A novel classification of posterior pelvic exenteration to assess prognosis in female patients with locally advanced primary rectal cancer: a retrospective cohort study from China PelvEx collaborative.

PURPOSE: Surgical techniques and the prognosis of posterior pelvic exenteration for locally advanced primary rectal cancer in female patients pose challenges that need to be addressed. Therefore, we investigated the short-term and survival outcomes of posterior pelvic exenteration in female patients using a novel Peking classification. METHODS: We retrospectively analysed a prospective database from China PelvEx Collaborative across three tertiary referral centres. A total of 172 patients who underwent combined resection for locally advanced primary rectal cancer were classified based on four subtypes (PPE-I [64/172], PPE-II [68/172], PPE-III [21/172], and PPE-IV [19/172]) according to the Peking classification; perioperative characteristics and short-term and oncological outcomes were analysed. RESULTS: Differences were significant among the four groups regarding colorectal reconstruction (p < 0.001), perineal reconstruction (p < 0.001), in-hospital complications (p < 0.05), and urinary retention (p < 0.05). The R0 resection rates for PPE-I, PPE-II, PPE-III, and PPE-IV were 90.6%, 89.7%, 90.5%, and 89.5%, respectively. The 5-year overall survival rates of the PPE-I, PPE-II, PPE-III, and PPE-IV groups were 73.4%, 68.8%, 54.7%, and 37.3%, respectively. Correspondingly, their 5-year disease-free survival rates were 76.0%, 62.5%, 57.7%, and 43.1%, respectively. Notably, the PPE-IV group demonstrated the lowest 5-year overall survival rate (p < 0.001) and 5-year disease-free survival rate (p < 0.001). CONCLUSION: The Peking classification can aid in determining suitable surgical techniques and conducting prognostic assessments in female patients with locally advanced primary rectal cancer.

Love-hate relationship between hepatitis B virus and type 2 diabetes: a Mendelian randomization study.

Objective: The impact of hepatitis B virus (HBV) on the risk of type 2 diabetes (T2D) remains a controversial topic. This study aims to analyze the causal relationship between HBV and T2D using Mendelian randomization (MR). Methods: Single nucleotide polymorphisms on chronic hepatitis B (CHB), liver fibrosis, liver cirrhosis, and T2D were obtained from BioBank Japan Project, European Bioinformatics Institute, and FinnGen. Mendelian randomization was utilized to evaluate exposure-outcome causality. Inverse variance weighted was used as the primary method for MR analysis. To assess horizontal pleiotropy and heterogeneity, we conducted MR-Egger intercept analysis and Cochran's Q test, and the robustness of the MR analysis results was evaluated through leave-one-out sensitivity analysis. Results: MR analysis revealed that CHB was associated with a decreased genetic susceptibility to T2D (OR, 0.975; 95% CI, 0.962-0.989; p < 0.001) while liver cirrhosis (OR, 1.021; 95% CI, 1.007-1.036; p = 0.004) as well as liver cirrhosis and liver fibrosis (OR, 1.015; 95% CI, 1.002-1.028; p = 0.020) were associated with an increased genetic susceptibility to T2D. MR-Egger intercept showed no horizontal pleiotropy (p > 0.05). Cochran's Q showed no heterogeneity (p > 0.05). Leave-one-out sensitivity analysis showed that the results were robust. Conclusion: CHB has the potential to act as a protective factor for T2D, but its effectiveness is constrained by viral load and disease stage. This protective effect diminishes or disappears as viral load decreases, and it transforms into a risk factor with the progression to liver fibrosis and cirrhosis.

Multi-Strategy Boosted Fick's Law Algorithm for Engineering Optimization Problems and Parameter Estimation.

To address the shortcomings of the recently proposed Fick's Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick's Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space. Second, by introducing the Gaussian local variation, the search diversity is increased, and the exploration capability and convergence efficiency are further improved. Further, a comprehensive learning strategy that simultaneously updates multiple individual parameters is introduced to improve search diversity and shorten the running time. Finally, the stability of the update is improved by adding a global search mechanism to balance the distribution of molecules on both sides during seagull updates. To test the competitiveness of the algorithms, the exploration and exploitation capability of the proposed FLAS is validated on 23 benchmark functions, and CEC2020 tests. FLAS is compared with other algorithms in seven engineering optimizations such as a reducer, three-bar truss, gear transmission system, piston rod optimization, gas transmission compressor, pressure vessel, and stepped cone pulley. The experimental results verify that FLAS can effectively optimize conventional engineering optimization problems. Finally, the engineering applicability of the FLAS algorithm is further highlighted by analyzing the results of parameter estimation for the solar PV model.

