bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relation between pre-existing quality management measures and prevention and containment of COVID-19 outbreaks in 159 nursing homes in Tuscany: a mixed methods study
Authors: Poldrugovac Mircha , Barsanti Sara , Pardini Emiliano , Klazinga Niek S , Kringos Dionne S 
Published: April 2024

BackgroundNursing homes were often the focus of COVID-19 outbreaks. Many factors are known to influence the ability of a nursing home to prevent and contain a COVID-19 outbreak. The role of an organisation’s quality management prior to the pandemic is not yet clear. In the Italian region of Tuscany nursing home performance indicators have been regularly collected since before the pandemic, providing the opportunity to better understand this relationship.ObjectivesTo test if there is a difference in the results achieved by nursing homes in Tuscany on 13 quality management indicators, when grouped by severity of COVID-19 outbreaks; and to better understand how these indicators may be related to the ability to control COVID-19 outbreaks, from the perspective of nursing homes.MethodsWe used a mixed methods sequential explanatory design. Based on regional and national databases, 159 nursing homes in Tuscany were divided into four groups by outbreak severity. We tested the significance of the differences between the groups with respect to 13 quality management indicators. The potential relation of these indicators to COVID-19 outbreaks was discussed with 29 managers and other nursing homes’ staff through four group interviews.ResultsThe quantitative analysis showed significant differences between the groups of nursing homes for 3 of the 13 indicators. From the perspective of nursing homes, the indicators might not be good at capturing important aspects of the ability to control COVID-19 outbreaks. For example, while staffing availability is seen as essential, the staff-to-bed ratio does not capture the turn-over of staff and temporary absences due to positive COVID-19 testing of staff.ConclusionsThough currently collected indicators are key for overall performance monitoring and improvement, further refinement of the set of quality management indicators is needed to clarify the relationship with nursing homes’ ability to control COVID-19 outbreaks.
Randomised trial of ‘hybrid’ water-assisted colonoscopy (modified water immersion) versus water exchange colonoscopy: WAVE study
Authors: Ahmad Ahmir , Buenaventura Anna , Motes Belma , Misra Ravi , Man Ripple , Dhillon Angad , Bassett Paul , Thiruvilangam Kowshika , Saunders Brian P 
Published: April 2024

Background and aimsColonoscopy practice has evolved with a trend towards water-assisted insertion. A recent national survey suggests a hybrid approach to colonic distension during insertion, with water used predominately to the splenic flexure and carbon dioxide (CO2) with water used thereafter to the caecum, is commonly used. This contrasts with the water exchange technique where no CO2is used. This study aimed to evaluate whether a hybrid or water exchange technique allows more efficient colonoscopy.MethodsThis prospective, randomised controlled trial was conducted between March 2021 and June 2022. Participants were randomised 1:1 to hybrid or water exchange colonoscopy, performed by four experienced colonoscopists. The primary outcome was total procedure time with secondary outcomes of caecal intubation time, caecal intubation rate, polyp detection, loop formation, number of ancillary manoeuvres, sedation use and patient comfort.Results256 patients were invited, and 246 were randomised to either hybrid or water exchange colonoscopy. 122 patients were included in each arm of the primary analysis. Total procedure time was greater in the water exchange group compared with hybrid (29 vs 25 min, p=0.009). Patient reposition episodes occurred more frequently in the water exchange group vs hybrid group (5.5 vs 5, p=0.003) and left colon Boston Bowel Preparation Scale (BBPS) score was improved. No difference was seen in all other outcomes.ConclusionA hybrid technique, compared with water exchange, enabled faster colonoscopy without adversely impacting sedation requirement, caecal intubation, overall bowel cleansing and patient comfort. This technique appears to maximise the advantages of both water and CO2to enable more efficient colonoscopy.Trial registration numberNCT04710706.
Placental circulating T cells: a novel, allogeneic CAR-T cell platform with preserved T-cell stemness, more favorable cytokine profile, and durable efficacy compared to adult PBMC-derived CAR-T
Authors: Ruggeri Barbaro Natalia , Drashansky Theodore , Tess Kristina , Djedaini Mansour , Hariri Robert , He Shuyang , van der Touw William , Karasiewicz Kathy 
Published: April 2024

