Edge Transport and Self-Assembly of Passive Objects in a Chiral Active Fluid*

Topological edge flow and dissipationless odd viscosity are two remarkable features of chiral active fluids composed of active spinners. These features can significantly influence the dynamics of suspended passive particles and the interactions between the particles. By computer simulations, we investigate the transport phenomenon of anisotropic passive objects and the self-assembly behavior of passive spherical particles in the active spinner fluid. It is found that in confined systems, nonspherical passive objects can stably cling to boundary walls and are unidirectionally and robustly transported by edge flow of spinners. Furthermore, in an unconfined system, passive spherical particles are able to form stable clusters that spontaneously and unidirectionally rotate as a whole. In these phenomena, strong particle-wall and interparticle effective attractions play a vital role, which originate from spinner-mediated depletion-like interactions and can be largely enhanced by odd viscosity of spinner fluids. Our results thus provide new insight into the robust transport of cargoes and the nonequilibrium self-assembly of passive intruders.

Associations between patterns in comorbid diagnostic trajectories of individuals with schizophrenia and etiological factors

Schizophrenia is a heterogeneous disorder, exhibiting variability in presentation and outcomes that complicate treatment and recovery. To explore this heterogeneity, we leverage the comprehensive Danish health registries to conduct a prospective, longitudinal study from birth of 5432 individuals who would ultimately be diagnosed with schizophrenia, building individual trajectories that represent sequences of comorbid diagnoses, and describing patterns in the individual-level variability. We show that psychiatric comorbidity is prevalent among individuals with schizophrenia (82%) and multi-morbidity occur more frequently in specific, time-ordered pairs. Three latent factors capture 79% of variation in longitudinal comorbidity and broadly relate to the number of co-occurring diagnoses, the presence of child versus adult comorbidities and substance abuse. Clustering of the factor scores revealed five stable clusters of individuals, associated with specific risk factors and outcomes. The presentation and course of schizophrenia may be associated with heterogeneity in etiological factors including family history of mental disorders.

Ab Initio Molecular Dynamics Study of the Structure and Properties of Nb-Doped Zr-Cu-Al Amorphous Alloys

In this paper, experiments were carried out on (Zr0.5Cu0.4Al0.1)100-xNbx (x = 0, 3, 6 at.%) amorphous alloys, and the corresponding ab initio molecular dynamics simulation was performed. The results showed that stable structures of Nb-centered and Al-centered icosahedral (-like) atomic clusters were formed after a small amount of (3 at.%) Nb was added. Stable and close-packed backbone structures were formed by the means of interconnection and matching of the two kinds of stable clusters in the alloys, which also enhanced the overall heterogeneity of the structures, thereby improving the strength and macroscopic plasticity. In addition, when more (6 at.%) Nb was added, the stable Al-centered clusters were replaced by some stable Nb-centered clusters in the alloys, and the stability and heterogeneity of the structures were partly reduced, which reduced the strength and macroscopic plasticity.

Identifying and Describing Subtypes of Spontaneous Empathic Facial Expression Production in Autistic Adults

Background: It is unclear whether atypical patterns of facial expression production metrics in autism reflect the dynamic and nuanced nature of facial expressions or a true diagnostic difference. Further, the heterogeneity observed across autism symptomatology suggests a need for more adaptive and personalized social skills programs. For example, it would be useful to have a better understanding of the different expressiveness profiles within the autistic population and how they differ from neurotypicals to help develop systems that train facial expression production and reception. Methods:We used automated facial coding and an unsupervised clustering approach to limit inter-individual variability in facial expression production that may have otherwise obscured group differences in previous studies, allowing an "apples-to-apples" comparison between autistic and neurotypical adults. Specifically, we applied k-means clustering to identify subtypes of facial expressiveness in an autism group (N=27) and a neurotypical control group (N=57) separately. The two most stable clusters from these analyses were then further characterized and compared on the basis of their expressiveness and emotive congruence to emotionally charged stimuli. Results: Our main finding was that autistic adults show heightened spontaneous facial expressions in response to negative emotional images. The group effect did not extend to positive emotional images, and we did not find evidence for greater incongruous (i.e., inappropriate) facial expressions in autism. Conclusion: These findings build on previous work suggesting valence-specific effects of autism on emotional empathy and suggest the need for intervention programs to focus on social skills in the context of both negative and positive emotions.

