bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contemporary Management of Distal Anterior Cerebral Artery Aneurysms: A Dual-Trained Neurosurgeon's Perspective
Authors: Furtado Sunil V. , Jayakumar Dravya , Perikal Parichay J. , Mohan Dilip 
Published: September 2021

Abstract

Objectives Distal anterior cerebral artery (DACA) aneurysms are a subset of aneurysms located in the anterior circulation but away from the circle of Willis. We analyze the clinical presentation and outcomes of two treatment groups—surgical and endovascular—for DACA aneurysms managed by a dual-trained neurosurgeon.

Material and Methods A retrospective evaluation of radiological and operative/interventional data of 34 patients with 35 DACA aneurysms over a 12-year period was analyzed. Twenty-seven patients underwent surgery, whereas seven underwent endovascular coiling of the aneurysms. Modified Fisher grade and World Federation of Neurosurgical Societies scale (WFNS) were used to note the subarachnoid hemorrhage (SAH) severity.

Statistical Analysis Categorical data were presented as frequency and percentage, while noncategorical data were represented as mean ± SD. Statistical significance for difference in outcome between the two groups was analyzed using Chi-square test, and p < 0.05 was considered statistically significant.

Results Of 34 patients, 33 presented with a bleed and 23.5% patients were noted to have another aneurysm in addition to the DACA aneurysm. Patients who underwent clipping for another aneurysm along with the DACA aneurysm in a single surgical exercise had a poor outcome compared with those who underwent surgery for the lone DACA aneurysm (7 vs. 20, p = 0.015). Most patients in both surgical (70.37%) and endovascular (85.71%) groups had good outcome (mRS ≤ 2).

Conclusions A good outcome can be achieved with either surgery or endovascular coiling in the management of DACA aneurysms. In patients with multiple aneurysms, SAH with aneurysmal rupture of DACA should be managed first; the other unruptured aneurysm may be operated after an interval to avoid morbidity.
Inclusive Education for Children with Special Needs and Autism: Status in the Schools of Puducherry, India
Authors: Mukkiri Sujitha , Kandasamy Preeti , Subramanian Mahadevan , Chandrasekaran Venkatesh , Kattimani Shivanand 
Published: September 2021

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder affecting social interaction and communication. Children with autism are at risk of school exclusion because of the inherent problems in behavior and communication. This study was performed in Puducherry to determine whether inclusive education is practiced for children with ASD and to ascertain the difficulties faced by educators in the schools that enroll children with ASD. This study was performed in the mainstream (regular) and special schools of Puducherry between November 2018 and February 2019. The mainstream schools were contacted over the telephone, by e-mail, and by regular post, and the investigator contacted the special schools in person. Data were collected using a semistructured proforma. A total of 66 schools participated in the survey (60 mainstream and 6 special schools). Among 60 mainstream schools comprising 3,967 children, 18 (0.45%) were children with special needs (CWSN) and 2 (0.05%) had ASD. In the six special schools comprising 2,167 children, 1,844 (85%) were CWSN and 323 (15%) were those with ASD. Among the 60 mainstream schools, only 14 (23.3%) schools had provision for special educators, accounting for 42 teachers. Behavioral problems were commonly reported in special schools. Enrollment of children with ASD is negligible in mainstream schools compared with special schools in Puducherry. More studies are needed to explore the reason for the same and to explore the feasibility of providing access to disabled children in mainstream schools.
Revisiting Intraoperative 2D USG with Saline–Air Mixture as Contrast for Resection of Eloquent Area Glioma in Resource-Deficient Countries
Authors: Sahoo Sushanta K. , Salunke Pravin , Ahuja Chirag Kamal 
Published: September 2021

Abstract

Background Advanced ultrasound, intraoperative magnetic resonance imaging (MRI), neuromonitoring, and aminolevulenic acid have improved the resection and safety of eloquent area gliomas. However, availability of these modern gadgets is a major concern in resource-deficient countries. A two-dimensional ultrasonography 2D USG is cheaper, provides real-time imaging, and is already established but underutilized instrument.

Objective Here, we revisited the principles of 2D USG and used it for eloquent-area glioma surgery.

