Miracle-dz/UniFAR-FTUs huggingface dataset:
Optomechanical thermal intermodulation noise

Thermal fluctuations limit the sensitivity of precision measurements ranging from laser interferometer gravitational wave observatories to optical atomic clocks. In optical cavities, thermally induced fluctuations of length or refractive index create frequency noise. Here we investigate a previously unreported broadband noise process, thermal intermodulation noise, originating from the transduction of frequency fluctuations by the inherent nonlinearity of the cavity-laser detuning response. We show that the Brownian motion of a thin Si$_3$N$_4$ membrane in an optomechanical cavity at room temperature, instead of transducing into the phase quadrature only, also creates intensity noise for resonant laser excitation. We study the laser detuning dependence of this noise and also demonstrate that its magnitude scales with the quartic power of the ratio of the optomechanical coupling rate to the cavity linewidth. Both dependencies are in excellent agreement with our developed theoretical model. We use a phononic crystal membrane with a low mass, soft-clamped defect mode and operate in a regime where quantum fluctuations of radiation pressure are expected to dominate the motion of the membrane (i.e. a nominal quantum cooperativity of unity). However, we find that the thermal intermodulation noise exceeds the vacuum fluctuations by more than two orders of magnitude, even within the phononic bandgap, thereby preventing the observation of ponderomotive squeezing. Our work shows that a new noise source arises as a consequence of nonlinear cavity transduction, which is broadly relevant to cavity-based measurements, and is especially pronounced when thermally induced frequency fluctuations are comparable to the optical linewidth.
Behavior Variations and Their Implications for Popularity Promotions: From Elites to Mass on Weibo

The boom in social media with regard to producing and consuming information simultaneously implies the crucial role of online user influence in determining content popularity. In particular, understanding behavior variations between the influential elites and the mass grassroots is an important issue in communication. However, how their behavior varies across user categories and content domains and how these differences influence content popularity are rarely addressed. From a novel view of seven content domains, a detailed picture of the behavior variations among five user groups, from the views of both the elites and mass, is drawn on Weibo, one of the most popular Twitter-like services in China. Interestingly, elites post more diverse content with video links, while the mass possess retweeters of higher loyalty. According to these variations, user-oriented actions for enhancing content popularity are discussed and testified. The most surprising finding is that the diverse content does not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively. For the first time, our results demonstrate the possibility of highly individualized strategies of popularity promotions in social media, instead of a universal principle.
Non-genetic inheritance restraint of cell-to-cell variation

Heterogeneity in physical and functional characteristics of cells (e.g. size, cycle time, growth rate, protein concentration) proliferates within an isogenic population due to stochasticity in intracellular biochemical processes and in the distribution of resources during divisions. Conversely, it is limited in part by the inheritance of cellular components between consecutive generations. Here we introduce a new experimental method for measuring proliferation of heterogeneity in bacterial cell characteristics, based on measuring how two sister cells become different from each other over time. Our measurements provide the inheritance dynamics of different cellular properties, and the ‘inertia’ of cells to maintain these properties along time. We find that inheritance dynamics are property specific and can exhibit long-term memory (∼10 generations) that works to restrain variation among cells. Our results can reveal mechanisms of non-genetic inheritance in bacteria and help understand how cells control their properties and heterogeneity within isogenic cell populations.
Sparse recovery of noisy data using the Lasso method

We present a detailed analysis of the unconstrained $\ell_1$-method Lasso method for sparse recovery of noisy data. The data is recovered by sensing its compressed output produced by randomly generated class of observing matrices satisfying a Restricted Isometry Property. We derive a new $\ell_1$-error estimate which highlights the dependence on a certain compressiblity threshold: once the computed re-scaled residual crosses that threshold, the error is driven only by the (assumed small) noise and compressiblity. Here we identify the re-scaled residual as a key quantity which drives the error and we derive its sharp lower bound of order square-root of the size of the support of the computed solution.
A Bayesian nonparametric approach for inferring drug combination effects on mental health in people with HIV

