bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of Manihot esculenta leaves on physiological and production parameters of Sasso breeder hens
Authors: Odile Raphaëlle Ngueda Djeuta , Kwassi Tona , Benjamin Adjei Mensah , Oke Oyegunle , Okanlawo Onagbessan , Kokou Tona
Published: April 2022

Background

The world population is increasing, leading to competition between humans and animals for the use of farm produce. The use of non‐conventional resources in poultry feed is increasingly being explored. Cassava (Manihot esculenta) have been studied in layers and broilers feed. However, there is very little information on the impact of the leaves on breeder hens.

Objectives

This study was conducted to determine the effect of M. esculenta leaf meal (MELM) on breeder hen performance, hatching egg quality and blood parameters.

Methods

A total of 180 hens and 24 cocks Sasso breeders at 32 weeks of age were used and equally assigned into two dietary treatments having six replicates of 15 hens and two cocks each. The dietary treatments were basal diet supplemented with 0% MELM and basal diet supplemented with the MELM group (5% MELM). Data were collected on feed intake, egg production, feed conversion ratio as well as egg quality indices during the experiment. The blood samples were collected from 18 birds per treatment (three3 per replication) for the determination of total protein, uric acid, triglycerides and total cholesterol at 45th week of age.

Results

The results showed that there was no significant difference on the feed intake of the birds across the treatments. Average egg weight and egg production were higher with a lower (p < 0.05) feed conversion rate in the MELM group hens. Total protein, uric acids, total cholesterol and triglyceride level increased significantly (p < 0.05) in hens fed 5 % of MELM. The proportions of yolk, egg shell and Haugh unit showed no significant difference between the treatments, while the proportion of albumen and yolk colour increased significantly (p < 0.05) in the MELM group hens.

Conclusions

It was concluded that 5% MELM can be used as feed ingredients in formulating breeder hen diets to improve productive performance.
The story of rRNA expansion segments: Finding functionality amidst diversity
Authors: Hariharan Nivedita , Ghosh Sumana , Palakodeti Dasaradhi
Published: April 2022

Expansion segments (ESs) are multinucleotide insertions present across phyla at specific conserved positions in eukaryotic rRNAs. ESs are generally absent in bacterial rRNAs with some exceptions, while the archaeal rRNAs have microexpansions at regions that coincide with those of eukaryotic ESs. Although there is an increasing prominence of ribosomes, especially the ribosomal proteins, in fine‐tuning gene expression through translation regulation, the role of rRNA ESs is relatively underexplored. While rRNAs have been established as the major catalytic hub in ribosome function, the presence of ESs widens their scope as a species‐specific regulatory hub of protein synthesis. In this comprehensive review, we have elaborately discussed the current understanding of the functional aspects of rRNA ESs of cytoplasmic eukaryotic ribosomes and discuss their past, present, and future.

This article is categorized under:

RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems

Translation > Ribosome Structure/Function

Translation > Regulation
A free boundary problem describing migration into rubbers – Quest for the large time behavior
Authors: Aiki Toyohiko , Kumazaki Kota , Muntean Adrian
Published: April 2022

In many industrial applications, rubber‐based materials are routinely used in conjunction with various penetrants or diluents in gaseous or liquid form. It is of interest to estimate theoretically the penetration depth as well as the amount of diffusants stored inside the material. In this framework, we prove the global solvability and explore the large time‐behavior of solutions to a one‐phase free boundary problem with nonlinear kinetic condition that is able to describe the migration of diffusants into rubber. The key idea in the proof of the large time behavior is to benefit from a contradiction argument, since it is difficult to obtain uniform estimates for the growth rate of the free boundary due to the use of a Robin boundary condition posed at the fixed boundary.
The Use of the δ18OP to Study P Cycling in the Environment
Authors: Pfahler V. , Adu-Gyamfi J. , O’Connell D. , Tamburini F.
Published: April 2022

Phosphorus (P) fertilizers are known to increase crop productivity; however, when applied in excess, it can cause serious environment pollution. Monitoring P pollution in natural environments using stable isotopes has been difficult because P has only one stable isotope (31P) making the use of P stable isotope tracing not an option. Radioactive P isotopes (32P and 33P) have been used but its drawbacks are the short half-life, health risks and safety procedures required to apply them in agricultural catchments. Phosphorus in organic and inorganic P forms is strongly bonded to oxygen (O), which has three stable isotopes, providing a system to track P cycling in agricultural catchments and environment using the stable isotopes of O in phosphate (δ18O-PO4). In recent years, various studies have indicated that the analysis of the stable isotopic composition of oxygen (O) bound to P (δ18Op) to better understand P cycling in the environment, has become a promising tracer (surrogate) to investigate soil P transformation, plant P uptake and to trace the sources of P from the soil to water bodies and the environment. The chapter outlines the background and examples of δ18Op studies in sediments, soils, fresh water, mineral fertilizers and plants.
Extraction Protocol
Authors: Pfahler V. , Adu-Gyamfi J. , O’Connell D. , Tamburini F.
Published: April 2022

