bluuebunny/crossref_metadata_2025 huggingface dataset:
Interaction between Bovine Serum Albumin in Fresh Milk Cream and Encapsulated and Non-Encapsulated Polyphenols of Tamarillo
Authors: Liu Chen , Nguyen Bao Viet , Diep Tung Thanh , Yoo Michelle Ji Yeon
Published: August 2023

The fortification of dairy products with polyphenols is known to deliver additional health benefits. However, interactions between polyphenols may form complexes and cause a loss of functionality overall. This study aimed to investigate potential interactions between polyphenols, in encapsulated and non-encapsulated forms, extracted from tamarillo fruit and bovine serum albumin (BSA) from fresh milk cream. Fortification with tamarillo extract was made at 1, 2 and 3% (w/w), and the resultant changes in physicochemical, rheological and functional properties were studied. With an increase in fortification, the absorbance of protein–ligand in the protein–polyphenol complex was decreased by up to 55% and 67% in UV and fluorescent intensities, respectively. Chlorogenic acid and kaempferol-3-rutinoside were more affected than delphinidin-3-rutinoside and pelargonidin-3-rutinoside. Static quenching was the main mechanism in the fluorescence spectra. Tryptophan and tyrosine residues were the two major aromatic amino acids responsible for the interactions with BSA. There were at least three binding sites near the tryptophan residue on BSA. The rheological property remained unaffected after the addition of non-encapsulated tamarillo extracts. Antioxidant capacity was significantly decreased (p < 0.05) after the addition of encapsulated extracts. This may be explained by using a low concentration of maltodextrin (10% w/w) as an encapsulating agent and its high binding affinity to milk proteins.
An Overview of Japanese Encephalitis in Australia: Trends, Impact and Interventions
Authors: Kwa Faith A. A. , Kendal Evie , Xiao Junhua
Published: August 2023

Japanese Encephalitis (JE) is caused by infection with the Japanese encephalitis virus (JEV). This vector-borne disease has been listed as a nationally notifiable public health risk in various countries. Predominantly found in Southeast Asia, JE can result in long-term neurological and psychiatric sequelae in both adults and children and is the leading cause of viral encephalitis in this region. Globally, there are an estimated 100,000 cases of JE and 25,000 associated deaths per year. Despite the significant effort to stop JE transmission through implementing vaccination programmes, JEV infection continues to be a global problem, with recent outbreaks in several countries, including outside of Asia. This raises a public health alert and establishes a need for future surveillance. Here, we reviewed the recent JE epidemic in Australia, focusing on its trends, impact and intervention. We recommend that a collaborative research effort together with a globally unified disease control strategy is urgently required to improve our understanding of JEV transmission and surveillance and to be better prepared for future outbreaks.
Utilization Potential of Tree-of-Heaven Species Biomass—A Review
Authors: Terzopoulou Paschalina , Kamperidou Vasiliki , Barboutis Ioannis
Published: August 2023

Climate change and the subsequent increase in global mean air temperature already present a significant impact on forest vegetation. Especially in the near future, several forest species are expected to be in danger of extinction or compelled to migrate to colder regions. Some common species will be replaced by highly climate-tolerant species, primarily exotic plants, among others. The tree of Ailanthus, or “tree-of-heaven”, as it is widely known, constitutes a rapidly growing plant, considered to be native to parts of China, while since the middle of the 18th century, it gradually spread to Europe and North America. This species demonstrates a preference for warmer, drier environments, although it can also survive in a variety of habitats and endure pollution of urban areas. It is a species with several uses, such as for animal feed, fuel, timber, pharmaceutical applications, etc., while its suitability for specialized applications of high-added-value is constantly being investigated. Its wood has a desirable appearance and characteristics that are comparable to those of other hardwood species of similar densities/weight. This article discusses some of the most important characteristics of Ailanthus wood and presents a comprehensive and constructive review of the chemistry, pharmacology, traditional and innovative uses, quality control, biological resistance, potential utilization in bioenergy and biofuels and wood products (e.g., wood-based panels, other advanced structure materials, etc.), use challenges and limitations, in order to contribute to the utilization potential assessment of this species biomass.
Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review
Authors: D’Ulizia Arianna , D’Andrea Alessia , Grifoni Patrizia , Ferri Fernando
Published: August 2023

Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years. This work systematically reviews the literature focused on facial cues of deception. The most relevant methods applied in the literature of the last decade have been surveyed and classified according to the main steps of the facial-deception-detection process (video pre-processing, facial feature extraction, and decision making). Moreover, datasets used for the evaluation and future research directions have also been analysed.
Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network
Authors: Chen Zhuoran , Zhang Yujin , Wang Yongqi , Tian Jin , Wu Fei
Published: August 2023

