Talar and Calcaneal Coordinate Axes Definitions across Foot Pathologies.

The understanding of foot and ankle biomechanics is improving as new technology provides more detailed information about the motion of foot and ankle bones with biplane fluoroscopy, as well as the ability to analyze the hindfoot under weightbearing conditions with weightbearing computed tomography. Three-dimensional anatomical coordinate systems are necessary to describe the 3D alignment and kinematics of the foot and ankle. The lack of standard coordinate systems across research study sites can significantly alter experimental data analyses used for pre-surgical evaluation and post-operative outcome assessments. Clinical treatment paradigms are changing based on the expanding knowledge of complex pes planovalgus morphologies or progressive collapsing foot deformity, which is present in both neurologic and non-neurologic populations. Four patient cohorts were created from 10 flexible PCFD, 10 rigid PCFD, 10 adult cerebral palsy, and 10 asymptomatic control patients. Six coordinate systems were tested on both the talus and calcaneus for all groups. The aim of this study was to evaluate axes definitions for the subtalar joint across four different patient populations to determine the influence of morphology on the implementation of previously defined coordinate systems. Different morphologic presentations from various pathologies have a substantial impact on coordinate system definitions, given that numerous axes definitions are defined through geometric fits or manual landmark selection. Automated coordinate systems that align with clinically relevant anatomic planes are preferred. Principal component axes are automatic, but do not align with clinically relevant planes and should not be used for such analysis where anatomic planes are critical.

Unraveling the characteristics of microplastics in agricultural soils upon long-term organic fertilizer application: A comprehensive study using diversity indices.

Microplastics negatively impact soil health and productivity. Organic fertilizers constitute significant contributors of microplastics in agricultural soils. Nevertheless, comprehensive data on the diversity of microplastics in long-term fertilized soils remain unavailable. In this study, we assessed the presence of microplastics in soils subjected to application of three different organic fertilizers (pig manure, chicken manure, and sludge composts) over 12 years, and evaluated the potential ecological risks posed by microplastic accumulation. The average microplastic abundance in soil was 368.88 ± 207.97 (range: 90-910) items/kg. Microplastic abundance differed among fertilization treatments, with substantial increases of 16.67%, 71.67%, and 61.43% upon low to high application of the three treatments, respectively. Overall, the microplastics predominantly comprised fibers (70.94%) and fragments (25.25%), of which a substantial proportion constituted light-colored microplastics (transparent and white). The size of microplastics was mainly concentrated in the 1-2 mm range (39.96%), with rayon, polypropylene, polyester, and polyethylene being identified as the major types. The risk assessment indices of the three treatments were 229.38, 257.64, and 175.89, respectively, and were all classified as level 4 (high risk). The microplastic diversity integrated index and principal component analysis revealed that microplastics were uniformly distributed throughout the 0-20 cm soil depth consequent to tillage activity. Together, these findings provide a comprehensive assessment of microplastic pollution in long-term fertilized soils and serve as a scientific basis for reducing microplastic contamination in agricultural soils.

An arm swing enhances the proximal-to-distal delay in joint extension during a countermovement jump.

An abundance of degrees of freedom (DOF) exist when executing a countermovement jump (CMJ). This research aims to simplify the understanding of this complex system by comparing jump performance and independent functional DOF (fDOF) present in CMJs without (CMJNoArms) and with (CMJArms) an arm swing. Principal component analysis was used on 39 muscle forces and 15 3-dimensional joint contact forces obtained from kinematic and kinetic data, analyzed in FreeBody (a segment-based musculoskeletal model). Jump performance was greater in CMJArms with the increased ground contact time resulting in higher external (p = 0.012), hip (p < 0.001) and ankle (p = 0.009) vertical impulses, and slower hip extension enhancing the proximal-to-distal joint extension strategy. This allowed the hip muscles to generate higher forces and greater time-normalized hip vertical impulse (p = 0.006). Three fDOF were found for the muscle forces and 3-dimensional joint contact forces during CMJNoArms, while four fDOF were present for CMJArms. This suggests that the underlying anatomy provides mechanical constraints during a CMJ, reducing the demand on the control system. The additional fDOF present in CMJArms suggests that the arms are not mechanically coupled with the lower extremity, resulting in additional variation within individual motor strategies.