Studies showed that the δ18OP is a useful tool to study P in the environment. Adequate extraction protocols for the targeted P pools of the study are a prerequisite for a successful study. Likewise, for most environmental samples, including water, soil, sediment and plant samples, it is crucial that the samples are processed as soon as possible after they have been taken to avoid any alterations of the original δ18OP signature. This is especially true when more bioavailable P pools, like soluble reactive P (SRP) in water samples, are extracted and analysed. Brucite precipitation of water samples should be directly done in the field, fresh soil and sediment samples have to be extracted within 7 days (if microbial P is targeted, on the day of sampling), and plant samples have to be extracted within a few hours of sampling or be frozen. The chapter briefly describes the P cycle in aquatic and terrestrial ecosystems and give an overview about extracting the most common P pools for δ18OP analysis: soluble reactive P in water samples, sequentially extracted P pools of soil, sediment, fertilizer and plant samples.
Modifications and Issues During Purification
Authors: Pfahler V. , Adu-Gyamfi J. , Watzinger A. , Tamburini F.
Published: April 2022

Depending on the extract, it is necessary to modify the purification protocol slightly. Each sample is different and despite a thorough testing of the purification protocol, issues might occur. The three modifications suggested include (1) adjustments in pH, (2) magnesium ammonium phosphate (MAP) precipitation and (3) reductions, prior to A1, of cations like iron (Fe), silica (Si) and calcium (Ca) which could cause interferences during the purification process. Some of the major issues often encountered are (1) no APM precipitation due to the presence of high carbonate concentrations, (2) the presence of high organic matter that requires additional steps in the protocol, (3) crystals not dissolving and (4) discoloration of solution.
How to Design a Study Including the Analysis of δ18OP
Authors: Pfahler V. , Adu-Gyamfi J. , Tamburini F.
Published: April 2022

To plan a research study, one needs to (1) establish a research question, (2) make a set of observations, (3) form a hypothesis in an attempt to explain the observations and (4) test the hypothesis based on the data collected. The following questions should be addressed when designing a study including the analysis of δ18OP: (i) what is the research hypothesis? (ii) what is the main objective of the study? (iii) what are the aims to address these objectives? and (iv) which techniques are appropriate to address such research question. In addition, one needs to consider (1) which kind of samples needs to be collected, e.g. soil, vegetation or water? (2) in case of soil and sediment samples, which sampling depths and increments need to be sampled? (3) which P pools need to be extracted and analysed for the corresponding δ18OP values? (4) when and how often should samples be taken and (5) how many samples can be processed per week?
Conclusions and Way Forward
Authors: Adu-Gyamfi J. , Pfahler V. , Heng L. , Tamburini F.
Published: April 2022

With an increasing number of researchers using the δ18OP method to investigate P cycling in the environment, it is necessary to conduct an inter-laboratory comparison study for the purification protocol as well as the measurement of silver phosphate with the TC/EA-IRMS like Watzinger et al. (2021) did. For the δ18OP method to progress, further fundamental research as well as field and laboratory studies need to be conducted. To the best of our knowledge, the effect of synthesizing enzymes on the δ18OP has not been investigated yet, despite the importance of those enzymes in the P cycle.
xxAI - Beyond Explainable Artificial Intelligence
Authors: Holzinger Andreas , Goebel Randy , Fong Ruth , Moon Taesup , Müller Klaus-Robert , Samek Wojciech
Published: April 2022

The success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, forestry, health, etc.) has therefore led to an demand for trust, which can be fostered if the methods can be interpreted and thus explained to humans. The research field of explainable artificial intelligence (XAI) provides the necessary foundations and methods. Historically, XAI has focused on the development of methods to explain the decisions and internal mechanisms of complex AI systems, with much initial research concentrating on explaining how convolutional neural networks produce image classification predictions by producing visualizations which highlight what input patterns are most influential in activating hidden units, or are most responsible for a model’s decision. In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering systems, structured explanations). In addition, we also intend to move beyond simply providing model explanations to directly improving the transparency, efficiency and generalization ability of models. We hope this volume presents not only exciting research developments in explainable AI but also a guide for what next areas to focus on within this fascinating and highly relevant research field as we enter the second decade of the deep learning revolution. This volume is an outcome of the ICML 2020 workshop on “XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.”
XAI and Strategy Extraction via Reward Redistribution
Authors: Dinu Marius-Constantin , Hofmarcher Markus , Patil Vihang P. , Dorfer Matthias , Blies Patrick M. , Brandstetter Johannes , Arjona-Medina Jose A. , Hochreiter Sepp
Published: April 2022

