5,182 research outputs found
Platelet-activating factor receptor in health and disease.
Background Platelet-activating factor receptor (PAFR) expression has been linked to anthropogenic particulate matter (PM). Traffic-related air pollution (TRAP) now accounts for the majority of this PM. PAFR expression has also been linked to an increased risk of infection from Streptococcus pneumoniae (S. pneumoniae). Children with asthma and sickle cell disease (SCD) have a significantly increased risk of morbidity and mortality from invasive pneumococcal disease (IPD). PAFR expression has not yet been investigated in relation to TRAP-generated PM, nor has constitutive expression been investigated in these children at increased risk of IPD. Methods PM10 was collected from roadside traffic using the Cyclone device. A549 cells were exposed to the collected PM10 and flow cytometry was undertaken to measure PAFR expression by median fluorescence intensity (MFI). Exposed A549 cells also underwent assays to determine bacterial adhesion (colony-forming units, CFU) using D39 S. pneumoniae species. In both experiments, Dulbecco’s phosphate buffered saline (DPBS) was used as a control. In a separate study, children aged 1 – 17 years were recruited into 4 groups: 2 disease groups (children with asthma, and those with SCD); and 2 control groups (healthy children, and children with atopy but not asthma). Nasal epithelial cells were collected and PAFR expression (MFI) measured by flow cytometry. 24-hour PM10 pollution (μg/m3) data were also collected for each participant. Results TRAP-related PM caused a significant increase in PAFR expression in A549 cells when exposed to a concentration of 10 ug/ml (p < 0.05). Bacterial adhesion (CFU) was significantly raised in A549 cells exposed to TRAP PM verses the control wells (p < 0.05). In children, PAFR expression in SCD was notably raised when compared to all other groups (p < 0.001). There was no 7 significant difference in the PAFR expression in those with asthma versus the control groups. 24% of the children within the study demonstrated exposure to PM10 levels above the WHO daily safety limit. Conclusion PAFR expression and subsequent bacterial adhesion is increased following exposure to TRAP. PAFR is shown to be constitutively raised in those with SCD and this may explain some of the reported risk from IPD. Air pollution levels in London remain above safe limits despite public health initiatives trying to decrease them
Cyclic proof systems for modal fixpoint logics
This thesis is about cyclic and ill-founded proof systems for modal fixpoint logics, with and without explicit fixpoint quantifiers.Cyclic and ill-founded proof-theory allow proofs with infinite branches or paths, as long as they satisfy some correctness conditions ensuring the validity of the conclusion. In this dissertation we design a few cyclic and ill-founded systems: a cyclic one for the weak Grzegorczyk modal logic K4Grz, based on our explanation of the phenomenon of cyclic companionship; and ill-founded and cyclic ones for the full computation tree logic CTL* and the intuitionistic linear-time temporal logic iLTL. All systems are cut-free, and the cyclic ones for K4Grz and iLTL have fully finitary correctness conditions.Lastly, we use a cyclic system for the modal mu-calculus to obtain a proof of the uniform interpolation property for the logic which differs from the original, automata-based one
Investigating the Innate Immune Systems of Bats and Their Roles as Zoonotic Viral Reservoirs
The zoonotic spillover of viral pathogens from wild animal reservoirs into human populations remains the leading cause of emerging and re-emerging infectious diseases globally. Bats represent important viral reservoirs, notorious for the diversity and richness of the viruses they host, several of which are highly pathogenic when transmitted to humans. Remarkably, bats appear to host an abundance of these viruses without exhibiting any clinical signs of disease. A dominant hypothesis for this ability suggests that bats can control viral replication early in the innate immune response, which acts as the first line of defence against infection. However, bat immunology remains fundamentally understudied, largely due to their high species diversity and the lack of accessible reagents required for bat research. Therefore, in this work we explored and characterised