Enhancing identification of early-stage lung adenocarcinomas through solid component analysis of three-dimensional computed tomography images.

OBJECTIVES: As the role of segmentectomy expands in managing early-stage lung adenocarcinoma, precise preoperative assessments of tumor invasiveness via computed tomography become crucial. This study aimed to evaluate the effectiveness of solid component analysis of three-dimensional (3D) computed tomography images and establish segmentectomy criteria for early-stage lung adenocarcinomas. METHODS: This retrospective study included 101 cases with adenocarcinoma diagnoses, with patients undergoing segmentectomy for clinical stage 0 or IA between 2012 and 2017. The solid component volume (3D-volume) and solid component ratio (3D-ratio) of tumors were calculated using 3D computed tomography. Additionally, based on two-dimensional (2D) computed tomography, the solid component diameter (2D-diameter) and solid component ratio (2D-ratio) were calculated. The area under the receiver-operating characteristic curve (AUC) was calculated for each method, facilitating predictions of mortality and recurrence within 5 years. The AUC of each measurement was compared with those of invasive component diameter (path-diameter) and invasive component ratio (path-ratio) obtained through pathology analysis. RESULTS: The predictive performance of 3D-volume did not differ significantly from that of path-diameter, whereas 2D-diameter exhibited less predictive accuracy (AUC: 3D-volume, 2D-diameter, and path-diameter: 0.772, 0.624, and 0.747, respectively; 3D-volume vs. path-diameter: p = 0.697; 2D-diameter vs. path-diameter: p = 0.048). Results were similar for the solid component ratio (AUC: 3D-ratio, 2D-ratio, path-ratio: 0.707, 0.534, and 0.698, respectively; 3D-ratio vs. path-ratio: p = 0.882; 2D-ratio vs. path-ratio: p = 0.038). CONCLUSION: Solid component analysis using 3D computed tomography offers advantages in prognostic prediction for early-stage lung adenocarcinomas.

Hydro-chemical characterization and irrigation suitability assessment of a tropical decaying river in India.

Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station's data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+-HCO3- types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3- + CO32-) outperform strong acids (Cl- + SO42-). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p < 0.05). The results also revealed that river water for irrigation during monsoon is suitable compared to the pre-monsoon season. Anthropogenic interventions including riverbed agriculture, and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river, which needs substantial attention from the various stakeholders in a participatory, and sustainable manner.

Traditional Processing Can Enhance the Medicinal Effects of Polygonatum cyrtonema by Inducing Significant Chemical Changes in the Functional Components in Its Rhizomes.

The aims of this study were to explore the significant chemical changes in functional components induced by the traditional processing method and evaluate whether this method based on nine cycles of steaming and drying can effectively enhance the medicinal effects of Polygonatum cyrtonema rhizome. A global analysis on dynamic changes in secondary metabolites during nine processing cycles was performed, and the significantly differentially accumulated secondary metabolites were initially identified based on the secondary metabolome. Unsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLA-DA) on secondary metabolites clearly showed that processing significantly increased the global accumulation of secondary metabolites in processed P. cyrtonema rhizomes compared to unprocessed crude rhizomes. The first six processing cycles induced drastic changes in the accumulation of functional components, while the last three did not induce further changes. The accumulations of most functional components were significantly enhanced after the first three cycles and stabilized after six cycles; meanwhile, the first three cycles also led to numerous new components. However, the enhancing effects were unavoidably reversed or weakened under continued processing lasting 6-9 cycles. Furthermore, continued processing also reduced the contents of a small number of original components to undetectable levels. Processing induced some significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, among which the first three processing cycles enhanced the synthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. In conclusion, this study not only reveals that processing can effectively enhance the medicinal effects, by two main mechanisms including enhancing chemical synthesis and inducing structural transformation of functional components, but also provides theoretical guidance for the optimization of the traditional processing method based on nine cycles of steaming and drying for achieving optimal effects on enhancing the medicinal effects of P. cyrtonema rhizome.

Comprehensive Analysis of Drug-Induced Parkinson-like Events.

