bluuebunny/crossref_metadata_2025 huggingface dataset:
Vertebrobasilar dissecting aneurysms: microsurgical management in 42 patients
Authors: Frisoli Fabio A. , Srinivasan Visish M. , Catapano Joshua S. , Rudy Robert F. , Nguyen Candice L. , Jonzzon Soren , Korson Clayton , Karahalios Katherine , Lawton Michael T.
Published: December 2021

OBJECTIVE

Vertebrobasilar dissecting (VBD) aneurysms are rare, and patients with these aneurysms often present with thromboembolic infarcts or subarachnoid hemorrhage (SAH). The morphological nature of VBD aneurysms often precludes conventional clip reconstruction or coil placement and encourages parent artery exclusion or endovascular stenting. Treatment considerations include aneurysm location along the vertebral artery (VA), the involvement of the posterior inferior cerebellar artery (PICA), and collateral blood flow. Outcomes after endovascular treatment have been well described in the neurosurgical literature, but microsurgical outcomes have not been detailed. Patient outcomes from a large, single-surgeon, consecutive series of microsurgically managed VBD aneurysms are presented, and 3 illustrative case examples are provided.

METHODS

The medical records of patients with dissecting aneurysms affecting the intracranial VA (V4), basilar artery, and PICA that were treated microsurgically over a 19-year period were reviewed. Patient demographics, aneurysm characteristics, surgical procedures, and clinical outcomes (according to modified Rankin Scale [mRS] scores at last follow-up) were analyzed.

RESULTS

Forty-two patients with 42 VBD aneurysms were identified. Twenty-six aneurysms (62%) involved the PICA, 14 (33%) were distinct from the PICA origin on the V4 segment of the VA, and 2 (5%) were located at the vertebrobasilar junction. Thirty-four patients (81%) presented with SAH with a mean Hunt and Hess grade of 3.2 at presentation. Six (14%) of the 42 patients had been previously treated using endovascular techniques. Nineteen aneurysms (45%) underwent clip wrapping, 17 (40%) were treated with bypass trapping, and 6 (14%) underwent parent artery sacrifice. The complete aneurysm obliteration rate was 95% (n = 40), and the surgical complication rate was 7% (n = 3). The 8 patients with unruptured VBD aneurysms were significantly more likely to be discharged home (n = 6, 75%) compared with 34 patients with ruptured aneurysms (n = 9, 27%; p = 0.01). Good outcomes (mRS score ≤ 2) were observed in 20 patients (48%). Eight patients (19%) died.

CONCLUSIONS

These data demonstrate that patients with VBD aneurysms often present after a rupture in poor neurological condition, but favorable results can be achieved with open microsurgical repair in almost half of such cases. Microsurgery remains a viable treatment option, with the choice between bypass trapping and clip wrapping largely dictated by the specific location of the aneurysm and its relationship to the PICA.
METEOROLOGICAL FACTORS IMPACT ON THE QUALITY OF NATURAL WATER FACILITIES ON THE TERRITORY OF THE BAGAEVSKAYA IRRIGATION SYSTEM
Authors:   Novocherkassk Engineering and Land Reclamation Institute – branch of the Don State Agrarian University, Drovovozova T. I. , Kirilenko A. A. ,   Russian Scientific Research Institute of Land Improvement Problems
Published: December 2021

Purpose: analysis of the influence of atmospheric precipitation and air temperature on the formation of geochemical background of water bodies in the zone of collector canals of the Bagaevskaya irrigation system. Materials and Methods: analysis of the report on atmospheric precipitation and chemical measurements of waste and natural water samples from collector canals for the period 2014–2020; assessment of long-term (1900–2020) dynamics of the average annual temperature of atmospheric air. Results. The assessment of the chemical composition of water intake waters was carried out by the sum of the main salt-forming ions at the confluence of collector canals into water bodies, in the background and control sections. Samples of waste and natural water were analyzed separately for the sulfates, chlorides, hydrocarbons content indicators. A relationship between the Total Dissolved Solids and the precipitation amount 30 days before the date of sampling (inclusive) was revealed. The trend of the value of the average annual temperature of atmospheric air is determined in respect to the territory of the Lower Don. Conclusions. The concentration of substances in the water inputs (the Podpolnaya and Western Manych rivers) at the outlet of drainage and waste waters from the BG-MS-6 and BG-MS-10 canals does not exceed the concentration in the background section, which indicates a natural (conditionally-natural) background of water bodies. The dynamics of mineralization indicates that the ionic composition of water bodies within the Bagaevskaya irrigation system is formed due to the input of salts by surface runoff into the composition of atmospheric moisture, as well as in the process of groundwater feed. On a more global scale (on the whole territory of the Lower Don), the geochemical background of small water bodies is influenced by a gradual increase in the average annual air temperature.
Managing Stages of Film Sound Production
Authors: Riazantsev Lev , Yevdokymenko Yevheniia
Published: December 2021