key components of bat innate immunity to gain a better understanding of bats as viral reservoirs and contribute to the currently limited literature. Here, we demonstrated the in vitro transcriptomic response of the bat model species, Pteropus alecto (P.alecto) upon stimulation with the bat henipavirus Cedar virus and also with a type III bat interferon (paIFNλ). These investigations highlighted key transcripts, some of which were immune-related, in the response of bats to the separate stimuli and presents a foundation for further research into significant genes concerned in bat viral infection. Building from genome-wide transcriptomics, three distinctive bat innate immune genes representative of different stages of interferon signalling were selected for comparative genomics and functional characterisation. Our work demonstrated the conservation of genes between bats and humans, including IRF7, IFIT5 and IFI35. Specific findings for IRF7 included its successful translocation to the cell nucleus upon stimulation. IFIT5 and IFI35 were specifically selected for exploration due to previous research demonstrating the respective antiviral and conflicting anti- or pro-viral roles of these genes in humans. Significantly, our research demonstrated the direct antiviral action of P.alecto IFIT5 against negative-sense RNA viruses. Collectively, our findings offer valuable contributions to the field of bat antiviral immunity and provide the framework for future investigative studies into the role and function of the bat innate immune system and bat viral tolerance mechanisms
Towards A Practical High-Assurance Systems Programming Language
Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation.
Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code.
To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process
Robustness and Interpretability of Neural Networks’ Predictions under Adversarial Attacks
Le reti neurali profonde (DNNs) sono potenti modelli predittivi, che superano le capacità umane in una varietà di task. Imparano sistemi decisionali complessi e flessibili dai dati a disposizione e raggiungono prestazioni eccezionali in molteplici campi di apprendimento automatico, dalle applicazioni dell'intelligenza artificiale, come il riconoscimento di immagini, parole e testi, alle scienze più tradizionali, tra cui medicina, fisica e biologia. Nonostante i risultati eccezionali, le prestazioni elevate e l’alta precisione predittiva non sono sufficienti per le applicazioni nel mondo reale, specialmente in ambienti critici per la sicurezza, dove l'utilizzo dei DNNs è fortemente limitato dalla loro natura black-box. Vi è una crescente necessità di comprendere come vengono eseguite le predizioni, fornire stime di incertezza, garantire robustezza agli attacchi avversari e prevenire comportamenti indesiderati.
Anche le migliori architetture sono vulnerabili a piccole perturbazioni nei dati di input, note come attacchi avversari: manipolazioni malevole degli input che sono percettivamente indistinguibili dai campioni originali ma sono in grado di ingannare il modello in predizioni errate. In questo lavoro, dimostriamo che tale fragilità è correlata alla geometria del manifold dei dati ed è quindi probabile che sia una caratteristica intrinseca delle predizioni dei DNNs. Questa
condizione suggerisce una possibile direzione al fine di ottenere robustezza agli attacchi: studiamo la geometria degli attacchi avversari nel limite di un numero infinito di dati e di pesi per le reti neurali Bayesiane, dimostrando che, in questo limite, sono immuni agli attacchi avversari gradient-based. Inoltre, proponiamo alcune tecniche di training per migliorare la robustezza delle architetture deterministiche. In particolare, osserviamo sperimentalmente che ensembles di reti neurali addestrati su proiezioni casuali degli input originali in spazi basso-dimensionali sono più resistenti agli attacchi.
Successivamente, ci concentriamo sul problema dell'interpretabilità delle predizioni delle reti nel contesto delle saliency-based explanations. Analizziamo la stabilità delle explanations soggette ad attacchi avversari e dimostriamo che, nel limite di un numero infinito di dati e di pesi, le interpretazioni Bayesiane sono più stabili di quelle fornite dalle reti deterministiche. Confermiamo questo comportamento in modo sperimentale nel regime di un numero finito di dati.
