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Allostery is a fundamental property of proteins that represents the functional coupling between distantly located sites. In different manifestations, this property underlies signal transduction, gene expression, and regulation -- elementary reactions in networks comprising cellular information and metabolic processing systems. In this work, we present a reduced theoretical model for allostery that encompasses the many diverse mechanisms described in past work. What emerges is a basic classification of different forms of allostery into two groups: those that couple distant sites through soft normal modes within a single state, and those that possess multiple states, with effective interactions between sites emerging only upon transitioning between these states. This work serves to unify the extensive past theoretical work on the phenomenology and machinery of allostery in proteins.

Fully supervised deep models have shown promising performance for many medical segmentation tasks. Still, the deployment of these tools in clinics is limited by the very timeconsuming collection of manually expert-annotated data. Moreover, most of the state-ofthe-art models have been trained and validated on moderately homogeneous datasets. It is known that deep learning methods are often greatly degraded by domain or label shifts and are yet to be built in such a way as to be robust to unseen data or label distributions. In the clinical setting, this problematic is particularly relevant as the deployment institutions may have different scanners or acquisition protocols than those from which the data has been collected to train the model. In this work, we propose to address these two challenges on the detection of clinically significant prostate cancer (csPCa) from bi-parametric MRI. We evaluate the method proposed by (Kervadec et al., 2018), which introduces a size constaint loss to produce fine semantic cancer lesions segmentations from weak circle scribbles annotations. Performance of the model is based on two public (PI-CAI and Prostate158) and one private databases. First, we show that the model achieves on-par performance with strong fully supervised baseline models, both on in-distribution validation data and unseen test images. Second, we observe a performance decrease for both fully supervised and weakly supervised models when tested on unseen data domains. This confirms the crucial need for efficient domain adaptation methods if deep learning models are aimed to be deployed in a clinical environment. Finally, we show that ensemble predictions from multiple trainings increase generalization performance.

We discuss in this article the creation and organization of a national core facility for bioimage analysis, based on a distributed team. F-BIAS federates bioimage analysts across France and relies on them to deliver services to the researchers of this territory. The main challenge in implementing this structure is to provide significant scientific value to its members, thereby encouraging their active participation and persuading their respective host teams to support their involvement. F-BIAS accomplished this by creating a professional network that mitigates the negative effects of isolation experienced by its members, who are often the sole bioimage analyst within their local teams, and fosters the development of their technical skills. In a second phase we capitalized on F-BIAS to create a virtual, remotely-operating core facility for bioimage analysis, offering consultations and collaborative project services to the scientific community of France. The services are organized so that they also contribute to the technical proficiency of the analysts. To promote the creation of similar structures, we present and discuss here the organization of this nationally distributed bioimage analysis service core, highlighting successes and challenges.

Semiconductor nanowires are believed to play a crucial role for future applications in electronics, spintronics and quantum technologies. A potential candidate is HgTe but its sensitivity to nanofabrication processes restrain its development. A way to circumvent this obstacle is the selective area growth technique. Here, in-plane HgTe nanostructures are grown thanks to selective area molecular beam epitaxy on a semi-insulating CdTe substrate covered with a patterned SiO$_{\mathrm{2}}$ mask. The shape of these nanostructures is defined by the in-plane orientation of the mask aperture along the <$110$>, <$1\bar{\mathrm{1}}0$>, or <$100$> direction, the deposited thickness, and the growth temperature. Several micron long in-plane nanowires can be achieved as well as more complex nanostructures such as networks, diamond structures or rings. A good selectivity is achieved with very little parasitic growth on the mask even for a growth temperature as low as $140${\deg}C and growth rate up to $0.5$ ML/s. For <$110$> oriented nanowires, the center of the nanostructure exhibits a trapezoidal shape with {$111$}B facets and two grains on the sides, while <$1\bar{\mathrm{1}}0$> oriented nanowires show {$111$}A facets with adatoms accumulation on the sides of the top surface. Transmission electron microscopy observations reveal a continuous epitaxial relation between the CdTe substrate and the HgTe nanowire. Measurements of the resistance with fourpoint scanning tunneling microscopy indicates a good electrical homogeneity along the main NW axis and a thermally activated transport. This growth method paves the way toward the fabrication of complex HgTe-based nanostructures for electronic transport measurements., Comment: 18 pages and 8 figures. Submitted to Nanotechnology

