Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting F v /F m and evaluating cotton drought tolerance using hyperspectral and 1D-CNN

The chlorophyll fluorescence parameter Fv/Fm is significant in abiotic plant stress. Current acquisition methods must deal with the dark adaptation of plants, which cannot achieve rapid, real-time, and high-throughput measurements. However, increased inputs on different genotypes based on hyperspectral model recognition verified its capabilities of handling large and variable samples. Fv/Fm is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, Fv/Fm hyperspectral prediction of different cotton varieties, and drought tolerance evaluation, are worth exploring. In this study, 80 cotton varieties were studied. The hyperspectral cotton data were obtained during the flowering, boll setting, and boll opening stages under normal and drought stress conditions. Next, One-dimensional convolutional neural networks (1D-CNN), Categorical Boosting (CatBoost), Light Gradient Boosting Machines (LightBGM), eXtreme Gradient Boosting (XGBoost), Decision Trees (DT), Random Forests (RF), Gradient elevation decision trees (GBDT), Adaptive Boosting (AdaBoost), Extra Trees (ET), and K-Nearest Neighbors (KNN) were modeled with F v /F m. The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the F v /F m prediction drought tolerance coefficient and the manually measured drought tolerance coefficient were similar. Therefore, cotton varieties with different drought tolerance degrees can be monitored using hyperspectral full band technology to establish a 1D-CNN model. This technique is non-destructive, fast and accurate in assessing the drought status of cotton, which promotes smart-scale agriculture.
Potential gross and net N2O production by the gut of different termite species are related to the abundance of nitrifier and denitrifier groups

Termites can be a significant source of N2O emissions in tropical and subtropical ecosystems. The emission rates vary greatly between species, with many species creating emission hotspots while others acting as net sinks. We studied the relationships between net and gross N2O production/consumption and the abundances of eight nitrifier and denitrifier groups (as determined by functional marker genes) in termite gut homogenates for eleven species from five feeding guilds. Overall, the abundance of nitrite reducers and, to a lesser extent, nitrifiers in the gut was strongly correlated with gross N2O production, whereas N2O reduction was correlated with the abundance of nosZ1 N2O reducers and the nosZ1/nirK ratio. Our results show that the differences in gross and net N2O production rates between termite species are primarily due to differences in nitrifier and denitrifier abundances, as well as the ratio of related functional gene marker abundances in the guts. N2O production rates were negatively correlated to the ratio of nosZ to nir. N2O production rates by live individuals measured for a subset of termite species were lower in the wood-feeding than in other species. Our results suggest that the differences in functional gene abundances may be associated with feeding guild, although this should be tested on a larger number of termite species.
Putting external validation performance of major bleeding risk models into context

When developing predictive models, model simplicity and performance often need to be balanced. We propose a novel methodology to put the performance of bleeding risk prediction models ORBIT, ATRIA, HAS-BLED, CHADS2, and CHA2DS2-VASc into perspective. Instead of comparing the existing models’ performance against the 0.5–1 AUROC scale, we suggest estimating a prediction task specific AUROC scale, lower bound AUROC (lbAUROC) and upper bound AUROC (ubAUROC), to help assess the balance between model simplicity and performance and determine whether more complex models could significantly improve the ability to predict the outcome. We validate the existing bleeding risk prediction models by applying them to a cohort of new users of warfarin and a cohort of new users of direct oral anticoagulants (DOACs) separately, across a set of four observational databases. Then, we develop the lbAUROC-ubAUROC scale by using the validation data to train regularized logistic regression models. The internal validation AUROC of the model that includes only age and gender variables was used to estimate the lbAUROC. The internal validation AUROC of the model that includes thousands of candidate variables was used to estimate the ubAUROC. The age and gender only models achieved AUROCs between 0.50 and 0.56 (lower bound) and the large-scale models achieved AUROCs between 0.67 and 0.72 and between 0.70 and 0.77 (upper bound) within the target cohorts of warfarin new users and DOACs new users, respectively. The AUROC of existing bleeding risk prediction models fall between the upper-bound and lower-bound of predictive models. Our study showed that this context of the predictability of the outcome is essential when evaluating risk prediction models to be administered in actual practice.
A perspective on oligonucleotide therapy: Approaches to patient customization