In reinforcement learning, an agent interacts with an environment from which it receives rewards, that are then used to learn a task. However, it is often unclear what strategies or concepts the agent has learned to solve the task. Thus, interpretability of the agent’s behavior is an important aspect in practical applications, next to the agent’s performance at the task itself. However, with the increasing complexity of both tasks and agents, interpreting the agent’s behavior becomes much more difficult. Therefore, developing new interpretable RL agents is of high importance. To this end, we propose to use Align-RUDDER as an interpretability method for reinforcement learning. Align-RUDDER is a method based on the recently introduced RUDDER framework, which relies on contribution analysis of an LSTM model, to redistribute rewards to key events. From these key events a strategy can be derived, guiding the agent’s decisions in order to solve a certain task. More importantly, the key events are in general interpretable by humans, and are often sub-tasks; where solving these sub-tasks is crucial for solving the main task. Align-RUDDER enhances the RUDDER framework with methods from multiple sequence alignment (MSA) to identify key events from demonstration trajectories. MSA needs only a few trajectories in order to perform well, and is much better understood than deep learning models such as LSTMs. Consequently, strategies and concepts can be learned from a few expert demonstrations, where the expert can be a human or an agent trained by reinforcement learning. By substituting RUDDER’s LSTM with a profile model that is obtained from MSA of demonstration trajectories, we are able to interpret an agent at three stages: First, by extracting common strategies from demonstration trajectories with MSA. Second, by encoding the most prevalent strategy via the MSA profile model and therefore explaining the expert’s behavior. And third, by allowing the interpretation of an arbitrary agent’s behavior based on its demonstration trajectories.
Beyond the Visual Analysis of Deep Model Saliency
Authors: Bargal Sarah Adel , Zunino Andrea , Petsiuk Vitali , Zhang Jianming , Murino Vittorio , Sclaroff Stan , Saenko Kate
Published: April 2022

Increased explainability in machine learning is traditionally associated with lower performance, e.g. a decision tree is more explainable, but less accurate than a deep neural network. We argue that, in fact, increasing the explainability of a deep classifier can improve its generalization. In this chapter, we survey a line of our published work that demonstrates how spatial and spatiotemporal visual explainability can be obtained, and how such explainability can be used to train models that generalize better on unseen in-domain and out-of-domain samples, refine fine-grained classification predictions, better utilize network capacity, and are more robust to network compression.
ECQ$$^{\text {x}}$$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
Authors: Becking Daniel , Dreyer Maximilian , Samek Wojciech , Müller Karsten , Lapuschkin Sebastian
Published: April 2022

The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations. Such increases in memory and computational demands make deep learning prohibitive for resource-constrained hardware platforms such as mobile devices. Recent efforts aim to reduce these overheads, while preserving model performance as much as possible, and include parameter reduction techniques, parameter quantization, and lossless compression techniques.

In this chapter, we develop and describe a novel quantization paradigm for DNNs: Our method leverages concepts of explainable AI (XAI) and concepts of information theory: Instead of assigning weight values based on their distances to the quantization clusters, the assignment function additionally considers weight relevances obtained from Layer-wise Relevance Propagation (LRP) and the information content of the clusters (entropy optimization). The ultimate goal is to preserve the most relevant weights in quantization clusters of highest information content.

Experimental results show that this novel Entropy-Constrained and XAI-adjusted Quantization (ECQ$$^{\text {x}}$$

x

) method generates ultra low-precision (2–5 bit) and simultaneously sparse neural networks while maintaining or even improving model performance. Due to reduced parameter precision and high number of zero-elements, the rendered networks are highly compressible in terms of file size, up to 103$$\times $$
×
compared to the full-precision unquantized DNN model. Our approach was evaluated on different types of models and datasets (including Google Speech Commands, CIFAR-10 and Pascal VOC) and compared with previous work.
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
Authors: Mamalakis Antonios , Ebert-Uphoff Imme , Barnes Elizabeth A.
Published: April 2022

In recent years, artificial intelligence and specifically artificial neural networks (NNs) have shown great success in solving complex, nonlinear problems in earth sciences. Despite their success, the strategies upon which NNs make decisions are hard to decipher, which prevents scientists from interpreting and building trust in the NN predictions; a highly desired and necessary condition for the further use and exploitation of NNs’ potential. Thus, a variety of methods have been recently introduced with the aim of attributing the NN predictions to specific features in the input space and explaining their strategy. The so-called eXplainable Artificial Intelligence (XAI) is already seeing great application in a plethora of fields, offering promising results and insights about the decision strategies of NNs. Here, we provide an overview of the most recent work from our group, applying XAI to meteorology and climate science. Specifically, we present results from satellite applications that include weather phenomena identification and image to image translation, applications to climate prediction at subseasonal to decadal timescales, and detection of forced climatic changes and anthropogenic footprint. We also summarize a recently introduced synthetic benchmark dataset that can be used to improve our understanding of different XAI methods and introduce objectivity into the assessment of their fidelity. With this overview, we aim to illustrate how gaining accurate insights about the NN decision strategy can help climate scientists and meteorologists improve practices in fine-tuning model architectures, calibrating trust in climate and weather prediction and attribution, and learning new science.
Towards Explainability for AI Fairness
Authors: Zhou Jianlong , Chen Fang , Holzinger Andreas
Published: April 2022

