bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiplexed CRISPR-Cas system targeting ASFV genes
in vivo
: solution lies within
Authors: Zhang Mengjia , Lang Yifei , Li Wentao
Published: August 2024

The emergence and spread of the African swine fever virus (ASFV) posed a significant threat to the global swine breeding industry, calling for innovative approaches benefiting viral containment and control. A recent study (Z. Zheng, L. Xu, H. Dou, Y. Zhou, X., et al., Microbiol Spectr 12: e02164-23, 2024,
https://doi.org/10.1128/spectrum.02164-23
) established a multiplexed CRISPR-Cas system targeting the genome of ASFV and tested the consequent antiviral activity both
in vitro
and
in vivo
. Application of this system showed a significant reduction of viral replication
in vitro
, while the germline-edited pigs expressing this system exhibited normal growth with continuous guide RNA expression. Although no survival advantage was observed upon ASFV challenge compared with nonengineered pigs, this marks the first attempt of germline editing to pursue ASFV resistance and paves the way for future disease-resistant animal breeding approaches utilizing CRISPR-Cas technology.
Diagnostic accuracy of alternative biomarkers for acute aortic syndrome: a systematic review
Authors: Wren Joshua , Goodacre Steve , Pandor Abdullah , Essat Munira , Clowes Mark , Cooper Graham , Hinchliffe Robert , Reed Matthew J , Thomas Steven , Wilson Sarah
Published: August 2024

Background

D-dimer is the only biomarker currently recommended in guidelines for the diagnosis of acute aortic syndrome (AAS). We undertook a systematic review to determine whether any alternative biomarkers could be useful in AAS diagnosis.

Methods

We searched electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2024. Diagnostic studies were eligible if they examined biomarkers other than D-dimer for diagnosing AAS compared with a reference standard test in people presenting to the ED with symptoms of AAS. Case-control studies were identified but excluded due to high risk of bias. Selection of studies, data extraction and risk of bias assessments using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool were undertaken independently by at least two reviewers. We used narrative synthesis to summarise the findings.

Results

We identified 2017 citations, included 13 cohort studies (n=76–999), and excluded 38 case-control studies. Methodological quality was variable, with most included studies having unclear or high risk of bias and applicability concerns in at least one item of the QUADAS‐2 tool. Only two studies reported biomarkers with sensitivity and specificity comparable to D-dimer (ie, >90% and >50%, respectively). Wanget alreported 99.1% sensitivity and 84.9% specificity for soluble ST2; however, these findings conflicted with estimates of 58% sensitivity and 70.8% specificity reported in another study. Chun and Siu reported 95.6% sensitivity and 56.1% specificity for neutrophil count, but this has not been confirmed elsewhere.

Conclusion

There are many potential alternative biomarkers for AAS but few have been evaluated in more than one study, study designs are often weak and reported biomarker accuracy is modest or inconsistent between studies. Alternative biomarkers to D-dimer are not ready for routine clinical use.

PROSPERO registration number

CRD42022252121.
Beyond air pollution: a national assessment of cooking-related burns in Ghana
Authors: Daouda Misbath , Seyram Kaali , Amankwah Georgette Owusu , Seidu Iddrisu , Kar Abhishek , Abubakari Sulemana , Malagutti Flavio , Awuni Sule , Razak Abdul , Apraku Edward , Peprah Peter , Lee Alison G , Mehta Sumi , Jack Darby , Asante Kwaku Poku
Published: August 2024

Introduction

Household energy transitions have the potential to reduce the burden of several health outcomes but have narrowly focused on those mediated by reduced exposure to air pollution, despite concerns about the burden of injury outcomes. Here, we aimed to describe the country-level incidence of severe cooking-related burns in Ghana and identify household-level risk factors for adults and children.

Methods

We conducted a national household energy use survey including 7389 households across 370 enumeration areas in Ghana in 2020. In each household, a pretested version of the Clean Cooking Alliance Burns Surveillance Module was administered to the primary cook. We computed incidence rates of severe cooking-related burns and conducted bivariate logistic regression to identify potential risk factors.

Results

We documented 129 severe cooking-related burns that had occurred in the previous year. The incidence rate (95% CI) of cooking-related burns among working-age females was 17 (13 to 21) per 1000 person-years or 8.5 times higher than that of working-age males. Among adults, the odds of experiencing a cooking-related burn were 2.29 (95% CI 1.02 to 5.14) and 2.40 (95% CI 1.04 to 5.55) times higher among primary wood and charcoal users respectively compared with primary liquified petroleum gas users. No child burns were documented in households where liquified petroleum gas was primarily used.

Conclusion

Using a nationally representative sample, we found that solid fuel use doubled the odds of cooking-related burns compared with liquified petroleum gas. Ghana’s efforts to expand access to liquified petroleum gas should focus on safe use.
Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer
Authors: Wang Zixiang , Wang Bingyu , Feng Yuan , Ye Jinwen , Mao Zhonghao , Zhang Teng , Xu Meining , Zhang Wenjing , Jiao Xinlin , Zhang Qing , Zhang Youzhong , Cui Baoxia
Published: August 2024

Background

Cervical cancer has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors such as programmed cell death protein 1 (PD-1) blockade are promising therapeutic agents, but their efficacy when combined with neoadjuvant chemotherapy (NACT) has not been fully tested, and how they alter the tumor microenvironment has not been comprehensively elucidated.

