Natural products are a sustainable resource for drug discovery, but their identification in complex mixtures remains a daunting task. We present an automated pipeline that compares, harmonizes and ranks the annotations of LC-HRMS data by different tools. When applied to 7,400 extracts derived from 6,566 strains belonging to 86 actinomycete genera, it yielded 150,000 molecules after processing over 50 million MS features. The web-based Molecules Gateway provides a highly interactive access to experimental and calculated data for these molecules, along with the metadata related to extracts and producer strains. We show how the Molecules Gateway can be used to rapidly identify known hard to find microbial products, unreported analogs of known families and not yet described metabolites. The Molecules Gateway, which complements available repositories, contains annotated MS data, both acquired and computationally processed under an identical workflow, making it suitable for global analyses which reveal a large and untapped chemical diversity afforded by actinomycetes.
While barnacle cement protein cp19k (from Megabalanus rosa) possesses remarkable adhesion properties and spider silk protein MaSp1 (from Nephila clavata dragline silk) demonstrates exceptional toughness, their advancements in medical biomaterials are significantly hindered by their limitations in antimicrobial properties. In this study, composite nanocomplexes incorporating chitosan and proteins derived from barnacle cement and spider silk were designed and biofabricated for enhanced antibacterial properties. The impact of chitosan's molecular weight on the properties of nanocomplexes comprising cp19k-MaSp1/chitosan, MaSp1/chitosan, and cp19k/chitosan was evaluated. The results revealed that low molecular weight chitosan (LMWC, Mw = 1 kDa) forms nanocomplexes that exhibit distinct structural differences in comparison to those formed with high molecular weight chitosan (HMWC, Mw ≥ 150 kDa). Furthermore, cp19k-MaSp1/C150k exhibited the most potent antibacterial activity against E. coli and S. aureus, surpassing the performance of cp19k, MaSp1, cp19k-MaSp1, and chitosan individually, achieving inhibition by disrupting the bacterial cell membrane structure and elevating the intracellular ROS level. Meanwhile, On day 6, the viability of HUVECs (Human Umbilical Vein Endothelial Cells) of cp19k-MaSp1/C150k had attained a level of 145.21 ± 6.23 %, representing a substantial elevation when compared to C150k. The remarkable biocompatibility of nanocomplexes cp19k-MaSp1/C150k holds potential for application in wound dressings and tissue repair.
Delayed healing of wounds in diabetics is mainly due to tissue inflammation, poor vasculature, lack of neovascularization, and bacterial infection. Therefore, a therapeutic protocol that disrupts this cycle and speeds healing is urgently needed. Despite attempts to enhance wound dressing effectiveness through hydrogels with diverse complexes such as bacterial cellulose (BC) combined with chitosan, BC/ chitosan/hyaluronic acid, and BC/chitosan/collagen, the toughness and adhesion properties of hydrogel remain constrained, leading to inadequate and uncontrollable wound healing. To address the challenge, we have devised an innovative solution by integrating barnacle cement protein (cp19k) and spider silk protein (major ampullate spidroin 1, MaSp1) into a BC matrix, complemented by chitosan. This development has led to the creation of a novel BC-based composite hydrogel BC/cp19k-MaSp1/C150k. The composite hydrogel stands out with its remarkable mechanical (3.92 Mpa) and adhesion properties (8.4 kPa) compared to its BC/C150k counterpart. Meanwhile, the BC/cp19k-MaSp1/C150k hydrogel also demonstrated antimicrobial activity, coagulation, and biocompatibility. The BC/cp19k-MaSp1/C150k hydrogel showed an exceptional capacity to enhance wound healing in a diabetic rat model, achieving a significant wound closure rate of over 98 % on day 14 when compared to BC and commercially available dressing 3 M™ Tegaderm™. This advancement holds significant promise in revolutionizing wound management for diabetics.