Focusing on mitochondria in the brain: from biology to therapeutics.

Mitochondria have multiple functions such as supplying energy, regulating the redox status, and producing proteins encoded by an independent genome. They are closely related to the physiology and pathology of many organs and tissues, among which the brain is particularly prominent. The brain demands 20% of the resting metabolic rate and holds highly active mitochondrial activities. Considerable research shows that mitochondria are closely related to brain function, while mitochondrial defects induce or exacerbate pathology in the brain. In this review, we provide comprehensive research advances of mitochondrial biology involved in brain functions, as well as the mitochondria-dependent cellular events in brain physiology and pathology. Furthermore, various perspectives are explored to better identify the mitochondrial roles in neurological diseases and the neurophenotypes of mitochondrial diseases. Finally, mitochondrial therapies are discussed. Mitochondrial-targeting therapeutics are showing great potentials in the treatment of brain diseases.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator.

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

Exploring Information Exchange between Thesium chinense and Its Host Prunella vulgaris through Joint Transcriptomic and Metabolomic Analysis.

BACKGROUND: Thesium chinense known as the "plant antibiotic" is a facultative root hemi-parasitic herb while Prunella vulgaris can serve as its host. However, the molecular mechanisms underlying the communication between T. chinense and its host remained largely unexplored. The aim of this study was to provide a comprehensive view of transferred metabolites and mobile mRNAs exchanged between T. chinense and P. vulgaris. RESULTS: The wide-target metabolomic and transcriptomic analysis identified 5 transferred metabolites (ethylsalicylate, eriodictyol-7-O-glucoside, aromadendrin-7-O-glucoside, pruvuloside B, 2-ethylpyrazine) and 50 mobile genes between T. chinense and P. vulgaris, as well as haustoria formation related 56 metabolites and 44 genes. There were 4 metabolites (ethylsalicylate, eriodictyol-7-O-glucoside, aromadendrin-7-O-glucoside and pruvuloside B) that are transferred from P. vulgaris to T. chinense, whereas 2-ethylpyrazine was transferred in the opposite direction. Furthermore, we inferred a regulatory network potentially involved in haustoria formation, where three metabolites (N,N'-Dimethylarginine/SDMA, NG,NG-Dimethyl-L-arginine, 2-Acetoxymethyl-anthraquinone) showed significant positive correlations with the majority of haustoria formation-related genes. CONCLUSIONS: These results suggested that there was an extensive exchange of information with P. vulgaris including transferred metabolites and mobile mRNAs, which might facilitate the haustoria formation and parasition of T. chinense.

Association between the MDR1 rs1045642 polymorphism and breast cancer risk: An updated meta­analysis.

Multidrug resistance 1 (MDR1) is a transmembrane transporter on the cell membrane. As an ATP-dependent efflux pump, MDR1 is mainly responsible for the adsorption, distribution, metabolism, excretion and transportation of anticancer drugs to cancer cells. Mutations of the MDR1 gene may be associated with the incidence of cancer. In the past decade, associations found between the MDR1 rs1045642 polymorphism and breast cancer have been inconsistent and inconclusive. Therefore, the present study performed a meta-analysis including studies published up until August 16, 2023 to systematically evaluate the association between the MDR1 rs1045642 polymorphism and breast cancer risk. A total of 21 published case studies involving 6,815 patients with breast cancer and 9,227 healthy participants were included in the meta-analysis. Overall, the MDR1 rs1045642 polymorphism was not significantly associated with breast cancer-associated risk. However, in the subgroup analysis, the MDR1 rs1045642 polymorphism was found to be notably associated with a higher risk of breast cancer among Asian populations in recessive models [TT vs. CT + CC; odds ratio (OR)=1.393; 95% confidence interval (CI), 1.143-1.698; P=0.001; I2<25%]. The MDR1 C3435T polymorphism was also associated with a notable decrease in the incidence of breast cancer in mixed ethnicity populations (TT and CT + CC; OR=0.578; 95% CI, 0.390-0.856; P=0.006; I2<25%). In Caucasian populations, the MDR1 rs1045642 polymorphism was not associated with breast cancer risk. In conclusion, the present meta-analysis demonstrated that the MDR1 rs1045642 polymorphism may increase the risk of breast cancer in Asian populations, is associated with a reduced risk of breast cancer in mixed populations but has no notable effect in Caucasian populations.