Methods

In this study, we conducted single-cell RNA sequencing using 46,950 cells from nine human cervical cancer tissues representing sequential different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and identified the crucial factors involved in combination therapy. Cell–cell communication analysis was performed between tumor and immune cells. In addition, THP-1-derived and primary monocyte-derived macrophages were cocultured with cervical cancer cells and phagocytosis was detected by flow cytometry. The antitumor activity of blocking CD74 was validated in vivo using a CD74 humanized subcutaneous tumor model.

Results

Pathway enrichment analysis indicated that NACT activated cytokine and complement-related immune responses. Cell–cell communication analysis revealed that after NACT therapy, interaction strength between T cells and cancer cells decreased, but intensified between macrophages and cancer cells. We verified that macrophages were necessary for the PD-1 blockade to exert antitumor effects in vitro. Additionally, CD74-positive macrophages frequently interacted with the most immunoreactive epithelial subgroup 3 (Epi3) cancer subgroup during combination NACT. We found that CD74 upregulation limited phagocytosis and stimulated M2 polarization, whereas CD74 blockade enhanced macrophage phagocytosis, decreasing cervical cancer cell viability in vitro and in vivo.

Conclusions

Our study reveals the dynamic cell–cell interaction network in the cervical cancer microenvironment influenced by combining NACT and PD-1 blockade. Furthermore, blocking tumor-associated macrophage-derived CD74 could augment neoadjuvant therapeutic efficacy.
Acute ischemic stroke with or without asymptomatic intracranial hemorrhage after endovascular treatment: a propensity-score matching study
Authors: Zhang Xiaoxi , Shen Fang , Rui Luo , Hanchen Liu , Shen Hongjian , Hongye Xu , Manyue Ge , Hua Weilong , Zhang Lei , Zhang Yongxin , Xing Pengfei , Li Zifu , Liu Jianmin , Yang Pengfei
Published: August 2024

Background

The long-term follow-up of asymptomatic intracranial hemorrhage (aICH) in patients with acute ischemic stroke after endovascular treatment (EVT) remains controversial.

Objective

To evaluate the potential effect of aICH in a real-world practice setting using a matched prospective database.

Methods

This observational cohort study enrolled patients between January 2015 and December 2022 in a prospective database. Eligible patients with occlusions in the anterior circulation were given endovascular treatment and achieved successful reperfusion. The primary outcome was functional independence (modified Rankin Scale (mRS) score 0–2). Propensity score (PS)-weighted multivariable logistic regression analyses were adjusted and were repeated in subsequent 1:1 PS-matched cohorts.

Results

732 patients, 516 without any ICH and 216 with aICH, were included. 418 and 348 patients were identified after matching in the aICH substudy and hemorrhagic infarction type aICH substudy, respectively. In the postmatched population, patients with aICH had worse functional outcomes (mRS score 0–2) at 90 days than patients without any ICH (37.8% vs 55.5%: P<0.001). Worse functional outcomes were seen in patients with aICH who were older (OR=5.59 (95% CI 2.91 to 10.74)), had higher baseline National Institutes of Health Stroke Scale score (OR=6.80 (95% CI 3.72 to 12.43)), lower baseline Alberta Stroke Program Early CT Score (OR=2.08 (95% CI 1.23 to 3.51)), and who received general anesthesia (OR=3.37 (95% CI 1.92 to 5.90)).

Conclusions

This matched-control study largely confirmed that asymptomatic ICH after EVT is associated with worse functional outcomes, and the harmful effect is more significant in older patients and those with severe baseline clinical and radiological features.
Added value of FDG-PET for detection of progressive supranuclear palsy
Authors: Buchert Ralph , Huppertz Hans-Jürgen , Wegner Florian , Berding Georg , Brendel Matthias , Apostolova Ivayla , Buhmann Carsten , Poetter-Nerger Monika , Dierks Alexander , Katzdobler Sabrina , Klietz Martin , Levin Johannes , Mahmoudi Nima , Rinscheid Andreas , Quattrone Andrea , Rogozinski Sophia , Rumpf Jost-Julian , Schneider Christine , Stoecklein Sophia , Spetsieris Phoebe G , Eidelberg David , Sabri Osama , Barthel Henryk , Wattjes Mike P , Höglinger Günter
Published: August 2024

Background

Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features.

Methods

The retrospective study included 41 PSP patients, 21 with Richardson’s syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score).

Results

The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP.

