Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting Aggrecanases for Osteoarthritis Therapy: From Zinc Chelation to Exosite Inhibition

Osteoarthritis (OA) is the most common degenerative joint disease. In 1999, two members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family of metalloproteinases, ADAMTS4 and ADAMTS5, or aggrecanases, were identified as the enzymes responsible for aggrecan degradation in cartilage. The first aggrecanase inhibitors targeted the active site by chelation of the catalytic zinc ion. Due to the generally disappointing performance of zinc-chelating inhibitors in preclinical and clinical studies, inhibition strategies tried to move away from the active-site zinc in order to improve selectivity. Exosite inhibitors bind to proteoglycan-binding residues present on the aggrecanase ancillary domains (called exosites). While exosite inhibitors are generally more selective than zinc-chelating inhibitors, they are still far from fulfilling their potential, partly due to a lack of structural and functional data on aggrecanase exosites. Filling this gap will inform the design of novel potent, selective aggrecanase inhibitors.
Spin-polarized transport of electrons from polycrystalline Fe3O4 to amorphous Si

Polycrystalline Fe3O4∕amorphous Si heterostructure was prepared by facing-target sputtering and its microstructure and electrical transport properties were studied. The polycrystalline Fe3O4 layer was grown in column structure. The electrical transport mechanism across the disordered interface between polycrystalline Fe3O4 and amorphous Si layers is tunneling above the Verwey temperature [Nature (London) 144, 327 (1939)] of 120K. Nonlinear I‐V characteristics of the Schottky diode reveal thermionic emission∕diffusion mechanism below the Verwey temperature, and Schottky barrier height is 0.27eV, calculated by a standard theory of thermionic emission∕diffusion. Based on a simplified band structure, the spin polarization of the polycrystalline Fe3O4 layer was determined to be ∼45%.
Isolation and Genomic Analysis of an Intracellular Mycobacterium gordonae from a Free-Living Acanthamoeba sp. in a Hospital Environment in Lima, Peru

Mycobacterium gordonae is a nontuberculous mycobacterium found in diverse environments and is considered an opportunistic pathogen in immunocompromised patients. We report the draft genome sequence of a Mycobacterium gordonae strain isolated from a free-living amoeba found in a nosocomial environment in Lima, Peru. ABSTRACT Mycobacterium gordonae is a nontuberculous mycobacterium found in diverse environments and is considered an opportunistic pathogen in immunocompromised patients. We report the draft genome sequence of a Mycobacterium gordonae strain isolated from a free-living amoeba found in a nosocomial environment in Lima, Peru.
Origins of Massive Field Stars in the Galactic Center: a Spectroscopic Study

Outside of the known star clusters in the Galactic Center, a large number of evolved massive stars have been detected; but their origins remain uncertain. We present a spectroscopic study of eight such stars, based on new Gemini GNIRS and NIFS near-infrared observations. This work has led to the discovery of a new O If+ star. We compare the reddening-corrected J-K vs K diagram for our stars with the massive ones in the Arches cluster and use stellar evolutionary tracks to constrain their ages and masses. The radial velocities of both the stars and their nearby H II regions are also reported. All of the stars are blueshifted relative to the Arches cluster by > 50 km/s. We find that our source P35 has a velocity consistent with that of the surrounding molecular gas. The velocity gradient of nearby ionized gas along the Gemini GNIRS long slit, relative to P35 and the adjacent -30-0 km/s molecular cloud, can best be explained by a pressure-driven flow model. Thus, P35 most likely formed in situ. Three more of our stars have radial velocities different from their adjacent molecular gas, indicating that they are interlopers. The four stars closest to the Arches cluster have similar spectra, ages and masses to known cluster members, suggesting that they were likely ejected from the cluster via three-body interactions. Therefore, we find that the relatively isolated stars partly formed in situ and partly were ejected from the known star clusters in the Galactic Center.
Neutron detectors for the ESS diffractometers