Materials and Methods Fifty-eight patients with eloquent area gliomas were operated in last 2 years with the aid of 2D USG with 6-13 MHz curvilinear probe. Preoperative diagnosis was high-grade glioma in 38 and low-grade glioma (LGG) in 20 patients. Tumors were categorized as predominantly hyperechoic (27), uniformly hyperechoic (7), mixed echogenicity (21), and cystic (3).

Results Intraoperatively, 2D USG could define the tumor margins in 46 cases. Of these, USG suggested gross total excision in 38 patients and subtotal in 8 patients. The findings matched with follow-up MRI in 34 patients who showed hyperechogenicity (predominant/uniform). Injecting saline with air in to the resection cavity and insinuating through adjacent brain parenchyma helped in detecting residual lesion in three cystic gliomas and in two LGG where the tumor cavity collapsed.

Conclusion 2D USG is a helpful tool in eloquent area glioma surgery, especially in resource-limited countries. Visualization through adjacent parenchyma and injection of saline–air mixture in to the resection cavity helped in delineating residual lesion. Extent of resection is best monitored by 2D USG when tumor appeared hyperechoic (predominant/uniform).
Hyperammonemic Encephalopathy Associated with 5-Flurouracil Infusion in Head and Neck Cancer: Case Report and Review of the Literature
Authors: Das Saikat , Ahmad Aftab , Prasun Pallav , Kharade Vipin , Gupta Manish 
Published: September 2021

Abstract
Hyperammonemic encephalopathy is an uncommon, potentially lethal adverse effect of 5-fluorouracil (5-FU). Being one of the most common and versatile chemotherapy agents, it is important to understand this important side effect of 5FU. There is paucity of data in this subject. Here, we report a case of 5FU-induced encephalopathy in a patient on induction chemotherapy for head and neck cancer. In this case report, the clinical presentation, diagnosis, and management of 5FU-induced encephalopathy is reported.
Defective myelination in an RNA polymerase III mutant leukodystrophic mouse
Authors: Merheb Emilio , Cui Min-Hui , DuBois Juwen C. , Branch Craig A. , Gulinello Maria , Shafit-Zagardo Bridget , Moir Robyn D. , Willis Ian M. 
Published: September 2021

SignificancePathogenic mutations in subunits of RNA polymerase (Pol) III cause a prevalent autosomal recessive form of leukodystrophy. However, understanding of the mechanisms of pathogenesis, including how ubiquitously expressed Pol III mutations affect primarily the central nervous system, has been limited by the absence of an animal model of the disease. We show that conditional knockin of pathogenicPolr3amutations in the Olig2 lineage in mice results in growth, neurobehavioral, and hypomyelination phenotypes, reflecting a subset of clinical features of Pol III-related leukodystrophy patients. Myelination defects in the mice identify neural-specific roles for Pol III transcription. The phenotypes of Pol III-related leukodystrophic mice enable genetic and pharmacological approaches aimed at mitigating the consequences of this disease in humans.
Inequality in mortality between Black and White Americans by age, place, and cause and in comparison to Europe, 1990 to 2018
Authors: Schwandt Hannes , Currie Janet , Bär Marlies , Banks James , Bertoli Paola , Bütikofer Aline , Cattan Sarah , Chao Beatrice Zong-Ying , Costa Claudia , González Libertad , Grembi Veronica , Huttunen Kristiina , Karadakic René , Kraftman Lucy , Krutikova Sonya , Lombardi Stefano , Redler Peter , Riumallo-Herl Carlos , Rodríguez-González Ana , Salvanes Kjell G. , Santana Paula , Thuilliez Josselin , van Doorslaer Eddy , Van Ourti Tom , Winter Joachim K. , Wouterse Bram , Wuppermann Amelie 
Published: September 2021

