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Brillouin Microscopy is a novel label-free optical technique that enables the measurement of a material's mechanical properties at the sub-micron scale in a non-invasive and non-contact way; in the last few years, its applications in the life sciences have extensively expanded. To date, many custom-built Brillouin Microscopes suffer from temporal instabilities that impact their performances, showing drifts in the acquired spectra during time that may lead to inconsistencies between data acquired at different days. A further challenge for standard Brillouin Microscopes is the calibration of the spectrometer: the currently accepted protocol in literature uses known Brillouin shifts of water and methanol to reconstruct the dispersion curve, but this approach is highly influenced by external factors that are unrelated to spectrometer's performances. Manual and frequent realignments of the spectrometer and repeated calibrations with standard materials are thus needed to address these issues. Here, we show an innovative method to remove temporal instabilities of a standard Brillouin Microscope by inserting an Electro-Optic Modulator (EOM) that can be used: i) as a reference signal during measurements; ii) as a calibrator, allowing the reconstruction of the spectrometer dispersion curve with high precision, in an automatic pipeline and without the need for reference samples; iii) as a tool to detect and compensate for temporal drifts through a feedback control in a closed loop. We here show that our Brillouin Microscope, equipped with an EOM and a tuneable laser, is able to automatically acquire data for more than 2 days without the need to realign the spectrometer; retrieved Brillouin shifts and widths showed superior stability in time than standard Brillouin Microscopes.

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different--often self-built--spectrometers, a consensus statement is particularly timely to assure unified advancement., Comment: Main Text & Supplementary Text: 56 pages, 3 Figures, 2 Supplementary Figures, 1 Supplementary Table

Eukaryotic cells are characterized by a stiff nucleus whose effect in modeling the collective behavior of cell aggregates is usually underestimated. However, increasing experimental evidence links nuclear modifications with phenotypic transition, like the one between epithelial and mesenchymal states. In this work, we explore the effect of short-range repulsive forces in the non-equilibrium dynamics of the self-propelled Voronoi model. We show that the competition between steric repulsions (representing nuclear/cellular compressibility) and Vertex interactions (mimicking cell-cell adhesion/interaction and cytoskeleton organization) generate a variety of non-equilibrium phase transitions from Motility-Induced Phase Separation to mesenchymal-like phases up to disordered confluent configurations. Notably, we found that tuning the nucleus's effective size/compressibility provides an additional way to cross the boundary between the different possible phases in line with experimental observations., Comment: 11 pages, 4 figures

We report on the statistical confirmation of a second planet inside the TIC 393818343 system. The first planet TIC 393818343 b has been confirmed and classified as a Warm Jupiter planet with a period of P = (16.24921 +- 0.00003) days. The second planet in the system has an orbital period of P = (7.8458 +- 0.0023) days and orbits 2.05 times closer to its host star. The second planet was initially spotted by the Las Cumbres Observatory (LCOGT) and amateur astronomers. This Super-Neptunian exoplanet marks TIC 393818343 as a multi-planetary system.

Asymmetric partition of fate determinants during cell division is a hallmark of cell differentiation. Recent works suggested that such a mechanism is hijacked by cancer cells to increase both their phenotypic heterogeneity and plasticity and in turn their fitness. To quantify fluctuations in the partitioning of cellular elements, imaging-based approaches are used, whose accuracy is limited by the difficulty of detecting cell divisions. Our work addresses this gap proposing a general method based on high-throughput flow cytometry measurements coupled with a theoretical frameworks. We applied our method to a panel of both normal and cancerous human colon cells, showing that different kinds of colon adenocarcinoma cells display very distinct extents of fluctuations in their cytoplasm partition, explained by an asymmetric division of their size. To test the accuracy of our population-level protocol, we directly measure the inherited fractions of cellular elements from extensive time-lapses of live-cell laser scanning microscopy, finding excellent agreement across the cell types. Ultimately, our flow cytometry-based method promise to be accurate and easily applicable to a wide range of biological systems where the quantification of partition fluctuations would help accounting for the observed phenotypic heterogeneity and plasticity, Comment: 17 pages, 4 figures