The ambitious instrument suite for the future European Spallation Source whose civil construction started recently in Lund, Sweden, demands a set of diverse and challenging requirements for the neutron detectors. For instance, the unprecedented high flux expected on the samples to be investigated in neutron diffraction or reflectometry experiments requires detectors that can handle high counting rates, while the investigation of sub-millimeter protein crystals will only be possible with large-area detectors that can achieve a position resolution as low as 200 μm. This has motivated an extensive research and development campaign to advance the state-of-the-art detector and to find new technologies that can reach maturity by the time the ESS will operate at full potential. This paper presents the key detector requirements for three of the Time-of-Flight (TOF) diffraction instrument concepts selected by the Scientific Advisory Committee to advance into the phase of preliminary engineering design. We discuss the detector technologies commonly employed at the existing similar instruments and their major challenges for ESS. The detector technologies selected by the instrument teams to collect the diffraction patterns are also presented. Analytical calculations, Monte-Carlo simulations, and real experimental data are used to develop a generic method to estimate the event rate in the diffraction detectors. We apply this method to make predictions for the future diffraction instruments, and thus provide additional information that can help the instrument teams with the optimisation of the detector designs.
Using Diagrammatic Reasoning for Theorem Proving in a Continuous Domain

This project looks at using diagrammatic reasoning to prove mathematical theorems. The work is motivated by a need for theorem provers whose reasoning is readily intelligible to human beings. It should also have practical applications in mathematics teaching. We focus on the continuous domain of analysis a geometric subject, but one which is taught using a dry algebraic formalism which many students find hard. The geometric nature of the domain makes it suitable for a diagram-based approach. However it is a difficult domain, and there are several problems, including handling alternating quantifiers, sequences and generalisation. We developed representations and reasoning methods to solve these. Our diagram logic isn't complete, but does cover a reasonable range of theorems. It utilises computers to extend diagrammatic reasoning in new directions – including using animation. This work is tested for soundness, and evaluated empirically for ease of use. We demonstrate that computerised diagrammatic theorem proving is not only possible in the domain of real analysis, but that students perform better using it than with an equivalent algebraic computer system.
A light-responsive injectable hydrogel with remodeling tumor microenvironment for light-activated chemodynamic therapy.

Chemodynamic therapy (CDT) based on Fenton-like reaction is often limited by the tumor microenvironment (TME), which has insufficient hydrogen peroxide, and single CDT treatment is often less efficacious. To overcome these limitations, in this study, we designed a hydrogel-based system to enhance the redox stress (EOH) by loading the composite nanomaterial Cu-Hemin-Au, into the agarose hydrogels. The hydrogels could reach the tumor site upon intratumoral injection, and then coagulate and stay for extended period. Once irradiated with near-infrared light, the Cu-Hemin-Au loaded into the EOH acted as a photothermal agent to convert the light energy into heat, and the EOH gradually heated up and softened, releasing the Cu-Hemin-Au residing in it to achieve photothermal therapy (PTT). Benefiting from the glucose oxidase (GOx)-like activity of the Au nanoparticles (NPs), glucose in the tumor cells was largely consumed, and H2 O2 was generated in situ, and then Cu-Hemin-Au reacted with sufficient H2 O2 to generate a large amount of ROS to kill the tumors, which promoted the complete inhibition of tumor growth in mice during the treatment cycle. The hydrogel system for the synergistic enhancement of oxidative stress achieved good PTT/CDT synergy, providing a novel inspiration for the next generation of hydrogels for application in antitumor therapy. This article is protected by copyright. All rights reserved.
Stability of a dominant sponge-symbiont in spite of antibiotic-induced microbiome disturbance.

Marine sponges are known for their complex and stable microbiomes. However, the lack of a gnotobiotic sponge-model and experimental methods to manipulate both the host and the microbial symbionts currently limit our mechanistic understanding of sponge-microbial symbioses. We have used the North Atlantic sponge species Halichondria panicea to evaluate the use of antibiotics to generate gnotobiotic sponges. We further asked whether the microbiome can be reestablished via recolonization with the natural microbiome. Experiments were performed in marine gnotobiotic facilities equipped with a custom-made, sterile, flow-through aquarium system. Bacterial abundance dynamics were monitored qualitatively and quantitatively by 16S rRNA gene amplicon sequencing and qPCR, respectively. Antibiotics induced dysbiosis by favoring increase of opportunistic, antibiotic-resistant bacteria, resulting in more complex, but less specific bacteria-bacteria interactions than in untreated sponges. The abundance of the dominant symbiont, Candidatus Halichondribacter symbioticus, remained overall unchanged, reflecting its obligately symbiotic nature. Recolonization with the natural microbiome could not reverse antibiotic-induced dysbiosis. However, single bacterial taxa that were transferred, successfully recolonized the sponge and affected bacteria-bacteria interactions. By experimentally manipulating microbiome composition, we could show the stability of a sponge-symbiont clade despite microbiome dysbiosis. This study contributes to understanding both host-bacteria and bacteria-bacteria interactions in the sponge holobiont. This article is protected by copyright. All rights reserved.
Assessing single-locus CRISPR/Cas9-based gene drive variants in the mosquito Aedes aegypti via single-generation crosses and modeling