AI explainability is becoming indispensable to allow users to gain insights into the AI system’s decision-making process. Meanwhile, fairness is another rising concern that algorithmic predictions may be misaligned to the designer’s intent or social expectations such as discrimination to specific groups. In this work, we provide a state-of-the-art overview on the relations between explanation and AI fairness and especially the roles of explanation on human’s fairness judgement. The investigations demonstrate that fair decision making requires extensive contextual understanding, and AI explanations help identify potential variables that are driving the unfair outcomes. It is found that different types of AI explanations affect human’s fairness judgements differently. Some properties of features and social science theories need to be considered in making senses of fairness with explanations. Different challenges are identified to make responsible AI for trustworthy decision making from the perspective of explainability and fairness.
Logic and Pragmatics in AI Explanation
Authors: Tsai Chun-Hua , Carroll John M.
Published: April 2022

This paper reviews logical approaches and challenges raised for explaining AI. We discuss the issues of presenting explanations as accurate computational models that users cannot understand or use. Then, we introduce pragmatic approaches that consider explanation a sort of speech act that commits to felicity conditions, including intelligibility, trustworthiness, and usefulness to the users. We argue Explainable AI (XAI) is more than a matter of accurate and complete computational explanation, that it requires pragmatics to address the issues it seeks to address. At the end of this paper, we draw a historical analogy to usability. This term was understood logically and pragmatically, but that has evolved empirically through time to become more prosperous and more functional.
Explainable AI Methods - A Brief Overview
Authors: Holzinger Andreas , Saranti Anna , Molnar Christoph , Biecek Przemyslaw , Samek Wojciech
Published: April 2022

Explainable Artificial Intelligence (xAI) is an established field with a vibrant community that has developed a variety of very successful approaches to explain and interpret predictions of complex machine learning models such as deep neural networks. In this article, we briefly introduce a few selected methods and discuss them in a short, clear and concise way. The goal of this article is to give beginners, especially application engineers and data scientists, a quick overview of the state of the art in this current topic. The following 17 methods are covered in this chapter: LIME, Anchors, GraphLIME, LRP, DTD, PDA, TCAV, XGNN, SHAP, ASV, Break-Down, Shapley Flow, Textual Explanations of Visual Models, Integrated Gradients, Causal Models, Meaningful Perturbations, and X-NeSyL.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Authors: Molnar Christoph , König Gunnar , Herbinger Julia , Freiesleben Timo , Dandl Susanne , Scholbeck Christian A. , Casalicchio Giuseppe , Grosse-Wentrup Moritz , Bischl Bernd
Published: April 2022

An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified causal interpretations, and illustrate them with examples. We focus on pitfalls for global methods that describe the average model behavior, but many pitfalls also apply to local methods that explain individual predictions. Our paper addresses ML practitioners by raising awareness of pitfalls and identifying solutions for correct model interpretation, but also addresses ML researchers by discussing open issues for further research.
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
Authors: Salewski Leonard , Koepke A. Sophia , Lensch Hendrik P. A. , Akata Zeynep
Published: April 2022

Providing explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generating natural language explanations for VQA, we introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with natural language explanations. For each image-question pair in the CLEVR dataset, CLEVR-X contains multiple structured textual explanations which are derived from the original scene graphs. By construction, the CLEVR-X explanations are correct and describe the reasoning and visual information that is necessary to answer a given question. We conducted a user study to confirm that the ground-truth explanations in our proposed dataset are indeed complete and relevant. We present baseline results for generating natural language explanations in the context of VQA using two state-of-the-art frameworks on the CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation generation quality for different question and answer types. Additionally, we study the influence of using different numbers of ground-truth explanations on the convergence of natural language generation (NLG) metrics. The CLEVR-X dataset is publicly available athttps://github.com/ExplainableML/CLEVR-X.
Explaining the Predictions of Unsupervised Learning Models
Authors: Montavon Grégoire , Kauffmann Jacob , Samek Wojciech , Müller Klaus-Robert
Published: April 2022