Deep learning has injected a new sense of vitality into the field of image inpainting, allowing for the creation of more realistic inpainted images that are difficult to distinguish from the original ones. However, this also means that the malicious use of image inpainting technology to tamper with images could lead to more serious consequences. In this paper, we use an attention-based feature pyramid network (AFPN) to locate the inpainting traces left by deep learning. AFPN employs a feature pyramid to extract low- and high-level features of inpainted images. It further utilizes a multi-scale convolution attention (MSCA) module to optimize the high-level feature maps. The optimized high-level feature map is then fused with the low-level feature map to detect inpainted regions. Additionally, we introduce a fusion loss function to improve the training effectiveness. The experimental results show that AFPN exhibits remarkable precision in deep inpainting forensics and effectively resists JPEG compression and additive noise attacks.
Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module
Authors: Deng Jinxin , Liu Junbao , Ma Xiaoqin , Qin Xizhong , Jia Zhenhong
Published: August 2023

Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the span-based approach ignores the local features within the entities and the relative position features between the head and tail tokens, which affects the performance of entity recognition. To address these issues, we propose a nested entity recognition model using a convolutional block attention module and rotary position embedding for local features and relative position features enhancement. Specifically, we apply rotary position embedding to the sentence representation and capture the semantic information between the head and tail tokens using a biaffine attention mechanism. Meanwhile, the convolution module captures the local features within the entity to generate the span representation. Finally, the two parts of the representation are fused for entity classification. Extensive experiments were conducted on five widely used benchmark datasets to demonstrate the effectiveness of our proposed model.
A Method for Plotting Failure Envelopes of Unidirectional Polymer Composite Materials under Different Strain Rates
Authors: Liu Hao , Pang Yuezhao , Su Dandan , Wang Yifan , Dong Ge
Published: August 2023

This article emphasizes the significance of investigating the nonlinear behavior and strength characteristics of polymer composite materials under various strain rates. The study utilizes test results of a unidirectional (UD) composite material subjected to compression at different angles relative to the reinforcement direction, using quasi-static, static, and dynamic strain rates. The analysis focused on a UD layer experiencing compressive stresses perpendicular to the fiber reinforcement and in-plane shear stresses. A novel model is presented, enabling the calculation and prediction of the strength of a UD composite under uniaxial loading at different angles to the fiber direction, considering various strain rates. The developed model facilitates the derivation of equations for the failure envelopes of UD Carbon Fiber-Reinforced Polymers (CFRPs) under quasi-static, static, and dynamic loading conditions. To construct the failure envelopes of CFRPs, it is necessary to acquire experimentally determined values of tensile and compressive strength in the direction perpendicular to the reinforcement, as well as the ultimate strength in uniaxial compression of a specimen with reinforcement at a 45° angle to the loading axis. The failure envelopes generated using the proposed model exhibit excellent agreement with experimental data, with coefficients of determination ranging from 0.864 to 0.957, depending on the deformation rate. Consequently, the developed model holds promise for predicting the strength of other UD polymer composite materials.
Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
Authors: Asiedu Asante Bismark Kweku , Broni-Bediako Clifford , Imamura Hiroki
Published: August 2023

Multi-stage or multi-generator generative adversarial networks (GANs) have recently been demonstrated to be effective for speech enhancement. The existing multi-generator GANs for speech enhancement only use convolutional layers for synthesising clean speech signals. This reliance on convolution operation may result in masking the temporal dependencies within the signal sequence. This study explores self-attention to address the temporal dependency issue in multi-generator speech enhancement GANs to improve their enhancement performance. We empirically study the effect of integrating a self-attention mechanism into the convolutional layers of the multiple generators in multi-stage or multi-generator speech enhancement GANs, specifically, the ISEGAN and the DSEGAN networks. The experimental results show that introducing a self-attention mechanism into ISEGAN and DSEGAN leads to improvements in their speech enhancement quality and intelligibility across the objective evaluation metrics. Furthermore, we observe that adding self-attention to the ISEGAN’s generators does not only improves its enhancement performance but also bridges the performance gap between the ISEGAN and the DSEGAN with a smaller model footprint. Overall, our findings highlight the potential of self-attention in improving the enhancement performance of multi-generator speech enhancement GANs.
Advancements in Medical Imaging and Image-Guided Procedures: A Potential—Or Rather Likely—Paradigm Shift in Diagnosis and Therapy: Understand Disruption and Take Advantage of It!
Authors: Friebe Michael , Illanes Alfredo
Published: August 2023

What were our product development goals in medical imaging and image-guided therapies in recent decades [...]
A Computational (DFT) Study on the Anti-Malarial Drug: Lumefantrine
Authors: Kunduracioglu Ahmet
Published: August 2023