In Super-resolution, a varying-illumination image stack is required. This enriched the dataset typically necessitates precise mechanical control and micron scale optical alignment and repeatability. Here, we introduce a novel methodology for super-resolution microscopy called Stochastically Structured Illumination Microscopy (S2IM), which bypasses the need for illumination control instead exploiting the random, uncontrolled movement of the target object. We tested our methodology within the clinically relevant ophthalmoscopic setting, harnessing the inherent saccadic motion of the eye to induce stochastic displacement of the illumination pattern on the retina. We opted to avoid human subjects by utilizing a phantom eye model, featuring a retina composed of human induced pluripotent stem cells (iPSC) retinal neurons, and replicating the ocular saccadic movements by custom actuators. Our findings demonstrate that S$^2$IM unlocks scan-less super-resolution with a resolution enhancement of 1.91, with promising prospects also beyond ophthalmoscopy applications such as active matter or atmospheric/astronomical observation.

The ability of a cancer cell population to achieve heterogeneity in their phenotype distributions offers advantages in tumor invasiveness and drug resistance. Studying the mechanisms behind such observed heterogeneity in mammalian cells presents challenges due for instance to the prolonged proliferation times compared to widely studied unicellular organisms like bacteria and yeast. Here, we studied the response of leukemia cell populations to serum starvation via a protocol, we recently developed, that makes use of live cell fluorescence and flow cytometry in combination with a quantitative analytical model to follow the population proliferation while monitoring the dynamics of its phenotype distributions. We found that upon switching between a serum-rich to a serum-poor media, leukemia cells (i) maintain a memory of the previous environment up to one generation even in the presence of severe medium-depletion, before (ii) adapting their growth and division rates to the novel environment while preserving a sizer-like division strategy. Finally, looking at the mitochondria content of the proliferating vs non-proliferating cells, we found that the latter is characterized by a higher number of older mitochondria, suggesting a possible functional role of the observed asymmetric partitioning of (aged) mitochondria in leukemia cells., Comment: 10 pages, 4 figures, 1 table. arXiv admin note: text overlap with arXiv:2306.10905

Predicting interactions between biomolecules, such as protein-protein complexes, remains a challenging problem. Despite the many advancements done so far, the performances of docking protocols are deeply dependent on their capability of identify binding regions. In this context, we present a novel approach that builds upon our previous works modeling protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating another key binding property, such as the balance between hydrophilic and hydrophobic contributions, we define new binding matrices that serve an effective inputs for training a neural network. Our approach also allows for the quantitative definition of a typical binding site area - approximately 10\AA~in radius - where hydrophobic contribution and shape complementarity, which reflects the Lennard-Jones interaction, are maximized. Using this new architecture, CIRNet (Core Interacting Residues Network), we achieve an accuracy of approximately 0.82 in identifying pairs of core interacting residues on a balanced dataset. In a blind search for core interacting residues, CIRNet distinguishes these from decoys with a ROC AUC of 0.72. This protocol can enahnce docking algorithms by rescaling the proposed poses. When applied to the top ten models from three popular docking server, CIRNet improves docking outcomes, reducing the the average RMSD between the refined poses and the native state by up to 58%., Comment: 15 pages, 5 figures, 1 table

Increasingly, business opportunities available to fabless design teams in the semiconductor industry far exceed those addressable with on-prem compute resources. An attractive option to capture these electronic design automation (EDA) design opportunities is through public cloud bursting. However, security concerns with public cloud bursting arise from having to protect process design kits, third party intellectual property, and new design data for semiconductor devices and chips. One way to address security concerns for public cloud bursting is to leverage confidential containers for EDA workloads. Confidential containers add zero trust computing elements to significantly reduce the probability of intellectual property escapes. A key concern that often follows security discussions is whether EDA workload performance will suffer with confidential computing. In this work we demonstrate a full set of EDA confidential containers and their deployment and characterize performance impacts of confidential elements of the flow including storage and networking. A complete end-to-end confidential container-based EDA workload exhibits 7.13% and 2.05% performance overheads over bare-metal container and VM based solutions, respectively.

