bluuebunny/crossref_metadata_2025 huggingface dataset:
Lowari Tunnel Water Quality Evaluation: Implications for Tunnel Support, Potable Water Supply, and Irrigation in Northwestern Himalayas, Pakistan
Authors: Khan Jehanzeb , Ahmed Waqas , Waseem Muhammad , Ali Wajid , Rehman Inayat ur , Islam Ihtisham , Janjuhah Hammad Tariq , Kontakiotis George , Bathrellos George D. , Skilodimou Hariklia D.
Published: August 2023

Water ingress is a common and costly problem in tunnel construction, and understanding the hydrogeological characteristics of a site is crucial for mitigating its effects. This study presents a detailed analysis of the water ingress problems experienced during the construction and operation stages of the Lowari Tunnel in Khyber Pakhtunkhwa, Pakistan, and evaluates the suitability of the water for various purposes. The study found that the water quality varied significantly, depending on the geological conditions and water quantity, with the south portal of the tunnel dominated by Mg cations and bicarbonate anions. The water was found to be suitable for tunnel support systems, including concrete and steel installations, with negligible corrosion observed over the study period. However, the water coming out of the tunnel at the south portal was found to be unsuitable for drinking due to its low pH value. The Wilcox plot classified the water samples as excellent for irrigation, which could benefit local agriculture in the area. The findings of this study can provide valuable insights into the hydrogeological characteristics of the Lowari Tunnel, contributing to the design and construction of similar infrastructure projects in the region. Furthermore, these insights can help improve access to safe and reliable water sources for local communities.
Cascade Speech Translation for the Kazakh Language
Authors: Kozhirbayev Zhanibek , Islamgozhayev Talgat
Published: August 2023

Speech translation systems have become indispensable in facilitating seamless communication across language barriers. This paper presents a cascade speech translation system tailored specifically for translating speech from the Kazakh language to Russian. The system aims to enable effective cross-lingual communication between Kazakh and Russian speakers, addressing the unique challenges posed by these languages. To develop the cascade speech translation system, we first created a dedicated speech translation dataset ST-kk-ru based on the ISSAI Corpus. The ST-kk-ru dataset comprises a large collection of Kazakh speech recordings along with their corresponding Russian translations. The automatic speech recognition (ASR) module of the system utilizes deep learning techniques to convert spoken Kazakh input into text. The machine translation (MT) module employs state-of-the-art neural machine translation methods, leveraging the parallel Kazakh-Russian translations available in the dataset to generate accurate translations. By conducting extensive experiments and evaluations, we have thoroughly assessed the performance of the cascade speech translation system on the ST-kk-ru dataset. The outcomes of our evaluation highlight the effectiveness of incorporating additional datasets for both the ASR and MT modules. This augmentation leads to a significant improvement in the performance of the cascade speech translation system, increasing the BLEU score by approximately 2 points when translating from Kazakh to Russian. These findings underscore the importance of leveraging supplementary data to enhance the capabilities of speech translation systems.
Numerical Analysis Applying the Finite Element Method by Developing a Complex Three-Dimensional Biomodel of the Biological Tissues of the Elbow Joint Using Computerized Axial Tomography
Authors: Maya-Anaya Daniel , Urriolagoitia-Sosa Guillermo , Romero-Ángeles Beatriz , Martinez-Mondragon Miguel , German-Carcaño Jesús Manuel , Correa-Corona Martin Ivan , Trejo-Enríquez Alfonso , Sánchez-Cervantes Arturo , Urriolagoitia-Luna Alejandro , Urriolagoitia-Calderón Guillermo Manuel
Published: August 2023

Numerical analysis computational programs are applied to the research of biological tissues, which have complex forms. Continuous technological advance has facilitated the development of biomodels to evaluate biological tissues of different human body systems using computerized axial tomography to produce complex three-dimensional models that represent the morphological and physiological characteristics of the real tissues. Biomodels are applied to numerical analysis using the Finite Element Method and provide a perspective of the mechanical behavior in the system. In this study, a numerical evaluation was performed by developing a biomodel of the humerus, radius, and ulna (the elbow joint, composed of cortical bone, trabecular bone, and cartilage). Also introduced to the biomodel were the ligaments of the capsule joint, collateral ligaments of the ulna, and collateral ligaments of the radius. The biomodel was imported into a computer program to perform a numerical analysis considering the mechanical properties of cortical and trabecular bone (including elasticity modulus, shear modulus, Poisson relation, and density). The embedding conditions were defined to restrict displacements and rotations in the proximal zone of the humerus, applying a compression load to the other end of the biomodel at the distal area of the radius and ulna. The results are the direct consequence of how boundary conditions and external agents are applied to the structure to be analyzed, and the data obtained show how the behavior of the force applied through the component produces stresses and strains as a whole, as well as for each of the components. These stresses and strains can indicate zones with structural problems and the detection areas causing pain (assisting in a better diagnosis).
Towards a Semantics-Based Recommendation System for Cultural Heritage Collections
Authors: Li Jiayu , Bikakis Antonis
Published: August 2023