This study aims to investigate the spectroscopic and structural properties of the compound Lumefantrine, which is important in pharmacology because of its anti-malarial effect. The structural and spectroscopic properties of this molecule, such as bond lengths, bond angles, FT-IR and NMR spectra were handled computationally using a computational chemistry suite: Spar-tan’14. Both HF and DFT methods were used with different basis sets for the calculations. The results calculated by the software were compared to experimental results from the literature. Both computational and experimental results were exhibited as tables. Some calculated results, such as HOMO-LUMO boundary orbitals and electrostatic potential map, were also given as graphics and pictures.
Continuous-Flow Microwave Heating Inactivation Kinetics of α-Amylase from Bacillus subtilis and a Comparison with Conventional Heating Conditions
Authors: Tong Zhen , Ramaswamy Hosahalli S.
Published: August 2023

The inactivation kinetics of an α-amylase enzymatic time-temperature integrator (TTI) from Bacillus subtilis (BAA) under continuous-flow microwave (MW) and conventional heating conditions were evaluated and compared in this study. The TTI dispersed in a buffer solution (pH 5.0 to 6.9) at 20 °C initially, and it was continuously circulated through two helical coils connected in a series for heating. The two coils were positioned in two domestic microwave ovens (2450 MHz and 1000 W nominal capacity each) and connected by a short tube. The sample flow rates were adjusted to result in a specific exit temperature in the range of 65 to 80 °C. A short fully insulated helical coil at the exit of the second oven was used as a holding tube. Test samples were drawn either at the exit of the second MW oven or immediately after the holding tube. The decimal reduction times obtained under conventional batch heating conditions decreased from 66 to 24 s as the temperature changed from 70 to 75 °C at pH 5.0 while they decreased from 8 to 5 s under MW in the same temperature range, but at pH 6.0, they increased both under conventional and microwave heating conditions (138 to 120 s and 89 to 61 s, respectively). The D-values under conventional thermal holding were four–eight times higher than under a continuous-flow MW heating condition. By varying the pH, the D-values could be modified to suit the validation of appropriate processing conditions.
Evaluation of WRF Performance in Simulating an Extreme Precipitation Event over the South of Minas Gerais, Brazil
Authors: Garcia Denis William , Reboita Michelle Simões , Carvalho Vanessa Silveira Barreto
Published: August 2023

Extreme precipitation events are becoming increasingly frequent and intense in southeastern Brazil, leading to socio-economic problems. While it is not possible to control these events, providing accurate weather forecasts can help society be better prepared. In this study, we assess the performance of the Weather Research and Forecasting (WRF) model in simulating a period of extreme precipitation from 31 December 2021 to 2 January 2022 in the southern region of Minas Gerais (SMG) state in southeastern Brazil. We conducted five simulations using two nested grids: a 12 km grid (coarse resolution) and a 3 km grid (high resolution). For the coarse resolution, we tested the performance of five cumulus convection parameterization schemes: Kain–Fritsch, Betts–Miller–Janjic, Grell–Freitas, Grell–Devenyi, and New Tiedke. We evaluated the impact of these simulations on driving the high-resolution simulations. To assess the performance of the simulations, we compared them with satellite estimates, in situ precipitation measurements from thirteen meteorological stations, and other variables from ERA5 reanalysis. Based on the results, we found that the Grell–Freitas scheme has better performance in simulating the spatial pattern and intensity of precipitation for the studied region when compared with the other four analyzed schemes.
The 50th Anniversary of the Metaphorical Butterfly Effect since Lorenz (1972): Multistability, Multiscale Predictability, and Sensitivity in Numerical Models
Authors: Shen Bo-Wen , Pielke Roger A. , Zeng Xubin
Published: August 2023

Lorenz rediscovered the butterfly effect, which is defined as the sensitive dependence on initial conditions (SDIC), in 1963. In 1972, he used the term “butterfly” as a metaphor to illustrate how a small perturbation can lead to a tornado with a complex structure. The metaphorical butterfly effect, which celebrated its 50th anniversary in 2022, is not precisely the same as the original butterfly effect with SDIC. To commemorate the 50th anniversary, a Special Issue was launched and invited the submission of research and review articles that can help to enhance our understanding of both the original and metaphorical butterfly effects. The Special Issue also sought recent developments in idealized Lorenz models and real-world models that address multistability, multiscale predictability, and sensitivity. The call for papers was opened 15 months prior to the completion of the Special Issue and features nine selected papers. This editorial provides a brief review of Lorenz models, introduces the published papers, and summarizes each one of them.
Investigating the Seasonal Effect of Climatic Factors on Evapotranspiration in the Monsoon Climate Zone: A Case Study of the Yangtze River Basin
Authors: Wang Mengmeng , Li Miao , An Qing , Zhang Zhengjia , Lu Jing
Published: August 2023

