Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes

Pre-exposure prophylaxis (PrEP) is an important pillar to prevent HIV transmission. Because of experimental and clinical shortcomings, mathematical models that integrate pharmacological, viral- and host factors are frequently used to quantify clinical efficacy of PrEP. Stochastic simulations of these models provides sample statistics from which the clin- ical efficacy is approximated. However, many stochastic simulations are needed to reduce the associated sampling error. To remedy the shortcomings of stochastic simulation, we developed a numerical method that allows predicting the efficacy of arbitrary prophylactic regimen directly from a viral dynamics model, without sampling. We apply the method to var- ious hypothetical dolutegravir (DTG) prophylaxis scenarios. The approach is verified against state-of-the-art stochastic simulation. While the method is more accurate than stochastic simulation, it is superior in terms of computational performance. For example, a continuous 6-month prophylactic profile is computed within a few seconds on a laptop computer. The method’s computational performance, therefore, substantially expands the horizon of feasi- ble analysis in the context of PrEP, and possibly other applications.Pre-exposure prophylaxis (PrEP) is an important tool to prevent HIV transmission. However, experimental identification of parameters that determine prophylactic efficacy is extremely difficult. Clues about these parameters could prove essential for the design of next-generation PrEP compounds. Integrative mathematical models can fill this void: Based on stochastic simulation, a sample statistic can be generated, from which the prophylactic efficacy is estimated. However, for this sample statistic to be accurate, many simulations need to be performed. Here, we introduce a numerical method to directly compute the prophylactic efficacy from a viral dynamics model, without the need for sampling. Based on several examples with dolutegravir (DTG) -based short- and long-term PrEP, as well as post-exposure prophylaxis we demonstrate the correctness of the new method and its outstanding computational performance. Due to the method’s computational performance, a number of analyses, including formal sensitivity analysis, are becoming feasible with the proposed method.Peer Reviewe

Functional innovations of PIN auxin transporters mark crucial evolutionary transitions during rise of flowering plants

Flowering plants display the highest diversity among plant species and have notably shaped terrestrial landscapes. Nonetheless, the evolutionary origin of their unprecedented morphological complexity remains largely an enigma. Here, we show that the coevolution of cis-regulatory and coding regions of PIN-FORMED (PIN) auxin transporters confined their expression to certain cell types and directed their subcellular localization to particular cell sides, which together enabled dynamic auxin gradients across tissues critical to the complex architecture of flowering plants. Extensive intraspecies and interspecies genetic complementation experiments with PINs from green alga up to flowering plant lineages showed that PIN genes underwent three subsequent, critical evolutionary innovations and thus acquired a triple function to regulate the development of three essential components of the flowering plant Arabidopsis: shoot/root, inflorescence, and floral organ. Our work highlights the critical role of functional innovations within the PIN gene family as essential prerequisites for the origin of flowering plants

A Fair Resource Allocation Algorithm for Data and Energy Integrated Communication Networks

With the rapid advancement of wireless network technologies and the rapid increase in the number of mobile devices, mobile users (MUs) have an increasing high demand to access the Internet with guaranteed quality-of-service (QoS). Data and energy integrated communication networks (DEINs) are emerging as a new type of wireless networks that have the potential to simultaneously transfer wireless energy and information via the same base station (BS). This means that a physical BS is virtualized into two parts: one is transferring energy and the other is transferring information. The former is called virtual energy base station (eBS) and the latter is named as data base station (dBS). One important issue in such setting is dynamic resource allocation. Here the resource concerned includes both power and time. In this paper, we propose a fair data-and-energy resource allocation algorithm for DEINs by jointly designing the downlink energy beamforming and a power-and-time allocation scheme, with the consideration of finite capacity batteries at MUs and power sensitivity of radio frequency (RF) to direct current (DC) conversion circuits. Simulation results demonstrate that our proposed algorithm outperforms the existing algorithms in terms of fairness, beamforming design, sensitivity, and average throughput.</jats:p

Evaluating Learning-to-Rank Models for Prioritizing Code Review Requests using Process Simulation