Significance
From 1990 to 2018, the Black–White American life expectancy gap fell 48.9% and mortality inequality decreased, although progress stalled after 2012 as life expectancy plateaued. Had improvements continued at the 1990 to 2012 rate, the racial gap in life expectancy would have closed by 2036. Despite decreasing mortality inequality, income-based life expectancy gaps remain starker in the United States than in European countries. At the same time, European mortality improved strongly and even those U.S. populations with the longest life spans–White Americans living in the highest-income areas–experience higher mortality at all ages than Europeans in high-income areas in 2018. Hence, mortality rates of both Black and White Americans could fall much further in both high-income and low-income areas.
“How do you even know what ideophones mean?”
Authors: Nuckolls Janis B. 
Published: September 2021

AbstractUsing data from the Northern Pastaza (qvc) and Upper Napo Quichua (quw) dialects of Amazonian Ecuador, this paper argues that the semantics of ideophones, a highly marked form class of expressive words, is principled and describable with a combination of sensori-semantic features and a fine-grained typology of gestures, based on insights fromStreeck (2008)and others. Specifically, ideophones’ sensori-semantics are broken down into a semantic map consisting of 3 super- and 7 subcategorical distinctions. The greater the number of categories encoded by an ideophone’s semantics, the greater are the range of gestures used. Finally, gesture types identified byStreeck (2008)and others, were found among a very different group of people who are not western, educated, industrialized, rich, or democratic. Further research into ideophones and their gestures may find broader significance for ideophone semantics, and more generally, for the interrelations between language and gesture.
Dutch pragmatic markers in the left periphery
Authors: van der Wouden Ton , Foolen Ad 
Published: September 2021

Abstract
This chapter explores the left periphery of Dutch
utterances. Four positions hosting pragmatic markers (PMs) are
distinguished: the first sentence position (P1), a position
following P1 and two positions respectively preceding and following
a left dislocated constituent. We hypothesize that there are
correlations between these positions and pragmatic functions like
discourse coherence, subjectivity and intersubjectivity. A short
look is taken at asyndetic clusters of pragmatic markers in the left
periphery, we also assume functional motivations for the
linear ordering of PMs in the cluster. The goal of the chapter is
primarily descriptive, inspired by functionally oriented frameworks,
in particular Construction Grammar, Functional Discourse Grammar and
Interactional Linguistics. The illustrative data are mainly taken
from CGN, the corpus of spoken Dutch.
Interactions between distribution and functional uses in Italian
adversative pragmatic markers
Authors: Cimmino Doriana 
Published: September 2021

Abstract
This chapter describes the interaction between
distribution and functional uses of the Italian Adversative
Pragmatic Markers (APMs) ma and
però in spontaneous spoken language. These
forms are claimed to be distributionally and functionally
asymmetrical, as ma preferentially occurs in the
left periphery, performing a turn-taking function, while only
però can appear in the right periphery, with a
cohesive function. Results from a semasiological (form-to-function),
corpus-based and multi-level (prosodic, information and functional)
investigation show that APM asymmetry is not clear-cut. Although the
multi-level description supports Beeching and Detges’s (2014) function-periphery
asymmetry hypothesis as a general tendency, it also argues in favor
of a more fine-grained functional analysis.
Žinai ‘you know’ in Lithuanian discourse
Authors: Šinkūnienė Jolanta 
Published: September 2021

Abstract
This corpus-based study focuses on the frequency,
distribution patterns and functional profile of the pragmatic marker
žinai ‘you know’ in Lithuanian spoken discourse
and fiction. The data for the study comes from interviews,
spontaneous private conversations and a parallel corpus of English
and Lithuanian fiction texts. The results show that
žinai ‘you know’ can occupy clause initial,
medial and final positions, in which it can perform various
functions. They range from textual (indicating a word search or
hesitation, sustaining the flow of discourse, offering a
clarification, introducing information or arguments) to
interpersonal (making reference to common knowledge and seeking
understanding or agreement from the addressee).
Second person parentheticals of unintentional visual perception
in British English
Authors: Van Olmen Daniël 
Published: September 2021