Evapotranspiration (ET) plays an essential role in water balance and ecological environment changes. The Yangtze River Basin (YRB) is a typical monsoon climate zone. Most existing studies on the impact of climatic factors on annual ET have overlooked the seasonal effect. This study quantitatively analyzed the spatiotemporal variation characteristics of ET and its relationship with climatic factors at the annual and monthly scales in the YRB using high−spatial−resolution PML_V2 ET data from 2001 to 2020. Results showed that: (1) the spatiotemporal distribution of the regions with significant correlation between ET and individual climatic factors (i.e., air temperature, solar radiation and precipitation) in the YRB showed obvious cyclical changes in month, and the spatial change pattern is strongly related to the elevation; (2) the area proportion of the dominant climatic factors affecting annual ET in the study area was characterized by solar radiation > specific humidity > precipitation > air temperature > wind speed. However, monthly ET in most areas of the YRB was driven by solar radiation and air temperature, especially in summer and autumn, while ET in spring and winter was mainly driven by solar radiation, air temperature, and specific humidity.
Calculation of Resonance Fluorescence Scattering Cross Sections of Metal Particles in the Middle and Upper Atmosphere and Comparison of Their Detectability
Authors: Wang Kexin , Wang Zelong , Wu Yuxuan , Xia Yuan , Xun Yuchang , Wu Fuju , Jiao Jing , Du Lifang
Published: August 2023

Resonance fluorescence scattering is the physical mechanism with which lidar detects atmospheric metal layers. The resonance fluorescence scattering cross section is an important parameter for lidar data processing. In this work, the resonance fluorescence backscattering cross sections of most detectable metal atoms and ions in the atmosphere were calculated. The calculated maximum backscattering cross section of Na at the D2 line is 7.38 × 10−17 m2/sr; K at the D1 line is 7.37 × 10−17 m2/sr; Fe at the 372 nm line is 7.53 × 10−18 m2/sr; Fe at the 374 nm line is 6.98 × 10−18 m2/sr; Fe at the 386 nm line is 3.75 × 10−18 m2/sr; Ni at the 337 nm line is 4.05 × 10−18 m2/sr; and Ni at the 341 nm line is 2.05 × 10−17 m2/sr; Ca is 3.06 × 10−16 m2/sr; Ca+ is 1.12 × 10−16 m2/sr. The influence of the laser linewidth on the effective scattering cross section was discussed. If the laser linewidth is lower than 2 GHz to detect Na, the laser center frequency locked at the D2a line is a better option than the D2 line in order to obtain greater signals. If an unlocked lidar is used to detect Na, the frequency at D2a should be used as the laser center frequency when the effective scattering cross section of Na was calculated, because the absorption cross section of Na atom has two local maxima. This work proposes a quantifiable comparative method for assessing the observation difficulty of different metal particles by comparing their relative uncertainties in lidar observation. It is assumed that under the same observation conditions, the detectability of different metal atoms and ions is compared. Using Na as a basis for comparison, the relative uncertainty of Ni at 337 nm is the highest, about a factor of 21 larger than that of Na, indicating that it is the most difficult to be detected. The purpose of this work is to present a quantifiable comparison method for the detection difficulty of the metal particles by lidar in the middle and upper atmosphere, which has great significance for the design of the lidar system.
Low Transportation Emission Analysis and Projection Using LEAP: The Case of Qatar
Authors: Al-Jabir Maryam , Isaifan Rima J.
Published: August 2023

The transportation sector is a significant source of pollution and greenhouse gas (GHG) emissions contributing to global warming. Although research on the actual emissions from transport has been conducted in several parts of the world, very limited outcomes in this field have been reported in the Gulf region. This is especially true for road transportation, one of Qatar’s most significant sources of air pollution. To address this research gap, this study provides an invaluable resource for policymakers as it is the first to quantify the current and forecast future transport emissions. In this work, actual data on traffic counts were input into the Low-Emissions Analysis Platform (LEAP) tool, which has never been used in the context of Qatar, making it an outstanding addition to the research in this domain. The ultimate goal is to estimate the transportation emissions from road traffic in Qatar and investigate how different actions lead to three different scenarios that can impact it. Considering that the policy reformation in the transportation sector is crucial to reducing greenhouse gas emissions and climate change, a scenario analysis can provide theoretical support for policy development and implementation. The investigation used the actual vehicle count data from 2017 to 2021, while all future projections considered the duration from 2022 to 2050 using three scenarios. The first was based on the historical growth in the number of vehicles; the second was based on the business-as-usual scenario (BAU); and the third considered that the public transport shall be divided into three sub-scenarios, namely fuel-economy improvement, the electrification of public transportation, and the replacement of diesel or petrol fuel with compressed natural gas (CNG). The results show that carbon dioxide (CO2) emissions were the highest in all scenarios. Moreover, it was found that in the BAU, the light-duty vehicles (LDVs) category is the most significant contributor to GHG emissions compared to motorcycles and heavy-duty vehicles (HDVs). In addition, it was found that emissions can be reduced by improving public transportation, switching to a cleaner fuel, and reducing reliance on private vehicles.
New Equivalents of Kurepa’s Hypothesis for Left Factorial
Authors: Petojević Aleksandar , Gordić Snežana , Mandić Milinko , Ranitović Marijana Gorjanac
Published: August 2023

