bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Probiotics and Its Extracellular Products on the Growth Performance, Immune Response, and Aeromonas hydrophila Resistance of Grass Carp
Authors: Gao Xiantao , Peng Yuanzhi , Gao Xiaokun , Zhu Zhicheng , Zhang Jingjing , Cheng Wanli , Cai Minmin , Zheng Longyu , Huang Feng , Zhang Jibin
Published: May 2024

The goal of this study was to research the effects of Lactobacillus buchneri L3‐9 and its extracellular products on growth, immunity, intestinal microorganisms, and disease resistance to Aeromonas hydrophila in grass carp. A total of 120 fish (80–90 g) were distributed into 12 aquariums (10 fish/aquarium) in three replicates and fed 1% of their body weight for 21 days. The grass carp were fed four different diets: a control (group C), a diet supplemented with 1 × 106 cell g−1 of L. buchneri L3‐9 (group B), a diet supplemented with extracellular products of L. buchneri L3‐9 (group E), and a diet supplemented with L. buchneri L3‐9 and the extracellular products (group BE). Compared with the control diet, the B, E, and BE diets increased the weight gain rate by 4.49%, 2.59%, and 4.38%, respectively (p  < 0.05), and significantly decreased the feed conversion ratio by 0.72, 0.57, and 0.76, respectively (p < 0.05). Groups B, BE, and E showed significant decreases in mortality, with values of 26.67%, 16.67%, and 23.33%, respectively (p < 0.05). Group B showed a significant increase in superoxide dismutase (SOD) activity and total antioxidant capacity (T‐AOC) and decrease in malondialdehyde (MDA) content compared to the control group (p < 0.05). The highest catalase (CAT) activity was found in group E (p < 0.05). In group B, the expression of the gut proinflammatory gene TNF-α was downregulated (p < 0.05), and the expression of anti‐inflammatory genes, including TGF-β1, IL-10, and Tlr-8, was upregulated compared to that in the control group (p < 0.05). Groups BE and E showed an increased abundance of Lactobacillaceae. Additionally, a drastic decrease in the abundance of pathogenic bacteria such as Aeromonadaceae and Enterobacteriaceae was in these groups compared to the control group. In conclusion, L. buchneri L3‐9 and its extracellular products could improve the growth performance, immune responses, and resistance to A. hydrophila in grass carp. This study provides insights for the development and application of microecological preparations.
TABLE 1 from Humanized Patient-derived Xenograft Models of Disseminated Ovarian Cancer Recapitulate Key Aspects of the Tumor Immune Environment within the Peritoneal Cavity
Authors: Steinkamp Mara P. , Lagutina Irina , Brayer Kathryn J. , Schultz Fred , Burke Danielle , Pankratz Vernon S. , Adams Sarah F. , Hudson Laurie G. , Ness Scott A. , Wandinger-Ness Angela
Published: May 2024

Cytokines elevated in the ascites fluid of patients with ovarian cancer are also present in the huPDX ascites fluid
FIGURE 2 from Humanized Patient-derived Xenograft Models of Disseminated Ovarian Cancer Recapitulate Key Aspects of the Tumor Immune Environment within the Peritoneal Cavity
Authors: Steinkamp Mara P. , Lagutina Irina , Brayer Kathryn J. , Schultz Fred , Burke Danielle , Pankratz Vernon S. , Adams Sarah F. , Hudson Laurie G. , Ness Scott A. , Wandinger-Ness Angela
Published: May 2024

High M-CSF production influences the development of myeloid cells in huNBSGW PDX. A, Diagram of the huPDX study. B, Change in human CD45+ cell populations in peripheral blood before tumor engraftment versus end stage for each mouse (n = 3 mice/group). CD19+ B cells, CD3+ T cells, and CD11b+ myeloid cells make up the majority of human immune cells in the blood. Note that terminal samples from huPDX9 ms3 were not collected as the mouse died unexpectedly overnight. Mouse numbers are listed as ms1–3. C, Average percentage of human myeloid cells in the peripheral blood of huPDX before and after tumor challenge (values are the average ± SD). P values are based on an unpaired two-tailed Student t test. ***, P < 0.0005; **, P < 0.005; *, P < 0.05; n.s., not significant). D, Percentage of CD11b+ human myeloid cells in huPDX ascites fluid at end stage. E, M-CSF concentration in patient ascites fluid measured by ELISA. Samples were assayed in duplicate. Error bars are SD. F, M-CSF concentrations in PDX ascites fluid measured by cytokine array. G, M-CSF gene expression levels in patient samples as determined by RNA-seq analysis of primary patient samples and non-huPDX samples. H, GMCSF concentrations in PDX ascites fluid measured by cytokine array. Black dots represent the value for each sample. Bars are the average level and error bars are SEM. H = humanized NH = non-humanized.
Supplementary Table S2 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