Knee arthroplasty (KA) significantly improves pain and function in gonarthrosis. Despite its success, major complications like deep surgical site infection (SSI) remain a concern. Infection treatments often require aggressive protocols, including implant exchange and prolonged antimicrobial therapy, emphasizing the need for preoperative patient optimization to mitigate infection risk. Preoperative platelet count has been proposed as a predictive marker for postoperative infection. Beyond their hemostatic role, platelets possess antimicrobial properties and are considered the first line of defense against microbial proliferation within the surgical site. This database review aims to assess the relationship between preoperative platelet count and deep SSI. The National Surgery Quality Improvement Program was queried for patients who underwent primary total or unicompartmental KA between 2015 and 2022 and developed deep SSI within 30 days. Postoperative outcomes were analyzed with respect to platelet count as a continuous and categorical variable. Deep SSI was significantly more common in patients with preoperative platelet counts <150,000/μl compared to those with higher counts (0.61% vs 0.30%; P < .001). Regression analysis assessed the independent association of preoperative platelet count with deep SSI. The odds ratio for deep SSI with platelet count <50k/μl was 2.47 (1.42-4.28; P = .001), 1.87 (1.08-3.24; P = .026) for platelet count between 50-100k/μl, and 1.62 (1.32-2.00; P < .001) for platelet count between 100-150k/μl, all in reference to patients with platelet counts >150k/μl. Thrombocytopenia (<150,000 platelets/μl) is significantly associated with a higher incidence of deep SSI following KA. Preoperative platelet count may serve as a useful marker for infection risk.
For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax τ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5-9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic-physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available.
To compare the in-vitro retentive forces of Novaloc-TiN and Locator attachment systems for two-implant overdentures before and after insertion-removal and compressive cyclic loading, representing up to 12 months of wear. Insertion-removal and compression-cycles of two-implant Novaloc (Novaloc® TiN, Straumann) and Locator (Locator®, Straumann) overdenture attachments embedded into 3D-printed acrylic blocks were performed (n=10 each). For compressive cycling, a force approximating 66.7-N was applied at the center of the blocks using a Bose ElectroForce Fatigue Testing machine over a sum of 300,000 cycles per sample. The retentive force was recorded at baseline and after certain insertion-removal (23, 270, 540, and 1080) and compression-cycles (5k, 25k, 75k, 150k, and 300k). Deformation and crystallization were assessed using micro-computed tomography and differential scanning calorimetry coupled with thermal gravimetric analysis. Compressive cycling of two-implant overdenture yielded a greater retentive force for Locator attachments relative to Novaloc-TiN attachments at simulated mastication equivalent to one-week, one, three, six and 12-months of wear. However, Locator attachments displayed fluctuating retentive forces throughout the 300,000-cycle duration. Retention forces for Novaloc system had no significant differences between baseline and the following 300,000 cycles, indicating consistent retention throughout the cycling duration. Similarly, insertion-removal cycles resulted in retention loss for Locator, whereas Novaloc showed more consistency. Novaloc-TiN system offers superior durability following compressive and insertion-removal cycling. This indicates more longevity for retentive forces relative to Locator attachment system, despite the lower overall retentive forces of Novaloc attachments.
In this paper, we present a memory-efficient ECG based heartbeat classification for wearable devices enabled by multi-feature fusion and compressed bidirectional long short term memory (Bi-LSTM). A multi-feature fusion technique based on time intervals and under-the-curve areas is proposed to extract the main characteristic points of the ECG waveform with high accuracy and robustness against noise and artifacts. A Bi-LSTM network is developed to process the input sequence in both forward and backward directions, resulting in higher accuracy and a 28% smaller network size compared with a conventional LSTM. Multiple neural networks with varying sizes, including tiny (84k), small (150k), medium (478k), and large (1.25M) models, are developed to achieve high accuracy across all classes. The overall accuracy is 96.4% for the large model and 94.6% for the tiny model, while the F1 score across all classes exceeds 89.1% and 85.1%, respectively. The proposed models, compressed using post-training quantization techniques, achieve state-of-the art performance. The compressed large model with 8-bit integer quantization (INT8) achieves an accuracy of 88.4% with 1.3MB of memory, while the compressed tiny model with dynamic range quantization achieves 94.6% accuracy with only 139kB of memory.
