Search

Your search keyword '"Rulli, A"' showing total 5,017 results
Start Over Author "Rulli, A"
5,017 results on '"Rulli, A"'

1. Investigating the Scalability of Approximate Sparse Retrieval Algorithms to Massive Datasets

Author

Bruch, Sebastian, Nardini, Franco Maria, Rulli, Cosimo, Venturini, Rossano, and Venuta, Leonardo

Subjects
Computer Science - Information Retrieval
Abstract

Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, have long hindered their use in real-world retrieval systems. That changed with the recent development of approximate algorithms that leverage the distributional properties of sparse embeddings to speed up retrieval. Nonetheless, in much of the existing literature, evaluation has been limited to datasets with only a few million documents such as MSMARCO. It remains unclear how these systems behave on much larger datasets and what challenges lurk in larger scales. To bridge that gap, we investigate the behavior of state-of-the-art retrieval algorithms on massive datasets. We compare and contrast the recently-proposed Seismic and graph-based solutions adapted from dense retrieval. We extensively evaluate Splade embeddings of 138M passages from MsMarco-v2 and report indexing time and other efficiency and effectiveness metrics.

Published
2025

2. kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search

Author

Delfino, Leonardo, Erriquez, Domenico, Martinico, Silvio, Nardini, Franco Maria, Rulli, Cosimo, and Venturini, Rossano

Subjects
Computer Science - Information Retrieval
Abstract

Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel research-oriented ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. This abstraction ensures flexibility and facilitates an easy integration of new components. In this work, we detail the architecture of kANNolo and demonstrate that its flexibility does not compromise performance. The experimental analysis shows that kANNolo achieves state-of-the-art performance in terms of speed-accuracy trade-off while allowing fast and easy prototyping, thus making kANNolo a valuable tool for advancing ANN research. Source code available on GitHub: https://github.com/TusKANNy/kannolo., Comment: 7 pages, 3 figures

Published
2025

3. Educating Preschoolers Environmental to Actions: A Metacognition-Based Approach

Author

Martina Rulli, Elsa Bruni, Alberto Di Domenico, and Nicola Mammarella

Abstract

Metacognition is the process of thinking about one's own thinking, learning, and problem-solving strategies. It involves being aware of one's own cognitive processes and knowing how to regulate and monitor them. Sustainability, instead, refers to the ability to maintain or preserve resources and ecosystems for future generations. Here, we draw from the classical metacognitive approach and propose that metacognition plays an important role in sustainability. The PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses) Statement was used to perform and recode this review. Given that metacognitive abilities develop early during childhood, metacognition can be viewed as a useful approach to teach pro-environmental behavior from early childhood. Although the interaction between metacognition and sustainability in preschoolers appears to be insufficiently explored, the final aim of this review is to offer a new education-based perspective about metacognition that can be implemented in early childhood to foster pro-environmental actions in the longterm.

Published
2024

4. Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations

Author

Bruch, Sebastian, Nardini, Franco Maria, Rulli, Cosimo, and Venturini, Rossano

Subjects
Computer Science - Information Retrieval
Abstract

Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges, a recent algorithm called Seismic has enabled remarkably fast, highly-accurate approximate retrieval. Seismic statically prunes inverted lists, organizes each list into geometrically-cohesive blocks, and augments each block with a summary vector. At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and significantly outperforms the winning graph-based submissions to the BigANN 2023 Challenge. In this work, we speed up Seismic further by introducing two innovations to its query processing subroutine. First, we traverse blocks in order of importance, rather than arbitrarily. Second, we take the list of documents retrieved by Seismic and expand it to include the neighbors of each document using an offline k-regular nearest neighbor graph; the expanded list is then ranked to produce the final top-k set. Experiments on two public datasets show that our extension, named SeismicWave, can reach almost-exact accuracy levels and is up to 2.2x faster than Seismic.

Published
2024
Full Text
View/download PDF

6. Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations

Author

Bruch, Sebastian, Nardini, Franco Maria, Rulli, Cosimo, and Venturini, Rossano

Subjects
Computer Science - Information Retrieval
Abstract

Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design. Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences between learned embeddings and term frequency-based lexical models of relevance such as BM25. Recognizing this challenge, a great deal of research has gone into, among other things, designing retrieval algorithms tailored to the properties of learned sparse representations, including approximate retrieval systems. In fact, this task featured prominently in the latest BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on a large benchmark dataset by throughput and recall. In this work, we propose a novel organization of the inverted index that enables fast yet effective approximate retrieval over learned sparse embeddings. Our approach organizes inverted lists into geometrically-cohesive blocks, each equipped with a summary vector. During query processing, we quickly determine if a block must be evaluated using the summaries. As we show experimentally, single-threaded query processing using our method, Seismic, reaches sub-millisecond per-query latency on various sparse embeddings of the MS MARCO dataset while maintaining high recall. Our results indicate that Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and further outperforms the winning (graph-based) submissions to the BigANN Challenge by a significant margin.