Table S2 shows characteristics of the TNBC PDX tumors in which LP-184 efficacy was tested in vivo
Supplementary Figure S9 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

Figure S9 shows mouse body weight changes in a TNBC PDX model following treatment with LP-184, Olaparib or the combination
Supplementary Figure S7 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

Figure S7 shows mouse body weight changes in selected TNBC PDX models following LP-184 treatment
Supplementary Figure S6. from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

Figure S6 shows a heatmap with correlations between LP-184 activity in NCI0 cancer cell lines and transcript expression levels of Homoloous Recombination genes
Supplementary Figure S11 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

Figures S11 shows LP-184 activity in normal cells
FIGURE 6 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

LP-184 and Olaparib combination showed synergy in TNBC PDX models. A, The BRCA2-mutated HBCx10 TNBC PDX mouse tumor models were treated with the vehicle (saline), LP-184 (intravenously on days 1 and 8), olaparib (orally daily), or combination of LP-184 and olaparib. Tumor volumes at day 22 were displayed as mean ± SD (N = 3 mice/arm). B, The BRCA1-mutated HBCx28 TNBC PDX mouse tumor models were treated with the vehicle (saline), LP-184 (intravenously on days 1, 4, 8, and 11), olaparib (orally daily), or combination of LP-184 and olaparib. Tumor volumes at day 21 were displayed as mean ± SD (N = 3 mice/arm). Group means were compared using the unpaired t test. ns, not significant. *, P ≤ 0.05; ****, P ≤ 0.0001.
FIGURE 4 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

LP-184 is effective in HRD TNBC PDX models regardless of their sensitivity to PARPi. LP-184 treatment (4 mg/kg i.v.) on days 0, 2, 4, 6, 8 and 16, 18, 20, 22, 24 in HRD TNBC PDX models showing HBCx-24 (PARPi resistant) tumor growth curves (mean ± SD) for vehicle versus treatment arms (A), T168 (PARPi sensitive) tumor growth curves (mean ± SD) for vehicle versus treatment arms (B), and Waterfall plot illustrating tumor regression in all 10 LP-184–treated PDX models (C).
FIGURE 3 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

LP-184 is active in LuCaP 96 and LuCaP 86.2 prostate cancer organoids. Fluorescence microscopy images show organoid spheres after 5-day treatment with LP-184 and with Calcein AM staining for live cells and ethidium homodimer-1 staining for dead cells in LuCaP 86.2 (A) and LuCap 96 (B) organoids. Dose–response curves in LuCaP 86.2 (C) and LuCap 96 (D) organoids show mean organoid number/field on the Y axis and LP-184 test concentrations on the X axis.
Cloud-based machine learning algorithms for anomalies detection
Authors: Amarnath Raveendra N , Gurulakshmanan Gurumoorthi
Published: May 2024

Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.
Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN
Authors: Bhoopal Sowmyashree , Rao Mahesh , Krishnappa Chethan Hasigala
Published: May 2024

Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.
Development of mathematical methods for diagnosing kidney diseases using fuzzy set tools
Authors: Myrzakerimova Alua , Kolesnikova Kateryna
Published: May 2024

An approach based on fuzzy set theory is presented in the scientific article to enhance the efficiency of diagnosing kidney diseases by decreasing the time required for making medical decisions. The suggested approach employs fuzzy models and algorithms that consider the uncertainty and variability of clinical data to optimize the assessment of the functional state of the kidneys, taking into account various risk factors and individual characteristics of patients. The paper suggests a technique to develop a system of fuzzy decision rules. This technique combines E. Shortliff’s iterative rules with functions from the studied classes of kidney diseases. Mathematical modeling and experimental studies have indicated relatively high effectiveness in classifying different forms of kidney diseases. The results can be used to formulate intelligent decision support systems in clinical practice and improve diagnostic and monitoring processes. Moreover, the findings may aid in shaping more targeted and effective health policies at the national and regional levels, enhancing access to healthcare, and promoting the population’s overall health.
Ontological model of the process of intensification of teachers’ competencies
Authors: Bazarova Madina , Alibekkyzy Karlygash , Adikanova Saltanat , Bugubayeva Alina , Zhomartkyzy Gulnaz , Jaxalykova Akmaral , Baidildina Aizhan , Keribayeva Talshyn
Published: May 2024

