bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of deep brain stimulation on driving in Parkinson’s disease - a prospective controlled study
Authors: Buhmann Carsten , Fründt Odette , Mainka Tina , Vettorazzi Eik , Baspinar Ela , Schwarz Cindy , Südmeyer Martin , Gerloff Christian , Zangemeister Wolfgang , Pötter-Nerger Monika , Hidding Ute , Hamel Wolfgang , Moll Christian 
Published: October 2022

Abstract
About 50% of patients with Parkinson’s disease (PD) treated with deep brain stimulation (DBS) actively drive a car. In this controlled, three-armed study, non-demented active drivers with PD and DBS were recruited to prospectively examine their driving simulator performance before (Baseline) and 6–12 months after brain surgery (Follow-up). Data was compared to that of clinically matched PD patients, eligible for but without undergoing DBS and to age-matched healthy controls. Clinical Baseline characteristics and driving performance of PD patients with (n = 23) and without DBS (n = 29) did not differ significantly but driving safety in PD was worse compared to controls (n = 33). At Follow-up, driving performance was still comparable between both PD groups. Disease duration was associated with poorer driving performance. This first prospective study on driving safety in PD after DBS implantation indicates that DBS does not influence driving negatively, but also does not improve driving safety as hypothesized based on previous studies.
Modelling the ecological dynamics of mosquito populations with multiple co-circulating Wolbachia strains
Authors: Ogunlade Samson , Adekunle Adeshina , McBryde Emma , Meehan Michael 
Published: October 2022

Abstract
Wolbachia intracellular bacteria successfully reduce the transmissibility of arthropod-borne viruses (arboviruses) when introduced into virus-carrying vectors such as mosquitoes. Despite the progress made by introducing Wolbachia bacteria into the Aedes aegypti wild-type population to control arboviral infections, reports suggest that heat-induced loss-of-Wolbachia infection as a result of climate change may reverse these gains. Novel, supplemental Wolbachia strains that are more resilient to increased temperatures may circumvent these concerns, and could potentially act synergistically with existing variants. In this article, we model the ecological dynamics among three distinct mosquito (sub)populations: a wild-type population free of any Wolbachia infection; an invading population infected with a particular Wolbachia strain; and a second invading population infected with a distinct Wolbachia strain from that of the first invader. We explore how the range of possible characteristics of each Wolbachia strain impacts mosquito prevalence. Further, we analyse the differential system governing the mosquito populations and the Wolbachia infection dynamics by computing the full set of basic and invasive reproduction numbers and use these to establish stability of identified equilibria. Our results show that releasing mosquitoes with two different strains of Wolbachia did not increase their prevalence, compared with a single-strain Wolbachia-infected mosquito introduction and only delayed Wolbachia dominance.
Uncertainty in child custody cases after parental separation: context and decisionmaking process
Authors: Mendes Josimar Antônio de Alcântara , Ormerod Thomas 
Published: October 2022

Abstract
Context factors (e.g., a family’s developmental crisis) can affect the child custody decisionmaking process and the child’s best interests after parental separation. But what are these context factors, and how do they vary across different cultures and legal systems? This paper reports a cross-cultural qualitative study funded by the Brazilian Ministry of Education and was carried under a Naturalistic Decision-making approach. This study addresses factors that impact the decision-making of experienced legal actors in child custody cases. Interviews were conducted with 73 legal actors (judges, prosecutors, lawyers, psychologists and social workers) in Brazil and England. The data gathered were analysed employing a reflexive thematic analysis that generated seven themes addressing how uncertainty is structured by context factors in child custody cases after parental separation. The themes encompass three domains (‘family’, ‘family court’ and ‘legal-psychosocial’) that draw the attention to the sources of uncertainty in child custody cases, especially to the role of contextual players (family and children) in the child custody decision-making process.
Towards securing Wireless insulin pump system using unsupervised deep learning technique
Authors: M Shobana , s Poonkuzhali 
Published: October 2022