Published
2024
Full Text
View/download PDF

7. Efficient Multi-Vector Dense Retrieval Using Bit Vectors

Author

Nardini, Franco Maria, Rulli, Cosimo, and Venturini, Rossano

Subjects
Computer Science - Information Retrieval
Abstract

Dense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance of a passage w.r.t. to a query using efficient similarity measures. In this line, multi-vector representations show improved effectiveness at the expense of a one-order-of-magnitude increase in memory footprint and query latency by encoding queries and documents on a per-token level. Recently, PLAID has tackled these problems by introducing a centroid-based term representation to reduce the memory impact of multi-vector systems. By exploiting a centroid interaction mechanism, PLAID filters out non-relevant documents, thus reducing the cost of the successive ranking stages. This paper proposes ``Efficient Multi-Vector dense retrieval with Bit vectors'' (EMVB), a novel framework for efficient query processing in multi-vector dense retrieval. First, EMVB employs a highly efficient pre-filtering step of passages using optimized bit vectors. Second, the computation of the centroid interaction happens column-wise, exploiting SIMD instructions, thus reducing its latency. Third, EMVB leverages Product Quantization (PQ) to reduce the memory footprint of storing vector representations while jointly allowing for fast late interaction. Fourth, we introduce a per-document term filtering method that further improves the efficiency of the last step. Experiments on MS MARCO and LoTTE show that EMVB is up to 2.8x faster while reducing the memory footprint by 1.8x with no loss in retrieval accuracy compared to PLAID.

Published
2024

8. The Role of Reporting Guidelines in Research Publication

Author

Rulli, Danielle

Subjects
Report writing -- Laws, regulations and rules, Practice guidelines (Medicine) -- Usage, Methodology -- Analysis, Examinations -- Validity, Government regulation, Health
Abstract

A peer-reviewed journal preserves the quality of evidence in the literature by ensuring the manuscripts and research submitted is reliable and valid. For an article to be published, it must be of significance to the profession and body of evidence, have no flaws in the methodologies, contain suitable and complete statistical analyses, and appropriately interpreted results. Peer reviewers are experts whose role is to determine the quality of the research and how it is reported, protecting the profession and the public via the evidence on which it stands. The genesis of reporting guidelines can be found in medicine's effort to bring the quality of research methods in line with expected standards. Reporting guidelines build in quality control, requiring authors to clearly lay out the methodologies of how their research has been designed and conducted, and assisting peer reviewers in the standardized assessment of manuscripts. These reporting guidelines or 'checklists' have become mainstream over the last 20 years as the means for upholding ethical, reliable, and valid research in health professions research. Reporting guidelines are simply the means for authors to easily demonstrate that their research was designed and performed in a valid manner, and that their findings are therefore reliable. Keywords Reporting guidelines, checklist, manuscript, peer review NDHRA priority area, Professional development: Education (evaluation). Submitted for publication: 7/26/2024; accepted: 7/30/2024, INTRODUCTION The preparation of a research manuscript submission is just as important as the research study. The manuscript is a direct reflection of the research itself, demonstrating its rationale, design, [...]

Published
2025

12. A global dataset of the national green and blue water footprint of livestock feeds

Author

Camilla Govoni, Davide Danilo Chiarelli, and Maria Cristina Rulli

Subjects
Science
Abstract

Abstract Agriculture accounts for a significant share of global freshwater use, with the livestock sector contributing substantially. The production of animal feeds stands out as the step of the production chain responsible for the high water use associated with the consumption of animal-source foods. Estimating the water footprint (WF) of livestock feeds is crucial for sustainable animal farming. This study fills gaps in the existing literature by evaluating the WF of 140 animal feed products across 262 countries. Utilizing the WATNEEDS model, we quantify green and blue water requirements based on crop-specific areas and climatic data averaged between 2015 and 2019. Our analysis covers rainfed and irrigated conditions, considering crop yields, conversion factors, and feed use fractions. Results provide insights into the water footprint associated with diverse feed products globally, aiding in informed decision-making for sustainable livestock production systems. This dataset contributes to enhancing our understanding of the environmental impact of the livestock sector and informs on strategies for water resource management in the agri-food sector through more sustainable animal nutrition.