Traditional methods of measuring diet, mainly food frequency questionnaires, are not fit for purpose. Diet is complex, with over 150k different food items available in UK supermarkets. This review describes the limitations of nutritional assessment methods used in the past. It provides an overview of recent methods using newer technologies, including online tool myfood24. Limitations of dietary assessment methods include recall bias; inability to estimate portion sizes; lack of adaptability across diverse populations; and inadequate food composition data. New tools include mobile apps for real-time intake tracking; use of image-based approaches for portion sizes and food identification; sensor technologies such as smart utensils and bite counters. Online platforms provide an economical approach for large-scale epidemiology. myfood24 is an online tool, developed for research and validated using biomarkers. Having demonstrated success with >250,000 research participants in over 27 countries; myfood24 is also used in healthcare, for student education and other settings. myfood24 focuses on accurate data and ease of participant use. The underlying food composition database curated by nutritionists has fewer missing data and more nutrient variables than standard generic tables. The database includes diet quality and sustainability measures. A new app includes the novel myfood24 Diet Optimization Engine suggesting individual dietary changes to meet nutrition targets. New dietary assessment tools provide more accurate data with deeper insights into dietary behaviour. They need to be used in large epidemiological surveys; for public health population screening and with patients to fully realise their potential.
Domain-peptide interactions mediate a significant fraction of cellular protein networks, yet accurately predicting their specificity remains challenging. Peptide motifs typically have short, fuzzy sequence profiles, and their interactions are often weak and transient, limiting the size, coverage, and quality of experimentally validated domain-peptide datasets. Since true non-binders are rarely known, constructing negative examples often introduces bias. While structure-based prediction methods can achieve high accuracy, they are computationally demanding and difficult to scale to the proteome level. We introduce CLIPepPI, a dual-encoder model that leverages contrastive learning to embed domains and peptides into a shared space directly from sequence. Both encoders are initialized from a protein language model (ESM-C) and fine-tuned using lightweight LoRA adapters, enabling parameter-efficient training on positive pairs alone. To overcome data scarcity, we augment ~3K protein-peptide complexes from PPI3D with ~150K domain-peptide pairs derived from protein-protein interfaces. CLIPepPI further injects structural information by marking interface residues in the domain sequence, thus guiding the encoders toward binding regions and linking sequence-level learning with structural context. Competitive performance is achieved across three independent benchmarks: domain-peptide complexes from PPI3D, large-scale phage-library data from ProP-PD, and a curated dataset of nuclear export signal (NES) sequences. We demonstrate scalability and generalization through two applications: (i) proteome-wide NES scanning, and (ii) variant-effect prediction, where score changes in domain-peptide interactions between wild-type and mutant sequences discriminate pathogenic from benign variants. Together, CLIPepPI offers a scalable, structure-informed model for predicting domain-peptide specificity and generating meaningful embeddings suited for large-scale proteomic analyses. CLIPepPI is available at: https://bio3d.cs.huji.ac.il/webserver/clipeppi/.
Vision Transformers (ViTs) have shown remarkable performance across various computer vision tasks, but their fine-tuning for dense prediction tasks such as semantic segmentation remains computationally intensive. This work proposes a novel dual-task architectural application of the LyCORIS Low-Rank Adaptation for Convolutions (LyCORIS LoCon) framework, which introduces learnable low-rank convolutional modules into pre-trained ViTs. This method is applied to Depth Anything V2 (DAV2), augmenting its decoder to support dual-task outputs; monocular depth estimation and binary human semantic segmentation, without disrupting its original capabilities. By injecting only 150K trainable parameters, this approach significantly reduces the adaptation cost while achieving segmentation performance comparable to state-of-the-art models like SAM, MaskFormer, and SegFormer. Extensive experiments on filtered COCO1 and ImageNet subsets show that Conv-LoRA enhances task-specific learning with minimal computational overhead. The method achieves an mAP of 89.69% and an mIoU of 79.17% for human segmentation, performing competitively alongside state-of-the-art models like Mask2Former, while preserving the depth prediction accuracy of the base model.
