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Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration-response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration-response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions.

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The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) adopted Guideline M10 entitled "Bioanalytical Method Validation and Study Sample Analysis" in May 2022. In October 2023, approximately one year after the adoption of the ICH M10 guideline, a "Hot Topic" session was held during the AAPS PharmSci 360 meeting to discuss the implementation of the guideline. The session focused on items the bioanalytical community felt were challenging to implement or ambiguous within the guideline. These topics included cross-validation, parallelism, comparative bioavailability studies, combination drug stability, endogenous analyte bioanalysis, and dilution QCs. In addition, the regulatory perspective on the guideline was presented. This report provides a summary of the Hot Topic session.

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Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS.

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BACKGROUND: Phylogenies play a crucial role in biological research. Unfortunately, the search for the optimal phylogenetic tree incurs significant computational costs, and most of the existing state-of-the-art tools cannot deal with extremely large datasets in reasonable times. RESULTS: In this work, we introduce the new VeryFastTree code (version 4.0), which is able to construct a tree on 1 server using single-precision arithmetic from a massive 1 million alignment dataset in only 36 hours, which is 3 times and 3.2 times faster than its previous version and FastTree-2, respectively. This new version further boosts performance by parallelizing all tree traversal operations during the tree construction process, including subtree pruning and regrafting moves. Additionally, it introduces significant new features such as support for new and compressed file formats, enhanced compatibility across a broader range of operating systems, and the integration of disk computing functionality. The latter feature is particularly advantageous for users without access to high-end servers, as it allows them to manage very large datasets, albeit with an increase in computing time. CONCLUSIONS: Experimental results establish VeryFastTree as the fastest tool in the state-of-the-art for maximum likelihood phylogeny estimation. It is publicly available at https://github.com/citiususc/veryfasttree. In addition, VeryFastTree is included as a package in Bioconda, MacPorts, and all Debian-based Linux distributions.

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Seed colors and color patterns are critical for the survival of wild plants and the consumer appeal of crops. In common bean, a major global staple, these patterns are also essential in determining market classes, yet the genetic and environmental control of many pigmentation patterns remains unresolved. In this study, we genetically mapped variation for several important seed pattern loci, including T, Bip, phbw, and Z, which co-segregated with candidate genes PvTTG1, PvMYC1, PvTT8, and PvTT2, respectively. Proteins encoded by these genes are predicted to work together in MYB-bHLH-WD40 (MBW) complexes, propagating flavonoid biosynthesis across the seed coat as observed in Arabidopsis. Whole-genome sequencing of 37 accessions identified mutations, including seven unique parallel mutations in T (PvTTG1) and non-synonymous SNPs in highly conserved residues in bipana (PvMYC1) and z (PvTT2). A 612 bp intron deletion in phbw (PvTT8) eliminated motifs conserved since the Papilionoideae origin and corresponded to a 20-fold reduction in transcript abundance. In multi-location field trials of seven varieties with partial seed coat pigmentation patterning, the pigmented seed coat area correlated positively with ambient temperature, with up to 11-fold increases in the pigmented area from the coolest to the warmest environments. In controlled growth chamber conditions, an increase of 4°C was sufficient to cause pigmentation on an average additional 21% of the seed coat area. Our results shed light on key steps of flavonoid biosynthesis in common bean. They will inform breeding efforts for seed coat color/patterning to improve consumer appeal in this nutritious staple crop.

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BACKGROUND: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure. RESULTS: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software. CONCLUSIONS: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills.

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Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and "micro-shifting" techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks.

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The European sprat is a small plankton-feeding clupeid present in the northeastern Atlantic Ocean, in the Mediterranean Sea, and in the brackish Baltic Sea and Black Sea. This species is the target of a major fishery and, therefore, an accurate characterization of its genetic population structure is crucial to delineate proper stock assessments that aid ensuring the fishery's sustainability. Here, we present (i) a draft genome assembly, (ii) pooled whole genome sequencing of 19 population samples covering most of the species' distribution range, and (iii) the design and test of a single nucleotide polymorphism (SNP)-chip resource and use this to validate the population structure inferred from pooled sequencing. These approaches revealed, using the populations sampled here, three major groups of European sprat: Oceanic, Coastal, and Brackish with limited differentiation within groups even over wide geographical stretches. Genetic structure is largely driven by six large putative inversions that differentiate Oceanic and Brackish sprats, while Coastal populations display intermediate frequencies of haplotypes at each locus. Interestingly, populations from the Baltic and the Black Seas share similar frequencies of haplotypes at these putative inversions despite their distant geographic location. The closely related clupeids European sprat and Atlantic herring both show genetic adaptation to the brackish Baltic Sea, providing an opportunity to explore the extent of genetic parallelism. This analysis revealed limited parallelism because out of 125 independent loci detected in the Atlantic herring, three showed sharp signals of selection that overlapped between the two species and contained single genes such as PRLRA, which encodes the receptor for prolactin, a freshwater-adapting hormone in euryhaline species, and THRB, a receptor for thyroid hormones, important both for metabolic regulation and the development of red cone photoreceptors.