It is estimated that the human genome encodes 15% of proteins that are considered to be disease-modifying. Only 2% of these proteins possess a druggable site that the approved clinical candidates target. Due to this disparity, there is an immense need to develop therapeutics that may better mitigate the disease or disorders aroused by non-druggable and druggable proteins or enzymes. The recent surge in approved oligonucleotide therapeutics (OT) indicates the imminent potential of these therapies. Oligonucleotide-based therapeutics are of intermediate size with much-improved selectivity towards the target and fewer off-target effects than small molecules. The OTs include Antisense RNAs, MicroRNA (MIR), small interfering RNA (siRNA), and aptamers, which are currently being explored for their use in neurodegenerative disorders, cancer, and even orphan diseases. The present review is a congregated effort to present the past and present of OTs and the current efforts to make OTs for plausible future therapeutics. The review provides updated literature on the challenges and bottlenecks of OT and recent advancements in OT drug delivery. Further, this review deliberates on a newly emerging approach to personalized treatment for patients with rare and fatal diseases with OT.
Dietary rosemary extract modulated gut microbiota and influenced the growth, meat quality, serum biochemistry, antioxidant, and immune capacities of broilers

After the legislative ban on the utilization of antibiotics in animal feed, phytochemical substances gained increasing attention as alternatives to antibiotics because of their bioactivities and safety for animals. The present study aimed to investigate the influence of dietary rosemary extract (RE) on growth performance, meat quality, serum biochemistry, antioxidant and immune capacities, and gut microbiota composition of broilers. By exploring connections among RE, physiological characteristics of broilers, and key microbiota, we sought to provide evidence for the utilization of RE in poultry feed. A total of 280 1-d-old female AA broilers were randomly separated into five groups, and were fed a basal diet supplemented with 0, 250, 500, 750, and 1,000 mg/kg of RE, respectively. Results showed that with regard to growth performance, both 500 and 750 mg/kg RE reduced the broiler feed-to-gain ratio from 1 to 21 d (P = 0.018). Regarding meat quality, all compositions of dietary RE reduced cooking loss of breast muscle (P < 0.01), and 500 and 1,000 mg/kg RE reduced the cooking loss of thigh muscle (P = 0.045). Regarding serum biochemical indexes, 500 mg/kg RE reduced ALB, TCHO, HDL-C, and LDL-C, and 750 mg/kg RE reduced GLU, TP, ALB, UA, TG, TCHO, HDL-C, and LDL-C (P < 0.01). Regarding antioxidant and immune capacities, 250, 500, 750, and 1,000 mg/kg RE increased T-AOC, GSH-Px, SOD, CAT, IL-2, IgA, IgG, and IgM levels (P < 0.01), and decreased serum MDA level (P < 0.01). RE at 750 mg/kg showed similar effects on growth performance, meat quality, and antioxidative and immune capacities, but a better influence on serum biochemical indexes of broilers compared with 500 mg/kg. Further analysis was conducted to investigate the effect of 750 mg/kg dietary RE on the gut microbial composition of broilers, and the results showed that 750 mg/kg RE reduced the relative abundance of g_Lachnoclostridium, g_Escherichia_Shigella, and g_Marvinbryantia (P <0.05, LDA score >2), which were negatively correlated to antioxidative and immune-associated parameters (P < 0.05). In conclusion, 750 mg/kg dietary RE was shown to have certain beneficial effects on growth performance and meat quality, and hypolipidemic and hypoglycemic effects on broilers. Furthermore, dietary RE improved antioxidant and immune capacities, which was partially attributed to the reduced abundance of certain pathogenic bacteria in broilers.
Multi-omics analyses reveal the specific changes in gut metagenome and serum metabolome of patients with polycystic ovary syndrome