Infine, introduciamo il concetto di attacco avversario alle sequenze di amminoacidi per protein Language Models (LM). I modelli di Deep Learning per la predizione della struttura delle proteine, come AlphaFold2, sfruttano le architetture Transformer e il loro meccanismo di attention per catturare le proprietà strutturali e funzionali delle sequenze di amminoacidi. Nonostante l'elevata precisione delle predizioni, perturbazioni biologicamente piccole delle sequenze di input, o anche mutazioni di un singolo amminoacido, possono portare a strutture 3D sostanzialmente diverse. Al contempo, i protein LMs sono insensibili alle mutazioni che inducono misfolding o disfunzione (ad esempio le missense mutations). In particolare, le predizioni delle coordinate 3D non rivelano l'effetto di unfolding indotto da queste mutazioni. Pertanto, esiste un'evidente incoerenza tra l'importanza biologica delle mutazioni e il conseguente cambiamento nella predizione strutturale. Ispirati da questo problema, introduciamo il concetto di perturbazione avversaria delle sequenze proteiche negli embedding continui dei protein LMs. Il nostro metodo utilizza i valori di attention per rilevare le posizioni degli amminoacidi più vulnerabili nelle sequenze di input. Le mutazioni avversarie sono biologicamente diverse dalle sequenze di riferimento e sono in grado di alterare in modo significativo le strutture 3D.Deep Neural Networks (DNNs) are powerful predictive models, exceeding human capabilities in a variety of tasks. They learn complex and flexible decision systems from the available data and achieve exceptional performances in multiple machine learning fields, spanning from applications in artificial intelligence, such as image, speech and text recognition, to the more traditional sciences, including medicine, physics and biology. Despite the outstanding achievements, high performance and high predictive accuracy are not sufficient for real-world applications, especially in safety-critical settings, where the usage of DNNs is severely limited by their black-box nature. There is an increasing need to understand how predictions are performed, to provide uncertainty estimates, to guarantee robustness to malicious attacks and to prevent unwanted behaviours.
State-of-the-art DNNs are vulnerable to small perturbations in the input data, known as adversarial attacks: maliciously crafted manipulations of the inputs that are perceptually indistinguishable from the original samples but are capable of fooling the model into incorrect predictions. In this work, we prove that such brittleness is related to the geometry of the data manifold and is therefore likely to be an intrinsic feature of DNNs’ predictions. This negative
condition suggests a possible direction to overcome such limitation: we study the geometry of adversarial attacks in the large-data, overparameterized limit for Bayesian Neural Networks and prove that, in this limit, they are immune to gradient-based adversarial attacks. Furthermore, we propose some training techniques to improve the adversarial robustness of deterministic architectures. In particular, we experimentally observe that ensembles of NNs trained on random projections of the original inputs into lower dimensional spaces are more resilient to the attacks.
Next, we focus on the problem of interpretability of NNs’ predictions in the setting of saliency-based explanations. We analyze the stability of the explanations under adversarial attacks on the inputs and we prove that, in the large-data and overparameterized limit, Bayesian interpretations are more stable than those provided by deterministic networks. We validate this behaviour in multiple experimental settings in the finite data regime.