BackgroundChimeric antigen receptor (CAR)-T cell quality and stemness are associated with responsiveness, durability, and memory formation, which benefit clinical responses. Autologous T cell starting material across patients with cancer is variable and CAR-T expansion or potency can fail during manufacture. Thus, strategies to develop allogeneic CAR-T platforms including the identification and expansion of T cell subpopulations that correspond with CAR-T potency are an active area of investigation. Here, we compared CAR-T cells generated from healthy adult peripheral blood T cells versus placental circulating T (P-T) cells.MethodsCAR-T cells from healthy adult peripheral blood mononuclear cells (PBMCs) and P-T cells were generated using the same protocol. CAR-T cells were characterized in detail by a combination of multiparameter flow cytometry, functional assays, and RNA sequencing. In vivo antitumor efficacy and persistence of CAR-T cells were evaluated in a Daudi lymphoma xenograft model.ResultsP-T cells possess stemness advantages compared with T cells from adult PBMCs. P-T cells are uniformly naïve prior to culture initiation, maintain longer telomeres, resist immune checkpoint upregulation, and resist further differentiation compared with PBMC T cells during CD19 CAR-T manufacture. P-T CD19 CAR-T cells are equally cytotoxic as PBMC-CD19 CAR-T cells but produce less interferon gamma in response to lymphoma. Transcriptome analysis shows P-T CD19 CAR-T cells retain a stem-like gene signature, strongly associate with naïve T cells, an early memory phenotype, and a unique CD4 T cell signature compared with PBMC-CD19 CAR-T cells, which enrich for exhaustion and stimulated memory T cell signatures. Consistent with functional data, P-T CD19 CAR-T cells exhibit attenuated inflammatory cytokine and chemokine gene signatures. In a murine in vivo model, P-T CD19 CAR-T cells eliminate lymphoma beyond 90 days. PBMC-CD19 CAR-T cells provide a non-durable benefit, which only delays disease onset.ConclusionWe identified characteristics of T cell stemness enriched in P-T CD19 CAR-T which are deficient in PBMC-derived products and translate into response durability in vivo. Our findings demonstrate that placental circulating T cells are a valuable cell source for allogeneic CAR-T products. Stemness advantages inherent to P-T cells translate to in vivo persistence advantages and long-term durable activity.
Interpreting hydroxychloroquine blood levels for medication non-adherence: a pharmacokinetic study
Authors: Balevic Stephen , Sun Kai , Rogers Jennifer L , Eudy Amanda , Sadun Rebecca Eli , Maheswaranathan Mithu , Doss Jayanth , Criscione-Schreiber Lisa , O'Malley Tyler , Clowse Megan , Weiner Daniel 
Published: April 2024

ObjectiveCharacterise the relationship between hydroxychloroquine (HCQ) blood levels and the number of missed doses, accounting for dosage, dose timing and the large variability in pharmacokinetics (PK) between patients.MethodsWe externally validated a published PK model and then conducted dosing simulations. We developed a virtual population of 1000 patients for each dosage across a range of body weights and PK variability. Using the model, 10 Monte Carlo simulations for each patient were conducted to derive predicted whole blood concentrations every hour over 24 hours (240 000 HCQ levels at steady state). To determine the impact of missed doses on levels, we randomly deleted a fixed proportion of doses.ResultsFor patients receiving HCQ 400 mg daily, simulated random blood levels <200 ng/mL were exceedingly uncommon in fully adherent patients (<0.1%). In comparison, with 80% of doses missed, approximately 60% of concentrations were <200 ng/mL. However, this cut-off was highly insensitive and would miss many instances of severe non-adherence. Average levels quickly dropped to <200 ng/mL after 2–4 days of missed doses. Additionally, mean levels decreased by 29.9% between peak and trough measurements.ConclusionsWe propose an algorithm to optimally interpret HCQ blood levels and approximate the number of missed doses, incorporating the impact of dosage, dose timing and pharmacokinetic variability. No single cut-off has adequate combinations of both sensitivity and specificity, and cut-offs are dependent on the degree of targeted non-adherence. Future studies should measure trough concentrations to better identify target HCQ levels for non-adherence and efficacy.
Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis
Authors: Scott Jennifer , White Arthur , Walsh Cathal , Aslett Louis , Rutherford Matthew A , Ng James , Judge Conor , Sebastian Kuruvilla , O’Brien Sorcha , Kelleher John , Power Julie , Conlon Niall , Moran Sarah M , Luqmani Raashid Ahmed , Merkel Peter A , Tesar Vladimir , Hruskova Zdenka , Little Mark A 
Published: April 2024

ObjectiveANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting.MethodsWe studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse.ResultsDevelopment and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS.ConclusionsThis CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.
The effect of scalar hair on the charged black hole with the images from accretions disk
Authors: Sui Tao-Tao , Wang Zi-Liang , Guo Wen-Di 
Published: April 2024

AbstractIn this paper, we investigate the optical properties of a charged black hole with scalar hair (CSH) within the context of four-dimensional Einstein–Maxwell–Dilaton gravity. To achieve this, we consider three distinct toy models of thin accretion disks. The presence of dilaton coupling allows us to express both the solutions of CSH and the Reissner–Nordström (RN) black hole in terms of their mass (M) and charge (Q). Our findings reveal differences in the effective potentials $$V_{eff}$$