The purpose of this article is to analyse the main stages of sound production in film. The study aims to establish the main principles of film sound design, prove the importance of a rational approach to each stage in the context of their impact on the results of the study, and determine the role of sound in film dramaturgy. The research methodology is based on theoretical methods, namely an analysis of information sources, comparison of Ukrainian and foreign approaches to filmmaking, generalisation and systematisation of practical knowledge and experience of sound production in film from the first sound film to the present day. Scientific novelty. The management structure of sound production’s modern stages and their impact on creative and technical components of film soundtracks is analysed in detail for the first time. Conclusions. The article analyses the stages of sound production in film and establishes the main principles of sound design by studying Ukrainian and foreign approaches to creating sound in the film. The author summarises the rational approach to each stage in the context of their impact on the results of the study and examines the role of sound in film dramaturgy.
Women Involvement in Terrorism Action in Indonesia
Authors:   Universitas Pertahanan Republik Indonesia, Prayogo Hagung , Rishanti Fauziyah Aulia ,   Universitas Pertahanan Republik Indonesia
Published: December 2021

This research is motivated by the increasing cases of involvement of women in acts of terrorism in Indonesia, the participation of women in acts of terrorism raises anxiety in Indonesian society and shows that currently there is an increase in the development of radicalism against women, they not only act as protectors or protectors but also today. acting as a combatant and being directly involved in acts of terrorism such as being a suicide bomber, this development is a threat that must be overcome by the Indonesian government. This article explains the development of the threat of women's involvement in acts of terrorism in Indonesia and how strategies can be implemented to overcome this. The data in this study came from interviews, documentation, and literature review and sourced from sources such as Densus 88, and BNPT. The results of this study explain that the involvement of women in the activities of radical terrorist groups is a new trend to avoid suspicion by security forces and groups that use the role of women as ISIS groups, several factors influence women to become involved in the actions of radical terrorist groups, namely due to marriage ties, friendship. , as well as social media. To overcome the trend of women being involved in acts of terrorism in Indonesia, several strategies are needed to overcome them: a humanitarian approach, an academic approach, and assistance in increasing literacy for women.
A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma
Authors: He Shuai , Li Jin-Feng , Tian Hao , Sang Ye , Yang Xiao-Jing , Guo Gui-Xin , Yang Jin-E
Published: December 2021

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCC patients was constructed with linear support vector machine (SVM). RESULTS: An lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analyses suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.
Negotiating an International Legal Instrument on Biodiversity Beyond National Jurisdiction: A Look Ahead
Authors: Abegón-Novella Marta
Published: December 2021

The negotiation of the future Agreement governing the conservation and sustainable use of marine biological diversity of areas beyond national jurisdiction is in its final stage. Essentially a treaty for the protection of general interests, the Agreement can generate several benefits for the governance of the oceans. However, in the first three sessions of the intergovernmental conference, deep discrepancies have emerged with respect to the core issues of the package agreed in 2011. This article identifies various formulas and strategies that have been considered in the negotiations and incorporated in the Revised draft text as possible regulatory options with the potential to bring positions closer and facilitate the agreement: avoiding explicit reference to the legal status of marine genetic resources; the incorporation of differential and contextual norms; the introduction of due diligence obligations; the incorporation of internal soft law; and the reduction of the scope of the treaty. These options may help to provide flexibility and differentiation in the regulation but, as essentially pragmatic measures, they tend to sacrifice the ambition of the final Agreement. On the other hand, if States assume their real role and responsibility in the process –that of interpreters of general interest and custodians of marine biodiversity –they would be in a better position to find novel and more ambitious solutions for bringing this crucial Agreement to fruition. This article advocates a return to basics and the placing of the marine environment at the centre of the regulations.
Personality Impact on Alzheimer’s Disease — Signature and Vascular Imaging Markers: A PET-MRI Study
Authors: Giannakopoulos Panteleimon , Rodriguez Cristelle , Montandon Marie-Louise , Garibotto Valentina , Haller Sven , Herrmann François R.
Published: December 2021

Background:

Several studies postulated that personality is an independent determinant of cognitive trajectories in old age.

