bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid PV/fuel cell based system using integrated SEPIC-Cuk converter with crow search optimized PI controller
Authors: Sankar Yannam Ravi , Sekhar Koritala Chandra
Published: July 2024

The study investigates a micro grid incorporating photovoltaic (PV), fuel cell and battery systems with an optimized proportional integral (PI) controller. A PV system serves as the primary green energy source, while a fuel cell acts as a supplementary source to compensate for power fluctuations. A single ended primary inductance converter (SEPIC) integrated Cuk converter boosts PV system's DC output voltage, controlled by a crow search-optimized PI (CS-PI) controller. A PI controller controls the fuel cell's minimum DC output voltage, which is raised by a boost converter to produce a controlled output. The proposed converter demonstrates robust performance across varying operational conditions with a reduced settling time of 0.9 s and an efficiency of 96.2%. A bidirectional converter links the battery to the grid, facilitating energy transfer in both directions and accommodating boost and buck operating characteristics thereby maintaining a state-of-charge (SOC) of 60%. This study employs MATLAB to establish effective PV-fuel storage control approaches, optimize energy extraction, and ensure steady power for a grid-connected system.
The prediction of the usual solar irradiation in the Sahel using the artificial neural networks (case study: 50 MW power plant in Nouakchott)
Authors: Mohamed Soukeyna , Elvally Vatma , Mahmoud Abdel Kader , Zoubir Aoulmi
Published: July 2024

The development of a model for predicting meteorological variables using a physical approach was our solution for modeling a solar system, this modeling was carried out in two stages. The first step is to predict the meteorological variable (solar irradiation) at the plant level and the second step is to use a generated energy model to convert these irradiation forecasts into a forecast of the generated energy by the plant. In this study we modeled the solar irradiation curve of the Nouakchott power plant (50 MW) using artificial neural networks (ANN) which create adaptive identification methods and intelligent control laws based on the principal learning, which consists of memorizing previous results and generalizing future results, ultimately modeling the given system. The development of the curve is carried out by carrying out a series of experiments which made it possible to converge towards a methodology offering good precision, using the data measured from solar irradiation over two years at the level of the Nouakchott site. The evaluation of the solar irradiation forecasting model, by calculating the statistical parameters, made it possible to record a normalized average absolute error between 0.121 and 0.126 and a regression factor R (measures the correlation among output-target) with the aid of using 98.4% and 98.5% and the evaluation among specific present techniques in literature display the goodness of the proposed models.
Artificial rabbits optimization based reconfiguration and distributed generation allotment in the distribution network
Authors: Rao Ganney Poorna Chandra , Babu Pallikonda Ravi , Rupesh Mailugundla , Krishna Puvvula Venkata Rama
Published: July 2024

For the past few years, to reduce system power losses and maintain operating constraints, such as voltage stability, network reconfiguration has been crucial in determining the radial operating framework. Distributed generation (DG) is typically used to generate energy close to the site of consumption. This technology generates energy that is affordable, in contrast to conventional energy production. To lessen energy losses as well as boost voltage characteristics, the adopted methodology is centered on reconfiguration and DG distribution in the radial distribution network (RDN). In this work, the loss sensitivity factor (LSF) is used to determine the right DG position in RDN. After identifying the bus for DG positioning, the artificial rabbits optimization (ARO) technique is used to ascertain the ideal reconfigured network and DG size to lessen energy losses and enhance the voltage profile for RDN. The employed methodology is investigated on IEEE-33 and 69 RDN, respectively, for two cases of considering only reconfiguration without distributing units of DG and reconfiguration with an allotment of three DG units. The latter case showed better results compared to the case of only reconfiguration.
Effect of GA based PID controller in bidirectional converter
Authors: Kumar S. Satish , Bharathi K. , Mohan S. B. , Balakrishnan T. Suresh , Kumar J. Anish , Kumar M. Sasi
Published: July 2024

The bi-directional direct current to direct current (DC-DC) converter plays a crucial role in applications involving photovoltaic (PV)-based micro grids. These PV-based hybrid systems have become imperative to ensure uninterrupted power supply and meet local power demands. Selecting the right controller for this converter presents a significant challenge. Traditional proportional integral derivative (PID) controllers yield suboptimal efficiency due to their high delay and settling times. This issue can be overcome by implementing a PID controller based on genetic algorithms (GA). An interconnected grid system is employed to regulate the DC connection voltage across various load conditions, with PV serving as the means to sustain electricity generation. Numerous operational modes tailored to demand are explored, and control techniques are used to maintain the desired DC link voltage level, ultimately enhancing the efficiency of both the PV system and the bidirectional converter in on-grid configurations.
Micropower design of energy harvesting based on piezoelectric transducer array circuit
Authors: Juanda Enjang Akmad , Hakim Nurul Fahmi Arief , Sarosa Moechammad , Saputra Dede Irawan , Azhima Silmi Ath Thahirah Al , Qibtiya Mariya Al
Published: July 2024

