Green plastics: Direct production from grocery wastes to bioplastics and structural characterization by using synchrotron FTIR.

Lignocellulosic bioplastics were produced using four different green wastes: hemp, parsley stem, pineapple leaves and walnut shell. Two different solutions were used to dissolve the green wastes: trifluoroacetic acid (TFA) and pure water. The changes in their natural structures and the solvent effect during the regeneration in biofilm formation were investigated by using Synchrotron FTIR Microspectroscopy (SR-µFTIR). The presence of cellulose, hemicellulose and lignin components in the water-based biofilms was confirmed. After dissolving in TFA, the spectra demonstrated some additional bands especially in the hemicellulose region. This is due to the hydrolysis of ester bonds and conversion to carboxylic acids. Principal component analysis showed grouping due to different solvents and polymer addition. Hemp-PVA (Polyvinyl Alcohol) composite biofilms were obtained by adding polyvinyl alcohol to the hemp solution to give extra strength to the hemp biofilms. It has been shown that water-based hemp-PVA biofilms do not cause any significant spectral changes, comparing with pure hemp and PVA spectra. However, after dissolving in TFA, unlike water-based biofilms, it appears that TFA molecules are retained by PVA through hydrogen bonds of TFA's carboxylic acid and hydroxyl groups and distinct spectral regions belong to TFA bands are clearly identified.

Pure arterial malformation of the fetal PCA treated with flow diverter stent-case report and literature review.

INTRODUCTION: Thirty-three-year-old female underwent diagnostic work-up for chronic headaches. CASE REPORT: Cerebral DSA on the right side showed fetal posterior cerebral artery (PCA) with unusually shaped arterial malformation on its proximal part. Malformation was formed by multiple convolutions and loops with no arteriovenous shunting. During endovascular procedure, flow diverter stent was placed in the right C6 segment. Patient was put on double antiaggregation therapy. Four weeks after procedure, patient was admitted to ER with signs of dyphasia. No recent ischemic lesions were detected on emergent magnetic resonance. Dyphasia resolved spontaneously. On follow-up magnetic resonance angiography 5 years after the procedure, main portion of malformation has diminished with slightly filling of posterior part supplied by right P1. DISCUSSION: Here, reported finding is consistent with previously described pure arterial malformation (PAM). Main intention of our treatment was to induce malformation remodeling with flow diverter placement. Two main risks arise when using flow diverters on fetal PCA region. Firstly, there is possibility of occluding fetal PCA alongside malformation. On the contrary, high flow through fetal PCA could hinder malformation thrombosis. Some case series reported high rate of fetal PCA occlusion without ischemic sequelae in the PCA teritory. Others consider flow diverters highly ineffective treatment for the fetal PCA aneurysms due to low rate of complete aneurysm occlusion. CONCLUSION: We presented first reported case in which flow diverter was used to treat PAM. Slow malformation thrombosis leaves enough time for collateral circulation to recruit and supply territory of the vessel affected by the malformation.

Insights on the comparative affinity of ribonucleic acids with plant-based beta carboline alkaloid, harmine: Spectroscopic, calorimetric and computational evaluation.

