Search Results (17)

Background and Objectives: The prognostic nutritional index (PNI), a marker reflecting both nutritional and immune status, has been associated with prognosis in various malignancies. However, evidence in metastatic castration-resistant prostate cancer (mCRPC), particularly from non-Asian populations, remains limited. This study aimed to evaluate the prognostic value of baseline PNI and to develop a blood-based prognostic model in mCRPC patients treated with abiraterone acetate (AA), enzalutamide (ENZA), or cabazitaxel (CABA). Materials and Methods: This retrospective study included mCRPC patients treated with AA, ENZA, or CABA before or after docetaxel. PNI was calculated as: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm³). Patients were classified into low-PNI (≤40.8) and high-PNI (>40.8) groups using the median PNI value. Survival outcomes were analyzed using Kaplan–Meier and Cox regression methods. Results: A total of 299 patients were analyzed: 133 (44.5%) received AA, 106 (35.5%) ENZA, and 60 (20.0%) CABA. Patients with high PNI had significantly longer median overall survival (OS; 30.2 vs. 12.6 months, p < 0.001), radiologic progression-free survival (rPFS; 13.5 vs. 6.7 months, p < 0.001), and PSA progression-free survival (PSA-PFS; 10.2 vs. 5.1 months, p < 0.001). These associations remained significant across all treatment subgroups. In multivariate analysis, prostate surgery (HR: 0.6), high PNI (HR: 0.5), PSA response (HR: 0.5), and elevated ALP (HR: 1.6) were independent predictors of OS. A prognostic model incorporating PNI, alkaline phosphatase, and anemia stratified patients into four risk groups with distinct OS: 49.1, 30.8, 18.8, and 9.1 months, respectively. Conclusions: This is the largest study to date in a non-Asian mCRPC population showing that baseline PNI is a strong, independent prognostic factor for survival. The proposed blood-based tool may aid in clinical risk stratification, pending prospective validation. Full article

This study investigates the environmental regulation of sex expression and seed yield stability in four Thai dioecious hemp (Cannabis sativa L.) cultivars (RPF1, RPF2, RPF3, and RPF4) under different seasonal conditions to optimize seed production. The experiment was conducted across two planting periods (in-season and off-season) to evaluate the effects of varying day lengths and temperatures on growth, reproductive development, and yield. The results showed that shorter day lengths and lower temperatures during the off-season led to an increased proportion of female plants across all cultivars, except RPF3, which exhibited a stable female-to-male ratio. RPF4 had the highest increase in female plants (16%), followed by RPF1 and RPF2 (10%). Seed yield was significantly influenced by seasonal changes, with RPF3 and RPF4 consistently outperforming the other cultivars. In the in-season, RPF3 and RPF4 produced the highest seed yields, reaching 83.4 g/plant and 81.6 g/plant, respectively. During the off-season, both cultivars experienced a decline in seed yield (by 24–26%), primarily due to a reduction in seed production in secondary inflorescences. However, RPF3 compensated for this loss with a 31% increase in seed production at main inflorescences, ensuring yield stability. RPF4 maintained its high yield potential by increasing the proportion of female plants, offsetting the decline in seed yield per plant. Additionally, cumulative growing degree days (CGDD) at harvest were comparable between seasons, with values of 2434 °Cd (in-season) and 2502 °Cd (off-season), indicating that temperature accumulation remained within an optimal range for seed maturation. The study highlights the importance of cultivar selection based on yield component stability and adaptability to seasonal variations. These findings provide valuable insights for improving hemp seed production strategies in Thailand’s diverse agro-climatic conditions. Full article

This paper presents a novel approach for predicting the failure pressure of corroded pipelines by integrating empirical formulas into the loss function of a neural network-based prediction model. Traditional empirical formulas, such as ASME-B31G, DNV RP-F101, and PCORRC, have been widely used for their simplicity but often suffer from significant prediction errors due to the complex interactions between defect parameters and material properties. In contrast, artificial neural networks (ANNs) offer more accurate predictions but require substantial training data. To address these limitations, we propose an integrated loss function that combines the strengths of empirical formulas and the powerful fitting capabilities of ANNs. The proposed loss function incorporates an additional defect factor term predicted by the neural network to compensate for errors caused by varying defect conditions, thereby enhancing the model′s adaptability and accuracy. The model is trained using a diverse dataset of 60 burst test results from various literature sources, covering a wide range of corrosion scenarios. The results demonstrate that the proposed method significantly improves prediction accuracy compared to traditional empirical formulas and ANN models trained with standard loss functions. The proposed approach achieves a mean absolute percentage error (MAPE) of 2.52%, a root mean square error (RMSE) of 0.39 MPa, and a coefficient of determination (R²) of 0.9886 on the validation set. This study highlights the effectiveness of integrating empirical knowledge with data-driven models and provides a robust and accurate solution for predicting the failure pressure of corroded pipelines, contributing to enhanced pipeline integrity assessment and safety management. Full article