This article reveals the conceptualization and implementation of energy harvesting system that utilize piezoelectric arrays within environments marked by elevated ambient noise levels. The selected methodology involves conducting an empirical study where the system is introduced into a room with pronounced ambient noise. A series configuration is adopted for assembling the piezoelectric sensors. For this particular experiment, 36 piezoelectric sensor units were arranged, each equipped with a voltage doubler circuit, aiming to harness a specific micropower energy threshold. The experimental results validate the successful development of an energy harvesting mechanism employing a piezoelectric array within a noisy setting. Notably, the device functions optimally at a frequency of 250 Hz. Additionally, a series of controlled experimental tests were executed at a sound level of 95.8 dBA to assess the efficacy of the piezoelectric array. Measurements taken at the voltage doubler output reveal that the device achieves its peak output signal at 3.32 Vpp and 50.42 Hz. The maximum attainable direct current (DC) voltage stands at 1 Volt, complemented by a current of 0.45 mA.
Shunt active power filter control based on Z-source inverter fed by PV system
Authors: Abdelkarim Ahfouda , Youcef Bekakra , Toumi Djaafar , Ibrahim Ahmed , Zellouma Laid , Messoud Hattiri
Published: July 2024

In this paper, an integration of a Z-source inverter (ZSI)-based shunt active power filter (SAPF) fed by a photovoltaic (PV) system for the enhancement of power quality is proposed. The ZSI provides substantial advantages including improved boost functionality, reduced harmonics, and superior performance compared to traditional inverters. In this paper, the SAPF control is based on instantaneous reactive power theory. A proportional-integral (PI) controller is implemented for DC-link voltage control, with the primary aim of this study being the elimination of total harmonic distortion (THD) in the source current. To demonstrate the effectiveness of the proposed approach, simulations were conducted using MATLAB/Simulink across various operating conditions. The outcomes substantiate and validate the efficacy of the proposed method.
The impact of electric vehicles and photovoltaic energy integration on distribution network
Authors: Talbi Boutaina , Derri Mounir , Haidi Touria , Janyene Aberrahmane
Published: July 2024

The transition towards an eco-friendly and lasting energy system is enabled by the presence of electric vehicles (EVs) and the utilization of renewable energy resources. Despite its intermittent occurrence, solar energy empowers sunny areas to capture renewable energy from sunlight and produce electricity continuously all day long. This energy will be able to smooth the consumption peaks during peak hours and ensure the electrical network flexibility by being injected at various voltage levels. Electric vehicles are supplied by low voltage recharging stations and are seen as power loads that, if rapidly and simultaneously recharged, could potentially affect the stability of the electrical grid. This paper introduces an intelligent method for electric vehicle charging designed to mitigate the impact of simultaneous charging, specifically addressing voltage drop issues. Furthermore, this study demonstrates the positive impact of integrating photovoltaic energy into the distribution network, serving as support, and alleviating the afore cited impact. The simulation results obtained using MATLAB/Simulink illustrate the effectiveness of the proposed strategy in charging electric vehicles, particularly in reducing the observed voltage drop. There is a notable enhancement in voltage drop across all study cases, amounting to a 27 V improvement compared to charging without the proposed method.
Solar-powered seaweed powder milling: enhancing value in the blue economy
Authors: Nugraha I Made Aditya , Desnanjaya I Gusti Made Ngurah , Luthfiani Febi , Idrus Muhamad Amril
Published: July 2024

Design and build a seaweed powder milling machine with the blue economy concept is one form of increasing the added value of selling seaweed in Indonesia and utilizing solar energy which is so abundant in Indonesia by using photovoltaic system. This machine is expected to increase the income of seaweed cultivators, and indirectly support the blue economy policy which is one of the policies in Indonesia. Design and build this machine using several supporting components, such as solar panel, battery control unit (BCU), inverter, battery, and motor to drive the seaweed powder milling machine. Testing is carried out by measuring the voltage and current output, and adding environmental conditions. Apart from that, the economics of the machine are also analyzed. Based on the results obtained, the machine can produce electrical energy in the range of 396.95-646 Wh/day, and can operate for up to 131 minutes, with a seaweed flour output of 10-20 kg per hour. From an economic perspective, the payback period is 0.22 years, NPV of IDR 605,286,359.01, with an IRR of 449%. From this value it can be seen that economically the tool is profitable if used by seaweed cultivators.
Pursuance of a passive filter for a multi-coil EV charger employing a solar connected system to enhance power quality
Authors: Saini Manoj , Gautam Desh Deepak , Saini Sumit , Chaudhary Sunil Kumar , Srivastava Puneet Chandra , Srivastava Kiran , Sharma Amit Kumar
Published: July 2024

This study describes the use of a passive filter to enhance power quality (PQ) of a multi-coil charger for (EVs) using a solar connected system. Wireless charging solutions have been developed as a result of the rising demand for EVs, which use multi-coil chargers to transfer power wirelessly. However, the high-frequency switching used in these chargers can cause power quality issues, such as harmonic distortion, which can affect the performance of the EV and the grid. To reduce PQ incidence effect during charging, a MATLAB model has been created here. The proposed system provides an innovative approach to addressing power quality challenges associated with electric vehicle charging infrastructure. The integration of a passive filter for multi coil wireless power transfer (MCWPT) can significantly improve power quality and promote the use of clean energy in the transportation sector. This study contributes to the development of sustainable solutions for addressing the challenges associated with electric vehicle charging infrastructure.
Passivity-based fuzzy logic approach for optimal power extraction from PMSG-wind energy conversion
Authors: Chaturvedi Pallavi , Palwalia Dheeraj Kumar
Published: July 2024