Small molecules as ligands target multifunctional ribonucleic acids (RNA) for therapeutic engagement. This study explores how the anticancer DNA intercalator harmine interacts various motifs of RNAs, including the single-stranded A-form poly (rA), the clover leaf tRNAphe, and the double-stranded A-form poly (rC)-poly (rG). Harmine showed the affinity to the polynucleotides in the order, poly (rA) > tRNAphe > poly (rC)·poly (rG). While no induced circular dichroism change was detected with poly (rC)poly (rG), significant structural alterations of poly (rA) followed by tRNAphe and occurrence of concurrent initiation of optical activity in the attached achiral molecule of alkaloid was reported. At 25 °C, the affinity further showed exothermic and entropy-driven binding. The interaction also highlighted heat capacity (ΔC o p ) and Gibbs energy contribution from the hydrophobic transfer (ΔG hyd) of binding with harmine. Molecular docking calculations indicated that harmine exhibits higher affinity for poly (rA) compared to tRNAphe and poly (rC)·poly (rG). Subsequent molecular dynamics simulations were conducted to investigate the binding mode and stability of harmine with poly(A), tRNAphe, and poly (rC)·poly (rG). The results revealed that harmine adopts a partial intercalative binding with poly (rA) and tRNAphe, characterized by pronounced stacking forces and stronger binding free energy observed with poly (rA), while a comparatively weaker binding free energy was observed with tRNAphe. In contrast, the stacking forces with poly (rC)·poly (rG) were comparatively less pronounced and adopts a groove binding mode. It was also supported by ferrocyanide quenching analysis. All these findings univocally provide detailed insight into the binding specificity of harmine, to single stranded poly (rA) over other RNA motifs, probably suggesting a self-structure formation in poly (rA) with harmine and its potential as a lead compound for RNA based drug targeting.

Risk factors for secondary bladder cancer following prostate cancer radiotherapy.

This review investigates the complex landscape of secondary bladder cancer (SBC) after radiotherapy for prostate cancer (PCa). External beam radiotherapy (EBRT) poses an increased risk for SBC, while brachytherapy seems to be associated with smaller increased risks for SBC due to its targeted radiation delivery, sparing the surrounding bladder tissue. Secondary cancers in the bladder are the most frequently diagnosed secondary cancers in the PCa patient population treated with radiotherapy. Patient-related factors are pivotal, with age emerging as a dual-edged factor. While advanced age is a recognized risk for bladder cancer, younger PCa patients exhibit higher susceptibility to radiation-induced cancers. Smoking, a well-established bladder cancer risk factor, increases this vulnerability. Studies highlight the synergistic effect of smoking and radiation exposure, amplifying the likelihood of genetic mutations and SBC. The latency period of SBC, which spans years to decades, remains a critical aspect. There is a strong dose-response relationship between radiation exposure and SBC risk, with higher doses consistently being associated with a higher SBC risk. While specific models for therapeutic radiation-induced SBC are lacking, insights from related studies, like the Atomic Bomb survivor research, emphasize the bladder's sensitivity to radiation-induced cancer. Chemotherapy in combination with radiotherapy, although infrequently used in PCa, emerges as a potential risk for bladder cancer. Bladder cancer's complex epidemiology, encompassing risk factors, treatment modalities, and cancer types, provides a comprehensive backdrop. As research refines understanding, we hope that this review contributes to guide clinicians, inform patient care, and shape preventive strategies on SBC.

Racial, ethnic, and socioeconomic disparities in rates of stage IV prostate cancer after USPSTF category "D" recommendation against prostate-specific antigen screening: a retrospective cohort study.

Background: In 2012 the United States Preventative Services Task Force (USPSTF) changed its prostate-specific antigen (PSA) screening recommendation to a category "D". The purpose of this study is to examine racial, ethnic, and socioeconomic differences in risk of presentation with metastatic prostate cancer (mPCa) at time of diagnosis before and after the 2012 USPSTF category "D" recommendation. Methods: This is a population-based cohort study. We identified patients with mPCa at diagnosis within the National Cancer Database from 2004-2017. Logistic regression models were used to examine associations of mPCa with age, race, ethnicity, geographic location, education level, income, and insurance status. Linear regression models assuming underlying binomial distribution were fitted to annual percentage of mPCa at diagnosis for years 2012-2017 to evaluate the post category "D" recommendation era. Results: From 2004 to 2017, 88,987 patients presented with mPCa. A higher percentage of mPCa was noted post-USPSTF category "D" recommendation, with a disproportionately greater increase observed among Hispanics and non-Hispanic Blacks [Δslope/year: Hispanics (0.0092), non-Hispanic Blacks (0.0073) and non-Hispanic Whites (0.0070)]. Insurance status impacts race/ethnicity differently: uninsured Hispanics were 3.66 times more likely to present with mPCa than insured Hispanics, while uninsured non-Hispanic Blacks were 2.62 times more likely to present with mPCa than insured non-Hispanic Blacks. Household income appears to be associated with differences in mPCa, particularly among non-Hispanic Blacks. Those earning <$30,000 were more likely to present with mPCa compared to higher income brackets. Conclusions: Since the USPSTF grade "D" recommendation against PSA screening, the percentage of mPCa at diagnosis has increased, with a higher rate of increase among Hispanic and non-Hispanic Blacks compared to non-Hispanic Whites.

