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Bile acids (BA) are among the most abundant metabolites produced by the gut microbiome. Primary BAs produced in the liver are converted by gut bacterial 7-α-dehydroxylation into secondary BAs, which can differentially regulate host health via signaling based on their varying affinity for BA receptors. Despite the importance of secondary BAs in host health, the regulation of 7-α-dehydroxylation and the role of diet in modulating this process is incompletely defined. Understanding this process could lead to dietary guidelines that beneficially shift BA metabolism. Dietary fiber regulates gut microbial composition and metabolite production. We tested the hypothesis that feeding mice a diet rich in a fermentable dietary fiber, resistant starch (RS), would alter gut bacterial BA metabolism. Male and female wild-type mice were fed a diet supplemented with RS or an isocaloric control diet (IC). Metabolic parameters were similar between groups. RS supplementation increased gut luminal deoxycholic acid (DCA) abundance. However, gut luminal cholic acid (CA) abundance, the substrate for 7-α-dehydroxylation in DCA production, was unaltered by RS. Further, RS supplementation did not change the mRNA expression of hepatic BA producing enzymes or ileal BA transporters. Metagenomic assessment of gut bacterial composition revealed no change in the relative abundance of bacteria known to perform 7-α-dehydroxylation. P. ginsenosidimutans and P. multiformis were positively correlated with gut luminal DCA abundance and increased in response to RS supplementation. These data demonstrate that RS supplementation enriches gut luminal DCA abundance without increasing the relative abundance of bacteria known to perform 7-α-dehydroxylation.

Invasive plant species have been recognized as adversely affecting native ecosystems. Some of these plant species become problematic in disturbed environments such as urbanized, agricultural, and abandoned developed or farmed land. In some cases, they can dominate the invaded ground, preventing a transition back to the native plant community. In Tenerife (Canary Islands), the invasive plant species Ulex europaeus L. has established dense infestations in abandoned agricultural lands. Removing such invasive species in Tenerife through ecological restoration is crucial for restoring ecosystem functionality and promoting biodiversity. This study evaluates various management methods for U. europaeus in abandoned fields, assessing their impact on species richness, diversity, composition, and regeneration. The findings can inform management strategies to combat this invasive species, contributing to biodiversity conservation and ecosystem resilience. The study was conducted in two highly invaded areas, evaluating chemical (C), mechanical (M), mechanical and chemical (MC), and mechanical, chemical, and plantation treatments (MCP), along with a control, to analyze changes in species richness, diversity, and plant community. Results indicate that U. europaeus remains capable of reoccupying treated areas. The different treatments led to differences in species richness and composition. The MCP treatment yielded the best results if the planted native species grew faster than Ulex europaeus, preventing its establishment due to its shade-intolerant nature. However, continual control is required to eliminate U. europaeus regeneration from seeds that can persist for at least 30 years. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSDiuron, a herbicide derived from urea, has been shown to induce urinary bladder urothelial tumors in rodents, leading the U.S. Environmental Protection Agency (USEPA) to designate it as a ‘known/likely’ human carcinogen. In our laboratory, a series of studies investigating the carcinogenic mode of action (MoA) of Diuron have consistently revealed its cytotoxic effects on the urinary bladder urothelium. Prolonged exposure to relatively high doses of Diuron results in urothelial necrosis, regenerative hyperplasia, and eventually, the development of tumors. The hypothesis posited is that Diuron and its metabolites exert toxicity by causing damage to mitochondria, a phenomenon referred to as mitotoxicity. Our research focuses on evaluating how Diuron and its metabolites affect mitochondria isolated from both the urothelium and the liver, the primary organ for Diuron biotransformation. In this context, we present and discuss data pertaining to mitochondria isolated from the liver of Wistar rats exposed to Diuron or its metabolites 3-(3,4-diclorofenil)-1-metilureia (DCPMU) or 3,4-dichloroaniline (DCA) at concentrations ranging from 0.5 to 500 µM in vitro. The findings indicate that, at concentrations of 100 and 500 µM, the tested chemicals induce uncoupling of oxidative phosphorylation, as evidenced by the dissipation of mitochondrial membrane potential and basal oxygen consumption. Notably, at 500 µM, DCA causes mitochondrial swelling, a morphofunctional indicator of severe organelle damage. These outcomes underscore the classification of Diuron and its metabolites, DCA and DCPMU, as mitotoxic to liver cells, given the pronounced mitochondrial dysfunction they induce.Diuron, DCA, and DCPMU uncouple the electron transport chain in to hepatic mitochondria.Diuron, DCA, and DCPMU are mitotoxic agents.The metabolite DCA is the most toxic to hepatic mitochondria and can induce mitochondrial swelling.Diuron, DCA, and DCPMU uncouple the electron transport chain in to hepatic mitochondria.Diuron, DCA, and DCPMU are mitotoxic agents.The metabolite DCA is the most toxic to hepatic mitochondria and can induce mitochondrial swelling. [ABSTRACT FROM AUTHOR]

