bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multilocation proteins in organelle communication: Based on protein–protein interactions
Authors: Xiong Erhui , Cao Di , Qu Chengxin , Zhao Pengfei , Wu Zhaokun , Yin Dongmei , Zhao Quanzhi , Gong Fangping 
Published: February 2022

AbstractProtein–protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best‐characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation‐related multilocation proteins, can function as a “needle and thread” via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation‐related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylation‐related multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.
Commentary on “compatibility of rapid enteral feeding advances and noninvasive ventilation in preterm infants—an observational study”
Authors: Buck Catherine O. , Taylor Sarah N. 
Published: February 2022

AbstractNutritional support is essential in the critical care of the very preterm infant, however, the study of the potential role of nutrition in the multifactorial pathogenesis of bronchopulmonary dysplasia (BPD) is limited. Achievement of full enteral feeding faster has not been shown to be harmful and may be of benefit in extremely preterm infants. This commentary discusses the studied risk and potential benefits of more rapid enteral feeding advancement in extremely preterm infants.
Selection and characterization of thermotolerant Beauveria bassiana isolates and with insecticidal activity against the cotton‐melon aphid Aphis gossypii (Glover) (Hemiptera: Aphididae)
Authors: Mseddi Jihen , Ben Farhat‐Touzri Dalel , Azzouz Hichem 
Published: February 2022

AbstractBACKGROUNDCotton‐melon aphid Aphis gossypii (Glover) causes severe damage mainly to cucurbits. Twenty‐two Beauveria sp. isolates were simultaneously assessed for their pathogenicity and heat tolerance. The selected isolates were identified molecularly and characterized in terms of conidial germination rate, mycelial growth, conidial yield and endophytic activity.RESULTSScreening bioassays showed that the B. bassiana isolates B3, B7, B9 and B12 were the most toxic, inducing mortality equal to or slightly higher than the commercialized strain B. bassiana BNat (70.7%). Median lethal concentration (LC50) bioassays revealed that only isolate B12 had a significantly lower LC50 value (5.4 × 105 conidia ml−1) than strain BNat (5 × 106 conidia ml−1). The heat tolerance screening test (1 h of exposure to 45°C) allowed us to select isolates B3, B7, B9 and B12 with germination rates of 57.5% to 80.1% after 24 h incubation at 25°C, all significantly higher than strain BNat (22.1%). The germination rates of all isolates decreased significantly after 2 h of exposure to 45°C, with the exception of isolate B12 which displayed the highest thermotolerance (72% germination). The four selected isolates were able to endophytically colonize cucumber leaves when applied to the foliage. Inoculation of cucumber plants with isolate B12 did not affect cucumber plant growth. However, several plant growth parameters were improved 5 weeks after root inoculation.CONCLUSIONOn the basis of its potent toxicity and thermotolerance, isolate B12 is a good candidate for further development as a biopesticide for use in integrated pest management strategies for aphid control. © 2022 Society of Chemical Industry.
Covariate analysis of tusamitamab ravtansine, a DM4 anti‐CEACAM5 antibody‐drug conjugate, based on first‐in‐human study
Authors: Pouzin Clemence , Tod Michel , Chadjaa Mustapha , Fagniez Nathalie , Nguyen Laurent 
Published: February 2022