Vegetation restoration plays a crucial role in conserving soil and water, as well as rehabilitating ecosystems, by enhancing soil properties and vegetation attributes. The evaluation of the ecological consequences among different vegetation restoration types can be achieved by clarifying the impacts on carbon, water and nutrient storages. In this study, we selected four typical vegetation restoration types (Pinus tabuliformis forest (PTF), Platycladus orientalis forest (POF) and Robinia pseudoacacia forest (RPF) as typical planted forests, and the natural secondary forest (NSF) as the control treatment) in the eastern Loess Plateau of China. The soil properties (at 0–200 cm depth) and vegetation attributes (including arborous, shrubs and herbaceous plants) were measured, as well as calculated soil carbon, water and nutrient storages, with a total of 1600 soil samples and 180 vegetation survey plots. The partial redundancy analysis (pRDA) and correlation analysis were also used to analyze the contributions and relationships among environmental factors, soil eco-hydrology and nutrient supplement services in different forestry ecosystems. The results indicate the following: (1) NSF has the lowest soil bulk density (1.21 ± 0.184 g·cm⁻³). Soil properties varied significantly at vertical scales, and had obvious surface accumulation. (2) Soil moisture storages were better in natural forests than those in planted forests, with more drastic changes in soil moisture dynamics. (3) The soil carbon, nitrogen, and phosphorus storages significantly differed among four vegetation types, with the highest carbon storages in PTF (207.75 ± 0.674 t·ha⁻¹), the highest nitrogen storages in POF (5.54 t·ha⁻¹), and the highest phosphorus storages in RPF (4.33 t·ha⁻¹), respectively. (4) Soil carbon storages depend primarily on the coupling effect of soil properties and precipitation, while nutrient storage is mainly influenced by soil properties. The results quantify the significant differences in soil water, carbon, and nutrient storage across various vegetation restoration types, and reveal the individual and combined contributions of environmental factors, providing new insights into the mechanisms driving these differences. These findings offer practical guidance for the sustainable management of forest ecosystems and the optimization of ecological restoration strategies on the Loess Plateau. Full article

Xanthomonas oryzae pv. oryzicola (Xoc) is a notorious plant pathogen. Like most bacterial pathogens, Xoc has evolved a complex regulatory network to modulate the expression of various genes related to pathogenicity. Here, we have identified TfmR, a transcriptional regulator belonging to the TetR family, as a key player in the virulence mechanisms of this phytopathogenic bacterium. We have demonstrated genetically that tfmR is involved in the hypersensitive response (HR), pathogenicity, motility and extracellular polysaccharide production of this phytopathogenic bacterium. Our investigations extended to exploring TfmR’s interaction with RpfG and HrpX, two prominent virulence regulators in Xanthomonas species. We found that TfmR directly binds to the promoter region of RpfG, thereby positively regulating its expression. Notably, constitutive expression of RpfG partly reinstates the pathogenicity compromised by TfmR-deletion mutants. Furthermore, our studies revealed that TfmR also exerts direct positive regulation on the expression of the T3SS regulator HrpX. Similar to RpfG, sustained expression of HrpX partially restores the pathogenicity of TfmR-deletion mutants. These findings underscore TfmR’s multifaceted role as a central regulator governing key virulence pathways in Xoc. Importantly, our research sheds light on the intricate molecular mechanisms underlying the regulation of pathogenicity in this plant pathogen. Full article