METTL14 inhibits the proliferation, migration and invasion of prostate cancer cells by increasing m6A methylation of CDK4.

Background: Methyltransferase-like (METTL) plays an important role in various biological processes, but its role in prostate cancer (PCa) is still unclear. This study aimed to explore the mechanism by which methyltransferase-like 14 (METTL14) inhibits the physiological activity of PCa cells by increasing the N6-methyladenosine (m6A) modification of cyclin-dependent kinase 4 (CDK4). Methods: Clinical samples were collected for bioinformatics analysis. A PCa mouse model was constructed. Cell counting kit-8 (CCK-8), flow cytometry, colony formation assays, scratch assays, Transwell assays, real-time quantitative polymerase chain reaction (RT-qPCR), immunofluorescence and western blotting were used to detect the corresponding indicators. Results: METTL14 was found to be beneficial to inhibit the proliferation, invasion, and migration of PCa cells. When the m6A RNA increased, the half-life of CDK4 mRNA decreased after oe-METTL14 (overexpression of METTL14). Overexpression of CDK4 reversed the effect of oe-METTL14. Coimmunoprecipitation experiments revealed there were interactions between CDK4 and forkhead box M1 (FOXM1). Transfection of si-CDK4 was similar to transfection of oe-METTL14. After transfection with oe-FOXM1, the invasion and migration ability of cells increased, and cell apoptosis decreased. After transfection with si-FOXM1 alone, autophagy related 7 (ATG7) expression was significantly downregulated, and autophagy levels were reduced. The overexpression of ATG7 reversed the effect of si-FOXM1. The tumor volume and weight of the oe-METTL14 group mice were significantly reduced, and tumor proliferation was decreased in comparison to untreated tumor-bearing mice. Conclusions: METTL14 inhibits the invasion and migration of PCa cells and induces cell apoptosis by inhibiting CDK4 stability and FOXM1/ATG7-mediated autophagy.

A review of tanshinone compounds in prostate cancer treatment.

Prostate cancer (PCa) is one of the most common malignant epithelial tumors in men worldwide. PCa patients are initially sensitive to chemotherapy, but patients in the advanced stages of PCa eventually develop resistance, leaving them with limited therapeutic options. Therefore, it is very important to screen new drugs for treating PCa. Salvia miltiorrhiza is a common Chinese herbal medicine used in some Asian countries. It has many functions and is widely used to treat a variety of diseases, including heart diseases and cancers. For the past few years, research has shown that liposoluble constituents of tanshinones (TANs), including cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I, exhibit good anticancer activity in PCa. In this study, we review the progress of TAN compounds (cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I) in treating PCa over the past decade. These compounds can act on the same molecular mechanisms, as they have a very similar structure; they are also found to work slightly differently in PCa. According to current studies, compared with other TAN compounds, TAN IIA appears to hold more potential for treating PCa. The toxicity, side effects or biodistribution of Salvia miltiorrhiza and these four TANs need to be confirmed with further research. Findings obtained in this study may provide important information for the potential clinical application of cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I in the treatment of PCa.

Outpatient Patient Controlled Analgesia (PCA) for the Palliative Care Patient.

