bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mussel‐Inspired Ligand Clicking and Ion Coordination on 2D Black Phosphorus for Cancer Multimodal Imaging and Therapy
Authors: Li Zimu , Yu Yongkang , Zeng Wenfeng , Ding Fan , Zhang Dan , Cheng Wei , Wang Miao , Chen Hongzhong , Pan Guoqing , Mei Lin , Zeng Xiaowei , Gao Nansha 
Published: May 2022

AbstractAs a promising 2D nanocarrier, the biggest challenge of bare black phosphorus nanosheets (BP NSs) lies in the inherent instability, while it can be improved by surface modification strategies to a great extent. Considering the existing infirm BP NSs surface modification strategies, A mussels‐inspired strong adhesive biomimetic peptide with azide groups for surface modification to increase the stability of BP NSs is synthesized. The azide groups on the peptide can quickly and precisely bind to the targeting ligand through click chemistry, solving the problem of nonspecificity of secondary modification of other mussel‐mimicking materials. Besides, a catechol‐Gd3+ coordination network is further constructed for magnetic resonance imaging (MRI) and inducing intracellular endo/lysosome escape. The fabricated BP‐DOX@Gd/(DOPA)4‐PEG‐TL nanoplatform exhibits enhanced antitumor abilities through synergetic chemo/photothermal effects both in vitro and in vivo.
An Interlayer Containing Dissociated LiNO3 with Fast Release Speed for Stable Lithium Metal Batteries with 400 Wh kg−1 Energy Density
Authors: Yang Huicong , Liu Qingyun , Wang Yaozu , Ma Zhuoting , Tang Pei , Zhang Xiaoyin , Cheng Hui‐Ming , Sun Zhenhua , Li Feng 
Published: May 2022

AbstractLithium metal is an ideal electrode material for future rechargeable batteries. However, dendrite formation and unstable solid electrolyte interphase film lead to safety concerns and poor Coulombic efficiency (CE). LiNO3 significantly improves the performance of the lithium metal anode in ester electrolytes but its use is restricted by low solubility. To increase the content of LiNO3 in the cell, a poly‐(vinyl carbonate) organogel interlayer containing dissociated LiNO3 (LNO‐PVC) is placed between the cathode and anode. The dissociated LiNO3 effectively increases the LiNO3‐release rate and compensates for the LiNO3 consumed in ester electrolytes during cycling. Via this interlayer, the performance of the lithium metal anode is significantly improved. The average CE of a Li‐Cu cell reaches 98.6% at 0.5 mA cm−2‐1 h and 98.5% at 1 mA cm−2‐1 h for 300 cycles. Also, a Li||NCM811 pouch cell with LNO‐PVC interlayer can also reach a 400 Wh kg−1 energy density with a cycling life of 65 cycles. This strategy sheds light on the effect of the state of this salt on its release/dissolution kinetics, which is determined by the interactions between the salt and host material.
A Novel High‐Performance TiO2‐x/TiO1‐yNy Coating Material for Silicon Anode in Lithium‐Ion Batteries
Authors: Wang Sung Eun , Kim Min Ji , Lee Jin Woong , Chun Jinyoung , Choi Junghyun , Roh Kwang Chul , Kang Yun Chan , Jung Dae Soo 
Published: May 2022

