Miracle-dz/UniFAR-FTUs huggingface dataset:
2PM waveform from loop corrected soft theorems

We introduce a classical version of the loop corrected soft graviton theorem and we use it to compute the universal part of the one-loop (2PM) waveform up to sub-subleading order in the energy $\omega$ of the emitted graviton for spinless black-hole scattering. In particular, we compute the action of the soft operators on the classically resummed four-point amplitude, that can be written in terms of the exponential of the eikonal phase (and is therefore non-perturbative in the Newton's constant $G_N$) and then we perform the usual PM expansion in powers of $G_N$ . We find perfect agreement with the existing 2PM literature at the orders $\omega^{-1}$, $\log\omega$ and $\omega\log^2\omega$, which are universal. Furthermore, we use this method to compute the universal part of the $\omega\log\omega$ contribution to the 2PM waveform. Even if in the present analysis we limit ourselves to compute the soft 2PM waveform, our general formulae can be used to extract all universal PM orders of the terms connected with the infrared divergences, once the impulse at the corresponding precision is known. Our approach, based on the resummed eikonal amplitude, gives a unified picture of the various computations of the classical soft graviton behaviour that are present in the literature since the seminal paper by Weinberg in 1965.
ASAP-MPC: An Asynchronous Update Scheme for Online Motion Planning with Nonlinear Model Predictive Control

This paper presents a Nonlinear Model Predictive Control (NMPC) scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performance. Although there exist various ways in literature to reduce the solution times in NMPC, such times may not be low enough to allow real-time implementations. This paper presents ASAP-MPC, an approach to handle varying, sometimes restrictively large, solution times with an asynchronous update scheme, always allowing for full convergence and real-time execution. The NMPC algorithm is combined with a linear state feedback controller tracking the optimised trajectories for improved robustness against possible disturbances and plant-model mismatch. ASAP-MPC seamlessly merges trajectories, resulting from subsequent NMPC solutions, providing a smooth and continuous overall trajectory for the motion system. This frameworks applicability to embedded applications is shown on two different experiment setups where a state-of-the-art method fails: a quadcopter flying through a cluttered environment in hardware-in-the-loop simulation and a scale model truck-trailer manoeuvring in a structured lab environment.
The Rise and Fall of the Standard-Model Higgs: Electroweak Vacuum Stability during Kination

In this paper we investigate the vacuum stability of the non-minimally coupled Standard-Model Higgs during a phase of kinetic domination following the end of inflation. The non-minimal coupling to curvature stabilises the Higgs fluctuations during inflation while driving them towards the instability scale during kination, when they can classically overcome the potential barrier separating the false electroweak vacuum from the true one at super-Planckian field values. Avoiding the instability of the Standard-Model vacuum sets an upper bound on the inflationary scale that depends both on the strength of the non-minimal interaction and on the top quark Yukawa coupling. We find that for all combinations of parameters allowing for the existence of a barrier in the effective potential, classical vacuum stability is generically guaranteed. Interestingly enough, thanks to the explosive particle production in the tachyonic phase, the Higgs itself can be also appointed to the role of reheaton field responsible for the onset of the hot Big Bang era, setting an additional lower bound on the inflationary scale $\mathcal{H}_{\rm inf} \gtrsim 10^8 \text{ GeV}$. Overall, these constraints favour lower masses for the top quark, in agreement with the current measurements of the top quark pole mass. We perform our analysis semi-analytically in terms of the one-loop and three-loop running of the Standard-Model Higgs self-coupling and make use of lattice-based parametric formulas for studying the (re)heating phase derived in arXiv:2307.03774 . For a specific choice of $m_t=171.3 \text{ GeV}$ we perform also an extensive numerical scanning of the parameter space via classical lattice simulations, identifying stable/unstable regions and supporting the previous analytical arguments. For this fiducial value, the heating of the Universe is achieved at temperatures in the range $10^{-2} - 10^5 \text{ GeV}$.
Rethink Model Re-Basin and the Linear Mode Connectivity

Recent studies suggest that with sufficiently wide models, most SGD solutions can, up to permutation, converge into the same basin. This phenomenon, known as the model re-basin regime, has significant implications for model averaging. However, current re-basin strategies are limited in effectiveness due to a lack of comprehensive understanding of underlying mechanisms. Addressing this gap, our work revisits standard practices and uncovers the frequent inadequacies of existing matching algorithms, which we show can be mitigated through proper re-normalization. By introducing a more direct analytical approach, we expose the interaction between matching algorithms and re-normalization processes. This perspective not only clarifies and refines previous findings but also facilitates novel insights. For instance, it connects the linear mode connectivity to pruning, motivating a lightweight yet effective post-pruning plug-in that can be directly merged with any existing pruning techniques. Our implementation is available at https://github.com/XingyuQu/rethink-re-basin.
Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