Objective:

This study explores the impact of personality on widely used Alzheimer’s disease (AD) and vascular imaging markers.

Methods:

We examined the association between personality and three classical AD imaging markers (centiloid-based-amyloid load, MRI volumetry in hippocampus, and media temporal lobe atrophy), and two vascular MRI parameters (Fazekas score and number of cortical microbleeds) assessed at baseline and upon a 54-month-follow-up. Personality was assessed with the Neuroticism Extraversion Openness Personality Inventory-Revised. Regression models were used to identify predictors of imaging markers including sex, personality factors, presence of APOE ɛ4 allele and cognitive evolution over time.

Results:

Cortical GM volumes were negatively associated with higher levels of Conscientiousness both at baseline and follow-up. In contrast, higher scores of Openness were related to better preservation of left hippocampal volumes in these two time points and negatively associated with medial temporal atrophy at baseline. Amyloid load was not affected by personality factors. Cases with higher Extraversion scores displayed higher numbers of cortical microbleeds at baseline.

Conclusion:

Personality impact on brain morphometry is detected only in some among the routinely used imaging markers. The most robust associations concern the positive role of high levels of Conscientiousness and Openness on AD-signature MRI markers. Higher extraversion levels are associated with increased vulnerability to cortical microbleeds pointing to the fact that the socially favorable traits may have a detrimental effect on brain integrity in old age.
Hippocampus-Based Dynamic Functional Connectivity Mapping in the Early Stages of Alzheimer’s Disease
Authors: Wang Jianlin , Wang Pan , Jiang Yuan , Wang Zedong , Zhang Hong , Li Hongyi , Biswal Bharat B.
Published: December 2021

Background:

The hippocampus with varying degrees of atrophy was a crucial neuroimaging feature resulting in the declining memory and cognitive function in Alzheimer’s disease (AD). However, the abnormal dynamic functional connectivity (DFC) in both white matter (WM) and gray matter (GM) from the left and right hippocampus remains unclear.

Objective:

To explore the abnormal DFC within WM and GM from the left and right hippocampus across the different stages of AD.

Methods:

Current study employed the OASIS-3 dataset including 43 mild cognitive impairment (MCI), 71 pre-mild cognitive impairment (pre-MCI), and matched 87 normal cognitive (NC). Adopting the FMRIB’s Integrated Registration and Segmentation Tool, we obtained the left and right hippocampus mask. Based on above hippocampus mask as seed point, we calculated the DFC between left/right hippocampus and all voxel time series within whole brain. One-way ANOVA analysis was performed to estimate the abnormal DFC among MCI, pre-MCI, and NC groups.

Results:

We found that MCI and pre-MCI groups showed the common abnormalities of DFC in the Temporal_Mid_L, Cingulum_Mid_L, and Thalamus_L. Specific abnormalities were found in the Cerebelum_9_L and Precuneus of MCI group and Vermis_8 and Caudate_L of pre-MCI group. In addition, we found that DFC within WM regions also showed the common low DFC for the Cerebellum anterior lobe-WM, Corpus callosum, and Frontal lobe-WM in MCI and pre-MCI group.

Conclusion:

Our findings provided a novel information for discover the pathophysiological mechanisms of AD and indicate WM lesions were also an important cause of cognitive decline in AD.
Droplet Degeneration of Hippocampal and Cortical Neurons Signifies the Beginning of Neuritic Plaque Formation
Authors: Streit Wolfgang J. , Rotter Jonas , Winter Karsten , Müller Wolf , Khoshbouei Habibeh , Bechmann Ingo
Published: December 2021

Background:

Neuritic plaques contain neural and microglial elements, and amyloid-β protein (Aβ), but their pathogenesis remains unknown.

Objective:

Elucidate neuritic plaque pathogenesis.

Methods:

Histochemical visualization of hyperphosphorylated-tau positive (p-tau+) structures, microglia, Aβ, and iron.

