Specific Bands Research Articles

DNA markers for early detection of somaclonal variation in Berangan banana

Banana is Malaysia’s second most cultivated fruit, with 50% of the cultivation focusing on Berangan and Cavendish types. Bananas have been identified as one of the important fruits in the National Agrofood Policy (2011-2020) due to their demand in the domestic and export markets. Thus, FGV is committed to achieving a 50% market share of local banana production through tissue culture propagation techniques. This technique enables the mass production of banana planting material in a short period. However, one downside of this technique is the random occurrence of somaclonal variation, which has been reported for various crops. FGV’s clonal propagation aims to produce true-to-type banana planting materials, therefore, somaclonal variants are undesired. Hence, we aimed to establish a marker-based screening method for early detection and elimination of Berangan somaclonal variants prior to field planting. Berangan leaf samples with and without somaclonal variation were collected from different sites. The samples’ DNA was extracted using an in-house cetyltrimethylammonium bromide (CTAB) extraction method and screened using 145 Random Amplified Polymorphic DNA (RAPD) markers. RAPD markers that could distinguish somaclonal variation in the Berangan samples were developed into Sequence Characterized Amplified Region (SCAR) markers for routine screening. The SCAR markers were validated for their ability to reproduce the specific bands from the RAPD results. Of the 145 RAPD markers, three markers do not produce any bands, 47 RAPD markers produce monomorphic bands and 95 RAPD markers produce polymorphic bands. Three out of the 95 polymorphic RAPD markers were able to selectively distinguish the somaclonal variation in samples from similar sources. The three RAPD markers were developed into SCAR markers, and they successfully reproduced the specific bands from their respective RAPD results. Hence, the developed SCAR markers will be utilized for the early detection and elimination of Berangan somaclonal variants from selected sources.

The colours of the ocean using multispectral satellite imagery to estimate sea surface temperature and salinity in global coastal areas, the gulf of Mexico and the UK

Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.

TRMD: a transformer-based reverse design model for quad-band metasurface absorbers

Abstract Metasurfaces have the ability to manipulate electromagnetic waves, which allows for the creation of functions such as perfect absorbers. The goal of a perfect absorber is to achieve high absorption peaks within a specific frequency band. This paper introduces an improved metasurface absorber structure that can achieve efficient absorption in four different frequency bands within the range of 2-9 GHz. In the field of metasurface design, deep learning methods have been recently applied due to their powerful data processing capabilities. However, these methods have primarily used fully connected neural networks and Long Short-Term Memory (LSTM). Despite their capabilities, fully connected networks and LSTM struggle to capture the global information in absorption spectrum data, leading to less accurate predictions. In this study, it was observed that the Transformer model can effectively capture global information using Multi-Head Self-Attention (MHSA) and is not affected by the length of the data. Based on this observation, this paper presents a lightweight model consisting solely of an encoder, achieving a Mean Squared Error (MSE) that is one-twentieth of the State-of-the-Art (SOTA). This model predicts metasurface structure based on target absorption spectra, enabling users to rapidly obtain metasurface absorber structures directly from input absorption spectra. The model consists of two parts: embedding and encoder. The embedding processes input absorption spectra data and adds positional encoding, while the encoder extracts spectral data features. MHSA effectively captures contextual information of absorption spectra, emphasizing key feature information. The final model achieved a MSE convergence of 2 × 10−4 and a coefficient of determination (R 2)value of 0.998, successfully optimizing the design of multi-band metasurface absorbers. Moreover, the predicted results from the model exhibit an absorption spectrum that is highly consistent with the target spectrum.

A CNN-based Fault Diagnosis Method of Multi-function Integrated RF System using Frequency Domain Scanning with Lasso Regression

Multi-function Integrated RF System (MIRFS) is a key unit for improving the reliability and safety of advanced aircraft. Single-frequency point data are easily affected by various interferences and noises resulting in unreliable and insufficient information. To reduce the low fault diagnosis rate caused by this effect, a novel fault diagnosis method is proposed based on frequency domain scanning and Lasso regression in this paper. Firstly, performance parameter sequences under each fault condition are extracted using frequency domain scanning. By extracting the performance parameters of a specific frequency band as raw fault data, the reliability of the data is greatly increased. Secondly, Lasso regression prioritizes the performance parameter sequences according to their correlation with faults. This approach selects the optimal performance parameter sequences as the data source for fault diagnosis, resolving the issue of excessively high dimensions in fault data. Finally, convolutional neural networks (CNNs) achieve precise fault diagnosis for both soft and hard faults. The results confirm that the proposed method overcomes the low distinguishability of fault states for both single frequency point data and sequence feature data. Compared with diagnostic models using sequence feature data as datasets, the diagnostic accuracy for hard faults increased by 7.92%, and for soft faults by 5.34%.

