BACKGROUND: Abamectin (ABA) is considered a powerful insecticidal and anthelmintic agent. It is an intracellular product of Streptomyces avermitilis; is synthesized through complicated pathways and can then be extracted from mycelial by methanol extraction. ABA serves as a biological control substance against the root-knot nematode Meloidogyne incognita. This investigation is intended to reach a new strain of S. avermitilis capable of producing ABA effectively.
RESULTS: Among the sixty actinobacterial isolates, Streptomyces St.53 isolate was chosen for its superior nematicidal effectiveness. The mycelial-methanol extract of isolate St.53 exhibited a maximum in vitro mortality of 100% in one day. In the greenhouse experiment, the mycelial-methanol extract demonstrated, for the second-stage juveniles (J2s), 75.69% nematode reduction and 0.84 reproduction rate (Rr) while for the second-stage juveniles (J2s), the culture suspension demonstrated 75.38% nematode reduction and 0.80 reproduction rate (Rr). Molecular identification for St.53 was performed using 16 S rRNA gene analysis and recorded in NCBI Genbank as S. avermitilis MICNEMA2022 with accession number (OP108264.1). LC-MS was utilized to detect and identify abamectin in extracts while HPLC analysis was carried out for quantitative determination. Both abamectin B1a and abamectin B1b were produced and detected at retention times of 4.572 and 3.890 min respectively.
CONCLUSIONS: Streptomyces avermitilis MICNEMA2022 proved to be an effective source for producing abamectin as a biorational agent for integrated nematode management.

Lithium, a rare metal of strategic importance, has garnered heightened global attention. This investigation delves into the laboratory visible-near infrared and short-wavelength infrared reflectance (VNIR-SWIR 350 nm-2500 nm) spectral properties of lithium-rich rocks and stream sediments, aiming to elucidate their quantitative relationship with lithium concentration. This research seeks to pave new avenues and furnish innovative technical solutions for probing sedimentary lithium reserves. Conducted in the Tuanjie Peak region of Western Kunlun, Xinjiang, China, this study analyzed 614 stream sediments and 222 rock specimens. Initial steps included laboratory VNIR-SWIR spectral reflectance measurements and lithium quantification. Following the preprocessing of spectral data via Savitzky-Golay (SG) smoothing and continuum removal (CR), the absorption positions (Pos2210nm, Pos1910nm) and depths (Depth2210, Depth1910) in the rock spectra, as well as the Illite Spectral Maturity (ISM) of the rock samples, were extracted. Employing both the Successive Projections Algorithm (SPA) and genetic algorithm (GA), wavelengths indicative of lithium content were identified. Integrating the lithium-sensitive wavelengths identified by these feature selection methods, A quantitative predictive regression model for lithium content in rock and stream sediments was developed using partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN). Spectral analysis indicated that lithium is predominantly found in montmorillonite and illite, with its content positively correlating with the spectral maturity of illite and closely related to Al-OH absorption depth (Depth2210) and clay content. The SPA algorithm was more effective than GA in extracting lithium-sensitive bands. The optimal regression model for quantitative prediction of lithium content in rock samples was SG-SPA-CNN, with a correlation coefficient prediction (Rp) of 0.924 and root-mean-square error prediction (RMSEP) of 0.112. The optimal model for the prediction of lithium content in stream sediment was SG-SPA-CNN, with an Rp and RMSEP of 0.881 and 0.296, respectively. The higher prediction accuracy for lithium content in rocks compared to sediments indicates that rocks are a more suitable medium for predicting lithium content. Compared to the PLSR and SVR models, the CNN model performs better in both sample types. Despite the limitations, this study highlights the effectiveness of hyperspectral technology in exploring the potential of clay-type lithium resources in the Tuanjie Peak area, offering new perspectives and approaches for further exploration.