Abstract
With the advent of Internet of things (IoT) technology across various fields give arise to occurrence of many smart objects/things. These kind of smart objects involves even in medical area to achieve smart health care monitoring system, wearable devices, and medical implanted devices, in general those kind of systems were called as internet of medical things. Hence the tremendous increase in smart devices among medical domain has both pros and cons. Among many existing problems, security issues sounds to be most addressable problem in the IoT based medical application. These IoMT devices were considered to be resource constrained, so it does not possess enough security framework to fight against all sorts of malicious attack as well as data privacy of patients. The malicious attack in IoMT system can bring huge data loss and life threat to patients. The existing solutions suggested for IoMT security issues are relies on supervised learning, so this work is heavily based on unsupervised learning to improve the efficiency of the designed security model. In this paper, an intrusion detection system has been designed for most significant IoMT device namely Insulin pump system for diabetes treatment using deep learning technique in an unsupervised manner. In this model deep autoencoder has been utilized to classify the unauthorized insulin value from the legitimate insulin value and this model used insulin logs of several patients as its dataset. The performance of the designed model has been evaluated using the quality metrics like accuracy, precision, F1-measure, and recall. Furthermore the resultant model is compared and analyzed against existing methodology as well as traditional machine learning classifiers.
AT 1 inhibition mediated neuroprotection after experimental traumatic brain injury is dependent on neutrophils in male mice
Authors: Timaru-Kast Ralph , Coronel-Castello Shila P. , Krämer Tobias J. , Hugonnet André V. , Schäfer Michael K. E. , Sebastiani Anne , Thal Serge C. 
Published: October 2022

Abstract
Cerebral inflammation with invasion of neutrophils and lymphocytes is a crucial factor in the process of secondary brain damage after traumatic brain injury (TBI). In TBI the intrinsic renin-angiotensin system is an important mediator of cerebral inflammation, as inhibition of the angiotensin II receptor type 1 (AT1) reduces secondary brain damage and the invasion of neutrophil granulocytes into injured cerebral tissue. The present study investigated the role of immune cells in AT1 inhibition-mediated neuroprotection after experimental TBI. In four different cohorts, male mice were studied to examine effects of neutropenia (anti-Ly6G antibody mediated neutrophil depletion; C57BL/6) or lymphopenia (RAG1 deficiency, RAG1-/-) alone, or in combination with candesartan-mediated AT1 inhibition. The present results demonstrate that reduction of neutrophils (neutrophil depletion in wild type mice) and of lymphocytes (RAG1-/-) as well as AT1 inhibition in (control antibody treated) wild type and RAG1-/- mice reduce brain damage and neuroinflammation after TBI compared to control groups, while in neutropenic mice, candesartan had no effect. However, AT1 inhibition was neuroprotective in RAG1-/- mice, but not in neutropenic mice. Therefore, the results indicate that AT1 inhibition mediated neuroprotection may be exerted by anti-inflammatory effects on neutrophils, with a subsequent reduction of neutrophil invasion.
Multiscale simulation and experimental measurements of the elastic response for constructional steel
Authors: Ye Yi-Cong , Zhao Feng-Yuan , Huang Cai-Min , Bai Shu-Xin , Chen Qiang 
Published: October 2022

Abstract
The elastic response of the medium carbon steel to the macroscopic stress is established for revealing the elastic properties at multiscale. Based on the multiscale correlation constitutive equations derived from this constitutive model, the effective elastic constants (EECs) of medium carbon steel are predicted. In addition, the diffraction elastic constants (DECs) of the constituents of the medium carbon steel are also evaluated. And then, the simple in-situ X-ray diffraction experiments was performed for the measurements of DECs and EECs of treated 35CrMo steel during the four-point bending. Compared with the different models and experiments, the results demonstrated that the developed constitutive model is in good agreement with the measured values of the EECs and DECs, and that the connection of the material properties may be revealed by the feasibility and credibility of the constitutive model for simulating the elastic response at multiscale.
An optimal design modeling of non-volatile dynamical reconfigurable FPGA using SOT-MRAM for ultra-low power applications
Authors: Hussain Karrar , Reddy C V Krishna , Kishore K Lal 
Published: October 2022

