Functional states of prelimbic and related circuits during the acquisition of a GO/noGO task in rats.

GO/noGO tasks enable assessing decision-making processes and the ability to suppress a specific action according to the context. Here, rats had to discriminate between 2 visual stimuli (GO or noGO) shown on an iPad screen. The execution (for GO) or nonexecution (for noGO) of the selected action (to touch or not the visual display) were reinforced with food. The main goal was to record and to analyze local field potentials collected from cortical and subcortical structures when the visual stimuli were shown on the touch screen and during the subsequent activities. Rats were implanted with recording electrodes in the prelimbic cortex, primary motor cortex, nucleus accumbens septi, basolateral amygdala, dorsolateral and dorsomedial striatum, hippocampal CA1, and mediodorsal thalamic nucleus. Spectral analyses of the collected data demonstrate that the prelimbic cortex was selectively involved in the cognitive and motivational processing of the learning task but not in the execution of reward-directed behaviors. In addition, the other recorded structures presented specific tendencies to be involved in these 2 types of brain activity in response to the presentation of GO or noGO stimuli. Spectral analyses, spectrograms, and coherence between the recorded brain areas indicate their specific involvement in GO vs. noGO tasks.

A Bayesian switching linear dynamical system for estimating seizure chronotypes.

Epilepsy is a disorder characterized by paroxysmal transitions between multistable states. Dynamical systems have been useful for modeling the paroxysmal nature of seizures. At the same time, intracranial electroencephalography (EEG) recordings have recently discovered that an electrographic measure of epileptogenicity, interictal epileptiform activity, exhibits cycling patterns ranging from ultradian to multidien rhythmicity, with seizures phase-locked to specific phases of these latent cycles. However, many mechanistic questions about seizure cycles remain unanswered. Here, we provide a principled approach to recast the modeling of seizure chronotypes within a statistical dynamical systems framework by developing a Bayesian switching linear dynamical system (SLDS) with variable selection to estimate latent seizure cycles. We propose a Markov chain Monte Carlo algorithm that employs particle Gibbs with ancestral sampling to estimate latent cycles in epilepsy and apply unsupervised learning on spectral features of latent cycles to uncover clusters in cycling tendency. We analyze the largest database of patient-reported seizures in the world to comprehensively characterize multidien cycling patterns among 1,012 people with epilepsy, spanning from infancy to older adulthood. Our work advances knowledge of cycling in epilepsy by investigating how multidien seizure cycles vary in people with epilepsy, while demonstrating an application of an SLDS to frame seizure cycling within a nonlinear dynamical systems framework. It also lays the groundwork for future studies to pursue data-driven hypothesis generation regarding the mechanistic drivers of seizure cycles.

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas.

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

Streptomyces avermitilis MICNEMA2022: a new biorational strain for producing abamectin as an integrated nematode management agent.

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. CONCLUSION: Streptomyces avermitilis MICNEMA2022 proved to be an effective source for producing abamectin as a biorational agent for integrated nematode management.

Differences in peripheral microcirculatory blood flow regulation in chronic kidney disease based on wavelet analysis of resting near-infrared spectroscopy.

Vascular impairment is closely related to increased mortality in chronic kidney disease (CKD). The objective of this study was to assess impairments in the regulation of peripheral microvascular perfusion in patients with CKD based on time-frequency spectral analysis of resting near-infrared spectroscopy (NIRS) signals. Total hemoglobin (tHb) concentration and tissue saturation index (TSI) signals were collected using NIRS for a continuous 5 mins at 10 Hz from the forearm of 55 participants (34 CKD including 5 with end-stage renal disease, and 21 age-matched control). Continuous wavelet transform-based spectral analysis was used to quantify the spectral amplitude within five pre-defined frequency intervals (I, 0.0095-0.021 Hz; II, 0.021-0.052 Hz; III, 0.052-0.145 Hz; IV, 0.145-0.6 Hz and V, 0.6-2.0 Hz), representing endothelial, neurogenic, myogenic, respiratory and heartbeat activity, respectively. CKD patients showed lower tHb average spectral amplitude within the neurogenic frequency interval compared with controls (p = 0.014), consistent with an increased sympathetic outflow observed in CKD. CKD patients also showed lower TSI average spectral amplitude within the endothelial frequency interval compared with controls (p = 0.046), consistent with a reduced endothelial function in CKD. These findings demonstrate the potential of wavelet analysis of NIRS to provide complementary information on peripheral microvascular regulation in CKD.

Spectral changes in skin blood flow during pressure manipulations or sympathetic stimulation.

