Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific contribution of Reelin expressed by Cajal–Retzius cells or GABAergic interneurons to cortical lamination

Significance The present study highlights a fundamental role of GABAergic interneuron-derived Reelin in neuronal migration, in addition to CR cell–expressed Reelin. Further, we observed transient migratory deficits, indicating that Reelin expressed by either neuronal population is sufficient to reverse some lamination defects. We propose a model of Reelin action in corticogenesis based on the spatial and cell-specific expression of this key protein. Because several neuropsychiatric disorders are linked to Reelin deficits in interneurons, this study may provide a better understanding of the mechanisms associated with human brain disorders related to Reelin deficits.
How to incorporate biological insights into network models and why it matters

Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large‐scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task‐oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.
Using the genome to correct the ion transport defect in cystic fibrosis

Today, biomedicine faces one of its greatest challenges, i.e. treating diseases through their causative dysfunctional processes and not just their symptoms. However, we still miss a global view of the mechanisms and pathways involved in the pathophysiology of most diseases. In fact, disease mechanisms and pathways can be achieved by holistic studies provided by ‘omic’ approaches. Cystic fibrosis (CF), caused by mutations in the CF transmembrane conductance regulator (CFTR) gene which encodes an anion channel, is paradigmatic for monogenic disorders, namely channelopathies. A high number of ‘omics studies’ have focused on CF; namely, several cell‐based high‐throughput approaches were developed and applied towards a global mechanistic characterization of CF pathophysiology and the identification of novel and ‘unbiased’ drug targets. Notwithstanding, it is likely that, through the integration of all these ‘layers’ of large datasets into comprehensive disease maps, biological significance can be extracted so that the enormous potential of these approaches to identifying dysfunctional mechanisms and novel drugs may become a reality.
Quantiles for Counts

This article studies the estimation of conditional quantiles of counts. Given the discreteness of the data, some smoothness must be artificially imposed on the problem. We show that it is possible to smooth the data in a way that allows inference to be performed using standard quantile regression techniques. The performance and implementation of the estimators are illustrated by simulations and an application.
Surprisingly Happy to Have Helped: Underestimating Prosociality Creates a Misplaced Barrier to Asking for Help

Performing acts of kindness increases well-being, yet people can be reluctant to ask for help that would enable others’ kindness. We suggest that people may be overly reluctant because of miscalibrated expectations about others’ prosocial motivation, underestimating how positively others will feel when asked for help. A pretest identified that interest in asking for help was correlated with expectations of how helpers would think and feel, but a series of scenarios, recalled experiences, and live interactions among adult participants in the United States (total N = 2,118) indicated that those needing help consistently underestimated others’ willingness to help, underestimated how positively helpers would feel, and overestimated how inconvenienced helpers would feel. These miscalibrated expectations stemmed from underestimating helpers’ prosocial motivation while overestimating compliance motivation. This research highlights a limitation of construing help-seeking through a lens of compliance by scholars and laypeople alike. Undervaluing prosociality could create a misplaced barrier to asking for help when needed.
À propos de la conjecture de Manin pour les courbes elliptiques modulaires

Résumé. On prouve que la constante de Manin d’une courbe elliptique, semi-stable sur Q, de Weil forte, ayant bonne réduction en 2, vaut 1. Mots clefs: courbes elliptiques de Weil fortes, congruences entre formes modulaires, constante de Manin.
The Spectrum of Influenza in Children

Abstract Background Children constitute an important component of the influenza burden and community transmission, but the frequency of asymptomatic infection and post-influenza sequelae at the community level is poorly understood. Methods Two community-based prospective cohort studies (2011–2020, 2017–2020) and 1 case-ascertained study (2012–2017) were conducted in Managua, Nicaragua. Non-immunocompromised children aged 0–14 years with ≥1 influenza infections, determined by polymerase chain reaction and hemagglutination inhibition assay, were included. Results A total of 1272 influenza infections occurred in the household-based portion of the study. Influenza infection was asymptomatic in 84 (6.6%) infections, and the asymptomatic fraction increased with age (1.7%, 3.5%, and 9.1% for ages 0–1, 2–4, and 5–14, respectively; P < .001). Of asymptomatic children, 43 (51.2%) shed virus, compared to 1099 (92.5%) symptomatic children (P < .001). Also, 2140 cases of influenza occurred in the primary care portion of the study. Sequelae of influenza were rare, with the most common being pneumonia (52, 2.4%) and acute otitis media (71, 3.3%). A/H1N1 had higher age-adjusted odds of acute otitis media (odds ratio [OR] 1.99, 95% confidence interval [CI]: 1.14–3.48; P = .015) and hospitalization (OR 3.73, 95% CI: 1.68–8.67; P = .002) than A/H3N2. B/Victoria had higher age-adjusted odds of pneumonia (OR 10.99, 95% CI: 1.34–90.28; P = .026) than B/Yamagata. Conclusions Asymptomatic influenza infection is much less common in children than adults, although viral shedding still occurs in asymptomatic children. Post-influenza sequelae are rare in children in the community setting, and virus strain may be important in understanding the risk of sequelae.
Aridity differentially alters the stability of soil bacterial and fungal networks in coastal and inland areas of Australia