Specific drugs are well known to have the capacity to induce Parkinson-like symptoms. Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Pharmaceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with the associated drug trends and characteristics. Our findings indicate that similar to Parkinson's disease, age-related differences affect the onset of these reported events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, the side effects can vary depending on the site of action. These insights underline the importance of the swift identification of the drugs suspected of causing these events and the implementation of measures to reduce their side effects.

A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process.

The monoclonal antibody (mAb) manufacturing process comes with high profits and high costs, and thus mAb productivity is of vital importance. However, many factors can impact the cell culture process, and lead to mAb productivity reduction. Nowadays, the biopharma industry is actively employing manufacturing information systems, which enable the integration of both online data and offline data. Although the volume of data is large, related data mining studies for mAb productivity improvement are rare. Therefore, a data-driven approach is proposed in this study to leverage both the inline and offline data of the cell culture process to discover the causes of mAb productivity reduction. The approach consists of four steps, namely data preprocessing, phase division, feature extraction and fusion, and cluster comparing. First, data quality issues are solved during the data preprocessing step. Next, the inline data are divided into several phases based on the moving window k-nearest neighbor method. Then, the inline data features are extracted via functional data analysis and combined with the offline data features. Finally, the causes of mAb productivity reduction are identified using the contrasting clusters via the principal component analysis method. A commercial-scale cell culture process case study is provided in this research to verify the effectiveness of the approach. Data from 35 batches were collected, and each batch contained nine inline variables and seven offline variables. The causes of mAb productivity reduction were identified to be the lack of nutrients, and recommended actions were taken according to the result, which was subsequently proven by six validation batches.

Development of a Neural Network for Target Gas Detection in Interdigitated Electrode Sensor-Based E-Nose Systems.

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO2 gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0-11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO2 gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO2 (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves.

Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal Component Analysis (PCA).

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm3, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx.

Static and Dynamic Dysconnectivity in Early Psychosis: Relationship With Symptom Dimensions.

BACKGROUND AND HYPOTHESIS: Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors. STUDY DESIGN: We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations. STUDY RESULTS: Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049). CONCLUSIONS: Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

Impacts of land use and management methods on soil quality dynamics in central highlands of Ethiopia.

Land degradation often results in poor soil quality in many parts of Ethiopia, including the study area. To address this issue and promote sustainable land management practices, various land use and management methods (LUMMs) have been implemented. However, little information is available regarding how these management practices influence overall soil quality dynamics of the study area. This study aimed at evaluating soil quality dynamics in the Urago micro-watershed, central highlands of Ethiopia, under major LUMMs: barren land (BL), grassland (GL), established farm boundary (EFB), restored degraded land (RDL), and stone-supported soil bund (SSB). Forty-five disturbed and fifteen undisturbed soil samples were collected from the ploughed soil layer (0-20 cm) of each LUMM and analysed for selected physicochemical properties to be used as indicators of soil quality. Principal component analysis and multiple correlation were used to select the minimum data set (MDS) to evaluate the overall soil quality index (SQI). The MDS included SOC, clay content, exchangeable Mg2+, and available P, which could replace other indicators for assessing the overall soil quality dynamics of the study watershed. The result showed notable variations in particle-size fractions, soil organic carbon (SOC), total nitrogen (TN), available P (av. P), and exchangeable Na+, K+, and Mg2+ levels among the LUMMs. RDL had higher sand and silt contents than SSB, whereas SSB had higher clay content compared to RDL, GL, and BL. GL, RDL, and EFB showed significantly higher levels of SOC, TN, and av. P, respectively, compared to other LUMMs. The obtained SQI showed that GL had the highest score (0.847), followed by SSB (0.703), RDL (0.701), EFB (0.644), and BL (0.628). This underscores the significance of stone-supported soil bund and restored degraded land as an efficient management method to enhance soil quality and agro-ecosystem through conserving soil and encouraging sustainable farming practices.

[Main nutritional components of food herbs in mountainous areas of Guizhou Province].