Results:

Disintegration of large projection neurons in human hippocampus and neocortex presents as droplet degeneration: pretangle neurons break up into spheres of numerous p-tau+ droplets of various sizes, which marks the beginning of neuritic plaques. These droplet spheres develop in the absence of colocalized Aβ deposits but once formed become encased in diffuse Aβ with great specificity. In contrast, neurofibrillary tangles often do not colocalize with Aβ. Double-labelling for p-tau and microglia showed a lack of microglial activation or phagocytosis of p-tau+ degeneration droplets but revealed massive upregulation of ferritin in microglia suggesting presence of high levels of free iron. Perl’s Prussian blue produced positive staining of microglia, droplet spheres, and Aβ plaque cores supporting the suggestion that droplet degeneration of pretangle neurons in the hippocampus and cortex represents ferroptosis, which is accompanied by the release of neuronal iron extracellularly.

Conclusion:

Age-related iron accumulation and ferroptosis in the CNS likely trigger at least two endogenous mechanisms of neuroprotective iron sequestration and chelation, microglial ferritin expression and Aβ deposition, respectively, both contributing to the formation of neuritic plaques. Since neurofibrillary tangles and Aβ deposits colocalize infrequently, tangle formation likely does not involve release of neuronal iron extracellularly. In human brain, targeted deposition of Aβ occurs specifically in response to ongoing ferroptotic droplet degeneration thereby producing neuritic plaques.
Diagnosing Alzheimer’s Disease from Circulating Blood Leukocytes Using a Fluorescent Amyloid Probe
Authors: Black Stefanie A.G. , Stepanchuk Anastasiia A. , Templeton George W. , Hernandez Yda , Ota Tomoko , Roychoudhury Shyamosree , Smith Eric E. , Barber Philip A. , Ismail Zahinoor , Fischer Karyn , Zwiers Angela , Poulin Marc J. , Blennow Kaj , Zetterberg Henrik , Stys Peter K. , Tsutsui Shigeki
Published: December 2021

Background:

Toxic amyloid-β (Aβ) peptides aggregate into higher molecular weight assemblies and accumulate not only in the extracellular space, but also in the walls of blood vessels in the brain, increasing their permeability, and promoting immune cell migration and activation. Given the prominent role of the immune system, phagocytic blood cells may contact pathological brain materials.

Objective:

To develop a novel method for early Alzheimer’s disease (AD) detection, we used blood leukocytes, that could act as “sentinels” after trafficking through the brain microvasculature, to detect pathological amyloid by labelling with a conformationally-sensitive fluorescent amyloid probe and imaging with confocal spectral microscopy.

Methods:

Formalin-fixed peripheral blood mononuclear cells (PBMCs) from cognitively healthy control (HC) subjects, mild cognitive impairment (MCI) and AD patients were stained with the fluorescent amyloid probe K114, and imaged. Results were validated against cerebrospinal fluid (CSF) biomarkers and clinical diagnosis.

Results:

K114-labeled leukocytes exhibited distinctive fluorescent spectral signatures in MCI/AD subjects. Comparing subjects with single CSF biomarker-positive AD/MCI to negative controls, our technique yielded modest AUCs, which improved to the 0.90 range when only MCI subjects were included in order to measure performance in an early disease state. Combining CSF Aβ42 and t-Tau metrics further improved the AUC to 0.93.

Conclusion:

Our method holds promise for sensitive detection of AD-related protein misfolding in circulating leukocytes, particularly in the early stages of disease.
Design of diversified teaching platform of college aerobics course based on artificial intelligence
Authors: Huang Fengping
Published: December 2021

In order to improve the diversified teaching effect of a college aerobics course, effectively improve the accuracy of student grouping on the teaching platform, a diversified teaching platform of college aerobics course based on artificial intelligence is designed. First of all, it puts forward the construction idea and design process of the network teaching platform, then designs the interface and function module of the teaching platform, and finally designs the grouping function of teaching objects, so as to complete the design of the diversified teaching platform of a college aerobics course based on artificial intelligence. The experimental results show that the grouping accuracy of students on the diversified teaching platform of college aerobics course based on artificial intelligence is greater than 75%, and the average score of students studying on the platform is 74.66. This explains why the designed platform can effectively provide the accuracy of grouping and the students’ performance.
Image recognition based on Boltzmann machine algorithm
Authors: Li Hao
Published: December 2021

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.
Depth-embedded instance segmentation network for urban scene parsing
Authors: Wang Zhifan , Xin Tong , Wang Shidong , Zhang Haofeng
Published: December 2021