Currently, there is a need to improve the education system and develop interdisciplinary research at all levels of education, from school to postgraduate education. The introduction of interdisciplinary connections contributes to the formation of a holistic understanding of natural phenomena and the connections between them. Thus, this knowledge becomes more meaningful and applicable in practice. This article proposes a conceptual model of the content of education in the form of a thesaurus and ontology. The use of these models will allow you to adaptively select and systematize educational information. The article also discusses the possibilities and experience of using ontological modeling and engineering for the conceptual description of school and higher education. In addition, the article discusses the development of an ontological model of the process of expanding teachers’ competencies with the integration of science, technology, engineering and mathematics (STEM) education. The use of ontological engineering methods will improve the quality of teacher education through the semantic description of knowledge in the subject area and the use of interdisciplinary and STEM approaches in the educational process.
 
Aspect term extraction from multi-source domain using enhanced latent Dirichlet allocation
Authors: Dhanal Radhika Jinendra , Ghorpade Vijay Ram
Published: May 2024

This study presents a comprehensive exploration of sentiment analysis across diverse domains through the introduction of a multi-source domain dataset encompassing hospitals, laptops, restaurants, cell phones, and electronics. Leveraging this extensive dataset, an enhanced latent Dirichlet allocation (E-LDA) model is proposed for topic modeling and aspect extraction, demonstrating superior performance with a remarkable coherence score of 0.5727. Comparative analyses with traditional LDA and other existing models showcase the efficacy of E-LDA in capturing sentiments and specific attributes within different domains. The extracted topics and aspects reveal valuable insights into domain-specific sentiments and aspects, contributing to the advancement of sentiment analysis methodologies. The findings underscore the significance of considering multi-source datasets for a more holistic understanding of sentiment in diverse text corpora.
Potato leaf disease detection through ensemble average deep learning model and classifying the disease severity
Authors: Chowdhury Nishu , Sultana Jeenat , Rahman Tanim , Chowdhury Tanjia , Khan Fariba Tasnia , Chakraborty Arpita
Published: May 2024

The varying crop species, symptoms of crop diseases, and environmental conditions make early detection of potato leaf disease difficult. Potato leaf diseases are difficult to identify in their early stages because of these reasons. An ensemble model is developed using the ResNet50V2 and DenseNet201 transfer learning algorithms in this study for identifying potato leaf diseases. For this work, 5,702 images were collected from the potato leaf disease dataset and the Plant Village Potato dataset. The datasets include valid, test, and train subdirectories, and the images are taken on 5 epochs. By including three more dense layers in each model and then ensemble that model, the performance of leaf classification may also be improved. Accurately and appropriately, the suggested ensemble averaging model identifies potato leaf phases. So, the accuracy of the suggested ensemble model is achieved with perfect precision. On the second level, the severity of the disorder is assessed using the K mean clustering algorithm. To determine the disease's severity, this system examines each pixel in the early and late blight images. It will be classified as severe if more than 50% of the pixels are damaged.
Deep learning for economic transformation: a parametric review
Authors: Tariq Usman , Ahmed Irfan , Khan Muhammad Attique , Bashir Ali Kashif
Published: May 2024

Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.
Extracting contextual insights from user reviews for recommender systems: a novel method
Authors: Madani Rabie , Ez-Zahout Abderrahmane , Omary Fouzia
Published: May 2024

Recommender systems (RS) primarily rely on user feedback as a core foundation for making recommendations. Traditional recommenders predominantly rely on historical data, which often presents challenges due to data scarcity issues. Despite containing a substantial wealth of valuable and comprehensive knowledge, user reviews remain largely overlooked by many existing recommender systems. Within these reviews, there lies an opportunity to extract valuable insights, including user preferences and contextual information, which could be seamlessly integrated into recommender systems to significantly enhance the accuracy of the recommendations they provide. This paper introduces an innovative approach to building context-aware RS, spanning from data extraction to ratings prediction. Our approach revolves around three essential components. The first component involves corpus creation, leveraging Dbpedia as a data source. The second component encompasses a tailored named entity recognition (NER) mechanism for the extraction of contextual data. This NER system harnesses the power of advanced models such as bidirectional encoder representations from transformers (BERT), bidirectional long short term memory (Bi-LSTM), and bidirectional conditional random field (Bi-CRF). The final component introduces a novel variation of factorization machines for the prediction of ratings called contextual factorization machines. Our experimental results showcase robust performance in both the contextual data extraction phase and the ratings prediction phase, surpassing the capabilities of existing state-of-the-art methods. These findings underscore the significant potential of our approach to elevate the quality of recommendations within the realm of context-aware recommender systems.
Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification
Authors: Novika Andien Dwi , Girsang Abba Suganda
Published: May 2024