AbstractTusamitamab ravtansine is an anti‐CEACAM5 antibody‐drug conjugate indicated in patients with solid tumors. Based on a previous developed semimechanistic model describing simultaneously pharmacokinetic (PK) of SAR408701, two of its active metabolites: DM4 and methyl‐DM4 and naked antibody, with integration of drug‐to‐antibody data, the main objective of the present analysis was to evaluate covariate’s impact in patients from phase I/II study (n = 254). Demographic and pathophysiologic baseline covariates were explored to explain interindividual variability on each entity PK parameter. Model parameters were estimated with good precision. Five covariates were included in the final PK model: body surface area (BSA), tumor burden, albumin, circulating target, and gender. Comparison of BSA‐adjusted dosing and flat dosing supported the current BSA‐based dosing regimen, to limit under and over exposure in patients with extreme BSA. Overall, this model characterized accurately the PKs of all entities and highlighted sources of PK variability. By integrating mechanistic considerations, this model aimed to improve understanding of the SAR408701 complex disposition while supporting key steps of clinical development.
Amyloidosis with recurrent oral and peptic ulcers mimicking Behcet's syndrome: A case report
Authors: Peng Jiajing , Liu Tian 
Published: February 2022

AbstractIntroductionAmyloidosis and Behcet's syndrome (BS) are systemic diseases that affect multiple systems. Similar mucocutaneous damage occurs in amyloidosis and BS, thus causing a delayed or incorrect diagnosis. The prognosis of patients with amyloidosis is poor. Therefore, exclusion of other diagnoses is important before confirming the presence of BS.Case ReportWe report a case of a 43‐year‐old woman who initially presented with peptic ulcers, recurrent oral ulcers, and suspicious genital ulcers resembling BS. She was treated with systemic steroids at the local hospital for approximately 2 years. However, her symptoms were not relieved, and the gradual presence of macroglossia, hoarseness, periorbital purpura, proteinuria, and cardiac involvement strongly suggested amyloidosis. She was diagnosed with immunoglobulin light chain amyloidosis (lambda type) after extensive examinations, including an elevated monoclonal protein concentration, Congo red staining, and immunohistochemical staining of abdominal fat. She was then treated with a chemotherapy regimen (melphalan and dexamethasone).ConclusionRecurrent oral ulcers can be the first manifestation of amyloidosis. The diagnosis of BS requires the exclusion of other diseases, and amyloidosis should be considered as a differential diagnosis.
Validation of supercritical water oxidation to destroy perfluoroalkyl acids
Authors: McDonough Jeffrey T. , Kirby John , Bellona Christopher , Quinnan Joseph A. , Welty Nicklaus , Follin John , Liberty Ken 
Published: February 2022

AbstractSome of the same unique physical and chemical properties that make per‐ and polyfluoroalkyl substances (PFAS) desirable for a wide range of commercial applications render them recalcitrant to many liquid treatment technologies. As developments in PFAS‐related toxicological studies increasingly suggest potential adverse human health effects, our industry has made great progress in the past several years on concentrating PFAS into small volume waste streams via adsorption and separation mechanisms. Coupled with residual PFAS‐containing commercial products that are being phased out, management of these concentrated waste streams presents an urgent need for the development and validation of destructive treatment technologies. Here, we field‐validate supercritical water oxidation to treat a concentrated waste stream of 12 perfluoroalkyl acids (PFAAs) with liquid and gaseous analysis, adhering to the recent Other Test Method 45 for stack emission sampling from the United States Environmental Protection Agency (USEPA) and USEPA Method 537.1, with quality control and quality assurance protocols from the Department of Defense/Department of Energy Quality Systems Manual 5.3. Results generated suggest greater than 99.999% destruction and removal efficiency of these 12 PFAAs after two ∼120‐min continuous flow trials, with an overall defluorination percentage of approximately 62.6%.
Caffeine and canopy cover interact to alter biofilm nutrient content, benthic invertebrates, and insect emergence
Authors: Marshall Melanie M. , Metzner Gabrielle K. , McCluney Kevin E. 
Published: February 2022