In large-scale, active software projects, one of the main challenges with code review is prioritizing the many Code Review Requests (CRRs) these projects receive. Prior studies have developed many Learning-to-Rank (LtR) models in support of prioritizing CRRs and adopted rich evaluation metrics to compare their performances. However, the evaluation was performed before observing the complex interactions between CRRs and reviewers, activities and activities in real-world code reviews. Such a pre-review evaluation provides few indications about how effective LtR models contribute to code reviews. This study aims to perform a post-review evaluation on LtR models for prioritizing CRRs. To establish the evaluation environment, we employ Discrete-Event Simulation (DES) paradigm-based Software Process Simulation Modeling (SPSM) to simulate real-world code review processes, together with three customized evaluation metrics. We develop seven LtR models and use the historical review orders of CRRs as baselines for evaluation. The results indicate that employing LtR can effectively help to accelerate the completion of reviewing CRRs and the delivery of qualified code changes. Among the seven LtR models, LambdaMART and AdaRank are particularly beneficial for accelerating completion and delivery, respectively. This study empirically demonstrates the effectiveness of using DES-based SPSM for simulating code review processes, the benefits of using LtR for prioritizing CRRs, and the specific advantages of several LtR models. This study provides new ideas for software organizations that seek to evaluate LtR models and other artificial intelligence-powered software techniques

Common and distinct equity preferences in children and adults

Fairness plays a crucial role in children’s social life and has garnered considerable attention. However, previous research and theories primarily examined the development of children’s fairness behaviors in the conflict between self-interest motivation and fairness-complying motivation, neglecting the influence of advantage-seeking motivation. Moreover, despite the well-established role of gain/loss frame in human decision-making, it remains largely unclear whether the framing effect modulates fairness behaviors in children. It was hypothesized that children would exhibit advantage-seeking motivation resulting in more selfish behaviors in the loss context. To examine the hypothesis, we combined an adapted dictator game and computational modeling to investigate various motivations underlying fairness behaviors of children in both loss and gain contexts and to explore the developmental directions by contrasting children and adults. In addition, the current design enabled the dissociation between fairness knowledge and behaviors by asking participants to decide for themselves (the first-party role) or for others (the third-party role). This study recruited a total of 34 children (9–10 years, Mage = 9.82, SDage = 0.38, 16 females) and 31 college students (Mage = 19.81, SDage = 1.40, 17 females). The behavioral results indicated that children behaved more selfishly in first-party and more fairly in third-party than adults, without any significant framing effects. The computational results revealed that both children and adults exhibited aversion to advantageous and disadvantageous inequity in third-party. However, they showed distinct preferences for advantageous inequity in first-party, with advantage-seeking preferences among children and aversion to advantageous inequity among adults. These findings contribute to a deeper understanding of children’s social preferences and their developmental directions

Model-based predictions of protective HIV pre-exposure prophylaxis adherence levels in cisgender women

Most human immunodeficiency virus (HIV) infections occur in cisgender women in resource-limited settings. In women, self-protection with emtricitabine/tenofovir disoproxil fumarate pre-exposure prophylaxis (FTC/TDF-PrEP) constitutes a major pillar of HIV prevention. However, clinical trials in women had inconsistent outcomes, sparking uncertainty about adherence requirements and reluctance in evaluating on-demand regimens. We analyzed data from published FTC/TDF-PrEP trials to establish efficacy ranges in cisgender women. In a ‘bottom-up’ approach, we modeled hypotheses in the context of risk-group-specific, adherence–efficacy profiles and challenged those hypotheses with clinical data. We found that different clinical outcomes were related to the proportion of women taking the product, allowing coherent interpretation of the data. Our analysis showed that 90% protection was achieved when women took some product. We found that hypotheses of putative male/female differences were either not impactful or statistically inconsistent with clinical data. We propose that differing clinical outcomes could arise from pill-taking behavior rather than biological factors driving specific adherence requirements in cisgender women

ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, which progressively upgrades the quality of generated images~by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 not only achieves the state-of-the-art on MS-COCO with zero-shot FID score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300

Bacterial Microbiota and Metabolic Character of Traditional Sour Cream and Butter in Buryatia, Russia