The ubiquitous availability of cost-effective cameras has rendered large scale collection of street view data a straightforward endeavour. Yet, the effective use of these data to assist autonomous driving remains a challenge, especially lack of exploration and exploitation of stereo images with abundant perceptible depth. In this paper, we propose a novel Depth-embedded Instance Segmentation Network (DISNet) which can effectively improve the performance of instance segmentation by incorporating the depth information of stereo images. The proposed network takes binocular images as input to observe the displacement of the object and estimate the corresponding depth perception without additional supervisions. Furthermore, we introduce a new module for computing the depth cost-volume, which can be integrated with the colour cost-volume to jointly capture useful disparities of stereo images. The shared-weights structure of Siamese Network is applied to learn the intrinsic information of stereo images while reducing the computational burden. Extensive experiments have been carried out on publicly available datasets (i.e., Cityscapes and KITTI), and the obtained results clearly demonstrate the superiority in segmenting instances with different depths.
Interval basic uncertain information and related aggregations in decision making
Authors: Jin LeSheng , Mesiar Radko , Yager Ronald , Kaya Sema Kayapinar
Published: December 2021

The recently proposed basic uncertain information can directly present numerical uncertainties for given real values, but it cannot handle given interval values which themselves also have uncertainties. Against this background, this work proposes the concept of interval basic uncertain information which serves as a generalization of basic uncertain information and involves two types of uncertainties. We analyze some basic operations, weighted arithmetic mean and preference transformation for interval basic uncertain information. The Rule-based decisions and the comprehensive certainty of interval basic uncertain information are also discussed. An illustrative example of multi-source multi-criteria evaluation under interval basic uncertain information environment is presented.
Sensitivity analysis of environmental impact of milk powder spray drying system based on intelligent algorithm
Authors: Zhang Zunhao , Zhang Junxia , Tian Wei , Li Yang , Song Yahui , Zeng Qi
Published: December 2021

The increasing market demand for milk powder has not only promoted the production capacity of milk powder, but also increased the impact on the environment. Therefore, it is very important to study the relationship between the environmental impact of milk powder spray drying (MPSD) system and system-related parameters and identify the key parameters to improve the efficiency of the sustainable improvement of the system. Treed Gaussian Process (TGP) and Standardized Regression Coefficients (SRC)methods are used to analyze the sensitivity of the system to environmental impacts. The results show that the inlet air temperature of the drying tower has the greatest impact on the environment of the system, accounting for about 82%, followed by the atomization pressure and the feed pump speed, accounting for about 9% and 8% respectively. Moreover, not only the environmental performance of the system should be improved, but also the quality of milk powder should be guaranteed when optimizing the parameters such as the inlet air temperature of drying tower. This study can help the manufacturers of milk powder and related equipment to determine the priority of improving the system from the perspective of environmental protection.
Multi response optimization of machining parameters for an annealed Monel K 500 alloy in drilling using Machine learning techniques and ANN
Authors: Sanjay Chintakindi , Alsamhan Ali , Abidi Mustufa Haider
Published: December 2021

Manufacturing companies are focusing on continuous process development to thrive in today’s quality-conscious market. It is particularly relevant to investigate machining processes for advanced materials such as superalloys. Drilling is a major operation that is used in the majority of manufacturing processes. Hence, this research work is focused on investigating the drilling performance of the Monel K500. The output responses under consideration are metal removal rate (MRR), surface roughness, and tool wear. Various contemporary techniques were utilized in this work, namely machine learning methods, artificial neural networks, principal component analysis, and grey relation analysis using uncoated, coated, and HSS (high-speed steel) drills. After annealing, the softened material can be easily machined to increase the MRR and decrease tool wear and surface roughness. The experimental results show that, after annealing, the surface roughness values for HSS drills have been reduced by 23.86%, uncoated drills by 27.29%, and coated drills by 29.27%, respectively. Moreover, tool wear values for HSS drills decreased by 28.51%, uncoated drills by 34.7%, and coated drills by 33.71%, based on the relative error approach. MRR values for HSS drills increased by 20.51 %, uncoated drills by 23.08%, and coated drills by 23.5%, respectively. For PCA (principal component analysis), feed (47%), and for GRA (gray relation analysis), feed (40.1%) will be the significant parameter followed by speed, and both methods have identified the same experimental run values for optimization of cutting parameters. The theoretical values were predicted using machine learning methods, which utilized the Python language using the Google Colab and then validated with experimental values. The predicted values obtained by the decision tree are close to the measured values as compared to support vector regression and K-nearest neighbor based on relative error. The estimated values obtained by the ANN (artificial neural networks) approach, using Easy NN plus software, match well with the actual values, with a slight deviation.
Abnormal actions detection of robotic arm via 3D convolution neural network and support vector data description
Authors: Yang Qingbo , Xu Fangzhou , Leng Jiancai
Published: December 2021