AbstractProtective surface coatings on Si anodes are promising for improving the electrochemical performance of lithium‐ion batteries (LIBs). Nevertheless, most coating materials have severe issues, including low initial coulombic efficiency, structural fracture, morphology control, and complicated synthetic processing. In this study, a multifunctional TiO2‐x/TiO1‐yNy (TTN) formed via a facile and scalable synthetic process is applied as a coating material for Si anodes. A thin layer of amorphous TiO2 is uniformly coated onto Si nanoparticles by a simple sol–gel method and then converted into a two phase TiO2‐x/TiO1‐yNy via nitridation. The lithiated TiO2‐x provides high ionic and electrical conductivity, while TiO1‐yNy can improve mechanical strength that alleviates volume change of Si to address capacity fading issue. Owing to these synergetic advantages, TiO2‐x/TiO1‐yNy‐coated Si (Si@TTN) exhibits excellent electrochemical properties, including a high charge capacity of 1650 mA h g–1 at 0.1 A g–1 and 84% capacity retention after 100 cycles at 1 A g–1. Moreover, a significantly enhanced rate performance can be achieved at a high current density. This investigation presents a facile and effective coating material to use as the high‐capacity silicon anode in the emerging Si anode technology in LIBs.
A Critical Review on Nanowire‐Motors: Design, Mechanism and Applications
Authors: Wang Dashuang , Mukhtar Aiman , Humayun Muhammad , Wu Kaiming , Du Zhilan , Wang Shushen , Zhang Yuxin 
Published: May 2022

AbstractNanowire‐motors (NW−Ms) are promoting the rapid development of emerging biomedicine and environmental governance, and are an important branch of micro‐nano motors in the development of nanotechnology. In recent years, huge research breakthroughs have been made in these fields in terms of the fascinating microstructure, conversion efficiency and practical applications of NW−Ms. This review article introduces the latest milestones in NW−Ms research, from production methods, driving mechanisms, control methods to targeted drug delivery, sewage detection, sensors and cell capture. The dynamics and physics of micro‐nano devices are reviewed, and finally the current challenges and future research directions in this field are discussed. This review further aims to provide certain guidance for the driving of NW−Ms to meet the urgent needs of emerging applications.
Congenital bilateral renal dysplasia in a Cavalier King Charles Spaniel
Authors: Tusa Nicole V. , Levy Nyssa A. 
Published: May 2022

AbstractThis report describes a case of congenital bilateral renal dysplasia in a Cavalier King Charles Spaniel (CKCS) puppy, which has not been previously reported in the breed. An 8‐week‐old male intact CKCS was presented to a University Teaching Hospital for the evaluation of vomiting and lab work revealed azotemia. The puppy was treated with medical management involving fluid therapy, antibiotics, and supportive care. Due to the development of unresponsive oliguric renal failure, puppy's level of care intensified and peritoneal dialysis was initiated and continued over a 12‐day course. After initiating peritoneal dialysis, azotemia improved but the patient acutely declined after an episode of regurgitation and ultimately cardiopulmonary arrest. Postmortem examination revealed bilateral persistent fetal glomeruli, tubular dysplasia, and rare tubular necrosis. While congenital renal dysplasia has been reported in other canine breeds, to the authors’ knowledge, this is the first report of congenital renal dysplasia in a CKCS.
Regulation of Molecular Farming Products
Authors: Hundleby Penny A. C. , D’Aoust Marc-André , Finkle Carolyn , Atkins Judith , Twyman Richard M. 
Published: May 2022

AbstractThe regulation of molecular farming is a complex topic because plants and plant-based systems are relative newcomers among the many production platforms available for recombinant proteins. The regulations specific for different types of product (human/veterinary pharmaceuticals and medical devices, cosmetics, diagnostics, and research reagents) must therefore be overlaid with the regulations governing hitherto unfamiliar production platforms, and this must be achieved in different jurisdictions that handle genetically modified organisms (and genetically modified plants in particular) in very different ways. This chapter uses examples of different product types and production methods in three different jurisdictions (the USA, the EU, and Canada) to demonstrate some of the challenges facing the regulatory authorities.
Production of Recombinant Proteins by Agrobacterium-Mediated Transient Expression
Authors: Spiegel Holger , Schillberg Stefan , Nölke Greta 
Published: May 2022