Abstract
The rapid development of reconfigurable FPGA circuits will facilitate their use and operation at low cost. The design of internal memory of FPGA circuit controls their future development. Spin RAM and Magnetic RAM (MRAM) are the best solutions for FPGA for reducing power and writing time. In terms of memory technology, MRAM is becoming more and more attractive in the industry due to its promising capabilities. The physical design range still causes significant radiation effects such as write error rate, access time, tunnel breakage, and low MRAM power consumption. However, random MRAM writing processes affect their yield, so process variations should be investigated, which increases the complexity of yield analysis. For further enhancement of non-volatile FPGA circuits, we propose an optimal design modeling of non-volatile dynamical reconfigurable FPGA using SOT-MRAM for ultra-low power applications. We first introduce a spin-orbit torque-based MRAM (SOT-MRAM), which significantly reduces exposure to radiation caused by offline reading and writing. We design SOT-MRAM based on a double barrier magnetic tunnel connector (DMTJ) with two reference layers to ensure high power and right length. After that, we develop an optimal SOT-MRAM design using the butterfly induced sunflower optimization (BSFO) algorithm which optimizes the design parameters of writing processes. Finally, the simulation results of proposed design can be compared with the existing state-of-art designs in terms of different simulation metrics.
Lactoferrin-chitosan-TPP nanoparticles: antibacterial action and extension of strawberry shelf-life
Authors: Duarte Larissa G.R. , Ferreira Natália C.A. , Fiocco Ana Clara T. R. , Picone Carolina S.F. 
Published: October 2022

Abstract
The aim of the present study was to produce lactoferrin (L) and chitosan (C) nanoparticles by ionic cross-linking with TPP and thereby increase the antimicrobial activity of biopolymers. The nanoparticles were synthesized in different proportions of biopolymers and TPP and characterized regarding their size, zeta potential, morphology, chemical interactions, structural characteristics and antibacterial activity. They were also applied as coatings on strawberries with the aim of increasing fruit shelf-life. Circular dichroism spectra revealed that the addition of TPP altered the secondary structure of lactoferrin. The nanoparticles 3.5L:5.5C:1TPP and 4.5L:4.5C:1TPP showed higher zeta potential values and lower hydrodynamic diameters (+39.30 mV, 81.87 nm and + 33.07 mV, 97.67 nm, respectively) and intense bacteriostatic action against S. aureus (0.0370 mg/ml and 0.0463 mg/ml, respectively).The minimum inhibitory concentration of these nanoparticles was three times lower than those of pure biopolymers. When applied to strawberries coating, the nanoparticles delayed the ripening and degradation of the fruit. These results confirm that it is possible to intensify the antimicrobial properties of lactoferrin and chitosan through ionic crosslinking with TPP and, thus, expand their use as natural food preservatives.
CGCoCoS-ODB: A Cell Granularity Correctness and Completeness Scheme for Outsourced DataBases
Authors: Cruz Bruno Ramos , Henríquez Lil Maria Xibai Rodríguez , Hernández Saúl Eduardo Pomares , Díaz Jesús García 
Published: October 2022