Traditional sour cream and butter are widely popular fermented dairy products in Russia for their flavor and nutrition, and contain rich microbial biodiversity, particularly in terms of lactic acid bacteria (LAB). However, few studies have described the microbial communities and metabolic character of traditional sour cream and butter. The objective of this study was to determine the bacterial microbiota and metabolic character of eight samples collected from herdsmen in Buryatia, Russia. Using single-molecule real-time (SMRT) sequencing techniques, we identified a total of 294 species and/or subspecies in 169 bacterial genera, belonging to 14 phyla. The dominant phylum was Firmicutes (81.47%) and the dominant genus was Lactococcus (59.28%). There were differences between the bacterial compositions of the sour cream and butter samples. The relative abundances of Lactococcus lactis, Lactococcus raffinolactis, and Acetobacter cibinongensis were significantly higher in sour cream than in butter, and the abundance of Streptococcusthermophilus was significantly lower in sour cream than in butter. Using a pure culture method, 48 strains were isolated and identified to represent seven genera and 15 species and/or subspecies. Among these isolates, Lactococccus lactis subsp. lactis (22.50%) was the dominant LAB species. Ultra-performance liquid chromatography–quadrupole–time of flight mass spectrometry at elevated energy was used in combination with statistical methods to detect metabolite differences between traditional sour cream and butter. A total of 27,822 metabolites were detected in all samples, and Lys-Lys, isohexanal, palmitic acid, Leu-Val, and 2′-deoxycytidine were the most dominant metabolites found in all samples. In addition, 27 significantly different metabolites were detected between the sour cream and butter samples, including short peptides, organic acids, and amino acids. Based on correlation analyses between the most prevalent bacterial species and the main metabolites in sour cream, we conclude that there may be a connection between the dominant LAB species and these metabolites. This study combined omics techniques to analyze the bacterial diversity and metabolic character of traditional sour cream and butter, and we hope that our findings will enrich species resource libraries and provide valuable resources for further research on dairy product flavor

Dynamics of oral microbiome acquisition in healthy infants: A pilot study

ObjectivesThe human oral microbiota is one of the most complex bacterial communities in the human body. However, how newborns initially acquire these bacteria remains largely unknown. In this study, we examined the dynamics of oral microbial communities in healthy infants and investigated the influence of the maternal oral microbiota on the acquisition of the infant's oral microbiota. We hypothesized that the infant oral microbial diversity increases with age.MethodsOne hundred and sixteen whole-salivary samples were collected from 32 healthy infants and their biological mothers during postpartum and 9- and 15-month well-infant visits. Bacterial genomic DNA was extracted and sequenced by Human Oral Microbe Identification using Next Generation Sequencing (HOMINGS) methods. The Shannon index was used to measure the microbial diversity of the infant-mother dyads (alpha diversity). The microbial diversity between the mother-infant dyads (beta-diversity) was calculated using the weighted non-phylogenetic Bray-Curtis distance in QIIME 1.9.1. Core microbiome analysis was performed using MicrobiomeAnalyst software. Linear discriminant analysis coupled with effect size analysis was used to identify differentially abundant features between mother and infant dyads.ResultsA total of 6,870,571 16S rRNA reads were generated from paired mother–infant saliva samples. Overall, oral microbial profiles significantly differed between the mother and infant groups (p &lt; 0.001). The diversity of the salivary microbiomes in the infants increased in an age-dependent manner, whereas the core microbiome of the mothers remained relatively stable during the study period. Breastfeeding and gender did not affect the microbial diversity in infants. Moreover, infants had a greater relative abundance of Firmicutes and a lower abundance of Actinobacteria, Bacteroidetes, Fusobacteria, and Proteobacteria than their mothers. The SparCC correlation analysis demonstrated constant changes in infants' oral microbial community network (p &lt; 0.05).ConclusionsThis study provides new evidence that the oral cavities of infants are colonized by a distinct group of bacterial species at birth. The acquisition and diversity of changes in oral microbial composition are dynamic during the first year of an infant's life. Before reaching the second birthday, the composition of the oral microbial community could be more similar to that of their biological mothers