Conclusions

FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.
Evaluating effects of silvicultural treatments on forest canopy structure outcomes
Authors: Alveshere Brandon C. , Bunce Amanda , Worthley Thomas E. , Fahey Robert T.
Published: August 2024

Traditional forest management can homogenize forests, and management strategies that can restore complexity and promote adaptive capacity to global change are needed. However, effects of different silvicultural practices on complexity of forest structural components, such as the canopy, are not well understood mechanistically. We conducted a coupled field and simulation-modeling study to evaluate: (1) how near-term effects of silviculture on canopy structure and complexity differ across treatment types and (2) how outcomes of common silvicultural treatments compare to a spectrum of randomly implemented removals. Thirteen different silvicultural treatments replicated across 12 study plots were simulated within 3D models derived from terrestrial lidar data. Treatment types often differed in their multi-dimensional structural outcomes, including vertical heterogeneity and canopy structural complexity. Moderate intensity thinning treatments that preferentially removed smaller trees increased near-term canopy structural complexity, while diameter-limit cutting often reduced complexity. Silvicultural treatments collectively produced a wide range of residual canopy conditions; however, variability among structural outcomes within individual treatment types or categories was limited relative to the range of possible outcomes from random removals. Most treatments induced shifts in canopy structure outside the spectrum of random removal, suggesting ample space for adapting management to promote forest heterogeneity.
EFFECTS OF SPINAL MOBILIZATION WITH LEG MOVEMENT VERSUS SPINAL EXTENSION-ORIENTED MOBILIZATION ON PAIN, AND DISABILITY IN PATIENTS WITH LUMBAR RADICULOPATHY
Authors: Halidadjiyah  , Sakulsriprasert Prasert , Chansirinukor Wunpen
Published: August 2024

Background: Either spinal mobilization with leg movement (SMWLM) or spinal extension-oriented mobilization (SEOM) has been proven to be effective for patients with lumbar radiculopathy (LR). Therefore, this study aimed to compare the effects of SMWLM versus SEOM on pain, disability, straight leg raise (SLR), and 5-m walk test in patients with LR.

Methods: Of the 58 patients screened for eligibility, 32 patients diagnosed with LR were randomly assigned to either intervention group, SWWLM versus SEOM, and 4 sessions were taken in 2 weeks by blinded physical therapists. Pain at rest and worst by visual analog scale (VAS), straight leg raise (SLR), 5-m walk test (5MWT), and disability by Modified Oswestry Disability Index (MODI) were assessed by a blinded assessor at baseline and after each treatment session.

Results: The patients in both groups showed significant improvement in VAS at rest, VAS at worst, SLR, 5MWT, and MODI ([Formula: see text] < 0.001). However, there was no significant difference between SMWLM and SEOM groups.

Conclusion: The result demonstrated that both SMWLM and SEOM were effective in treating patients with LR regarding pain at rest and worst, SLR, 5MWT, and MODI.
About this title - Powering the Energy Transition through Subsurface Collaboration: Proceedings of the 1st Energy Geoscience Conference
Authors: Gill C. E. , Goffey G. , Underhill J. R.
Published: August 2024

The first Energy Geoscience Conference was held in May 2023 to promote geoscientific collaboration towards achieving net zero. These proceedings papers cover hydrocarbons, carbon storage, ground modelling for wind projects, nuclear waste repositories, low temperature geothermal, salt tectonics, fault/fracture development, regional geological studies and exploration for lithium, hydrogen and helium.
LaMPost: AI Writing Assistance for Adults with Dyslexia Using Large Language Models
Authors: Goodman Steven M. , Buehler Erin , Clary Patrick , Coenen Andy , Donsbach Aaron , Horne Tiffanie N. , Lahav Michal , MacDonald Robert , Michaels Rain Breaw , Narayanan Ajit , Pushkarna Mahima , Riley Joel , Santana Alex , Shi Lei , Sweeney Rachel , Weaver Phil , Yuan Ann , Morris Meredith Ringel
Published: August 2024

The natural language capabilities demonstrated by large language models (LLMs) highlight an opportunity for new writing support tools that address the varied needs of people with dyslexia. We present LaMPost, a prototype email editor that draws upon our understanding of these needs to motivate AI-powered writing features, such as outlining main ideas, generating a subject line, suggesting changes, and rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying promising routes for further exploration (such as the popular “rewrite” and “subject line” features), while also finding that the current generation of LLMs may not yet meet the accuracy and quality thresholds to be useful for writers with dyslexia. In addition, knowledge of the AI did not alter participants’ perception of the system nor their feelings of autonomy, expression, and self-efficacy when writing emails. Our findings provide insight into the benefits and drawbacks of LLMs as writing support for adults with dyslexia, and they offer a foundation to build upon in future research.
Fast Algorithms for

ℓ
p

-Regression
Authors: Adil Deeksha , Kyng Rasmus , Peng Richard , Sachdeva Sushant
Published: August 2024

The

\(\ell _p\)

-norm regression problem is a classic problem in optimization with wide ranging applications in machine learning and theoretical computer science. The goal is to compute

\(\boldsymbol {\mathit {x}}^{\star } =\arg \min _{\boldsymbol {\mathit {A}}\boldsymbol {\mathit {x}}=\boldsymbol {\mathit {b}}}\Vert \boldsymbol {\mathit {x}}\Vert _p^p\)