Stenotrophomonas maltophilia is a major threat to the food industry and human health owing to its strong protease production and biofilm formation abilities. However, information regarding regulatory factors or potential mechanisms is limited. Herein, we observed that temperature differentially regulates biofilm formation and protease production, and a cAMP receptor-like protein (Clp) negatively regulates thermosensor biofilm formation, in contrast to protease synthesis. Among four c-di-GMP-related two-component systems (TCSs), promoter fusion analysis revealed that clp transcription levels were predominantly controlled by LotS/LotR, partially controlled by both RpfC/RpfG and a novel TCS Sm0738/Sm0737, with no obvious effect caused by Sm1912/Sm1911. Biofilm formation in Δclp and ΔTCSs strains suggested that LotS/LotR controlled biofilm formation in a Clp-mediated manner, whereas both RpfC/RpfG and Sm0738/Sm0737 may occur in a distinct pathway. Furthermore, enzymatic activity analysis combined with c-di-GMP level indicated that the enzymatic activity of c-di-GMP-related metabolism proteins may not be a vital contributor to changes in c-di-GMP level, thus influencing physiological functions. Our findings elucidate that the regulatory pathway of c-di-GMP-related TCSs and Clp in controlling spoilage or the formation of potentially pathogenic factors in Stenotrophomonas expand the understanding of c-di-GMP metabolism and provide clues to control risk factors of S. maltophilia in food safety. Full article

This study aimed to subject the albino peach palm to cooking and drying processes and characterize the raw pulp (RP), cooked pulp (CP), raw pulp flour (RPF), and cooked pulp flour (CPF). The product’s chemical composition, bioactive compounds, and physicochemical, color, thermal, morphological, and functional–technological properties were evaluated. The proximate composition showed that carbohydrates were the main constituents of all the products (69.59–72.08 g/100 g). The cooking process decreased the lipids (10.21 to 8.63 g/100 g), dietary fiber (13.64 to 12.81 g/100 g), and total sugar content (59.18 to 49.10 g/100 g) of the CP. The colorimetric parameters indicated a significant browning of the CP and CPF, which can be attributed to the Maillard reaction and lipid oxidation. After cooking, the total phenolic compound and ascorbic acid content decreased in the pulp. The RPF and CPF displayed different thermogravimetric behaviors. The spectral patterns in the infrared region showed the characteristic bands of organic compounds that are present in the structure of starches. The scanning electron microscopy showed amyloplast and fiber bundles with starches in the RP and gelatinized starch granules in the CP and CPF. The RPF presented small and heterogeneous starch granules with isolated amyloplast. The RPF and CPF showed different granulometric patterns and technological indices. The results suggest that the pulp and flour from the pulp of albino peach palms can be exploited by the food, pharmaceutical, and biotechnological industries. Full article

Nutrient recovery technologies have been constantly developed and optimised to address challenges in water and wastewater management, sanitation, and agri-food systems, while promoting sustainable management of resources and circular phosphorous economy. However, these technologies have been rarely explored beyond the laboratory-scale in developing countries where it is mostly needed. In this study, a nutrient recovery batch reactor system was installed at a local farm in the Philippines to process raw septage from an onsite sanitation system, a septic tank, to recover a high-value fertiliser for local crop production. The batch reactor was used for two processes, namely acid hydrolysis for pre-treatment of septage and chemical precipitation for recovered phosphorous fertiliser (RPF). The recovered fertiliser was then applied to produce eggplants and tomatoes, which are the common crops grown in the farm. Results show that an average of 290 g of RPF was produced for every 100 L of raw septage processed. With hydrolysis, 77% of the phosphate concentration were released as phosphates from the solid component of the raw septage. About 98.5% of phosphates were recovered from the hydrolysed septage. The RPF when applied to the farm’s eggplants and tomatoes has yields comparable to that of the commercial fertilisers. This study was able to demonstrate the potential of a resource-oriented sanitation system that promotes nutrient recycling towards sustainable agriculture that further leads to meeting the United Nations sustainable development goals, particularly zero hunger (goal 2), clean water and sanitation (goal 6), sustainable cities and communities (goal 11), and responsible consumption and production (goal 12). Full article

Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment. Full article

Mycobacterium tuberculosis variant bovis (MBO) has one of the widest known mammalian host ranges, including humans. Despite the characterization of this pathogen in the 1800s and whole genome sequencing of a UK strain (AF2122) nearly two decades ago, the basis of its host specificity and pathogenicity remains poorly understood. Recent experimental calf infection studies show that MBO strain Ravenel (MBO Ravenel) is attenuated in the cattle host compared to other pathogenic strains of MBO. In the present study, experimental infections were performed to define attenuation. Whole genome sequencing was completed to identify regions of differences (RD) and single nucleotide polymorphisms (SNPs) to explain the observed attenuation. Comparative genomic analysis of MBO Ravenel against three pathogenic strains of MBO (strains AF2122-97, 10-7428, and 95-1315) was performed. Experimental infection studies on five calves each, with either MBO Ravenel or 95-1315, revealed no visible lesions in all five animals in the Ravenel group despite robust IFN-γ responses. Out of 486 polymorphisms in the present analysis, 173 were unique to MBO Ravenel among the strains compared. A high-confidence subset of nine unique SNPs were missense mutations in genes with annotated functions impacting two major MBO survival and virulence pathways: (1) Cell wall synthesis & transport [espH (A103T), mmpL8 (V888I), aftB (H484Y), eccC₅ (T507M), rpfB (E263G)], and (2) Lipid metabolism & respiration [mycP₁(T125I), pks5 (G455S), fadD29 (N231S), fadE29 (V360G)]. These substitutions likely contribute to the observed attenuation. Results from experimental calf infections and the functional attributions of polymorphic loci on the genome of MBO Ravenel provide new insights into the strain’s genotype-disease phenotype associations. Full article

Introduction: Androgen receptor pathway inhibitors (ARPIs) have been increasingly offered to older patients with prostate cancer (PC). However, prognostic factors relevant to their outcome with ARPIs are still little investigated. Methods and Materials: The Meet-URO network ADHERE was a prospective multicentre observational cohort study evaluating and monitoring adherence to ARPIs metastatic castrate-resistant PC (mCRPC) patients aged ≥70. Cox regression univariable and multivariable analyses for radiographic progression-free (rPFS) and overall survival (OS) were performed. Unsupervised median values and literature-based thresholds where available were used as cut-offs for quantitative variables. Results: Overall, 234 patients were enrolled with a median age of 78 years (73–82); 86 were treated with abiraterone (ABI) and 148 with enzalutamide (ENZ). With a median follow-up of 15.4 months (mo.), the median rPFS was 26.0 mo. (95% CI, 22.8–29.3) and OS 48.8 mo. (95% CI, 36.8–60.8). At the MVA, independent prognostic factors for both worse rPFS and OS were Geriatric G8 assessment ≤ 14 (p < 0.001 and p = 0.004) and PSA decline ≥50% (p < 0.001 for both); time to castration resistance ≥ 31 mo. and setting of treatment (i.e., post-ABI/ENZ) for rPFS only (p < 0.001 and p = 0.01, respectively); age ≥78 years for OS only (p = 0.008). Conclusions: Baseline G8 screening is recommended for mCRPC patients aged ≥70 to optimise ARPIs in vulnerable individuals, including early introduction of palliative care. Full article

Objectives: To evaluate the management and outcome of idiopathic retroperitoneal fibrosis (iRPF) in Japan, and to identify its clinical biomarker. Methods: We retrospectively analyzed 129 patients with iRPF treated between January 2008 and May 2018 at 12 university and related hospitals. Patients treated with glucocorticoid were analyzed to identify a predictive biomarker. These patients were classified into three groups according to overall effectiveness (no change: NC, complete response: CR, and partial response groups: PR), and each parameter was compared statistically. Results: Male–female ratio was 5:1, and median age at diagnosis was 69 (33–86) years. Smoking history was reported in 59.6% of the patients. As treatment, 95 patients received glucocorticoid therapy with an overall response rate of 84%. As a result, serum concentration of IgG4 was significantly decreased in NC group compared with the other two groups (56.6 mg/dL vs. 255 mg/dL, 206 mg/dL, p = 0.0059 and 0.0078). ROC analysis was performed between the nonresponder (NC) and responder groups (CR + PR) to identify the cut-off value of serum IgG4 as a predictive marker. As a result, AUC of 0.793 was confirmed. Conclusions: Pre-treatment serum IgG4 concentration may have potential as a predictive biomarker of steroid treatment. Full article