In genetics and evolutionary biology, selection signatures refer to distinct genomic patterns that reflect the action of natural and artificial selection on populations over time. Detecting such signatures provides critical insights into adaptive evolution and breed differentiation, especially in livestock populations subjected to diverse production environments and breeding objectives. In this study, a total of 96 samples were collected from four different cattle breeds, namely, South African indigenous Nguni (n = 28), Bonsmara (n = 21), Angus (n = 22), and Simmental (n = 25). The samples were genotyped using the Illumina Bovine SNP 150K BeadChip and subjected to quality control. Selection signatures were identified using the integrated haplotype score (iHS) method and the fixation index (Fst) method to assess the genetic differences between breeds. The complementary application of within-population and cross-population approaches enabled the detection of both recent and divergent selective pressures. A total of twelve regions were found to be under selection, with Bos taurus autosome (BTA) 12 being common between Nguni and Bonsmara. Gene annotation analyses identified several genes, including FAM110B, CDK8, and FLT1 in Bonsmara cattle, whereas Nguni cattle indicated potential genes such as CRB1, PLA2G4A, and VASH2, with CDK8 common between Bonsmara and Nguni on BTA 12. Cross-population analyses further identified PLCXD3, FAM149B1, and GRIK2 as candidate genes differentiating Bonsmara from Nguni cattle, and TSPAN9 distinguishing Simmental from Angus cattle. These results indicated breed-specific adaptive divergence. The study revealed genomic regions that are under selection in South African Nguni, Bonsmara, and Simmental cattle, with less information for Angus cattle breeds. Several candidate genes were found to be associated with reproductive traits (such as sperm count and inseminations per conception), disease resistance (such as bovine respiratory disease), and calving ease. This study identifies breed-specific and shared genomic regions under selection across diverse cattle breeds, providing novel insights into the genetic basis of adaptation and production-related traits. These findings explain the potential application of selection signature analyses in genomic-assisted breeding programmes aimed at improving productivity, resilience, and sustainability of cattle populations.
Many real-world problems require reasoning across multiple scales, demanding models which operate not on single data points, but on entire distributions. We introduce generative distribution embeddings (GDE), a framework that lifts autoencoders to the space of distributions. In GDEs, an encoder acts on sets of samples, and the decoder is replaced by a generator which aims to match the input distribution. This framework enables learning representations of distributions by coupling conditional generative models with encoder networks which satisfy a criterion we call distributional invariance. We show that GDEs learn predictive sufficient statistics embedded in the Wasserstein space, such that latent GDE distances approximately recover the $W_2$ distance, and latent interpolation approximately recovers optimal transport trajectories for Gaussian and Gaussian mixture distributions. We systematically benchmark GDEs against existing approaches on synthetic datasets, demonstrating consistently stronger performance. We then apply GDEs to six key problems in computational biology: learning donor-level representations from single-nuclei RNA sequencing data (6M cells), capturing clonal dynamics in lineage-traced RNA sequencing data (150K cells), predicting perturbation effects on transcriptomes (1M cells), predicting perturbation effects on cellular phenotypes (20M single-cell images), designing synthetic yeast promoters (34M sequences), and spatiotemporal modeling of viral protein sequences (1M sequences).
Magnetic topological insulators have received significant interest due to their dissipationless edge states, which promise advances in energy-efficient electronic transport. However, the magnetic topological insulator state has typically been found in ferromagnets (FMs) that suffer from low magnetic ordering temperatures and stray fields. Identifying an antiferromagnetic topological insulator that exhibits the quantum anomalous Hall effect (QAHE) with a relatively high Néel temperature has been a longstanding challenge. Here, we focus on the recently discovered van der Waals (vdW) antiferromagnet (AFM) UOTe, which not only features a high Néel temperature (≈ 150K) but also exhibits intriguing Kondo interaction and topological characteristics. Our systematic analysis of the layer-dependent topological phases based on ab initio computations predicts the two-layer UOTe film to be an ideal 2D AFM Chern insulator in which the Hall conductivity is quantized with a fully compensated spin magnetization. By applying an in-plane strain or electric field, we show how the itinerancy of U-5f electrons can be manipulated to trigger a transition between the nontrivial (C  =  1) and trivial (C  =  0) phases. Interestingly, the three-layer UOTe film is found to have zero charge conductance but it hosts a quantized spin Hall conductivity (SHC) with finite magneto-electric coupling, suggesting the presence of an axion insulator-like state. The unique magnetic structure of UOTe supports a layer-tunable topology in which films with an odd number of layers are axion-like insulators, while films with an even number of layers are Chern insulators, and the bulk material is a Dirac semimetal. Our study offers a new intrinsic AFM materials platform for realizing correlated topological phases for next-generation spintronics applications and fundamental science studies.
