bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive transcriptomic profiling and mutational landscape of primary gastric linitis plastica
Authors: Liu Zhu , Hong Lian-Lian , Zheng Jin-Sen , Ling Zhe-Nan , Zhang Zhi-Long , Qi Ya-Nan , Zhang Xin-Yu , Zhu Tian-Yu , Wang Jiu-Li , Han Jing , Chen Xiang-Liu , Yu Qi-Ming , Wang Shi , Li Pei , Ling Zhi-Qiang 
Published: November 2022

Abstract
Background
Primary gastric linitis plastica (GLP) is a distinct phenotype of gastric cancer with poor survival. Comprehensive molecular profiles and putative therapeutic targets of GLP remain undetermined.

Methods
We subjected 10 tumor-normal tissue pairs to whole exome sequencing (WES) and whole transcriptome sequencing (WTS). 10 tumor samples were all GLP which involves 100% of the gastric wall macroscopically. TCGA data were compared to generate the top mutated genes and the overexpressed genes in GLP.

Results
Our results reveal that GLP has distinctive genomic and transcriptomic features, dysfunction in the Hippo pathway is likely to be a key step during GLP development. 6 genes were identified as significantly highly mutated genes in GLP, including AOX1, ANKRD36C, CPXM1, PTPN14, RPAP1, and DCDC1). MUC6, as a previously identified gastric cancer driver gene, has a high mutation rate (20%) in GLP. 20% of patients in our GLP cohort had CDH1 mutations, while none had RHOA mutations. GLP exhibits high immunodeficiency and low AMPK pathway activity. Our WTS results showed that 3 PI3K-AKT pathway-related genes (PIK3R2, AKT3, and IGF1) were significantly up-regulated in GLP. Two genes were identified using immunohistochemistry (IHC), IGF2BP3 and MUC16, which specifically expressed in diffuse-type-related gastric cancer cell lines, and its knockdown inhibits PI3K-AKT pathway activity.

Conclusions
We provide the first integrative genomic and transcriptomic profiles of GLP, which may facilitate its diagnosis, prognosis, and treatment.
Wahlerfolg der AfD und die Flüchtlingskrise: Spielen regionale Disparitäten eine Rolle?
Authors: Bartholomae Florian W. , Nam Chang Woon , Rafih Pierre 
Published: November 2022

ZusammenfassungWährend Populismus in Europa weiterhin weitverbreitet ist, sieht sich der Kontinent mit einer neuen Flüchtlingsdynamik konfrontiert. Nach der Krise von 2015/2016 ist die EU nun damit beschäftigt, die Herausforderungen der ukrainischen Zuwanderung besser und effizienter zu bewältigen. Aufgrund geopolitischer Veränderungen und zunehmender globaler Ungleichheit ist auch in Zukunft mit weiterer Zuwanderung zu rechnen. Da häufig die These vertreten wird, dass die Zuwanderung Populismus und die Unterstützung für rechtsextreme Parteien verstärkt, kann dies zu politischer Instabilität und Spaltung führen. In diesem Beitrag wird der Zusammenhang zwischen regionalen Disparitäten in der Flüchtlingskrise 2015/2016 und dem Erfolg der AfD bei der Bundestagswahl 2017 untersucht.
Behebung eines Webfehlers: Wie die Konjunkturkomponente demokratisch legitimiert und nachhaltiger werden könnte
Authors: Schuster Florian , Sigl-Glöckner Philippa 
Published: November 2022