Although combination antiretroviral therapy (ART) with three or more drugs is highly effective in suppressing viral load for people with HIV (human immunodeficiency virus), many ART agents may exacerbate mental health‐related adverse effects including depression. Therefore, understanding the effects of combination ART on mental health can help clinicians personalize medicine with less adverse effects to avoid undesirable health outcomes. The emergence of electronic health records offers researchers' unprecedented access to HIV data including individuals' mental health records, drug prescriptions, and clinical information over time. However, modeling such data is challenging due to high dimensionality of the drug combination space, the individual heterogeneity, and sparseness of the observed drug combinations. To address these challenges, we develop a Bayesian nonparametric approach to learn drug combination effect on mental health in people with HIV adjusting for sociodemographic, behavioral, and clinical factors. The proposed method is built upon the subset‐tree kernel that represents drug combinations in a way that synthesizes known regimen structure into a single mathematical representation. It also utilizes a distance‐dependent Chinese restaurant process to cluster heterogeneous populations while considering individuals' treatment histories. We evaluate the proposed approach through simulation studies, and apply the method to a dataset from the Women's Interagency HIV Study, showing the clinical utility of our model in guiding clinicians to prescribe informed and effective personalized treatment based on individuals' treatment histories and clinical characteristics.
Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.
The LOFAR view of intergalactic magnetic fields with giant radio galaxies

Context. Giant radio galaxies (GRGs) are physically large radio sources that extend well beyond their host galaxy environment. Their polarization properties are affected by the poorly constrained magnetic field that permeates the intergalactic medium on megaparsec scales. A low frequency (< 200 MHz) polarization study of this class of radio sources is now possible with LOFAR. Aims. Here we investigate the polarization properties and Faraday rotation measure (RM) of a catalog of GRGs detected in the LOFAR Two-meter Sky Survey. This is the first low frequency polarization study of a large sample of radio galaxies that were selected on their physical size. We explore the magneto-ionic properties of their under-dense environment and probe intergalactic magnetic fields using the Faraday rotation properties of their radio lobes. LOFAR is a key instrument for this kind of analysis because it can probe small amounts of Faraday dispersion (< 1 rad m−2), which are associated with weak magnetic fields and low thermal gas densities. Methods. We used RM synthesis in the 120−168 MHz band to search for polarized emission and to derive the RM and fractional polarization of each detected source component. We study the depolarization between 1.4 GHz and 144 MHz using images from the NRAO VLA Sky Survey. We investigate the correlation of the detection rate, the RM difference between the lobes, and the depolarization with different parameters as follows: the angular and linear size of the sources and the projected distance from the closest foreground galaxy cluster. In our sample, we also included 3C 236, which is one of the largest radio galaxies known. Results. From a sample of 240 GRGs, we detected 37 sources in polarization, all of which have a total flux density above 56 mJy. We detected significant RM differences between the lobes, which would be inaccessible at gigahertz frequencies, with a median value of ∼1 rad m−2. The fractional polarization of the detected GRGs at 1.4 GHz and 144 MHz is consistent with a small amount of Faraday depolarization (a Faraday dispersion < 0.3 rad m−2). Our analysis shows that the lobes are expanding into a low-density (< 10−5 cm−3) local environment that is permeated by weak magnetic fields (< 0.1 μG) with fluctuations on scales of 3−25 kpc. The presence of foreground galaxy clusters appears to influence the polarization detection rate up to 2R500. In general, this work demonstrates the ability of LOFAR to quantify the rarefied environments in which these GRGs exist and highlights them as an excellent statistical sample to use as high precision probes of magnetic fields in the intergalactic medium and the Milky Way.
Cosmic Gamma Ray Constraints on the Indirect Effects of Dark Matter

The observed anomalous excess of high-energy cosmic ray (CR) positrons is widely discussed as possible indirect evidence for dark matter (DM). However, any source of cosmic positrons is inevitably the source of gamma radiation. The least model dependent test of CR anomalies interpretation via DM particles decays (or annihilation) is connected with gamma-ray background due to gamma overproduction in such processes. In this work, we impose an observational constraint on gamma ray production from DM. Then, we study the possible suppression of gamma yield in the DM decays into identical final fermions. Such DM particles arise in the multi-component dark atom model. The influence of the interaction vertices on the gamma suppression was also considered. No essential gamma suppression effects are found. However, some minor ones are revealed.
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world’s second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.
Diffusion MRI as a descriptive imaging marker of the pathogenesis of treatment-resistant depression