, where

\(\boldsymbol {\mathit {x}}^{\star }\in \mathbb {R}^n,\boldsymbol {\mathit {A}}\in \mathbb {R}^{d\times n},\boldsymbol {\mathit {b}}\in \mathbb {R}^d\)

and

\(d\le n\)

. Efficient
high-accuracy
algorithms for the problem have been challenging both in theory and practice and the state-of-the-art algorithms require

\(poly(p)\cdot n^{\frac{1}{2}-\frac{1}{p}}\)

linear system solves for

\(p\ge 2\)

. In this article, we provide new algorithms for

\(\ell _p\)

-regression (and a more general formulation of the problem) that obtain a
high-accuracy
solution in

\(O(p n^{ {(p-2)}{(3p-2)}})\)

linear system solves. We further propose a new
inverse maintenance
procedure that speeds-up our algorithm to

\(\widetilde{O}(n^{\omega })\)

total runtime, where

\(O(n^{\omega })\)

denotes the running time for multiplying

\(n \times n\)

matrices. Additionally, we give the first
Iteratively Reweighted Least Squares (IRLS)
algorithm that is guaranteed to converge to an optimum in a few iterations. Our IRLS algorithm has shown exceptional practical performance, beating the currently available implementations in MATLAB/CVX by 10–50×.
Music Mode: Transforming Robot Movement into Music Increases Likability and Perceived Intelligence
Authors: Cuan Catie , Fisher Emre , Okamura Allison , Engbersen Tom
Published: August 2024

As robots enter everyday spaces like offices, the sounds they create affect how they are perceived. We present “Music Mode,” a novel mapping between a robot’s joint motions and sounds, programmed by artists and engineers to make the robot generate music as it moves. Two experiments were designed to characterize the effect of this musical augmentation on human users. In the first experiment, a robot performed three tasks while playing three different sound mappings. Results showed that participants observing the robot perceived it as more safe, animate, intelligent, anthropomorphic, and likable when playing the Music Mode Orchestra software. To test whether the results of the first experiment were due to the Music Mode algorithm, rather than music alone, we conducted a second experiment. Here, the robot performed the same three tasks, while a participant observed via video, but the Orchestra music was either linked to its movement or random. Participants rated the robots as more intelligent when the music was linked to the movement. Robots using Music Mode logged approximately 200 hours of operation while navigating, wiping tables, and sorting trash, and bystander comments made during this operating time served as an embedded case study. This article has both designerly contributions and engineering contributions. The contributions are as follows: (1) an interdisciplinary choreographic, musical, and coding design process to develop a real-world robot sound feature, (2) a technical implementation for movement-based sound generation, and (3) two experiments and an embedded case study of robots running this feature during daily work activities that resulted in increased likeability and perceived intelligence of the robot.
A Decade of SIG-Sponsored Community Events at SIGIR
Authors: Dietz Laura
Published: August 2024

When I attended the NeurIPS conference during my graduate student years, the parties of the Gatsby Neuroscience Unit weren't just social gatherings but pivotal points in my academic and professional journey. It was there where I connected with future collaborators, securing internship and post-doc opportunities that shaped my career. This experience underscored the value of helping students to build their network, a realization I carried with me to my first SIGIR conference in 2014. Observing a gap in such networking opportunities, I proposed the idea of organizing a similar event to foster connections, particularly for students and other newcomers to the research community.
Shaping the Future of Endangered and Low-Resource Languages---Our Role in the Age of LLMs: A Keynote at ECIR 2024
Authors: Mothe Josiane
Published: August 2024

Isidore of Seville is credited with the adage that
it is language that gives birth to a people, and not the other way around
, underlining the profound role played by language in the formation of cultural and social identity. Today, of the more than 7100 languages listed, a significant number are endangered. Since the 1970s, linguists, information seekers and enthusiasts have helped develop digital resources and automatic tools to support a wide range of languages, including endangered ones. The advent of Large Language Model (LLM) technologies holds both promise and peril. They offer unprecedented possibilities for the translation and generation of content and resources, key elements in the preservation and revitalisation of languages. They also present threat of homogenisation, cultural oversimplification and the further marginalisation of already vulnerable languages. The talk this paper is based on has proposed an initiatory journey, exploring the potential paths and partnerships between technology and tradition, with a particular focus on the Occitan language. Occitan is a language from Southern France, parts of Spain and Italy that played a major cultural and economic role, particularly in the Middle Ages. It is now endangered according to UNESCO. The talk critically has examined how human expertise and artificial intelligence can work together to offer hope for preserving the linguistic diversity that forms the foundation of our global and especially our European heritage while addressing some of the ethical and practical challenges that accompany the use of these powerful technologies. This paper is based on the keynote I gave at the 46th European Conference on Information Retrieval (ECIR 2024). As an alternative to reading this paper, a video talk is available online.
1