This study introduces an innovative hyperparameter optimization approach for enhancing multilayer perceptrons (MLP) using the Jaya algorithm. Addressing the crucial role of hyperparameter tuning in MLP’s performance, the Jaya algorithm, inspired by social behavior, emerges as a promising optimization technique without algorithm-specific parameters. Systematic application of Jaya dynamically adjusts hyperparameter values, leading to notable improvements in convergence speeds and model generalization. Quantitatively, the Jaya algorithm consistently achieves convergences at first iteration, faster convergence compared to conventional methods, resulting in 7% higher accuracy levels on several datasets. This research contributes to hyperparameter optimization, offering a practical and effective solution for optimizing MLP in diverse applications, with implications for improved computational efficiency and model performance.
Decision support system using Dhouib-Matrix-TP1 heuristic for pentagonal fuzzy transportation problem
Authors: Dhouib Souhail , Kharrat Aïda , Dhouib Saima , Sutikno Tole
Published: May 2024

In this paper, a decision support system (DSS) is provided to assist the decision-maker in obtaining the best solution for the transportation problem under uncertainty. Fuzzy parameters of the transportation problem are presented by pentagonal fuzzy numbers. The centroid ranking function transforms these pentagonal fuzzy numbers into crisp ones. Then, a novel, improved greedy method named Dhouib-Matrix-TP1 (DM-TP1) is used in order to help the decision-maker promptly find a suitable solution. Specifically, this DSS is composed of three components: the data base component considers a pentagonal fuzzy number; the Model Base component thinks through the original heuristic DM-TP1; and the User Interface component deliberates the convivial graphical output of the generated transportation plan solution using the Python programming language. Experiments in the literature on fuzzy transportation problems show that the novel proposed heuristic, DM-TP1, is easy to understand and allows the decision-maker to handle transportation problems under pentagonal fuzzy numbers. Also, the DM-TP1 is robust and can be applied to find a feasible initial solution in less time.
Method level static source code analysis on behavioral change impact analysis in software regression testing
Authors: Muthengi Fredrick Mugambi , Mugo David Muchangi , Mutua Stephen Makau , Musyoka Faith Mueni
Published: May 2024

Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons of software modifications. In assessing the impact of the changes made in software, static source code analysis plays a key role. However, static source code analysis can be a little complex depending on the reason for the expedition. Despite the work done so far, little focus has been made on the potential of changed methods analysis during static source code analysis in assessing the impact of the changes made in a software system. We propose and investigate a static source code analysis technique that would generate information on the modified methods in the source code. This study analyzes four aThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made.Java projects and shows that an analysis of the changed methods reveals the level of regression testing that ought to be conducted for the changes made.
Contribuições ao plano de ação climática do Estado de São Paulo
Authors: Metzger Jean Paul , Chaves Rafael , Sparovek Gerd , Pinter Adriano , Gerard Alexandre , Igari Alexandre , Almeida Danilo , Boscolo Danilo , Duarte Denise , Ditt Eduardo , di Giulio Gabriela , Oliveira Guaraci , Carrascosa Helena , Barcellos Isabel , Ortega Juliana , Mello Kaline , Antoniazzi Laura , Munhoz Leonardo , Schutzer Lie Shitara , Silva Lucia Sousa e , Ferreira Luciana , Pinto Luis Fernando Guedes , Nalon Marco , Rosa Marcos , Campos Marina , Domingos Marisa , Ruggiero Patricia , Tavares Paulo André , Molin Paulo Guilherme , Brancalion Pedro , Krainovic Pedro , Pardini Renata , Rodrigues Ricardo , Resende Roberto , Victor Rodrigo , Quadros Taruhim
Published: May 2024