While the use of semantic technologies is now commonplace in the cultural heritage sector and several semantically annotated cultural heritage datasets are publicly available, there are few examples of cultural portals that exploit these datasets and technologies to improve the experience of visitors to their online collections. Aiming to address this gap, this paper explores methods for semantics-based recommendations aimed at visitors to cultural portals who want to explore online collections. The proposed methods exploit the rich semantic metadata in a cultural heritage dataset and the capabilities of a graph database system to improve the accuracy of searches through the collection and the quality of the recommendations provided to the user. The methods were developed and tested with the Archive of the Art Textbooks of Elementary and Public Schools in the Japanese Colonial Period. However, they can easily be adapted to any cultural heritage collection dataset modelled in RDF.
An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation
Authors: Li Jianrong , Zhang Dandan , Shi Lei , Ke Ting , Zhang Chuanlei
Published: August 2023

In this paper, SEPAM_HRNet, a high-resolution pose-estimation model that incorporates the squeeze-and-excitation and pixel-attention-mask (SEPAM) module is proposed. Feature pyramid extraction, channel attention, and pixel-attention masks are integrated into the SEPAM module, resulting in improved model performance. The construction of the model involves replacing ordinary convolutions with the plug-and-play SEPAM module, which leads to the creation of the SEPAMneck module and SEPAMblock module. To evaluate the model’s performance, the YOGA2022 human yoga poses teaching dataset is presented. This dataset comprises 15,350 images that capture ten basic yoga pose types—Warrior I Pose, Warrior II Pose, Bridge Pose, Downward Dog Pose, Flat Pose, Inclined Plank Pose, Seated Pose, Triangle Pose, Phantom Chair Pose, and Goddess Pose—with a total of five participants. The YOGA2022 dataset serves as a benchmark for evaluating the accuracy of the human pose-estimation model. The experimental results demonstrated that the SEPAM_HRNet model achieved improved accuracy in predicting human keypoints on both the common objects in context (COCO) calibration set and the YOGA2022 calibration set, compared to other state-of-the-art human pose-estimation models with the same image resolution and environment configuration. These findings emphasize the superior performance of the SEPAM_HRNet model.
Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features
Authors: Yi Fen , Liu Hong , Wang You , Wu Sheng , Sun Cheng , Feng Peng , Zhang Jin
Published: August 2023

It is highly significant from a research standpoint and a valuable practice to identify diseases, symptoms, drugs, examinations, and other medical entities in medical text data to support knowledge maps, question and answer systems, and other downstream tasks that can provide the public with knowledgeable answers. However, when contrasted with other languages like English, Chinese words lack a distinct dividing line, and medical entities have problems such as long length and multiple entity types nesting. Therefore, to address these issues, this study suggests a medical named entity recognition (NER) approach that combines part-of-speech and stroke features. First, the text is fed into the BERT pre-training model to get the semantic representation of the text, while the part-of-speech feature vector is obtained using the part-of-speech dictionary, and the stroke feature of the text is extracted through a convolution neural network (CNN). The word vector is then joined with the part-of-speech and stroke feature vectors, respectively, and input into the BiLSTM and CRF layer for training. Additionally, to balance the disparity in data volume across several types of entities, the class-weighted loss function is included in the loss function. According to the experimental findings, our model’s F1 score on the CCKS2019 dataset reaches 78.65%, and the recognition performance exceeds many existing algorithms.
Tooth Discoloration Effect of BIOfactor Mineral Trioxide Aggregate: A 6-Month In Vitro Study
Authors: Mutlu Şeref Nur , Akbulut Makbule Bilge
Published: August 2023

The central objective of this research was to examine the color consistency of three materials, Biodentine (Septodont, France), Angelus MTA (Angelus, Brasil), and BIOfactor MTA (Imicryl, Turkey), when exposed to various irrigation solutions and to observe their impact on tooth discoloration. Each material was used to make sample cylinders (n = 18). After hardening, the samples were immersed for 24 h in either distilled water, 2.5% sodium hypochlorite (NaOCl), or 2% chlorhexidine gluconate (CHX). A spectrophotometer was used to measure the color changes. On the mandibular molars, access cavities were made in order to assess the discoloring impact of calcium silicate cements. In the pulp chambers of the teeth, Biodentine, Angelus MTA, and BIOfactor MTA were inserted (n = 17). Glass ionomer cement was used to seal the samples. Spectrophotometric measurements were made at five different intervals (the beginning, one week, one month, three months, and six months), after which color variance values were computed. The resulting information was statistically evaluated. In all solutions, Biodentine and BIOfactor MTA displayed similar ΔE values. When Angelus MTA was soaked in NaOCl, it discolored more than in CHX or distilled water. Distilled water and NaOCl both caused identical discoloration on all material groups when solutions were examined separately. With CHX, Biodentine changed colors more significantly. After a week and a month, all substances caused comparable tooth discoloration. Biodentine produced the most significant color shifts on teeth at the third and sixth months. Angelus MTA exhibited less color stability in NaOCl and Biodentine in CHX. While Biodentine induced significant discoloration, BIOfactor MTA only showed a moderate amount.
Innovating Industrial Training with Immersive Metaverses: A Method for Developing Cross-Platform Virtual Reality Environments
Authors: Almeida Lucas G. G. , Vasconcelos Nalini V. de , Winkler Ingrid , Catapan Márcio F.
Published: August 2023