Optomagnetic Micromirror Arrays for Mapping Large Area Stiffness Distributions of Biomimetic Materials.

A new device termed "Optomagnetic Micromirror Arrays" (OMA) is demonstrated capable of mapping the stiffness distribution of biomimetic materials across a 5.1 mm × 7.2 mm field of view with cellular resolution. The OMA device comprises an array of 50000 magnetic micromirrors with optical grating structures embedded beneath an elastic PDMS film, with biomimetic materials affixed on top. Illumination of a broadband white light beam onto these micromirrors results in the reflection of microscale rainbow light rays on each micromirror. When a magnetic field is applied, it causes each micromirror to tilt differently depending on the local stiffness of the biomimetic materials. Through imaging these micromirrors with low N.A. optics, a specific narrow band of reflection light rays from each micromirror is captured. Changing a micromirror's tilt angle also alters the color spectrum it reflects back to the imaging system and the color of the micromirror image it represents. As a result, OMA can infer the local stiffness of the biomimetic materials through the color change detected on each micromirror. OMA offers the potential for high-throughput stiffness mapping at the tissue-level while maintaining spatial resolution at the cellular level.

Proof of Concept: Autonomous Machine Vision Software for Botanical Identification.

HPTLC is a widely used and accepted technique for identification of botanicals. Current best practices involve subjective comparison of HPTLC-generated images between test samples and certified botanical reference materials based on specific bands. This research was designed to evaluate the potential of cutting-edge machine vision-based machine learning techniques to automate identification of botanicals using native HPTLC image data. HPTLC images from Ginger and its closely related species and common adulterants were used to create large, synthetic datasets using a deep conditional generative adversarial network. This synthetic dataset was used to train and validate a deep convolutional neural network capable of automatically identifying new HPTLC image data. Performance of both neural networks was evaluated over time using appropriate loss functions as an indicator of their progress during learning. Validation of the overall system was measured via the accuracy of the learned model when applied to real HPTLC data. The machine vision system was able to generate realistic synthetic HPTLC images that were successfully used to train a deep convolutional neural network. The resulting learned model achieved high-accuracy identification from HPTLC images corresponding to Ginger and six other related species. A proof-of-concept HPTLC image-based machine vision system for the identification of botanicals was proven to be feasible and a fully working prototype was validated for several species related to Ginger. This use of an autonomous machine-vision system for botanical identification removed the subjectivity inherent to human-based evaluation. The learned model also accurately evaluated botanical HPTLC images significantly faster than its human counterpart, which could save both time and resources.

Research on the Technology of a Compact Double-Layer Multispectral Filter-Wheel Mechanism Driven by a Single Motor

Spatial multispectral imaging technology can selectively image in specific spectral bands, and the filter wheel is a core component for multispectral selection. At present, there are relatively few types of spectral bands for the filter wheel under limited space/weight constraints. Addressing the challenges presented by this issue, this paper introduces an innovative design approach for the development of a double-layer or even multi-layer filter wheel that is operated by a solitary motor in conjunction with a differential gear mechanism, enabling a vast array of spectral segment combinations within a highly compact layout. A detailed design is implemented for the double-layer filter wheel, including comprehensive modal and dynamic analyses. The results of the modal analysis attested to the structural stability of the component, and the outcomes of the dynamic analysis validated the component’s timely and reliable switching capabilities. A prototype was meticulously crafted and subjected to rigorous testing. The switching functionality was validated during these tests, concurrently affirming the accuracy of the finite element analysis results. Additionally, spectral and application testing confirmed the number of spectral segments and the practical utility of the components. The research presented in this article introduces an innovative design concept for multispectral imaging filter-wheel mechanisms, providing a valuable reference and profound insights for the design and arrangement of a double-layer or even multi-layer filter wheel.

Seismic metamaterial design prediction based on joint neural network

SMs (SMs), artificial periodic composites utilized to mitigate seismic hazards by attenuating seismic waves within specific frequency bands, have garnered significant research interest in recent years. To expedite the determination of optimal structures within a limited design space, the joint neural network (JNN) incorporating a Depth Feedforward Network (DFN) and an Undercomplete Autoencoder (UAE) in series was devised. A dual-component SMs dataset was generated using curve functions for material parameter analysis. The UAE was trained to discern crucial features of SMs configurations. Dispersion curves for the sample dataset were computed using finite element method (FEM). Employing interval merging algorithm and normalized frequency labeling, data underwent dimensionality reduction and feature extraction, revealing that the pre-trained JNN exhibited an error less than 0.2 % compared to FEM, with a design time of merely 40 s. Adhering to the principles of optimal bandgaps and periodic symmetry, SMs were designed, combined, and subjected to frequency domain analysis, achieving an ultra-wide bandgap of 4.9–20 Hz. Inputting Helena Montana-02 and Chi-Chi seismic waveforms demonstrated reductions in peak seismic amplitudes by 52.6 % and 72.2 % respectively, validating the efficacy of the design model.

