Miracle-dz/UniFAR-FTUs huggingface dataset:
Initiation of Procalcitonin Screening as a Marker in Antibiotic Initiation of Procalcitonin Screening as a Marker in Antibiotic Therapy De-escalation Therapy De-escalation

Problem: The overuse of antibiotics has created antimicrobial resistance (AMR) resulting in treatment failure for bacterial infections. To combat AMR, clinicians should only prescribe antibiotic therapy when clinically warranted. It is easy to misdiagnose a viral infection. However, any delay in the initiation of antibiotic therapy in a bacterial infection may lead to increased morbidity and mortality. These patients with non-specific clinical symptoms may be misdiagnosed without a rapid and definitive diagnostic test. Methods: This quality improvement (QI) initiative utilized an evidence-based clinical guideline for procalcitonin (PCT) levels for antibiotic therapy de-escalation. WBC counts were collected two-days and one-day before ID work-up along with PCT levels and white blood cell (WBC) counts collected on day-1, day-3, and day-5 post infectious disease. Data was collected via prospective chart review including the number of positive PCT levels obtained versus positive cultures, average length of antibiotic use, number of different antibiotics used, and the time until the bacterial infection was confirmed or denied. Results: The implemented PCT clinical guideline allowed for faster identification of bacterial infections than standard blood cultures. There is significant evidence supporting a positive relationship between elevated PCT levels and positive cultures. Over time the level of PCT decreases showing a correlation with a reduction of the WBC count. Implications for Practice: Use of the PCT clinical guideline can identify whether there is a bacterial infection rapidly allowing for the initiation or de-escalation of antibiotic therapy preventing further AMR resistance.
Clumpy tori illuminated by the anisotropic radiation of accretion discs in active galactic nuclei

In this paper, we try to explain the observed correlation between the covering factor (CF) of hot dust and the properties of active galactic nuclei (AGNs), e.g., the bolometric luminosity ($L_{\rm{bol}}$) and black hole mass ($M_{\rm{BH}}$). Combining the possible dust distribution in the torus, the angular dependence of the radiation of the accretion disc, and the relation between the critical angle of torus and the Eddington ratio, there are eight possible models investigated in our work. We fit the observed CF with these models to determine the parameters of them. As a result, clumpy torus models can generally explain the observed correlations of tori, while the smooth models fail to produce the required CFs. However, there is still significant scatter even for the best-fitting model, which is the combination of a clumpy torus illuminated by the anisotropic radiation of accretion disc in an AGN. Although some of the observed scatter is due to the uncertainties in measuring $L_{\rm{bol}}$ and $M_{\rm{BH}}$, other factors are required in more realistic model. The models examined in this paper are not necessary to be the physical model of tori. However, the reasonable assumptions selected during this process should be helpful in constructing physical models of tori.
Fronto-Striatal Dynamic Connectivity is linked to Dopaminergic Motor Response in Parkinson's Disease

Background: Differences in dopaminergic motor response in Parkinson's disease (PD) patients are related to specific PD subtypes. An important factor driving dopaminergic response might lie in the temporal dynamics in corticostriatal connections. Objectives: The aim of this study is to determine if altered resting-state dynamic functional network connectivity (dFNC) is associated with dopaminergic motor response. Methods: We assessed static and dFNC in 32 PD patients and 18 healthy controls (HC). Patients were subgrouped according to their dopaminergic motor response as low and high responders using a median split. Results: Patients featuring high dopaminergic responses spent more time in a regionally more integrated state 1 compared to HC. Furthermore, dFNC between aMCC/dACC (anterior midcingulate cortex/dorsal anterior cingulate cortex) and putamen was lower in low responders during a more segregated state 2 and correlated with dopaminergic motor response. Conclusions: Alterations in temporal dynamics of fronto-striatal connectivity might underlie treatment response in PD.
Superposition of Droplet and Aerosol risk in the transmission of SARS-CoV-2