We introduce SiamTST, a novel representation learning framework for multivariate time series. SiamTST integrates a Siamese network with attention, channel-independent patching, and normalization techniques to achieve superior performance. Evaluated on a real-world industrial telecommunication dataset, SiamTST demonstrates significant improvements in forecasting accuracy over existing methods. Notably, a simple linear network also shows competitive performance, achieving the second-best results, just behind SiamTST. The code is available at https://github.com/simenkristoff/SiamTST., Comment: 14 pages, 3 figures, public codebase

Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available data is acquired during standard operation where extreme scenarios and transitory regimes such as major temperature increases or decreases are de-facto excluded. Acquisition of such data requires significant energy consumption and a dedicated facility, hindering the quantity and diversity of available data. Cost related constraints however do not allow for continuous year-around acquisition. To address this, we investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models. This approach alleviates the need for continuously acquiring extensive time series data, especially in contexts involving repetitive heating and cooling cycles in buildings. In our evaluation 1) we assess the performance of synthetic data generators independently, particularly focusing on SoTA AI-based methods; 2) we measure the utility of incorporating synthetically augmented data in a subsequent forecasting tasks where we employ a simple model in two distinct scenarios: 1) we first examine an augmentation technique that combines real and synthetically generated data to expand the training dataset, 2) we delve into utilizing synthetic data to tackle dataset imbalances. Our results highlight the potential of synthetic data augmentation in enhancing forecasting accuracy while mitigating training variance. Through empirical experiments, we show significant improvements achievable by integrating synthetic data, thereby paving the way for more robust forecasting models in low-data regime.

We present the first detection of electrons with kinetic energy in the 100 eV range with transition-edge sensors (TESs). This has been achieved with a $(100\times 100)$ $\mu$m$^2$ Ti-Au bilayer TES, with a critical temperature of about 84 mK. The electrons are produced directly in the cryostat by an innovative cold source based on field emission from vertically-aligned multiwall carbon nanotubes. We obtain a Gaussian energy resolution between 0.8 and 1.8 eV for fully-absorbed electrons in the $(90-101)$ eV energy range, which is found to be compatible with the resolution of this same device for photons in the same energy range. This work opens new possibilities for high-precision energy measurements of low-energy electrons., Comment: 5 pages, 7 figures

We present the largest optical photometry compilation of Gamma-Ray Bursts (GRBs) with redshifts ($z$). We include 64813 observations of 535 events (including upper limits) from 28 February 1997 up to 18 August 2023. We also present a user-friendly web tool \textit{grbLC} which allows users the visualization of photometry, coordinates, redshift, host galaxy extinction, and spectral indices for each event in our database. Furthermore, we have added a Gamma Ray Coordinate Network (GCN) scraper that can be used to collect data by gathering magnitudes from the GCNs. The web tool also includes a package for uniformly investigating colour evolution. We compute the optical spectral indices for 138 GRBs for which we have at least 4 filters at the same epoch in our sample and craft a procedure to distinguish between GRBs with and without colour evolution. By providing a uniform format and repository for the optical catalogue, this web-based archive is the first step towards unifying several community efforts to gather the photometric information for all GRBs with known redshifts. This catalogue will enable population studies by providing light curves (LCs) with better coverage since we have gathered data from different ground-based locations. Consequently, these LCs can be used to train future LC reconstructions for an extended inference of the redshift. The data gathering also allows us to fill some of the orbital gaps from Swift in crucial points of the LCs, e.g., at the end of the plateau emission or where a jet break is identified., Comment: 20 pages, 16 figures, 2 tables. Submitted to MNRAS, this version matches the third revision. The Online Materials and data will be available after the publication

The relentless pursuit of understanding matter at ever-finer scales has pushed optical microscopy to surpass the diffraction limit and produced the super-resolution microscopy which enables visualizing structures shorter than the wavelength of light. In the present work, we harnessed extreme ultraviolet beams to create a sub-{\mu}m grating structure, which was revealed by extreme ultraviolet structured illumination microscopy. This achievement marks the first step toward extending such a super-resolution technique into the X-ray regime, where achieving atomic-scale resolution becomes a charming possibility.