Unsupervised learning is a subfield of machine learning that focuses on learning the structure of data without making use of labels. This implies a different set of learning algorithms than those used for supervised learning, and consequently, also prevents a direct transposition of Explainable AI (XAI) methods from the supervised to the less studied unsupervised setting. In this chapter, we review our recently proposed ‘neuralization-propagation’ (NEON) approach for bringing XAI to workhorses of unsupervised learning such as kernel density estimation and k-means clustering. NEON first converts (without retraining) the unsupervised model into a functionally equivalent neural network so that, in a second step, supervised XAI techniques such as layer-wise relevance propagation (LRP) can be used. The approach is showcased on two application examples: (1) analysis of spending behavior in wholesale customer data and (2) analysis of visual features in industrial and scene images.
Towards Causal Algorithmic Recourse
Authors: Karimi Amir-Hossein , von Kügelgen Julius , Schölkopf Bernhard , Valera Isabel
Published: April 2022

Algorithmic recourse is concerned with aiding individuals who are unfavorably treated by automated decision-making systems to overcome their hardship, by offering recommendations that would result in a more favorable prediction when acted upon. Such recourse actions are typically obtained through solving an optimization problem that minimizes changes to the individual’s feature vector, subject to various plausibility, diversity, and sparsity constraints. Whereas previous works offer solutions to the optimization problem in a variety of settings, they critically overlook real-world considerations pertaining to the environment in which recourse actions are performed.

The present work emphasizes that changes to a subset of the individual’s attributes may have consequential down-stream effects on other attributes, thus making recourse a fundamcausal problem. Here, we model such considerations using the framework of structural causal models, and highlight pitfalls of not considering causal relations through examples and theory. Such insights allow us to reformulate the optimization problem to directly optimize for minimally-costly recourse over a space of feasible actions (in the form of causal interventions) rather than optimizing for minimally-distant “counterfactual explanations”. We offer both the optimization formulations and solutions to deterministic and probabilistic recourse, on an individualized and sub-population level, overcoming the steep assumptive requirements of offering recourse in general settings. Finally, using synthetic and semi-synthetic experiments based on the German Credit dataset, we demonstrate how such methods can be applied in practice under minimal causal assumptions.
Preparatory Knowledge
Authors: Zheng Zhiyong
Published: April 2022

Modern cryptography and information theory is a branch of mathematics which develops rapidly. Almost all mathematical knowledge, such as algebra, geometry, analysis, probability and statistics, has very important applications in information theory.
Cryptosystem and Authentication System
Authors: Zheng Zhiyong
Published: April 2022

In 1949, Shannon published a famous paper entitled “communication theory of secure systems” in the technical bulletin of Bell laboratory. Based on the mathematical theory of information established by him in 1948 (see Chap. 3), this paper makes a comprehensive discussion on the problem of secure communication and establishes the mathematical theory of secure communication system. It has a great impact on the later development of cryptography. It is generally believed that Shannon transformed cryptography from art (creative ways and methods) to science, so he is also known as the father of modern cryptography. The main purpose of this chapter is to introduce Shannon’s important ideas and results in cryptography theory, which is the cornerstone of the whole modern cryptography.
Lattice-Based Cryptography
Authors: Zheng Zhiyong
Published: April 2022

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The extracellular matrix molecule tenascin-C modulates cell cycle progression and motility of adult neural stem/progenitor cells from the subependymal zone
Authors: Schaberg Elena , Götz Magdalena , Faissner Andreas
Published: April 2022

Adult neurogenesis has been described in two canonical regions of the adult central nervous system (CNS) of rodents, the subgranular zone (SGZ) of the hippocampus and the subependymal zone (SEZ) of the lateral ventricles. The stem cell niche of the SEZ provides a privileged environment composed of a specialized extracellular matrix (ECM) that comprises the glycoproteins tenascin-C (Tnc) and laminin-1 (LN1). In the present study, we investigated the function of these ECM glycoproteins in the adult stem cell niche. Adult neural stem/progenitor cells (aNSPCs) of the SEZ were prepared from wild type (Tnc+/+) and Tnc knockout (Tnc−/−) mice and analyzed using molecular and cell biological approaches. A delayed maturation of aNSPCs in Tnc−/− tissue was reflected by a reduced capacity to form neurospheres in response to epidermal growth factor (EGF). To examine a potential influence of the ECM on cell proliferation, aNSPCs of both genotypes were studied by cell tracking using digital video microscopy. aNSPCs were cultivated on three different substrates, namely, poly-d-lysine (PDL) and PDL replenished with either LN1 or Tnc for up to 6 days in vitro. On each of the three substrates aNSPCs displayed lineage trees that could be investigated with regard to cell cycle length. The latter appeared reduced in Tnc−/− aNSPCs on PDL and LN1 substrates, less so on Tnc that seemed to compensate the absence of the ECM compound to some extent. Close inspection of the lineage trees revealed a subpopulation of late dividing aNSPCslate that engaged into cycling after a notable delay. aNSPCslate exhibited a clearly different morphology, with a larger cell body and conspicuous processes. aNSPCslate reiterated the reduction in cell cycle length on all substrates tested, which was not rescued on Tnc substrates. When the migratory activity of aNSPC-derived progeny was determined, Tnc−/− neuroblasts displayed significantly longer migration tracks. This was traced to an increased rate of migration episodes compared to the wild-type cells that rested for longer time periods. We conclude that Tnc intervenes in the proliferation of aNSPCs and modulates the motility of neuroblasts in the niche of the SEZ.
The role of bile acids in carcinogenesis
Authors: Režen Tadeja , Rozman Damjana , Kovács Tünde , Kovács Patrik , Sipos Adrienn , Bai Péter , Mikó Edit
Published: April 2022