Published
2024
Full Text
View/download PDF

16. Comparison of “IN-REC-SUR-E” and LISA in preterm neonates with respiratory distress syndrome: a randomized controlled trial (IN-REC-LISA trial)

Author

Vento, Giovanni, Paladini, Angela, Aurilia, C., Ozdemir, S. Alkan, Carnielli, V. P., Cools, F., Costa, S., Cota, F., Dani, C., Davis, P. G., Fattore, S., Fè, C., Finer, N., Fusco, F. P., Gizzi, C., Herting, E., Jian, M., Lio, A., Lista, G., Mosca, F., Nobile, S., Perri, A., Picone, S., Pillow, J. J., Polglase, G., Pasciuto, T., Pastorino, R., Tana, M., Tingay, D., Tirone, C., van Kaam, A. H., Ventura, M. L., Aceti, A., Agosti, M., Alighieri, G., Ancora, G., Angileri, V., Ausanio, G., Aversa, S., Balestri, E., Baraldi, E., Barbini, M. C., Barone, C., Beghini, R., Bellan, C., Berardi, A., Bernardo, I., Betta, P., Binotti, M., Bizzarri, B., Borgarello, G., Borgione, S., Borrelli, A., Bottino, R., Bracaglia, G., Bresesti, I., Burattini, I., Cacace, C., Calzolari, F., Campagnoli, M. F., Capasso, L., Capozza, M., Capretti, M. G., Caravetta, J., Carbonara, C., Cardilli, V., Carta, M., Castoldi, F., Castronovo, A., Cavalleri, E., Cavigioli, F., Cecchi, S., Chierici, V., Cimino, C., Cocca, F., Cocca, C., Cogo, P., Coma, M., Comito, V., Condò, V., Consigli, C., Conti, R., Corradi, M., Corsello, G., Corvaglia, L. T., Costa, A., Coscia, A., Cresi, F., Crispino, F., D’Amico, P., De Cosmo, L., De Maio, C., Del Campo, G., Di Credico, S., Di Fabio, S., Di Nicola, P., Di Paolo, A., Di Valerio, S., Distilo, A., Duca, V., Falcone, A., Falsaperla, R., Fasolato, V. A., Fatuzzo, V., Favini, F., Ferrarello, M. P., Ferrari, S., Nastro, F. Fiori, Forcellini, C. A., Fracchiolla, A., Gabriele, A., Galdo, F., Gallini, F., Gangemi, A., Gargano, G., Gazzolo, D., Gentile, M. P., Ghirardello, S., Giardina, F., Giordano, L., Gitto, E., Giuffrè, M., Grappone, L., Grasso, F., Greco, I., Grison, A., Guglielmino, R., Guidotti, I., Guzzo, I., La Forgia, N., La Placa, S., La Torre, G., Lago, P., Lanciotti, L., Lavizzari, A., Leo, F., Leonardi, V., Lestingi, D., Li, J., Liberatore, P., Lodin, D., Lubrano, R., Lucente, M., Luciani, S., Luvarà, D., Maffei, G., Maggio, A., Maggio, L., Maiolo, K., Malaigia, L., Mangili, G., Manna, A., Maranella, E., Marciano, A., Marcozzi, P., Marletta, M., Marseglia, L., Martinelli, D., Martinelli, S., Massari, S., Massenzi, L., Matina, F., Mattia, L., Mescoli, G., Migliore, I. V., Minghetti, D., Mondello, I., Montano, S., Morandi, G., Mores, N., Morreale, S., Morselli, I., Motta, M., Napolitano, M., Nardo, D., Nicolardi, A., Nider, S., Nigro, G., Nuccio, M., Orfeo, L., Ottaviano, C., Paganin, P., Palamides, S., Palatta, S., Paolillo, P., Pappalardo, M. G., Pasta, E., Patti, L., Paviotti, G., Perniola, R., Perotti, G., Perrone, S., Petrillo, F., Piazza, M. S., Piccirillo, A., Pierro, M., Piga, E., Pingitore, G. A., Pisu, S., Pittini, C., Pontiggia, F., Pontrelli, G., Primavera, A., Proto, A., Quartulli, L., Raimondi, F., Ramenghi, L., Rapsomaniki, M., Ricotti, A., Rigotti, C., Rinaldi, M., Risso, F. M., Roma, E., Romanini, E., Romano, V., Rosati, E., Rosella, V., Rulli, I., Salvo, V., Sanfilippo, C., Sannia, A., Saporito, A., Sauna, A., Scapillati, E., Schettini, F., Scorrano, A., Mantelli, S. Semeria, Sepporta, V., Sindico, P., Solinas, A., Sorrentino, E., Spaggiari, E., Staffler, A., Stella, M., Termini, D., Terrin, G., Testa, A., Tina, G., Tirantello, M., Tomasini, B., Tormena, F., Travan, L., Trevisanuto, D., Tuling, G., Tulino, V., Valenzano, L., Vedovato, S., Vendramin, S., Villani, P. E., Viola, S., Viola, V., Vitaliti, G., Vitaliti, M., Wanker, P., Yang, Y., Zanetta, S., and Zannin, E.