Shape complementarity of molecular surfaces at the interfaces is a well-known characteristic of protein-protein binding regions, and it is critical in influencing the stability of the complex. Measuring such complementarity is at the basis of methods for both the prediction of possible interactions and for the design/optimization of speficic ones. However, only a limited number of tools are currently available to efficiently and rapidly assess it. Here, we introduce Zepyros, a webserver for fast measuring of the shape complementarity between two molecular interfaces of a given protein-protein complex using structural information. Zepyros is implemented as a publicly available tool with a user-friendly interface. Our server can be found at the following link (all major browser supported): https://zepyros.bio-groups.com, Comment: 4 pages, 1 figure

We present our optical photometric observations of the 2022 eruption of the recurrent nova U Scorpii (U Sco) using 49,152 data points over 70 d following the optical peak. We have also analyzed its soft X-ray (0.3--1 keV) light curve by the Neil Gehrels Swift Observatory. During the 2022 eruption, the optical plateau stage started 13.8--15.0 d and ended 23.8--25.0 d after the optical peak. The soft X-ray stage started 14.6--15.3 d and ended 38.7--39.5 d after the optical peak. Both stages started later and had shorter durations, and the soft X-ray light curve peaked earlier and was less luminous compared to those during the U Sco 2010 eruption. These points suggest that there were differences in the envelope mass between the different cycles of the nova eruption. Furthermore, we have analyzed the optical eclipses during the 2022 eruption. The primary eclipse was first observed 10.4--11.6 d after the optical peak, earlier than the beginning of the optical plateau stage. This sequence of events can be explained by the receding ejecta photosphere associated with the expanding nova ejecta. We have determined the ingress and egress phases of the primary eclipses and estimated the outer radius of the optical light source centered at the white dwarf (WD). During the optical plateau stage, the source radius remained $\sim$1.2 times larger than the Roche volume radius of the primary WD, being close to the L1 point. When the optical plateau stage ended, the source radius drastically shrank to the tidal truncation radius within a few orbital periods. This previously unresolved phenomenon can be interpreted as a structural change in U Sco where the temporarily expanded accretion disk due to the nova wind returned to a steady state., Comment: 16 pages, 7 figures, 7 tables, accepted for publication in PASJ; doi:10.1093/pasj/psae010

It is shown that a Hopfield recurrent neural network, informed by experimentally derived brain topology, recovers the scaling picture recently introduced by Deco et al., according to which the process of information transfer within the human brain shows spatially correlated patterns qualitatively similar to those displayed by turbulent flows, although with a more singular exponent, 1/2 instead of 2/3. Both models employ a coupling strength which decays exponentially with the euclidean distance between the nodes, but their mathematical nature is very different, Hopf oscillators versus a Hopfield neural network, respectively. Hence, their convergence for the same data parameters, suggests an intriguing robustness of the scaling picture. The present analysis shows that the Hopfield model brain remains functional by removing links above about five decay lengths, corresponding to about one sixth of the size of the global brain. This suggests that, in terms of connectivity decay length, the Hopfield brain functions in a sort of intermediate ``turbulent liquid''-like state, whose essential connections are the intermediate ones between the connectivity decay length and the global brain size. Finally, the scaling exponents are shown to be highly sensitive to the value of the decay length, as well as to number of brain parcels employed. As a result, any quantitative assessment regarding the specific nature of the scaling regime must be taken with great caution.

