bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of OHMIC heating and ultrasound on functional properties of biodegradable gelatin‐based films
Authors: de Vargas Victoria Hermes , Flôres Simone Hickmann , Mercali Giovana Domeneghini , Marczak Ligia Damasceno Ferreira 
Published: March 2022

AbstractThis study aimed to evaluate the effect of ohmic heating (OH) and ultrasound (US) on the functional properties of biodegradable gelatin‐based films. For OH, electric field strength (30–120 V) and exposure time (5–20 min) were evaluated; for US, intensity (50%–100%) and the exposure time (5–20 min) were investigated. Regarding OH, higher mechanical properties (tensile strength and Young's modulus) values were obtained at the lowest electric field strength applied, regardless of the exposure time. For US, tensile strength and Young's modulus values significantly increased with increasing the US intensity and exposure time. Films produced with OH and US with optimized properties were compared to a control film (produced by conventional heating). X‐ray diffraction and thermogravimetric analysis indicated that films produced by US and OH showed a more ordered structure, with a higher crystallinity index, and were more thermally stable. The most pronounced effects on mechanical properties were obtained using OH: tensile strength increased from 1.53 MPa (control) to 3.86 MPa. US application reduced film opacity, resulting in a more transparent film with a smoother surface. Both treatments also significantly decreased the water vapor permeability but did not affect film thickness, moisture content, water solubility, and biodegradability.
Utility based approach in individualized optimal dose selection using machine learning methods
Authors: Li Pin , Taylor Jeremy M. G. , Boonstra Philip S. , Lawrence Theodore S. , Schipper Matthew J. 
Published: March 2022

AbstractThe goal in personalized medicine is to individualize treatment using patient characteristics and improve health outcomes. Selection of optimal dose must balance the effect of dose on both treatment efficacy and toxicity outcomes. We consider a setting with one binary efficacy and one binary toxicity outcome. The goal is to find the optimal dose for each patient using clinical features and biomarkers from available dataset. We propose to use flexible machine learning methods such as random forest and Gaussian process models to build models for efficacy and toxicity depending on dose and biomarkers. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non‐decreasing dose‐efficacy and dose‐toxicity relationships. Numerical utilities are elicited from clinicians for each potential bivariate outcome. For each patient, the optimal dose is chosen to maximize the posterior mean of the utility function. We also propose alternative approaches to optimal dose selection by adding additional toxicity based constraints and an approach taking into account the uncertainty in the estimation of the utility function. The proposed methods are evaluated in a simulation study to compare expected utility outcomes under various estimated optimal dose rules. Gaussian process models tended to have better performance than random forest. Enforcing monotonicity during modeling provided small benefits. Whether and how, correlation between efficacy and toxicity, was modeled, had little effect on performance. The proposed methods are illustrated with a study of patients with liver cancer treated with stereotactic body radiation therapy.
CeO2 Functionalized Cobalt Layered Double Hydroxide for Efficient Catalytic Oxygen‐Evolving Reaction
Authors: Li Yanyan , Zhang Xinyu , Zheng Zhiping 
Published: March 2022

AbstractWater splitting to produce hydrogen is an effective means to alleviate the energy crisis. The anodic oxygen‐evolving reaction (OER) limits the overall efficiency due to its high energy barrier. To address this, layered double hydroxides (LDHs) with high catalytic activities have been widely studied, especially those modified with CeO2, either bound to the surface or doped into interior. However, experimental evidence for the atomic‐level understanding of the mechanism for the enhanced catalytic performance is conspicuously missing. Herein, anchoring CeO2 nanoparticles onto Co LDH, based on the thoughts of loading capacity and size effect to regulate the properties of the interface and to optimize the performance, is attempted. The electronic interactions are studied by X‐ray photoelectron spectroscopy (XPS) and electron energy loss spectroscopy (EELS), revealing electron transfer from Co2+ to Ce4+ that leads to an increase in Co3+. The strong Lewis acidity of Co3+ helps the binding of OH−, which is conducive to the formation and transformation of oxygen‐containing intermediates. Providing evidence is the formation of one of the key intermediates Co‐OOH at a sizably reduced potential as monitored by in situ Raman spectroscopy. With this work, the atomic level correlation of site‐specific electronic interactions with the enhanced catalytic performance is clearly established.
Gelation‐Assisted Assembly of Large‐Area, Highly Aligned, and Environmentally Stable MXene Films with an Excellent Trade‐Off between Mechanical and Electrical Properties
Authors: Huang Xianwu , Huang Jiahui , Zhou Guishu , Wei Yanwei , Wu Peiyi , Dong Angang , Yang Dong 
Published: March 2022