Objectives. Considering three viral transmission routes: fomite contact, aerial transmission by droplets, and aerial transmission by aerosols, the aerial routes have been the focus of debate about the relative role of droplets and aerosols in SARS-CoV-2 infection. We seek to quantify infection risk in an enclosed space via short-range airborne transmission from droplets and long-range risk from aerosols toward focusing public health measures. Methods. Data from three published studies were analyzed to predict relative exposure at distances of 1 m and farther, mediated by droplet size divided into two bins: larger than 8 m and smaller than 75 m (medium droplets) and smaller than 8 m (small droplets or aerosols). The results at 1 m from an infectious individual were treated as a boundary condition to model infection risk at greater distance. At all distances, infection risk was treated as the sum of exposure to small and medium droplets. It was assumed that number of virions is proportional to droplet volume. Results. The largest infection risk (as exposure to droplet volume) came from medium droplets, close to the infectious individual out to approximately 1 m. Farther away, the largest risk was due to aerosols. For one model, medium droplet exposure disappeared at 1.8 m. Conclusions. Policy concerning social distancing for meaningful infection reduction relies on droplet exposure as a function of distance, yet within this construct droplet size determines respiratory deposition. This two-fold distance effect can be used to evaluate additional measures such as plexiglass barriers and masking.
School Distress in UK School Children: Characteristics and Consequences

As education becomes increasingly centralised, it is important to understand why some children and young people (CYP) in the UK have become too distressed to attend school, or who manage to attend school but at a considerable cost to their mental health and wellbeing. Using a case-control, concurrent embedded mixed-method research design, parents of CYP completed a bespoke online questionnaire. This included 948 parents of CYP who are currently (738), or have previously (209), struggled to attend school, 149 parents of age-matched control CYP, and parents of 25 CYP who have never attended school. We use the term School Distress (SD) as we found that for the majority of CYP (94.3%), school attendance was associated with emotional distress, with often harrowing accounts of distress provided by parents. Age of onset of SD was early (mean age 7.9 years), with a mean duration of 4 years. SD was associated with poor school attendance (with a cumulative total of 6,197 school days missed in the preceding 20 school days alone), poor academic attainment, significant anxiety symptomatology, and extreme demand avoidant behaviour. Neurodivergent CYP, and in particular, autistic CYP with complex presentations, such as multiple co-occurring neurodivergent conditions and/or multimodal sensory processing difficulties, were significantly overrepresented amongst CYP with SD. The majority of these CYP also experienced mental health difficulties. However, mental health difficulties in the absence of a neurodivergent profile were relatively rare, accounting for just 6.17% of cases. CYP with SD were also more likely than control CYP to have neurodivergent parents and siblings. Hence, whilst not a story of exclusivity relating solely to autism, SD is a story of complexity; occurring most frequently in CYP with complex neurodevelopmental profiles. Despite clear evidence that SD creates a context that engenders negative educational and mental health outcomes, parental reports revealed a dearth of support for these CYP. Moreover, whilst 97% of the CYP described here had once attended a mainstream school setting, only a minority of parents (35.6%) expressed a desire for their child to be educated in a mainstream school in the future. Similarly, only 24.3% of CYP with historical SD are currently educated in a mainstream classroom, with the majority now home-educated. Finally, the majority of parents of CYP with SD (85.56%) were not confident or no longer believed, school to be a suitable and proper place for their child to be educated.
Virtual reality-assessment of social interactions and prognosis in depression