Type 2 diabetes mellitus (T2DM) shares multiple modifiable and non-modifiable risk factors across populations, yet it is unclear whether these risk profiles differ in adults with depression-a group with elevated baseline metabolic risk. To identify clinical, behavioral, and sociodemographic predictors of incident T2DM among adults with depression and determine their implications for endocrine prevention and screening strategies. We conducted a retrospective cohort analysis of 40,585 adults with a documented depression diagnosis but no prior T2DM in the All of Us Research Program (median follow-up = 8.15 years). Incident T2DM was defined using electronic health record-based diagnosis codes. Cox proportional hazards models estimated adjusted hazard ratios (HRs) for T2DM as a function of gender, race, ethnicity, age, body mass index (BMI per 10 kg/m²), and household income. Sensitivity analyses incorporated cigarette smoking and multiple imputation for BMI. Higher BMI (HR = 1.76 per 10 kg/m²; 95% CI: 1.69-1.83), older age (HR = 1.03 per year; 95% CI: 1.027-1.032), male gender (HR = 1.38; 95% CI: 1.27-1.49), Black race (HR = 1.79; 95% CI: 1.64-1.95), and non-Hispanic ethnicity (HR = 1.37; 95% CI: 1.10-1.71) were independently associated with greater T2DM risk, while higher household income was protective (HR = 0.47 for >$150k vs. <$35k; 95% CI: 0.39-0.58), demonstrating a clear socioeconomic gradient. Associations were robust to smoking adjustment and multiple imputation, and were directionally consistent with patterns reported in prior general-population studies. Traditional demographic, socioeconomic, and clinical risk factors were associated with incident T2DM among adults with depression, supporting attention to established diabetes risk factors in psychiatric and endocrine prevention settings. Aggressive implementation of weight management, risk stratification, and early screening-particularly among socioeconomically disadvantaged patients-may reduce the burden of T2DM in psychiatric populations. Not applicable.
Two new RSV immunization products exist for infants: a prenatal vaccine (RSVpreF) and a long-acting monoclonal antibody for infants (nirsevimab). While both showed promise in clinical trials, implementation has varied across jurisdictions. Before rollout in Canada, we conducted an anonymized, online survey with a nationally representative sample of 1,015 expectant and recent birth parents. The primary outcome was product acceptability (disagree, undecided, agree). Odds ratios (ORs) and 95% confidence intervals (CIs) identified factors associated with agreement or indecision versus disagreement. Approximately 49% (n = 499) were expecting; 51% (n = 516) had a baby in the past year. Overall, 72% (n = 727) agreed to receive a product: 61% (n = 624) for RSVpreF and 60% (n = 608) for nirsevimab. RSVpreF agreement was higher among older parents (e.g., OR = 3.64; 95% CI, 1.99-6.67 for age 25-34 versus 18-24 years), those with a university degree (OR = 3.65; 95% CI, 2.01-6.64 versus high school), higher income (OR = 2.31; 95% CI, 1.01-5.28 for ≥$150k versus <$40k), or receipt/intention for Tdap (OR = 3.76; 95% CI, 2.37-5.96 versus no receipt/intention) or influenza (OR = 2.04; 95% CI, 1.29-3.21) vaccines. Agreement was higher among parents of children without a high-risk medical condition compared to those with (OR = 2.32; 95% CI, 1.44-3.72). Similar trends were observed for nirsevimab, with lower agreement also noted among those who self-researched antibodies (OR = 0.51; 95% CI, 0.30-0.88 versus not). As most respondents rated product safety (76%), effectiveness (71%), and disease severity risk (70%) as important in decision-making, providing clear information on these, alongside communications targeted to subgroups with lower agreement, may optimize uptake as programs rollout.