Published
2024
Full Text
View/download PDF

18. Neural Network Compression using Binarization and Few Full-Precision Weights

Author

Nardini, Franco Maria, Rulli, Cosimo, Trani, Salvatore, and Venturini, Rossano

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.6
Abstract

Quantization and pruning are two effective Deep Neural Networks model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression technique combining quantization with pruning. APB enhances the representational capability of binary networks using a few full-precision weights. Our technique jointly maximizes the accuracy of the network while minimizing its memory impact by deciding whether each weight should be binarized or kept in full precision. We show how to efficiently perform a forward pass through layers compressed using APB by decomposing it into a binary and a sparse-dense matrix multiplication. Moreover, we design two novel efficient algorithms for extremely quantized matrix multiplication on CPU, leveraging highly efficient bitwise operations. The proposed algorithms are 6.9x and 1.5x faster than available state-of-the-art solutions. We extensively evaluate APB on two widely adopted model compression datasets, namely CIFAR10 and ImageNet. APB delivers better accuracy/memory trade-off compared to state-of-the-art methods based on i) quantization, ii) pruning, and iii) combination of pruning and quantization. APB outperforms quantization in the accuracy/efficiency trade-off, being up to 2x faster than the 2-bit quantized model with no loss in accuracy., Comment: 15 pages, 6 figures, 3 tables

Published
2023

19. Distributed hydrodynamic modelling for assessing flood impacts on crops: Assessing flood-resilient crop management in a coastal basin of central Italy

Author

Thomas Lucaora, Antonio Annis, Fernando Nardi, Maria Cristina Rulli, and Davide Danilo Chiarelli

Subjects
Agriculture, Crops, Flood damage, Crop damage, 1D-2D flood modelling, HEC-RAS, Agriculture (General), S1-972, Agricultural industries, HD9000-9495
Abstract

Nuisance flooding phenomena do not only affect urban areas, but also agriculture, determining catastrophic damages to crops. The impact of floods on crops has been an underrated topic in scientific literature that focuses more on urban areas, probably because they tend to have higher densities of population, infrastructure, industries, and real estate properties, which translates into more substantial human and economic losses during floods. In this work, we proposed a quantitative physically based method for crop damage assessment based on distributed hydrodynamic modelling of flood events. It exploited a combined high-resolution 1D-2D hydraulic model and crop resistance interlinked dynamics. The method allows to quantify, with unprecedented high resolution, the spatially distributed probability of crop losses for different crop types for different flood recurrence intervals. The methodology is applied in a relevant case study in central Italy, the Marta River basin, that experienced impactful floods on the extended riverine agricultural lands. Modelling results allow to represent combined maximum water levels per flood scenario, ponding time of water on the field, and the maximum flow velocities considering growth stages of different crops. Results showed that April is the most vulnerable month with the greatest potential losses (4.47 × 109 kcal for the 200-year return period), due to the high concentration of winter cereals (>50 %) with high fragility at the flowering stage. A set of alternative crops that demonstrate to be more flood tolerant or to grow in a period offset from the peak flood occurrence were also assessed showing the potential use of the proposed methodology towards better agricultural management in floodplains.

Published
2025
Full Text
View/download PDF

20. Global Land‐Water Competition and Synergy Between Solar Energy and Agriculture

Author

Maddalena Curioni, Nikolas Galli, Giampaolo Manzolini, and Maria Cristina Rulli

Subjects
water‐energy‐food Nexus, global agro‐hydrology, solar energy land use, Environmental sciences, GE1-350, Ecology, QH540-549.5
Abstract