AbstractLayered MXene films have shown enormous potential for wide applications due to their high electrical conductivity and unique laminated microstructure. However, the intrinsic susceptibility to oxidation and the mechanical fragility of MXene films are the two major bottlenecks that prevent their widespread industrial applications. Here, a facile yet efficient assembly strategy is proposed to address these issues by increasing the alignment and compactness of MXene layers as well as strengthening the interlayer interactions. This method involves the gelation of MXene flakes with a multifunctional inorganic “mortar” polymer (ammonium polyphosphate, APP) followed by quasi‐solid‐state assembly enabled by a mechanical rolling process, by which the 3D gel network is transformed into 2D freestanding MXene films with unprecedented flake alignment and compactness. Besides, due to the multiple molecular‐level interactions (hydrogen bonding, coordination bonding, and electrostatic force) between APP and MXene flakes, the resultant MXene‐APP film (MAF) displays high mechanical strength (286.4 ± 20.3 MPa) and excellent electrical conductivity (8012.4 ± 325.6 S cm−1), along with remarkable environmental stability. As an application demonstration, MAF exhibits outstanding electromagnetic interference shielding effectiveness with long‐term durability, highlighting the great potential of this gelation‐assisted assembly strategy in fabricating large‐area, high‐performance MXene films for diverse real‐world applications.
2D π‐conjugated metal–organic frameworks for CO2 electroreduction
Authors: Yang Deren , Wang Xun 
Published: March 2022

AbstractElectrochemically converting CO2 molecules into valuable chemicals and fuels opens up a promising route to utilize CO2 source. To overcome the low efficiency and durability that hinder its practical applications, tremendous research efforts have been devoted to nano‐level or atomic‐level catalyst design. The advent of metal–organic frameworks (MOFs) provides novel opportunities for CO2 reduction catalysts, which may integrate the respective advantages of traditional catalysts and single‐atom catalysts. In this review, we summarize the recent advances in two‐dimensional (2D) π‐conjugated MOF catalysts and discuss their practical applications in CO2 reduction reaction (CO2RR). First, we systematically introduce the development of electrocatalysts for CO2RR applications. Meanwhile, various types of 2D porphyrin/phthalocyanine‐based MOFs and corresponding electrocatalytic performances arising from active‐site engineering, surface reconstruction, and thickness control are briefly overviewed. Finally, we highlight their major challenges and opportunities facing CO2RR, and hope that this review can offer new insight into MOF catalyst design.
A new criterion for $$\mathcal {M}, \mathcal {N}$$-adhesivity, with an application to hierarchical graphs
Authors: Castelnovo Davide , Gadducci Fabio , Miculan Marino 
Published: March 2022

AbstractAdhesive categoriesprovide an abstract framework for the algebraic approach to rewriting theory, where many general results can be recast and uniformly proved. However, checking that a model satisfies the adhesivity properties is sometimes far from immediate. In this paper we present a new criterion giving a sufficient condition for$$\mathcal {M},\mathcal {N}$$M,N-adhesivity, a generalisation of the original notion of adhesivity. We apply it to several existing categories, and in particular tohierarchical graphs, a formalism that is notoriously difficult to fit in the mould of algebraic approaches to rewriting and for which various alternative definitions float around.
A Faithful and Quantitative Notion of Distant Reduction for Generalized Applications
Authors: Santo José Espírito , Kesner Delia , Peyrot Loïc 
Published: March 2022