Background: The identification of benign and malignant pulmonary nodules (BPN and MPN) can significantly reduce mortality. However, a reliable and validated diagnostic model for clinical decision-making is still lacking. Methods: Enzyme-linked immunosorbent assay and electro chemiluminescent immunoassay were utilized to determine the serum concentrations of 7AABs (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5), and 4TTMs (CYFR21, CEA, NSE and SCC) in 260 participants (72 BPNs and 188 early-stage MPNs), respectively. The malignancy probability was calculated using Artificial intelligence pulmonary nodule auxiliary diagnosis system, or Mayo model. Along with age, sex, smoking history and nodule size, 18 variables were enrolled for model development. Baseline comparison, univariate ROC analysis, variable correlation analysis, lasso regression, univariate and stepwise logistic regression, and decision curve analysis (DCA) was used to reduce and screen variables. A nomogram and DCA were built for model construction and clinical use. Training (60%) and validation (40%) cohorts were used to for model validation. Results: Age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was screened out from 18 variables and utilized to establish the regression model for identifying BPN and early-stage MPN, as well as nomogram and DCA for clinical practical use. The AUC of the nomogram in the training and validation cohorts were 0.884 and 0.820, respectively. Moreover, the calibration curve showed high coherence between the predicted and actual probability. Conclusion: This diagnostic model and DCA could provide evidence for upgrading or maintaining the current clinical decision based on malignancy probability stratification. It enables low and moderate risk or ambiguous patients to benefit from more precise clinical decision stratification, more timely detection of malignant nodules, and early treatment. [ABSTRACT FROM AUTHOR]

Background: Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification. Method: RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC). Results: Two distinctm6A modification patterns were identified based on 23m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit. Conclusion: Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of minimizing the sum of a nonconvex differentiable function and a difference of convex (DC) function, where the differentiable function is not restricted to the global Lipschitz gradient continuity assumption. This problem covers a broad range of applications in machine learning and statistics, such as compressed sensing, signal recovery, sparse dictionary learning, matrix factorization, etc. We first take inspiration from the Nesterov acceleration technique and the DC algorithm to develop a novel algorithm for the considered problem. We then study the subsequential convergence of our algorithm to a critical point. Furthermore, we justify the global convergence of the whole sequence generated by our algorithm to a critical point and establish its convergence rate under the Kurdyka–Łojasiewicz condition. Numerical experiments on the nonnegative matrix completion problem are performed to demonstrate the efficiency of our algorithm and its superiority over well-known methods. [ABSTRACT FROM AUTHOR]

To study how species composition and physiognomical structure of Chamaecyparis montane mixed cloud forest in Taiwan changes along main soil, topographical and microclimatic variables on a small scale, we established a 1-ha Lalashan Forest Dynamics Plot in northern Taiwan (24°43' N, 121°26' E, elevation 1758-1782 m a.s.l.). Plot is affected by prevailing NE wind direction related to winter monsoon, and by seasonal fog frequency with highest fog density in autumn and winter. We finished the first census of all woody species in August 2020, and collected environmental factors related to soil and topographical properties, measured microclimate and soil moisture within the plot. In total, we recorded 5220 individuals with diameter at breast height ≥ 1 cm. These belong to 65 species, 42 genera and 29 families, with a basal area of 69.1 m² ha-1, and are dominated by Chamaecyparis obtusa var. formosana, Rhododendron formosanum and Quercus sessilifolia. Modified TWINSPAN classified vegetation into three types (ridge, east-facing slope and valley). Unconstrained ordination showed that the main gradients behind species composition changes are related to windwardness and convexity. Both east-facing slope type and valley type have relatively lower temperatures than ridge type, especially during summer. Convexity is related to soil moisture gradient (from dryer convex to wet concave sites). From soil properties, pH is negatively and phosphorus is positively related to topographical convexity. Collected data will serve as a baseline for future resurveys and monitoring changes within this montane cloud forest. [ABSTRACT FROM AUTHOR]

To analyze the effects of extending lymphocyte cultivation time on the Mitotic Index, frequency of first-division cells, and dose estimation after irradiating blood samples with different doses of radiation. Blood samples from two healthy male volunteers were separately irradiated with three doses (3, 5, and 6 Gy) using a 60Co gamma source (average dose rate: 1.48 kGy.h−1) and cultivated in vitro for conventional (48 h) and extended (56, 68, and 72 h) amounts of time. Colcemid (0.01 µg.mL−1) was added at the beginning of the culture period. Cells were fixed, stained with fluorescence plus Giemsa (FPG), and analyzed under a light microscope. The effects of prolonged culture duration on the Mitotic Index (MI), frequency of first-division cells (M1 cells), and the First-Division Mitotic Index (FDMI) were investigated. The estimation of delivered doses was conducted using a conventional 48h-culture calibration curve. Overall, cells presented higher MI (up to 12-fold) with the extension of culture, while higher radiation doses led to lower MI values (up to 80% reduction at 48 h). Cells irradiated with higher doses (5 and 6 Gy) had the most significant increase (5- to 12-fold) of MI as the cultivation was prolonged. The frequency of M1 cells decreased with the prolongation of culture for all doses (up to 75% reduction), while irradiated cells presented higher frequencies of M1 cells than non-irradiated ones. FDMI increased for all irradiated cultures but most markedly in those irradiated with higher doses (up to 10-fold). The conventional 48h-culture calibration curve proved adequate for assessing the delivered dose based on dicentric frequency following a 72-hour culture. Compared to the conventional 48-hour protocol, extending the culture length to 72 hours significantly increased the Mitotic Index and the number of first-division metaphases of irradiated lymphocytes, providing slides with a better scorable metaphase density. Extending the culture time to 72 hours, combined with FPG staining to score exclusively first-division metaphases, improved the counting of dicentric chromosomes. The methodology presented and discussed in this study can be a powerful tool for dicentric-based biodosimetry, especially when exposure to high radiation doses is involved. [ABSTRACT FROM AUTHOR]