Circularly Polarized Variations of Truncated Corner Square Microstrip Antennas Employing a U-slot

Circularly polarized designs of square microstrip antennas employing U-slot are presented in a 2400 MHz frequency spectrum. Design with a U-slot employing unequal lengths or the corner truncated patch with equal length U-slot yields axial ratio bandwidths of 3.84% and 2.2%, respectively. Against the conventional offset feed patches or the truncated corner square patches, the proposed designs decrease the frequency and patch size by 10% and 22%, respectively. Reduction in the frequency and patch area, while employing the U-slot is not being discussed in the reported papers on the circularly polarized designs, and hence it is the new contribution in the present study. The U-slot cut configurations exhibit a radiation pattern in the broadside with a gain of larger than 6 dBi across the complete reflection coefficient bandwidth. To obtain a wideband and circular polarized response using a single patch, a reconfigurable design of a corner truncated square patch employed with a U-slot is presented. A design methodology is presented based on the resonant length formulation to obtain reconfigurable U-slot cut design as per specific band start frequency in wideband response or center frequency in the axial ratio bandwidth in the circular polarized antenna. This helps to design these configurations for the specific frequency band. Simulated results in the proposed design are verified using the measurements that reflect a close agreement.

Stochastic Modeling of a Base Station in 5G Wireless Networks for Energy Efficiency

ABSTRACTThe potential benefits of 5G networks, such as faster data speeds and improved user experiences, come with a critical challenge—efficiently preserving energy in base stations (BSs). Addressing this challenge is crucial, necessitating a focus on maximizing the energy efficiency of these stations. BSs play a vital role in providing coverage and capacity by using different frequency bands adapting to diverse communication needs. The choice of a specific frequency band can impact network performance, data rates, coverage range, and signal propagation. This study emphasizes the crucial challenge of preserving energy in 5G BSs and underscores the significance of strategic frequency band selection for optimizing energy efficiency and network performance. For this, stochastic models are introduced for BSs, specifically the Markov and semi‐Markov models, which help choose frequency bands based on system traffic. Closed‐form solutions for steady‐state system size probabilities in these models are derived. A sensitivity analysis is conducted to assess power consumption in various scenarios. Furthermore, a comparison between analytical findings and simulation outcomes regarding power consumption is presented. The convergence of results in both methodologies emphasizes a significant level of agreement; that is, the introduced stochastic models provide insights and are validated by a close alignment between analytical and discrete‐event simulation outcomes. We have shown the behavior of power consumption with respect to three different distributions named deterministic, exponential, and hypo‐exponential. This research highlights the importance of strategic frequency band selection for 5G BSs to optimize energy efficiency and meet the demands of evolving communication networks.

Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues

Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost.

Enhanced Wavelet Scattering Network for Image Inpainting Detection

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization, and lastly by employing an innovative combination of texture segmentation with noise variance estimations. The DT-CWT offers significant advantages due to its shift-invariance, enhancing its robustness against subtle manipulations during the inpainting process. Furthermore, its directional selectivity allows for the detection of subtle artifacts introduced by inpainting within specific frequency bands and orientations. Various neural network architectures were evaluated and proposed. Lastly, we propose a fusion detection module that combines texture analysis with noise variance estimation to give the forged area. Also, to address the limitations of existing inpainting datasets, particularly their lack of clear separation between inpainted regions and removed objects—which can inadvertently favor detection—we introduced a new dataset named the Real Inpainting Detection Dataset. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives.