AbstractWe introduce a call-by-name lambda-calculus$$\lambda J$$λJwith generalized applications which integrates a notion of distant reduction that allows to unblock$$\beta $$β-redexes without resorting to the permutative conversions of generalized applications. We show strong normalization of simply typed terms, and we then fully characterize strong normalization by means of a quantitative typing system. This characterization uses a non-trivial inductive definition of strong normalization –that we relate to others in the literature–, which is based on a weak-head normalizing strategy. Our calculus relates to explicit substitution calculi by means of a translation between the two formalisms which is faithful, in the sense that it preserves strong normalization. We show that our calculus$$\lambda J$$λJand the well-know calculus$$\varLambda J$$ΛJdetermine equivalent notions of strong normalization. As a consequence,$$\varLambda J$$ΛJinherits a faithful translation into explicit substitutions, and its strong normalization can be characterized by the quantitative typing system designed for$$\lambda J$$λJ, despite the fact that quantitative subject reduction fails for permutative conversions.
Categorical Foundations of Gradient-Based Learning
Authors: Cruttwell Geoffrey S. H. , Gavranović Bruno , Ghani Neil , Wilson Paul , Zanasi Fabio 
Published: March 2022

AbstractWe propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as MSE and Softmax cross-entropy, shedding new light on their similarities and differences. Our approach to gradient-based learning has examples generalising beyond the familiar continuous domains (modelled in categories of smooth maps) and can be realized in the discrete setting of boolean circuits. Finally, we demonstrate the practical significance of our framework with an implementation in Python.
Linearity and Uniqueness: An Entente Cordiale
Authors: Marshall Danielle , Vollmer Michael , Orchard Dominic 
Published: March 2022

AbstractSubstructural type systems are growing in popularity because they allow for a resourceful interpretation of data which can be used to rule out various software bugs. Indeed, substructurality is finally taking hold in modern programming; Haskell now has linear types roughly based on Girard’s linear logic but integrated via graded function arrows, Clean has uniqueness types designed to ensure that values have at most a single reference to them, and Rust has an intricate ownership system for guaranteeing memory safety. But despite this broad range of resourceful type systems, there is comparatively little understanding of their relative strengths and weaknesses or whether their underlying frameworks can be unified. There is often confusion about whether linearity and uniqueness are essentially the same, or are instead ‘dual’ to one another, or somewhere in between. This paper formalises the relationship between these two well-studied but rarely contrasted ideas, building on two distinct bodies of literature, showing that it is possible and advantageous to have both linear and unique types in the same type system. We study the guarantees of the resulting system and provide a practical implementation in the graded modal setting of the Granule language, adding a third kind of modality alongside coeffect and effect modalities. We then demonstrate via a benchmark that our implementation benefits from expected efficiency gains enabled by adding uniqueness to a language that already has a linear basis.
A Predicate Transformer for Choreographies
Authors: Jongmans Sung-Shik , van den Bos Petra 
Published: March 2022

AbstractConstruction and analysis of distributed systems is difficult; choreographic programming is a deadlock-freedom-by-construction approach to simplify it. In this paper, we present a new theory of choreographic programming. It supports for the first time: construction of distributed systems that require decentralised decision making (i.e., if/while-statements with multiparty conditions); analysis of distributed systems to provide not only deadlock freedom but also functional correctness (i.e., pre/postcondition reasoning). Both contributions are enabled by a single new technique, namely a predicate transformer for choreographies.
PEQtest: Testing Functional Equivalence
Authors: Jakobs Marie-Christine , Wiesner Maik 
Published: March 2022