Kurepa’s hypothesis for the left factorial has been an unsolved problem for more than 50 years. In this paper, we have proposed new equivalents for Kurepa’s hypothesis for the left factorial. The connection between the left factorial and the continued fractions is given. The new equivalent based on the properties of the integer part of real numbers is proven. Moreover, a new equivalent based on the properties of two well-known sequences is given. A new representation of the left factorial is listed. Since derangement numbers are closely related to Kurepa’s hypothesis, we made some notes about the derangement numbers and defined a new sequence of natural numbers based on the derangement numbers. In this paper, we indicate a possible direction for further research through solving quadratic equations.
A Study on Existence and Controllability of Conformable Impulsive Equations
Authors: Mahmudov Nazim I. , Akgün Gülbahar
Published: August 2023

We study the existence/uniqueness of conformable fractional type impulsive nonlinear systems as well as the controllability of linear/semilinear conformable fractional type impulsive controlled systems. Using the conformable fractional derivative approach, we introduce the conformable controllability operator and the conformable controllability Gramian matrix in order to obtain the necessary and sufficient conditions for the complete controllability of linear impulsive conformable systems. We present a set of sufficient conditions for the controllability of the conformable semilinear impulsive systems.
Electroforming as a Novel One-Step Manufacturing Method of Structured Aluminum Foil Current Collectors for Lithium-Ion Batteries
Authors: Scherzl Phillip , Kaupp Michael , El Mofid Wassima , Sörgel Timo
Published: August 2023

Conventionally, cathode current collectors for lithium-ion batteries (LIB) consist of an aluminum foil generally manufactured by a rolling process. In the present work, a novel one-step manufacturing method of structured aluminum foil current collectors for lithium-ion batteries by electroforming is introduced. For this, a low-temperature chloride-based ionic liquid was used as an electrolyte and a rotating cylinder out of stainless steel as a temporary substrate. It was shown that the structure of the aluminum foils can be adjusted from dense and flat to three-dimensional by choosing an appropriate substrate rotation speed and current density. Scanning electron microscopy (SEM) and white light interferometry (WLI) were utilized to analyze the foils’ surface morphology, structure and topography. The SEM analysis of the aluminum foils showed that the rolling process produced a foil with small grains, while electrodeposition resulted in foils with different degrees of grain growth and seed formation. This was in total agreement with WLI results that revealed significant differences in terms of roughness parameters, including the peak-to-valley difference Rpv, the root-mean-square roughness Rq and the arithmetic mean roughness Ra. These were, respectively, equal to 6.8 µm, 0.35 µm and 0.279 µm for the state-of-the-art foil and up to 96.6 µm, 10.92 µm and 8.783 µm for the structured electroformed foil. Additionally, cyclic voltammetry (CV) of the aluminum foils was used to investigate their passivation behavior within the typical LIB cathode potential operation window. The strong decrease in the current density during the second cycle compared to the first cycle, where an anodic peak appeared between 4.0 and 4.4 V vs. Li/Li+, demonstrated that passivation occurs in the same manner as observed for commercial Al current collectors.
The Investigation of Triple-Lithiated Transition Metal Oxides Synthesized from the Spent LiCoO2
Authors: Kosenko Alexandra , Pushnitsa Konstantin , Chernyavsky Vladislav , Novikov Pavel , Popovich Anatoliy A.
Published: August 2023

The environmentally friendly closed cycle of the regeneration process of spent LiCoO2 was successfully developed and the following synthesis of triple-lithiated transition metal oxides was carried out. A hydrometallurgy recycling route with the usage of 1.5 mol/L of malic acid and 3 vol.% of H2O2 as a leaching solution for cobalt extraction was chosen. The efficiency of the cobalt extraction reached 95%. The obtained material was investigated using an X-ray diffraction analysis and the EDX and SEM methods. The electrochemical behavior of the synthesized NCM111 was analyzed and compared to the commercially available material of the same type. The material demonstrated a specific discharge capacity on the first cycle of 163.7 mAh/g. The cyclic resource of the material turned out to be unsatisfactory. In addition, perspective cathode materials, such as NCM622 and NCM811, were obtained. The synthesized materials were analyzed using XRD, SEM, EDX, charge–discharge and cycle life tests, and the CVA and EIS methods. The initial specific discharge capacities of the NCM622 and NCM811 were 168 and 187 mAh/g, respectively. On the fifth cycle, the NCM622 demonstrated an increasing capacity—to 179 mAh/g, unlike NCM811, as the capacity of this material decreased to 141 mAh/g.
Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD)
Authors: Ramzy Ahmed , Torki Marwan , Abdeen Mohamed , Saif Omar , ElNainay Mustafa , Alshanqiti AbdAllah , Nabil Emad
Published: August 2023