Esta obra integra a série Biota Síntese e constitui-se como a Nota Técnico-Científica 1. O foco do Biota Síntese é a coprodução, de forma colaborativa e transdisciplinar, de soluções baseadas na natureza. Essas soluções englobam um amplo espectro de ações, da conservação ao uso sustentável e à restauração, que se utilizam de processos ou serviços ecossistêmicos para enfrentar desafios societais, gerando benefícios tanto para a sociedade quanto para a biodiversidade. Diante do compromisso do governo do estado de São Paulo de zerar suas emissões líquidas de carbono e contribuir para aumentar sua resiliência às mudanças climáticas, foi concebido o Plano de Ação Climática (PAC). No seu Eixo#4 – Resiliência e Soluções Baseadas na Natureza –, o PAC conta com os programas AgroLegal e ReflorestaSP para atingir a meta de restauração de 1,5 milhão de hectares até 2050. Para tanto, foi necessário um detalhamento do Eixo#4 do PAC e dos programas de restauração, o que levou ao estabelecimento de diretrizes e estratégias que fundamentam o plano executivo e que são apresentadas nesta obra.
Análise de mapeamento de biomassa e carbono no Estado de São Paulo
Authors:   Universidade de São Paulo. Instituto de Estudos Avançados, Nascimento Nathália , Chaves Rafael Barreiro ,   Secretaria de Meio Ambiente, Infraestrutura e Logística do Estado de São Paulo, Brancalion Pedro Henrique ,   Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
Published: May 2024

Esta Nota Técnico-Científica 3 integra a Série Biota Síntese e traz os resultados preliminares do mapeamento da biomassa acima do solo e do potencial de aumento da biomassa no futuro no estado de São Paulo. O objetivo principal deste material é auxiliar o Governo do Estado de São Paulo, parceiro institucional do projeto Biota Síntese, a planejar ações estratégicas e o desenvolvimento de políticas públicas relacionadas ao carbono no estado. Isso envolve considerar tanto o potencial de sequestro e o estoque de carbono existente na biomassa atual do estado quanto os possíveis ganhos de biomassa por meio de iniciativas de restauração de ecossistemas. Adicionalmente, a nota pretende informar a sociedade, de forma mais ampla, sobre a importância desse assunto, que vem ganhando maior atenção não só do poder público e das universidades, mas também da sociedade civil e do setor privado.
Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities
Authors: Foucault Cedric , Meyniel Florent
Published: May 2024

Humans face a dynamic world that requires them to constantly update their knowledge. Each observation should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each observation. Although crucial for characterizing the learning process, these dynamic adjustments have only been investigated empirically in magnitude learning. Another important type of learning is probability learning. The latter differs from the former in that individual observations are much less informative and a single one is insufficient to distinguish environmental changes from stochasticity. Do humans dynamically adapt their learning rate for probabilities? What determinants drive their dynamic adjustments in magnitude and probability learning? To answer these questions, we measured the subjects’ learning rate dynamics directly through real-time continuous reports during magnitude and probability learning. We found that subjects dynamically adapt their learning rate in both types of learning. After a change point, they increase their learning rate suddenly for magnitudes and prolongedly for probabilities. Their dynamics are driven differentially by two determinants: change-point probability, the main determinant for magnitudes, and prior uncertainty, the main determinant for probabilities. These results are fully in line with normative theory, both qualitatively and quantitatively. Overall, our findings demonstrate a remarkable human ability for dynamic adaptive learning under uncertainty, and guide studies of the neural mechanisms of learning, highlighting different determinants for magnitudes and probabilities.
Talent hat, cross-border mobility, and career development in China
Authors: Huang Yurui , Cheng Xuesen , Tian Chaolin , Jiang Xunyi , Ma Langtian , Ma Yifang
Published: May 2024

This study investigates the influence of cross-border recruitment programs in China, which confer scientists with a “talent hat,” including an attractive startup package, on their future performance and career development. By curating a unique data set from China’s 10-year talent recruitment program, we employed multiple matching designs to quantify the effects of the cross-border recruitment with talent hats on early-career STEM scholars. Our findings indicate that the cross-border talents perform better than their comparable contenders who move without talent hats and those who do not move in terms of publication numbers, citation, publication quality, and collaborator numbers, given equivalent scientific performance before relocation. Moreover, we observed that scholars in experimental fields derive more significant benefits from the talent program than those in nonexperimental fields. Finally, we investigated how the changes in the scientific environment of scientists affect their future performance. We found that talents who reassembled their collaboration network with new collaborators in new institutions after job replacement experienced significant improvements in their academic performance. However, shifting research directions entails risks, which results in a subsequent decrease in future academic performances following the relocation. This study has significant implications for young scientists, research institutions, and governments concerning cultivating cross-border talents.
Estimating Nursing Home Quality with Selection
Authors: Olenski Andrew , Sacher Szymon
Published: May 2024