AbstractThe agroinfiltration of plant tissue is a robust method that allows the rapid and transient expression of recombinant proteins. Using wild-type plants as biomass, agroinfiltration exploits the ability of plants to synthesize even complex multimeric proteins that require oxidative folding and/or post-translational modifications, while avoiding the expensive and time-consuming creation of stably transformed plant lines. Here we describe a generic method for the transient expression of recombinant proteins inNicotiana benthamianaat the small to medium laboratory scale, including appropriate binary vectors, the design and cloning of expression constructs, the transformation, selection, and cultivation of recombinantAgrobacterium tumefaciens, the infiltration of plants using a syringe or vacuum device, and finally the extraction of recombinant proteins from plant tissues.
Guiding Refugees Through European Bureaucracy: Designing a Trustworthy Mobile App for Document Management
Authors: Amard Alexandre , Hoess Alexandra , Roth Tamara , Fridgen Gilbert , Rieger Alexander 
Published: May 2022

AbstractAfter being granted asylum in European countries, refugees need to go through a multitude of administrative processes before they can participate in society. However, these processes are often challenging, as refugees struggle to understand them, lack instructions for managing paperwork, and do not possess the required language skills. Prior research emphasizes the role of information and communication technologies to simplify and enable refugee-friendly administrative processes. However, recent research and existing applications mainly focus on information retrieval and do not offer assistance for understanding official letters, completing administrative forms, and managing corresponding documents. Furthermore, refugees are often reluctant to use existing applications as they do not trust their host country’s governments and public authorities. In this research, we aim to address this functional and trust gap. We follow a design science research approach to develop a design for a refugee-centric and trustworthy mobile application that assists refugees along administrative processes. In doing so, we identify three design principles that may guide the development of such applications for refugees.
Comments on classical AdS flux vacua with scale separation
Authors: Apers Fien , Montero Miguel , Van Riet Thomas , Wrase Timm 
Published: May 2022

Abstract
AdS flux vacua with a parametric separation between the AdS and KK scales have been conjectured to be in the Swampland. We study flux compactifications of massive IIA supergravity with O6 planes which are claimed to allow moduli-stabilised and scale separated AdS3 and AdS4 vacua at arbitrary weak coupling and large volume. A recent refinement of the AdS Distance Conjecture is shown to be inconsistent with this class of AdS3 vacua because the requisite discrete higher form symmetries are absent. We further perform a tree-level study of non-perturbative decays for the nonsupersymmetric versions of the AdS3 solutions, and find that the vacua are stable within this approximation. Finally, we provide an initial investigation of the would-be dual CFT2s and CFT3s. We study roughly a dozen different models and find for all AdS4 DGKT-type vacua that the dual operators to the lightest scalars have integer dimensions. For the putative CFT2 dual theories of the AdS3 vacua we find no integer dimensions for the operators.
Uniqueness for degenerate parabolic equations in weighted $$L^1$$ spaces
Authors: Nobili Camilla , Punzo Fabio 
Published: May 2022

AbstractWe study uniqueness of solutions to degenerate parabolic problems, posed in bounded domains, where no boundary conditions are imposed. Under suitable assumptions on the operator, uniqueness is obtained for solutions that satisfy an appropriate integral condition; in particular, such condition holds for possibly unbounded solutions belonging to a suitable weighted $$L^1$$

L
1

space.
Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender
Authors: Marinucci Ludovica , Mazzuca Claudia , Gangemi Aldo 
Published: May 2022

AbstractBiases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology.
Source and rank-dependent utility
Authors: Abdellaoui Mohammed , Zank Horst 
Published: May 2022

AbstractFoundations are provided for rank-dependent preferences within the popular two-stage framework of Anscombe–Aumann, in which risk and ambiguity feature as distinct sources of uncertainty. We advance the study of attitudes towards ambiguity without imposing expected utility for risk. As a result, in our general model, ambiguity attitude can be captured by non-additive subjective probabilities as under Choquet expected utility or by a specific utility for ambiguity as in recursive expected utility or, if required, by both. The key property for preferences builds on (discrete) rates of substitution which are standardly applied in economics. By demanding consistency for these rates of substitution across events and within or across sources of uncertainty, we obtain a model that nests popular theories for risk and ambiguity. This way, new possibilities for theoretical and empirical analyses of these theories emerge.
A survey on computational taste predictors
Authors: Malavolta Marta , Pallante Lorenzo , Mavkov Bojan , Stojceski Filip , Grasso Gianvito , Korfiati Aigli , Mavroudi Seferina , Kalogeras Athanasios , Alexakos Christos , Martos Vanessa , Amoroso Daria , Di Benedetto Giacomo , Piga Dario , Theofilatos Konstantinos , Deriu Marco Agostino 
Published: May 2022