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Diffusion MRI as a descriptive imaging marker of the pathogenesis of treatment-resistant depression Julie Coloigner, Jean-Marie Batail, Isabelle Corouge, Jean-Christophe Ferré, Dominique Drapier, Christian Barillot
Help in hand after traumatic events: a randomized controlled trial in health care professionals on the efficacy, usability, and user satisfaction of a self-help app to reduce trauma-related symptoms

ABSTRACT Background: Despite the fact that many people are affected by trauma and suffer from posttraumatic stress symptoms (PTSS) there is a lack of easy-accessible interventions to self-manage these symptoms. Mobile apps may deliver low-intensity self-help to reduce trauma-related symptoms and empower individuals following trauma, such as high-risk professionals who are regularly exposed to potentially traumatic events. In this randomized controlled trial, we examined the efficacy, and evaluated the usability and user satisfaction of the app ‘SUPPORT Coach’ as a self-help tool to reduce trauma-related symptoms. Methods: Health care professionals (e.g. nurses, physicians, paramedics and ambulance drivers) completed an online screening on PTSS (T0). They were randomized when at least one PTSS was reported, either to the intervention (1 month unlimited access to SUPPORT Coach) or control condition (no access to SUPPORT Coach). Self-reported PTSS, negative trauma-related cognitions, psychological resilience, and social support were assessed online at baseline (T1), post-condition (T2), and 1 month follow-up (T3). Results: Of the 1175 participants screened, 287 (24.4%) indicated at least one posttraumatic stress symptom and were randomized. The majority of intervention condition participants (83%, n = 103) used SUPPORT Coach; they were slightly to moderately satisfied with the app. There was no significant group difference in change in PTSS and social support after one-month app usage. However, the intervention condition showed a greater decline in negative trauma-related cognitions at T2 and T3, and a larger increase in psychological resilience at T3 than the control condition. Conclusions: SUPPORT Coach without guidance could potentially provide easy-accessible self-help to diminish negative trauma-related cognitions, and strengthen resilience in coping with adversities. However, since the attrition rate was substantially higher in the intervention than in control condition, our findings should be interpreted with caution and warrant replication.
Contractile functions of Piezo1 channels in murine arteries

Piezo1 proteins are Na+ and Ca2+ permeable mechanosensitive ion channels. Expressed on endothelial cell membranes, where they sense the shear stress forces created by blood flow, Piezo1 channels are essential for vascular development. The functional significance of vascular Piezo1 channels in adult physiology and pathology, however, remains largely unexplored. Therefore, using an inducible, endothelial-specific Piezo1 knock-out mouse line this study sought to identify and investigate the functional role of this channel in adult arterial vessels. Four morphologically and physiologically distinct arteries, the aorta, mesenteric, saphenous and carotid arteries, were selected and their responses to the vasoconstrictor phenylephrine (PE) and the vasorelaxant acetylcholine (ACh) were assessed using wire myography. While loss of endothelial Piezo1 did not alter or had only a small effect on the overall sensitivity or responsiveness of these vessels to either PE or ACh, further investigation revealed a significant increase in the endothelium-derived hyperpolarizing factor (EDHF) component of ACh-induced relaxation in mesenteric arteries. This anti-EDHF effect of Piezo1 was vascular bed specific, no difference between control or knock-out mice being observed in saphenous or carotid arteries. This finding led to the identification of a flow-stimulated vasoconstriction response in mesenteric vessels and furthermore provided an explanation for the reduced elevation in blood pressure detected in endothelial Piezo1 knock-out mice during exercise. Mesenteric arteries contract during exercise, enabling blood to be directed to tissues actively involved in physical movement. The data support the hypothesis that Piezo1 channels have specific importance in whole body physical exercise, sensing increased blood flow at the endothelium to elevate tone in the underlying vascular smooth muscle cells of visceral arteries, thus redirecting blood to the muscles.
A Comparison of Cs-137 γ Rays and 320-kV X-Rays in a Mouse Bone Marrow Transplantation Model