Date: 26 March 2024.
Report on the 8th Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI 2024) at CHIIR 2024
Authors: Frummet Alexander , Papenmeier Andrea , Fröbe Maik , Kiesel Johannes , Adlakha Vaibhav , Braunschweiler Norbert , Dubiel Mateusz , Ghosh Satanu , Gohsen Marcel , Kreutz Christin , Momeni Milad , Nilles Markus , Cherumanal Sachin Pathiyan , Pirmoradi Abbas , Thomas Paul , Trippas Johanne R. , Zelch Ines , Zendel Oleg
Published: August 2024

Conversational Agents are increasingly integrated into our daily routines, assisting us with various tasks, from simple commands such as scheduling events to more complex conversational search interactions. Such conversational search systems are traditionally evaluated with word-overlap metrics such as F1 score and accuracy. The full-day workshop on Search-Oriented Conversational Artificial Intelligence (SCAI) at CHIIR 2024 explored the evaluation of conversational search systems from the user's perspective. This interactive workshop included multiple panel discussions and working groups focused on developing and discussing innovative, user-centered evaluation methods for these systems. This paper, co-authored by both organizers and participants of the workshop, presents a summary of the insights gathered from the panel discussions and working groups.

Date
: 14 March 2024.

Website
: https://scai.info/scai-2024/.
Report on the 1st International Workshop on Open Web Search (WOWS 2024) at ECIR 2024
Authors: Farzana Sheikh Mastura , Fröbe Maik , Granitzer Michael , Hendriksen Gijs , Hiemstra Djoerd , Potthast Martin , de Vries Arjen P. , Zerhoudi Saber
Published: August 2024

The first International Workshop on Open Web Search (WOWS) was held on Thursday, March 28th, at ECIR 2024 in Glasgow, UK. The full-day workshop had two calls for contributions: the first call aimed at scientific contributions to building, operating, and evaluating search engines cooperatively and the cooperative use of the web as a resource for researchers and innovators. The second call for implementations of retrieval components aimed to gain practical experience with joint, cooperative evaluation of search engines and their components. In total, 2 papers were accepted for the first call, and 11 software components were submitted for the second. The workshop ended with breakout sessions on how the Open-WebSearch.eu project can incorporate collaborative evaluations and a hub of search engines.

Date
: 28 March 2024.

Website
: https://opensearchfoundation.org/wows2024/.
Graph Neural Networks for Recommendation Leveraging Multimodal Information
Authors: Malitesta Daniele
Published: August 2024

Recommender systems act as filtering algorithms to provide users with items that might meet their interests according to the expressed preferences and items' characteristics. As of today, the collaborative filtering paradigm, along with deep learning techniques to learn high-quality users' and items' representations, constitute the de facto standard for personalized recommendation, showing remarkable recommendation accuracy performance. Nevertheless, recommendation remains a highly-challenging task. Among the most debated open issues in the community, this thesis considers two algorithmic and conceptual ones, namely: (i) the inexplicable nature of users' preferences, especially when they come in the form of implicit feedback; (ii) the effective exploitation of the collaborative information in the designing and training of recommendation models.

In domains such as fashion, food, and media content recommendation, the shallow item's profile can be enhanced through the
multimodal
characteristics describing items [Malitesta et al., 2023]. Driven by these assumptions, in the first part of this thesis, we apply multimodal deep learning strategies for multimedia recommendation; the scope is to study and design recommendation algorithms based upon the principles of multimodality to possibly match each item's characteristic to the implicit preference expressed by the user [Deldjoo et al., 2022], thus addressing the (i) issue.

Recent collaborative filtering approaches profile users and items through embedding vectors in the latent space. However, such models disregard structural properties naturally encoded into the user-item interaction data. Indeed, recommendation datasets are easily describable under the topology of a bipartite and undirected graph, with users and items being the graph nodes connected at multiple distance hops. In this respect, the application of
graph neural networks
, recent deep learning techniques specifically tailored to learn from non-euclidean data, can provide a refined representation of users and items to mine near- and long-distance relationships in the user-item graphs [Anelli et al., 2023b]. Indeed, this is one possible solution to exploit the collaborative information, which is effectively propagated within the user-item graph, addressing the (ii) issue.

Conclusively, this thesis aims to match the two families of recommendation strategies by leveraging graph neural networks and multimodal information data [Anelli et al., 2022]. In doing so, other numerous micro-aspects within the two macro-areas (introduced above) are examined. Indeed, the thesis is a systematic compendium of careful analyses regarding, among others, reproducibility, novel evaluation dimensions [Anelli et al., 2023a], and tasks/scenarios complementary to recommendation.

Awarded by
: Politecnico di Bari, Bari, Italy on 30 January 2024.

Supervised by
: Tommaso Di Noia.

Available at
: https://hdl.handle.net/11589/264941.
New Algorithms and Methodologies for Building Information Retrieval Collections
Authors: Otero David
Published: August 2024

Information retrieval systems play a crucial role in addressing users' information needs by aiding their exploration of vast collections of information. This thesis is framed in a critical information retrieval research aspect: evaluation. In particular, we propose new approaches for creating annotated test collections. Such collections are essential for evaluating retrieval systems' effectiveness in controlled experiments. Reflecting real-world conditions accurately in these test collections is pivotal for progress in the field.