Abstract Despite the importance of soil bacterial and fungal communities for ecosystem services and human welfare, how their ecological networks respond to climatic aridity have yet been evaluated. Here, we collected soil samples from 47 sites across 2500 km in coastal and inland areas of eastern Australia with contrasting status of aridity. We found that the diversity of both bacteria and fungi significantly differed between inland and coastal soils. Despite the significant differences in soil nutrient availability and stoichiometry between the inland and coastal regions, aridity was the most important predictor of bacterial and fungal community compositions. Aridity has altered the potential microbial migration rates and further impacted the microbial assembly processes by increasing the importance of stochasticity in bacterial and fungal communities. More importantly, ecological network analysis indicated that aridity enhanced the complexity and stability of the bacterial network but reduced that of the fungal network, possibly due to the contrasting impacts of aridity on the community‐level habitat niche breadth and overlaps. Our work paves the way towards a more comprehensive understanding of how climate changes will alter soil microbial communities, which is integral to predicting their long‐term consequences for ecosystem sustainability and resilience to future disturbances.
Comparison of 24‐hour urinary creatinine clearance and estimated glomerular filtration rate based on a panel of filtration markers in patients with chronic kidney disease

Diagnosis and management of chronic kidney disease (CKD) requires accurate assessment of glomerular filtration rate (GFR). In practice, GFR is typically estimated by equations based on creatinine concentration in blood, but creatinine is affected by non‐GFR factors such as age and sex. Alternative filtration markers such as cystatin C, beta‐trace protein (BTP), and beta‐2 microglobulin (B2M) may be less dependent on age and sex, but equations combining these markers have not been investigated in patients with chronic kidney disease (CKD). In this cross‐sectional study of 50 patients with CKD stage 3–4, we compared kidney function estimates based on creatinine, cystatin C, BTP, B2M, or a combination of markers. Compared to the creatinine/cystatin C combination equation, the panel equation yielded a mean difference of only 2.8 ml/min/1.73 m2, indicating that switching to the panel equation would be unlikely to affect management.
Tumor‐associated macrophages in liver cancer: From mechanisms to therapy

Multidimensional analyses have demonstrated the presence of a unique tumor microenvironment (TME) in liver cancer. Tumor‐associated macrophages (TAMs) are among the most abundant immune cells infiltrating the TME and are present at all stages of liver cancer progression, and targeting TAMs has become one of the most favored immunotherapy strategies. In addition, macrophages and liver cancer cells have distinct origins. At the early stage of liver cancer, macrophages can provide a niche for the maintenance of liver cancer stem cells. In contrast, cancer stem cells (CSCs) or poorly differentiated tumor cells are key factors modulating macrophage activation. In the present review, we first propose the origin connection between precursor macrophages and liver cancer cells. Macrophages undergo dynamic phenotypic transition during carcinogenesis. In this course of such transition, it is critical to determine the appropriate timing for therapy and block specific markers to suppress pro‐tumoral TAMs. The present review provides a more detailed discussion of transition trends of such surface markers than previous reviews. Complex crosstalk occurs between TAMs and liver cancer cells. TAMs play indispensable roles in tumor progression, angiogenesis, and autophagy due to their heterogeneity and robust plasticity. In addition, macrophages in the TME interact with other immune cells by directing cell‐to‐cell contact or secreting various effector molecules. Similarly, tumor cells combined with other immune cells can drive macrophage recruitment and polarization. Despite the latest achievements and the advancements in treatment strategies following TAMs studies, comprehensive discussions on the communication between macrophages and cancer cells or immune cells in liver cancer are currently lacking. In this review, we discussed the interactions between TAMs and liver cancer cells (from cell origin to maturation), the latest therapeutic strategies (including chimeric antigen receptor macrophages), and critical clinical trials for hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) to provide a rationale for further clinical investigation of TAMs as a potential target for treating patients with liver cancer.
Line integration and second order partial differential equations over Cayley-Dickson algebras