Abstract The food and energy systems face mounting challenges due to increasing demands and sustainability constraints, which impact their ability to efficiently utilize natural resources, such as land and freshwater. Among these challenges, competition for land between large‐scale renewable energy production plants and agriculture poses a risk, especially for photovoltaics. Agrivoltaics offers an opportunity to synergistically use land for simultaneous production of energy and food. Recent studies have investigated the upscaling potential of agrivoltaics, moving from field scale analyses to larger‐scale suitability assessments. Yet, studies addressing the interaction between crop dynamics and local climatic factors, as well as explicitly investigating hydrological dynamics of agrivoltaics across crops and climates, are still limited. Here, we first superpose a spatial data set of existing photovoltaic farms with different land use/land cover maps to assess the magnitude of land use competition associated with photovoltaics. Then, we use a spatialized agro‐hydrological model to simulate the response to different levels of radiation attenuation of 22 non‐irrigated crops in their harvested areas across the globe. We find that 22%–35% of rainfed harvested areas globally would maintain their yields if converted to agrivoltaics, while 13%–16% of ground‐mounted photovoltaic plants globally are associated with a cropland to non‐cropland transition. While carrying the typical limitations and uncertainties of global studies, our results may offer novel possibilities for cross‐crop and cross‐location comparisons of agrivoltaic experiences, as well as a basis to have a deeper and cross‐scale understanding of the feasibility of photovoltaics.

Published
2025
Full Text
View/download PDF

28. Behavioral Health in Dental Settings: Resources to Support Integration

Author

Adrienne Lapidos, Rachael Matulis, Sarah Neil Mockridge, and Danielle Rulli

Subjects
Oral Health, dental care, medical/dental integration, mental health, substance use disorders, behavioral health, Dentistry, RK1-715
Abstract

Background Decades of research evidence has shown that mental health and substance use are impacted by oral health, and that oral health is impacted by mental health and substance use. As our nation experiences alarming increases in mental health and substance use needs, patients in dental settings are increasingly likely to present with such needs, impacting their ability to successfully access oral health services and improve their oral health.Discussion However, dental providers can leverage existing resources and practices toward best supporting patients’ behavioral health, and, ultimately, their oral health. This article includes concepts, tools, and resources to support dental professionals in developing financially sustainable shifts to move the dial toward integrated oral health and behavioral health care.

Published
2024
Full Text
View/download PDF

29. C.E. Credit. Why Integrate Dentistry and Behavioral Health: What Every Oral Health Provider Should Know About Mental Health and Substance Use

Author

Rachael Matulis, Adrienne Lapidos, Sarah Neil Mockridge, and Danielle Rulli

Subjects
Oral Health, Dental Care, Medical/Dental Integration, Mental Health, Substance Use Disorders, Behavioral Health, Dentistry, RK1-715
Abstract

Background Mental health and substance use challenges have reached crisis levels in the U.S. Nearly one in three American adults had either a substance use disorder or any mental illness in 2022, and nearly one in four adolescents aged 12 to 17 had a substance use disorder or major depressive episode in the same year. The behavioral health workforce is not able to keep up with this demand, leading to a scarcity of resources and services for individuals with mental health and substance use needs. This need increasingly impacts all health care settings, including dental settings, where providers often have limited resources and supports for addressing the mental health and substance use needs of patients – here referred to with the umbrella term of “behavioral health.”Discussion Because of the bi-directional relationship between oral health and behavioral health, providing high-quality dental care often involves considering many aspects of a person’s health, particularly the behavioral health factors that influence oral health. Mental health challenges such as depression, anxiety, or a substance use disorder can negatively impact oral health, and the reverse is also true; poor oral health can create or worsen challenges with behavioral health. This paper provides an overview of the bidirectional connections between oral health and behavioral health, and reviews disparities facing marginalized communities who lack access to care. It also offers promising practices and key resources for dental providers interested in better integrating oral health and behavioral health services.Continuing Education Credit Available The practice worksheet is available online in the supplemental material tab for this article. A CDA Continuing Education quiz is online for this article:https://www.cdapresents360.com/learn/catalog/view/20

Published
2024
Full Text
View/download PDF

30. Differential impact of genetic deletion of TIGIT or PD-1 on melanoma-specific T-lymphocytes

Author

Gwenann Cadiou, Tiffany Beauvais, Lucine Marotte, Sylvia Lambot, Cécile Deleine, Caroline Vignes, Malika Gantier, Melanie Hussong, Samuel Rulli, Anne Jarry, Sylvain Simon, Bernard Malissen, and Nathalie Labarriere