Tetrahydrocurcumin (THC), as a novel food supplement, has generated significant interests for its potential impact on health and nutrition. Pepsin serves as the primary enzyme involved in the digestive mechanism. This research investigated the conformational and functional alterations of pepsin induced by THC using multispectral techniques and computer simulations. The results showed that THC enters the cavity of pepsin, in which hydrophobic forces play a major role. The binding constant is 1.044 × 104 M-1 at 310 K. The upregulation or downregulation effect of THC on pepsin activity depends on its concentration. Molecular docking outcomes indicated that THC was encapsulated by various amino acids and established H-bonds with Tyr189 and Ser294, revealing that hydrogen bonds also contribute to maintaining the stability of THC-pepsin complex. In addition, the altered activity of pepsin may be related to the interaction between THC and the amino acids at the active site (Asp32) according to energy contribution results. 3D fluorescence spectroscopy, CD spectra and molecular dynamic simulations show that THC causes conformational changes in pepsin. The existence of THC makes pepsin structure to be less dense, leading to the decrease of energy traps. This suggests that pepsin becomes conformationally more suitable to bind to THC.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Irrigated agricultural lands in arid and semi-arid regions are prone to soil degradation. Remote sensing technology has proven useful for mapping and monitoring the extent of this issue. To accurately discern soil salinity, it is essential to choose appropriate spectral wavelengths. This study evaluated the potential of the land degradation index (LDI) using the visible and near infrared (VNIR) and the short wavelength infrared (SWIR) spectral bands compared to that of soil salinity indices by integrating only the VNIR wavelengths. Landsat-OLI and Sentinel-MSI data, acquired 2 weeks apart, were rigorously preprocessed and used. This research was conducted over irrigated agricultural land in Morocco, which is well known for its semi-arid climate and moderately saline soil. Furthermore, a field soil survey was conducted and 42 samples with variable electrical conductivity (EC) were collected for index calibration and validation of the results. The results showed that the visual analysis of the derived maps based on the examined indices exhibited a clear spatial pattern of gradual soil salinity changes extending from the elevated upstream plateau to the downstream of the plain, which limits agricultural activities in the southwestern sector of the study area. The results of this study show that LDI is effective in identifying soil salinity, as indicated by a coefficient of determination (R2) of 0.75 when using Sentinel-MSI and 0.72 with Landsat-OLI. The R2 value of 0.89 and root mean square error (RMSE) of 0.87 dS/m for soil salinity maps generated from LDI with Sentinel-MSI demonstrate high accuracy. In contrast, the R2 value of 0.83 and RMSE of 1.24 dS/m for maps produced from Landsat-OLI indicate lower accuracy. These findings indicate that high-resolution Sentinel-MSI data significantly improved the prediction of salinity-affected soils. Furthermore, this study highlights the benefits of using VNIR and SWIR bands for precise soil salinity mapping.

BACKGROUND: Feline osteoarthritis (OA) leads to chronic pain and somatosensory sensitisation. In humans, sensory exposure can modulate chronic pain. Recently, electroencephalography (EEG) revealed a specific brain signature to human OA. However, EEG pain characterisation or its modulation does not exist in OA cats, and all EEG were conducted in sedated cats, using intradermal electrodes, which could alter sensory (pain) perception.
METHODS: Cats (n=11) affected by OA were assessed using ten gold-plated surface electrodes. Sensory stimuli were presented in random orders: response to mechanical temporal summation, grapefruit scent and mono-chromatic wavelengths (500 nm-blue, 525 nm-green and 627 nm-red light). The recorded EEG was processed to identify event-related potentials (ERP) and to perform spectral analysis (z-score).
RESULTS: The procedure was well-tolerated. The ERPs were reported for both mechanical (F3, C3, Cz, P3, Pz) and olfactory stimuli (Cz, Pz). The main limitation was motion artifacts. Spectral analysis revealed a significant interaction between the power of EEG frequency bands and light wavelengths (p<0.001). All wavelengths considered, alpha band proportion was higher than that of delta and gamma bands (p<0.044), while the latter was lower than the beta band (p<0.016). Compared to green and red, exposure to blue light elicited distinct changes in EEG power over time (p<0.001).
CONCLUSIONS: This is the first demonstration of EEG feasibility in conscious cats with surface electrodes recording brain activity while exposing them to sensory stimulations.
CONCLUSIONS: The identification of ERPs and spectral patterns opens new avenues for investigating feline chronic pain and its potential modulation through sensory interventions.

