Determining the relationship between oral health and physical function in elderly people.

BACKGROUND: Poor oral conditions in the elderly may have numerous effects on general health, including physical fitness and performance. OBJECTIVES: This study aimed to determine the relationship between oral health and physical function in elderly people. METHODS: Physical function and oral health parameters were compared using parametric comparison tests and Pearson correlation analyses. In addition, principal components analysis, hierarchical clustering and multidimensional scaling analysis clustered the patients' physical and oral health scores. The relationship between the groups was also determined using decision tree analysis. RESULTS: A total of 112 elderly patients participated in the study. Grip strength (GS) was higher in patients with high chewing ability, and Timed Up and Go (TUG) scores were lower in the high oral health group (p < .05). GS was correlated with Decay, Missing, and Filled Teeth Index (DMFT) and the number of remaining and functional teeth (p < .05). According to principal component analysis, it was seen that there were three components (oral, functional and quality of life (QoL) parameters), and the features that were related to each other were gathered together. TUG and GS showed the highest relative importance among physical function criteria in the classification based on chewing ability. They were GS and physical activity for oral health-related QoL. CONCLUSION: In the elderly, higher physical function parameters, especially GS may be an indicator of a better oral health and oral health-related QoL. Preventive physical rehabilitation practices, in addition to oral treatments, may be effective in improving oral health in the elderly.

The contribution of different sperm parameters to better explain ram semen cryoresistance.

In order to determine an effective procedure for explaining ram sperm cryoresistance and develop a new model for breeders classification, a retrospective study was conducted using sperm analysis data obtained over two consecutive years from a total of 82 sessions of ram semen cryopreservation. In each session, fresh ejaculates from eight males were collected via artificial vagina, pooled and frozen in liquid nitrogen vapors. After thawing, a total of 19,084 sperm tracks and 11,319 morphometric measurements were analysed. Clustering analyses were applied to establish motile and morphometric sperm subpopulations. Additionally, plasma and acrosome membrane integrity, as well mitochondrial activity using flow cytometry immediately after sperm thawing and following hypoosmotic shock test (HOST) was assessed. To develop a Ram Sperm Cryoresistance Index, Principal Component Analyses (PCA) using 22 variables were conducted. In the first PCA, the parameters that best explain cryoresistance include total motility (TM), motile subpopulation 2 (motSP2, which groups slow, very linear spermatozoa with low lateral head displacement), morphometric subpopulation 1 (morphSP1, grouping spermatozoa with the smallest head size and lowest shape values), sperm plasma membrane integrity immediately after thawing and following hypoosmotic shock test. These parameters collectively account for 77.34 % of the accumulated variance. To emphasize their importance, a second PCA was performed, revealing significant higher weighting coefficients for the quantity (TM) and quality (motSP2) of sperm movement after thawing, compared to the head size and shape of the thawed sperm (morphSP1). Furthermore, HOST Viability played a more decisive role than what was observed under isotonic conditions.

Modulation of reaching by spatial attention.

Attention is needed to perform goal-directed vision-guided movements. We investigated whether the direction of covert attention modulates movement outcomes and dynamics. Right-handed and left-handed volunteers attended to a spatial location while planning a reach toward the same hemifield, the opposite one, or planned a reach without constraining attention. We measured behavioral variables as outcomes of ipsilateral and contralateral reaching and the tangling of behavioral trajectories obtained through principal component analysis as a measure of the dynamics of motor control. We found that the direction of covert attention had significant effects on the dynamics of motor control, specifically during contralateral reaching. Data suggest that motor control was more feedback-driven when attention was directed leftward than when attention was directed rightward or when it was not constrained, irrespectively of handedness. These results may help to better understand the neural bases of asymmetrical neurological diseases like hemispatial neglect.

New evidence for the sensorimotor mismatch theory of weight perception and the size-weight illusion.