We aim to introduce innovative techniques for efficiently assembling reliable test collections, facilitating broader research and development in information retrieval. The thesis first proposes a new method for building new pooled test collections without requiring costly evaluation campaigns [Otero et al., 2021b]. This approach simplifies and economizes the process of building new benchmarks. Then, we introduce a novel adjudication method for determining which pooled documents warrant human judgment, aiming to reduce the need for extensive expert assessments [Otero et al., 2023a]. This method is both cost-effective and efficient. Additionally, the thesis presents a fresh perspective on evaluating adjudication methods, emphasizing statistical significance, an aspect often overlooked in previous document adjudication research [Otero et al., 2023b]. As a demonstration of the methods explored in this thesis, we applied them to develop a new test collection whose construction process we describe here as an example of the use of reduced-budget methods [Otero et al., 2021a, 2020].

In summary, this thesis integrates established information retrieval knowledge with new methodologies to create annotated collections that are both cost-effective and reliable. This fusion is crucial for advancing the development of more effective retrieval systems.

Awarded by
: Universidade da Coruña, A Coruña, Spain on 5 April 2024.

Supervised by
: Javier Parapar, Álvaro Barreiro.

Available at
: http://hdl.handle.net/2183/36378.
Effective, Efficient, and Robust Learning Algorithms for Ranking and Classification
Authors: Marcuzzi Federico
Published: August 2024

Over the past decade, machine learning has gained significant traction and is now deployed across diverse domains, including information systems, finance, healthcare, cybersecurity, autonomous driving, and more. As machine learning finds applications in various sensitive scenarios, the demand for models that exhibit accuracy and robustness during the operational phase has grown exponentially. One crucial factor that profoundly shapes the quality of machine learning models revolves around the training data they rely upon and the input data encountered at the operational phase. Therefore, the development of data-aware algorithms is of paramount importance in achieving high-quality machine-learning models. This thesis contributes to this overarching objective by delving into the development of data-aware algorithms, emphasizing the importance of this awareness during both the training and operational phases of machine learning models. The research presented in this thesis focuses on two primary domains. The first domain is information retrieval, with a particular emphasis on enhancing both the efficiency of learning-to-rank learning algorithms and the effectiveness of the learned models in solving ranking tasks. The thesis includes three works in this domain: Marcuzzi et al. [2022] provides a novel algorithm to detect and remove
consistent-outliers
documents from the training data. In Marcuzzi et al. [2023], we designed a new learning algorithm that handles the problem of gradient incoherencies affecting LambdaRank-based algorithms. Finally, in Lucchese et al. [2023], we designed a new sampling function for the
Selective Gradient Boosting
algorithm to exploit the most useful low-ranked non-relevant document. The second domain is adversarial machine learning, which focuses on increasing the robustness of binary classifiers against adversarial inputs encountered at the operational phase. Furthermore, the research in this domain focuses on providing certifiable models to efficiently assess robustness against adversarial machine learning attacks. In this regard, in Calzavara et al. [2021], we designed a novel robust learning algorithm to train ensembles of decision trees robust to evasion attacks along with its polynomial robustness-certification algorithm designed to compute a robustness lower bound. Finally, in Calzavara et al. [2022], we provided a new evaluation metric named
Resilience
to better access the security of machine learning models.

Awarded by
: Università Ca' Foscari di Venezia, Venice, Italy on 19 April 2024.

Supervised by
: Claudio Lucchese.

Available at
: https://federicomarcuzzi.github.io/resources/thesis_phd.pdf.
Data Mesh: A Systematic Gray Literature Review
Authors: Goedegebuure Abel , Kumara Indika , Driessen Stefan , Van Den Heuvel Willem-Jan , Monsieur Geert , Tamburri Damian Andrew , Nucci Dario Di
Published: August 2024

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has piqued the practitioners’ interest, and considerable gray literature exists. At the same time, we observe a lack of academic attempts at defining and building upon the concept. Hence, in this article, we aim to start from the foundations and characterize the data mesh architecture regarding its design principles, architectural components, capabilities, and organizational roles. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The resulting review provides insights into practitioners’ perspectives on the four key principles of data mesh: data as a product, domain ownership of data, self-serve data platform, and federated computational governance. Moreover, due to the comparability of data mesh and SOA (service-oriented architecture), we mapped the findings from the gray literature into the reference architectures from the SOA academic literature to create the reference architectures for describing three key dimensions of data mesh: organization of capabilities and roles, development, and runtime. Finally, we discuss open research issues in data mesh, partially based on the findings from the gray literature.
Pharma[e]cology: How the Gut Microbiome Contributes to Variations in Drug Response
Authors: Trepka Kai R. , Olson Christine A. , Upadhyay Vaibhav , Zhang Chen , Turnbaugh Peter J.
Published: August 2024