Bile acids are soluble derivatives of cholesterol produced in the liver that subsequently undergo bacterial transformation yielding a diverse array of metabolites. The bulk of bile acid synthesis takes place in the liver yielding primary bile acids; however, other tissues have also the capacity to generate bile acids (e.g. ovaries). Hepatic bile acids are then transported to bile and are subsequently released into the intestines. In the large intestine, a fraction of primary bile acids is converted to secondary bile acids by gut bacteria. The majority of the intestinal bile acids undergo reuptake and return to the liver. A small fraction of secondary and primary bile acids remains in the circulation and exert receptor-mediated and pure chemical effects (e.g. acidic bile in oesophageal cancer) on cancer cells. In this review, we assess how changes to bile acid biosynthesis, bile acid flux and local bile acid concentration modulate the behavior of different cancers. Here, we present in-depth the involvement of bile acids in oesophageal, gastric, hepatocellular, pancreatic, colorectal, breast, prostate, ovarian cancer. Previous studies often used bile acids in supraphysiological concentration, sometimes in concentrations 1000 times higher than the highest reported tissue or serum concentrations likely eliciting unspecific effects, a practice that we advocate against in this review. Furthermore, we show that, although bile acids were classically considered as pro-carcinogenic agents (e.g. oesophageal cancer), the dogma that switch, as lower concentrations of bile acids that correspond to their serum or tissue reference concentration possess anticancer activity in a subset of cancers. Differences in the response of cancers to bile acids lie in the differential expression of bile acid receptors between cancers (e.g. FXR vs. TGR5). UDCA, a bile acid that is sold as a generic medication against cholestasis or biliary surge, and its conjugates were identified with almost purely anticancer features suggesting a possibility for drug repurposing. Taken together, bile acids were considered as tumor inducers or tumor promoter molecules; nevertheless, in certain cancers, like breast cancer, bile acids in their reference concentrations may act as tumor suppressors suggesting a Janus-faced nature of bile acids in carcinogenesis.
A clinical decision aid for patients with suspected midfacial and mandibular fractures (the REDUCTION-I study): a prospective multicentre cohort study
Authors: Rozema Romke , Moumni Mostafa El , de Vries Gysbert T. , Spijkervet Frederik K. L. , Verbeek René , Kleinbergen Jurrijn Y. J. , Bens Bas W. J. , Doff Michiel H. J. , van Minnen Baucke ,   the REDUCTION study group
Published: April 2022

Purpose

To assess physical examination findings related to maxillofacial trauma to identify patients at risk of midfacial and mandibular fractures and then to construct a clinical decision aid to rule out the presence of midfacial and mandibular fractures in emergency department patients.

Methods

We performed a prospective multicentre cohort study in four hospitals in the Netherlands, including consecutive patients with maxillofacial trauma. Each patient received a standardized physical examination consisting of 15 and 14 findings for midfacial and mandibular traumas, respectively. Consequently, clinical decision aids were constructed with the focus being on ruling out the presence of midfacial and mandibular fractures, and diagnostic accuracy was calculated.

Results

A total of 993 consecutive patients were identified of whom 766 and 280 patients were suspected of midfacial and mandibular fractures, respectively. Midfacial fractures were diagnosed in 339 patients (44.3%), whereas mandibular fractures were observed in 66 patients (23.6%). The decision aid for midfacial trauma consisting of peri-orbital hematoma, epistaxis, ocular movement limitation, infra-orbital nerve paresthesia, palpable step-off and tooth mobility or avulsion, produced a sensitivity of 89.7 (86.0–92.5), a specificity of 42.6 (38.0–47.4), and a negative predictive value of 83.9% (78.4–88.2). The decision aid for mandibular trauma consisting of the angular compression test, axial chin pressure test, objective malocclusion, tooth mobility or avulsion and the tongue blade bite test resulted in a sensitivity of 98.5 (91.9–99.7), a specificity of 34.6 (28.5–41.2), and a negative predictive value of 98.7% (92.8–99.8).

Conclusion

The constructed clinical decision aids for maxillofacial trauma may aid in stratifying patients suspected for midfacial and mandibular fractures to reduce unnecessary diagnostic imaging.