Abstract
In Outsourced Databases (ODB), Authenticated Query Processing (AQP) schemes offer to database owners the capabilities of verifying correctness, completeness, and/or freshness over query response, assuming untrusted providers. This paper focuses on the first two capabilities that are oriented to ensure integrity in structured data. The AQP solutions work at different level of granularity, mainly at tuple or cell; being the cell granularity the most recent approach that improves the precision of the query response. Another aspect of AQP solutions is the type of query that supports; selection, range, join, among others. Currently, only one solution works at cell level granularity and exclusively provides the correctness capability for selection queries. In this work, we propose a cryptographic scheme for authenticated query processing a cell level granularity that allows the data owner to verify the correctness and completeness of the selection and range query response. The scheme is based on a novel combination of cuckoo filters, bitmaps, and MACs. The cuckoo filter was used to achieve correctness and the bitmaps along with the MACS were used to ensure completeness. The performance of the scheme was evaluated using the Census-Income database. This data set contains weighted census data extracted from the 1994 and 1995 Current Population Surveys conducted by the U.S. Census Bureau. The experiments were carried out in two phases, the first phase consists of preparing the data. The second phase is the query process, where the queries are divided into select and range queries. The proposed scheme has the ability to provide correctness and completeness of a single cell without retrieving all the other attributes of the same row, minimizing network and storage costs. The scheme was designed and tested against attacks such as rows scrambling, columns scrambling, among others.
Generalized Gradient Emphasis Learning for Off-Policy Evaluation and Control with Function Approximation
Authors: Cao Jiaqing , Liu Quan , Wu Lan , Fu Qiming , Zhong Shan 
Published: October 2022

Abstract
Emphatic temporal-difference (TD) learning (Sutton et al. 2016) is a pioneering off-policy reinforcement learning method involving the use of the followon trace. The recently proposed Gradient Emphasis Learning (GEM, Zhang et al. 2020) algorithm is used to fix the problems of unbounded variance and large emphasis approximation error introduced by the followon trace from the perspective of stochastic approximation. In this paper, we rethink GEM and introduce a novel generalized GEM(β) algorithm to learn the true emphasis. The key to the construction of the generalized GEM(β) algorithm is introducing a tunable hyper-parameter β that is not necessarily the same as the discount factor γ to the GEM operator. We then apply the emphasis estimated by the proposed GEM(β) algorithm to the value estimation gradient and the policy gradient, respectively, yielding the corresponding emphatic TD variant for off-policy evaluation and actor-critic algorithm for off-policy control. Finally, we demonstrate empirically the advantage of the proposed algorithms across a range of problems, for both off-policy evaluation and off-policy control, and for both linear and nonlinear function approximation.
Extracting Entities and Relations in Analyst Stock Ratings News
Authors: Krstev Ivan , Mishkovski Igor , Mirchev Miroslav , Golubova Blagica , Gramatikov Sasho 
Published: October 2022

Abstract
Massive volumes of finance-related data are created on the Internet daily,whether on question-answering forums, news articles, or stocks analysis sites. This data can be critical in the decision-making process fortargeting investments in the stock market. Our research paper aimsto extract information from such sources in order to utilize the volumes of data, which is impossible to process manually. In particular,analysts’ ratings on the stocks of well-known companies are considereddata of interest. Two subdomains of Information Extraction will be performed on the analysts’ ratings, Named Entity Recognition and RelationExtraction. The former is a technique for extracting entities from araw text, giving us insights into phrases that have a special meaning inthe domain of interest. However, apart from the actual positions andlabels of those phrases, it lacks the ability to explain the mutual relations between them, bringing up the necessity of the latter model, whichexplains the semantic relationships between entities and enriches theamount of information we can extract when stacked on top of the NamedEntity Recognition model. This study is based on the employment of different models for word embedding and different Deep Learning classification architectures for extracting the entities and predicting relationsbetween them. Furthermore, the multilingual abilities of a joint pipelineare being explored by combining English and German corpora. For bothsubtasks, we record state-of-the-art performances of 97.69% F1 score fornamed entity recognition and 89.70% F1 score for relation extraction.
Potential additional effects of iron chelators on antimicrobial-impregnated central venous catheters
Authors: Itoh Kazuhiro , Tsutani Hiroshi , Mitsuke Yasuhiko , Iwasaki Hiromichi 
Published: October 2022