Robotic arms are powerful assistants in many industrial production environments, and they run periodically in accordance with preset actions to complete specified operations. However, they may act abnormally when encountering unexpected situation and then lead to unnecessary loss. Recognizing the abnormal actions of robotic arms through surveillance video can automatically help us to understand their operating status and discover possible abnormalities in time. We designed a deep learning architecture based on 3D convolution for abnormal action recognition. The 3D convolutional layer can extract the spatial and temporal features of the robotic arm movements from the video frame difference sequence. The features are compressed and streamlined by the maximum pooling layer to obtain concise and effective robotic arm action features. Finally, the fully connected layer is used to classify the features to recognize the abnormal robotic arm tasks. Support vector data description (SVDD) model is employed to detect abnormal actions of the robotic arm, and the well-trained SVDD model can distinguish the normal actions from the three kinds of abnormal actions with the Area Under Curve (AUC) 99.17%.
Asso approach for classifying gene expression data based on optimal features
Authors: Susmi S. Jacophine
Published: December 2021

Gene expression profiles are sequences of numbers, and the need to analyze them has now increased significantly. Gene expression data contain a large number of genes and models used for cancer classification. As the wealth of these data being produced, new prediction, classification and clustering techniques are applied to the analysis of the data. Although there are a number of proposed methods with good results, there is still limited diagnostics and a lot of problems still to be solved. To solve the difficulty, in this paper, an efficient gene expression data classification is proposed. To predict the cancer class of patients from the gene expression profile, this paper presents a novel classification framework in the manner of three steps namely, Pre-processing, feature selection and classification. In pre-processing, missing value is filled and redundant data are removed. To attain the enhanced classification outcomes, the important features are selected from the database with the help of Adaptive Salp Swarm Optimization (ASSO) algorithm. Then, the selected features are given to the multi kernel SVM (MKSVM) to classify the gene expression data namely, BRCA, KIRC, COAD, LUAD and PRAD. The performance of proposed methodology is analyzed in terms of different metrics namely, accuracy, sensitivity and specificity. The performance of proposed methodology is 4.5% better than existing method in terms of accuracy.
Towards an intelligent personal assistant for hearing impaired people
Authors: Utsuki-Alexander Taku , Rios-Martinez Jorge , Madera Francisco A. , Pérez-Espinosa Humberto
Published: December 2021

This work has been focused on the part of the population with hearing impairment who owns a dog and that worries about not listening the dog barks, specially when a risky situation is taking place at home. A survey was carried out on people with deafness problems to find out hazard situations which they are exposed at home. A system prototype was developed to be integrated as a component of ambient intelligence (AmI) for ambient assisted living (AAL) that serves to Hearing Impaired People (HIP). The prototype detects dog barks and notifies users through both a smart mobile app and a visual feedback. It consists of a connection between a Raspberry Pi 3 card and a ReSpeaker Mic Array v2.0 microphone array; a communication module with a smartphone was implemented, which displays written messages or vibrations when receiving notifications. The cylinder-shaped device was designed by the authors and sent it to 3D print with a resin material. The prototype recognized the barking efficiently by using a machine learning model based on Support Vector Machine technique. The prototype was tested with deaf people which were satisfied with precision, signal intensity, and activation of lights.
Handling temporality in human activity reasoning
Authors: Morveli-Espinoza Mariela , Nieves Juan Carlos , Tacla Cesar Augusto
Published: December 2021

Human-aware Artificial Intelligent systems are goal directed autonomous systems that are capable of interacting, collaborating, and teaming with humans. Activity reasoning is a formal reasoning approach that aims to provide common sense reasoning capabilities to these interactive and intelligent systems. This reasoning can be done by considering evidences –which may be conflicting–related to activities a human performs. In this context, it is important to consider the temporality of such evidence in order to distinguish activities and to analyse the relations between activities. Our approach is based on formal argumentation reasoning, specifically, Timed Argumentation Frameworks (TAF), which is an appropriate technique for dealing with inconsistencies in knowledge bases. Our approach involves two steps: local selection and global selection. In the local selection, a model of the world and of the human’s mind is constructed in form of hypothetical fragments of activities (pieces of evidences) by considering a set of observations. These hypothetical fragments have two kinds of relations: a conflict relation and a temporal relation. Based on these relations, the argumentation attack notion is defined. We define two forms of attacks namely the strong and the weak attack. The former has the same characteristics of attacks in TAF whereas for the latter the TAF approach has to be extended. For determining consistent sets of hypothetical fragments, that are part of an activity or are part of a set of non-conflicting activities, extension-based argumentation semantics are applied. In the global selection, the degrees of fulfillment of activities is determined. We study some properties of our approach and apply it to a scenario where a human performs activities with different temporal relations.
Algorithmic music generation by harmony recombination with genetic algorithm
Authors: Lopez-Rincon Omar , Starostenko Oleg , Lopez-Rincon Alejandro
Published: December 2021