Next-generation data networks need to support Tb/s rates. In-phase and quadrature (IQ) modulation combine phase and intensity information to increase the density of encoded data, reduce overall power consumption by minimising the number of channels, and increase noise tolerance. To reduce errors when decoding the received signal, intersymbol interference must be minimised. This is achieved with pure phase modulation, where the phase of the optical signal is controlled without changing its intensity. Phase modulators are characterised by the voltage required to achieve a $\pi$ phase shift V$_{\pi}$, the device length L, and their product V$_{\pi}$L. To reduce power consumption, IQ modulators are needed with$<$1V drive voltages and compact (sub-cm) dimensions, which translate in V$_\pi$L$<$1Vcm. Si and LiNbO$_3$ (LN) IQ modulators do not currently meet these requirements, because V$_{\pi}$L$>$1Vcm. Here, we report a double single-layer graphene (SLG) Mach-Zehnder modulator (MZM) with pure phase modulation in the transparent regime, where optical losses are minimised and remain constant with increasing voltage. Our device has $V_{\pi}L\sim$0.3Vcm, matching state-of-the-art SLG-based MZMs and plasmonic LN MZMs, but with pure phase modulation and low insertion loss ($\sim$5dB), essential for IQ modulation. Our $V_\pi L$ is$\sim$5 times lower than the lowest thin-film LN MZMs, and$\sim$3 times lower than the lowest Si MZMs. This enables devices with complementary metal-oxide semiconductor compatible V$_\pi$L ($<$1Vcm) and smaller footprint than LN or Si MZMs, improving circuit density and reducing power consumption by one order of magnitude.

We propose a novel approach to detect the binding between proteins making use of the anomalous diffraction of natively present heavy elements inside the molecule 3D structure. In particular, we suggest considering sulfur atoms contained in protein structures at lower percentages than the other atomic species. Here, we run an extensive preliminary investigation to probe both the feasibility and the range of usage of the proposed protocol. In particular, we (i) analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus the differences in the patterns produced by bound proteins with respect to their non-bonded states can be exploited to rapidly assess protein complex formation. Next, we (ii) carried out analyses on the abundances of sulfurs in the different proteomes and molecular dynamics simulations on a representative set of protein structures to probe the typical motion of sulfur atoms. Finally, we (iii) suggest a possible experimental procedure to detect protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale even between other biological molecules, thus paving the way to the development of a novel field of research based on a synchrotron light source., Comment: 9 pages, 4 figures

The integration of optoelectronic devices, such as transistors and photodetectors (PDs), into wearables and textiles is of great interest for applications such as healthcare and physiological monitoring. These require flexible/wearable systems adaptable to body motions, thus materials conformable to non-planar surfaces, and able to maintain performance under mechanical distortions. Here, we prepare fibre PDs combining rolled graphene layers and photoactive perovskites. Conductive fibres ($\sim$500$\Omega$/cm) are made by rolling single layer graphene (SLG) around silica fibres, followed by deposition of a dielectric layer (Al$_{2}$O$_{3}$ and parylene C), another rolled SLG as channel, and perovskite as photoactive component. The resulting gate-tunable PDs have response time$\sim$5ms, with an external responsivity$\sim$22kA/W at 488nm for 1V bias. The external responsivity is two orders of magnitude higher and the response time one order of magnitude faster than state-of-the-art wearable fibre based PDs. Under bending at 4mm radius, up to$\sim$80\% photocurrent is maintained. Washability tests show$\sim$72\% of initial photocurrent after 30 cycles, promising for wearable applications.

Urbanization has underscored the importance of understanding the pedestrian wind environment in urban and architectural design contexts. Pedestrian Wind Comfort (PWC) focuses on the effects of wind on the safety and comfort of pedestrians and cyclists, given the influence of urban structures on the local microclimate. Traditional Computational Fluid Dynamics (CFD) methods used for PWC analysis have limitations in computation, cost, and time. Deep-learning models have the potential to significantly speed up this process. The prevailing state-of-the-art methodologies largely rely on GAN-based models, such as pix2pix, which have exhibited training instability issues. In contrast, our work introduces a convolutional neural network (CNN) approach based on the U-Net architecture, offering a more stable and streamlined solution. The process of generating a wind flow prediction at pedestrian level is reformulated from a 3D CFD simulation into a 2D image-to-image translation task, using the projected building heights as input. Testing on standard consumer hardware shows that our model can efficiently predict wind velocities in urban settings in real time. Further tests on different configurations of the model, combined with a Pareto front analysis, helped identify the trade-off between accuracy and computational efficiency. This CNN-based approach provides a fast and efficient method for PWC analysis, potentially aiding in more efficient urban design processes.