This study explores the under-researched domain of patient-controlled analgesia (PCA) for cancer pain management in adult outpatients, focusing on the transition from patient-controlled analgesia pumps (PCA pump) to oral medications. While existing literature primarily addresses the use of PCA in inpatient settings, this descriptive study investigates the initiation of outpatient PCA in palliative care patients. The retrospective chart review includes data from all admissions between July 1, 2014, and December 31, 2020. Among the 49 identified patients, 41 were admitted for cancer-related pain, with an indication for PCA such as insufficient pain relief, highly fluctuating pain, or inadequate response to other routes. Of these patients, 13 were successfully transitioned from outpatient PCA to oral opioids. The study underscores the effective use of PCA as a transitional tool following a pain crisis that necessitates inpatient admission. Future research avenues could explore healthcare utilization, length of stay, and required outpatient resources, such as home visits or telehealth, for optimal PCA use in outpatient settings.

The value of synthetic magnetic resonance imaging in the diagnosis and assessment of prostate cancer aggressiveness.

Background: Synthetic magnetic resonance imaging (SyMRI) is a fast, standardized, and robust novel quantitative technique that has the potential to circumvent the subjectivity of interpretation in prostate multiparametric magnetic resonance imaging (mpMRI) and the limitations of existing MRI quantification techniques. Our study aimed to evaluate the potential utility of SyMRI in the diagnosis and aggressiveness assessment of prostate cancer (PCA). Methods: We retrospectively analyzed 309 patients with suspected PCA who had undergone mpMRI and SyMRI, and pathologic results were obtained by biopsy or PCA radical prostatectomy (RP). Pathological types were classified as PCA, benign prostatic hyperplasia (BPH), or peripheral zone (PZ) inflammation. According to the Gleason Score (GS), PCA was divided into groups of intermediate-to-high risk (GS ≥4+3) and low-risk (GS ≤3+4). Patients with biopsy-confirmed low-risk PCA were further divided into upgraded and nonupgraded groups based on the GS changes of the RP results. The values of the apparent diffusion coefficient (ADC), T1, T2 and proton density (PD) of these lesions were measured on ADC and SyMRI parameter maps by two physicians; these values were compared between PCA and BPH or inflammation, between the intermediate-to-high-risk and low-risk PCA groups, and between the upgraded and nonupgraded PCA groups. The risk factors affecting GS grades were identified via univariate analysis. The effects of confounding factors were excluded through multivariate logistic regression analysis, and independent predictive factors were calculated. Subsequently, the ADC+Sy(T2+PD) combined models for predicting PCA risk grade or GS upgrade were constructed through data processing analysis. The diagnostic performance of each parameter and the ADC+Sy(T2+PD) model was analyzed. The calibration curve was calculated by the bootstrapping internal validation method (200 bootstrap resamples). Results: The T1, T2, and PD values of PCA were significantly lower than those of BPH or inflammation (P≤0.001) in both the PZ or transitional zone. Among the 178 patients with PCA, intermediate-to-high-risk PCA group had significantly higher T1, T2, and PD values but lower ADC values compared with the low-risk group (P<0.05), and the diagnostic efficacy of each single parameter was similar (P>0.05). The ADC+Sy(T2+PD) model showed the best performance, with an area under the curve (AUC) 0.110 [AUC =0.818; 95% confidence interval (CI): 0.754-0.872] higher than that of ADC alone (AUC =0.708; 95% CI: 0.635-0.774) (P=0.003). Among the 68 patients initially classified as PCA in the low-risk group by biopsy, PCA in the postoperative upgraded GS group had significantly higher T1, T2, and PD values but lower ADC values than did those in the nonupgraded group (P<0.01). In addition, the ADC+Sy(T2+PD) model better predicted the upgrade of GS, with a significant increase in AUC of 0.204 (AUC =0.947; 95% CI: 0.864-0.987) compared with ADC alone (AUC =0.743; 95% CI: 0.622-0.841) (P<0.001). Conclusions: Quantitative parameters (T1, T2, and PD) derived from SyMRI can help differentiate PCA from non-PCA. Combining SyMRI parameters with ADC significantly improved the ability to differentiate between intermediate-to-high risk PCA from low-risk PCA and could predict the upgrade of low-risk PCA as confirmed by biopsy.