Large pretrained protein language models (PLMs) have improved protein property and structure prediction from sequences via transfer learning, in which weights and representations from PLMs are repurposed for downstream tasks. Although PLMs have shown great promise, currently there is little understanding of how the features learned by pretraining relate to and are useful for downstream tasks. We perform a systematic analysis of transfer learning using PLMs, conducting 370 experiments across a comprehensive suite of factors including different downstream tasks, architectures, model sizes, model depths, and pretraining time. We observe that while almost all down-stream tasks do benefit from pretrained models compared to naive sequence representations, for the majority of tasks performance does not scale with pretraining, and instead relies on low-level features learned early in pretraining. Our results point to a mismatch between current PLM pretraining paradigms and most applications of these models, indicating a need for better pretraining methods.
Insulin resistance alters the coupling between cerebral blood flow and glucose metabolism in younger and older adults: Implications for neurovascular coupling

Rising rates of insulin resistance and an ageing population are set to exact an increasing toll on individuals and society. Here we examine the contribution of insulin resistance and age to the coupling of cerebral blood flow and glucose metabolism; a critical process in the supply of energy for the brain. Thirty-four younger (20-42 years) and 41 older (66-86 years) healthy adults underwent a simultaneous resting state MR/PET scan, including arterial spin labelling. Rates of cerebral blood flow and glucose metabolism were derived using a functional atlas of 100 brain regions. Older adults had lower cerebral blood flow than younger adults in 95 regions, reducing to 36 regions after controlling for cortical atrophy and blood pressure. Younger and older insulin sensitive adults showed small, negative correlations between relatively high rates of regional cerebral blood flow and glucose metabolism. This pattern was inverted in insulin resistant older adults, who showed hypoperfusion and hypometabolism across the cortex, and a positive coupling. In insulin resistant younger adults, coupling showed inversion to positive correlations, although not to the extent seen in older adults. Our findings suggest that the normal course of ageing and insulin resistance alter the rates and coupling of cerebral blood flow and metabolism. They underscore the criticality of insulin sensitivity to brain health across the adult lifespan.
Mathematical modeling suggests heterogeneous replication of Mycobacterium tuberculosis in rabbits

Tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), remains a major health problem with 10.6 million cases of the disease and 1.6 million deaths in 2021. It is well understood that pulmonary TB is due to replication of Mtb in the lung but quantitative details of Mtb replication and death in lungs of patients and how these rates are related to the degree of lung pathology are unknown. We performed experiments with rabbits infected with a novel, virulent clinical Mtb isolate of the Beijing lineage, HN878, carrying an unstable plasmid pBP10. In our in vitro experiments we found that pBP10 is more stable in HN878 strain than in a more commonly used laboratory-adapted Mtb strain H37Rv (the segregation coefficient being s = 0.10 in HN878 vs. s = 0.18 in H37Rv). Interestingly, the kinetics of plasmid-bearing bacteria in lungs of Mtb-infected rabbits did not follow an expected monotonic decline; the percent of plasmid-bearing cells increased between 28 and 56 days post-infection and remained stable between 84 and 112 days post-infection despite a large increase in bacterial numbers in the lung at late time points. Mathematical modeling suggested that such a non-monotonic change in the percent of plasmid-bearing cells can be explained if the lung Mtb population consists of several (at least 2) sub-populations with different replication/death kinetics: one major population expanding early and being controlled/eliminated, while another, a smaller population expanding at later times causing a counterintuitive increase in the percent of plasmid-bearing cells. Given that HN878 forms well circumscribed granulomas in rabbits, our results suggest independent bacterial dynamics in subsets of such granulomas. Our model predictions can be tested in future experiments in which HN878-pBP10 dynamics in individual granulomas is followed over time.
Microbial Electrosynthesis from CO2 reaches Productivity of Syngas and Chain Elongation Fermentations