Religious studies are a rich land for Natural Language Processing (NLP). The reason is that all religions have their instructions as written texts. In this paper, we apply NLP to Islamic Hadiths, which are the written traditions, sayings, actions, approvals, and discussions of the Prophet Muhammad, his companions, or his followers. A Hadith is composed of two parts: the chain of narrators (Sanad) and the content of the Hadith (Matn). A Hadith is transmitted from its author to a Hadith book author using a chain of narrators. The problem we solve focuses on the classification of Hadiths based on their origin of narration. This is important for several reasons. First, it helps determine the authenticity and reliability of the Hadiths. Second, it helps trace the chain of narration and identify the narrators involved in transmitting Hadiths. Finally, it helps understand the historical and cultural contexts in which Hadiths were transmitted, and the different levels of authority attributed to the narrators. To the best of our knowledge, and based on our literature review, this problem is not solved before using machine/deep learning approaches. To solve this classification problem, we created a novel Author-Based Hadith Classification Dataset (ABCD) collected from classical Hadiths’ books. The ABCD size is 29 K Hadiths and it contains unique 18 K narrators, with all their information. We applied machine learning (ML), and deep learning (DL) approaches. ML was applied on Sanad and Matn separately; then, we did the same with DL. The results revealed that ML performs better than DL using the Matn input data, with a 77% F1-score. DL performed better than ML using the Sanad input data, with a 92% F1-score. We used precision and recall alongside the F1-score; details of the results are explained at the end of the paper. We claim that the ABCD and the reported results will motivate the community to work in this new area. Our dataset and results will represent a baseline for further research on the same problem.
Cost-Effective Cultivation of Native PGPB Sinorhizobium Strains in a Homemade Bioreactor for Enhanced Plant Growth
Authors: Manzano-Gómez Luis Alberto , Rincón-Rosales Reiner , Flores-Felix José David , Gen-Jimenez Adriana , Ruíz-Valdiviezo Víctor Manuel , Ventura-Canseco Lucia María Cristina , Rincón-Molina Francisco Alexander , Villalobos-Maldonado Juan José , Rincón-Molina Clara Ivette
Published: August 2023

The implementation of bioreactor systems for the production of bacterial inoculants as biofertilizers has become very important in recent decades. However, it is essential to know the bacterial growth optimal conditions to optimize the production and efficiency of bioinoculants. The aim of this work was to identify the best nutriment and mixing conditions to improve the specific cell growth rates (µ) of two PGPB (plant growth-promoting bacteria) rhizobial strains at the bioreactor level. For this purpose, the strains Sinorhizobium mexicanum ITTG-R7T and Sinorhizobium chiapanecum ITTG-S70T were previously reactivated in a PY-Ca2+ (peptone casein, yeast extract, and calcium) culture medium. Afterward, a master cell bank (MCB) was made in order to maintain the viability and quality of the strains. The kinetic characterization of each bacterial strain was carried out in s shaken flask. Then, the effect of the carbon and nitrogen sources and mechanical agitation was evaluated through a factorial design and response surface methodology (RSM) for cell growth optimization, where µ was considered a response variable. The efficiency of biomass production was determined in a homemade bioreactor, taking into account the optimal conditions obtained during the experiment conducted at the shaken flask stage. In order to evaluate the biological quality of the product obtained in the bioreactor, the bacterial strains were inoculated in common bean (Phaseolus vulgaris var. Jamapa) plants under bioclimatic chamber conditions. The maximum cell growth rate in both PGPB strains was obtained using a Y-Ca2+ (yeast extract and calcium) medium and stirred at 200 and 300 rpm. Under these growth conditions, the Sinorhizobium strains exhibited a high nitrogen-fixing capacity, which had a significant (p < 0.05) impact on the growth of the test plants. The bioreactor system was found to be an efficient alternative for the large-scale production of PGPB rhizobial bacteria, which are intended for use as biofertilizers in agriculture.
Establishing Compliance between Spectral, Colourimetric and Photometric Indicators in Resazurin Reduction Test
Authors: Sychev Alexander V. , Lavrova Anastasia I. , Dogonadze Marine Z. , Postnikov Eugene B.
Published: August 2023