Effect of viral hepatitis on type 2 diabetes: A Mendelian randomization study.

BACKGROUND: The effects of viral hepatitis (VH) on type 2 diabetes (T2D) remain controversial. AIM: To analyze the causal correlation between different types of VH and T2D using Mendelian randomization (MR). METHODS: Single nucleotide polymorphisms of VH, chronic hepatitis B (CHB), chronic hepatitis C (CHC) and T2D were obtained from the BioBank Japan Project, European Bioinformatics Institute, and FinnGen. Inverse variance weighted, MR-Egger, and weighted median were used to test exposure-outcome associations. The MR-Egger intercept analysis and Cochran's Q test were used to assess horizontal pleiotropy and heterogeneity, respectively. Leave-one-out sensitivity analysis was used to evaluate the robustness of the MR analysis results. RESULTS: The MR analysis showed no significant causal relationship between VH and T2D in Europeans [odds ratio (OR) = 1.028; 95% confidence interval (CI): 0.995-1.062, P = 0.101]. There was a negative causal association between CHB and T2D among East Asians (OR = 0.949; 95%CI: 0.931-0.968, P < 0.001), while there was no significant causal association between CHC and T2D among East Asians (OR = 1.018; 95%CI: 0.959-1.081, P = 0.551). Intercept analysis and Cochran's Q test showed no horizontal pleiotropy or heterogeneity (P > 0.05). Sensitivity analysis showed that the results were robust. CONCLUSION: Among East Asians, CHB is associated with a reduced T2D risk, but this association is limited by HBV load and cirrhosis. Although VH among Europeans and CHC among East Asians are not associated with the risk of T2D, focusing on blood glucose in patients with CHC is still relevant for the early detection of T2D induced by CHC-mediated pathways of hepatic steatosis, liver fibrosis, and cirrhosis.

Adverse birth outcomes and childhood overweight at age of 3-8 years in a prospective cohort study in Tianjin, China.

OBJECTIVES: To explore associations between adverse birth outcomes and childhood overweight at 3-8 years of age. DESIGN: A prospective cohort study. SETTING: Six central urban districts of Tianjin, China. PARTICIPANTS: 1681 woman-child pairs. METHODS: 1681 woman-child pairs were followed up for 8 years in Tianjin, China. Demographic and clinical information including birth outcomes was collected longitudinally, commencing from first antenatal care visit till postpartum period. Offspring height and weight were measured at 3-8 years of age. High and low weight/length ratios (WLR) at birth were, respectively, defined as ≥90th and ≤10th gestational week and sex-specific percentiles. Overweight for children at 3-5 and 6-8 years of age were, respectively, defined as body mass index (BMI)-for-age and -sex above the 2 z-score and 1 z-score curves of the WHO's child growth standards. Binary logistic regression analysis was used to obtain ORs and 95% CI with a stepwise backward selection method to select independent predictors. PRIMARY OUTCOMES MEASURES: Childhood overweight. RESULTS: Of 1681 children, 10.7% (n=179) and 27.8% (n=468) developed overweight at 3-5 and 6-8 years of age, respectively. Large for gestational age (LGA) was associated with increased risk of overweight at 3-5 years of age (aOR: 1.86, 95% CI: 1.27 to 2.72) while high WLR at birth was associated with increased risk of overweight at 6-8 years of age (1.82, 1.41 to 2.34). Low WLR at birth was associated with decreased risk of overweight at 6-8 years of age (0.52, 0.30 to 0.90). CONCLUSIONS: LGA and high WLR at birth predicted childhood overweight at 3-5 and 6-8 years of age, respectively. Low WLR at birth was associated with decreased risk of childhood overweight at 6-8 years of age.