AbstractThe optimisation of the cyclical adjustment procedure under the German debt brake should focus on how to contribute to a democratically legitimate and sustainable fiscal policy. In this article, we discuss four criteria that should be satisfied: The method must (i) be sufficiently determined, (ii) take the current state of research into account, (iii) incentivise a policy that enhances supply-side capacities, and (iv) be transparent. We show that today’s form of the cyclically adjusted budget balance does not satisfy these criteria, and we propose a reform. Finally, we discuss several recent trends in German fiscal policy, questioning whether such a reform can save the debt brake’s plausibility.
3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients
Authors: Di Napoli Alberto , Tagliente Emanuela , Pasquini Luca , Cipriano Enrica , Pietrantonio Filomena , Ortis Piermaria , Curti Simona , Boellis Alessandro , Stefanini Teseo , Bernardini Antonio , Angeletti Chiara , Ranieri Sofia Chiatamone , Franchi Paola , Voicu Ioan Paul , Capotondi Carlo , Napolitano Antonio 
Published: November 2022

Abstract
Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient’s mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient’s mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients.

Key Points
• 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients.
• The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.
STAn: analysis of data traces using an event-driven interval temporal logic
Authors: Panizo Laura , Gallardo María-del-Mar 
Published: November 2022

AbstractThe increasing integration of systems into people’s daily routines, especially smartphones, requires ensuring correctness of their functionality and even some performance requirements. Sometimes, we can only observe the interaction of the system (e.g. the smartphone) with its environment at certain time points; that is, we only have access to the data traces produced due to this interaction. This paper presents the toolSTAn, which performs runtime verification on data traces that combine timestamped discrete events and sampled real-valued magnitudes.STAnuses theSpinmodel checker as the underlying execution engine, and analyzes traces against properties described in the so-called event-driven interval temporal logic () by transforming each formula into a network of concurrent automata, written inPromela, that monitors the trace. We present two different transformations for online and offline monitoring, respectively. Then,Spinexplores the state space of the automata network and the trace to return a verdict about the corresponding property. We use the proposal to analyze data traces obtained during mobile application testing in different network scenarios.
Spoof, Bluff, Go For It: A Defence of Spoofing
Authors: Khorasanee Kasim 
Published: November 2022

AbstractSpoofing—placing orders on financial exchanges intending to withdraw them prior to execution—is widely legally prohibited. I argue instead on two main grounds that spoofing should be permitted and legalised. The first is that spoofing as a form of bluffing remains within the market practice of making legally binding offers—as opposed to lying or betraying trust—and primarily concerns the spoofer’s personal information. As a form of bluffing spoofing helps prevent financial speculators, in particular high-frequency algorithmic traders, from easily profiting by other market actors reliably revealing their underlying preferences through their market activity. The second is that at the systemic level permitting spoofing would benefit non-speculative actors who place orders to hedge economic risk and whose activities provide the raison d’être for financial exchanges, differentiating them from simple forums for gambling. I also address potential concerns that legalised spoofing would drive speculators out of financial markets entirely and, therefore, undermine market liquidity. This work contributes to the wider debate in business ethics regarding bluffing by illustrating the acceptability of bluffs which do not betray counterparty trust or reliance on testimony by remaining within the framework of market practices.
Phosphorylated chitosan accelerates dermal wound healing in diabetic wistar rats
Authors: Anushree U. , Punj Pratik , Vasumathi  , Bharati Sanjay 
Published: November 2022