Skin blood flow is commonly determined by laser Doppler flowmetry (LDF). It has been suggested that pathophysiological conditions can be assessed by analysis of specific frequency domains of the LDF signals. We tested whether physiological stimuli that activate myogenic and neurogenic mechanisms would affect relevant portions of the laser Doppler spectrum. LDF sensors were placed on the right forearm of 14 healthy volunteers for myogenic (six females) and 13 for neurogenic challenge (five females). Myogenic responses were tested by positioning the arm ∼50° above/below heart level. Neurogenic responses were tested by immersing the left hand into an ice slurry with and without topical application of local anaesthetic. Short-time Fourier analyses were computed over the range of 0.06 to 0.15 Hz for myogenic and 0.02 to 0.06 Hz for neurogenic. No significant differences in spectral density were observed (P = 0.40) in the myogenic range with arm above (7 ± 54 × 10-4 dB) and below heart (7 ± 14 × 10-4 dB). Neurogenic spectral density showed no significant increase from baseline to cold pressor test (0.0017 ± 0.0013 and 0.0038 ± 0.0039 dB; P = 0.087, effect size 0.47). After application of anaesthetic, neurogenic spectral density was unchanged between the baseline and cold pressor test (0.0014 ± 0.0025 and 0.0006 ± 0.0005 dB; P = 0.173). These results suggest that changes in the myogenic and neurogenic spectral density of LDF signals did not fully reflect the skin vascular function activated by pressure manipulation and sympathetic stimulation. Therefore, LDF myogenic and neurogenic spectral density data should be interpreted with caution.

Ictal fast activity chirps as markers of the epileptogenic zone.

The identification of the epileptogenic zone (EZ) boundaries is crucial for effective focal epilepsy surgery. We verify the value of a neurophysiological biomarker of focal ictogenesis, characterized by a low-voltage fast-activity ictal pattern (chirp) recorded with intracerebral electrodes during invasive presurgical monitoring (stereoelectroencephalography [SEEG]). The frequency content of SEEG signals was retrospectively analyzed with semiautomatic software in 176 consecutive patients with focal epilepsies that either were cryptogenic or presented with discordant anatomoelectroclinical findings. Fast activity seizure patterns with the spectrographic features of chirps were confirmed by computer-assisted analysis in 95.4% of patients who presented with heterogeneous etiologies and diverse lobar location of the EZ. Statistical analysis demonstrated (1) correlation between seizure outcome and concordance of sublobar regions included in the EZ defined by visual analysis and chirp-generating regions, (2) high concordance in contact-by contact analysis of 68 patients with Engel class Ia outcome, and (3) that discordance between chirp location and the visually outlined EZ correlated with worse seizure outcome. Seizure outcome analysis confirms the fast activity chirp pattern is a reproducible biomarker of the EZ in a heterogeneous group of patients undergoing SEEG.

Altered neural oscillations in classical galactosaemia during sentence production.

Classical galactosaemia (CG) is a hereditary disease in galactose metabolism that despite dietary treatment is characterized by a wide range of cognitive deficits, among which is language production. CG brain functioning has been studied with several neuroimaging techniques, which revealed both structural and functional atypicalities. In the present study, for the first time, we compared the oscillatory dynamics, especially the power spectrum and time-frequency representations (TFR), in the electroencephalography (EEG) of CG patients and healthy controls while they were performing a language production task. Twenty-one CG patients and 19 healthy controls described animated scenes, either in full sentences or in words, indicating two levels of complexity in syntactic planning. Based on previous work on the P300 event related potential (ERP) and its relation with theta frequency, we hypothesized that the oscillatory activity of patients and controls would differ in theta power and TFR. With regard to behavior, reaction times showed that patients are slower, reflecting the language deficit. In the power spectrum, we observed significant higher power in patients in delta (1-3 Hz), theta (4-7 Hz), beta (15-30 Hz) and gamma (30-70 Hz) frequencies, but not in alpha (8-12 Hz), suggesting an atypical oscillatory profile. The time-frequency analysis revealed significantly weaker event-related theta synchronization (ERS) and alpha desynchronization (ERD) in patients in the sentence condition. The data support the hypothesis that CG language difficulties relate to theta-alpha brain oscillations.

Distinct Spectral Profiles of Awake Resting EEG in Disorders of Consciousness: The Role of Frequency and Topography of Oscillations.