Drugs represent our first, and sometimes last, line of defense for many diseases, yet despite decades of research we still do not fully understand why a given drug works in one patient and fails in the next. The human gut microbiome is one of the missing puzzle pieces, due to its ability to parallel and extend host pathways for drug metabolism, along with more complex host–microbiome interactions. Herein, we focus on the well-established links between the gut microbiome and drugs for heart disease and cancer, plus emerging data on neurological disease. We highlight the interdisciplinary methods that are available and how they can be used to address major remaining knowledge gaps, including the consequences of microbial drug metabolism for treatment outcomes. Continued progress in this area promises fundamental biological insights into humans and their associated microbial communities and strategies for leveraging the microbiome to improve the practice of medicine.
What Structural Biology Tells Us About the Mode of Action and Detection of Toxicants
Authors: le Maire Albane , Bourguet William
Published: August 2024

The study of the adverse effects of chemical substances on living organisms is an old and intense field of research. However, toxicological and environmental health sciences have long been dominated by descriptive approaches that enable associations or correlations but relatively few robust causal links and molecular mechanisms. Recent achievements have shown that structural biology approaches can bring this added value to the field. By providing atomic-level information, structural biology is a powerful tool to decipher the mechanisms by which toxicants bind to and alter the normal function of essential cell components, causing adverse effects. Here, using endocrine-disrupting chemicals as illustrative examples, we describe recent advances in the structure-based understanding of their modes of action and how this knowledge can be exploited to develop computational tools aimed at predicting properties of large collections of compounds.
Neural pathways associated with reduced rigidity during pallidal deep brain stimulation for Parkinson’s disease
Authors: Lecy Emily , Linn-Evans Maria E. , Amundsen-Huffmaster Sommer L. , Palnitkar Tara , Patriat Remi , Chung Jae Woo , Noecker Angela M. , Park Michael C. , McIntyre Cameron C. , Vitek Jerrold L. , Cooper Scott E. , Harel Noam , Johnson Matthew D. , MacKinnon Colum D.
Published: August 2024

Subject-specific computational models of pallidal deep brain stimulation, in conjunction with quantitative measures of forearm rigidity, were used to examine the neural pathways mediating stimulation-induced changes in rigidity in people with Parkinson’s disease. The model uniquely included internal, efferent and adjacent pathways of the basal ganglia. The results demonstrate that reductions in rigidity evoked by deep brain stimulation were principally mediated by the activation of globus pallidus internus efferent pathways.
Chronic morphine treatment induces sex- and synapse-specific cellular tolerance on thalamo-cortical mu opioid receptor signaling
Authors: Jaeckel Elizabeth R. , Arias-Hervert Erwin R. , Perez-Medina Alberto L. , Schulz Stefan , Birdsong William T.
Published: August 2024

Opioid signaling within the anterior cingulate cortex (ACC) is important for opioid modulation of affective pain. Glutamatergic medial thalamus (MThal) neurons synapse in the ACC and opioids, acting through mu opioid receptors (MORs), acutely inhibit synaptic transmission from MThal synapses. However, the effect of chronic opioid exposure on MThal-ACC synaptic transmission is not known. Here, we demonstrate that chronic morphine treatment induces cellular tolerance at these synapses in a sex-specific and phosphorylation-dependent manner.
Sustained fentanyl exposure inhibits neuronal activity in dissociated striatal neuronal-glial cocultures through actions independent of opioid receptors
Authors: Yarotskyy Viktor , Nass Sara R. , Hahn Yun-Kyung , Contois Liangru , McQuiston A. Rory , Knapp Pamela E. , Hauser Kurt F.
Published: August 2024

Acute fentanyl exposure attenuated the activity of striatal medium spiny neurons (MSNs) in vitro and in dopamine D2, but not D1, receptor-expressing MSNs in ex vivo slices. By contrast, sustained fentanyl exposure suppressed the spontaneous activity of MSNs cocultured with glia through a nonopioid receptor-dependent mechanism modulated, in part, by α1-adrenoceptors. Fentanyl exposure can affect striatal function via a nonopioid receptor mechanism of action that appears mediated by α1-adrenoreceptor-expressing striatal neurons and/or astroglia.
Mechanical Remodeling of Nuclear Biomolecular Condensates
Authors: Soggia Giulia , ElMaghloob Yasmin , Boromangnaeva Annie-Kermen , Al Jord Adel
Published: August 2024

Organism health relies on cell proliferation, migration, and differentiation. These universal processes depend on cytoplasmic reorganization driven notably by the cytoskeleton and its force-generating motors. Their activity generates forces that mechanically agitate the cell nucleus and its interior. New evidence from reproductive cell biology revealed that these cytoskeletal forces can be tuned to remodel nuclear membraneless compartments, known as biomolecular condensates, and regulate their RNA processing function for the success of subsequent cell division that is critical for fertility. Both cytoskeletal and nuclear condensate reorganization are common to numerous physiological and pathological contexts, raising the possibility that mechanical remodeling of nuclear condensates may be a much broader mechanism regulating their function. Here, we review this newfound mechanism of condensate remodeling and venture into the contexts of health and disease where it may be relevant, with a focus on reproduction, cancer, and premature aging.
Novel Therapeutic Targets and Biomarkers Associated with Bladder Cancer‐Associated Fibroblasts (CAFs) Promoted by Bisphenol A
Authors: Luo Yuan , Liu Xinyue , Liu Yuanting
Published: August 2024