Clinical Trial Registration

The study was registered at ClinicalTrials.gov with the identifier NCT03314480.
The importance of transparency in naming conventions, designs, and operations of safety features: from modern ADAS to fully autonomous driving functions
Authors: Murtaza Mohsin , Cheng Chi-Tsun , Fard Mohammad , Zeleznikow John
Published: April 2022

This paper investigates the importance of standardising and maintaining the transparency of advanced driver-assistance systems (ADAS) functions nomenclature, designs, and operations in all categories up until fully autonomous vehicles. The aim of this paper is to reveal the discrepancies in ADAS functions across automakers and discuss the underlying issues and potential solutions. In this pilot study, user manuals of various brands are reviewed systematically and critical analyses of common ADAS functions are conducted. The result shows that terminologies used to describe ADAS functions vary widely across manufacturers and sometimes do not reflect their fundamental functions intuitively. Operational conditions and control procedures also vary across the selected models under this study. Due to this lack of consensus across the industry, drivers are not aware or well informed about ADAS functions in their vehicles, leading to a very low utilization rate and may lead to misuse of those functions. This paper provides insightful suggestions for the transport industry, Artificial Intelligence (AI) experts, and regulators to design frameworks and guidelines in governing the naming convention, operating conditions, control procedures, and information disclosure of ADAS. Such guidelines can be the foundations for regulating future AI-based self-driving functions.
Treating Agricultural Runoff with a Mobile Carbon Filtration Unit
Authors: Phillips Bryn M. , Fuller Laura B. McCalla , Siegler Katie , Deng Xin , Tjeerdema Ron S.
Published: April 2022

Several classes of pesticides have been shown to impair water quality in California, including organophosphates, pyrethroids and neonicotinoids. Vegetative treatment systems (VTS) can reduce pesticide loads and associated toxicity in agricultural runoff, but many water-soluble pesticides such as neonicotinoids are not effectively treated by VTS, and VTS installation is not always an option for growers required to remove non-crop vegetation for food safety concerns. Recent studies have shown that biochar filtration can be used to remove soluble contaminants, especially when coupled with other VTS components. We evaluated a mobile carbon filter system consisting of a trailer-mounted tank containing approximately 600L (~ 180 kg) of biochar. Input water from a 437-hectare agricultural drainage was pre-filtered and treated with biochar during two multi-week study periods. Laboratory toxicity tests and chemical and nutrient analyses were conducted on input and output water. Pesticide concentrations were initially reduced by greater than 99%. Treatment efficacy declined linearly and was expected to remain at least 50% effective for up to 34 weeks. Toxicity was assessed with Ceriodaphnia dubia, Hyalella azteca and Chironomus dilutus. Significant input toxicity was reduced to non-toxic levels in 6 of 16 samples. Some input concentrations of the neonicotinoid imidacloprid and the pyrethroid cypermethrin exceeded organism-specific toxicity thresholds and benchmarks, but the overall causes of toxicity were complex mixtures of agricultural chemicals. Nutrients were not reduced by the biochar. Results demonstrate the utility of biochar in treating agricultural runoff and provide measures of the longevity of biochar under field conditions.
The Value of the Biomarkers Neuron-Specific Enolase and S100 Calcium-Binding Protein for Prediction of Mortality in Children Resuscitated After Cardiac Arrest
Authors: Bangshøj Johanne , Liebetrau Benedikte , Wiberg Sebastian , Gjedsted Jakob , Kjærgaard Jesper , Hassager Christian , Wanscher Michael
Published: April 2022

The aim of the present study was to assess the ability of the biomarkers neuron-specific enolase (NSE) and S100 calcium-binding protein b (S100b) to predict 30 day mortality in children resuscitated from cardiac arrest (CA). It was a prospective observational study at a single tertiary heart centre. Consecutive children were admitted after resuscitated in-hospital and out-of-hospital CA. Levels of NSE and S100b were analyzed from 12 to 24 hours, from 24 to 48 hours, and from 48 to 72 hours after admission. The primary endpoint was 30-day mortality. Differences in biomarker levels between survivors and non-survivors were analyzed with the Mann-Whitney U test. Receiver operating characteristics (ROC) curves were applied to assess the predictive ability of the biomarkers and the areas under the ROC curves (AUC) were presented. A total of 32 resuscitated CA patients were included, and 12 (38%) patients died within 30 days after resuscitation. We observed significantly higher levels of NSE and S100b in non-survivors compared to survivors at all timepoints from 12 to 72 hours after CA. NSE achieved AUCs from 0.91–0.98 for prediction of 30 day mortality, whereas S100b achieved AUCs from 0.93–0.94. An NSE cut-off of 61 μg/L sampled between 12–24 hours from admission achieved a sensitivity of 80% and a specificity of 100% for prediction of 30 day mortality. In children resuscitated from CA, the biomarkers NSE and S100b appear to be solid predictors of mortality after 30 days.
Development of Diaphragmatic Hernia in Patients with Penetrating Left Thoracoabdominal Stab Wounds
Authors: Yucel Metin , Kulali Fatma , Yildiz Abdullah
Published: April 2022

Background

This study aimed to investigate the consequences of repairing versus not repairing diaphragmatic injury caused by penetrating left thoracoabdominal stab wounds.