OBJECTIVE: To investigate the nutritional content of edible medicinal materials in mountainous areas of Guizhou Province and compare the comprehensive nutritional value of different varieties. METHODS: A total of 15 kinds of edible herbs were collected from Guizhou Province. According to the national standard, direct drying method, Kjeldahl nitrogen determination method, Soxhlet extraction method, high performance liquid chromatography, inductively coupled plasma mass spectrometry and other detection method were used to determine the content of general nutrients, fat soluble vitamins, minerals and ash. According to the weight ranking of nutritional indexes in principal component analysis and membership function analysis, the quality of 9 kinds of food and drug substances and 6 kinds of Chinese medicinal materials were evaluated and ranked. RESULTS: The eigenvalues of the first 4 principal components were greater than 1, and the cumulative contribution rate was 82.32%. Compared with membership function analysis, the top 5 in comprehensive evaluation were Eucommia, Epimedium and honeysuckle, all of which were food and drug substances. The contents of calcium(851.69 mg/100 g), phosphorus(270.22 mg/100 g) and potassium(1446.48 mg/100 g) were the highest. The contents of carotene(21 963.87 µg/100 g) and vitamin E(57.82 mg/100 g) were the highest in the fat-soluble vitamins of Herbimedium. The contents of various indexes of honeysuckle were relatively high. CONCLUSION: Food and pharmaceutical substances have both medicinal value and nutritional value, and the overall nutritional benefit is higher than that of Chinese medicinal materials.

Zambian indigenous chicken genetic resources: phenotypic characteristics and their production systems among small-scale farmers.

Introduction: Indigenous chickens are very important to households for income and protein. However, their performance is usually poor, especially under small-scale farmer management, despite their potential to perform better. The performance of these chickens can be improved by selective breeding. However, for this to be a success, there is a need to understand the phenotypic and production characteristics of these chickens fully. Hence, this study aimed to characterize the phenotypes of these chickens and their production system among small-scale farmers. Method: A structured questionnaire was administered to 177 small-scale farmers. A total of 538 chickens whose mean weight was 1.66 kg were individually phenotyped in Luapula, Muchinga, and Northern provinces of Zambia. Results: Ownership of the indigenous chickens was dominated by females (65.37%), with most (64.31%) having attained primary education. Most housed their chickens in family houses (42.03%). All the farmers let their chickens scavenge for their feed, with 45.58% of them providing basic supplementation. Most (84.10%) farmers bought their breed stock from within their community and had a mean flock size of 12.5 chickens/household, which they mostly (78.09%) kept as free-range. The majority (77.39%) practiced culling, with low productivity being the most common reason for culling (84.45%). Only 59.01% of farmers practiced selective breeding, while 86.22% practiced uncontrolled mating. The age at first mating for cocks and hens was 6.8 months and 6.34 months, respectively, with 6.73 months being the age at first egg. It takes 15.43 days to reach a mean clutch size of 13 eggs. The hatchability and mortality at 8 weeks were 83.44% and 67.57%, respectively. All chickens were sold as live chickens, and the majority (51.59%) of the farmers sold their chickens within the community at 7.23 months. Diseases and predators were the most common challenges affecting farmers in the study area. Consultations with veterinarians, vaccinations, and deworming were uncommon while treating sick chickens mostly using ethnoveterinary medicines was common. The most common qualitative traits were brown (27.88%) and mixed (26.77%) plumage color, white skins (91.45%) and shanks (48.70%), single comb type (91.08%), red earlobe (55.76%), and orange eyes (78.07%). All linear body measurements positively and significantly correlated with the body weight averaging 1.66kgs, an indicator that selection for any of them would result in a corresponding increase in body weight. Principal Component Analysis extracted two components with 69.38% of the total variation. Discussion: The diversity in phenotypes of these chickens and their production systems indicate huge potential for improvement by implementing breeding programs.

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs).

Mental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.

Attenuated total reflection Fourier-transform infrared spectroscopy reveals environment specific phenotypes in clonal Japanese knotweed.

BACKGROUND: Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP. RESULTS: Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters. CONCLUSIONS: Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

Major dietary patterns and their associations with total weight loss and weight loss composition 2-4 years after sleeve gastrectomy.