Lightcurves of many classical novae deviate from the canonical "fast rise - smooth decline" pattern and display complex variability behavior. We present the first TESS-space-photometry-based investigation of this phenomenon. We use TESS Sector 41 full-frame images to extract a lightcurve of the slow Galactic nova V606 Vul that erupted nine days prior to the start of the TESS observations. The lightcurve covers the first of two major peaks of V606 Vul that was reached 19 days after the start of the eruption. The nova reached its brightest visual magnitude V=9.9 in its second peak 64 days after the eruption onset, following the completion of Sector 41 observations. To increase the confidence level of the extracted lightcurve, we performed the analysis using four different codes implementing the aperture photometry (Lightkurve, VaST) and image subtraction (TESSreduce, tequila_shots) and find good agreement between them. We performed ground-based photometric and spectroscopic monitoring to complement the TESS data. The TESS lightcurve reveals two features: periodic variations (0.12771d, 0.01mag average peak-to-peak amplitude) that disappeared when the source was within 1mag of peak optical brightness and a series of isolated mini-flares (with peak-to-peak amplitudes of up to 0.5mag) appearing at seemingly random times. We interpret the periodic variations as the result of azimuthal asymmetry of the photosphere engulfing the nova-hosting binary that was distorted by and rotating with the binary. Whereas we use spectra to associate the two major peaks in the nova lightcurve with distinct episodes of mass ejection, the origin of mini-flares remains elusive., Comment: 31 pages, 10 figures, submitted to ApJ; comments welcome

A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for the implementation of effective control systems. This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings, aiming at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Recent advancements in deep learning have enabled the development of more sophisticated forecasting models compared to traditional feedback control systems. The proposed global Transformer architecture can be trained on the entire dataset encompassing all rooms, eliminating the need for multiple room-specific models, significantly improving predictive performance, and simplifying deployment and maintenance. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings. The proposed approach provides a novel solution to enhance the accuracy and efficiency of temperature forecasting, serving as a valuable tool to optimize energy consumption and decrease greenhouse gas emissions in the building sector.

Nowadays a wide number of applications based on magnetic materials relies on the properties arising at the interface between different layers in complex heterostructures engineered at the nanoscale. In ferromagnetic/heavy metal multilayers, such as the [Co/Pt]$_N$ and [Co/Pd]$_N$ systems, the magnetic proximity effect was demonstrated to be asymmetric, thus inducing a magnetic moment on the Pt(Pd) layer that is typically higher at the top Co/Pt(Pd) interface. In this work, advanced spectroscopic and imaging techniques were combined with theoretical approaches to clarify the origin of this asymmetry both in Co/Pt trilayers and, for the first time, in multilayer systems that are more relevant for practical applications. The different magnetic moment induced at the Co/Pt interfaces was correlated to the microstructural features, which are in turn affected by the growth processes that induce a different intermixing during the film deposition, thus influencing the interface magnetic profile., Comment: Main paper: 22 pages, 5 figures. Supporting information: 14 pages, 12 figures

The localization length r_{loc} associated with a fast secondary relaxation in glassy Laponite is determined by X-ray photon correlation spectroscopy (XPCS) through a Debye-Waller fit of the non-ergodicity parameter. Quantitative differences are observed between the time dependence (aging) of r_{loc} in spontaneously aged and rejuvenated samples. This behavior is also reflected in the calculated shear modulus which matches well with data obtained by rheological measurements., Comment: 6 pages, 5 figures