3D-micro-flower like [formula omitted] nanoplate solid solution: A facile and rapid room-temperature synthesis with excellent photocatalytic decomposition of antibiotics in water

This research addresses the environmental threat posed by residual antibiotics in water and explores the potential of the photocatalytic process as an eco-friendly solution for antibiotic wastewater treatment. Here in, a series of BiOBr1−xClx nanoplate solid solutions with varied Br:Cl molar ratios were synthesized through a simple co-precipitation method. These solid solutions exhibited superior visible-light-driven photocatalytic activity compared to pristine BiOCl and BiOBr. Notably, the BiOBr0.25Cl0.75 sample demonstrated exceptional performance, achieving 89 % and 99 % degradation efficiency for ciprofloxacin (CIP) and tetracycline hydrochloride (TCH), respectively, within 20 min under optimum conditions. The outstanding performance is attributed to factors such as a large specific surface area, suitable morphology and band gap, effective separation of photo-generated electron-hole pairs, and the presence of meso-size pores in the structure. Thermodynamic studies confirmed the exothermic and spontaneous nature of the photocatalytic reactions. The proposed degradation pathway of CIP and TCH, along with the photocatalytic mechanism, is elucidated. The solid solution, BiOBr1−xClx, exhibits facile recyclability, robust stability, and excellent adaptability to actual aquatic environments, establishing its potential as a promising eco-friendly photocatalyst for antibiotic pollution control.

Performance Shortfall, Top Management Team Faultlines, and R&D Investment: Evidence from China

With the increasing uncertainty of the world economic situation, enterprises are facing the pressure of downward performance, and R&D has been an important strategic choice to solve the performance fall problem of high-tech enterprises. The article tries to explore the changes in the R&D investment intensity of enterprises under different performance lag levels and investigate the influence of the combination of characteristics of top management team(TMT) members on the strategic decision-making process. It is found that the relationship between the degree of performance disparity and R&D investment intensity is inverted U-shaped; the strength of the fracture faultline of the top management team negatively regulates the relationship between the degree of performance disparity and R&D investment and makes the inverted U-shaped curve flat. A further study of specific characteristic fracture bands revealed that team fracture bands based on tenure characteristics had a significant negative moderating effect, while professional and functional background fracture bands had a significant positive moderating effect. This study not only theoretically expands the understanding of the performance shortall and TMT disconnection on the deep-seated motivation of enterprise R&D investment, but also provides practical references for solving the problem of performance gap, optimizing the allocation of TMT human resources, and rationally promoting enterprise R&D investment to achieve transformation.

Electrically Controlled Smart Window for Seasonally Adaptive Thermal Management in Buildings.

Smart windows offer a sustainable solution for energy-efficient buildings by adapting to various weather conditions. However, the challenge lies in achieving precise control over specific sunlight bands to effectively respond to complex weather changes and individual needs. Herein, a novel electrochromic smart window fabricated by integrating a self-assembled cellulose nanocrystals (CNCs) layer with an electrochromic poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) layer is reported, which exhibits high near-infrared transmittance, low solar reflectivity (26%), and infrared emissivity (20%) in winter, and low near-infrared transmittance, high solar reflectivity (91%), and infrared emissivity (≈94%) in summer. Interestingly, the material offers dynamic temperature control based on its photothermal properties, maintaining the internal temperature of buildings within a comfortable range of 20-25 °C from morning to night, particularly in winter with significant daily temperature fluctuations. Simulations show that the CNC-PED device demonstrates excellent energy-saving and CO2 emission reduction capacities across various global climate zones. This study presents a feasible pathway for constructing season-adaptive smart windows, making them suitable for energy-efficient buildings.

Multifunctional composite paper fabricated by graphene oxide/tannin decorated carbon fiber with excellent electromagnetic interference shielding, antibacterial and sound absorption properties

Considering the distinctive oxidative coupling characteristic of tannin acid, it was used as an interfacial interlayer to build a nano-coating structure on the surface of carbon fiber (CF)using amino-modified graphene oxide (GON) via Schiff base reaction. Furthermore, a kind of multifunctional composite paper (PTCF/GON) with excellent electromagnetic interference shielding, antibacterial and sound absorption properties was fabricated using surface modified CF (TCF/GON) and plant fibers. The increased active groups on the surface of TCF/GON improved its hydrophilicity and enhanced its dispersibility in water, which facilitated to form an effective interconnected conductive network of composite paper via traditional wet forming process. Composite paper fabricated by 30 wt% surface decorated CF (PTCF1.5/GON1.2) achieved an electromagnetic interference shielding effectiveness as high as 43.4 dB in X-band, shielding 99.9 % of incident electromagnetic energy. Meanwhile, PTCF1.5/GON1.2 exhibits good sound absorption performance in specific frequency bands, with a sound absorption coefficient of 0.98 around 1180 Hz. Besides, PTCF1.5/GON1.2 also exhibited excellent antibacterial properties. This strategy provides an economical and practical method for manufacturing multifunctional electromagnetic shielding materials with superior performance.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms.