Thrombocytopenia is associated with a variety of medical comorbidities seen in patients with head and neck cancer. However, it remains unclear if and how thrombocytopenia affects surgical outcomes. The purpose of this study was to measure the association between thrombocytopenia and 30-day adverse outcomes in patients undergoing head and neck cancer surgery with free flap reconstruction. This was a retrospective cohort study using the 2012 to 2022 American College of Surgeons National Surgical Quality Improvement Program databases. Patients undergoing resection and free flap reconstruction for malignant pathology of the oral cavity, oropharynx, hypopharynx, larynx, and salivary glands were included. Patients undergoing emergency surgery or with missing outcomes data were excluded. The predictor variable was platelet count coded as a binary variable: <150k/μL (thrombocytopenia) or >150k/μL. The primary outcome variable was return to the operating room for free flap salvage or hematoma evacuation coded based on International Classification of Diseases diagnoses. Covariates were categorized into demographic (age, sex), medical (hypertension, diabetes), and perioperative (concurrent procedures, reconstructive modality). Descriptive, bivariate, and bootstrapped multiple logistic regression statistics were performed to evaluate the association between thrombocytopenia and adverse outcomes. Youden J analysis was used to identify a platelet value at which complications were more likely to occur. An alpha of P < .05 was significant. A total of 4,993 subjects met the inclusion criteria. There were 281 subjects with thrombocytopenia (5.63%) and 213 who underwent reoperations (4.30%). In bivariate analysis, thrombocytopenia was the only significant risk factor for reoperation, and these subjects were 1.74 times more likely to return to the operating room (P = .015, relative risk = 1.74, 95% CI 1.11 to 2.71). In multivariate analysis adjusting for study covariates, thrombocytopenia was independently associated with 1.82 times greater odds of return to the operating room (P = .026, OR = 1.82, 95% CI 1.11 to 3.26). Cut point analysis suggested that a platelet value less than 136,000/μL was a risk factor for return to the operating room. Thrombocytopenia was independently associated with return to the operating room following ablative head and neck cancer procedures with free flap reconstruction.
Two genetically engineered Bacillus subtilis strains, BMV9 and BsB6, were evaluated in terms of culture medium (effect of nutrients on surfactin yield) and potential biotechnological applications of surfactin in agriculture and the petrochemical industry. BMV9 (spo0A3; abrB*; ΔmanPA; sfp+) is, to date, the highest surfactin producer reported scientifically, and BsB6 is a sfp+ laboratory derivative strain that has also demonstrated considerable production potential. To assess their performance, fermentation experiments were conducted in shake flasks using two different culture media, a mineral salt medium and a complex medium, each supplemented with 2% (w/v) glucose. Lipopeptides (surfactin and fengycin) were extracted and quantified at multiple time points (up to 48 h) via high-performance thin-layer chromatography (HPTLC). Optical density, residual glucose, and pH were monitored throughout the cultivation. In parallel, microbial growth in both media were also validated in small-scale cultivation approaches. Antifungal activity of culture supernatants and lipopeptide extracts was tested against two Diaporthe species, key phytopathogens in soybean crops. Given the agricultural relevance of these pathogens, the biocontrol potential of lipopeptides represents a sustainable alternative to conventional chemical fungicides. Additionally, oil displacement tests were performed to evaluate the efficacy of surfactin in enhanced oil recovery (EOR), bioremediation, and related petrochemical processes. High-resolution LC-MS/MS analysis enabled structural characterization and relative quantification of the lipopeptides. Overall, these investigations provide a comprehensive comparison of strain production performance and the associated impact of cultivation media, aiming to define the optimal conditions for economically viable surfactin production and to explore its broader biotechnological applications in agriculture and the petrochemical industry.
The biosynthesis of surfactin, a potent lipopeptide biosurfactant produced by Bacillus subtilis, imposes a significant metabolic burden on the host organism. Understanding the metabolic costs associated with surfactin production, specifically the ATP demand and precursor diversion resulting from the expression of the large srfAA-AD operon (srfA operon), is crucial for optimizing production strains. In this study, the metabolic burden and growth impacts associated with surfactin biosynthesis in B. subtilis were quantitatively evaluated by comparing a reference surfactin-producing strain (BMV9) with two mutant strains: BMV12, which lacks the srfA operon, and BMV33, which retains the srfA operon but lacks the sfp gene. Our analysis included theoretical calculations of ATP and NADPH + H+ requirements for de novo surfactin synthesis, and we measured growth behavior and carbon and nitrogen source consumption. Results indicated that surfactin production significantly reduces biomass yield (YX/S) and specific growth rates due to the metabolic costs of expressing the non-ribosomal peptide synthetase (NRPS) and the diversion of key precursors. Notably, BMV12 exhibited higher growth rates compared to the surfactin-producing strain. Proteome analyses further revealed differential protein abundance in non-surfactin-producing strains, indicating altered metabolic pathways that may relieve the metabolic burden associated with surfactin synthesis. These findings highlight the complex trade-offs between secondary metabolite production and cellular growth, providing a foundation for metabolic engineering strategies aimed at optimizing surfactin yields while minimizing metabolic costs.