The size-weight illusion is a phenomenon where a smaller object is perceived heavier than an equally weighted larger object. The sensorimotor mismatch theory proposed that this illusion occurs because of a mismatch between efferent motor commands and afferent sensory feedback received when lifting large and small objects (i.e., the application of too little and too much lifting force, respectively). This explanation has been undermined by studies demonstrating a separation between the perceived weight of objects and the lifting forces that are applied on them. However, this research suffers from inconsistencies in the choice of lifting force measures reported. Therefore, we examined the contribution of sensorimotor mismatch in the perception of weight in the size-weight illusion and in non-size-weight illusion stimuli and evaluated the use of a lifting force aggregate measure comprising the four most common lifting force measures used in previous research. In doing so, the sensorimotor mismatch theory was mostly supported. In a size-weight illusion experiment, the lifting forces correlated with weight perception and, contrary to some earlier research, did not adapt over time. In a non-size-weight illusion experiment, switches between lifting light and heavy objects resulted in perceiving the weight of these objects differently compared to no switch trials, which mirrored differences in the manner participants applied forces on the objects. Additionally, we reveal that our force aggregate measure can allow for a more sensitive and objective examination of the effects of lifting forces on objects.

Evaluation of Biochemical Characteristics in a Retrospective Cohort of COVID-19 Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has had a significant impact on global health and healthcare systems. This retrospective study aimed to assess the association between biochemical parameters and outcomes in COVID-19 patients in Jazan, Saudi Arabia. METHODS: After establishing the inclusion criteria and obtaining ethical approval, data from 156 reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients were collected from electronic medical records from a general hospital in Samtah, Jazan, from April 2020 to October 2021. The collected data included patient demographics and liver, kidney, heart, and electrolyte function marker levels. Descriptive, inferential, and principal component analyses were conducted. RESULTS: Survival rates varied according to age and body mass index (BMI). Statistical analysis demonstrated that the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), sodium (Na), potassium (K), blood urea nitrogen (BUN), creatinine (Cr), creatine kinase (CK), CK myocardial band (MB), and lactate dehydrogenase (LDH) were significantly higher (P < 0.05) than the reference values, as assessed using the one-sample t-test. Principal component analysis (PCA) also revealed an underlying pattern in the variation of these biochemical markers. These findings suggest that certain biochemical parameters may serve as useful indicators for monitoring the condition of COVID-19 patients. CONCLUSION: This retrospective study in Jazan, Saudi Arabia highlights the association between biochemical parameters and outcomes in COVID-19 patients. Elevated levels of markers of liver, kidney, heart, and electrolyte function suggest organ damage and dysregulation. The pattern identified through PCA provides insights into disease severity. Monitoring these parameters may serve as valuable indicators for assessing COVID-19 patients. Further research is needed to validate these findings, explore their potential for personalized treatment strategies, and improve patient outcomes during the ongoing pandemic.

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Per- and poly-fluoroalkyl substances in agricultural contexts and mitigation of their impacts using biochar: A review.

Growing concern over the presence of per- and polyfluoroalkyl substances (PFAS) in agricultural compartments (e.g., soil, water, plants, soil fauna) has led to an increased interest in scalable and economically feasible remediation technologies. Biochar is the product of pyrolyzing organic materials (crop waste, wood waste, manures, grasses) and has been used as a low-cost adsorbent to remove contaminants including PFAS. This review frames biochar as a strategy for mitigating the detrimental impacts of PFAS in agricultural systems and discusses the benefits of this strategy within the framework of the needs and challenges of contaminant remediation in agriculture. To gauge the optimal physicochemical characteristics of biochar in terms of PFAS adsorption, principal component analysis using >100 data points from the available literature was performed. The main biochar-based PFAS treatment strategies (water filtration, soil application, mixing with biosolids) were also reviewed to highlight the benefits and complications of each. Life cycle analyses on the use of biochar for contaminant removal were summarized, and data from selected studies were used to calculate (for the first time) the global warming potential and net energy demand of various agriculturally important biochar classes (crop wastes, wood wastes, manures) in relation to their PFAS adsorption performance. This review serves to identify key gaps in our knowledge of (i) PFAS adsorption by biochars in agricultural remediation applications and (ii) environmental costs/benefits of biochars in relation to their adsorptive properties toward PFAS. The concepts introduced in this review may assist in developing large-scale biochar-based PFAS remediation strategies to help protect the agricultural food production environment.