We use variational inference (VI), a technique from the machine learning literature, to estimate a mortality-based Bayesian model of nursing home quality accounting for selection. We demonstrate how one can use VI to quickly and flexibly estimate a high-dimensional economic model with large datasets. Using our facility quality estimates, we examine the correlates of quality and find that public report cards have near-zero correlation. We then show that in contrast to prior literature, higher quality nursing homes fared better during the pandemic: a one standard deviation increase in quality corresponds to 2.5% fewer Covid-19 cases.
Disentangling Reputation from Selection Effects in Markets with Informational Asymmetries - A Field Experiment
Authors: Alysandratos Theodore , Georganas Sotiris , Sutter Matthias
Published: May 2024

In markets with asymmetric information between sellers and buyers, feedback mechanisms are important to increase market efficiency and reduce the informational disadvantage of buyers. Feedback mechanisms might work because of self-selection of more trustworthy sellers into markets with such mechanisms or because of reputational concerns of sellers. We show in a field experiment how to disentangle self-selection from reputation effects. Based on 476 taxi rides with four different types of taxis, we find strong evidence for reputation effects, but little support for self-selection effects. We discuss policy implications of our findings.
Public School Funding, School Quality, and Adult Crime
Authors: Baron E. Jason , Hyman Joshua , Vasquez Brittany
Published: May 2024

This paper asks whether increasing public school funding can be an effective long-run crime-prevention strategy in the U.S. Specifically, we examine the effect of increases in funding early in children's lives on the likelihood that they are arrested as adults. We exploit quasi-experimental variation in public school funding, leveraging two natural experiments in Michigan and a novel administrative dataset linking the universe of Michigan public school students to adult criminal justice records. The first research design exploits variation in operating expenditures due to Michigan's 1994 school finance reform, Proposal A. The second design exploits variation in capital spending by leveraging close school district capital bond elections in a regression discontinuity framework. In both cases, we find that students exposed to additional funding during elementary school were substantially less likely to be arrested in adulthood. We show that the social benefits of increasing school funding are greater than the costs, even when considering only the crime-reducing benefits.
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Authors: Zhou Han , Wan Xingchen , Vulić Ivan , Korhonen Anna
Published: May 2024

Large pretrained language models are widely used in downstream NLP tasks via task- specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong task performance while updating much fewer parameters than full model fine-tuning (FFT). However, it is non-trivial to make informed design choices on the PEFT configurations, such as their architecture, the number of tunable parameters, and even the layers in which the PEFT modules are inserted. Consequently, it is highly likely that the current, manually designed configurations are suboptimal in terms of their performance-efficiency trade-off. Inspired by advances in neural architecture search, we propose AutoPEFT for automatic PEFT configuration selection: We first design an expressive configuration search space with multiple representative PEFT modules as building blocks. Using multi-objective Bayesian optimization in a low-cost setup, we then discover a Pareto-optimal set of configurations with strong performance-cost trade-offs across different numbers of parameters that are also highly transferable across different tasks. Empirically, on GLUE and SuperGLUE tasks, we show that AutoPEFT-discovered configurations significantly outperform existing PEFT methods and are on par or better than FFT without incurring substantial training efficiency costs.
L’Africa di Dacia Maraini, un altrove seducente o una realtà travolgente?
Authors: Abdo Samah
Published: May 2024