The resazurin reduction test is one of the basic tests for bacterial culture viability and drug resistance endorsed by the World Health Organisation. At the same time, conventional spectrophotometric and spectrofluorimetric methods demand rather bulky and expensive equipment. This induces a challenge for developing simpler approaches to sensor systems that are portable and applicable in resource-limited settings. In this work, we address two such alternative approaches, based on the colour processing of the microbiological plate’s photographic images and single-channel photometry with a recently developed portable microbiological analyser. The key results consist of establishing a sequential linear correspondence between the concentration of resorufin produced due to the reduction of resazurin by viable bacteria as determined by the UV-Vis studies, the intensity of the a* channel of the CIE L*a*b* colour space and the transmitted light intensity registered by a luxmeter under the LED illumination with a yellow colour filter. This route is illustrated with the chemical system “Hydrazine hydrate – resazurin”, isolating the target colour change-inducing reaction and the test of determining the minimal inhibition concentration of the antibacterial first-line drug isoniazid acting on the culture of the H37Rv strain of M. tuberculosis.
Hidden Glutathione Transferases in the Human Genome
Authors: Oakley Aaron J.
Published: August 2023

With the development of accurate protein structure prediction algorithms, artificial intelligence (AI) has emerged as a powerful tool in the field of structural biology. AI-based algorithms have been used to analyze large amounts of protein sequence data including the human proteome, complementing experimental structure data found in resources such as the Protein Data Bank. The EBI AlphaFold Protein Structure Database (for example) contains over 230 million structures. In this study, these data have been analyzed to find all human proteins containing (or predicted to contain) the cytosolic glutathione transferase (cGST) fold. A total of 39 proteins were found, including the alpha-, mu-, pi-, sigma-, zeta- and omega-class GSTs, intracellular chloride channels, metaxins, multisynthetase complex components, elongation factor 1 complex components and others. Three broad themes emerge: cGST domains as enzymes, as chloride ion channels and as protein–protein interaction mediators. As the majority of cGSTs are dimers, the AI-based structure prediction algorithm AlphaFold-multimer was used to predict structures of all pairwise combinations of these cGST domains. Potential homo- and heterodimers are described. Experimental biochemical and structure data is used to highlight the strengths and limitations of AI-predicted structures.
LVPT: Lazy Velocity Pseudotime Inference Method
Authors: Mao Shuainan , Liu Jiajia , Zhao Weiling , Zhou Xiaobo
Published: August 2023

The emergence of RNA velocity has enriched our understanding of the dynamic transcriptional landscape within individual cells. In light of this breakthrough, we embarked on integrating RNA velocity with cellular pseudotime inference, aiming to improve the prediction of cell orders along biological trajectories beyond existing methods. Here, we developed LVPT, a novel method for pseudotime and trajectory inference. LVPT introduces a lazy probability to indicate the probability that the cell stays in the original state and calculates the transition matrix based on RNA velocity to provide the probability and direction of cell differentiation. LVPT shows better and comparable performance of pseudotime inference compared with other existing methods on both simulated datasets with different structures and real datasets. The validation results were consistent with prior knowledge, indicating that LVPT is an accurate and efficient method for pseudotime inference.
AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes
Authors: Gordon Catriona H. , Hendrix Emily , He Yi , Walker Mark C.
Published: August 2023

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a growing class of natural products biosynthesized from a genetically encoded precursor peptide. The enzymes that install the post-translational modifications on these peptides have the potential to be useful catalysts in the production of natural-product-like compounds and can install non-proteogenic amino acids in peptides and proteins. However, engineering these enzymes has been somewhat limited, due in part to limited structural information on enzymes in the same families that nonetheless exhibit different substrate selectivities. Despite AlphaFold2’s superior performance in single-chain protein structure prediction, its multimer version lacks accuracy and requires high-end GPUs, which are not typically available to most research groups. Additionally, the default parameters of AlphaFold2 may not be optimal for predicting complex structures like RiPP biosynthetic enzymes, due to their dynamic binding and substrate-modifying mechanisms. This study assessed the efficacy of the structure prediction program ColabFold (a variant of AlphaFold2) in modeling RiPP biosynthetic enzymes in both monomeric and dimeric forms. After extensive benchmarking, it was found that there were no statistically significant differences in the accuracy of the predicted structures, regardless of the various possible prediction parameters that were examined, and that with the default parameters, ColabFold was able to produce accurate models. We then generated additional structural predictions for select RiPP biosynthetic enzymes from multiple protein families and biosynthetic pathways. Our findings can serve as a reference for future enzyme engineering complemented by AlphaFold-related tools.
Functional Recovery after the Application of Amniotic Tissues and Methylene Blue during Radical Prostatectomy—A Pilot Study
Authors: Barski Dimitri , Tsaur Igor , Boros Mihaly , Brune Jan , Otto Thomas
Published: August 2023