There has been extensive activity exploring the doping of semiconducting two-dimensional (2D) transition metal dichalcogenides in order to tune their electronic and magnetic properties. The outcome of doping depends on various factors, including the intrinsic properties of the host material, the nature of the dopants used, their spatial distribution as well as their interactions with other types of defects. A thorough atomic-level analysis is essential to fully understand these mechanisms. In this work, vanadium doped WSe$_2$ monolayer grown by molecular beam epitaxy is investigated using four-dimensional scanning transmission electron microscopy (4D-STEM). Through center of mass-based reconstruction, atomic scale maps are produced, allowing the visualization of both the electric field and the electrostatic potential around individual V atoms. To provide quantitative insights, these results are successfully compared with multislice image simulations based on ab initio calculations, accounting for lens aberrations. Finally, a negative charge around the V dopants is detected as a drop in the electrostatic potential, unambiguously demonstrating that 4D-STEM can be used to detect and to accurately analyze single dopant charge states in semiconducting 2D materials., Comment: 17 pages, 4 figures and Supporting Information

We study the origin of bimodal emission in AlGaN/AlN QD superlattices displaying high internal quantum efficiency (around 50%) in the 230-300 nm spectral range. The secondary emission at longer wavelengths is linked to the presence of cone-like defects starting at the first AlN buffer/superlattice interface and propagating vertically. These defects are associated with a dislocation that produces strong shear strain, which favors the formation of 30{\deg} faceted pits. The cone-like structures present Ga enrichment at the boundary facets and larger QDs within the defect. The bimodality is attributed to the differing dot size/composition within the defects and at the defect boundaries, which is confirmed by the correlation of microscopy results and Schr\"odinger-Poisson calculations.

Historically, the resolution of optical imaging systems was dictated by diffraction, and the Rayleigh criterion was long considered an unsurpassable limit. In superresolution microscopy, this limit is overcome by manipulating the emission properties of the object. However, in passive imaging, when sources are uncontrolled, reaching sub-Rayleigh resolution remains a challenge. Here, we implement a quantum-metrolgy-inspired approach for estimating the separation between two incoherent sources, achieving a sensitivity five orders of magnitude beyond the Rayleigh limit. Using a spatial mode demultiplexer, we examine scenes with bright and faint sources, through intensity measurements in the Hermite-Gauss basis. Analysing sensitivity and accuracy over an extensive range of separations, we demonstrate the remarkable effectiveness of demultiplexing for sub-Rayleigh separation estimation. These results effectively render the Rayleigh limit obsolete for passive imaging.

Epitaxial growth has become a promising route to achieve highly crystalline continuous two-dimensional layers. However, high-quality layer production with expected electrical properties is still challenging due to the defects induced by the coalescence between imperfectly aligned domains. In order to control their intrinsic properties at the device scale, the synthesized materials should be described as a patchwork of coalesced domains. Here, we report multi-scale and multistructural analysis on highly oriented epitaxial WS$_2$ and WSe$_2$ monolayers using scanning transmission electron microscopy (STEM) techniques. Characteristic domain junctions are first identified and classified based on the detailed atomic structure analysis using aberration corrected STEM imaging. Mapping orientation, polar direction and phase at the micrometer scale using four-dimensional STEM enabled to access the density and the distribution of the specific domain junctions. Our results validate a readily applicable process for the study of highly oriented epitaxial transition metal dichalcogenides, providing an overview of synthesized materials from large scale down to atomic scale with multiple structural information., Comment: 22 pages, 6 figures and Supplementary Information

Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the detection of prostate cancer (PCa). However, characterizing prostate lesions aggressiveness in mp-MRI sequences is impossible in clinical practice, and biopsy remains the reference to determine the Gleason score (GS). In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading. After encoding the information on a latent space, the network is separated in two branches: 1) the first branch performs prostate segmentation 2) the second branch uses this zonal prior as an attention gate for the detection and grading of prostate lesions. The model was trained and validated with a 5-fold cross-validation on an heterogeneous series of 219 MRI exams acquired on three different scanners prior prostatectomy. In the free-response receiver operating characteristics (FROC) analysis for clinically significant lesions (defined as GS > 6) detection, our model achieves 69.0% $\pm$14.5% sensitivity at 2.9 false positive per patient on the whole prostate and 70.8% $\pm$14.4% sensitivity at 1.5 false positive when considering the peripheral zone (PZ) only. Regarding the automatic GS group

Momentum resolved 4D-STEM, also called center of mass (CoM) analysis, has been used to measure the long range built-in electric field of a silicon p-n junction. The effect of different STEM modes and the trade-off between spatial resolution and electric field sensitivity are studied. Two acquisition modes are compared: nanobeam and low magnification (LM) modes. A thermal noise free Medipix3 direct electron detector with high speed acquisition has been used to study the influence of low electron beam current and millisecond dwell times on the measured electric field and standard deviation. It is shown that LM conditions can underestimate the electric field values due to a bigger probe size used but provide an improvement of almost one order of magnitude on the signal-to-noise ratio, leading to a detection limit of 0.011MV/cm. It is observed that the CoM results do not vary with acquisition time or electron dose as low as 24 $e^-/A^2$, showing that the electron beam does not influence the built-in electric field and that this method can be robust for studying beam sensitive materials, where a low dose is needed.

A key issue in the development of high-performance semiconductor devices is the ability to properly measure active dopants at the nanometer scale. 4D scanning transmission electron microscopy and off-axis electron holography have opened up the possibility of studying the electrostatic properties of a p-n junction with nm-scale spatial resolution. The complete description of a p-n junction must take into account the precise evolution of the concentration of dopants around the junction, since the sharpness of the dopant transition directly influences the built-in potential and the maximum electric field. Here, a contacted silicon p-n junction is studied through in-situ biased 4D-STEM. Measurements of electric field, built-in voltage, depletion region width and charge density in the space charge region are combined with analytical equations as well as finite-element simulations in order to evaluate the quality of the junction interface. The nominally-symmetric, highly doped ($N_A = N_D = 9\space x \space10^{18} cm^{-3}$) junction presents an electric field and built-in voltage much lower than expected for an abrupt junction. These experimental results are consistent with electron holography data. All measured junction parameters are compatible with the presence of an intermediate region with a graded profile of the dopants at the p-n interface. This hypothesis is also consistent with the evolution of the electric field with bias. These results demonstrate that in-situ biased 4D-STEM enables a better understanding of the electrical properties of semiconductor p-n junctions with nm-scale resolution., Comment: 13 pages, 5 figures

It is well known that the classical single linkage algorithm usually fails to identify clusters in the presence of outliers. In this paper, we propose a new version of this algorithm, and we study its mathematical performances. In particular, we establish an oracle type inequality which ensures that our procedure allows to recover the clusters with large probability under minimal assumptions on the distribution of the outliers. We deduce from this inequality the consistency and some rates of convergence of our algorithm for various situations. Performances of our approach is also assessed through simulation studies and a comparison with classical clustering algorithms on simulated data is also presented.

Several physical mechanisms have been proposed to explain allostery in proteins. They differ by the number of internal states that they assume a protein to occupy, leaving open the question of what controls the emergence of these distinct physical forms of allostery. Here, we analyze a simplified model of protein allostery under a range of physical and evolutionary constraints. We find that two archetypal mechanisms can emerge through evolution: a single-state mechanism where ligand binding induces a displacement along a soft normal mode or a multi-state mechanism where ligand binding induces a switch across an energy barrier to a different stable state. Importantly, whenever the two mechanisms are possible, the multi-state mechanism confers a stronger allosteric effect and thus a selective advantage. This work defines the essential constraints on single or multi-state allostery, and sets the stage for a physical theory of its evolutionary origins.