Copaifera mildbraedii Desf.: Phytochemical Composition of Extracts, Essential Oil, and In Vitro Biological Activities of Bark.

Copaifera mildbraedii Desf. is an evergreen tree with an umbrella-like crown. It is distributed from south-eastern Nigeria eastward to the Central African Republic (CAR). The aim of this study was to assess the chemical composition and biological activities of C. mildbraedii bark, as well as the chemical composition of the essential oil. Ethyl acetate (EtOAc) and methanol (MeOH) extracts showed a high total phenolic content (TPC) (149.9 and 148.8 mg GAE/g dry residue (dr), respectively), which was related to good antioxidant activity (DPPH) with an IC50 of 21.2 and 12.9 µg/mL, respectively. High-performance liquid chromatography coupled with diode array detector (HPLC-DAD) analysis revealed seven phenolic compounds with myricitrin (13.3 mg/g dr) and 2,4-dihydroxy-3,6-dimethyl benzoic acid (30.7 mg/g dr) as major compounds, while gas chromatography-mass spectrometry (GC-MS) analysis enabled detection of 13 volatile compounds (3 before and 10 after derivatization). Thirty compounds were identified in the essential oil, which corresponds to 65% of all identified compounds. Among the latter, E,E-farnesylacetone and γ-gurjunene were considered as major compounds (8.08 and 10.43%, respectively). The EtOAc extract showed a potent potential, simultaneously, against anti-acetylcholinesterase (AChE), anti-15-lipoxygenase (15-LOX), anti-xanthine oxidase (XOD), and cytotoxic (OVCAR) activities, whereas cyclohexane (CYHA) and dichloromethane (DCM) extracts showed a cytotoxic effect with high percentages of inhibition (95.2%).

Association of dietary patterns with sarcopenia in adults aged 50 years and older.

PURPOSE: Although numerous studies have investigated the impact of dietary factors on the prevention of decreased muscle mass and function, limited research has examined the relationship between dietary patterns and sarcopenia. This study aimed to assess the associations between dietary patterns, and sarcopenia, muscle strength, and mass in adults following a Mediterranean diet residing in southern Italian cities. METHODS: This cross-sectional study utilized data from an existing database, comprising 528 individuals aged 50 years or older who underwent health-screening tests at the Clinical Nutrition Unit of the "R.Dulbecco" University Hospital. Strength was assessed through handgrip strength, and appendicular skeletal muscle mass was estimated using bioelectrical impedance analysis. Dietary intake information was collected through a food frequency questionnaire linked to the MetaDieta 3.0.1 nutrient composition database. Principal Component Analysis, a statistical technique identifying underlying relationships among different nutrients, was employed to determine dietary patterns. Multinomial logistic regression analysis was conducted to estimate the odds ratio for sarcopenia or low handgrip strength in relation to the lowest tertile of dietary pattern adherence compared to the highest adherence. RESULTS: The participants had a mean age of 61 ± 8 years. Four dietary patterns were identified, with only the Western and Mediterranean patterns showing correlations with handgrip strength and appendicular skeletal muscle mass. However, only the Mediterranean pattern exhibited a correlation with sarcopenia (r = - 0.17, p = 0.02). The highest tertile of adherence to the Mediterranean dietary pattern demonstrated significantly higher handgrip strength compared to the lowest tertile (III Tertile: 28.3 ± 0.5 kg vs I Tertile: 26.3 ± 0.5 kg; p = 0.01). Furthermore, even after adjustment, the highest tertile of adherence to the Mediterranean pattern showed a significantly lower prevalence of sarcopenia than the lowest adherence tertile (4% vs 16%, p = 0.04). The lowest adherence to the Mediterranean dietary pattern was associated with increased odds of having low muscle strength (OR = 2.38; p = 0.03; 95%CI = 1.05-5.37) and sarcopenia (OR = 9.69; p = 0.0295; %CI = 1.41-66.29). CONCLUSION: A high adherence to the Mediterranean dietary pattern, characterized by increased consumption of legumes, cereals, fruits, vegetables, and limited amounts of meat, fish, and eggs, is positively associated with handgrip strength and appendicular skeletal muscle mass. The highest adherence to this dietary model is associated with the lowest odds of low muscle strength and sarcopenia. Despite the changes brought about by urbanization in southern Italy compared to the past, our findings continue to affirm the superior benefits of the Mediterranean diet in postponing the onset of frailty among older adults when compared to other dietary patterns that are rich in animal foods.

Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses.

Objectives: This study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques. Methods: Data were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches. Results: PCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01). Conclusion: The study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians.

The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.

Sugar detection in adulterated honey using hyper-spectral imaging with stacking generalization method.

This paper develops a new hybrid, automated, and non-invasive approach by combining hyper-spectral imaging, Savitzky-Golay (SG) Filter, Principal Components Analysis (PCA), Machine Learning (ML) classifiers/regressors, and stacking generalization methods to detect sugar in honey. First, the 32 different sugar concentration levels in honey were predicted using various ML regressors. Second, the six ranges of sugar were classified using various classifiers. Third, the 11 types of honey and 100% sugar were classified using classifiers. The stacking model (STM) obtained R2: 0.999, RMSE: 0.493 ml (v/v), RPD: 40.2, a 10-fold average R2: 0.996 and RMSE: 1.27 ml (v/v) for predicting 32 sugar concentrations. The STM achieved a Matthews Correlation Coefficient (MCC) of 99.7% and a Kappa score of 99.7%, a 10-fold average MCC of 98.9% and a Kappa score of 98.9% for classifying the six sugar ranges and 12 categories of honey types and a sugar.

The effect of COVID-19 on cancer incidences in the U.S.

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin's lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.

Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology.

Aspergillus flavus and its toxic metabolites-aflatoxins infect and contaminate maize kernels, posing a threat to grain safety and human health. Due to the complexity of microbial growth and metabolic processes, dynamic mechanisms among fungal growth, nutrient depletion of maize kernels and aflatoxin production is still unclear. In this study, visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) combined with the scanning electron microscope (SEM) was used to elucidate the critical organismal interaction at kernel (macro-) and microscopic levels. As kernel damage is the main entrance for fungal invasion, maize kernels with gradually aggravated damages from intact to pierced to halved kernels with A. flavus were cultured for 0-120 h. The spectral fingerprints of the A. flavus-maize kernel complex over time were analyzed with principal components analysis (PCA) of hyperspectral images, where the pseudo-color score maps and the loading plots of the first three PCs were used to investigate the dynamic process of fungal infection and to capture the subtle changes in the complex with different hardness of the maize matrix. The dynamic growth process of A. flavus and the interactions of fungus-maize complexes were explained on a microscopic level using SEM. Specifically, fungus morphology, e.g., hyphae, conidia, and conidiophore (stipe) was accurately captured on the microscopic level, and the interaction process between A. flavus and nutrient loss from the maize kernel tissues (i.e., embryo, and endosperm) was described. Furthermore, the growth stage discrimination models based on PLSDA with the results of CCRC = 100 %, CCRV = 97 %, CCRIV = 93 %, and the prediction models of AFB1 based on PLSR with satisfactory performance (R2C = 0.96, R2V = 0.95, R2IV = 0.93 and RPD = 3.58) were both achieved. In conclusion, the results from both macro-level (Vis/NIR-HSI) and micro-level (SEM) assessments revealed the dynamic organismal interactions in A. flavus-maize kernel complex, and the detailed data could be used for modeling, and quantitative prediction of aflatoxin, which would establish a theoretical foundation for the early detection of fungal or toxin contaminated grains to ensure food security.

High-throughput mRNA sequencing of human placenta shows sex differences across gestation.