V

eff

, photon sphere radii $$r_{ph}$$

r

ph

, and innermost stable circular orbit $$r_{isco}$$

r

isco

between the CSH and RN black hole cases, which become increasingly pronounced as the charge parameter Q increases. However, no noticeable distinctions are observed concerning the critical impact parameter $$b_{ph}$$

b

ph

. When the ratio of the photon ring band and the lensed ring band exceeds 0.1, it may suggest the presence of a charged black hole with scalar hair. Furthermore, our results underscore the significant influence of the charge parameter Q on the brightness distributions of the direct, lensed ring, and photon ring for three standard emission functions. These findings emphasize the potential for distinguishing between CSH and RN black holes through an analysis of direct intensity and peak brightness in specific accretion disk models.
Scalar field perturbation around a rotating hairy black hole: quasinormal modes, quasibound states and superradiant instability
Authors: Lei Yun-He , Yang Zhen-Hao , Kuang Xiao-Mei 
Published: April 2024

AbstractWe consider the quasinormal modes, quasibound states and superradiant instability of a rotating hairy black hole, which possesses a Horndeski hair as deviation from Kerr black hole, under the perturbation of massive scalar field. With the use of the matrix method, we mainly calculate the eigenfrequencies related to those modes of the perturbation. Under the perturbation of the massless scalar field, the Horndeski hair and spin parameter have significant influences on the quasinormal frequency, but its imaginary part is always finite negative and no unstable mode is found. Under the perturbation of the massive scalar field, we focus on the eigenfrequencies of quasibound states and find the modes of which the imaginary part of eigenfrequencies is positive, indicating that the black hole undergoes superradiant instability. Then we scan the parameters and figure out a diagram in the space of Horndeski hair and spin parameters to distinguish the rotating hairy black hole with superradiant instability from the stable one.
Stoking the Flames: Understanding Escalation in an Online Harassment Community
Authors: Take Kejsi , Zhong Victoria , Geeng Chris , Bevensee Emmi , McCoy Damon , Greenstadt Rachel 
Published: April 2024

Online harassment remains a prevalent problem for internet users. Its impact is made orders of magnitude worse when multiple harassers coordinate to conduct networked attacks. This paper presents an analysis of 231 threads in Kiwi Farms, a notorious online harassment community. We find that networked online harassment campaigns consists of three phases: target introduction, network decision, and network response. The first stage consists of the initial narrative elements, that are approved or not in stage two and expanded in stage three. Narrative building is a common element of all three stages. The network plays a key role in narrative building, adding elements to the narrative in at least 80 % of the threads, resulting in sustained harassment. This finding is central to our model of Continuous Narrative Escalation (CNE), that has two parts: (1) narrative continuation, the action of repeatedly adding new information to the existing narrative and (2) escalation, the aggravation of harassment that occurs as a consequence.
In addition, we present insights from our analysis of 100 takedown requests threads, discussing received abuse reports. We find that these takedown requests are misused by the community and are used as elements to further fuel the narrative. We use our findings and framework to come up with a set of recommendations, that can inform harassment interventions and make online spaces safer.
Fulfilling OCaml Modules with Transparency
Authors: Clement Blaudeau , Rémy Didier , Radanne Gabriel 
Published: April 2024

ML modules come as an additional layer on top of a core language to offer
large-scale notions of composition and abstraction. They largely
contributed to the success of OCaml and SML. While modules are easy to write
for common cases, their advanced use may become tricky. Additionally,
despite a long line of works, their meta-theory remains difficult to
comprehend, with involved soundness proofs. In fact, the module layer of
OCaml does not currently have a formal specification and its implementation
has some surprising behaviors.
Building on previous translations from ML modules to Fω, we propose a type
system, called Mω, that covers a large subset of OCaml modules, including
both applicative and generative functors, and extended with transparent
ascription. This system produces signatures in an OCaml-like syntax extended
with Fω quantifiers. We provide a reverse translation from Mω signatures to
path-based source signatures along with a characterization of signature
avoidance cases, making Mω signatures well suited to serve as a new internal
representation for a typechecker.
The soundness of the type system is shown by elaboration in Fω. We improve
over previous encodings of sealing within applicative functors, by the
introduction of transparent existential types, a weaker form of existential
types that can be lifted out of universal and arrow types. This shines a new
light on the form of abstraction provided by applicative functors and brings
their treatment much closer to those of generative functors.
Newtonian Program Analysis of Probabilistic Programs
Authors: Wang Di , Reps Thomas 
Published: April 2024