This study exposed the implementation of a novel technique (VMAT LSL) for the planning of moving targets in lung stereotactic body radiation therapy (SBRT). This new technique has been compared to static conformal radiotherapy (3D-CRT), volumetric‐modulated arc therapy (VMAT), and dynamic conformal arc (DCA). The rationale of this study was to lower geometric complexity (54.9% lower than full VMAT) and hence ensure the reproducibility of the treatment delivery by reducing the risk for interplay errors induced by respiratory motion. Dosimetry metrics were studied with a cohort of 30 patients. Our results showed that leaf speed limitation provided conformal number (CN) close to the VMAT (median CN of VMAT LSL is 0.78 vs 0.82 for full VMAT) and was a significant improvement on 3D-CRT and DCA with segment‐weight optimized (respectively 0.55 and 0.57). This novel technique is an alternative to VMAT or DCA for lung SBRT treatments, combining independence from the patient's breathing pattern, from the size and amplitude of the lesion, free from interplay effect, and with dosimetry metrics close to the best that could be achieved with full VMAT. [ABSTRACT FROM AUTHOR]

ABSTRACTBile acids (BA) are among the most abundant metabolites produced by the gut microbiome. Primary BAs produced in the liver are converted by gut bacterial 7-α-dehydroxylation into secondary BAs, which can differentially regulate host health via signaling based on their varying affinity for BA receptors. Despite the importance of secondary BAs in host health, the regulation of 7-α-dehydroxylation and the role of diet in modulating this process is incompletely defined. Understanding this process could lead to dietary guidelines that beneficially shift BA metabolism. Dietary fiber regulates gut microbial composition and metabolite production. We tested the hypothesis that feeding mice a diet rich in a fermentable dietary fiber, resistant starch (RS), would alter gut bacterial BA metabolism. Male and female wild-type mice were fed a diet supplemented with RS or an isocaloric control diet (IC). Metabolic parameters were similar between groups. RS supplementation increased gut luminal deoxycholic acid (DCA) abundance. However, gut luminal cholic acid (CA) abundance, the substrate for 7-α-dehydroxylation in DCA production, was unaltered by RS. Further, RS supplementation did not change the mRNA expression of hepatic BA producing enzymes or ileal BA transporters. Metagenomic assessment of gut bacterial composition revealed no change in the relative abundance of bacteria known to perform 7-α-dehydroxylation. P. ginsenosidimutans and P. multiformis were positively correlated with gut luminal DCA abundance and increased in response to RS supplementation. These data demonstrate that RS supplementation enriches gut luminal DCA abundance without increasing the relative abundance of bacteria known to perform 7-α-dehydroxylation.

Background: This study aimed to develop a nomogram for predicting temporary acute agitated delirium after surgery in patients with chronic subdural hematoma (CSH) without neurological compromise and hospitalized in the neurosurgery. Methods: We included 289 patients with chronic subdural hematoma (CSH) from the medical information system of Yuebei People's Hospital of Shaoguan City, Guangdong Province, and collected 16 clinical indicators within 24 h of admission. We used the least absolute shrinkage and selection operator (LASSO) regression to identify risk factors. We established a multivariate logistic regression model and constructed a nomogram. We performed internal validation by 1,000 bootstrap samples; we plotted a receiver operating curve (ROC) and calculated the area under the curve (AUC), sensitivity, and specificity. We also evaluated the calibration of our model by the calibration curve and the Hosmer-Lemeshow goodness-of-fit test (HL test). We performed a decision curve analysis (DCA) and a clinical impact curve (CIC) to assess the net clinical benefit of our model. Results: The nomogram included alcoholism history, hepatic insufficiency, verbal rating scale for postoperative pain (VRS), pre-hospital modified Rankin Scale (mRS), and preoperative hematoma thickness as predictors. Our model showed satisfactory diagnostic performance with an AUC value of 0.8474 in the validation set. The calibration curve and the HL test showed good agreement between predicted and observed outcomes (p = 0.9288). The DCA and CIC showed that our model had a high predictive ability for the occurrence of postoperative delirium in patients with CSDH. Conclusion: We identified alcoholism, liver dysfunction, pre-hospital mRS, preoperative hematoma thickness, and postoperative VRS pain as predictors of postoperative delirium in chronic subdural hematoma patients. We developed and validated a multivariate logistic regression model and a nomogram. [ABSTRACT FROM AUTHOR]

We address the so-called DC (difference-of-convex functions) composite minimization problems (or DC composite programs) whose objective function is a composition of a DC function with a continuously differentiable mapping. We first develop an algorithm named DC composite algorithm (DCCA in short) for unconstrained DC composite programs and further extend to DC composite programs with constraints of inclusion associated with a smooth mapping and a closed convex set. The convergence analysis of the proposed algorithms is investigated. Applications of DCCA for two different problems, computation of the numerical radius of a square matrix and minimization of composite energies, are presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Riemannian version of the difference of convex algorithm (DCA) to solve a minimization problem involving the difference of convex (DC) function. The equivalence between the classical and simplified Riemannian versions of the DCA is established. We also prove that under mild assumptions the Riemannian version of the DCA is well defined and every cluster point of the sequence generated by the proposed method, if any, is a critical point of the objective DC function. Some duality relations between the DC problem and its dual are also established. To illustrate the algorithm's effectiveness, some numerical experiments are presented. [ABSTRACT FROM AUTHOR]