Objective The purpose of this study was to investigate the specific alterations in gut microbiome and serum metabolome and their interactions in patients with polycystic ovary syndrome (PCOS). Methods The stool samples from 32 PCOS patients and 18 healthy controls underwent the intestinal microbiome analysis using shotgun metagenomics sequencing approach. Serum metabolome was analyzed by ultrahigh performance liquid chromatography quadrupole time-of-flight mass spectrometry. An integrative network by combining metagenomics and metabolomics datasets was constructed to explore the possible interactions between gut microbiota and circulating metabolites in PCOS, which was further assessed by fecal microbiota transplantation (FMT) in a rat trial. Results Fecal metagenomics identified 64 microbial strains significantly differing between PCOS and healthy subjects, half of which were enriched in patients. These changed species showed an ability to perturb host metabolic homeostasis (including insulin resistance and fatty acid metabolism) and inflammatory levels (such as PI3K/Akt/mTOR signaling pathways) by expressing sterol regulatory element-binding transcription factor-1, serine/threonine-protein kinase mTOR, and 3-oxoacyl-[acyl-cattier-protein] synthase III, possibly suggesting the potential mechanisms of gut microbiota underlying PCOS. By integrating multi-omics datasets, the panel comprising seven strains (Achromobacter xylosoxidans, Pseudomonas sp. M1, Aquitalea pelogenes, Porphyrobacter sp. HL-46, Vibrio fortis, Leisingera sp. ANG-Vp, and Sinorhizobium meliloti) and three metabolites [ganglioside GM3 (d18:0/16:0), ceramide (d16:2/22:0), and 3Z,6Z,9Z-pentacosatriene] showed the highest predictivity of PCOS (AUC: 1.0) with sensitivity of 0.97 and specificity of 1.0. Moreover, the intestinal microbiome modifications by FMT were demonstrated to regulate PCOS phenotypes including metabolic variables and reproductive hormones. Conclusion Our findings revealed key microbial and metabolite features and their interactions underlying PCOS by integrating multi-omics approaches, which may provide novel insights into discovering clinical diagnostic biomarkers and developing efficient therapeutic strategies for PCOS.
Effects of jet nebulization on ventilator performance with different invasive ventilation modes: A bench study

Background The effects of jet nebulization on ventilator performance in the volume control mode (VC) and pressure control mode (PC) of ventilation have not been determined. Objectives The present study investigated the impact of jet nebulization on ventilator performance in different modes in vitro. Methods Two types of jet nebulizer (ventilator-integrated jet nebulizers, external jet nebulizer) and six types of ventilator were connected with a simulated lung to simulate aerosol therapy during mechanical ventilation. The ventilation modes were set to VC and PC, and the driving flows of external jet nebulizer were set at 4 L/min and 8 L/min, respectively. Jet nebulizers were placed between patient airway and Y-piece or at 15 cm from the Y-piece in the inspiratory limb. The effects of jet nebulization were compared with the baseline of triggering performance, control performance, and tidal volume under different experimental conditions. Results Ventilator-integrated jet nebulizers had no effect on ventilator performance in different modes (all P > 0.05). However, the effects of external jet nebulizers on ventilator performance varied widely: for triggering performance, all parameters were increased in different modes and nebulization positions (all P < 0.05), including the time from the beginning of the inspiratory effort to the lowest value of airway pressure needed to trigger the ventilator (TPmin), the time to trigger (Ttrig), and the magnitude of airway pressure drop needed to trigger (Ptrig); for control performance, peak inspiratory pressure (Ppeak) and peak inspiratory flow(Pflow) were increased in the VC mode (P < 0.05), but not significantly changed in the PC mode (P > 0.05);the actual tidal volume (VT) and expiratory tidal volume monitored (VTe) were significantly increased (P < 0.05), however, the inspiratory tidal volume monitored (VTi) was not affected by jet nebulization in the VC mode. In the PC mode, there were no significant changes in VT, whereas VTi decreased and VTe increased (P < 0.05). The higher the driving flow of external jet nebulizers, the stronger the impact on ventilator performance (all P < 0.05). Conclusion Triggering performance was decreased in both the VC and PC modes when using an external jet nebulizer, while the effects of nebulization on control performance and tidal volume varied significantly.
Social insurance and earnings management: Too rich to be good