The preference for permanent magnet synchronous generators (PMSGs) in wind energy conversion systems (WECS) is due to their reliability, compactness, and efficiency. However, designing controllers for PMSG-WECS faces challenges from parameter uncertainties, nonlinearity, and grid integration. To address this, a novel passivity-based nonlinear controller (PBNC) is proposed to precisely track speed and torque. This unique PBNC employs a damped approach to address nonlinearity and integrates a fuzzy logic controller (FLPBNC) for robustness. The chosen strategy shapes energy dynamics using Lyapunov functions. The addition of damping elements ensures Lyapunov stability condition and boosts convergence while keeping the energy functions positive. The system design involves linking passive mechanical and electrical parts in a feedback loop. Meanwhile, for grid integration, a proportional-integral (PI) controller manages DC-link voltage and active power supply to the grid. MATLAB/Simulink simulations confirm the effectiveness of the proposed approach compared to conventional methods.
A nonlinear control in αβ reference frame for single-stage three-phase grid-connected photovoltaic systems with an LCL-filter
Authors: Madani Zakariae El , Yahya Abdelhafid , Malki Zakaria El
Published: July 2024

This paper presents a nonlinear control in the stationary αβ reference frame for single-stage three-phase grid-connected photovoltaic systems. The system under study consists of a photovoltaic generator (PVG), a three-phase inverter, and an LCL-filter on the grid side. The main control objectives are to extract the maximum power from the PVG and deliver that power into the grid with high-quality power i.e. unity power factor (UPF). In order to achieve these objectives, a nonlinear controller is designed by using backstepping technique. The performance of this controller is evaluated under standard and variable atmospheric conditions. Simulation results demonstrate that the proposed controller has successfully met all the specified objectives. This study highlights the potential of using the nonlinear control based on backstepping technique in photovoltaic systems with LCL-filters. The modeling and simulation of the complete system are carried out using MATLAB/Simulink environment.
Effect of water-based cooling on PV performance: case study
Authors: Shaaban Abdullah M. A. , El-Samahy Adel A. , Abed Kamal A. , Mosa Magdi A.
Published: July 2024

Solar energy, especially photovoltaic (PV), is one of the most common renewable energy resources. Panel temperature and dust are the common problems which have a great effect on the conversion performance of PV. These problems can be alleviated by cooling and cleaning in order to improve its efficiency. This paper investigates the effect of PV cooling on the energy harvesting. The study is carried out experimentally using two similar PV panels which are subjected to the same environmental conditions and connected to similar load. The proposed cooling system is applied to one of these panels while the other is left without cooling for comparison. Five cases of water cooling are tested; surface cooling in two ways, back cooling using sprayers with and without cotton net, and hybrid cooling. The effect of cooling can be noticed from the measured load voltage and power. It is found that the surface cooling is the most effective because it achieved the best improvement comparing to others. When the panel temperature decreased from 65 to 42 °C, the load voltage increased from 32.55 to 35.8 V and the load power from 4.57 to 5.37 watts, with an improvement of about 10% and 18%, respectively.
Boosting wind farm productivity: smart turbine placement with cutting-edge AI algorithms
Authors: Abdelouahad Bellat , Abderrahman Mansouri , Mansouri Khalifa
Published: July 2024

Efficient wind farm development necessitates careful planning of wind turbine placement. The primary aim of this optimization process is to strategically position turbines to minimize the wake effect. The ongoing study seeks to standardize wake losses across all turbines in the wind farm through the adoption of a novel diagonal layout. To achieve this objective, an objective function has been devised and employed by a genetic algorithm, aiming to maximize the energy production of the farm while avoiding the concentration of wake on specific turbines. This methodology was applied to the Gasiri wind farm using simulation. The results of the optimization show great promise, indicating a potential energy increase of 17% following the implementation of the optimized layout. Furthermore, the study highlights that the new turbine placements, characterized by higher nominal power, are more favorably aligned forward, in accordance with the wind direction, compared to their original positions. Additionally, a substantial reduction in the mechanical fatigue of the turbine blades was noted.
Efficient and robust nonlinear control MPPT based on artificial neural network for PV system
Authors: Abdouni Khadija , Ennasri Hind , Drighil Asmaa , Bahri Hicham , Bour Mohamed , Benboukous Mostafa
Published: July 2024

The objective of this paper is to optimize the energy generation of a photovoltaic system by proposing an improved maximum power point tracking (MPPT) technique. The proposed method combines an artificial neural network (ANN) with a backstepping controller to enhance the photovoltaic (PV) system’s efficiency and precision in diverse climatic conditions, including solar irradiance and temperature. The ANN is used to predict the optimal voltage at maximum power point (MPP) Vpv, ref, and the backstepping controller is used to control the DC/DC converter based on Vpv, ref. The results obtained using this technique are compared with those obtained from the perturbation and observation (P&O) technique. The proposed technique achieves better results than P&O in terms of efficiency, accuracy, stability, and response time. The simulations are performed on MATLAB/Simulink software.
Machine learning applications for predicting system production in renewable energy
Authors: Somasundaram Deepa , Muthukumar R. , Rajavinu N. , Ramaiyan Kalaivani , Kavitha P.
Published: July 2024