Subjects
Adoptive cell transfer, CD8+ T cell clones, gene editing, immunotherapy, melanoma, PD-1, Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Immune checkpoint (IC) blockade and adoptive transfer of tumor-specific T-cells (ACT) are two major strategies to treat metastatic melanoma. Their combination can potentiate T-cell activation in the suppressive tumor microenvironment, but the autoimmune adverse effects associated with systemic injection of IC blockers persist with this strategy. ACT of tumor-reactive T-cells defective for IC expression would overcome this issue. For this purpose, PD-1 and TIGIT appear to be relevant candidates, because their co-expression on highly tumor-reactive lymphocytes limits their therapeutic efficacy within the tumor microenvironme,nt. Our study compares the consequences of PDCD1 or TIGIT genetic deletion on anti-tumor properties and T-cell fitness of melanoma-specific T lymphocytes. Transcriptomic analyses revealed down-regulation of cell cycle-related genes in PD-1KO T-cells, consistent with biological observations, whereas proliferative pathways were preserved in TIGITKO T-cells. Functional analyses showed that PD-1KO and TIGITKO T-cells displayed superior antitumor reactivity than their wild-type counterpart in vitro and in a preclinical melanoma model using immunodeficient mice. Interestingly, it appears that TIGITKO T-cells were more effective at inhibiting tumor cell proliferation in vivo, and persist longer within tumors than PD-1KO T-cells, consistent with the absence of impact of TIGIT deletion on T-cell fitness. Taken together, these results suggest that TIGIT deletion, over PD-1 deletion, in melanoma-specific T-cells is a compelling option for future immunotherapeutic strategies.

Published
2024
Full Text
View/download PDF

33. Development of Teachers' Pedagogical Content Knowledge during Lesson Planning of Socioscientific Issues

Author

Minken, Zachary, Macalalag, Augusto Z., Clarke, Andre, Marco-Bujosa, Lisa M., and Rulli, Carol

Abstract

This case study addresses the pedagogical challenges teachers face in incorporating elements of socioscientific issues (SSI) when planning science and mathematics lessons. In order to effectively plan and teach SSI lessons, teachers must develop pedagogical content knowledge (PCK) specific to unpacking elements of SSI such as identifying an issue that is debatable and relevant to students' lives, employing reflective scientific skepticism, and evaluating multiple perspectives. This study was guided by the following research questions: 1) In what ways, if any, did teachers' knowledge and instructional design of SSI change throughout the intensive series of workshops? 2) What areas of SSI required additional support? To answer our research questions, we analyzed changes in lesson plans from 29 teachers, mostly science and secondary, over the course of three intensive workshops as part of the Integrating STEM in Everyday Life Conference Series. Over the five month period, teachers worked in groups and with mentors to design and implement SSI lessons. Our findings show that teachers demonstrated positive changes in all SSI elements over the course of the workshops. However, deeper analysis reveals that teachers struggled to balance the social and scientific aspects of SSI. Moreover, our analysis suggests that teachers did not focus on the discursive nature of SSI in their lesson plans. Implications of our study include ways in which professional development programs can cultivate teachers' PCK of SSI in order to better support them in planning and implementing SSI lessons.

Published
2021

34. Distilled Neural Networks for Efficient Learning to Rank

Author

Nardini, F. M., Rulli, C., Trani, S., and Venturini, R.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Performance
Abstract

Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task. Nevertheless, ensembles of regression trees outperform neural models both in terms of efficiency and effectiveness, particularly when scoring on CPU. In this paper, we propose an approach for speeding up neural scoring time by applying a combination of Distillation, Pruning and Fast Matrix multiplication. We employ knowledge distillation to learn shallow neural networks from an ensemble of regression trees. Then, we exploit an efficiency-oriented pruning technique that performs a sparsification of the most computationally-intensive layers of the neural network that is then scored with optimized sparse matrix multiplication. Moreover, by studying both dense and sparse high performance matrix multiplication, we develop a scoring time prediction model which helps in devising neural network architectures that match the desired efficiency requirements. Comprehensive experiments on two public learning-to-rank datasets show that neural networks produced with our novel approach are competitive at any point of the effectiveness-efficiency trade-off when compared with tree-based ensembles, providing up to 4x scoring time speed-up without affecting the ranking quality., Comment: This work has been submitted to the IEEE for possible publication

Published
2022
Full Text
View/download PDF

36. FÁRMACOS ANTIARRITMICOS

Author

De Brito Araújo Monteiro, Bianca, primary, Emy Dos Reis Tanno, Gabriela, additional, De Carvalho Soares, Isabela, additional, Clara Meira Morais, Maria, additional, Serafin Rulli Costa, Raphaela, additional, Maria Oliveira Ferreira, Ingridy, additional, Belle Moraes Gonçalves, Aline, additional, and Janussi Vacanti, Luciano, additional

Published
2024
Full Text
View/download PDF

42. Improved in vitro potency of Clofazimine derivatives against Neisseria species

Author

Masahide Yano, Jessica M. Lawson-Rulli, Reilly M. Coates, Jennifer Heldring, Marvin J. Miller, and Rui Liu

Subjects
Pharmacy and materia medica, RS1-441, Other systems of medicine, RZ201-999
Abstract