AbstractThe addition of trace contaminants to surface waters is recognized as a rising environmental concern and caffeine is among those most commonly detected. Classic toxicological analyses of single organisms have described some of the impacts of caffeine on freshwater organisms, but few have examined ecological interactions, food webs, or aquatic‐terrestrial linkages, especially in natural aquatic systems. This study used an in situ manipulative experiment to effects of caffeine addition on aquatic biofilms and biomass of aquatic and emergent insects. We used chemical diffusing substrata to introduce small concentrations of caffeine to streams and analyzed effects on downstream biofilm nutrient content, benthic macroinvertebrates, and emergent insects. We found that caffeine addition decreased the biomass of emergent insects by 56% and altered biofilm nutrient content in complex ways that interact with canopy cover, with stronger effects of caffeine addition under low canopy cover (higher light). Thus, the impacts of this common aquatic contaminant may influence the linked riparian food web by decreasing emergent insects, an important food source for riparian insectivores.
Effect of nature‐based physical activity on post‐traumatic growth among healthcare providers with post‐traumatic stress
Authors: Noushad Shamoon , Ansari Basit , Ahmed Sadaf 
Published: February 2022

AbstractThe purpose of this randomized control trial was to observe the effect of nature‐based physical activity in achieving post traumatic growth and to estimate the combined effect of nature and physical activity on the psychophysiological outcomes. A 3‐month therapy was provided to participants meeting eligibility criteria to receive the walk‐in nature (experimental group) or sit‐in nature (control group) in the 1:1 ratio. At baseline and 3‐month follow‐up, participants were assessed with Trauma Symptom Checklist 40, Traumatic Stress Scale, Post‐Traumatic Growth Inventory (PTGI), Cortisol, C‐Reactive Protein (CRP), Interleukin‐6 (IL‐6), Brain‐Derived Neurotropic Factor (BDNF) and heart rate variability. There was a significant effect of nature‐based physical activity on traumatic stress and post‐traumatic growth in comparison with the sit‐in control. A significant post‐interventional difference was observed in the mean PTGI score [F = 5.412, p = 0.022] between the experimental and control groups after 3 months of intervention. All the biochemical estimates, including CRP, BDNF, IL‐6, and cortisol levels, were significantly altered in both post‐intervention study groups (p < 0.01). Taken together, these results show that nature‐based physical activity significantly improves psychophysiological outcomes induced as a result of post‐traumatic growth and also reduces traumatic stress.
Local tumour response to neoadjuvant therapy with 2‐aminoethyl dihydrogen phosphate in dogs with soft tissue sarcoma
Authors: de Castro Patrícia Ferreira , Maria Durvanei Augusto , de Campos Fonseca Pinto Ana Carolina Brandão , Patricio Geni Cristina Fonseca , Matera Julia Maria 
Published: February 2022

AbstractBackgroundIn cases of soft tissue sarcoma (STS), neoadjuvant therapy is indicated to downstage the tumour prior to surgery to achieve enhanced local tumour control. The antineoplastic phospholipid compound 2‐aminoethyl dihydrogen phosphate (2‐AEH2F) is an alkyl phosphate ester capable of inhibiting cell proliferation and inducing cell death by modifying the asymmetry of phospholipids in the cytoplasmic membraneObjectivesThis clinical study was designed to investigate local antitumoural effects of neoadjuvant therapy with 2‐AEH2F in dogs with naturally occurring STSMaterial and methodsDogs (n = 11) received four consecutive weekly intravenous injections of 2‐AEH2F (70 mg/kg) prior to tumour resection. Tomographic (CT) and thermal (TE) images were used to investigate changes in tumour size and local temperature in response to treatmentResultsComparative analysis of CT images (n = 9/11) failed to reveal complete or partial remission according to selected assessment criteria (RECIST, WHO and volumetric). Comparative analysis of TE images (n = 10/11) revealed significantly (p = 0.01416) lower temperatures in tumoural areas relative to surrounding tissues over the course of treatmentConclusions2‐AEH2F had no cytoreductive effects when used at doses and intervals described in this study. However, significant drop in skin temperatures recorded in tumoural areas suggest induction of physiological changes.
Proactive interference and the development of working memory
Authors: Hamilton Mollie , Ross Ashley , Blaser Erik , Kaldy Zsuzsa 
Published: February 2022