BACKGROUND: Food intakes 1-2 years following bariatric surgery depend more on patients than the surgery's gastrointestinal tract changes. This study aimed to determine the major dietary patterns of patients after the first two years of sleeve gastrectomy and to investigate their associations with total weight loss (TWL) and the proportion of TWL as fat mass (FM) and fat-free mass (FFM) loss. METHODS: This cross-sectional study included 146 patients undergoing sleeve gastrectomy 2-4 years after surgery. Dietary patterns were determined using principal component analysis based on the 19 food groups. The percentage of FM loss relative to TWL (%FML) and FFM loss relative to TWL (%FFML) were calculated. A suboptimal clinical response was defined as a TWL of less than 25%. High FM loss and excessive FFM loss were defined based on the highest tertiles. Linear and logistic regression models were used to derive unstandardized (B) coefficients and odds ratios (OR), with dietary pattern scores serving as both a continuous and a binary variable (higher vs. lower adherence groups based on median). RESULTS: Two predominant dietary patterns were retained. Each 1-unit increase in the first dietary pattern score characterized by high intakes of fast foods, soft drinks, processed meats, sugar confectionary, salty snacks, grains, and organ meats was associated with higher %FFML (B = 1.99; 95% confidence interval (CI) 0.34, 3.66), lower %FML (B = - 1.84; 95% CI - 3.49, - 0.20), and higher odds of excessive FFM loss (OR = 1.84; 95% CI 1.09, 3.11). Participants with higher adherence to the first dietary pattern had lower %TWL, and greater odds of suboptimal clinical response and excessive FFM loss than those with lower adherence. Each 1-unit increase in score for the second dietary pattern characterized by a high intake of fruits, dairy, vegetables, legumes, eggs, nuts, red meats, poultry, and fish was associated with lower odds of suboptimal clinical response (OR = 0.51; 95% CI 0.31, 0.86). CONCLUSION: Patients should be encouraged to modify their diet by reducing the consumption of ultra-processed foods and increasing their intake of high-quality protein sources, fruits, and vegetables to achieve the best postoperative outcome.

Spiny dogfish, Squalus suckleyi, shows a good tolerance for hypoxia but need long recovery times.

Pacific spiny dogfish, Squalus suckleyi, move to shallow coastal waters during critical reproductive life stages and are thus at risk of encountering hypoxic events which occur more frequently in these areas. For effective conservation management, we need to fully understand the consequences of hypoxia on marine key species such as elasmobranchs. Because of their benthic life style, we hypothesized that S. suckleyi are hypoxia tolerant and able to efficiently regulate oxygen consumption, and that anaerobic metabolism is supported by a broad range of metabolites including ketones, fatty acids and amino acids. Therefore, we studied oxygen consumption rates, ventilation frequency and amplitude, blood gasses, acid-base regulation, and changes in plasma and tissue metabolites during progressive hypoxia. Our results show that critical oxygen levels (P crit) where oxyregulation is lost were indeed low (18.1% air saturation or 28.5 Torr at 13°C). However, many dogfish behaved as oxyconformers rather than oxyregulators. Arterial blood PO2 levels mostly decreased linearly with decreasing environmental PO2. Blood gases and acid-base status were dependent on open versus closed respirometry but in both set-ups ventilation frequency increased. Hypoxia below Pcrit resulted in an up-regulation of anaerobic glycolysis, as evidenced by increased lactate levels in all tissues except brain. Elasmobranchs typically rely on ketone bodies as oxidative substrates, and decreased concentrations of acetoacetate and ß-hydroxybutyrate were observed in white muscle of hypoxic and/or recovering fish. Furthermore, reductions in isoleucine, glutamate, glutamine and other amino acids were observed. After 6 hours of normoxic recovery, changes persisted and only lactate returned to normal in most tissues. This emphasizes the importance of using suitable bioindicators adjusted to preferred metabolic pathways of the target species in conservation physiology. We conclude that Pacific spiny dogfish can tolerate severe transient hypoxic events, but recovery is slow and negative impacts can be expected when hypoxia persists.

Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss.

BACKGROUND: Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. OBJECTIVE: This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years. MATERIALS AND METHODS: Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. RESULTS: The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. CONCLUSIONS: These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.