Abstract The yellow fever mosquito Aedes aegypti is a major vector of arthropod-borne viruses, including dengue, chikungunya, and Zika viruses. A novel approach to mitigate arboviral infections is to generate mosquitoes refractory to infection by overexpressing antiviral effector molecules. Such an approach requires a mechanism to spread these antiviral effectors through a population, for example, by using CRISPR/Cas9-based gene drive systems. Critical to the design of a single-locus autonomous gene drive is that the selected genomic locus is amenable to both gene drive and appropriate expression of the antiviral effector. In our study, we used reverse engineering to target 2 intergenic genomic loci, which had previously shown to be highly permissive for antiviral effector gene expression, and we further investigated the use of 3 promoters (nanos, β2-tubulin, or zpg) for Cas9 expression. We then quantified the accrual of insertions or deletions (indels) after single-generation crossings, measured maternal effects, and assessed fitness costs associated with various transgenic lines to model the rate of gene drive fixation. Overall, MGDrivE modeling suggested that when an autonomous gene drive is placed into an intergenic locus, the gene drive system will eventually be blocked by the accrual of gene drive blocking resistance alleles and ultimately be lost in the population. Moreover, while genomic locus and promoter selection were critically important for the initial establishment of the autonomous gene drive, it was the fitness of the gene drive line that most strongly influenced the persistence of the gene drive in the simulated population. As such, we propose that when autonomous CRISPR/Cas9-based gene drive systems are anchored in an intergenic locus, they temporarily result in a strong population replacement effect, but as gene drive-blocking indels accrue, the gene drive becomes exhausted due to the fixation of CRISPR resistance alleles.
Operator shifting for model-based policy evaluation

In model-based reinforcement learning, the transition matrix and reward vector are often estimated from random samples subject to noise. Even if the estimated model is an unbiased estimate of the true underlying model, the value function computed from the estimated model is biased. We introduce an operator shifting method for reducing the error introduced by the estimated model. When the error is in the residual norm, we prove that the shifting factor is always positive and upper bounded by $1+O\left(1/n\right)$, where $n$ is the number of samples used in learning each row of the transition matrix. We also propose a practical numerical algorithm for implementing the operator shifting.
Protein-to-genome alignment with miniprot

Abstract Motivation Protein-to-genome alignment is critical to annotating genes in non-model organisms. While there are a few tools for this purpose, all of them were developed over 10 years ago and did not incorporate the latest advances in alignment algorithms. They are inefficient and could not keep up with the rapid production of new genomes and quickly growing protein databases. Results Here, we describe miniprot, a new aligner for mapping protein sequences to a complete genome. Miniprot integrates recent techniques such as k-mer sketch and vectorized dynamic programming. It is tens of times faster than existing tools while achieving comparable accuracy on real data. Availability and implementation https://github.com/lh3/miniport.
Chat Control or Child Protection?

Ian Levy and Crispin Robinson’s position paper “Thoughts on child safety on commodity platforms” 1 is to be welcomed for extending the scope of the debate about the extent to which child safety concerns justify legal limits to online privacy. Their paper’s context is the laws proposed in both the UK and the EU to give the authorities the power to undermine end-to-end cryptography in online communications services, with a justification of preventing and detecting of child abuse and terrorist recruitment. Both jurisdictions plan to make it easier to get service firms to take down a range of illegal material from their servers; but they also propose to mandate client-side scanning – not just for known illegal images, but for text messages indicative of sexual grooming or terrorist recruitment. In this initial response, I raise technical issues about the capabilities of the technologies the authorities propose to mandate, and a deeper strategic issue: that we should view the child safety debate from the perspective of children at risk of violence, rather than from that of the security and intelligence agencies and the firms that sell surveillance software. The debate on terrorism similarly needs to be grounded in the context in which young people are radicalised. Both political violence and violence against children tend to be politicised and as a result are often poorly policed. Effective policing, particularly of crimes embedded in wicked social problems, must be locally led and involve multiple stakeholders; the idea of using ‘artificial intelligence’ to replace police officers, social workers and teachers is just the sort of magical thinking that leads to bad policy. The debate must also be conducted within the boundary conditions set by human rights and privacy law, and to be pragmatic must also consider reasonable police priorities.
Adaptive patch foraging in deep reinforcement learning agents

Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult to discover given current techniques in deep reinforcement learning. Here, we investigate deep reinforcement learning agents in an ecological patch foraging task. For the first time, we show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers, and approach optimal patch foraging behavior when accounting for temporal discounting. Finally, we show emergent internal dynamics in these agents that resemble single-cell recordings from foraging non-human primates, which complements experimental and theoretical work on the neural mechanisms of biological foraging. This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions, suggesting the emergence of foundational computations behind adaptive, intelligent behavior in both biological and artificial agents.
Neural Attentive Circuits

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural architectures that represent the data using sparsely interacting modules. These models can be more robust out-of-distribution, computationally efficient, and capable of sample-efficient adaptation to new data. However, they tend to make domain-specific assumptions about the data, and present challenges in how module behavior (i.e., parameterization) and connectivity (i.e., their layout) can be jointly learned. In this work, we introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) that jointly learns the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs are best understood as the combination of two systems that are jointly trained end-to-end: one that determines the module configuration and the other that executes it on an input. We demonstrate qualitatively that NACs learn diverse and meaningful module configurations on the NLVR2 dataset without additional supervision. Quantitatively, we show that by incorporating modularity in this way, NACs improve upon a strong non-modular baseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about 10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that NACs can achieve an 8x speedup at inference time while losing less than 3% performance. Finally, we find NACs to yield competitive results on diverse data modalities spanning point-cloud classification, symbolic processing and text-classification from ASCII bytes, thereby confirming its general purpose nature.
On the Impact of Temporal Concept Drift on Model Explanations

Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i.e. synchronous settings). While model performance often deteriorates due to temporal variation (i.e. temporal concept drift), it is currently unknown how explanation faithfulness is impacted when the time span of the target data is different from the data used to train the model (i.e. asynchronous settings). For this purpose, we examine the impact of temporal variation on model explanations extracted by eight feature attribution methods and three select-then-predict models across six text classification tasks. Our experiments show that (i)faithfulness is not consistent under temporal variations across feature attribution methods (e.g. it decreases or increases depending on the method), with an attention-based method demonstrating the most robust faithfulness scores across datasets; and (ii) select-then-predict models are mostly robust in asynchronous settings with only small degradation in predictive performance. Finally, feature attribution methods show conflicting behavior when used in FRESH (i.e. a select-and-predict model) and for measuring sufficiency/comprehensiveness (i.e. as post-hoc methods), suggesting that we need more robust metrics to evaluate post-hoc explanation faithfulness.
Superconductivty in a Luttinger liquid mediated by spin ﬂuctuations

We study spin-ﬂuctuation mediated divergent superconducting ﬂuctuations in a Luttinger liquid proximity-coupled to a spin chain. Our study provides insight into how spin-ﬂuctuations can induce superconductivity in a strongly correlated non-Fermi liquid with repulsive electronic interactions only. The electrons in the system are governed by the Extended Hubbard Hamiltonian and are coupled to a chain of localized spins modeled by the spin- 12 XX Hamiltonian. Using a multi-channel Luttinger liquid approach, we determine the phase diagram of the metal chain. We ﬁnd that spin-polarized triplet superconducting correlations persist for repulsive electronic interactions for sufﬁciently large interchain couplings.
Approximate Graph Colouring and Crystals

We show that approximate graph colouring is not solved by any level of the affine integer programming (AIP) hierarchy. To establish the result, we translate the problem of exhibiting a graph fooling a level of the AIP hierarchy into the problem of constructing a highly symmetric crystal tensor. In order to prove the existence of crystals in arbitrary dimension, we provide a combinatorial characterisation for realisable systems of tensors; i.e., sets of low-dimensional tensors that can be realised as the projections of a single high-dimensional tensor.
Logic in formal verification of computer systems. Some remarks

: Various logics are applied to speciﬁcation and veriﬁcation of both hardware and software systems. The problem with ﬁnding of proof is the most important disadvantage of proof-theoretical method. The proof-theoretical method presupposes the axiomatization of the logic. Proprieties of a system can also be checked using a model of the system. A model is constructed with the speciﬁcation language and checked using automatic model checkers. The model checking application presupposes the decidability of the task.