Abstract
Although central venous catheters (CVCs) play an important role in medical practice, their placement as foreign objects in the human body increases the risk of infection. Microbial colony and biofilm formation are likely to occur on CVCs. Infections caused by CVCs are referred to as catheter-related infections (CRIs), and can lead to longer hospital stays, increased medical costs, and in severe cases, bacteremia and death. The use of antimicrobial-impregnated catheters is recommended as a method to control CRIs. The efficacy of antimicrobial-impregnated catheters has been studied in basic experimental and clinical trials, but further improvements are still being sought, leading to studies on various combinations of iron chelators and antimicrobial agents to limit microbial and biofilm activity. Here, we hypothesize that a new type of CVC with an iron chelator in addition to antimicrobial agents might further suppress the occurrence of CRIs. This paper discusses the antimicrobial and anti-biofilm activities of iron chelators and proposes the possibility of creating a new type of CVC.
Computing a Minimum Subset Feedback Vertex Set on Chordal Graphs Parameterized by Leafage
Authors: Papadopoulos Charis , Tzimas Spyridon 
Published: October 2022

Abstract
Chordal graphs are characterized as the intersection graphs of subtrees in a tree and such a representation is known as the tree model. Restricting the characterization results in well-known subclasses of chordal graphs such as interval graphs or split graphs. A typical example that behaves computationally different in subclasses of chordal graph is the Subset Feedback Vertex Set (SFVS) problem: given a vertex-weighted graph G = (V,E) and a set S ⊆ V , we seek for a vertex set of minimum weight that intersects all cycles containing a vertex of S. SFVS is known to be polynomial-time solvable on interval graphs, whereas SFVS remains NP-complete on split graphs and, consequently, on chordal graphs. Towards a better understanding of the complexity of SFVS on subclasses of chordal graphs, we exploit structural properties of a tree model in order to cope with the hardness of SFVS. Here we consider variants of the leafage that measures the minimum number of leaves in a tree model. We show that SFVS can be solved in polynomial time for every chordal graph with bounded leafage. In particular, given a chordal graph on n vertices with leafage ℓ, we provide an algorithm for SFVS with running time nO(ℓ), thus improving upon nO(ℓ2), the running time of the previously known algorithm obtained for graphs with bounded mim-width. We complement our result by showing that SFVS is W[1]-hard parameterized by ℓ. Pushing further our positive result, it is natural to consider a slight generalization of leafage, the vertex leafage, which measures the minimum upper bound on the number of leaves of every subtree in a tree model. However, we show that it is unlikely to obtain a similar result, as we prove that SFVS remains NP-complete on undirected path graphs, i.e., chordal graphs having vertex leafage at most two. Lastly, we provide a polynomial-time algorithm for SFVS on rooted path graphs, a proper subclass of undirected path graphs and graphs of mim-width one, which is faster than the previously known algorithm obtained for graphs with bounded mim-width.
Perfect Matchings with Crossings
Authors: Aichholzer Oswin , Fabila-Monroy Ruy , Kindermann Philipp , Parada Irene , Paul Rosna , Perz Daniel , Schnider Patrick , Vogtenhuber Birgit 
Published: October 2022

Abstract
Note: Please see pdf for full abstract with equations.
For sets of $n=2m$ points in general position in the plane we consider straight-line drawings of perfect matchings on them. It is well known that such sets admit at least $C_m$ different plane perfect matchings, where $C_m$ is the $m$-th Catalan number. Generalizing this result we are interested in the number of drawings of perfect matchings which have $k$ crossings. We show the following results. (1)~For every $k\leq \frac{1}{64}n^2-O(n \sqrt{n})$, any set with an even number of $n$ points, $n$ sufficiently large, admits a perfect matching with exactly $k$ crossings. (2)~There exist sets of $n$ points ($n$ even) where every perfect matching has fewer than $\frac{5}{72}n^2$ crossings. (3)~The number of perfect matchings with at most $k$ crossings is superexponential in $n$ if $k$ is superlinear in $n$. (4)~Point sets in convex position minimize the number of perfect matchings with at most $k$ crossings for $k=0,1,2$, and maximize the number of perfect matchings with $\binom{n/2}{2}$ crossings and with ${\binom{n/2}{2}}\!-\!1$ crossings.