Abstract
This chapter is the first systematic corpus-based
study of parenthetical see, you see and do
you see in British English. It compares (the
relationship between) their clause positions and their uses. The
results indicate, inter alia, that see is not
simply a shorter form of you see but also that some
conflation exists between the three markers. Furthermore, they
confirm some of the hypothesized associations of particular
functions with the left versus right clause periphery (e.g.
see’s attention-getting use in clause-initial
position) while challenging others (e.g. you see
able to mark clauses in both their left and right periphery as
explaining a previous one). The chapter also questions the notion of
(inter)subjectivity’s value in the debate about peripheries and
functions.
Extracting cosmological parameters from N-body simulations using machine learning techniques
Authors: Lazanu Andrei 
Published: September 2021

Abstract
We make use of snapshots taken from the Quijote suite of simulations, consisting of 2000 simulations where five cosmological parameters have been varied (Ωm, Ωb, h, n
s and σ8) in order to investigate the possibility of determining them using machine learning techniques. In particular, we show that convolutional neural networks can be employed to accurately extract Ω
m and σ
8 from the N-body simulations, and that these parameters can also be found from the non-linear matter power spectrum obtained from the same suite of simulations using both random forest regressors and deep neural networks. We show that the power spectrum provides competitive results in terms of accuracy compared to using the simulations and that we can also estimate the scalar spectral index n
s from the power spectrum, at a lower precision.
TCN-HBP: A Deep Learning Method for Identifying Hormone-Binding Proteins from Amino Acid Sequences Based on a Temporal Convolution Neural Network
Authors: Guo Jing 
Published: September 2021

Abstract
Hormone-binding proteins (HBPs) are carrier proteins that specifically bind to targeted hormones. Some evidence suggests that the abnormal expression of HBPs causes various diseases. Therefore, it is significant to accurately identify HBPs to study these diseases. Recently, many researchers have proposed traditional machine learning methods to complete this work, but these methods are neither suitable for training on large-scale datasets nor take into account the contextual features of HBPs. In this paper, I propose a new deep learning method, TCN-HBP, to distinguish HBPs. TCN-HBP consists of a coding layer, embedding layer, convolutional neural network (CNN) layer and temporal convolutional network (TCN) layer. The coding and embedding layers extend the protein sequences into two-dimensional matrix data. The CNN layer convolves the matrix data to form feature maps. The TCN layer captures the contextual features present in the feature maps. Experiments show that the data generalization capabilities and recognition accuracy (99.15%) of TCN-HBP on large datasets perform better than previous methods.
Detection of Sick Laying Hens by Infrared Thermal Imaging and Deep Learning
Authors: Li Pei , Lu Huishan , Wang Fujie , Zhao Shouyao , Wang Ning 
Published: September 2021

Abstract
The laying hens are prone to get sick during the growing period, and the temperature will fluctuate within a relative range when the disease occurs. This temperature change range can be used as a sign of pathological phenomena in the laying hens. In order to find the floating range of the body surface temperature of the laying hens raised in the poultry house in both healthy and pathological states, and the areas where there is a significant difference in the body surface temperature of the two, a detection method combining infrared thermal imaging technology and neural network is proposed. First, use an infrared thermal imager to obtain an infrared image of the body surface of a layer, and then use a convolutional neural network to establish a recognition model for the characteristic area of the layer, and extract the highest temperature of the region of interest in a healthy and pathological layer. Finally, analyse the temperature difference of each area of interest in the chicken body under these two conditions. The test results show that the accuracy of the convolutional neural network recognition model is 97%; the temperature fluctuation range of the three characteristic areas of healthy and pathological layers are different, and the maximum temperature difference area is 7.8°C.
A New Method for Geophysical Induced Polarization Inversion Based on Stochastic Medium Model and Sample-Compressed Artificial Neural Network
Authors: Liu Weiqiang , Yang Liangyong 
Published: September 2021

Abstract
Induced polarization (IP) is a near-surface geophysical exploration method. Inverting the electrical properties of the underground medium from surface apparent IP parameters is a highly nonlinear problem. To further improve the accuracy, the artificial neural network (ANN) algorithm is applied to the two-dimensional (2D) IP data inversion for the first time. We firstly produced smooth geo-electric models based on the stochastic medium theory, and obtained the corresponding theoretical responses through forward modelling. Then, we compressed the responses and models through image compression technology. Finally, the above compressed responses and models were used as input and output samples to train an optimal network system for inversion. We tested the algorithm with synthetic examples. The results show that ANN can improve the longitudinal resolution of the inversion results and make the inversion results more focused.
Artificial Intelligence (AI) Technology Assisted Emergency Management in Oil Industry
Authors: Wang Lei , Gao Shenghan , Chu Shengli , JiayiLi  , Jin Shuli , Li Na , Zhang Lianfang 
Published: September 2021