Microbial electrosynthesis allows the electrochemical upgrading of CO2. However, higher productivities and energy efficiencies are needed to reach a viability that can make the technology transformative. Here we show how a biofilm-based microbial porous cathode in a directed flow-through electrochemical system can continuously reduce CO2 to even-chain C2-C6 carboxylic acids during 248 days. We demonstrate a 3-fold higher biofilm concentration, volumetric current density, and productivity than the state of the art, up to a new record of -35 kA m-3cathode and 69 kgC m-3cathode day-1, at 60-97% and 30-35% faradaic and energy efficiencies, respectively. Most notably, the volumetric productivity resembles those achieved in lab-scale and industrial syngas (CO-H2-CO2) fermentation and chain elongation fermentation. This work highlights key design parameters for efficient electricity-driven microbial CO2 reduction. There is need and room to improve the rates of electrode colonization and microbe-specific kinetics to scale-up the technology. Graphical abstract
Protein property prediction based on local environment by 3D equivariant convolutional neural networks

Predicting the properties of proteins is an important procedure in protein engineering. It determines the subspace of mutations for protein modifications, which is critical to the success of the project, but heavily relies on the knowledge and experience of scientists. In this study, we propose a novel deep 3D-CNN model, Eq3DCNN, specifically designed for local environment-related tasks in protein engineering. Eq3DCNN uses basic atom descriptors and their coordinates as inputs, utilizing customized data augmentations to enhance its training efficiency. To make the Eq3DCNN extracted features with more generalization capability, we incorporated a rotation equivariant module to get rotation invariant features. Using cross-validations with different data splitting strategies and under the scenarios of zero-shot predictions, we demonstrate that Eq3DCNN outperformed other 3D-CNN models in stability predictions, and also well-preformed on other prediction tasks, such as the binding pocket and the secondary structure predictions. Our results also identified the key factors that contribute to the model’s accuracy and the scope of its applications. These findings may help scientists in designing better mutation experiments and increasing the success rate in protein engineering.
Degradation of indole-3-acetic acid by plant-associated microbes

Plant-associated microbiota affect pant growth and development by regulating plant hormones homeostasis. Indole-3-acetic acid (IAA), a well-known plant hormone, can be produced by various plant-associated bacteria. However, the prevalence of microbes with the capacity to degrade IAA in the rhizosphere has not been systematically studied. In this study, we analyzed the IAA degradation capabilities of bacterial isolates from the roots of Arabidopsis and rice. Using genomics analysis and in vitro assays, we found that 21 out of 189 taxonomically diverse bacterial isolates possess the ability to degrade IAA. Through comparative genomics and transcriptomic assays, we identified iac-like or iad-like operon in the genomes of these IAA degraders. Additionally, the regulator of the operon was found to be highly conserved among these strains through protein structure similarity analysis. Some of the IAA degraders could utilize IAA as their sole carbon and energy source. In planta, most of the IAA degrading strains mitigated Arabidopsis seedling root growth inhibition (RGI) triggered by exogenous IAA. Importantly, we observed increased colonization preference of IAA degraders from soil to root according to the frequency of the biomarker genes in metagenome-assembled genomes (MAGs) collected from different habitats, suggesting that there is a close association between IAA degraders and IAA-producers. In summary, our findings further the understanding of the functional diversity and roles of plant-associated microbes.
Trackoscope: A Low-Cost, Open, Autonomous Tracking Microscope for Long-Term Observations of Microscale Organisms

Cells and microorganisms are motile, yet the stationary nature of conventional microscopes impedes comprehensive, long-term behavioral and biomechanical analysis. The limitations are twofold: a narrow focus permits high-resolution imaging but sacrifices the broader context of organism behavior, while a wider focus compromises microscopic detail. This trade-off is especially problematic when investigating rapidly motile ciliates, which often have to be confined to small volumes between coverslips affecting their natural behavior. To address this challenge, we introduce Trackoscope, a 2-axis autonomous tracking microscope designed to follow swimming organisms ranging from 10µm to 2mm across a 325cm2 area (equivalent to an A5 sheet) for extended durations—ranging from hours to days—at high resolution. Utilizing Trackoscope, we captured a diverse array of behaviors, from the air-water swimming locomotion of Amoeba to bacterial hunting dynamics in Actinosphaerium, walking gait in Tardigrada, and binary fission in motile Blepharisma. Trackoscope is a cost-effective solution well-suited for diverse settings, from high school labs to resource-constrained research environments. Its capability to capture diverse behaviors in larger, more realistic ecosystems extends our understanding of the physics of living systems. The low-cost, open architecture democratizes scientific discovery, offering a dynamic window into the lives of previously inaccessible small aquatic organisms.
BEND2 is a crucial player in oogenesis and reproductive aging