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic’s emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

Microbiota of semen samples with normozoospermia: analysis of real-time PCR data

The analysis of semen microbiota is difficult due to the lack of established criteria for interpretation of microbiological tests. The aim of the study was to determine the stable clusters of semen microbiota analyzed by real-time PCR in samples with normozoospermia. Semen samples of 227 men with normal spermiograms were included in the study. The quantity of total bacterial DNA and at least one group of microorganisms was more than 103 GE/ml in 107 (41.7%) samples. Four stable microbiota clusters with the prevalence of a specific microorganism group were distinguished in these samples: obligate anaerobes (OA) cluster (proportion in the centroid — 81.1%); Lactobacillus spp. cluster (proportion in the centroid — 64.3%); gram-positive facultative anaerobes (GPFA) cluster (proportion in the centroid — 92.5%); Enterobacteriaceae/Enterococcoccus (EE) cluster (proportion in the centroid — 80.8%). The clusters were ranked by frequency of occurrence: OA cluster was the most prevalent (43 (40.2%) of 107), second-most frequent were GPFA-cluster (27 (25.2%)) and Lactobacillus-cluster (22 (20.6%)). EE-dominated cluster was found in 15 (14.0%) cases.

Charge moderated preplanetary growth from single grains to giant aggregates

<p>In early phases of planet formation, bouncing and fragmentation barriers still represent major obstacles. Beginning at micrometer, dust can readily grow to sub-millimeter size in collisions due to cohesion before bouncing prevails. Later, streaming instabilities trigger further growth which might finally results into planetesimal formation by gravitational collapse. However, for streaming instabilities sub-millimeter grains might be too small, therefore there is gap of at least 1 order of magnitude in size which needs to be bridged.</p> <p>Here, we present our ongoing work how to bridge this gap by charge moderated aggregation [1]. When two (dielectric) grains collide they charge. This tribocharging or collisional charging is omnipresent in nature. We designed drop tower experiments in which we generated charges on glass and basalt grains by collisions in a shaker. In microgravity, the particle trajectories and collisions were observed, and charges were measured by applying an electric field.</p> <p>In early work, we analyzed millimeter-sized glass grain collisions with a copper plate. The coefficient of restitution increased with the charge on a single grain due to mirror charge forces. That means highly charged grains tend to stick more easily to surfaces than uncharged grains. The velocity where sticking is possible was increased by a factor of 100 up to several dm/s [2].<br /> <br />More recently, we used half millimeter basalt spheres and observed sticking events at several cm/s among grains themselves [3]. This is also way higher than predicted by adhesion. In a number of cases, we could observe the sequential formation of aggregates of up to ten single grains. During approach the grains are accelerated due to net charge Coulomb forces but likely also due to higher order charges on the surfaces in agreement to earlier measurements of strong permanent dipole moments [4]. Attraction increases collision cross-sections and the growth is sped up. Growth only stopped by the end of microgravity [3]. </p> <p>To observe the formation of still larger aggregates we developed a new setup, in which a dense cloud of 150 µm diameter basalt grains was continuously agitated slightly under microgravity and in vacuum. Here, the growth of a giant aggregate of centimeter size was observed collecting nearly all material in one cluster [5].</p> <p>To conclude, in experiments under various conditions, we see strong evidence that electrostatic charges on grains are able to conquer the bouncing barrier. We observed the bottom-up growth tracking individual particles, stable clusters emerging from dense regions and the formation of giant clusters during agitation. These are all bricks in the wall giving evidence that collisional charging might play a crucial role in planet formation.</p> <p><strong>References:</strong></p> <p>[1] Steinpilz, T.; Joeris, K.; Jungmann, F.; Wolf, D.; Brendel, L.; Teiser, J.; Shinbrot, T.; Wurm, G. Nature Physics 2020a, 16, 225-229.</p> <p>[2] Jungmann, F.; Steinpilz, T.; Teiser, J.; Wurm, G. Journal of Physics Communications 2018, 2 095009, 095009.</p> <p>[3] Jungmann, F.;Wurm, G. Astronomy and Astrophysics 2021, DOI: https://doi.org/10.1051/0004-6361/202039430.</p> <p>[4] Steinpilz, T.; Jungmann, F.; Joeris, K.; Teiser, J.; Wurm, G. New Journal of Physics 2020b, 22, 093025.</p> <p>[5] Teiser, J.; Kruss, M.; Jungmann, F.; Wurm, G. The Astrophysical Journal Letters 2021, 908, L22.</p>