Importance: Stratification of depression for personalised treatment is urgently needed to improve poor outcomes. Excessive self-blame-related motivations such as self-punishing tendencies have been proposed to play a key role in the onset and maintenance of depression. Their prognostic role, however, remains elusive. Objective: Use Virtual Reality (VR) to determine whether maladaptive self-blame-related action tendencies are associated with a poor prognosis for depression when treated as usual in primary care (pre-registered: NCT04593537). Design: Remote prospective cohort study (6/2020-6/2021) with four months follow-up. Settings: Online recruitment from primary care and self-report. Participants: n=879 pre-screened, n=164 eligible, n=101 completed baseline (age:18-66 years, mean=32.05{+/-}12.32, n=89 female), n=98 the VR-task, and n=93 the follow-up. Main inclusion criteria: at least one antidepressant medication trial and Patient Health Questionnaire-9[≥]15 at screening; main exclusion criteria: screening above threshold on validated self-report instruments for bipolar or alcohol/substance use disorders. Exposure(s): All participants completed a VR assessment via headsets sent to their homes, as well as online questionnaires to measure their clinical characteristics. Main outcomes and Measures: Primary: Quick Inventory of Depressive Symptomatology self-reported-16 score after four months. Hypotheses in the study were formulated before the data collection and pre-registered. Results: Contrary to our specific prediction, neither feeling like hiding nor creating a distance from oneself was associated with prognosis of depression during the follow-up period in the pre-registered regression model. Using a data-driven principal components analysis of all pre-registered continuous measures, a factor most strongly loading on punishing oneself for other people's wrongdoings ({beta}=.23, p=.01), a baseline symptom factor ({beta}=.30, p=.006) and Maudsley Staging Method treatment-resistance scores ({beta}=.28, p=.009) at baseline predicted higher depressive symptoms after four months. This relationship was confirmed by a significant interaction between feeling like punishing oneself for others' wrongdoings and time of monthly follow-up which was driven by higher depressive symptoms at last follow-up [F(1,84)=6.45, p=. 01, partial Eta Squared=0.07] in the subgroup who had reported feeling like punishing themselves at baseline. Our pre-registered statistical learning model prospectively predicted a cross-validated 19% of variance in depressive symptoms. Conclusions and Relevance: Feeling like punishing oneself is a relevant prognostic factor and should therefore be assessed and tackled in personalised care pathways for difficult-to-treat depression.
Magnetic field amplification and X-ray emission in galaxy minor mergers

We investigate the magnetic field evolution in a series of galaxy minor mergers using the N-body/smoothed particle hydrodynamics (SPH) code gadget. The simulations include the effects of radiative cooling, star formation and supernova feedback. Magnetohydrodynamics is implemented using the SPH method. We present 32 simulations of binary mergers of disc galaxies with mass ratios of 2:1 up to 100:1, whereby we have additionally varied the initial magnetic field strengths, disc orientations and resolutions. We investigate the amplification of a given initial magnetic field within the galaxies and an ambient intergalactic medium (IGM) during the interaction. We find that the magnetic field strengths of merger remnants with mass ratios up to 10:1 saturate at a common value of several μG. For higher mass ratios, the field strength saturates at lower values. The saturation values correspond to the equipartition value of magnetic and turbulent energy density. The initial magnetization, disc orientation and numerical resolution show only minor effects on the saturation value of the magnetic field. We demonstrate that a higher impact energy of the progenitor galaxies leads to a more efficient magnetic field amplification. The magnetic and turbulent energy densities are higher for larger companion galaxies, consistent with the higher impact energy supplied to the system. We present a detailed study of the evolution of the temperature and the bolometric X-ray luminosity within the merging systems. Thereby we find that magnetic fields cause a more efficient increase of the IGM temperature and the corresponding IGM X-ray luminosity after the first encounter. However, the presence of magnetic fields does not enhance the total X-ray luminosity. Generally, the final value of the X-ray luminosity is even clearly lower for higher initial magnetic fields.
Structural properties of dense hard spheres near random close packing

We numerically study structural properties of mechanically stable packings of hard spheres (HS), in a wide range of packing fractions $0.53 \le \phi \le 0.72$. Detailed structural information is obtained from the analysis of orientational order parameters, which clearly reveals a disorder-order phase transition at the random close packing (RCP) density, $\phi_{\rm c} \simeq 0.64$. Above $\phi_{\rm c}$ the crystalline nuclei form 3D-like clusters, which upon further desification, transform into alternating planar-like layers. We also find that particles with icosahedral symmetry survive only in a narrow density range in the vicinity of the RCP transition.
Neural data-driven model of spinal excitability changes induced by transcutaneous electrical stimulation in spinal cord injury subjects