In order to study the residual strength of buried pipelines with internal corrosion defects in seasonally frozen soil regions, we established a thermo-mechanical coupling model of a buried pipeline under differential frost heave by using the finite element elastoplastic analysis method. The material nonlinearity and geometric nonlinearity were considered as the basis of analysis. Firstly, the location of the maximum Mises equivalent stress in the inner wall of the buried non-corroded pipeline was determined. Furthermore, the residual strength of the buried pipeline with corrosion defects and the stress state of internal corrosion area in the pipeline under different defect parameters was analyzed by the orthogonal design method. Based on the data results of the finite element simulation calculation, the prediction formula of residual strength of buried pipelines with internal corrosion defects was obtained by SPSS (Statistical Product and Service Solutions) fitting. The prediction results were analyzed in comparison with the evaluation results of B31G, DNV RP-F101 and the experimental data of hydraulic blasting. The rationality of the finite element model and the accuracy of the fitting formula were verified. The results show that the effect degree of main factors on residual strength was in order of corrosion depth, corrosion length, and corrosion width. when the corrosion length exceeds 600 mm, which affects the influence degree of residual strength will gradually decrease. the prediction error of the fitting formula is small and the distribution is uniform, it can meet the prediction requirements of failure pressure of buried pipelines with internal corrosion defects in seasonally frozen soil regions. This method may provide some useful theoretical reference for the simulation real-time monitoring and safety analysis in the pipeline operation stage. Full article

In arid and semi-arid regions, plastic film mulching can effectively improve crop yield, but with the increase of service life, a lot of residual plastic film (R_PF) remains in the soil. The application of a R_PF to a soil will alter soil moisture processes, and thus, affect the soil water distribution and its effectiveness. A quadratic regression orthogonal design was used to study the effects of initial moisture content (I_MC), dry bulk density (D_BD), residual plastic film content (R_PFC), and the burial depth of R_PF on the migration time of wetting front (M_F), moisture content (M_C), and accumulative infiltration (A_I) of a test soil. It was found that I_MC, D_BD, and R_PFC were the main factors affecting M_C, M_F, and A_I, while the burial depth of R_PF had no significant influence. The order of influence for the factors affecting M_F was I_MC > D_BD > R_PFC, while the order of influence for the factors affecting M_C and A_I was D_BD > I_MC > R_PFC. R_PFC was parabolic in relation to M_F, M_C, and A_I, when it was in the range of 50–100 kg/hm², while within the same range M_C and A_I reached a maximum and M_F reached a minimum. The analysis of the interactive responses revealed that when the D_BD was greater than 1.29g/cm³, the M_F initially decreased and then increased with the increase of R_PFC. When the R_PFC was more than 100 kg/hm², the M_F initially increased and then decreased with the increase of the D_BD. When the D_BD was larger than 1.31 g/cm³, the A_I initially increased and then decreased with the increase of R_PFC. It was apparent that the R_PF not only had a blocking effect on the wetting front, but also affected the water flow. When the R_PFC was between 50 and 100 kg/hm², the soil M_C was significantly increased. It was suggested that the R_PF pollution area should increase the mechanical recovery of plastic film, standardize the use and recycling of agricultural R_PF, optimize the planting model, and establish a recyclable model for the treatment of R_PF pollution, and it was proposed that the R_PFC remaining after recovery of the R_PF should be less than 50 kg/hm².This study can prove the law of soil water movement in the residue film pollution area and provide reference and solution ideas for the comprehensive treatment of residue film pollution in farmland. Full article

Picture fuzzy model is a generalized structure of intuitionistic fuzzy model in the sense that it not only assigns the membership and nonmembership values in the form of orthopair $(\mu ,\nu )$ to an element, but it assigns a triplet $(\mu ,\eta ,\nu ),$ where $\eta$ denotes the neutral degree and the difference $\pi =1-(\mu +\eta +\nu )$ indicates the degree of refusal. The q-rung picture fuzzy set( $\mathrm{q}$ -RPFS) provides a wide formal mathematical sketch in which uncertain and vague conceptual phenomenon can be precisely and rigorously studied because of its distinctive quality of vast representation space of acceptable triplets. This paper discusses some properties including edge regularity, total edge regularity and perfect edge regularity of q-rung picture fuzzy graphs (q-RPFGs). The work introduces and investigates these properties for square q-RPFGs and q-RPF line graphs. Furthermore, this study characterizes how regularity and edge regularity of q-RPFGs structurally relate. In addition, it presents the concept of ego-networks to extract knowledge from large social networks under q-rung picture fuzzy environment with algorithm. Full article