ObjectiveTo compare and evaluate, a new viscoelastic point-of-care device, the Quantra QPlus® parameters with conventional hemostasis tests and TEG-5000® parameters.Design, Material and MethodsThis prospective homocentric observational study took place between January and June 2024 at the main community site Hospital, Jolimont's Hospital on adult patients undergoing elective cardiac surgery using cardiopulmonary bypass (CPB). Paired perioperative citrated and EDTA blood samples were collected and sent directly to the laboratory for analysis. The blood was analyzed in one hand with TEG-5000® (K, MA and alpha angle) in cardiac operating room and afterwards, simultaneously with Quantra QPlus® (CTH, FCS, PCS), citrate platelet count with specific fluorescent agent (PLT-F) and standard haemostasis testing (PT/INR, aPTT, Clauss Fibrinogen).ResultsMethod comparison analysis shows that Quantra's Parameters PCS and FCS were well correlated with PLT-F and Fibrinogen while TEG-5000® parameter were less. FCS and Alpha Angle predicted a Clauss Fibrinogen <150 mg/dL with an area under the curve (AUC) respectively of 0.899 (n = 66) and 0.815 (n = 54). PCS and MA predicted a Platelet count <150k/µL with an AUC of 0.793 (n = 62) and 0.807 (n = 54) respectively. CTH and K predicted an aPTT (actin FS on CS-5100) >29,0s with an AUC of 0.864 (n = 60) and 0.702 (n = 60) respectively.ConclusionStrong correlations were observed between quantra parameters FCS and PCS with Clauss Fibrinogen and Platelet count. While Performance from ROC curves for predicted thrombocytopenia <150k platelet/µL is similar with TEG-5000, they are better for predicted fibrinogen concentration and prolonged aPTT.
The Xinjiang Brown cattle (XJBC) is one of China's five major dual-purpose dairy and beef breeds. Analyzing the genetic diversity of the Xinjiang Brown cattle population lays the theoretical groundwork for identifying and conserving its genetic resources. This study employed the Illumina Bovine SNP 150K chip to analyze genetic diversity, inbreeding coefficient, kinship, and genetic distance in a population of 750 Xinjiang Brown cattle from three breeding farms in Xinjiang. Genetic diversity was assessed by calculating minimum allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), polymorphic information content (PIC), and linkage disequilibrium (LD). Population structure was analyzed using PCA. ROH was calculated to derive ROH-based inbreeding coefficients, pedigree-based inbreeding coefficients (FPED) were estimated using CFC software for comparison, and candidate genes within high-frequency ROH regions in Xinjiang Brown cattle were identified. A G matrix was constructed to analyze population kinship. Results revealed 94,173 high-quality SNP loci in Xinjiang Brown cattle, with an average MAF of 0.276, PIC of 0.376, Ho of 0.345, and He of 0.376. Breeding farm 3 exhibited the fastest LD decay, indicating relatively high genetic diversity across Xinjiang Brown cattle populations, with farm 3 demonstrating greater diversity. The IBS genetic distance was 0.313. The G matrix results aligned with the IBS distance matrix, both indicating close kinship among some individuals within the Xinjiang Brown cattle population. The ranges for average FPED and average FROH across farms were 0.0017-0.0189 and 0.0609-0.0878, respectively. Short ROH segments (0.5-2 Mb) constituted the largest proportion (51.31%) of all ROHs. Within high-frequency ROH enrichment regions, 61 genes, including LCORL, FAM110B, NR4A1, and PER2, were identified as potentially associated with economic traits in Xinjiang Brown cattle. These findings provide relevant marker sites for genomic selection in Xinjiang Brown cattle and lay a theoretical foundation for subsequent research.