A comprehensive RFD-FTC-DCA system is proposed for the hypersonic vehicle with saturation constraints to resist the control surface damage, actuator faults, disturbances and parameter perturbation. Firstly, the hypersonic vehicle fault model is established. Secondly, the robust fault detection (RFD) observer is designed to detect the control surface damage and actuator faults accurately under the influences of disturbances and parameter perturbation. Thirdly, the fault-tolerant control (FTC) law is designed to resist the control surface damage, actuator faults, disturbances and parameter perturbation simultaneously. Finally, a dynamic control allocation (DCA) algorithm is designed to reallocate the FTC law and limit the deflection angle and velocity within saturation constraint. The comparison simulation results are presented to demonstrate that the RFD observer can detect the faults accurately and reduce false alarm rates effectively, the FTC law can resist the control surface damage, actuator faults, disturbances and parameter perturbation simultaneously, and the tracing errors of attack angle, side-slip angle and roll angle are only $ \pm 0.7^\circ $ ± 0.7 ∘ , $ \pm 0.2^\circ $ ± 0.2 ∘ and $ \pm 1.5^\circ $ ± 1.5 ∘ , respectively, and the DCA algorithm can reallocate the FTC law and limit the deflection angle and velocity within saturation values. [ABSTRACT FROM AUTHOR]

DC (difference of convex functions) programming and DC algorithm (DCA) are powerful tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh Tao in its preliminary state, then the intensive research of the authors of this paper has led to decisive developments since 1993, and has now become classic and increasingly popular worldwide. For 35 years from their birthday, these theoretical and algorithmic tools have been greatly enriched, thanks to a lot of their applications, by researchers and practitioners in the world, to model and solve nonconvex programs from many fields of applied sciences. This paper is devoted to key open issues, recent advances and trends in the development of these tools to meet the growing need for nonconvex programming and global optimization. We first give an outline in foundations of DC programming and DCA which permits us to highlight the philosophy of these tools, discuss key issues, formulate open problems, and bring relevant answers. After outlining key open issues that require deeper and more appropriate investigations, we will present recent advances and ongoing works in these issues. They turn around novel solution techniques in order to improve DCA's efficiency and scalability, a new generation of algorithms beyond the standard framework of DC programming and DCA for large-dimensional DC programs and DC learning with Big data, as well as for broader classes of nonconvex problems beyond DC programs. [ABSTRACT FROM AUTHOR]

Purpose: For many thoracic tumors, patient respiration can introduce a significant amount of variability in tumor position that must be accounted for during radiotherapy. Of all existing techniques, real-time dynamic tumor tracking (DTT) represents the most ideal motion management strategy but can be limited by the treatment delivery technique. Our objective was to analyze the dosimetric performance of a dynamic conformal arc (DCA) approach to tumor tracking on standard linear accelerators that may offer similar dosimetric benefit, but with less complexity compared to intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT). Methods: Ten patients who previously received free-breathing VMAT for lung cancer were retrospectively analyzed. Patient 4D-CT and respiratory traces were simultaneously acquired prior to treatment and re-planned with DCA and VMAT using the Eclipse v15.6 Treatment Planning System with gated, deep inspiration breath hold (DIBH), and motion encompassment techniques taken into consideration, generating seven new plans per patient. DTT with DCA was simulated using an in-house MATLAB script to parse the radiation dose into each phase of the 4D-CT based on the patient's respiratory trace.Dose distributions were normalized to the same prescription and analyzed using dose volume histograms (DVHs).DVH metrics were assessed using ANOVA with subsequent paired t-tests. Results: The DCA-based DTT plans outperformed or showed comparable performance in their DVH metrics compared to all other combinations of treatment techniques while using motion management in normal lung sparing (p < 0.05). Normal lung sparing was not significantly different when comparing DCA-based DTT to gated and DIBH VMAT (p > 0.05), while both outperformed the corresponding DCA plans (p < 0.05). Simulated treatment times using DCA-based DTT were significantly shorter than both gating and DIBH plans (p < 0.05). Conclusions: A DCA-based DTT technique showed significant advantages over conventional motion encompassment treatments in lung cancer radiotherapy, with comparable performance to stricter techniques like gating and DIBH while conferring greater time-saving benefits. [ABSTRACT FROM AUTHOR]

Invasive plant species have been recognized as adversely affecting native ecosystems. Some of these plant species become problematic in disturbed environments such as urbanized, agricultural, and abandoned developed or farmed land. In some cases, they can dominate the invaded ground, preventing a transition back to the native plant community. In Tenerife (Canary Islands), the invasive plant species Ulex europaeus L. has established dense infestations in abandoned agricultural lands. Removing such invasive species in Tenerife through ecological restoration is crucial for restoring ecosystem functionality and promoting biodiversity. This study evaluates various management methods for U. europaeus in abandoned fields, assessing their impact on species richness, diversity, composition, and regeneration. The findings can inform management strategies to combat this invasive species, contributing to biodiversity conservation and ecosystem resilience. The study was conducted in two highly invaded areas, evaluating chemical (C), mechanical (M), mechanical and chemical (MC), and mechanical, chemical, and plantation treatments (MCP), along with a control, to analyze changes in species richness, diversity, and plant community. Results indicate that U. europaeus remains capable of reoccupying treated areas. The different treatments led to differences in species richness and composition. The MCP treatment yielded the best results if the planted native species grew faster than Ulex europaeus, preventing its establishment due to its shade-intolerant nature. However, continual control is required to eliminate U. europaeus regeneration from seeds that can persist for at least 30 years.