Association of longitudinal trajectories of general and abdominal adiposity during middle age with mental health and well-being in late life: A prospective analysis.

Single measures of adiposity markers, such as body mass index (BMI) and waist circumference (WC), are associated with adverse mental health outcomes; however, long-term patterns of adiposity and their health effects remain unclear. The current study assessed adiposity trajectories during a 14-year span beyond middle age and their relevance to mental well-being in late life, and the contribution of genetic and lifestyle factors to the trajectories. Based on a nationally representative sample with longitudinal anthropometric measures, adiposity trajectories were identified by latent mixture modeling, and logistic regression model was used to estimate their associations with mental well-being, with adjustment for confounders. Of the 3491 eligible participants included (mean [SD] age, 69.5 [8.9] years), five discrete BMI and four WC trajectory patterns were identified over 14 years. Compared with the low-stable BMI group (range, 22.8 to 22.9 kg/m²; representing stable healthy body weight), the high-stable group (range, 34.3 to 35.4 kg/m²; stable obese) was associated with increased risk of depression (odds ratio [OR], 1.63; 95 % CI, 1.28-2.07) and low subjective well-being (OR, 1.35; 95 % CI, 1.02-1.79). Compared with the low-stable WC group (range, 75 to 79 cm healthy WC), the high-increasing group (range, 114 to 121 cm) was associated with increased risk of depression (odds ratio [OR], 1.64; 95 % CI, 1.19-2.25) and low well-being (OR, 1.48; 95 % CI, 1.01-2.16). The adiposity trajectories, especially the high-stable/increasing groups, were driven by genetic factors in a dose-response manner, whereas the high/moderate-increasing groups were also behaviorally related. This longitudinal cohort study reveals that stably high trajectory patterns of central and general adiposity during middle age were associated with higher risk of depression and low well-being in late life. The findings indicate the importance of weight management beyond middle age, such as adherence to a healthy lifestyle, in promoting mental health and well-being.

Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes.

BACKGROUND: Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly the role of gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate gut microbiota and circulating metabolites associated with dietary fiber intake and their relationships with type 2 diabetes (T2D). METHODS: This study included up to 11 394 participants from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Diet was assessed with two 24-hour dietary recalls at baseline. We examined associations of dietary fiber intake with gut microbiome measured by shotgun metagenomics (350 species/85 genera and 1958 enzymes; n=2992 at visit 2), serum metabolome measured by untargeted metabolomics (624 metabolites; n=6198 at baseline), and associations between fiber-related gut bacteria and metabolites (n=804 at visit 2). We examined prospective associations of serum microbial-associated metabolites (n=3579 at baseline) with incident T2D over 6 years. RESULTS: We identified multiple bacterial genera, species, and related enzymes associated with fiber intake. Several bacteria (eg, Butyrivibrio, Faecalibacterium) and enzymes involved in fiber degradation (eg, xylanase EC3.2.1.156) were positively associated with fiber intake, inversely associated with prevalent T2D, and favorably associated with T2D-related metabolic traits. We identified 159 metabolites associated with fiber intake, 47 of which were associated with incident T2D. We identified 18 of these 47 metabolites associated with the identified fiber-related bacteria, including several microbial metabolites (eg, indolepropionate and 3-phenylpropionate) inversely associated with the risk of T2D. Both Butyrivibrio and Faecalibacterium were associated with these favorable metabolites. The associations of fiber-related bacteria, especially Faecalibacterium and Butyrivibrio, with T2D were attenuated after further adjustment for these microbial metabolites. CONCLUSIONS: Among United States Hispanics/Latinos, dietary fiber intake was associated with favorable profiles of gut microbiota and circulating metabolites for T2D. These findings advance our understanding of the role of gut microbiota and microbial metabolites in the relationship between diet and T2D.

Assessment of Disordered Linker Predictions in the CAID2 Experiment.