Globally, millions of infections that are resistant to antimicrobial agents are reported annually, leading to more than 700,000 fatalities. Among all, challenges arise particularly from nontuberculosis mycobacterial (NTM) and Gram-negative bacteria, as they exhibit limited treatment options in light of increasing reports of multi-drug resistant strains.Clofazimine (CFZ) is an antimycobacterial medication used to treat leprosy, and it is also known for its side effect of inducing skin pigmentation. The use of CFZ and its analogues against a broad range of Gram-negative bacteria has not been extensively investigated. In this study, we designed, synthesized and studied 11 CFZ analogues and identified examples with comparable or improved in vitro anti-bacterial activity relative to that of CFZ itself. This is the first report demonstrating in vitro activity of CFZ and its analogues against Neisseria species. The results of these studies may facilitate the development of CFZ analogues with limited side effects in humans.

Published
2024
Full Text
View/download PDF

43. Educating Preschoolers to Pro-Environmental Actions: A Metacognition-Based Approach

Author

Martina Rulli, Elsa Bruni, Alberto Di Domenico, and Nicola Mammarella

Subjects
metacognition, sustainability, Preschool children, Education, Special aspects of education, LC8-6691
Abstract

Metacognition is the process of thinking about one's own thinking, learning, and problem-solving strategies. It involves being aware of one's own cognitive processes and knowing how to regulate and monitor them. Sustainability, instead, refers to the ability to maintain or preserve resources and ecosystems for future generations. Here, we draw from the classical metacognitive approach and propose that metacognition plays an important role in sustainability. The PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses) Statement was used to perform and recode this review. Given that metacognitive abilities develop early during childhood, metacognition can be viewed as a useful approach to teach pro-environmental behavior from early childhood. Although the interaction between metacognition and sustainability in preschoolers appears to be insufficiently explored, the final aim of this review is to offer a new education-based perspective about metacognition that can be implemented in early childhood to foster pro-environmental actions in the long-term.

Published
2024
Full Text
View/download PDF

44. Atezolizumab and chemotherapy for advanced or recurrent endometrial cancer (AtTEnd): a randomised, double-blind, placebo-controlled, phase 3 trial

Author

Zola, Paolo, Casanova, Claudia, Arcangeli, Valentina, Antonuzzo, Lorenzo, Gadducci, Angiolo, Cosio, Stefania, Clamp, Andrew, Persic, Mojca, McNeish, Ian, Tookman, Laura, Redondo Sanchez, Andrés, Choi, Chel Hun, Baldini, Editta, Palaia, Innocenza, Benedetti Panici, Pierluigi, Takahashi, Nobutaka, Lombard, Janine, Ardizzoia, Antonio, Bologna, Alessandra, Herrero Ibáñez, Ana Maria, Musolino, Antonino, Márquez Vázquez, Raúl, Pietzner, Klaus, Braicu, Elena, Heinzelmann-Schwarz, Viola A., Powell, Melanie, Yokoyama, Yoshihito, Baron-Hay, Sally, Abeni, Chiara, Martin Lorente, Cristina, Cueva, Juan Fernando, Trillsch, Fabian, Heitz, Florian, Ataseven, Beyhan, Petru, Edgar, Heubner, MartinLeonhard, Sadozye, Azmat Hassanq, Dubey, Sidharth, Tazbirkova, Andrea, Tiley, Susan, Chrystal, Kathryn, Kim, Sang Wun, Fehr, Mathias, Scatchard, Kate, Anand, Anjana, Taylor, Alexandra, Watary, Hidemichi, Enomoto, Takayuki, Yoshihara, Kosuke, Selva-Nayagam, Sudarsha, Karki, Bhaskar, Harrison, Michelle, Wilkinson, Kate, Goh, Jeffrey, Glasgow, Amanda, Chantrill, Lorraine, Lee, Chulmin, Bertolini, Alessandro, Narducci, Filomena, Bellotti, Giovanna, Fusco, Vittorio, Aebi, Stefan, Del Grande, Maria, Colombo, Ilaria, Tokunaga, Hideki, Shigeta, Shogo, Goss, Geraldine, Siow, Zhen Rong, Steer, Christopher, Lin, Hao, Lee, Kwang-Beom, Di Meglio, Giovanni, Massa, Elena, De Marino, Elvira, Tortora, Vincenzo, Palacio Vazquez, Isabel, Tsuji, Kosuke, Tominaga, Eiichiro, Black, Allison, So, Kyeong A, Suh, Dong Hoon, Lee, Keun Ho, Kim, Yong Man, Fossati, Roldano, Carlucci, Luciano, Barberis, Massimo, Torri, Valter, Santoni, Anna, Colombo, Nicoletta, Biagioli, Elena, Harano, Kenichi, Galli, Francesca, Hudson, Emma, Antill, Yoland, Rabaglio, Manuela, Marmé, Frederic, Marth, Christian, Parma, Gabriella, Fariñas-Madrid, Lorena, Nishio, Shin, Allan, Karen, Lee, Yeh Chen, Piovano, Elisa, Pardo, Beatriz, Nakagawa, Satoshi, McQueen, John, Zamagni, Claudio, Manso, Luis, Takehara, Kazuhiro, Tasca, Giulia, Ferrero, Annamaria, Tognon, Germana, Lissoni, Andrea Alberto, Petrella, Mariacristina, Laudani, Maria Elena, Rulli, Eliana, Uggeri, Sara, and Barretina Ginesta, M Pilar