Storing apples for up to a year is a well-established practice aimed at providing a continuous, locally produced fruit supply to consumers and adapting to market trends for optimized profits. Temperature control is the cornerstone of postharvest conservation, and apples are typically kept at temperatures ranging from 0 to 3 °C. However, the energy-intensive process of the initial cool-down and subsequent temperature maintenance poses significant financial challenges with adverse effects on the carbon footprint. Higher storage temperatures could reduce cooling-related energy usage but also pose the risk of enhanced ripening and quality loss. This work explores different storage technologies aiming to reduce energy consumption, such as 1-methylcyclopropene, ultra-low oxygen, and a dynamically controlled atmosphere with raised temperatures. The integration of advanced monitoring and control systems, coupled with data analytics and energy management, in apple storage is also discussed. These strategies can be implemented without cost-intensive construction measures in standard storage facilities. Furthermore, beneficial side effects of higher storage temperatures in terms of a reduced occurrence of storage disorder symptoms and higher maintenance of quality attributes are also discussed for this special issue on sustainable horticultural production systems and supply chains. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of structured nonsmooth difference-of-convex (DC) minimization in which the first convex component is the sum of a smooth and a nonsmooth function, while the second convex component is the supremum of finitely many convex smooth functions. The existing methods for this problem usually have weak convergence guarantees or need to solve lots of subproblems per iteration. Due to this, we propose hybrid algorithms for solving this problem in which we first compute approximate critical points and then check whether these points are approximate D-stationary points. Under suitable conditions, we prove that there exists a subsequence of iterates of which every accumulation point is a D-stationary point. Some preliminary numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a machine learning technique with simple structure, fast learning speed, and excellent generalization ability, which has received a lot of attention since it was proposed. In order to further improve the sparsity of output weights and the robustness of the model, this paper proposes a sparse and robust ELM based on zero-norm regularization and a non-convex quadratic loss function. The zero-norm regularization obtains sparse hidden nodes automatically, and the introduced non-convex quadratic loss function enhances the robustness by setting constant penalties to outliers. The optimization problem can be formulated as the difference of convex functions (DC) programming. This DC programming is solved by using the DC algorithm (DCA) in this paper. The experiments on the artificial and Benchmark datasets verify that the proposed method has promising robustness while reducing the number of hidden nodes, especially on the datasets with higher outliers level. [ABSTRACT FROM AUTHOR]

Artificial insemination (AI) with liquid-stored semen is the most prevalent and efficient assisted reproduction technique in the modern pork industry. Pyruvate dehydrogenase complex component X (PDHX) was demonstrated to be associated with sperm metabolism and affected the boar sperm viability, motility, and fertility. Pyruvate Dehydrogenase Kinases (PDKs) are the key metabolic enzymes that regulate pyruvate dehydrogenase complex (PDHC) activity and also the conversion from glycolysis to oxidative phosphorylation. In the present study, two PDK inhibitors, Dichloroacetate (DCA) and Phenylbutyrate (4-PBA), were added to an extender and investigated to determine their regulatory roles in liquid-stored boar sperm at 17 °C. The results indicated that PDK1 and PDK3 were predominantly located at the head and flagella of the boar sperm. The addition of 2 mM DCA and 0.5 mM 4-PBA significantly enhanced the sperm motility, plasma membrane integrity (PMI), mitochondrial membrane potential (MMP), and ATP content. In addition, DCA and 4-PBA exerted their effects by inhibiting PDK1 and PDK3, respectively. In conclusion, DCA and 4-PBA were found to regulate the boar sperm metabolic activities via PDK1 and PDK3. These both can improve the quality parameters of liquid-stored boar sperm, which will help to improve and optimize liquid-stored boar semen after their addition in the extender. [ABSTRACT FROM AUTHOR]

Abstract Background The primary objective of the research was to develop a method using radiomics-based computed tomography (CT) to predict muscle invasion in bladder cancer (BCa) before surgery. Methods A total of 269 patients with bladder cancer were divided into two groups; training group (n = 188 cases) and validation group (n = 81 cases). Radiomics characteristics were determined by analyzing the CT images of each patient. The least absolute shrinkage and selection operator (LASSO) technique was used for developing a radiomics signature. Furthermore, logistic regression (LR), the support vector machine (SVM), decision tree (DT), and Artificial Neural Network (ANN) models were applied to differentiate between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Their performance was determined using the area under the receiver operating characteristic curve (AUC-ROC). In addition, accuracy, specificity, and sensitivity evaluations were also conducted. Results The radiomics signature was found to be successful in its prediction. A total of 1036 radiomics features were found in the 269 patients, and out of those, 16 were selected as the best predictors of radiomics features. The results revealed that the ANN classifier had the best performance, with a validation set accuracy of 0.950. Conclusions The current work used machine learning and radiomics techniques to successfully construct a prediction model for muscle invasion in bladder cancer. The ANN model produced significant outcomes that may be used in clinical diagnosis or therapy.

