasping, 45 s of rest, repeated 6 times). Twenty-three subjects were included at baseline, compared with six healthy subjects, and restudied after 6 weeks of rehabilitation. Motor/premotor cortical changes in oxyhemoglobin detected by NIRS were quantified as the area under the curve (AUC) for the total cortex (TOT-AUC) and for both affected (AFF-AUC) and unaffected hemispheres (UN-AUC). The ratio between AUC and the number of task repetitions performed identified the cortical metabolic cost (CMC) or the oxygenation increase for a single movement. Fugl–Meyer assessment of the upper extremity (FMA-UE) was also performed. At baseline, both total and hemispheric CMC were significantly higher in stroke patients than in healthy subjects and inversely correlated with FMA-UE. After rehabilitation, changes in total-CMC and unaffected-CMC, but not Affected-CMC, were inversely correlated with variations in the FMA-UE score. A value > 5000 a.u. for the ratio baseline TOT-CMC/days since stroke was associated with not reaching the clinically important difference for FMA-UE after rehabilitation. In subacute stroke the CMC, a biomarker assessed by NIRS during a motor task with the hemiparetic arm, may describe cortical time/treatment reorganization and favor patient selection for rehabilitation.

Critical mass dynamics, where a minority of committed individuals reaching a critical size can overturn stable social conventions, is typically modelled by taking into account pairwise interactions only. Here, the authors generalise the Naming Game model to higher-order social interactions, and show both numerically and analytically how the interplay of local interactions and group size influences global social outcomes.

Abstract Dark web marketplaces (DWMs) are online platforms that facilitate illicit trade among millions of users generating billions of dollars in annual revenue. Recently, two interview-based studies have suggested that DWMs may also promote the emergence of direct user-to-user (U2U) trading relationships. Here, we carefully investigate and quantify the scale of U2U trading around DWMs by analysing 31 million Bitcoin transactions among users of 40 DWMs between June 2011 and Jan 2021. We find that half of the DWM users trade through U2U pairs generating a total trading volume greater than DWMs themselves. We then show that hundreds of thousands of DWM users form stable trading pairs that are persistent over time. Users in such stable pairs turn out to be the ones with the largest trading volume on DWMs. Then, we show that new U2U pairs often form while both users are active on the same DWM, suggesting the marketplace may serve as a catalyst for new direct trading relationships. Finally, we reveal that stable U2U pairs tend to survive DWM closures and that they were not affected by COVID-19, indicating that their trading activity is resilient to external shocks. Our work unveils sophisticated patterns of trade emerging in the dark web and highlights the importance of investigating user behaviour beyond the immediate buyer-seller network on a single marketplace.

Abstract Visible markers are an important factor in social interactions. Some researchers have argued that one of their roles is to promote cooperation, but models designed to address this issue have yielded contradictory results. Here we present an experimental study of the effect of visible markers on the emergence of social norms where human subjects play a binary coordination game. Our results do not show different, marker-dependent behaviors. Instead, in practically all sessions participants achieved a global equilibrium disregarding the markers. Our findings suggest that simple markers may have a limited role in promoting the emergence of group-dependent social norms and call for further research investigating the role of markers in more sophisticated social settings.

People with multiple sclerosis (pwMS) are affected by a wide range of disabilities, including a decrease in bone mineral density (BMD) and a worsening of body composition (BC), which negatively impact their quality of life quality. This study aims to analyze the effects of nonpharmacological interventions—in particular, physical activity, nutritional approaches, and rehabilitation—on BC and BMD in pwMS. This systematic review and meta-analysis was performed following the updated version of the PRISMA guidelines. In July 2022, five databases (MEDLINE, Embase, The Cochrane Library, Google Scholar, Web of Science) and gray literature were screened. Relevant articles published between 1 January 1990 and 1 September 2022 in any language were included. Outcomes of interest were anthropometric, BC measures, and BMD. The RoB 2.0 tool was used to assess the risk of bias. After duplicates elimination, 1120 records were screened, and 36 studies were included. A total of 25 articles were focused on physical activity and rehabilitation, 10 on nutrition, and 1 on multimodal intervention. One-third of the studies were judged to be at high risk of bias. The meta-analysis showed a high degree of heterogeneity due to the high variability in disease severity and intervention duration, intensity, frequency, and type. In general, no intervention showed consistent positive effects on BC. However, the most promising interventions seemed to be high-intensity training and ketogenic diets. Only a few studies considered BMD, and the results are inconsistent. Nevertheless, more studies are needed in order to confirm these results.

Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients’ movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest’s and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients.

Abstract Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

Wireless networks are commonly used in public spaces, universities, and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyzed extensive data from the Wi-Fi network in a university campus in Italy during three periods, corresponding to partial lockdown, partial opening, and almost complete opening. We measured the probability distributions of groups and link activations at Wi-Fi access points, investigating how different areas are used in the presence of restrictions. We ranked the hotspots and the area they cover according to their crowding and to the probability of link formation, which is the relevant variable in determining potential outbreaks. We considered a recently proposed epidemic model on simplicial temporal networks, and we used the measured distributions to infer the change in the reproduction number in the three phases. Our data show that additional measures are necessary to limit the spread of epidemic in the total opening phase due to the dramatic increase in the number of contacts.

One of the objectives fostered in medical science is the so-called precision medicine, which requires the analysis of a large amount of survival data from patients to deeply understand treatment options. Tools like machine learning (ML) and deep neural networks are becoming a de-facto standard. Nowadays, computing facilities based on the Von Neumann architecture are devoted to these tasks, yet rapidly hitting a bottleneck in performance and energy efficiency. The in-memory computing (IMC) architecture emerged as a revolutionary approach to overcome that issue. In this work, we propose an IMC architecture based on resistive switching memory (RRAM) crossbar arrays to provide a convenient primitive for matrix-vector multiplication in a single computational step. This opens massive performance improvement in the acceleration of a neural network that is frequently used in survival analysis of biomedical records, namely the DeepSurv. We explored how the synaptic weights mapping strategy and the programming algorithms developed to counter RRAM non-idealities expose a performance/energy trade-off. Finally, we discussed how this application is tailored for the IMC architecture rather than being executed on commodity systems.

We designed and implemented an immersive virtual reality (VR) environment for upper limb rehabilitation, which possesses several notable features. First, by exploiting modern computer graphics its can present a variety of scenarios that make the rehabilitation routines challenging yet enjoyable for patients, thus enhancing their adherence to the therapy. Second, immersion in a virtual 3D space allows the patients to execute tasks that are closely related to everyday gestures, thus enhancing the transfer of the acquired motor skills to real-life routines. Third, in addition to the VR environment, we also developed a client app running on a PC that allows to monitor in real-time and remotely the patients’ routines thus paving the way for telerehabilitation scenarios. Here, we report the results of a feasibility study in a cohort of 16 stroke patients. All our patients showed a high degree of comfort in our immersive VR system and they reported very high scores of ownership and agency in embodiment and satisfaction questionnaires. Furthermore, and notably, we found that behavioral performances in our VR tasks correlated with the patients’ clinical scores (Fugl-Meyer scale) and they could thus be used to assess improvements during the rehabilitation program. While further studies are needed, our results clearly support the feasibility and effectiveness of VR-based motor rehabilitation processes.

Abstract The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and misinformation-driven panic have pushed customers and vendors towards the shadow economy. In particular, dark web marketplaces (DWMs), commercial websites accessible via free software, have gained significant popularity. Here, we analyse 851,199 listings extracted from 30 DWMs between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories including Personal Protective Equipment (PPE), medicines (e.g., hydroxyclorochine), and medical frauds. Finally, we compare trends in their temporal evolution with variations in public attention, as measured by Twitter posts and Wikipedia page visits. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs, especially now that real vaccines are available and in short supply. We anticipate our analysis will be of interest both to researchers and public agencies focused on the protection of public health.

Category systems exhibit striking agreement across many cultures, yet paradoxically individuals exhibit large variation in the categorization of novel stimuli. Here the authors show that critical mass dynamics explain the convergence of independent populations on shared category systems.