Reversed phase HPLC with UHPLC benefits for the determination of tocochromanols in the seeds of edible fruits in the Rosaceae family.

Rosaceae family includes several edible fruit species processed in vast quantities and generates large amounts of seeds valuable in tocopherols. In the present study, the composition of tocochromanols in the seeds of 141 samples was determined by reversed phase high-performance liquid chromatography (RPLC) with diode array detector (DAD), fluorescence detector (FLD) and confirmed by mass detector (MS). The thirteen species belonging to the Rosaceae family were classified by multivariate statistical analysis, hierarchical cluster analysis (HCA) and principal component analysis (PCA) into two groups based on tocochromanols content. Group 'A' includes pears (Pyrus communis), sweet cherry (Prunus avium), sour cherry (Prunus cerasus), apricots (Prunus armeniaca), hexaploid plums (Prunus domestica), diploid plums (Prunus cerasifera), raspberry (Rubus idaeus), and rose hip (Rosa rugosa); while group 'B' quince (Cydonia oblonga), Japanese quince (Chaenomeles japonica), strawberry (Fragaria × ananassa), dessert apples (Malus domestica), and crab apples (Malus spp.). Two rapid (6-7 min) and low pressure (7.2-8.1 MPa) separation methods were developed and validated using two core-shell columns (i) C18 and (ii) F5. The F5 achieved a separation of ß and γ isomers while the C18 column did not.

Evaluating the Prevalence and Clinical Significance of Fetal Posterior Cerebral Artery Variants via Magnetic Resonance Imaging: Insights From a Tertiary Healthcare Facility in Tamil Nadu.

Introduction The prevalence and clinical significance of fetal posterior cerebral artery (FPCA) variants are studied using magnetic resonance imaging (MRI) at Saveetha Medical College. This research focuses on the fetal origin of the posterior cerebral artery (PCA), a condition where the posterior communicating artery (PComA) is larger than the P1 segment of the PCA, affecting cerebral hemodynamics and associated with various cerebrovascular pathologies. Materials and methods This retrospective analysis employed MRI records from the Department of Radio Diagnosis at Saveetha Medical College, conducted between January 2013 and December 2023. The study included patients undergoing MRI for various neurological indications, with specific imaging protocols including T1- and T2-weighted sequences, diffusion-weighted imaging, and magnetic resonance angiography (MRA). Results The study confirmed a prevalence rate of FPCA variants within the expected range of 20%-30%. MRI findings were systematically analyzed by experienced radiologists to assess the presence and characteristics of FPCA variants. The relationship between these variants and clinical symptoms was explored, revealing significant correlations that emphasize the variants' impact on patient outcomes, particularly in the context of cerebrovascular events. Conclusion The study underlines the importance of recognizing FPCA variants due to their significant implications in neurovascular health and cerebrovascular accident (CVA) outcomes. These variants alter cerebral hemodynamics and can complicate neurosurgical and diagnostic procedures. Therefore, personalized treatment and management strategies are crucial for optimizing patient care.

Mathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusions.

The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish (GT), were used to produce teas at concentrations of 1, 2, and 3%, respectively. These teas were produced using 7 different steeping times: 2, 5, 10, 20, 30, 45, and 60 min. It was also aimed to optimize the regression equations utilizing these factors to identify parameters conducive to maximizing Zn, K, Cu, Mg, Ca, Na, and Fe levels; minimizing Al content, and maintaining Mn level at 5.3 mg/L. The optimal conditions for achieving a Mn content of 5.3 mg/L in black Turkish tea entailed steeping at a concentration of 1.94% for 11.4 min. Variations in K and Mg levels across teas were inconsistent with those observed for other minerals, whereas variations in Al, Cu, Fe, Mn, Na, and Zn levels exhibited a close relationship. Overall, mineral levels in tea can be predicted through regression analysis, and by mathematically optimizing the resultant equations, the requisite conditions for tea production can be determined to achieve maximum, minimum, or target mineral values.