The prolonged disorders of consciousness (PDOC) pose a challenge for an accurate clinical diagnosis, mainly due to patients' scarce or ambiguous behavioral responsiveness. Measurement of brain activity can support better diagnosis, independent of motor restrictions. Methods based on spectral analysis of resting-state EEG appear as a promising path, revealing specific changes within the internal brain dynamics in PDOC patients. In this study we used a robust method of resting-state EEG power spectrum parameter extraction to identify distinct spectral properties for different types of PDOC. Sixty patients and 37 healthy volunteers participated in this study. Patient group consisted of 22 unresponsive wakefulness patients, 25 minimally conscious patients and 13 patients emerging from the minimally conscious state. Ten minutes of resting EEG was acquired during wakefulness and transformed into individual power spectra. For each patient, using the spectral decomposition algorithm, we extracted maximum peak frequency within 1-14 Hz range in the centro-parietal region, and the antero-posterior (AP) gradient of the maximal frequency peak. All patients were behaviorally diagnosed using coma recovery scale-revised (CRS-R). The maximal peak frequency in the 1-14 Hz range successfully predicted both neurobehavioral capacity of patients as indicated by CRS-R total score and PDOC diagnosis. Additionally, in patients in whom only one peak within the 1-14 Hz range was observed, the AP gradient significantly contributed to the accuracy of prediction. We have identified three distinct spectral profiles of patients, likely representing separate neurophysiological modes of thalamocortical functioning. Etiology did not have significant influence on the obtained results.

Tilt-evoked, breathing-driven blood pressure oscillations: Independence from baroreflex-sympathoneural function.

PURPOSE: Orthostasis increases the variability of continuously recorded blood pressure (BP). Low-frequency (LF) BP oscillations (Mayer waves) in this setting are related to the vascular-sympathetic baroreflex. Mechanisms of increased high-frequency (HF) BP oscillations at the periodicity of respiration during orthostasis have received less research attention. A previously reported patient with post-neurosurgical orthostatic hypotension (OH) and vascular-sympathetic baroreflex failure had large tilt-evoked, breathing-driven BP oscillations, suggesting that such oscillations can occur independently of vascular-sympathetic baroreflex modulation. In the present study we assessed effects of orthostasis on BP variability in the frequency domain in patient cohorts with or without OH. METHODS: Power spectral analysis of systolic BP variability was conducted on recordings from 73 research participants, 42 with neurogenic OH [13 pure autonomic failure, 14 Parkinson's disease (PD) with OH, 12 parkinsonian multiple system atrophy, and 3 status post-brainstem neurosurgery] and 31 without OH (control group of 16 healthy volunteers and 15 patients with PD lacking OH), before, during, and after 5' of head-up tilt at 90 degrees from horizontal. The data were log transformed for statistical testing. RESULTS: Across all subjects, head-up tilting increased HF power of systolic BP variability (p = 0.001), without a difference between the neurogenic OH and control groups. LF power during orthostasis was higher in the control than in the OH groups (p = 0.009). CONCLUSIONS: The results of this observational cohort study confirm those based on our case report and lead us to propose that even in the setting of vascular-sympathetic baroreflex failure orthostasis increases HF power of BP variability.

Idiopathic cervical dystonia and non-motor symptoms: a pilot case-control study on autonomic nervous system.

PURPOSE: Non-motor symptoms, such as sleep disturbances, fatigue, neuropsychiatric manifestations, cognitive impairment, and sensory abnormalities, have been widely reported in patients with idiopathic cervical dystonia (ICD). This study aimed to clarify the autonomic nervous system (ANS) involvement in ICD patients, which is still unclear in the literature. METHODS: We conducted a pilot case-control study to investigate ANS in twenty ICD patients and twenty age-sex-matched controls. The Composite Autonomic System Scale 31 was used for ANS clinical assessment. The laser Doppler flowmetry quantitative spectral analysis, applied to the skin and recorded from indices, was used to measure at rest, after a parasympathetic activation (six deep breathing) and two sympathetic stimuli (isometric handgrip and mental calculation), the power of high-frequency and low-frequency oscillations, and the low-frequency/high-frequency ratio. RESULTS: ICD patients manifested higher clinical dysautonomic symptoms than controls (p < 0.05). At rest, a lower high-frequency power band was detected among ICD patients than controls, reaching a statistically significant difference in the age group of ≥ 57-year-olds (p < 0.05). In the latter age group, ICD patients showed a lower low-frequency/high-frequency ratio than controls at rest (p < 0.05) and after mental calculation (p < 0.05). Regardless of age, during handgrip, ICD patients showed (i) lower low-frequency/high-frequency ratio (p < 0.05), (ii) similar increase of the low-frequency oscillatory component compared to controls, and (iii) stable high-frequency oscillatory component, which conversely decreased in controls. No differences between the two groups were detected during deep breathing. CONCLUSION: ICD patients showed ANS dysfunction at clinical and neurophysiological levels, reflecting an abnormal parasympathetic-sympathetic interaction likely related to abnormal neck posture and neurotransmitter alterations.