Reproductive aging, characterized by a decline in female reproductive potential, is a significant biomedical challenge. A key factor in reproductive aging is the depletion of the ovarian reserve, the pool of primordial follicles in the ovary. Recent studies have implicated BEND2, a BEN domain-containing protein family member, in mammalian spermatogenesis. In the testis, Bend2 expresses two protein isoforms: full-length and truncated. Ablation of both proteins results in an arrested spermatogenesis. Because the Bend2 locus is on the X chromosome, and the Bend2-/y mutants are sterile, Bend2’s role in oogenesis remained elusive. In this study, we employed a novel Bend2 mutation that completely blocks the expression of the full-length BEND2 protein but allows the expression of the truncated BEND2 isoform. However, this mutation does not confer male sterility, allowing us to investigate BEND2’s role in mice’s oocyte quality, follicular dynamics, and fertility. Our findings demonstrate that full-length BEND2 is dispensable for male fertility, and its ablation leads to impaired oocyte quality, reduced follicular formation, and an accelerated decline in fertility. These results reveal a critical role for BEND2 in oogenesis and provide insights into the mechanisms underlying reproductive aging. Furthermore, these findings hold relevance for the diagnostic landscape of human infertility.
Reducing Cathodic Drift during Isoelectric Focusing using Microscale Immobilized pH Gradient Gels

Microfluidic analytical tools play an important role in miniaturizing proteomic assays for improved detection sensitivity, throughput, and automation. Microfluidic isoelectric focusing (IEF) can resolve proteoforms in lysate from low-to-single cell numbers. However, IEF assays often use carrier ampholytes (CAs) to establish a pH gradient for protein separation, presenting limitations like pH instability in the form of cathodic drift (migration of focused proteins toward the cathode). Immobilized pH gradient (IPG) gels reduce cathodic drift by covalently immobilizing the pH buffering components to a matrix. To our knowledge, efforts to implement IPG gels at the microscale have been limited to glass microdevices. To adapt IEF using IPGs to widely used microfluidic device materials, we introduce a polydimethylsiloxane (PDMS)-based microfluidic device and compare the microscale pH gradient stability of IEF established with IPGs, CAs, and a hybrid formulation of IPG gels and CAs (mixed-bed IEF). The PDMS-based IPG microfluidic device (μIPG) resolved analytes differing by 0.1 isoelectric point within a 3.5-mm separation lane over a 20-min focusing duration. During the 20-min duration, we observed markedly different cathodic drift velocities among the three formulations: 60.1 µm/min in CA-IEF, 2.5 µm/min in IPG-IEF (∼24-fold reduction versus CA-IEF), and 1.4 µm/min in mixed-bed IEF (∼43-fold reduction versus CA-IEF). Lastly, mixed-bed IEF in a PDMS device resolved green fluorescent protein (GFP) proteoforms from GFP-expressing human breast cancer cell lysate, thus establishing stability in lysate from complex biospecimens. µIPG is a promising and stable technique for studying proteoforms from small volumes.
PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers

Predicting protein-ligand binding affinity is crucial for drug discovery, as it enables efficient identification of drug candidates. We introduce PLAPT, a novel model utilizing transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT’s superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.
The Remote Sensing Algorithm for Analysis of Suspended Sediments Distribution in Lake Sihwa and Coastal Area

The study for detecting suspended sediment distribution in Lake Sihwa, which has a large surface area and coastal area, using remote sensing technique was carried out with development of satellite data collected since 1970. The research, however, analysis of spatial distribution and quantity, is not common in domestic study and useful algorithms have not been proposed. In this study, a suspended sediment algorithm was composed with in-situ data obtained in study area and remote sensing reflectance obtained in-water optical instrument, which has SeaWiFS wavelength bands. However, when the algorithm was applied to Landsat TM data, including an in-situ data set, and some problems arose. The composition of the algorithm which was structured with band difference and band ratio showed the correlation of =0.7649 with concentration of suspended sediments. And, between calculated and observed concentration of suspended sediments there was a correlation of =0.6959. However, remote sensing reflectance obtained from Landsat TM is not good for the estimation of concentration of suspended sediments, because of high concentration of chlorophyll and CDOM(colored dissolved organic matter).
Participatory Mapping of the Geography of Risk: Risk Perceptions of Children and Adolescents in Two Portuguese Towns

Abstract:This study identifies, characterizes, and represents children's and adolescents' perceptions of urban risks, using participatory mapping and Participatory Geographic Information Systems. Children and youth identified risks and georeferenced them using tools like CyberTracker, Google Earth and Google Maps embedded in a website. Data analysis showed that risk situations identified by the participants are real and concrete concerns, contextualized in their daily lives. This kind of study can be a starting point for community joint actions that include young people to help solve problems—from policy reviews, to effective structural and design changes, to general awareness-raising.