Abstract
Phosphorylated chitosan (PC), a water-soluble derivative of chitosan possesses several biological and chemical properties suitable for diabetic wound healing. In the present study, we report the synthesis and diabetic wound healing capabilities of PC. Elemental analysis, FT-IR, 13C-NMR and 31P-NMR techniques were employed for the chemical characterization of PC. In vitro, antioxidant properties of PC were determined in terms of Fe3+ reducing, metal chelating, lipid peroxidation and superoxide scavenging ability. The wound healing potential of PC was assessed in diabetic excisional wound rat model. PC exhibited good water solubility, and in vitro antioxidant capacity. Wound contraction was higher in PC-treated wounds (91.11%) as compared to untreated wounds (67.26%) on 14th-day post wound creation. Histopathology of PC-treated wounds revealed improved tissue morphology with higher number of fibroblasts, a thicker epithelial layer, enhanced collagen deposits and angiogenesis as compared to untreated wounds. An overall increase of 57% and 25% in hydroxylamine and hexosamine content respectively were noted as compared to untreated wounds. A significant (P ≤ 0.05) increase in SOD activity and a significant (P ≤ 0.05) decrease in lipid peroxides were recorded in PC-treated wounds as compared to untreated wounds. These observations demonstrated that PC can be used as an effective agent in diabetic wound healing.
Graphical Abstract
Illustration of phosphorylated chitosan (PC) synthesis and its wound healing potential: Chitosan was phosphorylated to impart diabetic wound healing properties. Chemical characterizations such as elemental analysis, FT-IR and NMR confirmed successful phosphorylation of chitosan. PC exhibited good in vitro antioxidant properties. To assess the diabetic wound healing potential, an excisional wound model was developed in diabetic rats. PC treatment demonstrated accelerated wound healing.
Variant analysis of grain size related genes in the genus Sorghum
Authors: Ananda Galaihalage K. S. , Norton Sally L. , Barnes Eve , Furtado Agnelo , Møller Birger Lindberg , Gleadow Roslyn , Henry Robert J. 
Published: November 2022

AbstractGrain size and weight are two of the most important determinants of crop yield. Key genes associated with the grain size and weight have been identified in major crops. However, studies on the genetic basis of the grain size and weight related genes in wild Sorghum are limited. In this study, we analysed the variation of grain size related genes using variant analysis of 15 accessions across one cultivated and six tertiary gene pool species representing the five subgenera of Sorghum. A wide variation in grain size related parameters was observed. The highest grain weight, width, and thickness was observed for the accession S. bicolor (L.) Moench 314,746, while the highest grain length was observed for the accession S. macrospermum E.D. Garber 302,367. The wild sorghum species exhibited high morphological diversity. The six candidate genes related to grain size, Sobic.001G335800 (qGW7/GL7), Sobic.001G341700 (GS3), Sobic.002G257900 (GW8), Sobic.003G035400 (GW5/qSW5), Sobic.004G107300 (GW2), and Sobic.009G053600 (GS5) showed polymorphism in the coding sequence regions including variants generating premature stop codons. These variants might contribute to the observed variation in grain size and weight. The tertiary wild sorghum species may be a useful source of genes for understanding and engineering grain size in sorghum and other cereals.
Structure of university licensing networks
Authors: Modic Dolores , Lužar Borut , Yoshioka-Kobayashi Tohru 
Published: November 2022

AbstractWe study the structure and evolution of networks of inventors involved in university licensing and patenting. In particular, we focus on networks of inventors that have successfully licensed a university patent (i.e.,licensing networks), and investigate levels of their fragmentation, cliquishness, and whether they exhibit the small world phenomenon. We find that these licensing networks are more fragmented and cliquish than the networks of inventors engaged in all (not necessarily licensed) patents (i.e.,patenting networks), and that they are not small worlds. Additionally, by comparing the created licensing networks to random subnetworks of the patenting networks, we find that concerns in regard to the potential effects of opportunistic behavior are, to some degree, justified. We detect an interesting collaboration behavior of inventors who license, which we designate as dualistic opportunistic behavior.
Somatic embryogenesis in Abies nebrodensis, an endangered Sicilian fir
Authors: Jouini Nourhene , Yahyaoui Emna , Tarraf Waed , İzgü Tolga , Benelli Carla , Lambardi Maurizio , Germanà Maria Antonietta 
Published: November 2022