The efficacy of trans-spinal direct current stimulation (tsDCS) as neurorehabilitation technology remains sub-optimal, partly due to the variability introduced by subject-specific neurophysiological features and stimulation conditions (e.g. electrode placement, stimulating amplitude, polarity, etc.) Hence, current therapies apply tsDCS in an open-loop fashion, resulting in a lack of standardized protocols for controlling elicited neuronal adaptations in closed-loop. Through the combination of high-density electromyogram (HD-EMG) decomposition, biophysical neuronal modelling and metaheuristic optimization, this work presents a novel neural data-driven framework for estimating subject-specific features and quantifying acute neuronal adaptations elicited by tsDCS on incomplete spinal cord injury subjects. This approach consists of calibrating the anatomical parameters (e.g. soma diameter) of in silico $\alpha-$motoneuron (MN) models for firing similarly to in vivo MNs decoded from HD-EMG. Assuming that cathodal-tsDCS elicits excitability changes in the MN pool, while preserving their anatomical parameters, optimization of an excitability gain common to the entire pool was performed to minimize discrepancies in firing rate and recruitment time between in vivo and in silico MNs after cathodal-tsDCS. This quantification of excitability changes on MN models calibrated in a person specific way enables closing the loop with neuro-modulation devices to tailor neurorehabilitation therapies. Clinical Relevance - This framework addresses a key limitation in non-invasive neuro-modulative technologies via a novel model-assisted framework that enables quantifying acute excitability changes induced on a person-specific in silico MN pool calibrated using in vivo neural data. This will enable the development of advanced controllers for modulating targeted neuronal adaptations in closed-loop.
Real-Time Free Space Semantic Segmentation for Detection of Traversable Space for an Intelligent Wheelchair*

The last decades saw a great innovation in computer vision. Recently, the field has been fundamental in the development of autonomous navigation systems. Modern assistive technologies, like smart wheelchairs, could employ autonomous navigation to assist users during operation. A prerequisite for such systems is to recognise the navigable space in real-time. The current research features an off-the-shelf powered wheelchair customised into an intelligent robot, which perceives the environment using Point Cloud Semantic Segmentation (PCSS). The implemented algorithm is used to distinguish between two conditions, traversable and non-traversable space, in real-time, using the aforementioned conditions as the two labelled classes. The accuracy of traversable space detection resulted as 99.64% while the accuracy of non-traversable space detection was 91.79%. The performance of the suggested method was invariant to changes in wheelchair velocity indicating that the latency of the suggested algorithm is within the tolerable limits for real-time operation.
Higher spin currents in Wolf space: III

The large  = 4 ? > linear superconformal algebra (generated by four spin- 1 2 ? > currents, seven spin-1 currents, four spin- 3 2 ? > currents and one spin-2 current) found by Sevrin, Troost and Van Proeyen (and other groups) was realized in the  = 4 ? > superconformal coset SU ( 5 ) SU ( 3 ) ? > theory previously. The lowest 16 higher spin currents of spins ( 1 , 3 2 , 3 2 , 2 ) ? > , ( 3 2 , 2 , 2 , 5 2 ) ? > , ( 3 2 , 2 , 2 , 5 2 ) ? > and ( 2 , 5 2 , 5 2 , 3 ) ? > are obtained by starting with the operator product expansions (OPEs) between the four spin- 3 2 ? > currents from the above large  = 4 ? > linear superconformal algebra and the lowest higher spin-1 current which is the same as the one in the Wolf space coset SU ( 5 ) SU ( 3 ) × SU ( 2 ) × U ( 1 ) ? > theory. These OPEs determine the four higher spin- 3 2 ? > currents and the next six higher spin-2 currents are obtained from the OPEs between the above four spin- 3 2 ? > currents associated with the  = 4 ? > supersymmetry and these four higher spin- 3 2 ? > currents. The four higher spin- 5 2 ? > currents can be determined by calculating the OPEs between the above four spin- 3 2 ? > currents and the higher spin-2 currents. Similarly, the higher spin-3 current is obtained from the OPEs between the four spin- 3 2 ? > currents and the higher spin- 5 2 ? > currents. The explicit relations between the above 16 higher spin currents and the corresponding 16 higher spin currents which were found in the extension of large  = 4 ? > nonlinear superconformal algebra previously are given. By examining the OPEs between the 16 currents from the large  = 4 ? > linear superconformal algebra and the 16 higher spin currents, the match with the findings of Beccaria, Candu and Gaberdiel is also given. The next 16 higher spin currents of spins ( 2 , 5 2 , 5 2 , 3 ) ? > , ( 5 2 , 3 , 3 , 7 2 ) ? > , ( 5 2 , 3 , 3 , 7 2 ) ? > and ( 3 , 7 2 , 7 2 , 4 ) ? > occur from the OPEs between the above lowest 16 higher spin currents.
AI for Urban Climate: An Eo-Based Approach for High-Resolution Mapping of Human Exposure to Heatwaves