The aging dynamics of a colloidal glass has been studied by multiangle Dynamic Light Scattering, Neutron Spin Echo, X-ray Photon Correlation Spectroscopy and Molecular Dynamics simulations. The two relaxation processes, microscopic (fast) and structural (slow), have been investigated in an unprecedentedly wide range of time and length scales covering both ergodic and nonergodic regimes. The microscopic relaxation time remains diffusive at all length scales across the glass transition scaling with wavevector Q as Q^{-2}. The length-scale dependence of structural relaxation time changes from diffusive, characterized by a Q^{-2}-dependence in the early stages of aging, to Q^{-1}- dependence in the full aging regime which marks a discontinuous hopping dynamics. Both regimes are associated with a stretched behaviour of the correlation functions. We expect these findings to provide a general description of both relaxations across the glass transition., Comment: 6 pages, 4 figures

An unexpected dichotomic long time aging behaviour is observed in a glassy colloidal clay suspension investigated by X-Ray Photon Correlation Spectroscopy and Dynamic Light Scattering. In the long time aging regime the intensity autocorrelations are non-exponential, following the Kohlrausch-Williams-Watts functional form with exponent bQ. We show that for spontaneously aged samples a stretched behaviour (\beta_Q < 1) is always found. Surprisingly a compressed exponent (\beta_Q > 1) appears only when the system is rejuvenated by application of a shear field. In both cases the relaxation times scale as Q^{-1}. These observations shed light on the origin of compressed exponential behaviour and helps in classifying previous results in the literature on anomalous dynamics., Comment: 5 pages, 5 figures

The high frequency collective dynamics of liquid hydrogen fluoride is studied by inelastic x-ray scattering on the coexistence curve at T = 239 K. The comparison with existing molecular dynamics simulations shows the existence of two active relaxation processes with characteristic time scales in the subpicosecond range. The observed scenario is very similar to that found in liquid water. This suggests that hydrogen bonded liquids behave similarly to other very different systems as simple and glass forming liquids, thus indicating that these two relaxation processes are universal features of the liquid state., Comment: 6 pages, 5 figures

This work aims at constraining the size, shape, and geometric albedo of the dwarf planet candidate 2002 MS4 through the analysis of nine stellar occultation events. Using multichord detection, we also studied the object's topography by analyzing the obtained limb and the residuals between observed chords and the best-fitted ellipse. We predicted and organized the observational campaigns of nine stellar occultations by 2002 MS4 between 2019 and 2022, resulting in two single-chord events, four double-chord detections, and three events with three to up to sixty-one positive chords. Using 13 selected chords from the 8 August 2020 event, we determined the global elliptical limb of 2002 MS4. The best-fitted ellipse, combined with the object's rotational information from the literature, constrains the object's size, shape, and albedo. Additionally, we developed a new method to characterize topography features on the object's limb. The global limb has a semi-major axis of 412 $\pm$ 10 km, a semi-minor axis of 385 $\pm$ 17 km, and the position angle of the minor axis is 121 $^\circ$ $\pm$ 16$^\circ$. From this instantaneous limb, we obtained 2002 MS4's geometric albedo and the projected area-equivalent diameter. Significant deviations from the fitted ellipse in the northernmost limb are detected from multiple sites highlighting three distinct topographic features: one 11 km depth depression followed by a 25$^{+4}_{-5}$ km height elevation next to a crater-like depression with an extension of 322 $\pm$ 39 km and 45.1 $\pm$ 1.5 km deep. Our results present an object that is $\approx$138 km smaller in diameter than derived from thermal data, possibly indicating the presence of a so-far unknown satellite. However, within the error bars, the geometric albedo in the V-band agrees with the results published in the literature, even with the radiometric-derived albedo.