Line integration of generalized functions is studied. Second order partial differential equations with piecewise continuous and generalized variable coefficients over Cayley-Dickson algebras are investigated. Formulas for integrations of such equations are deduced. For this purpose a non-commutative line integration is used. Examples of solutions of partial differential equations are given.
Identi fi cation of shared and unique susceptibility pathways among cancers of the lung , breast , and prostate from genome-wide association studies and tissue-speci fi c protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that Received: July 15, 2015. Revised: September 11, 2015. Accepted: October 12, 2015 © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com Human Molecular Genetics, 2015, Vol. 24, No. 25 7406–7420 doi: 10.1093/hmg/ddv440 Advance Access Publication Date: 19 October 2015
MultiViz: A Gephi Plugin for Scalable Visualization of Multi-Layer Networks

The process of visually presenting networks is an effective way to understand entity relationships within the networks since it reveals the overall structure and topology of the network. Real networks are extremely difficult to visualize due to their immense complexity, which includes vast amounts of data, several types of interactions, various subsystems and several levels of connectivity as well as changes over time. This paper introduces the"MultiViz Plugin,"a plugin for gephi, an open-source software tool for graph visualization and modification, in order to to visualize complex networks in a multi-layer manner. A collection of settings are availabe through the plugin to transform an existing network into a multi-layered network. The plugin supports several layout algorithms and lets user to choose which property of the network to be used as the layer. The goal of the study is to give the user complete control over how the network is visualized in a multi-layer fashion. We demonstrate the ability of the plugin to visualize multi-layer data using a real-life complex multi-layer datasets.
Two temperate super-Earths transiting a nearby late-type M dwarf

In the age of JWST, temperate terrestrial exoplanets transiting nearby late-type M dwarfs provide unique opportunities for characterising their atmospheres, as well as searching for biosignature gases. We report here the discovery and validation of two temperate super-Earths transiting LP 890-9 (TOI-4306, SPECULOOS-2), a relatively low-activity nearby (32 pc) M6V star. The inner planet, LP 890-9b, was first detected by TESS (and identified as TOI-4306.01) based on four sectors of data. Intensive photometric monitoring of the system with the SPECULOOS Southern Observatory then led to the discovery of a second outer transiting planet, LP 890-9c (also identified as SPECULOOS-2c), previously undetected by TESS. The orbital period of this second planet was later confirmed by MuSCAT3 follow-up observations. With a mass of 0.118$\pm$0.002 $M_\odot$, a radius of 0.1556$\pm$0.0086 $R_\odot$, and an effective temperature of 2850$\pm$75 K, LP 890-9 is the second-coolest star found to host planets, after TRAPPIST-1. The inner planet has an orbital period of 2.73 d, a radius of $1. 320_{-0.027}^{+0.053}$ $R_\oplus$, and receives an incident stellar flux of 4.09$\pm$0.12 $S_\oplus$. The outer planet has a similar size of $1. 367_{-0.039}^{+0.055}$ $R_\oplus$ and an orbital period of 8.46 d. With an incident stellar flux of 0.906 $\pm$ 0.026 $S_\oplus$, it is located within the conservative habitable zone, very close to its inner limit. Although the masses of the two planets remain to be measured, we estimated their potential for atmospheric characterisation via transmission spectroscopy using a mass-radius relationship and found that, after the TRAPPIST-1 planets, LP 890-9c is the second-most favourable habitable-zone terrestrial planet known so far. The discovery of this remarkable system offers another rare opportunity to study temperate terrestrial planets around our smallest and coolest neighbours.
Hardware Acceleration of Sampling Algorithms in Sample and Aggregate Graph Neural Networks