Persistent symptoms following SARS-CoV-2 infection in a random community sample of 508,707 people

Introduction Long COVID, describing the long-term sequelae after SARS-CoV-2 infection, remains a poorly defined syndrome. There is uncertainty about its predisposing factors and the extent of the resultant public health burden, with estimates of prevalence and duration varying widely. Methods Within rounds 3-5 of the REACT-2 study, 508,707 people in the community in England were asked about a prior history of COVID-19 and the presence and duration of 29 different symptoms. We used uni- and multivariable models to identify predictors of persistence of symptoms (12 weeks or more). We estimated the prevalence of symptom persistence at 12 weeks, and used unsupervised learning to cluster individuals by symptoms experienced. Results Among the 508,707 participants, the weighted prevalence of self-reported COVID-19 was 19.2% (95% CI: 19.1,19.3). 37.7% of 76,155 symptomatic people post COVID-19 experienced at least one symptom, while 14.8% experienced three or more symptoms, lasting 12 weeks or more. This gives a weighted population prevalence of persistent symptoms of 5.75% (5.68, 5.81) for one and 2.22% (2.1, 2.26) for three or more symptoms. Almost a third of people 8,771/28,713 (30.5%) with at least one symptom lasting 12 weeks or more reported having had severe COVID-19 symptoms ('significant effect on my daily life') at the time of their illness, giving a weighted prevalence overall for this group of 1.72% (1.69,1.76). The prevalence of persistent symptoms was higher in women than men (OR: 1.51 [1.46,1.55]) and, conditional on reporting symptoms, risk of persistent symptoms increased linearly with age by 3.5 percentage points per decade of life. Obesity, smoking or vaping, hospitalisation , and deprivation were also associated with a higher probability of persistent symptoms, while Asian ethnicity was associated with a lower probability. Two stable clusters were identified based on symptoms that persisted for 12 weeks or more: in the largest cluster, tiredness predominated, while in the second there was a high prevalence of respiratory and related symptoms. Interpretation A substantial proportion of people with symptomatic COVID-19 go on to have persistent symptoms for 12 weeks or more, which is age-dependent. Clinicians need to be aware of the differing manifestations of Long COVID which may require tailored therapeutic approaches. Managing the long-term sequelae of SARS-CoV-2 infection in the population will remain a major challenge for health services in the next stage of the pandemic.

Clusters of neuronal Neurofascin prefigure node of Ranvier position along single axons

The spacing of nodes of Ranvier crucially affects conduction properties along myelinated axons. It has been assumed that node position is primarily driven by the growth of myelin sheaths. Here, we reveal an additional mechanism of node positioning that is driven by the axon. We show through longitudinal live imaging of node formation dynamics that stable clusters of the cell adhesion molecule Neurofascin A accumulate at specific sites along axons prior to myelination. While some of these clusters change position upon encounter with growing myelin sheaths, others restrict sheath extension and are therefore predictive of future node position. Animals that lack full-length Neurofascin A showed increased internodal distances and less regular spacing of nodes along single axons. Together, our data reveal the existence of an axonal mechanism to position its nodes of Ranvier that does not depend on regulation of myelin sheath length.

Analysis and Augmentation of Small Datasets with Unsupervised Machine Learning

Analysis of small datasets presents a number of essential challenges not in the least due to insufficient sampling of characteristic patterns in the data making confident conclusions about the unknown distribution elusive and resulting in lower statistical confidence and higher error. In this work, a novel approach to augmentation of small datasets is proposed based on an ensemble of neural network models of unsupervised generative self-learning. Applying generative learning with an ensemble of individual models allowed to identify stable clusters of data points in the latent representations of the observable data. Several techniques of augmentation based on identified latent cluster structure were applied to produce new data points and enhance the dataset. The proposed method can be used with small and extremely small datasets to identify characteristics patterns, augment data and in some cases, improve accuracy of classification in the scenarios with strong deficit of labels.