We examine the relationship between social insurance contributions and earnings management for publicly listed firms in China. Our empirical results show that the social insurance contributions burden significantly reduces the degree of earnings management by reducing the level of free cash flow. Additionally, the negative relation between social insurance contributions burden and earnings management is more pronounced when the internal and external social insurance pressures are high and when the firms are large non-state-owned enterprises. We also discuss the heterogeneity among firms for different financing constraints, external financing environment, regional marketization, and internal and external corporate governance. Finally, we further find that under the dual collecting system, although the social security administration is a better collecting agency, the local tax bureau acting with full responsibility is more effective than the collecting system.
Lactate-driven macrophage polarization in the inflammatory microenvironment alleviates intestinal inflammation

Background Lactate has long been considered an intermediate by-product of glucose metabolism. However, in recent years, accumulating evidence reveals that lactate has unique biological activities. In previous studies, lactate signaling was shown to inhibit inflammation. Furthermore, in vitro experiments have shown that lactate can promote the transformation of pro-inflammatory macrophages into anti-inflammatory macrophages. However, no in vivo studies have shown whether lactate can alleviate inflammation. Methods RAW 264.7 macrophages were stimulated by LPS to induce an M1 phenotype, and cultured with low and high concentrations of lactate. The cells were then observed for phenotypic transformations and expression of inflammatory mediators and surface markers. The expression of inflammatory factors was also analyzed in the cell-free supernatant fraction. Further, a mouse model of DSS-induced colitis was established and treated with lactate. Colonic tissue injury was monitored by histopathological examinations. Results The in vitro experiments showed that lactate promoted the transformation of activated macrophages to M2 phenotype and decreased the expression of TLR4-mediated NF-κB signaling proteins and inflammatory factors. In the DSS-induced colitis mouse model, lactate promoted the phenotypic transformation of macrophages in colonic tissue, reduced inflammation and organ damage, inhibited the activation of TLR4/NF-κB signaling pathway, decreased the serum levels of pro-inflammatory factors, increased the expression of anti-inflammatory factors, promoted the repair of the intestinal mucosal barrier and reduced the severity of colitis. Conclusions Lactate inhibits the TLR/NF-κB signaling pathway and the production of pro-inflammatory factors by promoting polarization of macrophages. In addition, lactate promotesthe repair of the intestinal mucosal barrier and protects intestinal tissue in inflammation. Furthermore, lactate is relatively safe. Therefore, lactate is a promising and effective drug for treating inflammation through immunometabolism regulation.
Multifractal Correlation between Terrain and River Network Structure in the Yellow River Basin, China

As the most basic physical geographic elements, basin terrain and river networks have high spatial complexity and are closely related. However, there is little research on the correlation between terrain and river networks. In this paper, the Yellow River Basin was selected as the study area. Topographic factors of multiple dimensions were calculated. The influence of different topographic factors on the river network structure at different scales and their correlation from a multifractal perspective based on geographical detectors and a geographically weighted regression model were determined. The explanatory power of topography on the river network structure at different scales was: multifractal spectrum width > multifractal spectrum difference > slope > average elevation > elevation maximum > elevation minimum, which generally indicated that the topographic factor that has the greatest influence on the river network structure is the complexity and singularity of the terrain. The second-order clustering of regression coefficients from the results of the geographically weighted regression model revealed that the Yellow River basin was divided into three types of high-aggregation areas, which are dominated by the Qinghai-Tibet Plateau, the Loess Plateau, and the Huang-Huaihai Plain, respectively. The clustering results also revealed that the river network structure was affected by different key topographic factors in the different types of areas. This research studies and quantifies the relationship between basin topography and river network structure from a new perspective and provides a theoretical basis for unraveling the development of topography and river networks.
Scan2FEM: From Point Clouds to Structured 3D Models Suitable for Simulation