Abstract Observable social traits determine how we interact meaningfully in society even in our globalized world. While a popular hypothesis states that observable traits may help promote cooperation, the alternative explanation that they facilitate coordination has gained ground in recent years. Here we explore this possibility and present a model that investigates the role of ethnic markers in coordination games. In particular, we aim to test the role of reinforcement learning as the microscopic mechanism used by the agents to update their strategies in the game. For a wide range of parameters, we observe the emergence of a collective equilibrium in which markers play an assorting role. However, if individuals are too conformist or too greedy, markers fail to shape social interactions. These results extend and complement previous work focused on agent imitation and show that reinforcement learning is a good candidate to explain many instances where ethnic markers influence coordination.

In this work, we propose a multi-scale approach for modeling the ground deformation field of volcanic sources. The methodology is based on the use of Multiridge and ScalFun methods to analyze the elastic deformation fields by providing source information, such as the depth, the horizontal position, and its morphological features. This strategy is alternative with respect to the classical inverse approaches since it allows overcoming some aspects of the ambiguities related to the interpretation of DInSAR measurements. Indeed, the multi-scale method does not need a priori constraints on the model parameters to achieve an unambiguous solution. First, we argue on the general physical conditions so that the deformation field, generally represented by biharmonic functions, also satisfies Laplace’s equation and the law of homogeneity. This occurs in the case of sources with hydrostatic pressure-change embedded in an elastic half-space. In these conditions, the properties of harmonic and homogeneous functions can be employed to model the ground deformation fields using multi-scale procedures. Then, we demonstrate the soundness of the proposed approach through the application of Multiridge and ScalFun methods to synthetic tests. We analyze the fields generated by spherical, pipes- and sills-like sources and consider different model settings, as the layered half-space, the noisy, and the multi-source scenarios. For all these simulations, we achieve unconstrained information related to the source geometry with satisfying accuracy. Finally, in order to show the flexibility of the multi-scale approach in different volcanic environments, we use Multiridge and ScalFun methods for analyzing DInSAR measurements relevant to Uturuncu, Okmok, and Fernandina volcanoes, retrieving information about their volcanic systems. In the first case, we point out a transient source at a depth of 4.5 km b.s.l., which is activated during 2006–2007 together with the stable and well-known Altiplano Puna Magma Body (APMB); in the second one, we retrieve a concentrated body at a depth of 3.1 km b.s.l. during 2003–2004; and in the last case, we interpret the 2013 unrest episode as caused by a pipe-like source located at a depth of 1.7 km b.s.l.

In January 2021, retail investors coordinated on Reddit to target short-selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. Here, we analyse Reddit and financial data and rationalize the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades. First, we operationalize the concept of individual commitment in financial discussions. Second, we show that the increase of commitment within Reddit pre-dated the initial surge in price. Third, we reveal that initial committed users occupied a central position in the network of Reddit conversations. Finally, we show that the social identity of the broader Reddit community grew as the collective action unfolded. These findings shed light on financial collective action, as several observers anticipate it will grow in importance.

Introduction Few data exist on the use of edoxaban in cancer-associated venous thromboembolism (VTE) outside of clinical trials. Aim of this study was to evaluate the characteristics and outcomes of these patients in a real world clinical setting. Methods We retrospectively analyzed the characteristics of patients with cancer-associated VTE who were prescribed edoxaban. Follow-up at 3, 6, and 12 months was performed: VTE recurrences, bleedings, mortality, cancer progression and treatment, edoxaban interruption and its reason were assessed. Results Fifty-four patients, 38 females (70.4%), mean age 71 ± 14 years, were enrolled. In 38 patients (70.4%), the episode of VTE was the first one, in 28 (51.8%) it was an isolated deep vein thrombosis (DVT), in 13 (24.1%) a pulmonary embolism (PE) associated with DVT, in 13 (24.1%) an isolated PE. Median time between cancer and VTE diagnosis was 6 (interquartile range [IQR] 2–47) months. Median time between VTE diagnosis and edoxaban prescription was 36 (IQR 7–117) days. At 3, 6, and 12 months the incidence of all-cause mortality was 16.6, 22.2, and 38.8%, that of VTE recurrence 1.8, 1.8, and 3.7%, and that of major bleeding 7.4, 9.2, and 12.9%, respectively. No bleeding was fatal. Of the 33 patients alive at 12 months, 32 (96.9%) were still on edoxaban therapy, in seven (21.2%) cancer was in progression. Conclusion Our study, conducted on a real world population of patients with cancer-associated VTE, confirms the results of randomized controlled clinical trials, and supports the use of edoxaban as effective and safe treatment in this context.

Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started, when these goods were in shortage in the traditional economy. Here, we broaden the scope and depth of previous investigations by considering how DWMs responded to an ongoing pandemic after the initial shock. Our dataset contains listings from 194 DWMs collected until July 2021. We start by focusing on vaccines. We find 248 listings offering approved vaccines, like Pfizer/BioNTech and AstraZeneca, as well as vendors offering fabricated proofs of vaccination and COVID-19 passports. Then, we consider COVID-19 related products. We show that, as the regular economy has become able to satisfy the demand of these goods, DWMs have decreased their offer. Next, we analyse the profile of vendors of COVID-19 related products and vaccines. We find that most of them are specialized in a single type of listings and are willing to ship worldwide. Finally, we consider a broader set of listings mentioning COVID-19, in order to assess the general impact of the pandemic on the broader activity of DWMs. Among 10,330 such listings, we show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020. We anticipate that our results will be of interest to researchers, practitioners, and law enforcement agencies focused on the study and safeguard of public health.

In relation to the COVID-19 pandemic outbreak, a large body of research has identified a negative impact on individuals' affectivity, frequently documented by increased prevalence of anxiety and depression symptoms. For children, this research was less extensive, was mainly based on caregivers' reports and neglected personality assessment. In order to measure the impact of the pandemic, and the fears it caused, on primary school children's affect and personality, 323 (180 boys and 143 girls) Italian third, fourth and fifth graders were assessed between October and November 2020, namely during the second wave of COVID-19 infections in Italy, with validated self-reports of affect (Positive and Negative Affect Scale for Children, PANAS-C), fear of COVID-19 (Fear of COVID-19 Scale, FCV-19S) and personality (junior Temperament and Character Inventory, jTCI). In comparison with PANAS-C and jTCI normative scores collected prior to the pandemic, data obtained from children in 2020 showed unchanged affect scores in the overall sample, a decrease of Positive Affect in girls, and a decrease in the Harm Avoidance and an increase in the Self-Transcendence scales of personality. Fear of COVID-19 scores were positively correlated with Negative Affect scores and negatively predicted by children's personality profile of resilience (calculated using scores on the Harm Avoidance and the Self-Directedness scales of personality). These results suggested that Italian primary school children, especially boys, maintained their pre-pandemic levels of affect (or restored them after the first COVID-19 wave) and partially diverged from the typical development of personality in an apparently positive sense, namely toward more courageous/optimistic and spiritual profiles. This sort of children's post-traumatic growth might also be attributed to children's family and education systems, which should continue to be supported to promote and maintain community mental health.

Introduction Multiple sclerosis (MS) is one of the major causes of disability in young adults and affects mobility, compromising daily living activities and participation in social life. Cognitive domain is also frequently impaired in people with MS (PwMS), particularly the capacity to perform dual-task activities. Impaired cognitive processing abilities need to be treated, and motor and cognitive aspects need to be considered together. Recently, video game therapy (VGT) has been used in rehabilitation to improve motor outcomes and cognitive processing speed. The aim of this study is to test the efficacy of commercially available VGT on mobility and dual tasking in PwMS compared with standardised balance platform training (BPT).Methods and analysis This will be a parallel-assignment, double-blinded, randomised control trial. Forty-eight (24 per arm) PwMS with Expanded Disability Status Scale 4–5.5 will be randomly assigned to receive 1 hour training session over 4 weeks (three sessions/week) of either: (1) VGT on commercial video game console to train balance and mobility-related activities or (2) BPT to perform balance, postural stability and weight-shifting exercises with and without visual feedback. The same assessor will evaluate outcome measures at points: before and after the 12 training sessions and at 3 months of follow-up. The primary outcome will be functional mobility, assessed by the Timed Up and Go test. We will also evaluate gait, risk of fall, fatigue and health-related quality of life as well as cognitive and psychological aspects (depression, anxiety and attentional performance) and stability through posturographic evaluation. Dual-tasking assessment will be performed combining posturographic and neuropsychological tests. Data analysis will be performed to compare the efficacy of the two treatments.Ethics and dissemination Ethical approval have been granted from the local Ethics Committee. Study results will be communicated through high-quality journals and national and international conferences.Trial registration number NCT03353974