Revealing lineage-related signals in single-cell gene expression using random matrix theory.

Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690-695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissue-specific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes.

The structural transformation reversibility of biogas slurry derived dissolved organic matter and its binding properties with norfloxacin under temperature fluctuation.

The widespread use of biogas slurry could potentially raise the environmental risk of antibiotics. Dissolved organic matter (DOM), as the most active part of biogas slurry, was able to interact with antibiotics and play a crucial role in the structure and function of soil and aquatic ecosystems. The recent shifts in global climate patterns have garnered significant attention due to their substantial impact on temperature, thereby exerting a direct influence on the characteristics of DOM and subsequently on the environmental behavior of antibiotics. However, there is limited research concerning the impact of temperature on the binding of DOM and antibiotics. Thus, this study aimed to explore the temperature-dependent structural transformation and driving factors of biogas slurry-derived DOM (BSDOM). Additionally, the binding characteristics between BSDOM and the commonly used antibiotic norfloxacin (NOR) at different temperatures were studied by using multi spectroscopic methods and two-dimensional correlation spectroscopy (2D-COS) analysis. The results suggested that the temperature-dependent structural transformation of BSDOM was reversible, with a slight lag in the transition temperature under cooling (13 °C for heating and 17 °C for cooling). Heating promoted the conversion of protein-like to humic-like substances while cooling favored the decomposition of humic-like substances. BSDOM and NOR were static quenching, with oxygen-containing functional groups such as C-O and -OH playing an important role. Temperature influenced the order of binding, the activity of the protein fraction, and its associated functional groups. At temperatures of 25 °C and 40 °C, the fluorescent components were observed to exhibit consistent binding preferences, whereby the humic-like component demonstrated a greater affinity for NOR compared to the protein-like component. However, the functional group binding order exhibited an opposite trend. At 10 °C, a new protein-like component appeared and bound preferentially to NOR, when no C-O stretch corresponding to the amide was observed. The finding will contribute to a comprehensive understanding of the interaction mechanisms between DOM and antibiotics under climate change, as well as providing a theoretical basis to reduce the environmental risks of biogas slurry and antibiotics.

Synthesis, characterization, and biological study of new synthetic opioid hemorphin-4 peptides containing sterically restricted nonnatural amino acids.

Some new hemorphin-4 analogs with structures of Xxx-Pro-Trp-Thr-NH2 and Tyr-Yyy-Trp-Thr-NH2, where Xxx is 2-amino-3-(4-hydroxy-2,6-dimethylphenyl)propanoic acid or 2-amino-3-(4-dibenzylamino-2,6-dimethylphenyl)propanoic acid, and Yyy is (2S,4S)-4-amino-pyrrolidine-2-carboxylic acid, were synthesized and characterized by electrochemical and spectral analyses. In vivo anticonvulsant and antinociceptive activities of peptide derivatives were studied after intracerebroventricular injection in mice. The therapeutic effects of the modified peptides on seizures and pain in mice were evaluated to provide valuable insights into the potential applications of the novel compounds. Electrochemical characterization showed that the compounds behave as weak protolytes and that they are in a soluble, stable molecular form at physiological pH values. The antioxidant activity of the peptides was evaluated with voltammetric analyses, which were confirmed by applying the 2,2-Diphenyl-1-picrylhydrazyl method. The compounds showed satisfactory results regarding their structural stability, reaching the desired centers for the manifestation of biological activity without hydrolysis processes at 37°C and physiological pH. Dm-H4 and H4-P1 exhibited 100% and 83% potency to suppress the psychomotor seizures in the 6-Hz test compared to 67% activity of H4. Notably, only the H4-P1 had efficacy in blocking the tonic component in the maximal electroshock test with a potency comparable to H4. All investigated peptides containing unnatural conformationally restricted amino acids showed antinociceptive effects. The analogs Db-H4 and H4-P1 showed the most pronounced and long-lasting effect in both experimental models of pain induced by thermal and chemical stimuli. Dm-H4 produced a dose-dependent thermal antinociception and H4-P2 inhibited only formalin-induced pain behavior.

Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network.

Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Effect of stoichiometry upon the characteristics of quercetin-arginine cocrystals formulated through solution crystallization.

OBJECTIVE: The aim of this study is to demonstrate the effect of stoichiometry upon characteristics of quercetin-arginine (QCT-Arg) cocrystals. SIGNIFICANCE: Quercetin (QCT) is a most abundant flavonoid in vegetables and fruits and has been widely used as an antioxidant. However, its oral bioavailability remains low due to poor aqueous solubility. We illustrate that QCT-Arg cocrystals formulated through an optimized stoichiometry can be a useful approach for its solubilization. METHOD: Cocrystals were prepared using solvent evaporation method. Characterizations were performed through microscopic, spectroscopic, and thermal techniques. The stoichiometry was confirmed from the binary phase diagram which was prepared using thermograms derived from differential scanning calorimetric experiments. RESULT: Cocrystal formation was accompanied by the conversion of isotropic phase into anisotropic one. Thread-like cocrystals were formed, regardless of QCT-Arg stoichiometry and solvent's polarity. Spectral analyses suggested that cocrystal structure was held together by hydrogen bonding between QCT and Arg. We ruled out the existence of eutectic mixture based on the observation of two eutectic points in the binary phase diagram. CONCLUSION: Morphology of cocrystals remained unaffected by the solvent type, stoichiometry and the presence of surfactant. We noticed that the cocrystals could improve the aqueous solubility of QCT.

Insights into the enhanced adsorption of glyphosate by dissolved organic matter in farmland Mollisol: effects and mechanisms of action.

Dissolved organic matter (DOM) is easy to combine with residual pesticides and affect their morphology and environmental behavior. Given that the binding mechanism between DOM and the typical herbicide glyphosate in soil is not yet clear, this study used adsorption experiments, multispectral techniques, density functional theory, and pot experiments to reveal the interaction mechanism between DOM and glyphosate on Mollisol in farmland and their impact on the environment. The results show that the adsorption of glyphosate by Mollisol is a multilayer heterogeneous chemical adsorption process. After adding DOM, due to the early formation of DOM and glyphosate complex, the adsorption process gradually became dominated by single-layer chemical adsorption, and the adsorption capacity increased by 1.06 times. Glyphosate can quench the endogenous fluorescence of humic substances through a static quenching process dominated by hydrogen bonds and van der Waals forces, and instead enhance the fluorescence intensity of protein substances by affecting the molecular environment of protein molecules. The binding of glyphosate to protein is earlier, of which affinity stronger than that of humic acid. In this process, two main functional groups (C-O in aromatic groups and C-O in alcohols, ethers and esters) exist at the binding sites of glyphosate and DOM. Moreover, the complexation of DOM and glyphosate can effectively alleviate the negative impact of glyphosate on the soil. This study has certain theoretical guidance significance for understanding the environmental behavior of glyphosate and improving the sustainable utilization of Mollisol.

Potential of land degradation index for soil salinity mapping in irrigated agricultural land in a semi-arid region using Landsat-OLI and Sentinel-MSI data.

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.

[The role of spectral analysis of cough sounds in the diagnosis of COVID-19].

AIM: To evaluate the possibility of using spectral analysis of cough sounds in the diagnosis of a new coronavirus infection COVID-19. MATERIALS AND METHODS: Spectral toussophonobarography was performed in 218 patients with COVID-19 [48.56% men, 51.44% women, average age 40.2 (32.4; 51.0)], in 60 healthy individuals [50% men, 50% women, average age 41.7 (32.2; 53.0)] with induced cough (by inhalation of citric acid solution at a concentration of 20 g/l through a nebulizer). The recording was made using a contact microphone located on a special tripod at a distance of 15-20 cm from the face of the subject. The resulting recordings were processed in a computer program, after which spectral analysis of cough sounds was performed using Fourier transform algorithms. The following parameters of cough sounds were evaluated: the duration of the cough act (ms), the ratio of the energy of low frequencies (60-600 Hz) to the energy of high frequencies (600-6000 Hz), the frequency of the maximum energy of the cough sound (Hz). RESULTS: After statistical processing, it was found out that the parameters of the cough sound of COVID-19 patients differ from the cough of healthy individuals. The obtained data were substituted into the developed regression equation. Rounded to integers, the resulting number had the following interpretation: "0" - there is no COVID-19, "1" - there is COVID-19. CONCLUSION: The technique showed high levels of sensitivity and specificity. In addition, the method is characterized by sufficient ease of use and does not require expensive equipment, therefore it can be used in practice for timely diagnosis of COVID-19.