Renewable energy systems play pivotal role in addressing the global challenge of sustainable energy production. Efficiently harnessing energy from renewable sources requires accurate prediction models to optimize system production. This paper delves into the realm of predictive modeling, focusing on the utilization of machine learning techniques to forecast system production in renewable energy systems. The investigation incorporates a range of factors such as wind speed, sunshine, air pressure, radiation, air temperature, and relative air humidity, alongside temporal data ('Date-Hour (NMT)'). These factors undergo rigorous curation and preprocessing to ensure the reliability and quality of the predictive model. Various machine learning algorithms, including linear regression, decision tree, random forest, and support vector machine (SVM), are employed to examine the relationships between these factors and system production. The findings are assessed using metrics such as mean squared error, mean absolute error, and R-squared. Through comparative analysis, the study illuminates the strengths and limitations of each algorithm, providing valuable insights into their suitability for renewable energy forecasting. This paper adds to renewable energy research by examining how machine learning predicts system production. The insights are valuable for researchers, practitioners, and policymakers in sustainable energy development.
Dimensionality reduced deep learning-based state of health estimation of Lithium-Ion batteries using standard dataset
Authors: Srikantappa Vimala Channapatna , Devarakonda Seshachalam
Published: July 2024

Lithium-Ion batteries are used in everyday DC equipment’s, electric vehicle technology, and microgrid technology. The necessity to verify the battery's state is crucial for the dependent apps to continue operating without interruption due to advancements in battery technology & adaption. This study uses dimension decreases in input attributes along with deep learning methods to determine the state of health of lithium-Ion batteries (LIB). principal component analysis (PCA), a deep learning technique, is combined with recurrent neural networks (RNN) to reduce dimensionality. For the purpose of evaluating the effectiveness of the dimensionality reduction used in the data, the state of health (SOH) estimate using the RNN with and without PCA is compared. The use of PCA-powered RNNs using mean square error (MSE) as the loss function throughout the training and testing stages of state-of-health (SOH) estimation showed great performance in terms of loss. This was seen during the training and testing processes' respective testing and validation phases.
Optimal fuzzy controller for speed control of DC drive using salp swarm algorithm
Authors: Somasundaram Deepa , Arumugham Sasikala , Ramalingam Puviarasi , Dhandapani Kirubakaran , Ramaiyan Kalaivani
Published: July 2024

The inherent non-linearity of the system being investigated highlights the limitations of traditional proportional integral or PI tuning approaches. Consequently, the primary objective of this study is to construct and refine the PI controller by leveraging the salp swarm algorithm, aiming to enhance the performance of the DC drive output. Through the application of the salp swarm algorithm, the fuzzy PI controller undergoes dynamic online modifications, leading to optimal results. The controller's superior performance is achieved by employing an optimization approach to identify the optimal set of solutions for the Fuzzy PI parameters. Rigorous simulations are conducted to comprehensively evaluate the proposed salp swarm algorithm technique, assessing its viability and efficacy in real-world. Thorough simulations assess the viability of the salp swarm algorithm, evaluating its effectiveness in real-world applications. The study demonstrates the methodology's reliability through comparative analyses of DC/DC converters against alternative methods. In non-linear systems like the DC drive, innovative optimization strategies are shown to significantly boost PI controller performance. The findings offer valuable insights for advanced control system design.
Enhancing of a wind power system control using intelligent artificial control and multilayer inverter
Authors: Chandad Abdelmoumen , Hamouda Messaoud , Benharir Nedjadi , Bouzidi Mohammed
Published: July 2024

The improvement of overall power quality and optimal control of reactive and active power are issues that have attracted many researchers. The harmonic is considered a main stressful source for energy quality. This paper proposed the ability of artificial intelligence at controlling the active and reactive power and to reduce torque ripple and current harmonics thus improve energy quality and the stability of system, that by using the artificial intelligence controller and a neural network based space vector modulation with two levels inverter (NSVM-2L). These inverter hold the offer improved efficiency and have on account of their capability of high voltage operation compared with traditional inverters as reducing the harmonic. These results showed that the fuzzy logic controller's dynamic performance is very superior to that of the PID controller of DFIG. The fuzzy controller works well for helping us to minimize the rate of harmonic distortion of absorbed currents and for correctly adjusting active and reactive power and its stability of wind turbine compared to PID controller.
Revolutionizing motor maintenance: a comprehensive survey of state-of-the-art fault detection in three-phase induction motors
Authors: Bahgat Bahgat Hafez , Elhay Enas A. , Sutikno Tole , Elkholy Mahmoud M.
Published: July 2024