L’Africa costituisce un topos di rilievo negli scritti di Dacia Maraini La seduzione dell’altrove, Sguardo a Oriente e Caro Pier Paolo. Lontano da ogni tipo di pregiudizio o stereotipo, Maraini cerca di scoprire l’eterogeneità culturale del continente nero e le sue peculiarità etnico-tribali. Tuttavia, non risparmia critiche all’invadente distruzione delle culture primitive e degli animali selvatici. La scrittrice nota che, mentre i ricchi ad Addis Abeba e Nairobi si barricano nelle loro ville, la povertà del popolo è assoluta; il neocolonialismo impera ancora in diversi paesi e il potenziale economico di alcuni paesi africani come il Sudafrica e la Rhodesia potrebbe superare il potenziale economico di tutta l’Africa indipendente. Tanti di questi paesi che sono tutt’ora un serbatoio di povertà e bacino di conflitti, furono suddivisi sotto l’imperialismo secondo criteri arbitrari per rispondere a un immediato interesse materiale. Per far fronte alla miseria e alla guerra, alcuni sono costretti all’immigrazione clandestina, altri ricorrono alla prostituzione minorile, al traffico di droga, o a fare parte di gruppi terroristici che promettono grandi guadagni; come citato in uno studio che porta la testimonianza di tre giovani che hanno fatto parte di questi gruppi. Maraini discute il diritto all’istruzione negato a molti bambini – soprattutto le ragazze – a causa della povertà. Lo studio passa anche in rassegna le sfide che la donna africana deve affrontare, dalla violenza all’analfabetismo, dalla negata istruzione all’infibulazione. Tuttavia, bisogna ricordare che il 70% della popolazione africana ha meno di trent’anni; l’esplosione di giovinezza significa avere più forza. Questo continente è al bivio tra dramma ed energia esplosiva. Attraverso una scrittura impegnata, Maraini offre uno sguardo profondo sulla complessità del continente africano, facendo capire che l’Africa non va considerata solo come un continente problematico, ma soprattutto come terra ricca di bellezza e valore.
Remembering Donna Rockwell (1957–2023)
Authors: Bland Andrew M.
Published: May 2024

Donna embraced, embodied, and modeled mindful living and authentic loving. She approached neither mindfulness nor love as a buzzword or an obligatory topic. Rather, she profoundly lived and breathed those principles as her essence—ongoingly committed to cultivating clarity, wisdom, and perspective-taking through deep listening and to promoting inclusivity and compassion via caring relationships and unconditional positive regard.
Health and Productivity Benefits with Early Intensified Treatment in Patients with Type 2 Diabetes: Results from Korea
Authors: Tsotra Foteini , Malhotra Ankur , Peristeris Platon , Athanasiou Ioannis , Müller Malina , Bader Giovanni
Published: May 2024

The available evidence suggests positive health outcomes associated with early treatment intensification in Type 2 diabetes mellitus (T2DM). Our study estimated the productivity effects arising from improved health correlated with early intensified treatment in T2DM in Korea. Using a recently published methodology and model, we investigated the association between early intensified treatment and the probability of experiencing fewer diabetes-related complication events. Treatment strategies leading to better health outcomes are expected to be associated with social value through increased participation in paid and unpaid work activities. Therefore, we translated the lower incidence of complications into monetary terms related to productivity for the Korean population. We quantified productivity by considering (a) absenteeism, (b) presenteeism, (c) permanent loss of labor force, and (d) activity restriction. Deterministic and probabilistic sensitivity analyses in the base case parameter were performed. Approximately, 1.7 thousand (standard deviation [SD] ±580 events) micro- and macrovascular complication events could potentially be avoided by early treatment intensification. This led to a societal gain attributed to increased productivity of 23 million USD (SD ± $8.2 million). This article demonstrates the likelihood of achieving better health and productivity through early intensified treatment in diabetes.
A gradient-descent iterative learning control algorithm for a non-linear system
Authors: He Zhiying , Pu Hongji
Published: May 2024

Original iterative learning control (OILC) has been proved a powerful tool in dealing with the model-free control problems by repetitively corrections based on the control error. However, the steady-state error under widely-used proportional-type original iterative learning control (P-type OILC) is highly corresponded to the proportional learning gain, making the algorithm parameter-determined. Therefore, a new gradient-descent iterative learning control (GDILC) algorithm is proposed to achieve a parameter-free approach by simulating the gradient-descent process. First, GDILC problem is formulated mathematically. Next, the idea of the algorithm is proposed, the analyses of the convergence and the steady-state error are conducted and the algorithm is implemented. GDILC will generate a random correction with a gradient-descent upper bound, rather than a correction proportional to the error in P-type OILC. Finally, illustrative and application simulations are conducted to validate the algorithm. Results show that the algorithm will be convergent after adequate iterations under proper corrections. The steady-state error will be less affected by the algorithm parameters under GDILC than that under OILC.
Rare Nasopharyngeal Neuroendocrine Tumor in a 26-Year-Old Female: Case Report and Literature Review
Authors: Alsalah Qusai A. , Abufara Arein A. , Alsahouri Mohammad I. , Albzour Ameer S. , Hammouri Ahmad G. , Arafat Hasan , Abu Aqeel Bashir
Published: May 2024