Multiband photometric observations and their evaluation to instrumental magnitudes were performed using standard Johnson-Cousins filters (B, V, Rc) as well r and g Sloan filters, and not standard ones (R, G, B, and Clear filters). These were recorded from 9 observatories and from the MicroObservatory Robotic Telescope Network. The results describe the rapid ascent towards the maximum (2.5 magnitudes about in five days in the B filter) and the slow decrease after the maximum (0.0425 +/- 0.02 magnitudes/day in the B filter). The results highlight the strong variation of the B-V colour indices during the first 50 days (from -0.20 +/- 0.02 to +0.85 +/- 0.02) and V-R (from 0 +/- 0.01 to +0.50 +/- 0.01) after the explosion, presumably corresponding to the cooling of the stellar photosphere. At 50 days after the explosion the magnitude decrease from the maximum was observed to continue where it faded by 2.5 magnitudes (B filter), thus we propose SN 2023ixf is a Type II, subtype L, supernova (SNe).

Heterogeneity in the size distribution of cancer cell populations has been recently linked to drug resistance and invasiveness. However, despite many progresses have been made in understanding how such heterogeneous size distributions arise in fast-proliferating cell types -like bacteria and yeast-, comprehensive investigations on cancer cell populations are still lacking mainly due to the difficulties of monitoring the proliferation of the time scales typical of mammalian cells. From a reductionist cell dynamics point of view, the strategies allowing size homeostasis are roughly grouped into three classes, \emph{i.e.} timer, sizer, or adder. These strategies are empirically distinguishable given the phenomenological measurable relationship between the cell size at birth and at division, which requires following the proliferation at the single-cell level. Here, we show how it is possible to infer the growth regime and division strategy of leukemia cell populations using live cell fluorescence labeling and flow cytometry in combination with a quantitative analytical model where both cell growth and division rates depend on powers of the cell size. Using our novel approach, we found that the dynamics of the size distribution of leukemia Jurkat T-cells is quantitatively reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution given by the sum of at least three independent exponentially-distributed times and (iii) fluctuations up to 15\% of the inherited fraction of size at division with respect to the mother cell size. Finally, we note that our experimental and theoretical apparatus can be easily extended to other cell types and environmental conditions, allowing for a comprehensive characterization of the growth and division model different cells can adopt., Comment: 16 pages, 5 figures

Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating patterns. In this study, we present a novel storage method that harnesses naturally-occurring random structures to store an arbitrary pattern in a memory device. This method, the stochastic emergent storage (SES), builds upon the concept of emergent archetypes, where a training set of imperfect examples (prototypes) is employed to instantiate an archetype in an Hopfield-like network through emergent processes. We demostrate this non-Hebbian paradigm in the photonic domain by utilizing random transmission matrices, which govern light scattering in a white-paint turbid medium, as prototypes. Through the implementation of programmable hardware, we successfully realize and experimentally validate the capability to store an arbitrary archetype and perform classification at the speed of light. Leveraging the vast number of modes excited by mesoscopic diffusion, our approach enables the simultaneous storage of thousands of memories without requiring any additional fabrication efforts. Similar to a content addressable memory, all stored memories can be collectively assessed against a given pattern to identify the matching element. Furthermore, by organizing memories spatially into distinct classes, they become features within a higher-level categorical (deeper) optical classification layer.

Enhanced stabilisation of protein structures via the presence of inert excipients is a key mechanism adopted both by physiological systems and in biotechnological applications. While the intrinsic stability of proteins is ultimately fixed by their amino acid composition and organisation, the interactions between excipients and proteins together with their concentrations introduce an additional layer of complexity and in turn, method of modulating protein stability. Here, we combined experimental measurements with molecular dynamics simulations and graph-theory based analyses to assess the stabilising/destabilising effects of different kinds of osmolytes on proteins during heat-mediated denaturation. We found that (i) proteins in solution with stability-enhancing osmolytes tend to have more compact interaction networks than those assumed in presence of destabilising excipients; (ii) a strong negative correlation (R = -0.85) characterises the relationship between the melting temperature Tm and the preferential interaction coefficient defined by the radial distribution functions of osmolytes and water around the protein and (iii) a positive correlation exists between osmolyte-osmolyte clustering and the extent of preferential exclusion from the local domain of the protein, suggesting that exclusion may be driven by enhanced steric hindrance of aggregated osmolytes., Comment: 24 pages, 13 figures, 3 tables