Sampling is an important process in many GNN structures in order to train larger datasets with a smaller computational complexity. However, compared to other processes in GNN (such as aggregate, backward propagation), the sampling process still costs tremendous time, which limits the speed of training. To reduce the time of sampling, hardware acceleration is an ideal choice. However, state of the art GNN acceleration proposal did not specify how to accelerate the sampling process. What's more, directly accelerating traditional sampling algorithms will make the structure of the accelerator very complicated. In this work, we made two contributions: (1) Proposed a new neighbor sampler: CONCAT Sampler, which can be easily accelerated on hardware level while guaranteeing the test accuracy. (2) Designed a CONCAT-sampler-accelerator based on FPGA, with which the neighbor sampling process boosted to about 300-1000 times faster compared to the sampling process without it.
Neutrino pair condensate and its application to cosmology and astrophysics

Left-handed neutrinos interact attractively by Z-boson exchange. The Ginzburg-Landau mean ﬁeld calculation and the Bogoliubov transformation suggest that the attractive force leads to neutrino pair condensate and neutrino super-ﬂuidity. Neutrinos, as deﬁned by quasi-particle in the super-ﬂuid phase, behave as massless fermions. When the result of super-ﬂuid formation is applied to the early universe, horizon scale pair condensate may become a component of dark energy. A further accretion of other fermions from thermal cosmic medium gives a seed of primordial neutron stars consisting of proton, neutron, electron, and neutrino in beta-equilibrium, surrounded by left-handed neutrino pair condensate. This possibility may provide a mechanism of giving a part or the whole of the dark matter in the present universe, if a properly chosen small fraction condenses to neutrino super-ﬂuid and primordial neutron star not to over-close the universe. The proposal can be veriﬁed by measuring neutrino burst at primordial neutron star formation and by detecting super-ﬂuid relic neutrinos in atomic experiments at laboratories. tools statistical mechanics and condensed matter physics, the Ginzburg-Landau theory and the Bogoliubov transformation. Results suggest that super-ﬂuid formation is likely to occur in much the same way as condensate formation of the electron Cooper pair in superconductivity of metals. It is likely that the early universe at temperatures below 100 GeV is made of a mixed phase of ideal gas and a small fraction of super-ﬂuid neutrino pair condensates. Crucial question on observable consequences of super-ﬂuid neutrinos in PNS remains to be studied, but the proposal can be veriﬁed by high statistics data of neutrino burst at PNS formation.
Scalable Regularization of Scene Graph Generation Models using Symbolic Theories

Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art techniques can be divided into two families: one where the background knowledge is incorporated into the model in a subsymbolic fashion, and another in which the background knowledge is maintained in symbolic form. Despite promising results, both families of techniques face several shortcomings: the first one requires ad-hoc, more complex neural architectures increasing the training or inference cost; the second one suffers from limited scalability w.r. t. the size of the background knowledge. Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art. Our technique is model-agnostic, does not incur any cost at inference time, and scales to previously unmanageable background knowledge sizes. We demonstrate that our technique can improve the accuracy of state-of-the-art SGG models, by up to 33%.
Cooperative trajectory planning algorithm of USV-UAV with hull dynamic constraints

Eﬃcient trajectory generation in complex dynamic environments remains an open problem in the unmanned surface vehicle (USV). The perception of the USV is usually interfered with by the swing of the hull and the ambient weather, making it challenging to plan the optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed to ensure that USV can execute a safe and smooth path in the process of autonomous advance in multi-obstacle maps. Speciﬁcally, the unmanned aerial vehicle (UAV) plays the role of a ﬂight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation networkand3Dprojectiontransformation.Andthen,aninitialobstacleavoidancetrajectoryisgenerated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the eﬀectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.
Suppressing amplitude damping in trapped ions: Discrete weak measurements for a nonunitary probabilistic noise filter

The idea of exploiting maximally-entangled states as a resource lies at the core of several modalities of quantum information processing, including secure quantum communication, quantum computation, and quantum sensing. However, due to imperfections during or after the entangling gates used to prepare such states, the amount of entanglement decreases and their quality as a resource gets degraded. We introduce a low-overhead protocol to reverse this degradation by partially filtering out a specific type of noise relevant to many quantum technologies. We present two trapped-ion schemes for the implementation of a non-unitary probabilistic filter against amplitude damping noise, which can protect any maximally-entangled pair from spontaneous photon scattering during or after the two-qubit trapped-ion entangling gates. This filter can be understood as a protocol for single-copy quasi-distillation, as it uses only local operations to realise a reversal operation that can be understood in terms of weak measurements.