AbstractWorking memory (WM), the ability to maintain information in service to a task, is characterized by its limited capacity. Several influential models attribute this limitation in a large extent to proactive interference (PI), the phenomenon that previously encoded, now‐irrelevant information competes with relevant information. Here, we look back at the adult PI literature, spanning over 60 years, as well as recent results linking the ability to cope with PI to WM capacity. In early development, WM capacity is even more limited, yet an accounting for the role of PI has been lacking. Our Focus Article aims to address this through an integrative account: since PI resolution is mediated by networks involving the frontal cortex (particularly, the left inferior frontal gyrus) and the posterior parietal cortex, and since children have protracted development and less recruitment of these areas, the increase in the ability to cope with PI is a major factor underlying the increase in WM capacity in early development. Given this, we suggest that future research should focus on mechanistic studies of PI resolution in children. Finally, we note a crucial methodological implication: typical WM paradigms repeat stimuli from trial‐to‐trial, facilitating, inadvertently, PI and reducing performance; we may be fundamentally underestimating children's WM capacity.This article is categorized under:
Psychology > Memory
Neuroscience > Cognition
Neuroscience > Development
Automating Data Science
Authors: Brazdil Pavel , van Rijn Jan N. , Soares Carlos , Vanschoren Joaquin 
Published: February 2022

AbstractIt has been observed that, in data science, a great part of the effort usually goes into various preparatory steps that precede model-building. The aim of this chapter is to focus on some of these steps. A comprehensive description of a given task to be resolved is usually supplied by the domain expert. Techniques exist that can process natural language description to obtain task descriptors (e.g., keywords), determine the task type, the domain, and the goals. This in turn can be used to search for the required domain-specific knowledge appropriate for the given task. In some situations, the data required may not be available and a plan needs to be elaborated regarding how to get it. Although not much research has been done in this area so far, we expect that progress will be made in the future. In contrast to this, the area of preprocessing and transformation has been explored by various researchers. Methods exist for selection of instances and/or elimination of outliers, discretization and other kinds of transformations. This area is sometimes referred to asdata wrangling. These transformations can be learned by exploiting existing machine learning techniques (e.g., learning by demonstration). The final part of this chapter discusses decisions regarding the appropriate level of detail (granularity) to be used in a given task. Although it is foreseeable that further progress could be made in this area, more work is needed to determine how to do this effectively.
Automating the Design of Complex Systems
Authors: Brazdil Pavel , van Rijn Jan N. , Soares Carlos , Vanschoren Joaquin 
Published: February 2022

AbstractThis chapter discusses the issue of whether it is possible to automate the design of rather complex workflows needed when addressing more complex data science tasks. The focus here is on symbolic approaches, which continue to be relevant. The chapter starts by discussing some more complex operators, including, for instance, conditional operators and operators used in iterative processing. Next, we discuss the issue of introduction of new concepts and the changes of granularity that can be achieved as a result. We review various approaches explored in the past, such as constructive induction, propositionalization, reformulation of rules, among others, but also draw attention to some new advances, such as feature construction in deep NNs. It is foreseeable that in the future both symbolic and subsymbolic approaches will coexist in systems exhibiting a kind of functional symbiosis. There are tasks that cannot be learned in one go, but rather require a sub-division into subtasks, a plan for learning the constituents, and joining the parts together. Some of these subtasks may be interdependent. Some tasks may require an iterative process in the process of learning. This chapter discusses various examples that can stimulate both further research and some practical solutions in this rather challenging area.
Metadata Repositories
Authors: Brazdil Pavel , van Rijn Jan N. , Soares Carlos , Vanschoren Joaquin 
Published: February 2022