Methods

Diagnostic laparoscopy was performed to evaluate the left diaphragm in patients with penetrating left thoracoabdominal stab wounds who did not have an indication for emergency laparotomy. Patients who did not consent to laparoscopy were discharged without undergoing surgery. Post‐discharge radiological images of patients who underwent diaphragmatic repair and radiological images of patients who could not undergo laparoscopy, both during hospitalization and after discharge, were evaluated and compared.

Results

Diagnostic laparoscopy was performed on 109 patients. Diaphragmatic injuries were detected and repaired in 32 (29.36%) of these patients. Seventeen patients were lost to follow‐up. After a mean follow‐up of 57.67 months, none of the remaining 15 patients developed a diaphragmatic hernia. On the other hand, 43 patients refused to undergo diagnostic laparoscopy. Twenty of them were lost from follow‐up. The diaphragmatic injury was detected in seven of the remaining 23 patients (30.44%) during initial computed tomography (CT) examinations. In this group, the mean follow‐up time was 42.57 months, and delayed diaphragmatic hernia developed in one patient (14.30%). Patients who underwent diaphragmatic repair were compared to patients who did not undergo diagnostic laparoscopy but had diaphragmatic injuries detected on their CT. No statistical differences were detected.

Conclusions

Diaphragmatic injuries caused by penetrating stab wounds can sometimes heal spontaneously. However, diagnostic laparoscopy is still relevant for revealing and repairing possible diaphragmatic injuries.
Methotrexate significantly reduces the humoral vaccination response against SARS-CoV-2 in older but not younger patients with rheumatoid arthritis
Authors: Feuchtenberger Martin , Kovacs Magdolna Szilvia , Eder Anna , Nigg Axel , Schäfer Arne
Published: April 2022

To assess the humoral response to vaccination against SARS-CoV-2 in patients with rheumatoid arthritis treated with methotrexate (MTX). In total, 142 fully vaccinated individuals were included at 6 ± 1 weeks after their second vaccination [BioNTech/Pfizer (70.4%), AstraZeneca (20.4%), and Moderna (9.2%)]. The primary goal was to assess the humoral immune response as measured by titres of neutralising antibodies against the S1 antigen of SARS-CoV-2. In a cross-sectional, single-centre study, titres were compared between patient subgroups with (n = 80) and without (n = 62) methotrexate exposure. MTX patients showed a significantly reduced humoral response to vaccination in the oldest patient subgroup (> 70 years: P = 0.038), whereas titres of neutralising antibodies were not significantly different between MTX and non-MTX patients in patients less than 70 years of age (< 56 years: P = 0.234; 56–70 years: P = 0.446). In patients > 70 years, non-MTX patients showed a maximum immune response in 76.5% of cases, whereas this percentage was reduced to 53.7% in study participants on MTX medication (effect size d = 0.21). Older age in patients with rheumatoid arthritis in combination with methotrexate results in a significantly reduced humoral response after vaccination against SARS-CoV-2. Our data underline the importance of age regarding the humoral response and may support the temporary cessation of methotrexate, particularly in elderly patients in the context of vaccination against SARS-CoV-2.
Simulation framework for crystallization in melt flows of semi-crystalline polymers based on phenomenological models
Authors: Descher Stefan , Wünsch Olaf
Published: April 2022

Polymer components are shaped mostly out of the molten state. As in the case of semi-crystalline polymers, crystallization can be suppressed by shock cooling, thermal process design allows to influence the solid bodies properties. A simulation approach that enables to predict these properties based on a forecast of crystallinity is presented in this paper. The main effects to consider and possibilities of modeling and simulation are discussed. A detailed description of how to create an experimental foundation using dynamic scanning calorimetry (DSC) and a rheometer is provided. Suppression of crystallization is modeled by a novel phenomenological approach, based on data over a large band of cooling rates. Special focus is put on parameter identification and extension of insufficient DSC data. The mechanical behavior is modeled using a weighted approach based on a nonlinear-thermoviscoelastic model for the molten state and a highly viscous Newtonian model for the solid state. Parameterization of both models is highlighted. An implementation in OpenFOAM is documented, emphasizing specific methods that were applied. Results of simulations for a simplified profile extrusion and injection molding case are presented. Basic relationships are forecasted correctly by the method, and important findings are presented for both processes.