The metaverse has garnered significant attention for its potential to provide engaging and social experiences in virtual reality. Despite substantial investment and interest from industry, there remains a lack of academic research on the development and implementation of metaverses for industrial training. Notably, research indicates that virtual reality training is, on average, four times faster than classroom-based training. This study proposes a method for developing immersive metaverses for industrial training, leveraging specialized tools like Epic Games’ Unreal Engine software version 4.27.2. To assess the efficacy of this method, a cross-platform metaverse was developed, and a questionnaire was administered to game developers. The results indicate that even junior developers and those with limited experience can comprehend the method, suggesting that it is possible to develop immersive virtual worlds with an emphasis on professional training even without prior experience with 3D modeling or third-party licensing.
Cross-Platform UAV Swarm Key Management in Denied Environments
Authors: Yuan Lin , Feng Zhishang , Zhang Chang , Ji Huifang
Published: August 2023

As resources provided by single unmanned aerial vehicles (UAVs) are limited, we propose a cross-platform UAV swarm key management scheme for task scenarios in denied environments. In denied environments where the communication link is open and the UAV nodes may go invalid, secure communication is often at stake. To solve this problem, we propose a key management scheme which, based on the Chinese remainder theorem (CRT) and the Hash function, constructs a swarm key by combining the local key and the session key to reduce the overhead of individual UAV nodes in the swarm. Meanwhile, the swarm head node constructs broadcast messages according to the key update needs, which reduces the overhead of the member nodes, improves the efficiency of key updating, and fulfills the key establishment and updating of the UAV swarm. Experiments show that our proposed scheme has forward and backward security and can defend against collusion attacks and replay attacks; our method was compared with other methods on the MIRACL cryptographic library in Visual Studio 2019, and it was found that our method has a lower computing and communication overhead, provides a solution to cross-platform key management of UAV swarms in denied environments, and ensures safe communication of UAVs in the swarm.
Improving the Forecasts of Surface Latent Heat Fluxes and Surface Air Temperature in the GRAPES Global Forecast System
Authors: Liang Miaoling , Yuan Xing , Wang Wenyan
Published: August 2023

The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature.
Determining the Day-to-Day Occurrence of Low-Latitude Scintillation in Equinoxes at Sanya during High Solar Activities (2012–2013)
Authors: Jia Guodong , Luo Weihua , Yu Xiao , Zhu Zhengping , Chang Shanshan
Published: August 2023

Plasma irregularity in the equatorial and low-latitude ionosphere, which leads to ionospheric scintillation, can threaten the operation of radio-based communication and navigation systems. A method for forecasting scintillation activity is still pending. In this study, we examined the performance of ionospheric parameters, including the critical frequency (foF2), peak height of the F2-layer (hmF2), scale height (Hm) and virtual height (h’F), around local sunset from ground-based ionosonde observations, and also the characteristics of Equatorial Ionization Anomaly (EIA) derived from Gravity Recovery and Climate Experiment (GRACE) observations in equinoctial months (March–April and September–October) during high solar activities (2012–2013) at a low-latitude station at Sanya (18.3° N, 109.6° E; dip lat.: 12.8° N), China. Furthermore, the simplified linear growth rate of Rayleigh–Taylor (R–T) instability inferred from ionosonde measurements and EIA strength derived from GRACE observations were used to estimate the day-to-day occurrence of post-sunset scintillation. The results indicate that it is not adequate to determine whether scintillation in a low-latitude region would occur or not based on one ionospheric parameter around sunset. The simplified growth rate of R–T instability can be a good indicator for the day-to-day occurrence of scintillation, especially in combination with variations in EIA strength. An index including the growth rate and EIA variations for the prediction of the post-sunset occurrence of irregularity and scintillation is proposed; the overall prediction accuracy could be about 90%. Our results may provide useful information for the development of a forecasting model of the day-to-day variability of irregularities and scintillation in equatorial and low-latitude regions.
A Method for Extrapolating Continuous Functions by Generating New Training Samples for Feedforward Artificial Neural Networks
Authors: Yotov Kostadin , Hadzhikolev Emil , Hadzhikoleva Stanka , Cheresharov Stoyan
Published: August 2023