The escalating incidence of health issues linked to environmental pollutants, specifically endocrine‐disrupting chemicals (EDCs), has emerged as a dire consequence of modern industrialization. Bisphenol A (BPA), a widespread EDC, is under scrutiny for its potential role in exacerbating bladder cancer via the modulation of cancer‐associated fibroblasts (CAFs). CAFs are integral to the tumor microenvironment, influencing cancer progression through their interactions with immune cells and secretion of various factors and exosomes. By recognizing the critical role of CAFs, this study delves into their utility as therapeutic targets, focusing on the identification of reliable biomarkers within CAFs for bladder cancer. Through weighted correlation network analysis, genes associated with T cell activity were pinpointed, culminating in the creation of a CAFs‐based, immune‐related gene prognostic model. Central to this model is ANPEP, an enzyme whose expression level not only serves as an indicator of T cell infiltration but also implicates a substantial role in the CAF‐mediated immunotherapy responses for bladder cancer. Our investigation posits ANPEP as a linchpin in regulating CAF functions, offering a novel perspective wherein targeting ANPEP may reduce the adverse side effects commonly associated with traditional immunotherapies. Furthermore, ANPEP‐targeted strategies could lessen the tumor mutational burden in bladder cancer patients. Empirical evidence from our proliferation and invasion assays indicates that heightened ANPEP expression is correlated with diminished patient survival. These insights pave the way for tailored immunotherapeutic approaches in bladder cancer treatment, emphasizing the modulation of CAFs by ANPEP.
Design and Validation of an Instrument for the Evaluation of the Quality of Mother‐Child Bond and Attachment: “Cuestionario Vínculo y Apego Materno‐Filial” (VAMF Questionnaire)
Authors: Diaz-Ogallar Maria Antonia , Hernandez-Martinez Antonio , Linares-Abad Manuel , Martinez-Galiano Juan Miguel
Published: August 2024

The relationship between a mother and her newborn can be determined through two concepts: “bond” and “attachment.” Currently, there are no instruments that assess these phenomena jointly. Objective. This study aims to develop a valid and reliable instrument to determine the quality of the postnatal bond and attachment in the mother‐child relationship. Methods. In Spain, a multidisciplinary panel of experts was involved in creating the “Maternal‐Child Bond and Attachment (VAMF, for its initials in Spanish)” tool. The tool was piloted on a group of women and applied to the target population of women with children aged between 6 weeks and 18 months to determine the psychometric characteristics: internal consistency Cronbach’s α was used. An exploratory factor analysis was conducted, a study of convergent validity with the scale and predictive validity with the Maternal Postnatal Attachment Scale (MPAS) using Pearson’s correlation coefficient, and a study of reliability was carried out using the intraclass correlation coefficient. Results. 1155 women participated, with a mean age of 34.5 years. The VAMF showed good internal consistency for the scale with 29 items (α = 0.836). In the exploratory factor analysis (EFA), an explained variance of 49.71% was observed with 6 components. Convergent validity showed an adequate correlation, with a Pearson correlation coefficient with the MPAS scale of 0.679. The VAMF questionnaire presented an excellent predictive capacity in the bond subscale, with an area under the ROC curve of 0.90 (95% CI: 0.87–0.93), and a poor predictive capacity in the attachment subscale, with an area under the ROC curve of 0.69 (95% CI: 0.63–0.76) to predict MPAS scale scores. In the test‐retest, the VAMF presented a very good adequate degree of absolute agreement (ICC: 0.86; 95% CI: 0.72–0.93). Conclusions. The VAMF is a new valid and reliable instrument for determining the quality of the mother‐child bond and attachment.
Development of Root Caries Associated With the Use of Sugar‐Free Nicotine Lozenges: A Long‐Term Case Report
Authors: Ehrhardt Coral , Mäkinen Kauko K. , Cobb Charles M.
Published: August 2024

The authors present a case concerning an adult male patient who developed multiple sites of root caries adjacent to the area where he habitually held a sugar‐free nicotine lozenge that contained mannitol and maltodextrin. The root caries occurred despite the patient’s excellent oral hygiene, exemplary dietary habits, and clinically normal salivary flow. Between 1999 and 2008, he had only required two restorations to repair carious lesions. This patient had a 20+‐year habit of using smokeless tobacco before switching to a cessation aid nicotine lozenge in May of 2008. A full‐mouth series of radiographs taken in November 2009 revealed carious lesions on virtually every posterior tooth. The nicotine lozenge’s principal ingredients were mannitol (75.7%) and maltodextrin. According to the United States’ current Food and Drug Administration (FDA) guidelines, manufacturers can advertise these lozenges as sugar‐free. Thus, it is assumed by the public that these types of products are incapable of “causing a cavity.” However, this case report presents evidence suggesting that frequent use of sugar‐free nicotine lozenges may be associated with dental caries.