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Debiasing astro-photometric observations with corrections using statistics (DePhOCUS)

Photometric measurements allow the determination of an asteroid’s absolute magnitude, which often represents the sole means to infer its size. Photometric observations can be obtained in a variety of filters that can be unique to a specific observatory. Those observations are then calibrated into specific bands with respect to reference star catalogs. In order to combine all the different measurements for evaluation, photometric observations need to be converted to a common band, typically V-band. Current band-correction schemes in use by IAU’s Minor Planet Center (MPC), JPL’s Center for Near Earth Object Studies (CNEOS) and ESA’s NEO Coordination Centre (NEOCC) use average correction values for the apparent magnitude derived from photometry of asteroids as the corrections are dependent on the typically unknown spectrum of the object to be corrected. By statistically analyzing the photometric residuals of asteroids, we develop a new photometric correction scheme that does not only consider the band, but also accounts for reference catalog and observatory. We analyzed nearly 500000 observations submitted to the MPC from 468 asteroids with published and independently determined high confidence H and G values. We describe a new statistical photometry correction scheme for asteroid observations with debiased corrections. Testing this scheme on a reference group of asteroids, we see a 36% reduction in the photometric residuals. Moreover, the new scheme leads to a more accurate and debiased determination of the H-G magnitude system and, in turn, to more reliable inferred sizes. We discuss the significant shift in the corrections with this “DePhOCUS” debiasing system, its limitations, and the impact for photometric and physical properties of all asteroids, especially Near-Earth Objects.

Detection and isolation of Leishmania infantum from natural infected dog in Türkiye

Some clinical signs such as cachexia, alopecia, exfoliative dermatitis, hair loss, and swollen lymph nodes were observed in a one-year-old crossbred male dog living in a rural area of Kirikkale province, Türkiye, was presented to the veterinary clinic by its owner. Anaemia, leucopenia, hyperglobulinemia, and bilirubinemia were detected. Seropositivity was detected using Leishmania IgG/IgM Rapid Test. The amastigote forms of the parasite were observed in the lymph node aspirates. Viable promastigotes were observed in the samples obtained from the Novy-MacNeal-Nicolle medium (NNN) tubes. The parasite DNA was extracted from the promastigotes produced in the NNN medium using a DNA extraction kit according to the manufacturer's instructions. The specific bands indicating the gene regions of L. infantum (350 bp and 730 bp for HSP20 and HSP70, respectively) were observed. Additionally, BLASTn analysis revealed 100 % similarity with GenBank-deposited L. infantum sequences. The sequences were submitted to GenBank (Accession numbers: OR806945 and OR806946). According to the author's knowledge, it is the first dog isolate deposited in the collection of the Microbiology Reference Laboratories and Biological Products Department, General Directorate of Public Health, Ministry of Health, Ankara, Türkiye. The isolate (TR_L. infantum CanL-1_damla) has been preserved by cryopreservation at −150 °C for further studies.

Modeling and Optimizing Deep Brain Stimulation to Enhance Gait in Parkinson's Disease: Personalized Treatment with Neurophysiological Insights.

Although high-frequency deep brain stimulation (DBS) is effective at relieving many motor symptoms of Parkinson's disease (PD), its effects on gait can be variable and unpredictable. This is due to 1) a lack of standardized and robust metrics for gait assessment in PD patients, 2) the challenges of performing a thorough evaluation of all the stimulation parameters space that can alter gait, and 3) a lack of understanding for impacts of stimulation on the neurophysiological signatures of walking. In this study, our goal was to develop a data-driven approach to identify optimal, personalized DBS stimulation parameters to improve gait in PD patients and identify the neurophysiological signature of improved gait. Local field potentials from the globus pallidus and electrocorticography from the motor cortex of three PD patients were recorded using an implanted bidirectional neural stimulator during overground walking. A walking performance index (WPI) was developed to assess gait metrics with high reliability. DBS frequency, amplitude, and pulse width on the "clinically-optimized" stimulation contact were then systemically changed to study their impacts on gait metrics and underlying neural dynamics. We developed a Gaussian Process Regressor (GPR) model to map the relationship between DBS settings and the WPI. Using this model, we identified and validated personalized DBS settings that significantly improved gait metrics. Linear mixed models were employed to identify neural spectral features associated with enhanced walking performance. We demonstrated that improved walking performance was linked to the modulation of neural activity in specific frequency bands, with reduced beta band power in the pallidum and increased alpha band pallidal-motor cortex coherence synchronization during key moments of the gait cycle. Integrating WPI and GPR to optimize DBS parameters underscores the importance of developing and understanding personalized, data-driven interventions for gait improvement in PD.