Finally, we introduce the concept of adversarial perturbations of amino acid sequences for protein Language Models (LMs). Deep Learning models for protein structure prediction, such as AlphaFold2, leverage Transformer architectures and their attention mechanism to capture structural and functional properties of amino acid sequences. Despite the high accuracy of predictions, biologically small perturbations of the input sequences, or even single point mutations, can lead to substantially different 3d structures. On the other hand, protein language models are insensitive to mutations that induce misfolding or dysfunction (e.g. missense mutations). Precisely, predictions of the 3d coordinates do not reveal the structure-disruptive effect of these mutations. Therefore, there is an evident inconsistency between the biological importance of mutations and the resulting change in structural prediction. Inspired by this problem, we introduce the concept of adversarial perturbation of protein sequences in continuous embedding spaces of protein language models. Our method relies on attention scores to detect the most vulnerable amino acid positions in the input sequences. Adversarial mutations are biologically diverse from their references and are able to significantly alter the resulting 3D structures
Precision theoretical determination of electric-dipole matrix elements in atomic cesium
We compute the reduced electric-dipole matrix elements
with and in
cesium using the most complete to date ab initio relativistic coupled-cluster
method which includes singles, doubles, perturbative core triples, and valence
triples. Our results agree with previous calculations at the linearized single
double level but also show large contributions from nonlinear singles and
doubles as well as valence triples. We also calculate the normalized ratio
which is important for experimental determination of matrix elements. The
ratios display large deviations from the nonrelativistic limit
which we associate with Cooper-like minima. Several appendices are provided
where we document the procedure for constructing finite basis sets and our
implementation of the random phase approximation and Brueckner-orbitals method.Comment: 26 pages, 17 figures v.2: uncertainties for removal energies
provided, further comments and additional references added, typos correcte
Provable better quasi orders
It has recently been shown that fairly strong axiom systems such as
cannot prove that the antichain with three elements is a
better quasi order (). In the present paper, we give a complete
characterization of the finite partial orders that are provably
in such axiom systems. The result will also be extended to infinite orders. As
an application, we derive that a version of the minimal bad array lemma is weak
over . In sharp contrast, a recent result shows that the same
version is equivalent to -comprehension over the stronger base theory
Nonperturbative Aspects of Quantum Field Theory in Curved Spacetime
Quantum field theory in curved spacetime is perhaps the most reliable
framework in which one can investigate quantum effects in the presence of
strong gravitational fields. Nevertheless, it is often studied by means of
perturbative treatments. In this thesis, we aim at using the functional
renormalization group -- a nonperturbative realization of the renormalization
group -- as a technique to describe nonperturbative quantum phenomena in curved
spacetimes. The chosen system is an Unruh--DeWitt particle detector coupled to
a scalar quantum field. We discuss how to formulate such a system in terms of
an action and how one can compute its renormalization group flow in the case of
an inertial detector in flat spacetime, for simplicity. We learn, however, that
the results are divergent in the limit in which the detector's energy gap
vanishes. Possible workarounds are discussed at the end.
This thesis also presents a review of quantum field theory in curved
spacetimes by means of the algebraic approach, although it assumes no previous
experience with functional analysis. Hence, it fills a pedagogical gap in the
literature. Furthermore, we also review the functional renormalization group
and derive the Wetterich equation assuming a general field content that might
include both bosonic and fermionic fields. Such a derivation is also hardly
found in pedagogical introductions available in the high energy physics
literature.Comment: MSc thesis defended at the Federal University of ABC (Brazil) on 28
April 2023. xxiv + 152 pages, 22 figure
An aperiodic monotile
A longstanding open problem asks for an aperiodic monotile, also known as an
"einstein": a shape that admits tilings of the plane, but never periodic
tilings. We answer this problem for topological disk tiles by exhibiting a
continuum of combinatorially equivalent aperiodic polygons. We first show that
a representative example, the "hat" polykite, can form clusters called
"metatiles", for which substitution rules can be defined. Because the metatiles
admit tilings of the plane, so too does the hat. We then prove that generic
members of our continuum of polygons are aperiodic, through a new kind of
geometric incommensurability argument. Separately, we give a combinatorial,
computer-assisted proof that the hat must form hierarchical -- and hence
aperiodic -- tilings.Comment: 89 pages, 57 figures; Minor corrections, renamed "fylfot" to
"triskelion", added the name "turtle", added references, new H7/H8 rules (Fig
2.11), talk about reflection
Martin's conjecture for regressive functions on the hyperarithmetic degrees
We answer a question of Slaman and Steel by showing that a version of
Martin's conjecture holds for all regressive functions on the hyperarithmetic
degrees. A key step in our proof, which may have applications to other cases of
Martin's conjecture, consists of showing that we can always reduce to the case
of a continuous function.Comment: 12 page
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