Total variation (TV) regularizer has diffusely emerged in image processing. In this paper, we propose a new nonconvex total variation regularization method based on the generalized Fischer-Burmeister function for image restoration. Since our model is nonconvex and nonsmooth, the specific difference of convex algorithms (DCA) are presented, in which the subproblem can be minimized by the alternating direction method of multipliers (ADMM). The algorithms have a low computational complexity in each iteration. Experiment results including image denoising and magnetic resonance imaging demonstrate that the proposed models produce more preferable results compared with state-of-the-art methods.

In this study, the semi-aromatic poly (decamethylene terephthalamide) acid (PA10T) and an array of PA10T/1010 with various copolymerization content of the third monomer 1,10-decanedioic acid (DCA) were synthesized using a productive one-step melt polycondensation method. Importantly, the addition of DCA's effect for PA10T on crystallization behaviors, thermal properties, and mechanical performances were extensively investigated. Additionally, the results of DSC demonstrate that all the melting temperature of PA10T/1010 copolyamides are significantly lower than PA10T by introducing the third monomer DCA, and the heat stability has been not significantly affected according to the results of TGA, suggesting the improvement of processing properties. Besides, compared with PA10T, the bending modulus, breaking elongation, tensile strength, and izod impact strength of PA10T/1010-1 all increase obviously, when the feed ratio of DCA and terephthalic acid (PTA) is 10:90. Thus, introducing bio-based DCA for copolymerization is a promising method to enhance both the processing and mechanical properties of PA10T. [ABSTRACT FROM AUTHOR]

A wide variety and large number of bacterial species live in the gut, forming the gut microbiota. Gut microbiota not only coexist harmoniously with their hosts, but they also induce significant effects on each other. The composition of the gut microbiota can be changed due to environmental factors such as diet and antibiotic intake. In contrast, alterations in the composition of the gut microbiota have been reported in a variety of diseases, including intestinal, allergic, and autoimmune diseases and cancer. The gut microbiota metabolize exogenous dietary components ingested from outside the body to produce short-chain fatty acids (SCFAs) and amino acid metabolites. Unlike SCFAs and amino acid metabolites, the source of bile acids (BAs) produced by the gut microbiota is endogenous BAs from the liver. The gut microbiota metabolize BAs to generate secondary bile acids, such as lithocholic acid (LCA), deoxycholic acid (DCA), and their derivatives, which have recently been shown to play important roles in immune cells. This review focuses on current knowledge of the role of LCA, DCA, and their derivatives on immune cells. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma has obvious primary multidrug resistance and is generally resistant to cisplatin and other chemotherapy drugs and high glycolytic levels may be associated with chemotherapy resistance of cholangiocarcinoma cells. Dichloroacetate (DCA) is a specific inhibitor of PDK, which can promote mitochondrial aerobic oxidation process by activating PDH. In the past few years, there have been an increasing number of studies supporting the action of DCA against cancer, which also provided evidence for targeting metabolism to enhance the efficacy of cholangiocarcinoma chemotherapy. Methods: Glucose uptake and lactic acid secretion were used to detect cell metabolism level. Cell apoptosis and cell cycle were detected to confirm cell fate induced by cisplatin combined with DCA. Mito-TEMPO was used to inhibit mtROS to explore the relationship between oxidative stress and cell cycle arrest induced by DCA under cisplatin stress. Finally, PCR array and autophagy inhibitor CQ were used to explore the potential protective mechanism under cell stress. Results: DCA changed the metabolic model from glycolysis to aerobic oxidation in cholangiocarcinoma cells under cisplatin stress. This metabolic reprogramming increased mitochondrial reactive oxygen species (mtROS) levels, which promoted cell cycle arrest, increased the expression of antioxidant genes and activated autophagy. Inhibition of autophagy further increased the synergistic effect of DCA and cisplatin. Conclusion: DCA increased cisplatin sensitivity in cholangiocarcinoma cells via increasing the mitochondria oxidative stress and cell growth inhibition. Synergistic effects of DCA and CQ were observed in cholangiocarcinoma cells, which further increased the cisplatin sensitivity via both metabolic reprogramming and inhibition of the stress response autophagy. [ABSTRACT FROM AUTHOR]

It is undoubtedly that mathematical modelling and optimisation play a key role in the supply chain and the production management (SCPM). In this paper, we provide a survey on DC (Difference of Convex function) programming and DCA (DC Algorithm), a state-of-the-art optimisation approach for challenging problems in SCPM. DC programming and DCA constitute the backbone of non-convex programming and global optimisation. Whilst DC programming and DCA were widely and successfully investigated in many areas, it seems that they were not so much popular in the community of SCPM. There is therefore a need to further develop this efficient and scalable approach for SCPM applications, especially for large-scale problems in the context of Big data. For such purpose, this paper aims to present benchmark models and state-of-the-art DCA-based methods for solving challenging problems in SCPM systems. We prove that all the benchmark classes of optimisation models appeared in SCPM systems can be formulated/reformulated as a DC program and show how to solve these classes of problems by DCA-based algorithms. We offer the community of researchers in SCPM efficient algorithms in a unified DC programming framework to tackle various applications such as supply chain design, scheduling, multi-stage production/inventory system, vehicle routing, ... [ABSTRACT FROM AUTHOR]

