Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and Tolerability of Intra-Articular Injection of Adipose-Derived Mesenchymal Stem Cells GXCPC1 in 11 Subjects With Knee Osteoarthritis: A Nonrandomized Pilot Study Without a Control Arm

The current study aimed to determine the safety profile of intra-articular-injected allogeneic adipose-derived mesenchymal stem cells (ADSCs) GXCPC1 in subjects with knee osteoarthritis (OA) and its preliminary efficacy outcome. The 3 + 3 phase I study was designed with two dose-escalation cohorts: low dose (6.7 × 106 GXCPC1, N = 5) and high dose (4 × 107 GXCPC1, N = 6). The primary endpoint was safety, which was evaluated by recording adverse events throughout the trial; the secondary endpoints included total, pain, stiffness, and function subscales of the Western Ontario and McMaster Universities Arthritis Index (WOMAC), Visual Analogue Scale (VAS) for pain, and 12-Item Short Form (SF-12) health survey questionnaire. The GXCPC1 treatment was found to be safe after 1 year of follow-up with no treatment-related severe adverse events observed. When compared to baseline, subjects in both the low- and high-dose cohorts demonstrated improving trends in pain and knee function after receiving GXCPC1 treatment. Generally, the net change in pain (95% confidence interval (CI) = −7.773 to −2.561t at 12 weeks compared to baseline) and knee function (95% CI = −24.297 to −10.036t at 12 weeks compared to baseline) was better in subjects receiving high-dose GXCPC1. Although this study included a limited number of subjects without a placebo arm, it showed that the intra-articular injection of ADSCs was safe and well-tolerated in subjects with therapeutic alternatives to treat knee OA. However, a larger scale study with an appropriate control would be necessary for clinical efficacy in the following study.
Mars Molniya Orbit Atmospheric Resource Mining

This NIAC (NASA Advanced Innovative Concepts) work will focus on Mars and will build on previous efforts at analyzing atmospheric mining at Earth and the outer solar system. Spacecraft systems concepts will be evaluated and traded, to assess feasibility. However the study will primarily examine the architecture and associated missions to explore the closure, constraints and critical parameters through sensitivity studies. The Mars atmosphere consists of 95.5 percent CO2 gas which can be converted to methane fuel (CH4) and Oxidizer (O2) for chemical rocket propulsion, if hydrogen is transported from electrolyzed water on the Mars surface or from Earth. By using a highly elliptical Mars Molniya style orbit, the CO2 atmosphere can be scooped, ram-compressed and stored while the spacecraft dips into the Mars atmosphere at periapsis. Successive orbits result in additional scooping of CO2 gas, which also serves to aerobrake the spacecraft, resulting in a decaying Molniya orbit.
A Practical Guide to the Study of Distribution Limits

Factors that limit the geographic distribution of species are broadly important in ecology and evolutionary biology, and understanding distribution limits is imperative for predicting how species will respond to environmental change. Good data indicate that factors such as dispersal limitation, small effective population size, and isolation are sometimes important. But empirical research highlights no single factor that explains the ubiquity of distribution limits. In this article, we outline a guide to tackling distribution limits that integrates established causes, such as dispersal limitation and spatial environmental heterogeneity, with understudied causes, such as mutational load and genetic or developmental integration of traits limiting niche expansion. We highlight how modeling and quantitative genetic and genomic analyses can provide insight into sources of distribution limits. Our practical guide provides a framework for considering the many factors likely to determine species distributions and how the different approaches can be integrated to predict distribution limits using eco-evolutionary modeling. The framework should also help predict distribution limits of invasive species and of species under climate change.
Single-cell analysis reveals that cryptic prophage protease LfgB protects Escherichia coli during oxidative stress by cleaving antitoxin MqsA