This comprehensive review delves into electrical machine fault diagnosis techniques, with a particular emphasis on three-phase induction motors. It covers a variety of faults, including eccentricity, broken rotor bars, and bearing faults. It also covers techniques like motor current signature analysis (MCSA), partial discharge testing, and artificial intelligence (AI)-based approaches. This review focuses on fault diagnosis techniques for electrical machines, specifically eccentricity faults, squirrel cage rotor faults, and bearing faults. It discusses their efficacy, applications, and limitations, as well as the role of AI and neural network techniques in modern fault detection applications. The review covers not only eccentricity faults, but also stator or armature faults caused by insulation failure, as well as bearing faults classified as ball, train, outer, and inner races. It focuses on early detection to ensure optimal machine performance and reliability, while also providing insights into fault detection mechanisms. Modern ways of finding problems with machines, like non-negative matrix factorization, rectified stator current analysis, incremental broad learning, and AI-based methods, make machines work better and stop money from being lost. The review is a valuable resource for practitioners and researchers in the field, allowing them to make better decisions about maintenance strategies and increase machine efficiency.
THD analysis and its mitigation using DSTATCOM integrated with EV charging station in the distribution network
Authors: Dhami Kavita , Saggu Tejinder Singh
Published: July 2024

With the increase in carbon emissions, noise pollution and other environmental impacts caused by conventional vehicles, the demand for electric vehicles (EVs) is continuously increasing in the market. The transport sector has also been revolutionized with the use of EVs. The unique features such as reduction in noise pollution, carbon emissions and running costs and the capability of EVs to work in both grid-vehicle (G2V) and vehicle-grid (V2G) have made EVs popular nowadays. Still, it has several effects on the power distribution grid. There are several power issues due to the incorporation of electric vehicles (EVs) in the distribution network such as voltage instability, harmonics, and voltage fluctuations. This research paper focuses mainly on the harmonics caused in the system when EVs are connected to the distribution side. A distributed static compensator (DSTATCOM) based on the d-q theory is introduced to mitigate the harmonics along with the improvement in the voltage profile of the distribution side. By using MATLAB Simulink, the performance of DSTATCOM is validated and the comparison of the proposed approach is also done with that of similar work already existing in the literature.
Electric vehicle charging station components and current scenario
Authors: Wahsh Said , Mariah Ibrahim , Nashed Maged N. F.
Published: July 2024

Since the range of an electric vehicle (EV) is important, vehicles with the longest range are preferred. As a result, a survey is conducted on the longest-range vehicles commercially available to estimate the charging power required. EV range has recently increased significantly and can now be charged at home, adding to the benefits of EV. The paper will present the best three EV models with the longest range. So, the specifications of the most popular EV commercially available had been analyzed. Nonetheless, charging stations are still required, which is a critical issue. This paper discusses various approaches to EV charging stations that rely primarily on AC or DC power supply. The previous year’s accomplishments will be highlighted. The three EV charging station levels will be thoroughly addressed. It is primarily classified based on voltage, power and types. In this type of charging EV must equipped with rectifier to change AC-DC to charge batteries. The impact of rapid advancements in power electronics technology has been discussed as AC-DC converter, as its advancement will determine the future of EV charging stations. Renewable energy sources (hydropower, photovoltaic, and wind) are now essential as energy source. Recommendations for increasing EV sales will be made.
Suppletion in Global Perspective
Authors: Brodkin Dan
Published: July 2024

This paper documents and analyzes a system of suppletive alternations that are conditioned by top-down prosodic context. In Mandar (Austronesian), seven heads supplete at the right edge of the phonological phrase to satisfy an output constraint on foot structure. When phrase-external phonological context makes it possible to resolve this output constraint in a more optimal way, this suppletion is suspended. These effects suggest that the mechanism which regulates suppletion, vocabulary insertion, must be situated within a phonological calculus that can access global context and respond to output constraints.
Systematic position of Rivina humilis var. humilis, R. humilis var. bracteata and R. bengalensis based on nrDNA ITS and cpDNA rbcL & trnH-psbA sequence data
Authors: BANERJEE ARNAB , DWIVEDI MAYANK D. , MAITY DEBABRATA , PANDEY ARUN K.
Published: July 2024

The ITS and cpDNA sequences were obtained for three taxa of the family Phytolaccaceae, viz. Rivina bengalensis S.C.Srivast. & T.K.Paul, R. humilis L. var. humilis, and R. humilis var. bracteata D.Maity, Sonia Mitra, Manasi Mandal & Maiti. The MLtree strongly supports the Rivina humilis clade, which includes R. bengalensis and R. humilis var. bracteata. The synonymization of R. bengalensis is supported by both molecular and morphological data. The accessions of Rivina humilis var. bracteata group together in the R. humilis clade, but the results also show inter-population variation, which primarily supports the varietal status of the taxon. The broadly oblanceolate bracts which is abruptly narrowed from middle or above middle into a long subulate to narrowly triangular, acuminate apical lobe make the variety distinct from typical R. humilis. The combined rbcL and trnH-psbA dataset reveal congruent result for the sequenced Rivina spp. Present study is the first report on molecular systematics of R. humilis from India.
Appendopyricularia guttulata sp. nov., a lignicolous freshwater fungus from Dianchi Lake in Yunnan, China
Authors: WANG YING , BAO DAN-FENG , SHEN HONG-WEI , YANG LIN , LUO ZONG-LONG
Published: July 2024