We propose and study a version of the DCA (Difference-of-Convex functions Algorithm) using the ℓ 1 penalty function for solving nonsmooth DC optimisation problems with nonsmooth DC equality and inequality constraints. The method employs an adaptive penalty updating strategy to improve its performance. This strategy is based on the so-called steering exact penalty methodology and relies on solving some auxiliary convex subproblems to determine a suitable value of the penalty parameter. We present a detailed convergence analysis of the method and illustrate its practical performance by applying the method to two nonsmooth discrete optimal control problem. [ABSTRACT FROM AUTHOR]

The second part of our study is devoted to a detailed convergence analysis of two extensions of the well-known DCA method for solving DC (Difference of Convex functions) optimization problems to the case of general cone constrained DC optimization problems. We study the global convergence of the DCA for cone constrained problems and present a comprehensive analysis of a version of the DCA utilizing exact penalty functions. In particular, we study the exactness property of the penalized convex subproblems and provide two types of sufficient conditions for the convergence of the exact penalty method to a feasible and critical point of a cone constrained DC optimization problem from an infeasible starting point. In the numerical section of this work, the exact penalty DCA is applied to the problem of computing compressed modes for variational problems and the sphere packing problem on Grassmannian. [ABSTRACT FROM AUTHOR]

Current methods estimate the time delay (TD) before the onset of dynamic cerebral autoregulation (dCA) from verbal command to stand. A force sensor used during a sit‐to‐stand dCA measure provides an objective moment an individual stands (arise‐and‐off, AO). We hypothesized that the detection of AO would improve the accuracy of TD compared with estimation. We measured middle cerebral artery blood velocity (MCAv) and mean arterial pressure (MAP) for 60 s sitting followed by 2‐min standing, three times separated by 20 min. TD was calculated as the time from: (1) verbal command and (2) AO, until an increase in cerebrovascular conductance index (CVCi = MCAv/MAP). Sixty‐five participants were enrolled: young adults (n = 25), older adults (n = 20), and individuals post‐stroke (n = 20). The TD calculated from AO (x¯$$ \overline{x} $$ = 2.98 ± 1.64 s) was shorter than TD estimated from verbal command (x¯$$ \overline{x} $$ = 3.35 ± 1.72 s, η2 = 0.49, p < 0.001), improving measurement error by ~17%. TD measurement error was not related to age or stroke. Therefore, the force sensor provided an objective method to improve the calculation of TD compared with current methods. Our data support using a force sensor during sit‐to‐stand dCA measures in adults across the lifespan and post‐stroke. [ABSTRACT FROM AUTHOR]

Background: Commensal bacteria secrete metabolites that reach distant cancer cells through the circulation and influence cancer behavior. Deoxycholic acid (DCA), a hormone-like metabolite, is a secondary bile acid specifically synthesized by intestinal microbes. DCA may have both pro- and antineoplastic effects in cancers. Methods and results: The pancreatic adenocarcinoma cell lines, Capan-2 and BxPC-3, were treated with 0.7 µM DCA, which corresponds to the reference concentration of DCA in human serum. DCA influenced the expression of epithelial to mesenchymal transition (EMT)-related genes, significantly decreased the expression level of the mesenchymal markers, transcription factor 7- like 2 (TCF7L2), snail family transcriptional repressor 2 (SLUG), CLAUDIN-1, and increased the expression of the epithelial genes, zona occludens 1 (ZO-1) and E-CADHERIN, as shown by real-time PCR and Western blotting. Consequently, DCA reduced the invasion capacity of pancreatic adenocarcinoma cells in Boyden chamber experiments. DCA induced the protein expression of oxidative/nitrosative stress markers. Moreover, DCA reduced aldehyde dehydrogenase 1 (ALDH1) activity in an Aldefluor assay and ALDH1 protein level, suggesting that DCA reduced stemness in pancreatic adenocarcinoma. In Seahorse experiments, DCA induced all fractions of mitochondrial respiration and glycolytic flux. The ratio of mitochondrial oxidation and glycolysis did not change after DCA treatment, suggesting that cells became hypermetabolic. Conclusion: DCA induced antineoplastic effects in pancreatic adenocarcinoma cells by inhibiting EMT, reducing cancer stemness, and inducing oxidative/nitrosative stress and procarcinogenic effects such as hypermetabolic bioenergetics. [ABSTRACT FROM AUTHOR]