Published
2024
Full Text
View/download PDF

45. Efficacy and safety of ramucirumab plus carboplatin and paclitaxel in untreated metastatic thymic carcinoma: RELEVENT phase II trial (NCT03921671)

Author

Proto, C., Ganzinelli, M., Manglaviti, S., Imbimbo, M., Galli, G., Marabese, M., Zollo, F., Alvisi, M.F., Perrino, M., Cordua, N., Borea, F., de Vincenzo, F., Chella, A., Cappelli, S., Pardini, E., Ballatore, Z., Lucarelli, A., Ambrosini, E., Giuliano, M., Pietroluongo, E., Mulargiu, C., Fabbri, A., Prelaj, A., Occhipinti, M., Brambilla, M., Mazzeo, L., Beninato, T., Vigorito, R., Ruggirello, M., Greco, F.G., Calareso, G., Miliziano, D., Rulli, E., De Simone, I., Torri, V., de Braud, F.G.M., Pasello, G., De Placido, P., Berardi, R., Petrini, I., Zucali, P., Garassino, M.C., and Lo Russo, G.

Published
2024
Full Text
View/download PDF

48. TRAbectedin in adVanced rEtroperitoneal well differentiated/dedifferentiated Liposarcoma and Leiomyosarcoma (TRAVELL): results of a phase II study from the Italian Sarcoma Group

Author

Fabbroni, C., Grignani, G., Vincenzi, B., Fumagalli, E., De Pas, T.M., Mazzocca, A., Pantaleo, M.A., Brunello, A., Baldi, G.G., Boglione, A., Fatigoni, S., Berruti, A., Giordano, M., Marrari, A., Dei Tos, A.P., Alberton, A.S., Aliberti, S., Carlucci, L., Rulli, E., Casali, P.G., and Sanfilippo, R.

Published
2024
Full Text
View/download PDF

49. Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction

Author

Maria Vittoria Chiaruttini, Giulia Lorenzoni, Marco Daverio, Luca Marchetto, Francesca Izzo, Giovanna Chidini, Enzo Picconi, Claudio Nettuno, Elisa Zanonato, Raffaella Sagredini, Emanuele Rossetti, Maria Cristina Mondardini, Corrado Cecchetti, Pasquale Vitale, Nicola Alaimo, Denise Colosimo, Francesco Sacco, Giulia Genoni, Daniela Perrotta, Camilla Micalizzi, Silvia Moggia, Giosuè Chisari, Immacolata Rulli, Andrea Wolfler, Angela Amigoni, and Dario Gregori

Subjects
non-invasive ventilation, NIV, NIV failure, PICU, TIPNet, machine learning, Medicine (General), R5-920
Abstract

Background/Objectives: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities. Methods: Data were sourced from the TIPNet registry, which comprised patients admitted to 23 Italian Paediatric Intensive Care Units (PICUs). We selected patients between January 2010 and January 2024 who received non-invasive ventilation (NIV) as their initial approach to respiratory support. The study aimed to develop a predictive model for NIV failure, selecting the best Machine Learning technique, including Generalized Linear Models, Random Forest, Extreme Gradient Boosting, and Neural Networks. Additionally, an ensemble approach was implemented. Model performances were measured using sensitivity, specificity, AUROC, and predictive values. Moreover, the model calibration was evaluated. Results: Out of 43,794 records, 1861 admissions met the inclusion criteria, with 678 complete cases and 97 NIV failures. The RF model demonstrated the highest AUROC and sensitivity equal to 0.83 (0.64, 0.94). Base excess, weight, age, systolic blood pressure, and fraction of inspired oxygen were identified as the most predictive features. A check for model calibration ensured the model’s reliability in predicting NIV failure probabilities. Conclusions: This study identified highly sensitive models for predicting NIV failure in PICU patients, with RF as a robust option.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results