Due to their quantitative nature, probabilistic programs pose non-trivial challenges for designing compositional and efficient program analyses. Many analyses for probabilistic programs rely on iterative approximation. This article presents an interprocedural dataflow-analysis framework, called NPA-PMA, for designing and implementing (partially) non-iterative program analyses of probabilistic programs with unstructured control-flow, nondeterminism, and general recursion. NPA-PMA is based on Newtonian Program Analysis (NPA), a generalization of Newton's method to solve equation systems over semirings. The key challenge for developing NPA-PMA is to handle multiple kinds of confluences in both the algebraic structures that specify analyses and the equation systems that encode control flow: semirings support a single confluence operation, whereas NPA-PMA involves three confluence operations (conditional, probabilistic, and nondeterministic).
Our work introduces ω-continuous pre-Markov algebras (ωPMAs) to factor out common parts of different analyses; adopts regular infinite-tree expressions to encode probabilistic programs with unstructured control-flow; and presents a linearization method that makes Newton's method applicable to the setting of regular-infinite-tree equations over ωPMAs. NPA-PMA allows analyses to supply a non-iterative strategy to solve linearized equations. Our experimental evaluation demonstrates that (i) NPA-PMA holds considerable promise for outperforming Kleene iteration, and (ii) provides great generality for designing program analyses.
Identifying and Correcting Programming Language Behavior Misconceptions
Authors: Lu Kuang-Chen , Krishnamurthi Shriram 
Published: April 2024

Misconceptions about core linguistic concepts like mutable
variables, mutable compound data, and their interaction with scope
and higher-order functions seem to be widespread.
But
how do we detect
them, given that experts have blind spots and may not realize the
myriad ways in which students can misunderstand programs?
Furthermore, once identified, what can we do to correct them?
In this paper, we present a curated list of misconceptions, and an
instrument to detect them. These are distilled from student work
over several years and match and extend prior research. We also
present an automated, self-guided tutoring system. The tutor builds
on strategies in the education literature and is explicitly designed
around identifying and correcting misconceptions.
We have tested the tutor in multiple settings.
Our data consistently show that (a) the misconceptions we tackle are
widespread, and (b) the tutor appears to improve understanding.
CYCLE: Learning to Self-Refine the Code Generation
Authors: Ding Yangruibo , Min Marcus J. , Kaiser Gail , Ray Baishakhi 
Published: April 2024

Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers. However, their self-refinement capability is typically overlooked by the existing evaluations of code LMs, which focus only on the accuracy of the one-time prediction. For the cases when code LMs fail to implement the correct program, developers actually find it hard to debug and fix the faulty prediction since it is not written by the developers themselves. Unfortunately, our study reveals that code LMs cannot efficiently self-refine their faulty generations as well.
In this paper, we propose CYCLE framework, learning to self-refine the faulty generation according to the available feedback, such as the execution results reported by the test suites. We evaluate CYCLE on three popular code generation benchmarks, HumanEval, MBPP, and APPS. The results reveal that CYCLE successfully maintains, sometimes improves, the quality of one-time code generation, while significantly improving the self-refinement capability of code LMs. We implement four variants of CYCLE with varied numbers of parameters across 350M, 1B, 2B, and 3B, and the experiments show that CYCLE consistently boosts the code generation performance, by up to 63.5
Evaluating the Effectiveness of Deep Learning Models for Foundational Program Analysis Tasks
Authors: Chen Qian , Yu Chenyang , Liu Ruyan , Zhang Chi , Wang Yu , Wang Ke , Su Ting , Wang Linzhang 
Published: April 2024

While deep neural networks provide state-of-the-art solutions to a wide range of programming language tasks, their effectiveness in dealing with foundational program analysis tasks remains under explored. In this paper, we present an empirical study that evaluates four prominent models of code (i.e., CuBERT, CodeBERT, GGNN, and Graph Sandwiches) in two such foundational tasks: (1) alias prediction, in which models predict whether two pointers must alias, may alias or must not alias; and (2) equivalence prediction, in which models predict whether or not two programs are semantically equivalent. At the core of this study is CodeSem, a dataset built upon the source code of real-world flagship software (e.g., Linux Kernel, GCC, MySQL) and manually validated for the two prediction tasks.
Results show that all models are accurate in both prediction tasks, especially CuBERT with an accuracy of 89% and 84% in alias prediction and equivalence prediction, respectively. We also conduct a comprehensive, in-depth analysis of the results of all models in both tasks, concluding that deep learning models are generally capable of performing foundational tasks in program analysis even though in specific cases their weaknesses are also evident.
Our code and evaluation data are publicly available at https://github.com/CodeSemDataset/CodeSem.