The goal of the present study is to find a method for improving the predictive capabilities of feedforward neural networks in cases where values distant from the input–output sample interval are predicted. This paper proposes an iterative prediction algorithm based on two assumptions. One is that predictions near the statistical sample have much lower error than those distant from the sample. The second is that a neural network can generate additional training samples and use them to train itself in order to get closer to a distant prediction point. This paper presents the results of multiple experiments with different univariate and multivariate functions and compares the predictions made by neural networks before and after their training with the proposed iterative algorithm. The results show that, having passed through the stages of the algorithm, artificial neural networks significantly improve their interpolation performance in long-term forecasting. The present study demonstrates that neural networks are capable of creating additional samples for their own training, thus increasing their approximating efficiency.
Permutations, Signs, and Sum Ranges
Authors: Chobanyan Sergei , Domínguez Xabier , Tarieladze Vaja , Vidal Ricardo
Published: August 2023

The sum range SRx;X, for a sequence x=(xn)n∈N of elements of a topological vector space X, is defined as the set of all elements s∈X for which there exists a bijection (=permutation) π:N→N, such that the sequence of partial sums (∑k=1nxπ(k))n∈N converges to s. The sum range problem consists of describing the structure of the sum ranges for certain classes of sequences. We present a survey of the results related to the sum range problem in finite- and infinite-dimensional cases. First, we provide the basic terminology. Next, we devote attention to the one-dimensional case, i.e., to the Riemann–Dini theorem. Then, we deal with spaces where the sum ranges are closed affine for all sequences, and we include some counterexamples. Next, we present a complete exposition of all the known results for general spaces, where the sum ranges are closed affine for sequences satisfying some additional conditions. Finally, we formulate two open questions.
Barnyard Grass Stress Triggers Changes in Root Traits and Phytohormone Levels in Allelopathic and Non-Allelopathic Rice
Authors: Yan Qiling , Tong Jianhua , Li Shuyan , Peng Qiong
Published: August 2023

Despite the growing knowledge concerning allelopathic interference with barnyard grass, little is understood regarding the competitive physiological mechanisms of the interaction between allelopathic rice and herbicide-resistant barnyard grass. A hydroponic system was employed to investigate the root morphological traits and different phytohormonal changes in allelopathic and non-allelopathic rice cultivars when co-planted with quinclorac-resistant and -susceptible barnyard grass, respectively. The results show that shoot and root biomass were greater in PI. Barnyard grass stress induced an increase in shoot and root biomass in PI at 7 and 14 days of co-culturing rice and barnyard grass. Especially under the stress of quinclorac-resistant barnyard grass, the shoot biomass of PI increased by 23% and 68%, respectively, and the root biomass increased by 37% and 34%, respectively. In terms of root morphology, PI exhibited a significantly higher fine-root length, in root diameters of <0.5 mm, a greater number of root tips, and longer root tips compared to LE. The response to quinclorac-resistant barnyard grass stress was consistent in terms of the SA and JA content. The obvious accumulation of SA and JA was observed in two rice cultivars under quinclorac-resistant barnyard grass stress, with greater amounts of SA and JA in PI. The significant decrease in auxin (IAA) and abscisic acid (ABA) content in rice was detected from 7 to 14 days under co-culture stress. Additionally, highly significant and positive correlations were found between SA and JA content, and the number of root tips and root tip length at root diameters of 0–0.5 mm in rice.
Circadian Dysfunction in Adipose Tissue: Chronotherapy in Metabolic Diseases
Authors: Civelek Erkan , Ozturk Civelek Dilek , Akyel Yasemin Kubra , Kaleli Durman Deniz , Okyar Alper
Published: August 2023

Essential for survival and reproduction, the circadian timing system (CTS) regulates adaptation to cyclical changes such as the light/dark cycle, temperature change, and food availability. The regulation of energy homeostasis possesses rhythmic properties that correspond to constantly fluctuating needs for energy production and consumption. Adipose tissue is mainly responsible for energy storage and, thus, operates as one of the principal components of energy homeostasis regulation. In accordance with its roles in energy homeostasis, alterations in adipose tissue’s physiological processes are associated with numerous pathologies, such as obesity and type 2 diabetes. These alterations also include changes in circadian rhythm. In the current review, we aim to summarize the current knowledge regarding the circadian rhythmicity of adipogenesis, lipolysis, adipokine secretion, browning, and non-shivering thermogenesis in adipose tissue and to evaluate possible links between those alterations and metabolic diseases. Based on this evaluation, potential therapeutic approaches, as well as clock genes as potential therapeutic targets, are also discussed in the context of chronotherapy.
The Role and Regulation of the NKG2D/NKG2D Ligand System in Cancer
Authors: Tan Ge , Spillane Katelyn M. , Maher John
Published: August 2023