As climate change prospects emphasize the need for local adaptation, tackling heat exposure becomes a priority in Southern Europe. In the Greater Lisbon Area, heatwaves (HW) are already frequent, and their severity is expected to aggravate in the coming decades. Accordingly, there is a pressing need to pinpoint the neighborhoods where people are relatively more exposed to the excess heat. In this study, air temperature measurements from citizen owned stations are retrieved from open data sources, quality controlled and co-located with Earth Observation (EO) data to downscale the short-term numerical weather predictions (NWP) from 2.5km up to a metric scale (200m), using a Machine learning (ML) approach. While the NWP model resolves the regional physical processes, the ML model is able to disclose the air temperature response to more detailed local features, such as the urban heat island effect, supporting the local public health stakeholders in their decision-making.
Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots

Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by its lacking usability, as both end-users and researchers alike find fault with traditional EEG caps. In this paper, we compare the usability of four EEG recording devices for Steady-State Visually Evoked Potentials (SSVEP)-BCI applications: an EEG cap (active gel electrodes), two headbands (passive gel or active dry electrodes), and two adhesive electrodes placed on each mastoid. Ten able-bodied participants tested each device by completing an 8-target SSVEP paradigm. Setup times were recorded, and participants rated their satisfaction with each device. The EEG cap obtained the best classification accuracies (Median = 98.96%), followed by the gel electrode headband (Median = 93.75%), and the dry electrode headband (Median = 91.14%). The mastoid electrodes obtained classification accuracies close to chance level (Med = 29.69%). Unknowing of the classification accuracy, participants found the mastoid electrodes to be the most comfortable and discrete. The dry electrode headband obtained the lowest user satisfaction score and was criticized for being too uncomfortable. Participants also noted that the EEG cap was too conspicuous. The gel-based headband provided a good trade-off between BCI performance and user satisfaction.
Adatomo-Net: a Novel Deep Learning Approach for SAR Tomography Imaging and Autofocusing

Tomographic Synthetic aperture radar (TomoSAR) imaging algorithms for urban areas based on Compressed Sensing (CS) often have high time complexity due to many times iterations. Moreover, the phase error (PE) that exists will defocus TomoSAR imaging results. To reduce PE in the TomoSAR process, researchers use methods such as PS-InSAR, phase gradient autofocusing (PGA), etc. However, these methods are computationally expensive, which hinders the application of fast high-resolution TomoSAR imaging. In this paper, we merge the PE compensation into FISTA framework and proposed a novel deep learning approach for TomoSAR imaging. The network is based on Ada-LFISTA architecture, dubbed as AdaTomo-Net. Experiment results show that AdaTomo-Net has higher imaging accuracy and considerable computational efficiency compared with typical CS algorithms and learning-based algorithms such as LISTA in the presence of PE.