During an investigation into species diversity of lignicolous freshwater fungi from the plateau lakes in Yunnan Province, China, a hyphomycetous fungus was collected from Dianchi Lake. The new isolate was placed in Appendopyricularia as a sister taxon of Appendopyricularia juncicola within Barbatosphaeriaceae based on the phylogenetic analysis of combined ITS and LSU sequence data. The fungus is characterized by acrogenous, fusoid, guttulate conidia that are 3–4 septate and have a flexuous central apical appendage. It exhibits more pronounced guttulate appendages and more prominent denticle sporulation structure. Therefore, Appendopyricularia guttulata is introduced as a new species based on morphological characteristics and phylogenetic evidence. The detailed description and illustrations of the new species are provided, as well as the morphological comparison with similar taxa.
A new species of Porophyllum (Asteraceae: Tageteae) from the northwest of Argentina, and the first record of female plants of Porophyllum cabrerae
Authors: ANZUINELLI MILAGROS , SCHIAVINATO DARIO J. , BARTOLI ADRIANA
Published: July 2024

Porophyllum riparium, a new species of the genus Porophyllum, is described. Morphological details, illustration, geographical distribution, and photographs in its natural habitat are provided. This new species occurs in the northwest region of Argentina, in Salta, Jujuy and Tucumán Provinces, in the Yungas biogeographical province. It is usually found on rocky and sandy soils, as well as along riverbanks. Porophyllum riparium is characterized by being an annual or short-lived perennial, glaucous, odoriferous, monoecious herb, 15–80(–130) cm tall, with sessile or attenuate into a pseudopetiole, linear-elliptic leaves with submarginal and subapical semi-pellucid oil cavities, lemon-colored infundibuliform corollas and exserted brownish anthers. Additionally, female plants of Porophyllum cabrerae were documented for the first time, photographs and descriptions of their female florets are included. Also, a key to identify northwestern species of Porophyllum is provided.
Biophysical characterization of the phase separation of TDP-43 devoid of the C-terminal domain
Authors: Staderini Tommaso , Bigi Alessandra , Lagrève Clément , Marzi Isabella , Bemporad Francesco , Chiti Fabrizio
Published: July 2024

Background

Frontotemporal lobar degeneration with ubiquitin-positive inclusions (FTLD-TDP), amyotrophic lateral sclerosis (ALS) and limbic-predominant age-related TDP-43 encephalopathy (LATE) are associated with deposition of cytoplasmic inclusions of TAR DNA-binding protein 43 (TDP-43) in neurons. One complexity of this process lies in the ability of TDP-43 to form liquid-phase membraneless organelles in cells. Previous work has shown that the recombinant, purified, prion-like domain (PrLD) forms liquid droplets in vitro, but the behaviour of the complementary fragment is uncertain.

Methods

We have purified such a construct without the PrLD (PrLD-less TDP-43) and have induced its phase separation using a solution-jump method and an array of biophysical techniques to study the morphology, state of matter and structure of the TDP-43 assemblies.

Results

The fluorescent TMR-labelled protein construct, imaged using confocal fluorescence, formed rapidly (< 1 min) round, homogeneous and 0.5–1.0 µm wide assemblies which then coalesced into larger, yet round, species. When labelled with AlexaFluor488, they initially exhibited fluorescence recovery after photobleaching (FRAP), showing a liquid behaviour distinct from full-length TDP-43 and similar to PrLD. The protein molecules did not undergo major structural changes, as determined with circular dichroism and intrinsic fluorescence spectroscopies. This process had a pH and salt dependence distinct from those of full-length TDP-43 and its PrLD, which can be rationalized on the grounds of electrostatic forces.

Conclusions

Similarly to PrLD, PrLD-less TDP-43 forms liquid droplets in vitro through liquid–liquid phase separation (LLPS), unlike the full-length protein that rather undergoes liquid–solid phase separation (LSPS). These results offer a rationale of the complex electrostatic forces governing phase separation of full-length TDP-43 and its fragments. On the one hand, PrLD-less TDP-43 has a low pI and oppositively charged domains, and LLPS is inhibited by salts, which attenuate inter-domain electrostatic attractions. On the other hand, PrLD is positively charged due to a high isoionic point (pI) and LLPS is therefore promoted by salts and pH increases as they both reduce electrostatic repulsions. By contrast, full-length TDP-43 undergoes LSPS most favourably at its pI, with positive and negative salt dependences at lower and higher pH, respectively, depending on whether repulsive or attractive forces dominate, respectively.
Enhancing SNV identification in whole-genome sequencing data through the incorporation of known genetic variants into the minimap2 index
Authors: Guguchkin Egor , Kasianov Artem , Belenikin Maksim , Zobkova Gaukhar , Kosova Ekaterina , Makeev Vsevolod , Karpulevich Evgeny
Published: July 2024

Motivation

Alignment of reads to a reference genome sequence is one of the key steps in the analysis of human whole-genome sequencing data obtained through Next-generation sequencing (NGS) technologies. The quality of the subsequent steps of the analysis, such as the results of clinical interpretation of genetic variants or the results of a genome-wide association study, depends on the correct identification of the position of the read as a result of its alignment. The amount of human NGS whole-genome sequencing data is constantly growing. There are a number of human genome sequencing projects worldwide that have resulted in the creation of large-scale databases of genetic variants of sequenced human genomes. Such information about known genetic variants can be used to improve the quality of alignment at the read alignment stage when analysing sequencing data obtained for a new individual, for example, by creating a genomic graph. While existing methods for aligning reads to a linear reference genome have high alignment speed, methods for aligning reads to a genomic graph have greater accuracy in variable regions of the genome. The development of a read alignment method that takes into account known genetic variants in the linear reference sequence index allows combining the advantages of both sets of methods.