INTRODUCTION: Fetal sex affects fetal and maternal health outcomes in pregnancy, but this connection remains poorly understood. As the placenta is the route of fetomaternal communication and derives from the fetal genome, placental gene expression sex differences may explain these outcomes. OBJECTIVES: We utilized next generation sequencing to study the normal human placenta in both sexes in first and third trimester to generate a normative transcriptome based on sex and gestation. STUDY DESIGN: We analyzed 124 first trimester (T1, 59 female and 65 male) and 43 third trimester (T3, 18 female and 25 male) samples for sex differences within each trimester and sex-specific gestational differences. RESULTS: Placenta shows more significant sexual dimorphism in T1, with 94 T1 and 26 T3 differentially expressed genes (DEGs). The sex chromosomes contributed 60.6% of DEGs in T1 and 80.8% of DEGs in T3, excluding X/Y pseudoautosomal regions. There were 6 DEGs from the pseudoautosomal regions, only significant in T1 and all upregulated in males. The distribution of DEGs on the X chromosome suggests genes on Xp (the short arm) may be particularly important in placental sex differences. Dosage compensation analysis of X/Y homolog genes shows expression is primarily contributed by the X chromosome. In sex-specific analyses of first versus third trimester, there were 2815 DEGs common to both sexes upregulated in T1, and 3263 common DEGs upregulated in T3. There were 7 female-exclusive DEGs upregulated in T1, 15 female-exclusive DEGs upregulated in T3, 10 male-exclusive DEGs upregulated in T1, and 20 male-exclusive DEGs upregulated in T3. DISCUSSION: This is the largest cohort of placentas across gestation from healthy pregnancies defining the normative sex dimorphic gene expression and sex common, sex specific and sex exclusive gene expression across gestation. The first trimester has the most sexually dimorphic transcripts, and the majority were upregulated in females compared to males in both trimesters. The short arm of the X chromosome and the pseudoautosomal region is particularly critical in defining sex differences in the first trimester placenta. As pregnancy is a dynamic state, sex specific DEGs across gestation may contribute to sex dimorphic changes in overall outcomes.

On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data.

In this paper we propose the method for detecting potential anomalous cosmic ray particle tracks in big data image dataset acquired by Complementary Metal-Oxide-Semiconductors (CMOS). Those sensors are part of scientific infrastructure of Cosmic Ray Extremely Distributed Observatory (CREDO). The use of Incremental PCA (Principal Components Analysis) allowed approximation of loadings which might be updated at runtime. Incremental PCA with Sequential Karhunen-Loeve Transform results with almost identical embedding as basic PCA. Depending on image preprocessing method the weighted distance between coordinate frame and its approximation was at the level from 0.01 to 0.02 radian for batches with size of 10,000 images. This significantly reduces the necessary calculations in terms of memory complexity so that our method can be used for big data. The use of intuitive parameters of the potential anomalies detection algorithm based on object density in embedding space makes our method intuitive to use. The sets of anomalies returned by our proposed algorithm do not contain any typical morphologies of particle tracks shapes. Thus, one can conclude that our proposed method effectively filter-off typical (in terms of analysis of variance) shapes of particle tracks by searching for those that can be treated as significantly different from the others in the dataset. We also proposed method that can be used to find similar objects, which gives it the potential, for example, to be used in minimal distance-based classification and CREDO image database querying. The proposed algorithm was tested on more than half a million (570,000+) images that contains various morphologies of cosmic particle tracks. To our knowledge, this is the first study of this kind based on data collected using a distributed network of CMOS sensors embedded in the cell phones of participants collaborating within the citizen science paradigm.

Cascade reaction triggered colorimetric array for identification of organophosphorus pesticides congeners.