Abstract
Emergency management is considered to be a response to the risk of major accidents and disasters, while oil companies pay more attention to the post-emergency response and post-treatment, but do not care about the whole process of emergency management. Despite not necessarily receiving the most coverage, production safety accidents caused by unreasonable emergency management have become an urgent problem in petroleum industry. As the most advanced productive force in modern society, artificial intelligence is considered to lead the future, integrate into all aspects of today’s society, and even bring about industrial revolution. With the development of science and technology, artificial intelligence has been applied to all aspects of emergency management in petroleum industry, including intelligent prevention and emergency preparedness, monitoring and early warning, emergency response and rescue, post-recovery and reconstruction. The application of artificial intelligence can not only improve the level of emergency management, reduce the cost of labor expenditure, avoid casualties of emergency management personnel and field staff, but also improve the production efficiency of enterprises and maintain the safety of production in petroleum industry. The most important thing is that AI can involve the whole process of emergency management and bring industrial upgrading to the petroleum industry.
Meso-model Optimization of Composite Propellant Based on Hybrid Genetic Algorithm and Mass Spring System
Authors: Zhao Jiuling 
Published: September 2021

Abstract
The meso-structure of composite propellant directly affects its macroscopic properties. The primary premise of studying the macroscopic characteristics of composite propellant is to establish the meso-structure model which can reflect the actual formulation characteristics.In order to obtain the optimal composite propellant meso-structure model suitable for numerical analysis and calculation, the hybrid genetic algorithm was used to optimize the reconstructed meso-structure model. In order to avoid different degree of overlap between particles, the mass spring system was further improved. The resulting meso-structure model not only preserves the statistical characteristics of the propellant, but also meets the requirement of minimizing the optimal size. The reconstruction and optimization algorithm of the optimal meso-structure model are established in this paper, which has high theoretical significance and engineering application value for the prediction of mechanical properties of composite propellant.
Robust Unsupervised Feature Selection Based on Elastic Distance Loss
Authors: Pan Jinyan , Xie Youwei , Gao Yunlong , Gao Peng , Chen Baihua , Lin Xiangyujie 
Published: September 2021

Abstract
In recent years, it is a desirable way to introduce graph learning into feature selection, and it has achieved good performance in feature selection tasks. But three issues need to be concerned: (1) how to learn the similarity of samples during graph construction adaptively; (2) the performance of feature selection heavily depending on the construction of the graph; (3) most of the feature selection algorithms all use L2,1-norm for feature selection, which is not suitable for all data distributions. In this paper, we propose a robust feature selection framework to solve the above problems, which incorporated data reconstruction, graph embedding, and feature selection into a uniform framework. We use the probability of the neighbors to reconstruct the original data adaptively. Then, a novel adaptive loss function, namely, the elastic distance loss function is proposed. Finally, we compared seven state-of-art unsupervised feature selection algorithms in several benchmark datasets, and the experimental results proved the effectiveness of our proposed model.
Dynamic Optimization Algorithm Based on Multi-modal Niche Lion Swarm by Sensitive Individuals
Authors: Zhang Qi , Jiang Mingyan 
Published: September 2021

Abstract
Aiming at the problem that the model of complex dynamic system which is difficult to track its global optimal solution, this paper proposes a multi-modal niche lion swarm optimization algorithm based on sensitive individuals to solve the optimization problem of complex dynamic system. Sensitive individuals are set in the lion swarm algorithm to perceive changes in the dynamic environment, and to track the global optimal value in time. It combines with niche technology to increase the diversity of the population and avoids falling into local extremes. The dynamic environment is simulated by the dynamic function generator and compared with other optimization algorithm to verify the superiority of the algorithm proposed in this paper.