Background & Aims: The colonic epithelium requires continuous renewal by crypt resident intestinal stem cells (ISCs) and transit-amplifying (TA) cells to maintain barrier integrity, especially after inflammatory damage. The diet of high-income countries contains increasing amounts of sugar, such as sucrose. ISCs and TA cells are sensitive to dietary metabolites, but whether excess sugar affects their function directly is unknown. Methods: Here, we used a combination of 3-dimensional colonoids and a mouse model of colon damage/repair (dextran sodium sulfate colitis) to show the direct effect of sugar on the transcriptional, metabolic, and regenerative functions of crypt ISCs and TA cells. Results: We show that high-sugar conditions directly limit murine and human colonoid development, which is associated with a reduction in the expression of proliferative genes, adenosine triphosphate levels, and the accumulation of pyruvate. Treatment of colonoids with dichloroacetate, which forces pyruvate into the tricarboxylic acid cycle, restored their growth. In concert, dextran sodium sulfate treatment of mice fed a high-sugar diet led to massive irreparable damage that was independent of the colonic microbiota and its metabolites. Analyses on crypt cells from high-sucrose–fed mice showed a reduction in the expression of ISC genes, impeded proliferative potential, and increased glycolytic potential without a commensurate increase in aerobic respiration. Conclusions: Taken together, our results indicate that short-term, excess dietary sucrose can directly modulate intestinal crypt cell metabolism and inhibit ISC/TA cell regenerative proliferation. This knowledge may inform diets that better support the treatment of acute intestinal injury.

Understanding the relations between soil features and plant responses is important for agricultural production and nature conservation. The aim of this study was to investigate the importance of the influence of soils’ physical and chemical features on the composition of plant species and richness in grasslands studied on the limestone massif in eastern Serbia (Mt. Rtanj). The data set included 22 phytosociological releves, the same number of corresponding mixed soil samples (0-10 cm depth) and 8 soil profiles. Two vegetation types were distinguished by numerical classification as Saturejion montanae and Festucion valesiacae. The results of detrended correspondence analysis (DCA) indicated that the most important soil parameters affecting species composition in dry grasslands were humus, the content of calcium, soil exchange capacity and base saturation, in addition to total acidity, pH and soil depth. The communities of both vegetation types are developed on Leptosols and Phaeozems. Under similar physical and chemical conditions of the soil, the grassland vegetation exhibits differences in floristic composition.

HIV resistance to the Tat inhibitor didehydro-cortistatin A (dCA) in vitro correlates with higher levels of Tat-independent viral transcription and a seeming inability to enter latency, which rendered resistant isolates more susceptible to CTL-mediated immune clearance. Here, we investigated the ability of dCA-resistant viruses to replicate in vivo using a humanized mouse model of HIV infection. Animals were infected with WT or two dCA-resistant HIV-1 isolates in the absence of dCA and followed for 5 weeks. dCA-resistant viruses exhibited lower replication rates compared to WT. Viral replication was suppressed early after infection, with viral emergence at later time points. Multiplex analysis of cytokine and chemokines from plasma samples early after infection revealed no differences in expression levels between groups, suggesting that dCA-resistance viruses did not elicit potent innate immune responses capable of blocking the establishment of infection. Viral single genome sequencing results from plasma samples collected at euthanasia revealed that at least half of the total number of mutations in the LTR region of the HIV genome considered essential for dCA evasion reverted to WT. These results suggest that dCA-resistant viruses identified in vitro suffer a fitness cost in vivo, with mutations in LTR and Nef pressured to revert to wild type. [ABSTRACT FROM AUTHOR]

This paper addresses the emerging practical requirement that wireless information and power transfer are employed simultaneously in a multiple-input single-output (MISO) secrecy channel. Transmit beamforming without artificial noise and that with artificial noise are considered. In addition, perfect and imperfect channel state information of both the legitimate receivers and eavesdroppers are investigated. These four scenarios correspond to four optimization problems. The considered problems aim to maximize the secrecy rate with constraints on the transmit power and the harvested energy at each receiver. Since these optimization problems are nonconvex, we propose the efficient algorithms based on Difference of Convex (DC) functions programming and DC Algorithm (DCA)—an innovative approach in nonconvex optimization. Exploiting the special structure of these problems, we utilize the semidefinite relaxation technique and S-procedure to reformulate them as general DC programs. The efficient algorithms based on general DCA are developed to solve the corresponding general DC programs. Numerical results are presented which illustrate the effectiveness of our proposed algorithms, they outperform the existing approaches in terms of both quality and rapidity. [ABSTRACT FROM AUTHOR]

In white-box cryptography, early protection techniques have fallen to the automated Differential Computation Analysis attack (DCA), leading to new countermeasures and attacks. A standard side-channel countermeasure, Ishai-Sahai-Wagner’s masking scheme (ISW, CRYPTO 2003) prevents Differential Computation Analysis but was shown to be vulnerable in the white-box context to the Linear Decoding Analysis attack (LDA). However, recent quadratic and cubic masking schemes by Biryukov-Udovenko (ASIACRYPT 2018) and Seker-Eisenbarth-Liskiewicz (CHES 2021) prevent LDA and force to use its higher-degree generalizations with much higher complexity. In this work, we study the relationship between the security of these and related schemes to the Learning Parity with Noise (LPN) problem and propose a new automated attack by applying an LPN-solving algorithm to white-box implementations. The attack effectively exploits strong linear approximations of the masking scheme and thus can be seen as a combination of the DCA and LDA techniques. Different from previous attacks, the complexity of this algorithm depends on the approximation error, henceforth allowing new practical attacks on masking schemes which previously resisted automated analysis. We demonstrate it theoretically and experimentally, exposing multiple cases where the LPN-based method significantly outperforms LDA and DCA methods, including their higher-order variants. This work applies the LPN problem beyond its usual post-quantum cryptography boundary, strengthening its interest for the cryptographic community, while expanding the range of automated attacks by presenting a new direction for breaking masking schemes in the white-box model.