Preservation of cultural heritage is important to prevent singular objects or sites of cultural importance to decay. One aspect of preservation is the creation of a digital twin. In case of a catastrophic event, this twin can be used to support repairs or reconstruction, in order to stay faithful to the original object or site. Certain activities in prolongation of such an objects lifetime may involve adding or replacing structural support elements to prevent a collapse. We propose an automatic method that is capable of transforming a point cloud into a geometric representation that is suitable for structural analysis. We robustly find cuboids and their connections in a point cloud to approximate the wooden beam structure contained inside. We export the necessary information to perform structural analysis, on the example of the timber attic of the UNESCO World Heritage Aachen Cathedral. We provide evaluation of the resulting cuboids’ quality and show how a user can interactively refine the cuboids in order to improve the approximated model, and consequently the simulation results.
C2C: Fine-grained Configuration-driven System Call Filtering

Configuration options allow users to customize application features according to the desired requirements. While the code that corresponds to disabled features is never executed, it still resides in process memory and comprises part of the application's attack surface, e.g., it can be reused for the construction of exploit code. Automatically reducing the attack surface of disabled application features according to a given configuration is thus a desirable defense-in-depth capability. The intricacies of modern software design and the complexities of popular programming languages, however, introduce significant challenges in automatically deriving the mapping of configuration options to their corresponding application code. In this paper, we present Configuration-to-Code (C2C), a generic configuration-driven attack surface reduction technique that automatically maps configuration options to application code using static code analysis and instrumentation. C2C operates at a fine-grained level by pruning configuration-dependent conditional branches in the control flow graph, allowing the precise identification of a given configuration option's code at the basic block level. At runtime, C2C reduces the application's attack surface by filtering any system calls required exclusively by disabled features. Using popular applications, we show how security-critical system calls (such as execve) can be automatically disabled when not needed, limiting an attacker's vulnerability exploitation capabilities. System call filtering also reduces the exposed attack surface of the underlying Linux kernel, neutralizing 32 additional CVEs (for a total of 88) compared to previous software specialization techniques.
Unbiased Pairwise Learning from Implicit Feedback

Implicit feedback is prevalent in the real-world and widely used to construct recommender systems. However, utilizing the implicit feedback data is considerably more complicated than the case with its explicit counterpart. This is because implicit feedback provides only positive feedback, and we cannot know whether the non-interacted feedback is positive or negative. Furthermore, positive feedback for popular items is more frequently observed than rare ones; the relevance of such popular items is often overestimated. Existing solutions to the challenges have shown to be subject to a bias towards the ideal losses of interest or accepts a simple pointwise approach, which is inappropriate for the top-N recommendation task. In this work, we ﬁrst deﬁne an ideal pairwise loss function that should be used to optimize ranking metrics and propose an unbiased estimator for this ideal pairwise loss. Then, we propose a corresponding algorithm called Unbiased Bayesian Personalized Ranking . The pairwise algorithm addressing the two major difﬁculties has not yet been investigated, and the proposed algorithm is the ﬁrst pairwise method to solve the two major challenges in a theoretically principal way. Through theoretical analysis, we provide critical statistical properties of the proposed unbiased estimator and a variance reduction technique. The empirical evaluations using semi-synthetic and real-world datasets demonstrate the practical strength of our approach.