US homeland security concerns regarding the potential misuse of some radiation sources used in radiobiological research, for example, cesium-137 (137Cs), have resulted in recommendations by the National Research Council to conduct studies into replacing these sources with suitable X-ray instruments. The objective of this research is to compare the effectiveness of an X-RAD 320 irradiator (PXINC 2010) with a 137Cs irradiator (Gammacell-1000 Unit) using an established bone marrow chimeric model. Using measured radiation doses for each instrument, we characterized the dose–response relationships for bone marrow and splenocyte ablation, using a cytotoxicity-hazard model. Our results show that the X-RAD 320 photon energy spectrum was suitable for ablating bone marrow at the 3 exposure levels used, similar to that of 137Cs photons. However, the 320-kV X-rays were not as effective as the much higher energy γ rays at depleting mouse splenocytes. Furthermore, the 3 X-ray levels used were less effective than the higher energy γ rays in allowing the successful engraftment of donor bone marrow, potentially as a result of the incomplete depletion of the spleen cells. More defined studies are warranted for determining whether bone marrow transplantation in mice can be successfully achieved using 320-kV X-rays. A higher X-ray dose then used is likely needed for transplantation success.
Consumption of fruit and vegetables and the risk of type 2 diabetes: a 4-year longitudinal study among Swedish adults

A low intake of fruit and vegetables is a significant contributor to the global burden of disease. The aim of this study was to estimate the size of the risk of type 2 diabetes (T2D) of a low intake and to investigate possible sex differences. In this regard, this study used a longitudinal data from the Stockholm Public Health Cohort located in Sweden, collected in 2010 and 2014. The analysis included 14 718 men and 20 589 women aged 25 to 84 years. Fruit and vegetable intake, separately <2 servings/d or combined <4 servings/d (one serving corresponding to 100 g) was set as a cut-point for low intake. The sex difference at baseline was examined. Sex-stratified logistic regression was performed with onset of T2D as the outcome and fruit and vegetable intake at baseline as the exposure with adjustment for other known risk factors. Results indicate that men consumed significantly (P < 0⋅001) less fruit and vegetables compared with women. A 62 % higher risk to develop T2D over the 4-year period was observed in men who had low vegetable intake compared with high intake after adjusting for age, education, BMI, smoking, alcohol and physical activity (OR 1⋅62; 95 % CI 1⋅00, 2⋅63). In women, a significantly higher risk of T2D was also observed with a low intake of vegetables, but not after adjustment. The present study suggests that higher consumption of vegetables seems to be protective for the onset of T2D in men. Thus, increasing the intake of vegetables in men should be a public health priority.
Story contents and intensity of the anxious symptomatology in children and adolescents with Autism Spectrum Disorder

ABSTRACT This study aimed to analyse and compare the storytelling of 25 children with Autism Spectrum Disorder (ASD) with a comparison group of 25 children with typical development. Children’s narratives were transcribed verbatim, and their forms and contents were analysed. The two groups were matched according to the narrative cohesion of the story using the Bears Family Projective Test, equivalent verbal age, sex, and socioeconomic level. No differences in the forms of the stories emerged, but compared with the narratives of the typical development group, the narrative contents of the ASD group showed more adaptive and maladaptive behaviours of the characters, more problems, and less use of the atmosphere outside the home. These contents are related to the intensity of the anxious symptomatology indicated by the ASD group, their family members and teachers.
Force production of human cytoplasmic dynein is limited by its processivity

We present a novel experimental framework allowing for determination of the maximal force generation of weakly processive motors. Cytoplasmic dynein is a highly complex motor protein that generates forces toward the minus end of microtubules. Using optical tweezers, we demonstrate that the low processivity (ability to take multiple steps before dissociating) of human dynein limits its force generation due to premature microtubule dissociation. Using a high trap stiffness whereby the motor achieves greater force per step, we reveal that the motor’s true maximal force (“stall force”) is ~2 pN. Furthermore, an average force versus trap stiffness plot yields a hyperbolic curve that plateaus at the stall force. We derive an analytical equation that accurately describes this curve, predicting both stall force and zero-load processivity. This theoretical model describes the behavior of a kinesin motor under low-processivity conditions. Our work clarifies the true stall force and processivity of human dynein and provides a new paradigm for understanding and analyzing molecular motor force generation for weakly processive motors.