AbstractRefactoring a program without changing the program’s functional behavior is challenging. To prevent that behavioral changes remain undetected, one may apply approaches that compare the functional behavior of original and refactored programs. Difference detection approaches often use dedicated test generators and may be inefficient (i.e., execute (some of) the non-modified code twice). In contrast, proving functional equivalence often requires expensive verification. Therefore, we proposePEQtest, which aims at localized functional equivalence testing thereby relying on existing tests or test generators. To this end,PEQtestderives a test program from the original program by replacing each code segment being refactored with program code that encodes the equivalence of the original and its refactored code segment. The encoding is similar to program encodings used by some verification-based equivalence checkers. Furthermore, we prove that the test program derived byPEQtestindeed checks functional equivalence. Moreover, we implementedPEQtestin a prototype and evaluate it on several examples. Our evaluation shows thatPEQtestsuccessfully detects refactored programs that change the program behavior and that it often performs better than the state-of-the-art equivalence checkerPEQcheck.
SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning
Authors: Dutta Saikat , Huang Zixin , Misailovic Sasa 
Published: March 2022

AbstractProbabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during inference and debugging are left entirely to the developers and typically require significant statistical expertise. A common class of problems when writing probabilistic programs is the lack of convergence of the probabilistic programs to their posterior distributions.We present SixthSense, a novel approach for predicting probabilistic program convergence ahead of run and its application to debugging convergence problems in probabilistic programs. SixthSense’s training algorithm learns a classifier that can predict whether a previously unseen probabilistic program will converge. It encodes the syntax of a probabilistic program asmotifs– fragments of the syntactic program paths. The decisions of the classifier are interpretable and can be used to suggest the program features that contributed significantly to program convergence or non-convergence. We also present an algorithm for augmenting a set of training probabilistic programs that uses guided mutation.We evaluated SixthSense on a broad range of widely used probabilistic programs. Our results show that SixthSense features are effective in predicting convergence of programs for given inference algorithms. SixthSense obtained Accuracy of over 78% for predicting convergence, substantially above the state-of-the-art techniques for predicting program properties Code2Vec and Code2Seq. We show the ability of SixthSense to guide the debugging of convergence problems, which pinpoints the causes of non-convergence significantly better by Stan’s built-in warnings.
Finding Semantic Bugs Fast
Authors: Grätz Lukas , Hähnle Reiner , Bubel Richard 
Published: March 2022

AbstractFinding semantic bugs in code is difficult and requires precious expert time. Lacking comprehensive formal specifications, deductive verification is not an option. We propose an incremental specification procedure: With the help of automatic verification tools, a domain expert is guided through program runs and source code locations. The expert validates a run at certain locations and creates lightweight annotations. Formal methods training is not required. We demonstrate by example that this approach is capable to quickly detect different kinds of semantic bugs. We position our approach in the middle ground between fully-fledged deductive verification and bug finding without semantic guidance.
On type 0 string theory in solvable RR backgrounds
Authors: Skrzypek Torben , Tseytlin Arkady A. 
Published: March 2022

Abstract
Motivated by a possibility of solving non-supersymmetric type 0 string theory in AdS5× S5 background using integrability, we revisit the construction of type 0 string spectrum in some solvable examples of backgrounds with RR fluxes that are common to type IIB and type 0B theories. The presence of RR fluxes requires the use of a Green-Schwarz description for type 0 string theory. Like in flat space, the spectrum of type 0 theory can be derived from the type II theory spectrum by a (−1)F orbifolding, i.e. combining the untwisted sector where GS fermions are periodic with the twisted sector where GS fermions are antiperiodic (and projecting out all spacetime fermionic states). This construction of the type 0 spectrum may also be implemented using Melvin background that allows to continuously interpolate between the type II and type 0 theories. As an illustration, we discuss the type 0B spectrum in the pp-wave background which is the Penrose limit of AdS5× S5 with RR 5-form flux and also in the pp-wave background which is the Penrose limit of AdS3× S3× T4 supported by mixed RR and NSNS 3-form fluxes. We show that increasing the strength of the RR flux increases the value of the effective normal ordering constant (which determines the mass of the type 0 tachyon in flat space) and thus effectively decreases the momentum-space domain of instability of the ground state. We also comment on the semiclassical sector of states of type 0B theory in AdS5× S5.
LHC lifetime frontier and visible decay searches in composite asymmetric dark matter models
Authors: Kamada Ayuki , Kuwahara Takumi 
Published: March 2022