SummaryThis chapter presents a review of online repositories where researchers can share data, code, and experiments. In particular, it covers OpenML, an online platform for sharing and organizing machine learning data automatically. OpenML contains thousands of datasets and algorithms, and millions of experimental results. We describe the basic philosophy involved, and its basic components: datasets, tasks, flows, setups, runs, and benchmark suites. OpenML has API bindings in various programming languages, making it easy for users to interact with the API in their native language. One important feature of OpenML is the integration into various machine learning toolboxes, such as Scikit-learn, Weka, and mlR. Users of these toolboxes can automatically upload all their results, leading to a large repository of experimental results.
Metalearning Approaches for Algorithm Selection I (Exploiting Rankings)
Authors: Brazdil Pavel , van Rijn Jan N. , Soares Carlos , Vanschoren Joaquin 
Published: February 2022

SummaryThis chapter discusses an approach to the problem of algorithm selection, which exploits the performance metadata of algorithms (workflows) on prior tasks to generate recommendations for a given target dataset. The recommendations are in the form of rankings of candidate algorithms. The methodology involves two phases. In the first one, rankings of algorithms/workflows are elaborated on the basis of historical performance data on different datasets. These are subsequently aggregated into a single ranking (e.g. average ranking). In the second phase, the average ranking is used to schedule tests on the target dataset with the objective of identifying the best performing algorithm. This approach requires that an appropriate evaluation measure, such as accuracy, is set beforehand. In this chapter we also describe a method that builds this ranking based on a combination of accuracy and runtime, yielding good anytime performance. While this approach is rather simple, it can still provide good recommendations to the user. Although the examples in this chapter are from the classification domain, this approach can be applied to other tasks besides algorithm selection, namely hyperparameter optimization (HPO), as well as the combined algorithm selection and hyperparameter optimization (CASH) problem. As this approach works with discrete data, continuous hyperparameters need to be discretized first.
Evaluating Recommendations of Metalearning/AutoML Systems
Authors: Brazdil Pavel , van Rijn Jan N. , Soares Carlos , Vanschoren Joaquin 
Published: February 2022

AbstractThis chapter discusses some typical approaches that are commonly used to evaluate metalearning and AutoML systems. This helps us to establish whether we can trust the recommendations provided by a particular system, and also provides a way of comparing different competing approaches. As the performance of algorithms may vary substantially across different tasks, it is often necessary to normalize the performance values first to make comparisons meaningful. This chapter discusses some common normalization methods used. As often a given metalearning system outputs a sequence of algorithms to test, we can study how similar this sequence is from the ideal sequence. This can be determined by looking at a degree of correlation between the two sequences. This chapter provides more details on this issue. One common way of comparing systems is by considering the effect of selecting different algorithms (workflows) on base-level performance and determining how the performance evolves with time. If the ideal performance is known, it is possible to calculate the value of performance loss. The loss curve shows how the loss evolves with time or what its value is at the maximum available time (i.e., the time budget) given beforehand. This chapter also describes the methodology that is commonly used in comparisons involving several metalearning/AutoML systems with recourse to statistical tests.
Automating Workflow/Pipeline Design
Authors: Brazdil Pavel , van Rijn Jan N. , Soares Carlos , Vanschoren Joaquin 
Published: February 2022

SummaryThis chapter discusses the design of workflows (or pipelines), which represent solutions that involve more than one algorithm. This is motivated by the fact that many tasks require such solutions. This problem is non-trivial, as the number of possible workflows (and their configurations) can be rather large. This chapter discusses various methods that can be used to restrict the design options and thus reduce the size of the configuration space. These include, for instance, ontologies and context-free grammars. Each of these formalisms has its merits and shortcomings. Many platforms have resorted to planning systems that use operators. These can be designed to be in accordance with the given ontologies or grammars. As the search space may be rather large, it is important to leverage prior experience. This topic is addressed in one of the sections, which discusses rankings of plans that have proved to be useful in the past. The workflows/pipelines that have proved successful in the past can be retrieved and used as plans in future tasks. Thus, it is possible to exploit both planning and metalearning.