Amniotic tissues and methylene blue (MB) provide the ability for neuroregeneration, and MB enables intraoperative neurostaining. We first combined the techniques to explore a neuroprotective effect on early functional outcomes in a retrospective proof-of-concept trial of 14 patients undergoing radical prostatectomy (RP). The patients were followed up at a median of 13 months, and the continence and potency rates were reported. Early recovery of continence was found after three months. No effect on potency was detected. The findings indicate the feasibility of this tissue-engineering strategy, and justify prospective comparative studies.
Profile Characterization of Biogenic Amines in Glioblastoma Patients Undergoing Standard-of-Care Treatment
Authors: Aboud Orwa , Liu Yin , Dahabiyeh Lina , Abuaisheh Ahmad , Li Fangzhou , Aboubechara John Paul , Riess Jonathan , Bloch Orin , Hodeify Rawad , Tagkopoulos Ilias , Fiehn Oliver
Published: August 2023

Introduction: Biogenic amines play important roles throughout cellular metabolism. This study explores a role of biogenic amines in glioblastoma pathogenesis. Here, we characterize the plasma levels of biogenic amines in glioblastoma patients undergoing standard-of-care treatment. Methods: We examined 138 plasma samples from 36 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma at multiple stages of treatment. Untargeted gas chromatography–time of flight mass spectrometry (GC-TOF MS) was used to measure metabolite levels. Machine learning approaches were then used to develop a predictive tool based on these datasets. Results: Surgery was associated with increased levels of 12 metabolites and decreased levels of 11 metabolites. Chemoradiation was associated with increased levels of three metabolites and decreased levels of three other metabolites. Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). The metabolites sorbitol and N-methylisoleucine were identified as important predictive features and confirmed via SHAP. Conclusion: To our knowledge, this is the first study to describe plasma biogenic amine signatures throughout the treatment of patients with glioblastoma. A larger study is needed to confirm these results with hopes of developing a diagnostic algorithm.
Paving the Way for Memory Enhancement: Development and Examination of a Neurofeedback System Targeting the Medial Temporal Lobe
Authors: Koizumi Koji , Kunii Naoto , Ueda Kazutaka , Nagata Keisuke , Fujitani Shigeta , Shimada Seijiro , Nakao Masayuki
Published: August 2023

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with drug-resistant epilepsy, and we observed an increasing difference in NF signal between conditions (Up–Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF’s potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF’s application to memory and provides valuable insights into developing more efficient memory enhancement strategies.
Understanding Fibroblast Heterogeneity in Form and Function
Authors: Parker Jennifer B. , Valencia Caleb , Akras Deena , DiIorio Sarah E. , Griffin Michelle F. , Longaker Michael T. , Wan Derrick C.
Published: August 2023

Historically believed to be a homogeneous cell type that is often overlooked, fibroblasts are more and more understood to be heterogeneous in nature. Though the mechanisms behind how fibroblasts participate in homeostasis and pathology are just beginning to be understood, these cells are believed to be highly dynamic and play key roles in fibrosis and remodeling. Focusing primarily on fibroblasts within the skin and during wound healing, we describe the field’s current understanding of fibroblast heterogeneity in form and function. From differences due to embryonic origins to anatomical variations, we explore the diverse contributions that fibroblasts have in fibrosis and plasticity. Following this, we describe molecular techniques used in the field to provide deeper insights into subpopulations of fibroblasts and their varied roles in complex processes such as wound healing. Limitations to current work are also discussed, with a focus on future directions that investigators are recommended to take in order to gain a deeper understanding of fibroblast biology and to develop potential targets for translational applications in a clinical setting.
Characterization of the Ang/Tie2 Signaling Pathway in the Diaphragm Muscle of DMD Mice
Authors: Lin Yiming , McClennan Andrew , Hoffman Lisa
Published: August 2023

In Duchenne muscular dystrophy (DMD), angiogenesis appears to be attenuated. Local administration of angiopoietin 1 (Ang1) has been shown to reduce inflammation, ischemia, and fibrosis in DMD mice. Ang1 is a vital vascular stabilizing factor that activates the endothelial cell receptor Tie2, leading to downstream pro-survival PI3K/Akt pathway activation and eNOS phosphorylation. In this study, we aimed to characterize the Ang/Tie2 signaling pathway within the diaphragm muscle of mouse models of DMD. Utilizing ELISA, immunoblots, and RT-qPCR, we demonstrated that Ang1 was downregulated, while the antagonist angiopoietin 2 (Ang2) was upregulated, leading to a decreased Ang1/Ang2 ratio. This correlated with a reduction in the phosphorylated Tie2/total Tie2 ratio. Interestingly, no significant differences in Akt or eNOS phosphorylation were observed, although DMD murine models did have elevated total Akt protein concentrations. These observations suggest that Ang1/Tie2 signaling may be dysregulated in the diaphragm muscle of DMD and further investigations may lead to new therapeutic interventions for DMD.