AbstractSomatic embryogenesis, as a promising biotechnological tool for many conifer trees, has never been applied for the Abies nebrodensis species. Although all the encouraging results previously obtained by the EU LIFE (European LIFE program) funded projects in over ten years, the critically endangered Sicilian fir remains alarmingly close to extinction. In this study, we reported the first protocol of somatic embryogenesis obtained from mature zygotic embryos of the Abies nebrodensis. Seeds from Abies adult trees with specific identification numbers (IN) were collected and full seeds were identified by X-ray. Different experiments were carried out for callus initiation, from both zygotic immature and mature embryos, testing different culture media. The immature embryos did not give embryogenic tissue (ET). Embryogenic callus (EC) was successfully induced from mature embryos with variable frequencies (0–40%). Schenk and Hilderbrandt (SH) was the most suitable initiation medium where the obtained callus initiation rate reached up to 40% for IN7 (first experiment). 6-benzylaminopurine (BAP) showed to be essential to induce EC (second experiment). IN8 presented the highest callus initiation rate (40%) among all tested donor trees, whereas IN13 recorded the lowest rate with 4% (third experiment). ET maturation from each singular embryo of IN7, IN8, IN10 and IN21 was successfully achieved in SH medium containing 37,83 µM abscisic acid (ABA), 8% of polyethylene glycol (PEG-4000) and 4% maltose. The encapsulation technology was assessed on the obtained ET and its proliferation was observed after encapsulation.
High current limits in chemical vapor deposited graphene spintronic devices
Authors: Belotcerkovtceva Daria , Panda J. , Ramu M. , Sarkar Tapati , Noumbe Ulrich , Kamalakar M. Venkata 
Published: November 2022

AbstractUnderstanding the stability and current-carrying capacity of graphene spintronic devices is key to their applications in graphene channel-based spin current sensors, spin-torque oscillators, and potential spin-integrated circuits. However, despite the demonstrated high current densities in exfoliated graphene, the current-carrying capacity of large-scale chemical vapor deposited (CVD) graphene is not established. Particularly, the grainy nature of chemical vapor deposited graphene and the presence of a tunnel barrier in CVD graphene spin devices pose questions about the stability of high current electrical spin injection. In this work, we observe that despite structural imperfections, CVD graphene sustains remarkably highest currents of 5.2 × 108 A/cm2, up to two orders higher than previously reported values in multilayer CVD graphene, with the capacity primarily dependent upon the sheet resistance of graphene. Furthermore, we notice a reversible regime, up to which CVD graphene can be operated without degradation with operating currents as high as 108 A/cm2, significantly high and durable over long time of operation with spin valve signals observed up to such high current densities. At the same time, the tunnel barrier resistance can be modified by the application of high currents. Our results demonstrate the robustness of large-scale CVD graphene and bring fresh insights for engineering and harnessing pure spin currents for innovative device applications.
Scene text recognition via context modeling for low-quality image in logistics industry
Authors: Heng Herui , Li Peiji , Guan Tuxin , Yang Tianyu 
Published: November 2022

AbstractText recognition has been applied in many fields recently, such as robot vision, video retrieval, and scene understanding. However, minimal research has been conducted in the field of logistics wherein images of express sheets captured by cameras are mostly curved, distorted, and have low resolution. In this study, a new method is proposed to address the aforementioned research gap while simultaneously considering irregular and low-resolution English letters. The entire approach comprises a rectification module, a convolutional neural network (CNN) extractor, a semantic context module (SCM), a global context module (GCM), and a lightweight transformer decoder that can exhibit improved training speed. In particular, we propose the idea of context modeling in our proposed method. (1) The proposed SCM is introduced to capture full-image dependencies and generates rich semantic context information. (2) We propose the GCM, which not only enhances long-range dependencies from the output of SCM but also outputs abundant pixel information to the self-attention decoder. (3) To solve the low-resolution text recognition problem in a large number of express sheet scenes, we propose Chinese datasets for improving intelligent logistics. Experiments conducted on six public benchmarks demonstrate that the developed method achieves better robustness to low-resolution and irregular text images.
Triangular modular curves of small genus
Authors: Duque-Rosero Juanita , Voight John 
Published: November 2022