The family of human NKG2D ligands (NKG2DL) consists of eight stress-induced molecules. Over 80% of human cancers express these ligands on the surface of tumour cells and/or associated stromal elements. In mice, NKG2D deficiency increases susceptibility to some types of cancer, implicating this system in immune surveillance for malignancy. However, NKG2DL can also be shed, released via exosomes and trapped intracellularly, leading to immunosuppressive effects. Moreover, NKG2D can enhance chronic inflammatory processes which themselves can increase cancer risk and progression. Indeed, tumours commonly deploy a range of countermeasures that can neutralise or even corrupt this surveillance system, tipping the balance away from immune control towards tumour progression. Consequently, the prognostic impact of NKG2DL expression in human cancer is variable. In this review, we consider the underlying biology and regulation of the NKG2D/NKG2DL system and its expression and role in a range of cancer types. We also consider the opportunities for pharmacological modulation of NKG2DL expression while cautioning that such interventions need to be carefully calibrated according to the biology of the specific cancer type.
Establishment of Wild-Derived Strains of Japanese Quail (Coturnix japonica) in Field and Laboratory Experiments
Authors: Goto Tatsuhiko , Konno Satoshi , Konno Miwa
Published: August 2023

Domestic quail are used as both farm and laboratory animals. As the wild ancestor of quails is “endangered,” field studies are needed to conserve them. If wild-derived strains of quail are established, they will be unique genetic resources for both farm and laboratory animals. The purpose of the present study was to conduct a field study and create wild-derived quail strains using the breeding stocks in Tokachi, Hokkaido, Japan. Field observations from 2019 to 2022 indicate that wild quails migrate and stay at reproductive sites from late April to late October. Our estimations of the approximate ages of the observed and captured quails imply that adult males have intermittent reproductive opportunities from May to August. Morphological traits collected from adult and juvenile quails in the 2019–2022 population were similar to those previously reported for Japan’s 1914 and 1970 populations. Using natural mating of captured wild males and domestic (Dom) females, we established the W50 and W75 strains, which possessed 50% and 75% genetic contributions from the wild stocks. These unique genetic resources can be applied for future conservation and experimental use to understand the domestication history and genetic basis of quantitative traits.
A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression
Authors: Imoto Hiroaki , Rauch Nora , Neve Ashish J. , Khorsand Fahimeh , Kreileder Martina , Alexopoulos Leonidas G. , Rauch Jens , Okada Mariko , Kholodenko Boris N. , Rukhlenko Oleksii S.
Published: August 2023

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.
Deployment of an Automated Method Verification-Graphical User Interface (MV-GUI) Software
Authors: Nagabhushana Priyanka , Rütsche Cyrill , Nakas Christos , Leichtle Alexander B.
Published: August 2023

Clinical laboratories frequently conduct method verification studies to ensure that the process meets quality standards for its intended use, such as patient testing. They play a pivotal role in healthcare, but issues such as accurate statistical assessment and reporting of verification data often make these studies challenging. Missteps can lead to false conclusions about method performance, risking patient safety or leading to incorrect diagnoses. Despite a requirement for accredited labs to document method performance, existing solutions are often expensive and complex. Addressing these issues, we present Method Verification-Graphical User Interface (MV-GUI), a software package designed for ease of use. It is platform-independent, capable of statistical analysis, and generates accreditation-ready reports swiftly and efficiently. Users can input patient data from one or more .CSV files, and MV-GUI will produce comprehensive reports, including statistical comparison tables, regression plots, and Bland–Altman plots. While method validation, which establishes the performance of new diagnostic tools, remains a crucial concern for manufacturers, MV-GUI primarily streamlines the method verification process. The software aids both medical practitioners and researchers and is designed to be user-friendly, even for non-experienced users. Requiring no internet connection, MV-GUI can operate in restricted IT environments, making method verification widely accessible and efficient.
A Fast and Effective Spike Sorting Method Based on Multi-Frequency Composite Waveform Shapes
Authors: Wang Ruixue , Xu Yuchen , Zhang Yiwei , Hu Xiaoling , Li Yue , Zhang Shaomin
Published: August 2023