We compare two transmission electron microscopy (TEM) based techniques that can provide highly spatially resolved quantitative measurements of magnetic induction fields at high sensitivity. To this end, the magnetic induction of a ferromagnetic NiFe nanowire has been measured and compared to micromagnetic modelling. State-of-the-art electron holography has been performed using the averaging of large series of holograms to improve the sensitivity of the measurements. These results are then compared those obtained from pixelated (or 4D) scanning transmission electron microscopy (STEM). This emerging technique uses a pixelated detector to image the local diffraction patterns as the beam is scanned over the sample. For each diffraction pattern, the deflection of the beam is measured and converted into magnetic induction, while scanning the beam allows to build a map. Aberration corrected Lorentz (field-free) configurations of the TEM and STEM were used for an improved spatial resolution. We show that the pixelated STEM approach, even when performed using an old generation of charge-coupled device camera, provides better sensitivity at the expense of spatial resolution. A more general comparison of the two techniques is given.

Controlling the strain level in nanowire heterostructures is critical for obtaining coherent interfaces of high crystalline quality and for the setting of functional properties such as photon emission, carrier mobility or piezoelectricity. In a nanowire axial heterostructure featuring a sharp interface, strain is set by the materials lattice mismatch and the nanowire radius. Here, we show that introducing a graded interface in nanowire heterostructures offers an additional parameter to control strain. For a given interface length and lattice mismatch, we first derive theoretically the maximum nanowire radius below which coherent growth is possible. We validate these findings by growing and characterizing various In(Ga)As/GaAs nanowire heterostructures with graded interfaces. Furthermore, we perform a complete chemical and structural characterization of the interface by combining energy-dispersive X-ray spectroscopy and high resolution transmission electron microscopy. In the case of coherent growth, we directly observe that the mismatch strain relaxes elastically on the side walls of the nanowire around the interface area, while the core of the nanowire remains partially strained. Moreover, our experimental data show good agreement with finite element calculations. This analysis confirms in particular that mechanical strain is largely reduced by interface grading. Overall, our work extends the parameter space for the design of nanowire heterostructures, thus opening new opportunities for nanowire optoelectronics., Comment: 8 pages, 6 figures

In crystal growth, surfactants are additive molecules used in dilute amount or as dense, permeable layers to control surface morphologies. Here, we investigate the properties of a strikingly different surfactant: a two-dimensional and covalent layer with close atomic packing, graphene. Using in situ, real time electron microscopy, scanning tunneling microscopy, kinetic Monte Carlo simulations, and continuum mechanics calculations, we reveal why metallic atomic layers can grow in a two-dimensional manner below an impermeable graphene membrane. Upon metal growth, graphene dynamically opens nanochannels called wrinkles, facilitating mass transport, while at the same time storing and releasing elastic energy via lattice distortions. Graphene thus behaves as a mechanically active, deformable surfactant. The wrinkle-driven mass transport of the metallic layer intercalated between graphene and the substrate is observed for two graphene-based systems, characterized by different physico-chemical interactions, between graphene and the substrate, and between the intercalated material and graphene. The deformable surfactant character of graphene that we unveil should then apply to a broad variety of species, opening new avenues for using graphene as a two-dimensional surfactant forcing the growth of flat films, nanostructures and unconventional crystalline phases.

Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov's accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting). Substantial numerical evidence is provided on both synthetic and real-life data sets to assess the excellent performance of the method in a large variety of prediction problems. It is empirically shown that AGB is much less sensitive to the shrinkage parameter and outputs predictors that are considerably more sparse in the number of trees, while retaining the exceptional performance of gradient boosting.