Results

In this paper, we present the minimap2_index_modifier tool, which enables the construction of a modified index of a reference genome using known single nucleotide variants and insertions/deletions (indels) specific to a given human population. The use of the modified minimap2 index improves variant calling quality without modifying the bioinformatics pipeline and without significant additional computational overhead. Using the PrecisionFDA Truth Challenge V2 benchmark data (for HG002 short-read data aligned to the GRCh38 linear reference (GCA_000001405.15) with parameters k = 27 and w = 14) it was demonstrated that the number of false negative genetic variants decreased by more than 9500, and the number of false positives decreased by more than 7000 when modifying the index with genetic variants from the Human Pangenome Reference Consortium.
Biochemical characterisation and production kinetics of high molecular-weight (HMW) putative antibacterial proteins of insect pathogenic Brevibacillus laterosporus isolates
Authors: Babar Tauseef K. , Glare Travis R. , Hampton John G. , Hurst Mark R. H. , Narciso Josefina
Published: July 2024

Background

Bacterial genomes often encode structures similar to phage capsids (encapsulins) and phage tails which can be induced spontaneously or using genotoxic compounds such as mitomycin C. These high molecular-weight (HMW) putative antibacterial proteins (ABPs) are used against the competitive strains under natural environment. Previously, it was unknown whether these HMW putative ABPs originating from the insect pathogenic Gram-positive, spore-forming bacterium Brevibacillus laterosporus (Bl) isolates (1821L, 1951) are spontaneously induced during the growth and pose a detrimental effect on their own survival. Furthermore, no prior work has been undertaken to determine their biochemical characteristics.

Results

Using a soft agar overlay method with polyethylene glycol precipitation, a narrow spectrum of bioactivity was found from the precipitated lysate of Bl 1951. Electron micrographs of mitomycin C- induced filtrates showed structures similar to phage capsids and contractile tails. Bioactivity assays of cell free supernatants (CFS) extracted during the growth of Bl 1821L and Bl 1951 suggested spontaneous induction of these HMW putative ABPs with an autocidal activity. Sodium dodecyl sulphate-polyacrylamide gel electrophoresis of spontaneously induced putative ABPs showed appearance of ~ 30 kDa and ~ 48 kDa bands of varying intensity across all the time intervals during the bacterial growth except in the initial hours. Statistically, spontaneously induced HMW putative ABPs of Bl 1951 exhibited a significant decrease in the number of viable cells of its producer strain after 18 h of growth in liquid. In addition, a significant change in pH and prominent bioactivity of the CFS of this particular time period was noted. Biochemically, the filtered supernatant derived from either Bl 1821L or Bl 1951 maintained bioactivity over a wide range of pH and temperature.

Conclusion

This study reports the spontaneous induction of HMW putative ABPs (bacteriocins) of Bl 1821L and Bl 1951 isolates during the course of growth with potential autocidal activity which is critically important during production as a potential biopesticide. A narrow spectrum of putative antibacterial activity of Bl 1951 precipitate was found. The stability of HMW putative ABPs of Bl 1821L and Bl 1951 over a wide range of pH and temperature can be useful in expanding the potential of this useful bacterium beyond the insecticidal value.
Identification of volatile metabolites produced from levodopa metabolism by different bacteria strains of the gut microbiome
Authors: Pennington Taylor , Eshima Jarrett , Smith Barbara S.
Published: July 2024

Interspecies pathways in the gut microbiome have been shown to metabolize levodopa, the primary treatment for Parkinson’s disease, and reduce its bioavailability. While the enzymatic reactions have been identified, the ability to establish the resulting macromolecules as biomarkers of microbial metabolism remains technically challenging. In this study, we leveraged an untargeted mass spectrometry-based approach to investigate volatile organic compounds (VOCs) produced during levodopa metabolism by Enterococcus faecalis, Clostridium sporogenes, and Eggerthella lenta. We cultured these organisms with and without their respective bioactive metabolites and detected levodopa-induced shifts in VOC profiles. We then utilized bioinformatics to identify significant differences in 2,6-dimethylpyrazine, 4,6-dimethylpyrimidine, and 4,5-dimethylpyrimidine associated with its biotransformation. Supplementing cultures with inhibitors of levodopa-metabolizing enzymes revealed specific modulation of levodopa-associated diazines, verifying their relationship to its metabolism. Furthermore, functional group analysis depicts strain-specific VOC profiles that reflect interspecies differences in metabolic activity that can be leveraged to assess microbiome functionality in individual patients. Collectively, this work identifies previously uncharacterized metabolites of microbe-mediated levodopa metabolism to determine potential indicators of this activity and further elucidate the metabolic capabilities of different gut bacteria.
Uric acid to albumin ratio as a novel predictor for coronary slow flow phenomenon in patients with chronic coronary syndrome and non-obstructive coronary arteries
Authors: Zhang Xiao-jiao , Hou Ai-jie , Luan Bo , Wang Cheng-fu , Li Jia-jin
Published: July 2024

Background

The plasma uric acid to albumin ratio (UAR) is considered as a novel indicator for Inflammation. However, the association between UAR and coronary slow flow phenomenon (CSFP) remains unclear.