Neuroendocrine neoplasms (NENs) represent a collection of highly varied tumors that originate from neuroendocrine cells. They are considered rare tumors that predominantly affect the lungs. Epithelial NENs can be categorized into neuroendocrine tumors (NETs) and neuroendocrine carcinomas. It is extremely rare for NET grade 1 (NET G1) to exist in the nasopharynx, these tumors are slow-growing and the onset of symptoms and identification of the tumor may take several years. The majority of cases occur in ages between 60 and 65 years. In this article, we present a case of a 26-year-old female who presented with recurrent epistaxis and nasal obstruction for one and a half years. Magnetic resonance imaging revealed a substantial nasopharyngeal mass. Subsequently, a biopsy was conducted, and the histopathological results indicated a NET G1. Our literature review revealed 5 cases of NET G1 in the nasopharynx, with our patient being the youngest among all published cases.
Do Capital Asset and Labor Conditions Matter in Loan Pricing? Evidence From Capital and Labor Redeployability
Authors: Chen Yangyang , Ng Jeffrey , Wang Chong
Published: May 2024

This article offers the novel insight that loan pricing is affected by the redeployability of borrowers’ capital assets and labor. We find that both capital and labor redeployability are negatively related to loan spread, suggesting that borrowers with higher redeployability enjoy more favorable loan pricing. This finding is consistent with redeployability promoting reduced cost stickiness and enhanced liquidity, which in turn reduces borrowers’ probability of default and lenders’ loan losses given default. Our cross-sectional test results reveal that the relation between redeployability and loan pricing is stronger for firms with more growth opportunities, which is consistent with lenders viewing redeployability as an important way to minimize potential loan losses from risky investments. Also, the relation between redeployability and loan pricing is weaker for loans with more stringent nonpricing terms, suggesting that strict terms may protect lenders and make redeployability less important in loan pricing.

JEL Classification: D22, G21, G32, J62.
Semantic Web Practices: Infrastructural Politics and the Future of the Web
Authors: Halford Susan , Weal Mark , Hardcastle Faranak , Gibbins Nicholas , Pearman-Kanza Samantha , Pope Catherine
Published: May 2024

In the past thirty years, the Web has developed from its inception as a layer of protocols on top of the Internet to use by more than 5 billion people and organizations. This has driven the creation of vast quantities of data and led to deep concerns about the politics of digital data and computational methods. To date, critical investigation of these concerns has focused on large commercial platforms built on top of the Web, and their use of machine learning methods. Meanwhile, less attention has been paid to the underlying design and protocols of Web itself, and how these might be implicated in the very same process and concerns. We explore ongoing endeavors to transform the Web from a library of documents intended for humans to a “semantic Web” using symbolic artificial intelligence to enable machine reasoning across multiple heterogenous data sources. In principle, this would transform the production and circulation of knowledge at Web scale. We present the findings from an experimental, interdisciplinary study exploring the epistemological politics and sociomaterial practices involved in situated accomplishment of the semantic Web. Our findings have consequences for the future of the Web and the future of Web-based platforms.
Children consider “who” and “what” when reasoning about rule changes: A comparative study of children from two cultures
Authors: Guo Rui , Li Dandan , Zhao Xin
Published: May 2024

Children’s normative knowledge develops early. While prior studies focus on rule compliance and violation, only limited research explores children’s views on rule changes, especially in non-Western cultures. This study investigates how Chinese children aged 4–7 ( N = 154) reason about rule changes, and compares their responses with US counterparts in the work of Zhao and Kushnir. Chinese children considered both “who” created the rules and “what” consequences the rule changes may bring about when judging changeability. For game rules, like US children, Chinese children considered both individual authority (including adult and peer authority) and collective agreement when judging who can change game rules. Compared to US children, Chinese children more often believe that the adult rule-maker’s child could also change the rule. Furthermore, although both Chinese and US children thought one could not change moral rules, Chinese children were less likely than US children to think one could change conventional rules. Exploratory analyses of children’s justifications suggest that Chinese children emphasize consequences more than US children when judging whether rules can be changed. This study offers valuable insights into the development of normative reasoning and cultural influences on children’s views on rule malleability.