Accurate spike sorting to the appropriate neuron is crucial for neural activity analysis. To improve spike sorting performance, it is essential to fully leverage each processing step, including filtering, spike detection, feature extraction, and clustering. However, compared to the latter two steps that were widely studied and optimized, the filtering process was largely neglected. In this study, we proposed a fast and effective spike sorting method (MultiFq) based on multi-frequency composite waveform shapes acquired through an optimized filtering process. When combined with the classical PCA-Km spiking sorting algorithm, our proposed MultiFq significantly improved its sorting performance and achieved as high performance as the complex Wave-clus did in both the simulated and in vivo datasets. But, the combined method was about 10 times faster than Wave-clus (0.16 s vs. 2.06 s in simulated datasets; 0.46 s vs. 2.03 s in in vivo datasets). Furthermore, we demonstrated the compatibility of our MultiFq by combining it with other sorting algorithms, which consistently resulted in significant improvement in sorting accuracy with the maximum improvement at 35.04%. The above results demonstrated that our proposed method could significantly improve the sorting performance with low computation cost and good compatibility by leveraging the multi-frequency composite waveform shapes.
3D-Printed Bioreceptive Tiles of Reaction–Diffusion (Gierer–Meinhardt Model) for Multi-Scale Algal Strains’ Passive Immobilization
Authors: Abdallah Yomna K. , Estévez Alberto T.
Published: August 2023

The current architecture practice is shifting towards Green Solutions designed, produced, and operated domestically in a self-sufficient decentralized fashion, following the UN sustainability goals. The current study proposes 3D-printed bioreceptive tiles for the passive immobilization of multi-scale-length algal strains from a mixed culture of Mougeotia sp., Oedogonium foveolatum, Zygnema sp., Microspora sp., Spirogyra sp., and Pyrocystis fusiformis. This customized passive immobilization of the chosen algal strains is designed to achieve bioremediation-integrated solutions in architectural applications. The two bioreceptive tiles following the reaction-diffusion, activator-inhibitor Grier–Meinhardt model have different patterns: P1: Polar periodic, and P2: Strip labyrinth, with niche sizes of 3000 µm and 500 µm, respectively. The results revealed that P2 has a higher immobilization capacity for the various strains, particularly Microspora sp., achieving a growth rate 1.65% higher than its activated culture density compared to a 1.08% growth rate on P1, followed by P. fusiformis with 1.53% on P2 and 1.3% on P1. These results prove the correspondence between the scale and morphology of the strip labyrinth pattern of P2 and the unbranched filamentous and fusiform large unicellular morphology of the immobilized algal strains cells, with an optimum ratio of 0.05% to 0.75% niche to the cell scale. Furthermore, The Mixed Culture method offered an intertwining net that facilitated the entrapment of the various algal strains into the bioreceptive tile.
Numerical Simulation of Severe Damage to a Historical Masonry Building by Soil Settlement
Authors: Ertürk Atmaca Esin , Genç Ali Fuat , Altunişik Ahmet Can , Günaydin Murat , Sevim Barış
Published: August 2023

Historical masonry structures, which constitute an important part of the historical heritage, exhibit brittle behavior under tensile stresses due to earthquakes or soil movements/settlements. Therefore, they are sensitive to deformation. The soil–structure interaction problems play an important role in the damage to historical masonry structures. Different settlements, slips, and deformations in foundations causes damage to, and/or partial collapse the load-bearing walls. This study provides a numerical simulation of a historic masonry building on the north coast of Turkey, dating from 19th century, which suffered severe damage due to soil settlement by excavation activity near the building. FE models of the building with and without the soil–structure system were created to identify the damage to the building following soil settlement. The height of the soil domain (bounded as a fixed boundary condition) was accepted in the range of 1.5 m to 15 m. The damage propagation between the numerically obtained damage and the existing damage to the building was detailed and compared. In terms of displacement, the maximum vertical and horizontal displacement values reflecting the existing damage to the building were determined as 22 mm and 85 mm, respectively. The soil depth of 6 m was also considered acceptable for deciding the soil–structure interaction. For this, the sum of the rigid basement and the soil depth can be selected to be equal to the total height of the building participating in free vibration. It was concluded that the numerical procedure employed provided an effective representation of existing damage to a building due to soil settlement. Moreover, the procedure described can be adopted for possible collapse simulation.
Performance and Mechanism of Zn-Contaminated Soil through Microbe-Induced Calcium Carbonate Precipitation
Authors: Xing Wei , Zhou Feng , Zhu Rui , Wang Xudong , Chen Tingzhu
Published: August 2023