A modern agriculture uses alternative pest control methods to boost productivity, leading to an accumulation of organophosphorus (OPPs) congeners. This necessitates an intuitive and quick way to identify OPPs congeners. A colorimetric sensor for detecting OPPs congeners using a double-enzyme cascade reaction has been successfully designed and constructed in this study. The OPPs regulate the color changes induced by manganese dioxide nanoflowers (MnO2 NFs) and specific alkaline phosphatases (ALP) during the etching of gold nanopyramids (Au NBPs). The ascorbic acid (AA) produced by ALP hydrolysis inhibits Au NBPs etching by MnO2 NFs oxidized 3, 3', 5, 5'-tetramethylbenzidine (TMB). By inhibiting ALP catalytic activity, OPPs prevent AA formation. In this process, Au NBPs will undergo further etching, resulting in various colors so they can be analyzed semi-quantitatively with the naked eye. It has been found that different types of OPPs inhibit enzymes differently and therefore result in varying degrees of etching of Au NBPs. Principal Component Analysis (PCA) is performed by smart devices that convert R, G, and B signals into digital signals. This colorimetric array tests various foods (tea, apple, and cabbage). Colorimetric visualization sensors combined with data analysis will be used in real-life product development.

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Identification of functional biomarkers of Peganum harmala and Hypericum perforatum using PCA-constructed secondary metabolite maps.

Peganum harmala L. (P. harmala), also known as Espand, Harmel, or Syrian rue, and Hypericum perforatum L. (H. perforatum), commonly known as St. John's wort, are two of the widely cultivated industrial crops and used worldwide in antihepatoma-related products. However, their main functional substances are still not clear, thus impeding the efficacy evaluations and quality controls of relative products around the world. In this work, the anti-hepatoma biomarkers of P. harmala and H. perforatum were clarified through the development of principal components analysis (PCA)-HPLC secondary metabolite mapping models. The chemical fingerprints of plant extracts were profiled by HPLC and then mapped to produce the secondary metabolite models using PCA. The models correlated the chemical information with the anti-hepatoma activities of plant extracts, thus indicating the functional inhibitors of P. harmala and H. perforatum against hepatoma cells. The activities of the identified compounds were validated by cytotoxic and apoptotic assays. The major inhibitors of P. harmala and H. perforatum against human hepatoma were determined to be harmine and quercetin, respectively. The IC50 values and the induced apoptotic rate of harmine on HepG2 cells were 20.7 ± 2.8 µM and 46.7 ± 3.5 %, respectively. The IC50 values and the induced apoptotic rate of quercetin on HepG2 cells were 49.5 ± 6.6 µM and 38.7 ± 2.6 %, respectively. In conclusion, the results significantly expanded the understanding of the biochemical foundations of P. harmala and H. perforatum, thus evidently supporting their current applications around the world. Moreover, harmine and quercetin could be used as biomarkers to evaluate the efficacy and quality of related products of industrial crops in therapeutic and health-improving applications.

Graphene Oxide, Carbon Nanotubes, and Polyelectrolytes-Based Impedanciometric E-Tongue for Estrogen Detection in Complex Matrices.

Currently, it is necessary to maintain the quality of aquifers and water bodies, which means the need for sensors that detect molecules as emerging pollutants (EPs) at low concentrations in aqueous complex solutions. In this work, an electronic tongue (e-tongue) prototype was developed to detect 17ß-estradiol in tap water. To achieve such a prototype, an array of sensors was prepared. Each sensor consists of a solid support with interdigitated electrodes without or with thin films prepared with graphene oxide, nanotubes, and other polyelectrolytes molecules adsorbed on them. To collect data from each sensor, impedance spectroscopy was used to analyze the electrical characteristics of samples of estrogen solutions with different concentrations. To analyze the collected data from the sensors, principal components analysis (PCA) method was used to create a three-dimensional plane using the calculated principal components, namely PC1 and PC2, and the estrogen concentration values. Then, damped least squares (DLS) was used to find the optimal values for the hyperplane calibration, as the sensitivity of this e-tongue was not represented by a straight line but by a surface. For the collected data, from nanotubes and graphene oxide sensors, a calibration curve for concentration given by the 10PC1×0.492-PC2×0.14-14.5 surface was achieved. This e-tongue presented a detection limit of 10-16 M of 17ß-estradiol in tap water.