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

The marine-derived compound TAG alleviates Parkinson's disease by restoring RUBCN-mediated lipid metabolism homeostasis.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and its prevalence is increasing. Currently, no effective therapies for PD exist. Marine-derived natural compounds are considered important resources for the discovery of new drugs due to their distinctive structures and diverse activities. In this study, tetrahydroauroglaucin (TAG), a polyketide isolated from a marine sponge, was found to have notable neuroprotective effects on MPTP/MPP+-induced neurotoxicity. RNA sequencing analysis and metabolomics revealed that TAG significantly improved lipid metabolism disorder in PD models. Further investigation indicated that TAG markedly decreased the accumulation of lipid droplets (LDs), downregulated the expression of RUBCN, and promoted autophagic flux. Moreover, conditional knockdown of Rubcn notably attenuated PD-like symptoms and the accumulation of LDs, accompanied by blockade of the neuroprotective effect of TAG. Collectively, our results first indicated that TAG, a promising PD therapeutic candidate, could suppress the accumulation of LDs through the RUBCN-autophagy pathway, which highlighted a novel and effective strategy for PD treatment.

A strategic study of acupuncture for diabetic kidney disease based on meta-analysis and data mining.

Objective: The specific benefit and selection of acupoints in acupuncture for diabetic kidney disease (DKD) remains controversial. This study aims to explore the specific benefits and acupoints selection of acupuncture for DKD through meta-analysis and data mining. Methods: Clinical trials of acupuncture for DKD were searched in eight common databases. Meta-analysis was used to evaluate its efficacy and safety, and data mining was used to explore its acupoints selection. Results: Meta-analysis displayed that compared with the conventional drug group, the combined acupuncture group significantly increased the clinical effective rate (risk ratio [RR] 1.35, 95% confidence interval [CI] 1.20 to 1.51, P < 0.00001) and high-density lipoprotein cholesterol (mean difference [MD] 0.36, 95% CI 0.27 to 0.46, P < 0.00001), significantly reduced the urinary albumin (MD -0.39, 95% CI -0.42 to -0.36, P < 0.00001), urinary microalbumin (MD -32.63, 95% CI -42.47 to -22.79, P < 0.00001), urine ß2-microglobulin (MD -0.45, 95% CI -0.66 to -0.24, P < 0.0001), serum creatinine (MD -15.36, 95% CI -21.69 to -9.03, P < 0.00001), glycated hemoglobin A1c (MD -0.69, 95% CI -1.18 to -0.19, P = 0.006), fasting blood glucose (MD -0.86, 95% CI -0.90 to -0.82, P < 0.00001), 2h postprandial plasma glucose (MD -0.87, 95% CI -0.92 to -0.82, P < 0.00001), total cholesterol (MD -1.23, 95% CI -2.05 to -0.40, P = 0.003), triglyceride (MD -0.69, 95% CI -1.23 to -0.15, P = 0.01), while adverse events were comparable. Data mining revealed that CV12, SP8, SP10, ST36, SP6, BL20, BL23, and SP9 were the core acupoints for DKD treated by acupuncture. Conclusion: Acupuncture improved clinical symptoms, renal function indices such as uALB, umALB, uß2-MG, and SCR, as well as blood glucose and blood lipid in patients with DKD, and has a favorable safety profile. CV12, SP8, SP10, ST36, SP6, BL20, BL23, and SP9 are the core acupoints for acupuncture in DKD, and this program is expected to become a supplementary treatment for DKD.

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang-Ball Curves.

A method for the approximate merging of disk Wang-Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design.

A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems.

The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb's law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC'17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC'17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems.

Enhancing Deep Edge Detection through Normalized Hadamard-Product Fusion.

Deep edge detection is challenging, especially with the existing methods, like HED (holistic edge detection). These methods combine multiple feature side outputs (SOs) to create the final edge map, but they neglect diverse edge importance within one output. This creates a problem: to include desired edges, unwanted noise must also be accepted. As a result, the output often has increased noise or thick edges, ignoring important boundaries. To address this, we propose a new approach called the normalized Hadamard-product (NHP) operation-based deep network for edge detection. By multiplying the side outputs from the backbone network, the Hadamard-product operation encourages agreement among features across different scales while suppressing disagreed weak signals. This method produces additional Mutually Agreed Salient Edge (MASE) maps to enrich the hierarchical level of side outputs without adding complexity. Our experiments demonstrate that the NHP operation significantly improves performance, e.g., an ODS score reaching 0.818 on BSDS500, outperforming human performance (0.803), achieving state-of-the-art results in deep edge detection.