Zn is a toxic heavy metal that seriously endangers human health and ecological stability. For a long time, traditional remediation techniques have been used to remediate Zn-contaminated soil prone to other problems such as secondary contamination. In recent years, due to the great danger posed by Zn pollution, there has been an increasing interest in applying eco-friendly and sustainable methods to remediate Zn-contaminated soil. Therefore, in this study, microbially induced calcium carbonate precipitation (MICP) technology was used to bioremediate zinc ions by transforming ionic heavy metals into insoluble solid-phase minerals. Through the unconfined compressive strength (UCS) test, direct shear (DS) test, and penetration test (PT), the results showed that the unconfined compressive strength of the treated specimens increased by 187.2~550.5%, the cohesion increased significantly compared with the internal friction angle of specimens, and the permeability coefficient can be reduced by at least one order of magnitude. During the treatment of Zn pollutants, the mobility of heavy metal zinc ions was significantly reduced, the percentage of exchangeable state Zn content was significantly reduced, and the leaching concentration of zinc ions in Zn-contaminated soil was reduced to about 20 mg/L, which was significantly lower than the limit in the standard (100 mg/L). These results were further confirmed by scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses, which indicated coprecipitation of calcium carbonate (CaCO3) and ZnCO3. The microbial solidification/stabilization of Zn-contaminated soil was most effective when the curing age of 28 d, the cementation solution concentration of 1 mol/L, and the cementation solution ratio of 1:2. Therefore, the bio-immobilization of zinc ions by MICP has the potential for application as a low-cost and eco-friendly method for heavy metal remediation.
Multimodal Deep Neural Network-Based Sensor Data Anomaly Diagnosis Method for Structural Health Monitoring
Authors: Nong Xingzhong , Luo Xu , Lin Shan , Ruan Yanmei , Ye Xijun
Published: August 2023

Due to sensor failure, noise interference and other factors, the data collected in the structural health monitoring (SHM) system will show a variety of abnormal patterns, which will bring great uncertainty to the structural safety assessment. This paper proposes an automatic data anomaly diagnosis method for SHM based on a multimodal deep neural network. In order to improve the detection accuracy, both two-dimensional and one-dimensional features of the sensor data are fused in the multimodal deep neural network. The network consists of two convolutional neural network (CNN) channels, one a 2D-CNN channel for extracting time–frequency features of sensor data and the other a 1D-CNN channel for extracting raw one-dimensional features of sensor data. After convolution and pooling operations for the sensor data by the 2D channel and 1D channel separately, the two types of extracted features are flattened into one-dimensional vectors and concatenated at the concatenation layer. The concatenated vector is then fed into fully connected layers for final SHM data anomaly classification. In order to evaluate the reliability of the proposed method, the monitored data lasting for one month of a long-span cable-stayed bridge were used for training, validation, and testing. Six types of training conditions (missing, minor, outlier, over-range oscillation, trend, and drift) are studied and analyzed to address the issue of imbalanced training data. With an accuracy rate of 95.10%, the optimal model demonstrates the effectiveness and capability of the proposed method. The proposed method shows a promising future as a reliable AI-assisted digital tool for safety assessment in structural health monitoring systems.
Topical Treatments for Basal Cell Carcinoma and Actinic Keratosis in the United States
Authors: Tan Isabella J. , Pathak Gaurav N. , Silver Frederick H.
Published: August 2023

Skin cancer is an overarching label used to classify a variety of cutaneous malignancies. Surgical excision procedures are the commonly used treatments for these lesions; however, the choice to perform operative intervention may be influenced by other factors. Established research and literature suggest that topical treatments limit the need for surgical intervention and its commonly associated adverse effects, including infection and scarring. In addition, the growing indications for the usage of topical therapies in BCC treatment, as well as their increased availability and therapeutic options, allow for their greater applicability in the dermatology clinic. Certain topical therapies have been highlighted in research, especially those targeting basal cell carcinoma (BCC) and actinic keratosis (AK). There is also a clear correlation between cost and treatment outcomes, considering BCC’s ever-growing prevalence and the proportion of excised lesions being reported as malignant. This review will discuss BCC and AK lesion criteria that result in the most successful outcomes using topical treatments, then highlight the various topical treatment options, and finally address their clinical significance moving forward.
Lynch Syndrome Biopathology and Treatment: The Potential Role of microRNAs in Clinical Practice
Authors: Ascrizzi Serena , Arillotta Grazia Maria , Grillone Katia , Caridà Giulio , Signorelli Stefania , Ali Asad , Romeo Caterina , Tassone Pierfrancesco , Tagliaferri Pierosandro
Published: August 2023