Fabrication of Aluminum Foil Integrated Pegylated Gold Nanoparticle Surface-Enhanced Raman Scattering Substrate for the Detection and Classification of Uropathogenic Bacteria.

Rapid detection and classification of pathogenic microbes for food hygiene, healthcare, environmental contamination, and chemical and biological exposures remain a major challenge due to nonavailability of fast and accurate detection methods. The delay in clinical diagnosis of the most frequent bacterial infections, particularly urinary tract infections (UTIs), which affect about half of the population at least once in their lifetime, can be fatal if not detected and treated appropriately. In this work, we have fabricated aluminum (Al) foil integrated pegylated gold nanoparticles (AuNPs) as a potential surface-enhanced Raman scattering (SERS) substrate, which is used for the detection and classification of uropathogens, namely, E. coli, S. aureus, and P. aeruginosa directly from the culture without any pretreatment. The substrate is first drop cast with bacterial pellets and then pegylated AuNPs, and the interaction of two on Al foil base gives identifiable characteristic Raman peaks with good reproducibility. With the use of chemometric methods such as principal component analysis (PCA), the Al foil-based SERS substrate offers a quick, effective detection and classification of three strains of UTI bacteria with the least bacterial concentration (105 cells mL-1) necessary for clinical diagnosis. In addition, this substrate was able to detect E. coli positive clinical samples by giving SERS fingerprint information directly from centrifuged urine samples within minutes. The stability of pegylated AuNPs provides for its application at the point of care with rapid and easy detection of uropathogens as well as the possibility of advancement in healthcare applications.

Estimation of plant pollution removal capacity based on intensive air quality measurements.

This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2) from multiple monitoring stations. The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis.

We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis.

An Analysis of Capsaicin, Dihydrocapsaicin, Vitamin C and Flavones in Different Tissues during the Development of Ornamental Pepper.

As a fruit and vegetable crop, the ornamental pepper is not just highly ornamental but also rich in nutritional value. The quality of ornamental pepper fruits is given in their contents of capsaicin, vitamin C (VC), flavonoids and total phenols. The study concentrated on the accumulation of capsaicin and dihydrocapsaicin in different tissues of 18 peppers during fruit growth and development. The results showed that the pericarp and placenta contained significantly higher levels of capsaicin than dihydrocapsaicin. Additionally, the placenta contained significantly higher levels of both capsaicin and dihydrocapsaicin compared to the pericarp. The content of capsaicin was in the range of 0-6.7915 mg·g-1, the range of dihydrocapsaicin content was 0-5.329 mg·g-1. Interestingly, we found that the pericarp is rich in VC (5.4506 mg·g-1) and the placenta is high in flavonoids (4.8203 mg·g-1) and total phenols (119.63 mg·g-1). The capsaicin is the most important component using the correlation analysis and principal component analysis. The qPCR results substantiated that the expression of genes in the placenta was significantly higher than that in the pericarp and that the expression of genes in green ripening stage was higher than that in red ripening stage. This study could be utilized to select the best ripening stages and tissues to harvest peppers according to the use of the pepper and to the needs of producers. It not only provides a reference for quality improvement and processing for consumers and market but also provides a theoretical basis for high-quality pepper breeding.

Mapping protein conformational landscapes from crystallographic drug fragment screens.

Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COLAV (COnformational LAndscape Visualization), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP-1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens also enable detailed mapping of proteins' conformational landscapes.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

Purpose: To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. Methods: We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results: Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). Conclusion: The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.