Methods

A total of 1328 individuals with chronic coronary syndrome (CCS) receiving coronary angiography (CAG) and found no obvious obstructive stenosis (< 40%) were included in this study. 79 individuals developed CSFP and were divided into CSFP group. The 1:2 age-matched patients with normal coronary blood flow were allocated to the control group (n = 158). The clinical characteristics, laboratory parameters including uric acid, albumin ratio, UAR and the angiographic characteristics were compared between the two groups.

Results

Patients with CSFP had a higher level of uric acid (392.3 ± 85.3 vs. 273.8 ± 71.5, P < 0.001), UAR (10.7 ± 2.2 vs. 7.2 ± 1.9, P < 0.001), but a lower level of plasma albumin (36.9 ± 4.2 vs. 38.5 ± 3.6, P = 0.003). Moreover, UAR increased as the numbers of vessels involved in CSFP increased. The logistic regression analysis demonstrated that UAR was independent predictors for CSFP. The Receiver operating characteristic (ROC) curve analysis showed that when UAR was more than 7.9, the AUC was 0.883 (95% CI: 0.840–0.927, p < 0.001), with the sensitivity and specificity were 78.2% and 88.2% respectively.

Conclusion

Combined uric acid with plasma albumin, UAR could serve as an independent predictor for CSFP.
Carbon-ion radiotherapy alone vs. standard dose photon radiation with carbon-ion radiotherapy boost for high-grade gliomas: a retrospective study
Authors: Li XiaoJun , Zhang YanShan , Ye YanCheng , Tian SuQing , Hu TingChao , Chai HongYu , Zhang TianE , Wen Faxin
Published: July 2024

Background

This study aimed to compare the survival outcome and side effects in patients with primary high-grade glioma (HGG) who received carbon ion radiotherapy (CIRT) alone or as a boost strategy after photon radiation (photon + CIRTboost).

Patients and methods

Thirty-four (34) patients with histologically confirmed HGG and received CIRT alone or Photon + CIRTboost, with concurrent temozolomide between 2020.03–2023.08 in Wuwei Cancer Hospital & Institute, China were retrospectively reviewed. Overall survival (OS), progression-free survival (PFS), and acute and late toxicities were analyzed and compared.

Results

Eight WHO grade 3 and 26 grade 4 patients were included in the analysis. The median PFS in the CIRT alone and Photon + CIRTboost groups were 15 and 19 months respectively for all HGG cases, and 15 and 17.5 months respectively for grade 4 cases. The median OS in the CIRT alone and Photon + CIRTboost groups were 28 and 31 months respectively for all HGG cases, and 21 and 19 months respectively for grade 4 cases. No significant difference in these survival outcomes was observed between the CIRT alone and Photon + CIRTboost groups. Only grade 1 acute toxicities were observed in CIRT alone and Photon + CIRTboost groups. CIRT alone group had a significantly lower ratio of acute toxicities compared to Photon + CIRTboost (3/18 vs. 9/16, p = 0.03). No significant difference in late toxicities was observed.

Conclusion

Both CIRT alone and Photon + CIRTboost with concurrent temozolomide are safe, without significant differences in PFS and OS in HGG patients. It is meaningful to explore whether dose escalation of CIRTboost might improve survival outcomes of HGG patients in future randomized trials.
Assessing risk for severe domestic violence and related homicides perpetrated by partners and in-laws: adapted danger assessments for women in abusive relationships in India
Authors: Sabri Bushra , Campbell Jacquelyn C. , Khan Naseem Ahmad , Tahir Mohammad , Khan Mohd Arif , Khan Mohd Naseem
Published: July 2024

Despite domestic violence and related homicides perpetrated by partners and/or in-laws being a significant public health problem in India, there are no reliable and valid instruments to identify and intervene with women in domestic violence relationships. Continued domestic violence can escalate to severe, near-lethal, or lethal violence or homicide. The Danger Assessment (DA) is a risk assessment instrument designed to assess the likelihood of severe, near-lethal, or lethal violence in abusive relationships. However, the DA is not designed to determine the risk of future severe, near-lethal, or lethal violence by in-laws. In-law abuse plays a significant role in domestic violence-related homicides in India and other countries with similar cultural norms. This study addressed this gap by developing the Danger Assessment for in-laws (DA-L) to assess risk from in-laws, alongside the Danger Assessment for Women in India (DA-WI) to assess risk from partners. The study also examined the psychometric properties of the DA-L and DA-WI. Longitudinal data from 150 women in India were used to measure the reliability and validity of the two versions of the DA. The original DA items and additional risk items were examined using relative risk ratios for their relationship with severe violence at three-month follow-ups. Predictive validity was tested with the receiver operating characteristic curve. The study resulted in reliable and valid measures (11 items DA-L and 26-items DA-WI) of risk. The versions of the DA can be useful for practitioners in India and those working with Indian women in the US and other countries. The DAs can be used for identifying women in domestic violence relationships who are at risk for future severe domestic violence and guide the provision of tailored safety plans.