In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries.

The number one cause of death in the United States is consistently cardiovascular disease (CVD). Studies have proven that the buildup of cardiac adipose tissue (CAT) around the heart is a biomarker of CVD. MRI is the gold standard for imaging CAT but is expensive and not widely available. Ultrasound is less expensive and portable, but the images are noisy, and it is difficult to identify or quantify CAT. The aim of this project is to use spectral analysis of raw radiofrequency (RF) ultrasound data as input for a machine learning classifier to automatically classify regions-of-interest (ROIs) around the heart as containing CAT or not. ROIs are labeled using corresponding MRI images of the same patients. A previous study used a random forest classifier with 9 spectral parameters as input to classify tissue types in echocardiograms. This project focuses on improving this classifier by experimenting with properties of the chosen ROIs. Experiments were performed independently varying the ROI circumference (length), width, the threshold CAT thickness used for labeling an ROI as CAT, and the signal level required for valid processing. Additional experiments explored the impact of the anatomical location of each ROI as an input. The addition of two parameters indicating the distance of each ROI from the two left and right myocardium intersections as well as the use of the optimal ROI parameters as determined from experimentation resulted in an accuracy of 75.5%. This demonstrates feasibility of this approach for identifying CAT around the heart and will lead to future work in estimating the thickness of fat in each ROI.

OBJECTIVE: Vocalizations from infants, particularly sounds associated with respiratory distress, are fundamental for observational scoring of respiratory tract issues. Listening to these infant sounds is a prevalent technique for decision-making in newborn intensive care units. Expiratory grunting, indicative of the severity and presence of potential conditions, is valuable, however, this evaluative method is subjective and prone to error. This study investigates the potential of computer-aided analysis to offer an objective scale for assessing the severity of respiratory tract problems, utilizing digital recordings of grunting sounds.
METHODS: The original data set is formed with a total of 189 grunting sound segments collected from 38 infants. Multiple evaluation approaches were performed to reveal the relation between spectral characteristics of the recordings and the severity or existence of respiratory distress.
RESULTS: Three spectral features were evaluated as prominently related to hospital stay duration and respiratory distress. The harmonic ratio of the recordings was graded as the most-related spectral feature that would characterize the severity.
CONCLUSIONS: The potential of an innovative and objective grading approach is first investigated for replacing the human ear with a computer-aided evaluation system. The results are promising and the detected relation between expert ear-based scoring and harmonic ratio suggests that the spectral character of the grunting sounds would reflect the nature of respiratory conditions. Moreover, this study underlines those spectral features of digital grunting recordings that would be functional for automated prediction and decision-making.

Autonomic nervous system (ANS) activity causes acute variations in blood pressure (BP) and heart rate (HR). These systems are challenged during high intensity interval exercise (HIIE). However, BP variability (BPV) and HR variability (HRV) response to HIIE is unknown. We characterized BPV and HRV during an acute HIIE bout using spectral low frequency [LF] and high frequency [HF] domains. We hypothesized that BPV would increase and HRV would decrease during high-intensity and active-recovery of HIIE compared to baseline [BL] and BPV would reduce and HRV would increase during cool down, post-HIIE, and 30 min post-HIIE compared to BL. HIIE involved 10 min of alternating high-intensity and active-recovery (approximately 70% and 10% of Wattmax) on a recumbent stepper. We did a secondary analysis on 23 datasets. The participants were 25 ± 1.5 years, 48% females. Our results showed high-intensity BPV LF was not significantly different from BL while HF increased. HRV LF and HF decreased compared to BL. During active-recovery, LF and HF for BPV and HRV increased greater than high-intensity. HRV LF and HF returned to BL after 30 min of recovery, whereas BPV HF was higher compared to BL. The rapid switching during HIIE uniquely modulates cardiovascular and ANS.