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.
A content spectral-based text representation
Authors: Crespo-Sanchez Melesio , Lopez-Arevalo Ivan , Aldana-Bobadilla Edwin , Molina-Villegas Alejandro
Published: December 2021

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.
Question type and answer related keywords aware question generation
Authors: Zhang Jianfei , Rong Wenge , Chen Dali , Xiong Zhang
Published: December 2021

The traditional end-to-end Neural Question Generation (NQG) models tend to generate generic and bland questions, as there are two obscure points: 1) the modifications of the answer in the context can be used as the clues to the answer mentioned in the question, while they are generally not unique and can be used independently for generating diverse questions; 2) the same question content can also be asked in diverse ways, which depends on personal preference in practice. The above-mentioned two points are indeed two variables to conduct question generation, but they are not annotated in the original dataset and are thus ignored by the traditional end-to-end models. In this paper we propose a framework that clarifies those two points through two sub-modules to better conduct question generation. We take experiments based on the GPT-2 model and the SQuAD dataset, and prove that our framework can improve the performance measured by similarity metrics, while it also provides appropriate alternatives for controllable diversity enhancement.
Neurodegenerative diseases categorization by applying the automatic model selection and hyperparameter optimization method
Authors: Fuentes-Ramos Mirta , Sánchez-DelaCruz Eddy , Meza-Ruiz Iván-Vladimir , Loeza-Mejía Cecilia-Irene
Published: December 2021

Neurodegenerative diseases affect a large part of the population in the world and also in Mexico, deteriorating gradually the quality of patients’ life. Therefore, it is important to diagnose them with a high degree of reliability. In order to solve it, various computational methods have been applied in the analysis of biomarkers of human gait. In this study, we propose employing the automatic model selection and hyperparameter optimization method that has not been addressed before for this problem. Our results showed highly competitive percentages of correctly classified instances when discriminating binary and multiclass sets of neurodegenerative diseases: Parkinson’s disease, Huntington’s disease, and Spinocerebellar ataxias.
Parkinson’s Disease Progression and Statins: Hydrophobicity Matters
Authors: Lewis Mechelle M. , Albertson Richard M. , Du Guangwei , Kong Lan , Foy Andrew , Huang Xuemei
Published: December 2021

Background:

Recent randomized clinical trials using hydrophobic statins reported no influence on Parkinson’s disease (PD) clinical progression. Hydrophobicity is a key determinant for blood-brain barrier penetrance.

Objective:

Investigate a potential effect of statins on PD progression.

Methods:

Statin use was determined at baseline and subtyped according to hydrophobicity in 125 PD patients participating in the PD Biomarker Program (PDBP, 2012–2015) at our site. Clinical ( N = 125) and susceptibility MRI ( N = 86) data were obtained at baseline and 18-months. Movement Disorders Society-Unified PD Rating Scales were used to track progression of non-motor (MDS-UPDRS-I) and motor (MDS-UPDRS-II) symptoms, and rater-based scores (MDS-UPDRS-III) of patients in the “on” drug state. R2* values were used to capture pathological progression in the substantia nigra. Associations between statin use, its subtypes, and PD progression were evaluated with linear mixed effect regressions.

Results:

Compared to statin non-users, overall statin or lipophilic statin use did not significantly influence PD clinical or imaging progression. Hydrophilic statin users, however, demonstrated faster clinical progression of non-motor symptoms [MDS-UPDRS-I (β= 4.8, p = 0.010)] and nigral R2* (β= 3.7, p = 0.043). A similar trend was found for MDS-UPDRS-II (β= 3.9, p = 0.10), but an opposite trend was observed for rater-based MDS-UPDRS-III (β= –7.3, p = 0.10). Compared to lipophilic statin users, hydrophilic statin users also showed significantly faster clinical progression of non-motor symptoms [MDS-UPDRS-I (β= 5.0, p = 0.020)], but R2* did not reach statistical significance (β= 2.5, p = 0.24).