Abstract
The LHC lifetime frontier will probe dark sector in near future, and the visible decay searches at fixed-target experiments have been exploring dark sector. Composite asymmetric dark matter with dark photon portal is a promising framework explaining the coincidence problem between dark matter and visible matter. Dark strong dynamics provides rich structure in the dark sector: the lightest dark nucleon is the dark matter, while strong annihilation into dark pions depletes the symmetric components of the dark matter. Dark photons alleviate cosmological problems. Meanwhile, dark photons make dark hadrons long-lived in terrestrial experiments. Moreover, the dark hadrons are produced through the very same dark photon. In this study, we discuss the visible decay searches for composite asymmetric dark matter models. For a few GeV dark nucleons, the LHC lifetime frontier, MATHUSLA and FASER, has a potential to discover their decay when kinetic mixing angle of dark photon is ϵ ≳ 10−4. On the other hand, fixed-target experiments, in particular SeaQuest, will have a great sensitivity to dark pions with a mass below GeV and with kinetic mixing ϵ ≳ 10−4 in addition to the LHC lifetime frontier. These projected sensitivities to dark hadrons in dark photon parameter space are comparable with the future sensitivities of dark photon searches, such as Belle-II and LHCb.
Virasoro entanglement Berry phases
Authors: de Boer Jan , Espíndola Ricardo , Najian Bahman , Patramanis Dimitrios , van der Heijden Jeremy , Zukowski Claire 
Published: March 2022

Abstract
We study the parallel transport of modular Hamiltonians encoding entanglement properties of a state. In the case of 2d CFT, we consider a change of state through action with a suitable diffeomorphism on the circle: one that diagonalizes the adjoint action of the modular Hamiltonian. These vector fields exhibit kinks at the interval boundary, thus together with their central extension they differ from usual elements of the Virasoro algebra. The Berry curvature associated to state-changing parallel transport is the Kirillov-Kostant symplectic form on an associated coadjoint orbit, one which differs appreciably from known Virasoro orbits. We find that the boundary parallel transport process computes a bulk symplectic form for a Euclidean geometry obtained from the backreaction of a cosmic brane, with Dirichlet boundary conditions at the location of the brane. We propose that this gives a reasonable definition for the symplectic form on an entanglement wedge.
PCSK9 acts as a key regulator of Aβ clearance across the blood–brain barrier
Authors: Mazura Alexander D. , Ohler Anke , Storck Steffen E. , Kurtyka Magdalena , Scharfenberg Franka , Weggen Sascha , Becker-Pauly Christoph , Pietrzik Claus U. 
Published: March 2022

AbstractDespite the neurodegenerative disorder Alzheimer’s disease (AD) is the most common form of dementia in late adult life, there is currently no therapy available to prevent the onset or slow down the progression of AD. The progressive cognitive decline in AD correlates with a successive accumulation of cerebral amyloid-β (Aβ) due to impaired clearance mechanisms. A significant percentage is removed by low-density lipoprotein receptor-related protein 1 (LRP1)-mediated transport across the blood–brain barrier (BBB) into the periphery. Circulating proprotein convertase subtilisin/kexin type 9 (PCSK9) binds to members of the low-density lipoprotein receptor protein family at the cell surface and targets them for lysosomal degradation, which reduces the number of functional receptors. However, the adverse impact of PCSK9 on LRP1-mediated brain Aβ clearance remains elusive. By using an established BBB model, we identified reduced LRP1-mediated brain-to-blood Aβ clearance due to PCSK9 across different endothelial monolayer in vitro. Consequently, the repetitive application of FDA-approved monoclonal anti-PCSK9 antibodies into 5xFAD mice decreased the cerebral Aβ burden across variants and aggregation state, which was not reproducible in brain endothelial-specific LRP1−/− 5xFAD mice. The peripheral PCSK9 inhibition reduced Aβ pathology in prefrontal cortex and hippocampus–brain areas critically involved in memory processing—and prevented disease-related impairment in hippocampus-dependent memory formation. Our data suggest that peripheral inhibition of PCSK9 by already available therapeutic antibodies may be a novel and easily applicable potential AD treatment.