Lynch syndrome (LS), also known as Hereditary Non-Polyposis Colorectal Cancer (HNPCC), is an autosomal dominant cancer syndrome which causes about 2–3% of cases of colorectal carcinoma. The development of LS is due to the genetic and epigenetic inactivation of genes involved in the DNA mismatch repair (MMR) system, causing an epiphenomenon known as microsatellite instability (MSI). Despite the fact that the genetics of the vast majority of MSI-positive (MSI+) cancers can be explained, the etiology of this specific subset is still poorly understood. As a possible new mechanism, it has been recently demonstrated that the overexpression of certain microRNAs (miRNAs, miRs), such as miR-155, miR-21, miR-137, can induce MSI or modulate the expression of the genes involved in LS pathogenesis. MiRNAs are small RNA molecules that regulate gene expression at the post-transcriptional level by playing a critical role in the modulation of key oncogenic pathways. Increasing evidence of the link between MSI and miRNAs in LS prompted a deeper investigation into the miRNome involved in these diseases. In this regard, in this study, we discuss the emerging role of miRNAs as crucial players in the onset and progression of LS as well as their potential use as disease biomarkers and therapeutic targets in the current view of precision medicine.
Alberta Childhood Cancer Survivorship Research Program
Authors: Harper Andrew , Schulte Fiona , Guilcher Gregory , Truong Tony , Reynolds Kathleen , Spavor Maria , Logie Natalie , Lee Joon , Fidler-Benaoudia Miranda
Published: August 2023

Adverse outcomes after childhood cancer have been assessed in a range of settings, but most existing studies are historical and ascertain outcomes only after 5-year survival. Here, we describe the Alberta Childhood Cancer Survivorship Research Program and its foundational retrospective, population-based cohort of Albertan residents diagnosed with a first primary neoplasm between the ages of 0 and 17 years from 1 January 2001 to 31 December 2018. The cohort was established in collaboration with the Alberta Cancer Registry and Cancer in Young People in Canada program and has been linked to existing administrative health databases and patient-reported outcome questionnaires. The cohort comprised 2581 survivors of childhood cancer, 1385 (53.7%) of whom were 5-year survivors. Approximately 48% of the cohort was female, 46% of the cohort was diagnosed between 0 and 4 years of age, and the most frequent diagnoses were leukemias (25.3%), central nervous system tumors (24.2%), and lymphomas (14.9%). Detailed treatment information was available for 1745 survivors (67.6%), with manual abstraction ongoing for those with missing data. By the study exit date, the median time since diagnosis was 5.6 years overall and 10.3 years for 5-year survivors. During the follow-up time, 94 subsequent primary cancers were diagnosed, 16,669 inpatient and 445,150 ambulatory/outpatient events occurred, 396,074 claims were reported, and 408 survivors died. The results from this research program seek to inform and improve clinical care and reduce cancer-related sequelae via tertiary prevention strategies.
Incidence and Predictors for Oncologic Etiologies in Chinese Children with Pituitary Stalk Thickening
Authors: Li Mario W. T. , Poon Sarah W. Y. , Cheung Claudia , Wong Chris K. C. , Shing Matthew M. K. , Chow Terry T. W. , Lee Samantha L. K. , Pang Gloria S. W. , Kwan Elaine Y. W. , Poon Grace W. K. , Yau Ho-Chung , Tung Joanna Y. L. , Liu Anthony P. Y.
Published: August 2023

Background: With the increasing use of magnetic resonance imaging (MRI) in the evaluation of children with endocrine disorders, pituitary stalk thickening (PST) poses a clinical conundrum due to the potential for underlying neoplasms and challenges in obtaining a tissue biopsy. The existing literature suggests Langerhans cell histiocytosis (LCH) to be the commonest (16%) oncologic cause for PST, followed by germ cell tumors (GCTs, 13%) (CCLG 2021). As the cancer epidemiology varies according to ethnicity, we present herein the incidence and predictors for oncologic etiologies in Hong Kong Chinese children with PST. Methods: Based on a territory-wide electronic database, we reviewed patients aged < 19 years who presented to three referral centers with endocrinopathies between 2010 and 2022. Records for patients who underwent at least one MRI brain/pituitary were examined (n = 1670): those with PST (stalk thickness ≥ 3 mm) were included, while patients with pre-existing cancer, other CNS and extra-CNS disease foci that were diagnostic of the underlying condition were excluded. Results: Twenty-eight patients (M:F = 10:18) were identified. The median age at diagnosis of PST was 10.9 years (range: 3.8–16.5), with central diabetes insipidus (CDI) and growth hormone deficiency (GHD) being the most frequent presenting endocrine disorders. At a median follow-up of 4.8 years, oncologic diagnoses were made in 14 patients (50%), including 13 GCTs (46%; germinoma = 11, non-germinoma = 2) and one LCH (4%). Among patients with GCTs, 10 were diagnosed based on histology, two by abnormal tumor markers and one by a combination of histology and tumor markers. Three patients with germinoma were initially misdiagnosed as hypophysitis/LCH. The cumulative incidence of oncologic diagnoses was significantly higher in boys and patients with PST at presentation ≥6.5 mm, CDI or ≥2 pituitary hormone deficiencies at presentation and evolving hypopituitarism (all p < 0.05 by log-rank). Conclusions: A higher rate of GCTs was observed in Chinese children with endocrinopathy and isolated PST. The predictors identified in this study may guide healthcare providers in Asia in clinical decision making. Serial measurement of tumor markers is essential in management.