Development of a drying method for proteins based on protein-hyaluronic acid precipitation.

This study aims to develop a new method to dry proteins based on protein-hyaluronic acid (HA) precipitation and apply the precipitation-redissolution technique to develop highly concentrated protein formulations. Lysozyme was used as a model protein and HA with various molecular weights (MW) were investigated. Under low ionic strength, low-MW HA (e.g., MW: around 5 K) did not induce lysozyme precipitation. Conversely, high-MW HA (e.g., MW: approximately from 40 K to 1.5 M) precipitated more than 90 % of lysozyme. The dried lysozyme-HA precipitates had moisture levels between 4 % and 5 %. The lysozyme-HA precipitates could be redissolved using PBS (pH 7.4, ionic strength: ∼ 163 mM). The viscosity of the reconstituted solution was dependent on HA MW, e.g., 4 cP for HA40K, and 155 cP for HA1.5 M, suggesting low-MW HA might be a proper excipient for highly concentrated solution formulations for subcutaneous/intraocular injection and high-MW HA may fit for other applications. The tertiary structure of lysozyme after the precipitation-redissolution steps had no significant difference from that of the original lysozyme as confirmed by fluorescence spectroscopy. The denaturation temperature of lysozyme after the precipitation-redissolution steps and that of the original lysozyme were close, indicating they possessed similar thermal stability. The accelerated stability study revealed that lysozyme stored in the dry precipitates was more physically stable than that in the buffer solution. Overall, this new drying technique is suitable for drying proteins and exhibits several benefits such as minimum energy consumption, cost effectiveness, high production yield, and mild processing conditions. In addition, the precipitation-redissolution technique proposed in this study can potentially be used to develop highly concentrated formulations, especially for proteins experiencing poor stability in the liquid state.

Disulfide bridge-targeted metabolome mining unravels an antiparkinsonian peptide.

Peptides are a particular molecule class with inherent attributes of some small-molecule drugs and macromolecular biologics, thereby inspiring continuous searches for peptides with therapeutic and/or agrochemical potentials. However, the success rate is decreasing, presumably because many interesting but less-abundant peptides are so scarce or labile that they are likely 'overlooked' during the characterization effort. Here, we present the biochemical characterization and druggability improvement of an unprecedented minor fungal RiPP (ribosomally synthesized and post-translationally modified peptide), named acalitide, by taking the relevant advantages of metabolomics approach and disulfide-bridged substructure which is more frequently imprinted in the marketed peptide drug molecules. Acalitide is biosynthetically unique in the macrotricyclization via two disulfide bridges and a protease (AcaB)-catalyzed lactamization of AcaA, an unprecedented precursor peptide. Such a biosynthetic logic was successfully re-edited for its sample supply renewal to facilitate the identification of the in vitro and in vivo antiparkinsonian efficacy of acalitide which was further confirmed safe and rendered brain-targetable by the liposome encapsulation strategy. Taken together, the work updates the mining strategy and biosynthetic complexity of RiPPs to unravel an antiparkinsonian drug candidate valuable for combating Parkinson's disease that is globally prevailing in an alarming manner.

Comparative genomics analysis reveals genetic characteristics and nitrogen fixation profile of Bradyrhizobium.

Bradyrhizobium is a genus of nitrogen-fixing bacteria, with some species producing nodules in leguminous plants. Investigations into Bradyrhizobium have recently revealed its substantial genetic resources and agricultural benefits, but a comprehensive survey of its genetic diversity and functional properties is lacking. Using a panel of various strains (N = 278), this study performed a comparative genomics analysis to anticipate genes linked with symbiotic nitrogen fixation. Bradyrhizobium's pan-genome consisted of 84,078 gene families, containing 824 core genes and 42,409 accessory genes. Core genes were mainly involved in crucial cell processes, while accessory genes served diverse functions, including nitrogen fixation and nodulation. Three distinct genetic profiles were identified based on the presence/absence of gene clusters related to nodulation, nitrogen fixation, and secretion systems. Most Bradyrhizobium strains from soil and non-leguminous plants lacked major nif/nod genes and were evolutionarily more closely related. These findings shed light on Bradyrhizobium's genetic features for symbiotic nitrogen fixation.