ABSTRACT Although toxin/antitoxin (TA) systems are ubiquitous, beyond phage inhibition and mobile element stabilization, their role in host metabolism is obscure. One of the best-characterized TA systems is MqsR/MqsA of Escherichia coli, which has been linked previously to protecting gastrointestinal species during the stress it encounters from the bile salt deoxycholate as it colonizes humans. However, some recent whole-population studies have challenged the role of toxins such as MqsR in bacterial physiology since the mqsRA locus is induced over a hundred-fold during stress, but a phenotype was not found upon its deletion. Here, we investigate further the role of MqsR/MqsA by utilizing single cells and demonstrate that upon oxidative stress, the TA system MqsR/MqsA has a heterogeneous effect on the transcriptome of single cells. Furthermore, we discovered that MqsR activation leads to induction of the poorly characterized yfjXY ypjJ yfjZF operon of cryptic prophage CP4-57. Moreover, deletion of yfjY makes the cells sensitive to H2O2, acid, and heat stress, and this phenotype was complemented. Hence, we recommend yfjY be renamed to lfgB (less fatality gene B). Critically, MqsA represses lfgB by binding the operon promoter, and LfgB is a protease that degrades MqsA to derepress rpoS and facilitate the stress response. Therefore, the MqsR/MqsA TA system facilitates the stress response through cryptic phage protease LfgB. IMPORTANCE The roles of toxin/antitoxin systems in cell physiology are few and include phage inhibition and stabilization of genetic elements; yet, to date, there are no single-transcriptome studies for toxin/antitoxin systems and few insights for prokaryotes from this novel technique. Therefore, our results with this technique are important since we discover and characterize a cryptic prophage protease that is regulated by the MqsR/MqsA toxin/antitoxin system in order to regulate the host response to oxidative stress. The roles of toxin/antitoxin systems in cell physiology are few and include phage inhibition and stabilization of genetic elements; yet, to date, there are no single-transcriptome studies for toxin/antitoxin systems and few insights for prokaryotes from this novel technique. Therefore, our results with this technique are important since we discover and characterize a cryptic prophage protease that is regulated by the MqsR/MqsA toxin/antitoxin system in order to regulate the host response to oxidative stress.
Animal and plant protein intake during infancy and childhood DNA methylation: a meta-analysis in the NutriPROGRAM consortium

ABSTRACT Background Higher early-life animal protein intake is associated with a higher childhood obesity risk compared to plant protein intake. Differential DNA methylation may represent an underlying mechanism. Methods We analysed associations of infant animal and plant protein intakes with DNA methylation in early (2−6 years, N = 579) and late (7̄−12 years, N = 604) childhood in two studies. Study-specific robust linear regression models adjusted for relevant confounders were run, and then meta-analysed using a fixed-effects model. We also performed sex-stratified meta-analyses. Follow-up analyses included pathway analysis and eQTM look-up. Results Infant animal protein intake was not associated with DNA methylation in early childhood, but was associated with late-childhood DNA methylation at cg21300373 (P = 4.27 × 10¯8, MARCHF1) and cg10633363 (P = 1.09 × 10¯7, HOXB9) after FDR correction. Infant plant protein intake was associated with early-childhood DNA methylation at cg25973293 (P = 2.26 × 10−7, C1orf159) and cg15407373 (P = 2.13 × 10−7, MBP) after FDR correction. There was no overlap between the findings from the animal and plant protein analyses. We did not find enriched functional pathways at either time point using CpGs associated with animal and plant protein. These CpGs were not previously associated with childhood gene expression. Sex-stratified meta-analyses showed sex-specific DNA methylation associations for both animal and plant protein intake. Conclusion Infant animal protein intake was associated with DNA methylation at two CpGs in late childhood. Infant plant protein intake was associated with DNA methylation in early childhood at two CpGs. A potential mediating role of DNA methylation at these CpGs between infant protein intake and health outcomes requires further investigation.
Lifelogging As An Extreme Form of Personal Information Management - What Lessons To Learn

Personal data includes the digital footprints that we leave behind as part of our everyday activities, both online and offline in the real world. It includes data we collect ourselves, such as from wearables, as well as the data collected by others about our online behaviour and activities. Sometimes we are able to use the personal data we ourselves collect, in order to examine some parts of our lives but for the most part, our personal data is leveraged by third parties including internet companies, for services like targeted advertising and recommendations. Lifelogging is a form of extreme personal data gathering and in this article we present an overview of the tools used to manage access to lifelogs as demonstrated at the most recent of the annual Lifelog Search Challenge benchmarking workshops. Here, experimental systems are showcased in live, real time information seeking tasks by real users. This overview of these systems' capabilities show the range of possibilities for accessing our own personal data which may, in time, become more easily available as consumer-level services.
Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages

This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs). One is the expansion of supported languages to previously unseen ones. The second relates to the lack of data in low-resource languages. Model fine-tuning through MT instructions (MTInstruct) is a straightforward approach to the first challenge. However, MTInstruct is limited by weak cross-lingual signals inherent in the second challenge. AlignInstruct emphasizes cross-lingual supervision via a cross-lingual discriminator built using statistical word alignments. Our results based on fine-tuning the BLOOMZ models (1b1, 3b, and 7b1) in up to 24 unseen languages showed that: (1) LLMs can effectively translate unseen languages using MTInstruct; (2) AlignInstruct led to consistent improvements in translation quality across 48 translation directions involving English; (3) Discriminator-based instructions outperformed their generative counterparts as cross-lingual instructions; (4) AlignInstruct improved performance in 30 zero-shot directions.
A Toolbox for Modelling Engagement with Educational Videos

With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. This work presents the PEEKC dataset and the TrueLearn Python library, which contains a dataset and a series of online learner state models that are essential to facilitate research on learner engagement modelling. TrueLearn family of models was designed following the "open learner" concept, using humanly-intuitive user representations. This family of scalable, online models also help end-users visualise the learner models, which may in the future facilitate user interaction with their models/recommenders. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytics practitioners. The experiments show the utility of both the dataset and the library with predictive performance significantly exceeding comparative baseline models. The dataset contains a large amount of AI-related educational videos, which are of interest for building and validating AI-specific educational recommenders.
Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications

Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper.
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning

Federated learning (FL) enables multiple participants to train a global machine learning model without sharing their private training data. Peer-to-peer (P2P) FL advances existing centralized FL paradigms by eliminating the server that aggregates local models from participants and then updates the global model. However, P2P FL is vulnerable to (i) honest-but-curious participants whose objective is to infer private training data of other participants, and (ii) Byzantine participants who can transmit arbitrarily manipulated local models to corrupt the learning process. P2P FL schemes that simultaneously guarantee Byzantine resilience and preserve privacy have been less studied. In this paper, we develop Brave, a protocol that ensures Byzantine Resilience And privacy-preserving property for P2P FL in the presence of both types of adversaries. We show that Brave preserves privacy by establishing that any honest-but-curious adversary cannot infer other participants' private data by observing their models. We further prove that Brave is Byzantine-resilient, which guarantees that all benign participants converge to an identical model that deviates from a global model trained without Byzantine adversaries by a bounded distance. We evaluate Brave against three state-of-the-art adversaries on a P2P FL for image classification tasks on benchmark datasets CIFAR10 and MNIST. Our results show that the global model learned with Brave in the presence of adversaries achieves comparable classification accuracy to a global model trained in the absence of any adversary.
Finite-size effects in the two-dimensional BCS-BEC crossover

We study the finite-size effects on the BCS-BEC crossover in two dimensions, occurring in confined fermionic superfluids. We analyze several thermodynamic properties, such as the chemical potential, the energy gap and the superfluid density, taking into account unavoidable quantum fluctuations, and, by means of renormalization group procedure, we detect the putative Berezinskii-Kosterlitz-Thouless phase transition at finite-size.
Upside down Rayleigh-Marchenko: a practical, yet exact redatuming scheme for seabed seismic acquisitions

Ocean-bottom seismic plays a crucial role in resource exploration and monitoring. However, despite its undoubted potential, the use of coarse receiver geometries poses challenges to accurate wavefield redatuming. This in turn, affects the quality of subsequent imaging and reservoir charactherization products. We propose a reciprocal version of the Rayleigh-Marchenko method, called upside down Rayleigh-Marchenko, where all spatial integrals are performed over the (usually much better-sampled) source carpet; this results in a theoretically exact redatuming scheme, which can handle irregular and sparse receiver geometries. The proposed method requires availability of multi-component receivers and either dual-sensor sources or a pre-processing step of model-based source deghosting, and utilizes only the down-going component of the receiver-side wavefield; as such, it can be interpreted as a full-wavefield extension of the mirror imaging method commonly used in seabed settings. Two synthetic examples are used to showcase the effectiveness of the proposed method, starting from the ideal scenario of finely and regularly sampled sources and receivers, and later considering different levels of decimation for the receiver array. Migrated images as well as common-angle gathers reveal that our method can be used to produce structural and amplitude-friendly imaging outputs with minimal data pre-processing.
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey

Due to the fact that roughly sixty percent of the human body is essentially composed of water, the human body is inherently a conductive object, being able to, firstly, form an inherent electric field from the body to the surroundings and secondly, deform the distribution of an existing electric field near the body. Body-area capacitive sensing, also called body-area electric field sensing, is becoming a promising alternative for wearable devices to accomplish certain tasks in human activity recognition (HAR) and human-computer interaction (HCI). Over the last decade, researchers have explored plentiful novel sensing systems backed by the body-area electric field, like the ring-form smart devices for sign language recognition, the room-size capacitive grid for indoor positioning, etc. On the other hand, despite the pervasive exploration of the body-area electric field, a comprehensive survey does not exist for an enlightening guideline. Moreover, the various hardware implementations, applied algorithms, and targeted applications result in a challenging task to achieve a systematic overview of the subject. This paper aims to fill in the gap by comprehensively summarizing the existing works on body-area capacitive sensing so that researchers can have a better view of the current exploration status. To this end, we first sorted the explorations into three domains according to the involved body forms: body-part electric field, whole-body electric field, and body-to-body electric field, and enumerated the state-of-art works in the domains with a detailed survey of the backed sensing tricks and targeted applications. We then summarized the three types of sensing frontends in circuit design, which is the most critical part in body-area capacitive sensing, and analyzed the data processing pipeline categorized into three kinds of approaches. The outcome will benefit researchers for further body-area electric field explorations. Finally, we described the challenges and outlooks of body-area electric sensing, followed by a conclusion, aiming to encourage researchers to further investigations considering the pervasive and promising usage scenarios backed by body-area capacitive sensing.
A Cold-Atom Particle Collider

A major objective of the strong ongoing drive to realize quantum simulators of gauge theories is achieving the capability to probe collider-relevant physics on them. In this regard, a highly pertinent and sought-after application is the controlled collisions of elementary and composite particles, as well as the scattering processes in their wake. Here, we propose particle-collision experiments in a cold-atom quantum simulator for a $1+1$D $\mathrm{U}(1)$ lattice gauge theory with a tunable topological $\theta$-term, where we demonstrate an experimentally feasible protocol to impart momenta to elementary (anti)particles and their meson composites. We numerically benchmark the collisions of moving wave packets for both elementary and composite particles, uncovering a plethora of rich phenomena, such as oscillatory string dynamics in the wake of elementary (anti)particle collisions due to confinement. We also probe string inversion and entropy production processes across Coleman's phase transition through far-from-equilibrium quenches. We further demonstrate how collisions of composite particles unveil their internal structure. Our work paves the way towards the experimental investigation of collision dynamics in state-of-the-art quantum simulators of gauge theories, and sets the stage for microscopic understanding of collider-relevant physics in these platforms.
FeynGrav and Recent Progress in Computational Perturbative Quantum Gravity

This article reviews recent progress in computational quantum gravity caused by the framework that efficiently computes Feynman’s rules. The framework is implemented in the FeynGrav package, which extends the functionality of the widely used FeynCalc package. FeynGrav provides all the tools to study quantum gravitational effects within the standard model. We review the framework, provide the theoretical background for the efficient computation of Feynman rules, and present the proof of its completeness. We review the derivation of Feynman rules for general relativity, Horndeski gravity, Dirac fermions, Proca field, electromagnetic field, and SU(N) Yang–Mills model. We conclude with a discussion of the current state of the FeynGrav package and discuss its further development.
Self-supporting Structure of Bird Nests

The mystery behind the bird nest's construction is not well understood. Our study focuses on the stability of a self-supporting nest-like structure. Firstly, we derived a stable/unstable phase boundary for the structure at the fixed coefficient of friction with varying geometrical parameters through force analysis. Structures with a lower height and greater friction coefficient between rods are more stable. The theoretical phase boundary matched the experiment results well. Then we investigate the nest structure's stability under applied weight. Static structures with lower height and more rods (five>four>three) are more stable. Our theory also predicts a transition from plastic phase to elastic phase. These theoretical predictions are all confirmed by experiment. In the experiment, we also find that wet rod structures are more stable than dry ones. The structures can support up to 100 times of it's weight. Finally, we test the nest structure's stability under vibration. When there are no weights applied, we are able to identify the appropriate geometric configuration that can withstand the greatest vibration ($1g$ of vibration acceleration and vibration energy up to $4\times10^{-4}$ times of a rod's maximum potential energy). The critical vibration energy and acceleration depend on the applied weights. They are increased by two and one order of magnitude respectively under proper weight. We also make potential energy analysis to explain the stability of the structure.