We report on a direct measurement of cation and anion sub-lattice strain in an InAs/GaSb type-II strained layer superlattice (T2SLs) using atomic resolution Z-contrast imaging and advanced image processing. Atomic column positions in InAs and GaSb are determined by separating the cation and anion peak intensities. Analysis of the InAs/GaSb T2SLs reveals the compressive strain in the nominal GaSb layer and tensile strain at interfaces between constituent layers, which indicate In incorporation into the nominal GaSb layer and the formation of GaAs like interfaces, respectively. The results are compared with the model-dependent X-ray diffraction measurements in terms of interfacial chemical intermixing and strain. Together, these techniques provide a robust measurement of atomic-scale strain which is vital to determine T2SL properties., Comment: 15 pages, double spaced, 7 figures

This paper deals with nonparametric estimation of conditional den-sities in mixture models in the case when additional covariates are available. The proposed approach consists of performing a prelim-inary clustering algorithm on the additional covariates to guess the mixture component of each observation. Conditional densities of the mixture model are then estimated using kernel density estimates ap-plied separately to each cluster. We investigate the expected L 1 -error of the resulting estimates and derive optimal rates of convergence over classical nonparametric density classes provided the clustering method is accurate. Performances of clustering algorithms are measured by the maximal misclassification error. We obtain upper bounds of this quantity for a single linkage hierarchical clustering algorithm. Lastly, applications of the proposed method to mixture models involving elec-tricity distribution data and simulated data are presented.

Collaborative recommendation is an information-filtering technique that attempts to present information items that are likely of interest to an Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In its most common form, the problem is framed as trying to estimate ratings for items that have not yet been consumed by a user. Despite wide-ranging literature, little is known about the statistical properties of recommendation systems. In fact, no clear probabilistic model even exists which would allow us to precisely describe the mathematical forces driving collaborative filtering. To provide an initial contribution to this, we propose to set out a general sequential stochastic model for collaborative recommendation. We offer an in-depth analysis of the so-called cosine-type nearest neighbor collaborative method, which is one of the most widely used algorithms in collaborative filtering, and analyze its asymptotic performance as the number of users grows. We establish consistency of the procedure under mild assumptions on the model. Rates of convergence and examples are also provided., Comment: Published in at http://dx.doi.org/10.1214/09-AOS759 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In its most common form, the problem is framed as trying to estimate ratings for items that have not yet been consumed by a user. Despite wide-ranging literature, little is known about the statistical properties of recommendation systems. In fact, no clear probabilistic model even exists allowing us to precisely describe the mathematical forces driving collaborative filtering. To provide an initial contribution to this, we propose to set out a general sequential stochastic model for collaborative recommendation and analyze its asymptotic performance as the number of users grows. We offer an in-depth analysis of the so-called cosine-type nearest neighbor collaborative method, which is one of the most widely used algorithms in collaborative filtering. We establish consistency of the procedure under mild assumptions on the model. Rates of convergence and examples are also provided.

Conducting nanowires possess remarkable physical properties unattainable in bulk materials. However our understanding of their transport properties is limited by the difficulty of connecting them electrically. In this Letter we investigate phototransport in both bulk silicon and silicon nanowires using a superconducting multimode resonator operating at frequencies between 0.3 and 3 GHz. We find that whereas the bulk Si response is mainly dissipative, the nanowires exhibit a large dielectric polarizability. This technique is contactless and can be applied to many other semiconducting nanowires and molecules. Our approach also allows to investigate the coupling of electron transport to surface acoustic waves in bulk Si and to electro-mechanical resonances in the nanowires.

The paper presents a multiplicative bias reduction estimator for nonparametric regression. The approach consists to apply a multiplicative bias correction to an oversmooth pilot estimator. In Burr et al. [2010], this method has been tested to estimate energy spectra. For such data set, it was observed that the method allows to decrease bias with negligible increase in variance. In this paper, we study the asymptotic properties of the resulting estimate and prove that this estimate has zero asymptotic bias and the same asymptotic variance as the local linear estimate. Simulations show that our asymptotic results are available for modest sample sizes., Comment: 29 pages

We demonstrate the growth of GaN/AlN quantum well structures by plasma-assisted molecular-beam epitaxy by taking advantage of the surfactant effect of Ga. The GaN/AlN quantum wells show photoluminescence emission with photon energies in the range between 4.2 and 2.3 eV for well widths between 0.7 and 2.6 nm, respectively. An internal electric field strength of $9.2\pm 1.0$ MV/cm is deduced from the dependence of the emission energy on the well width., Comment: Submitted to APL