Conclusion:

This study suggests that hydrophilic, but not lipophilic, statins may be associated with faster PD progression. Future studies may have clinical and scientific implications.
Cryopreservation of Induced Pluripotent Stem Cell-Derived Dopaminergic Neurospheres for Clinical Application
Authors: Hiramatsu Satoe , Morizane Asuka , Kikuchi Tetsuhiro , Doi Daisuke , Yoshida Kenji , Takahashi Jun
Published: December 2021

Background:

Pluripotent stem cell (PSC)-derived dopaminergic (DA) neurons are an expected source of cell therapy for Parkinson’s disease. The transplantation of cell aggregates or neurospheres, instead of a single cell suspension has several advantages, such as keeping the 3D structure of the donor cells and ease of handling. For this PSC-based therapy to become a widely available treatment, cryopreservation of the final product is critical in the manufacturing process. However, cryopreserving cell aggregates is more complicated than cryopreserving single cell suspensions. Previous studies showed poor survival of the DA neurons after the transplantation of cryopreserved fetal ventral-mesencephalic tissues.

Objective:

To achieve the cryopreservation of induced pluripotent stem cell (iPSC)-derived DA neurospheres toward clinical application.

Methods:

We cryopreserved iPSC-derived DA neurospheres in various clinically applicable cryopreservation media and freezing protocols and assessed viability and neurite extension. We evaluated the population and neuronal function of cryopreserved cells by the selected method in vitro. We also injected the cells into 6-hydroxydopamine (6-OHDA) lesioned rats, and assessed their survival, maturation and function in vivo.

Results:

The iPSC-derived DA neurospheres cryopreserved by Proton Freezer in the cryopreservation medium Bambanker hRM (BBK) showed favorable viability after thawing and had equivalent expression of DA-specific markers, dopamine secretion, and electrophysiological activity as fresh spheres. When transplanted into 6-OHDA-lesioned rats, the cryopreserved cells survived and differentiated into mature DA neurons, resulting in improved abnormal rotational behavior.

Conclusion:

These results show that the combination of BBK and Proton Freezer is suitable for the cryopreservation of iPSC-derived DA neurospheres.
Different inference approaches for the estimators of the sushila distribution
Authors: de Oliveira Peres Marcos Vinicius , de Oliveira Ricardo Puziol , Martinez Edson Zangiacomi , Achcar Jorge Alberto
Published: December 2021

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.
Agent-based modeling for benchmarking banking regulation regimes: Application for the CBDC
Authors: Nechitailo Vladimir , Penikas Henry
Published: December 2021

COVID-19 pandemic challenges the sustainability of the modern financial system. International central bankers claim that banks are solid. They have accumulated significant capital buffers. Those buffers should be further more augmented by 2027 in line with Basel III reforms. However, disregarding such a consecutive rise in the banking capital adequacy requirements, the US financial authorities undertook an unprecedented step. First time in the country history they lowered the reserve requirement to zero at the end of March 2020. Friedrich von Hayek demonstrated the fragility of the modern fractional reserve banking systems. Together with Ludwig von Mises (von Mises, 1978) he was thus able to predict the Great Depression of 1929 and explain its mechanics much in advance. Thus, we wish to utilize the agent-based modeling technique to extend von Hayek’s rationale to the previously unstudied interaction of capital adequacy and reserve requirement regulation. We find that the full reserve requirement regime even without capital adequacy regulation provides more stable financial environment than the existing one. Rise in capital adequacy adds to modern banking sustainability, but it still preserves the system remarkably fragile compared to the full reserve requirement. We also prove that capital adequacy regulation is redundant when the latter environment is in place. We discuss our findings application to the potential Central Bank Digital Currencies regulation.
Note on “Parameters estimators of irregular right-angled triangular distribution”
Authors: Lamond Bernard F. , Zephyr Luckny
Published: December 2021

Simple estimators were given in (Kachiashvili & Topchishvili, 2016) for the lower and upper limits of an irregular right-angled triangular distribution together with convenient formulas for removing their bias. We argue here that the smallest observation is not a maximum likelihood estimator (MLE) of the lower limit and we present a procedure for computing an MLE of this parameter. We show that the MLE is strictly smaller than the smallest observation and we give some bounds that are useful in a numerical solution procedure. We also present simulation results to assess the bias and variance of the MLE.