AbstractTriangular modular curves are a generalization of modular curves that arise from quotients of the upper half-plane by congruence subgroups of hyperbolic triangle groups. These curves also arise naturally as a source of Belyi maps with monodromy $$\text {PGL}_2(\mathbb {F}_q)$$

PGL
2

(

F
q

)

or $$\text {PSL}_2(\mathbb {F}_q)$$

PSL
2

(

F
q

)

. We present a computational approach to enumerate Borel-type triangular modular curves of low genus, and we carry out this enumeration for prime level and small genus.
Fulfilling social needs through anthropomorphic technology? A reflection on existing research and empirical insights of an interview study
Authors: Christoforakos Lara , Diefenbach Sarah 
Published: November 2022

AbstractAs interactive technologies, such as chatbots or voice assistants, increasingly become social counterparts and resemble human interaction partners in many ways, the question arises whether they are also able to address users’ social needs. This paper explores whether interaction with technology can address social needs and what role technology anthropomorphism plays in this. While previous research shows somewhat contradictory results potentially related to challenges of applied assessment methods of anthropomorphism and social needs, we complement this by means of a qualitative interview study (n = 8). Our study findings support a potential of anthropomorphic technology to address users’ social needs but also highlight differences between the quality of human-technology and interpersonal interaction. In addition, our findings hint at a social desirability bias, since people see social need fulfillment through technology as silly or inappropriate. Design and societal implications are discussed.Practical Relevance: This article explores the potential of technology to address users’ social needs and discusses practical implications for marketing and design, e.g., how technologies should be designed in order to affect users’ social needs and which contexts of application might be suitable. Moreover, the article also reflects on societal implications resulting from a potential effect of interaction with technology on users’ social needs.
Optimization Model of Raw Material Selection Process for Complex Industry Based on Improved Sequential Quadratic Programming Algorithm
Authors: He Xiuli , Zhang Yang , Hong Mengna , Li Jigeng 
Published: November 2022

AbstractRaw materials for industrial production mainly come from nature with wide sources and uneven quality, while production requires strictly with the materials’ quality and their quality indicators are very mutual coupling. In actual production, unreasonable combination and formula often exist in the process of raw material selection, impeding the improvement of production efficiency, quality, energy-saving, and emission reduction. Unfortunately, research on this actual issue is currently vacant, so the main object of this paper is to address the selection of raw materials and formulations in actual production. Based on the designed logical combination algorithm and improved SQP algorithm, this study established the batch combination model and formula calculation model from the perspective of optimal combination and formula. Finally, a practical example of actual production is given to demonstrate the utility and application of this research.
MnBi2Te4 – a good platform for topological quantum physics study
Authors: Tan Weilun , Liu Jing , Li Hui , Guan Dandan , Jia Jin-Feng 
Published: November 2022

AbstractMagnetic topological insulator MnBi2Te4 is an intrinsic van der Waals layer structure compound. The interplay between magnetism and topology makes MnBi2Te4 a good platform to investigate controllable topological phase transition and emerging physical states such as quantum anomalous Hall state and Weyl semimetal phase. Crystal characterization showed a rhombohedral unit cell composing of Te-Bi-Te-Mn-Te-Bi-Te septuple layer (SL) coupled antiferromagnetically. Systematically investigation of surface states with angle-resolved photoemission spectroscopy and of bulk states with transport measurement showed detailed electronic structure of MnBi2Te4 crystal. Rich topological phases were observed in MnBi2Te4. Temperature, doping and external magnetic field could affect the different topological phases and induce phase transitions in certain conditions. Quantum anomalous Hall effect (QAHE) was realized at as high as 6.5 K in 5-SLs MnBi2Te4 flake. Furthermore, the negative to positive magnetoresistance transition and the thickness dependent QAHE Chern number of MnBi2Te4 provide strong evidences for the Weyl semimetal states in this material. Based on experiments done from 2019 to 2022, our review should shed light on future research opportunities on MnBi2Te4 compound.