AbstractTaste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.
Discovery of Slow Variables in a Class Of Multiscale Stochastic Systems Via Neural Networks
Authors: Zieliński Przemysław , Hesthaven Jan S. 
Published: May 2022

AbstractFinding a reduction of complex, high-dimensional dynamics to its essential, low-dimensional “heart” remains a challenging yet necessary prerequisite for designing efficient numerical approaches. Machine learning methods have the potential to provide a general framework to automatically discover such representations. In this paper, we consider multiscale stochastic systems with local slow-fast timescale separation and propose a new method to encode in an artificial neural network a map that extracts the slow representation from the system. The architecture of the network consists of an encoder–decoder pair that we train in a supervised manner to learn the appropriate low-dimensional embedding in the bottleneck layer. We test the method on a number of examples that illustrate the ability to discover a correct slow representation. Moreover, we provide an error measure to assess the quality of the embedding and demonstrate that pruning the network can pinpoint essential coordinates of the system to build the slow representation.
C-reactive protein elevation predicts in-hospital deterioration after aneurysmal subarachnoid hemorrhage: a retrospective observational study
Authors: Alessandro Ostini , Rene Warschkow , Stefan Wolf , Miodrag Filipovic , Martin Seule , Oliver Bozinov , Urs Pietsch 
Published: May 2022

Abstract

Background
There is increasing evidence that inflammation plays a role in the pathogenesis of aneurysmal subarachnoid hemorrhage (aSAH) and in the development of delayed cerebral ischemia (DCI). However, the assessment and interpretation of classically defined inflammatory parameters is difficult in aSAH patients. The objective of this study was to investigate the relationship between easily assessable findings (hyperventilation, fever, white blood cell count (WBC), and C-reactive protein (CRP)) and the occurrence of DCI and unfavorable neurological outcome at discharge in aSAH patients.

Methods
Retrospective analysis of prospectively collected data from a single center cohort. We evaluated the potential of clinical signs of inflammation (hyperventilation, fever) and simple inflammatory laboratory parameters CRP and WBC to predict unfavorable outcomes at discharge and DCI in a multivariate analysis. A cutoff value for CRP was calculated by Youden’s J statistic. Outcome was measured using the modified Rankin score at discharge, with an unfavorable outcome defined as modified Rankin scale (mRS) > 3.

Results
We included 97 consecutive aSAH patients (63 females, 34 males, mean age 58 years) in the analysis. Twenty-one (22%) had major disability or died by the time of hospital discharge. Among inflammatory parameters, CRP over 100 mg/dl on day 2 was an independent predictor for worse neurological outcome at discharge. The average C-reactive protein level in the first 14 days was higher in patients with a worse neurological outcome (96.6, SD 48.3 vs 56.3 mg/dl, SD 28.6) in the first 14 days after aSAH. C-reactive protein on day 2 was an indicator of worse neurological outcome. No inflammatory parameter was an independent predictor of DCI. After multivariate adjustment, DCI, increased age, and more than 1 day of mechanical ventilation were significant predictors of worse neurological outcome.

Conclusions
Early elevated CRP levels were a significant predictor of worse neurological outcome at hospital discharge and may be a useful marker of later deterioration in aSAH.
Tooth agenesis in German orthodontic patients with non-syndromic craniofacial disorder: a retrospective evaluation of panoramic radiographs
Authors: Weise C. , Lehmann M. , Schulz M. C. , Reinert S. , Koos B. , Weise H. 
Published: May 2022

Abstract
Objectives
The study objective was to evaluate the tooth agenesis in German orthodontic patients with non-syndromic cleft lip and/or palate and Robin sequence compared to a control group without craniofacial disorder.

Materials/methods
A total of 108 panoramic radiographs were examined using the binary system of Tooth Agenesis Code (TAC) (excluding the third molar). Patients were divided into the craniofacial disorder group 1 (n = 43) and the healthy control group 2 (n = 65). Parameters such as skeletal class malformation, sex, localization of the cleft, craniofacial disorder, and interobserver reliability were assessed.

Results
Permanent tooth agenesis was observed in 44% of group 1 and 14% in group 2 with a statistically significant higher prevalence (p = 0.00162 (χ2)). Fourteen different TAC patterns were observed in group 1, ten of these occurring only once in separate patients. The distribution of the TAC codes in group 2 showed nine different possibilities of TAC code patterns; seven TACs were unique. In group 1, the most frequently absent teeth were the maxillary lateral incisor of the left side (30%); in group 2, the second premolar of the lower jaw on the right side (9%). Male patients with craniofacial disorder showed a higher percentage of tooth agenesis than female.

Conclusion
The data presented here shows a statistically significant higher prevalence of tooth agenesis in German patients with non-syndromic craniofacial disorder.

Clinical relevance
Radiographic evaluation enables the diagnosis of tooth agenesis. Recognizing early on the higher prevalence of tooth agenesis in patients exhibiting a craniofacial disorder is an important issue when developing long-term and comprehensive interdisciplinary treatment.
On the minimum number of resources for a perfect schedule
Authors: Benmansour Rachid , Braun Oliver 
Published: May 2022

AbstractIn the single-processor scheduling problem with time restrictions there is one main processor and B resources that are used to execute the jobs. A perfect schedule has no idle times or gaps on the main processor and the makespan is therefore equal to the sum of the processing times. In general, more resources result in smaller makespans, and as it is in practical applications often more economic not to mobilize resources that will be unnecessary and expensive, we investigate in this paper the problem to find the smallest number B of resources that make a perfect schedule possible. We show that the decision version of this problem is NP-complete, derive new structural properties of perfect schedules, and we describe a Mixed Integer Linear Programming (MIP) formulation to solve the problem. A large number of computational tests show that (for our randomly chosen problem instances) only $$B=3$$

B
=
3

or $$B=4$$

B
=
4

resources are sufficient for a perfect schedule.
Out of the shadows: automatic fish detection from acoustic cameras
Authors: Connolly R. M. , Jinks K. I. , Shand A. , Taylor M. D. , Gaston T. F. , Becker A. , Jinks E. L. 
Published: May 2022

AbstractEfficacious monitoring of fish stocks is critical for efficient management. Multibeam acoustic cameras, that use sound-reflectance to generate moving pictures, provide an important alternative to traditional video-based methods that are inoperable in turbid waters. However, acoustic cameras, like standard video monitoring methods, produce large volumes of imagery from which it is time consuming and costly to extract data manually. Deep learning, a form of machine learning, can be used to automate the processing and analysis of acoustic data. We used convolutional neural networks (CNNs) to detect and count fish in a publicly available dual-frequency identification sonar (DIDSON) dataset. We compared three types of detections, direct acoustic, acoustic shadows, and a combination of direct and shadows. The deep learning model was highly reliable at detecting fish to obtain abundance data using acoustic data. Model accuracy for counts-per-image was improved by the inclusion of shadows (F1 scores, a measure of the model accuracy: direct 0.79, shadow 0.88, combined 0.90). Model accuracy for MaxN per video was high for all three types of detections (F1 scores: direct 0.90, shadow 0.90, combined 0.91). Our results demonstrate that CNNs are a powerful tool for automating underwater acoustic data analysis. Given this promise, we suggest broadening the scope of testing to include a wider range of fish shapes, sizes, and abundances, with a view to automating species (or ‘morphospecies’) identification and counts.