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Constraints on phenotypic evolution can lead to patterns of convergent evolution, by limiting the 'pool' of potential phenotypes in the face of endogenous (functional, developmental) or exogenous (competition, predation) selective pressures. Evaluation of convergence depends on integrating ecological and morphological data within a robust, comparative phylogenetic context. The staggering diversity of teleost fishes offers a multitude of lineages adapted for similar ecological roles, and therefore, offers numerous replicated evolutionary experiments for exploring phenotypic convergence. However, our understanding of fish feeding systems has been primarily shaped by marine species, with the monolithic exception of freshwater cichlids. Here we use piranhas and pacus (Serrasalmidae) to explore the evolution of different feeding ecologies and their morphological proxies in Neotropical freshwater environments. Specifically, we explore whether convergence is more widespread among plant-eating fishes, arising from strong constraints on phenotypic evolution in herbivores. Using osteological micro-computed tomographic imaging (µCT), we describe the major axes of morphological variation in pacus and piranhas, regarding their diet and feeding behaviors. Next, we evaluated whether herbivorous niches are less labile than other dietary guilds and whether herbivorous species' phenotypes evolve at a slower evolutionary rate than other taxa. We then assess how convergent herbivorous taxa are, using three different suites of morphological characters (dental, jaw, and abdominal morphometrics). Ecologically, herbivory is not a dead end, exhibiting similar observed transition rates as those between carnivores and omnivores. However, we documented widespread convergence in herbivores and that herbivores have slower rates of phenotypic evolution than carnivores. Most instances of convergence are found in herbivorous taxa, specifically in frugivores and folivores. Moreover, instances of 'complete' convergence, indicated by positive convergence metrics observed in more than one morphometric dataset, were only found in herbivores. Herbivores do appear to evolve under constrained circumstances, but this has not limited their ecological lability.

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Introduction The goal of total knee arthroplasty is to replace diseased cartilage and bone with an artificial implant to improve the patient's quality of life. The knee has historically been reconstructed to the patient's mechanical axis (MA). However, kinematically aligned techniques have been increasingly used. Kinematic alignment requires less soft-tissue resection and aligns the knee with what is anatomically natural to the patient, while there is concern that kinematically aligned knees will lead to earlier failure due to potential unequal weight distribution on the implant. The purpose of this study is to compare the parallelism from the floor of the joint-line cuts using kinematic and mechanical alignment and understand if the MA is a proper estimation of the tibial-ankle axis (TA). Methods A retrospective study was conducted by recruiting all high tibial osteotomy and distal femoral osteotomy recipients operated on by two surgeons in two MedStar Health hospitals from 01/2013 to 07/2020 with full-length films in preparation for restorative procedures. Baseline osteoarthritis was graded using the Kellgren-Lawrence classification system with all patients presenting as Grade 0. The TA and the joint-line orientations of the MA and kinematic axis (KA) were measured on 66 legs. The average distance from parallelism to the ground was compared between the MA and the KA and between the MA and the TA using a paired t-test. Results KA joint-line orientation (1.705° deviation) was more parallel to the floor in the bipedal stance phase than the MA (2.316° deviation, p=0.0156). The MA (2.316° deviation) was not a proper estimation of the TA (4.278° deviation, p=0.0001). Conclusion By utilizing the KA technique, the restoration of the natural joint line, as well as a joint that is more parallel to the floor in the stance phase compared to the MA, is achieved. The parallelism to the ground of the KA during the bipedal stance phase suggests an even load distribution across the knee. In addition, due to its similarity to the KA and anatomical significance in weight-bearing distribution, further investigation into the hip-to-calcaneal axis as an approximation of the joint line is warranted.

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Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.

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Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

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With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.

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To meet the demand for the track's geometric parameter detection equipment for train speed and high-speed aerodynamics tests, a zero-calibration gauge device is designed with the centering limit, height adjustment and horizontal display function in this paper. The bending situation of the zero-calibration gauge is analyzed and the processing technology is studied, which ensures the rationality and realizability of the design of zero-calibration gauge. Then the gauge zero value and the parallelism of the working surface of zero-calibration gauge have been experimentally tested. The experimental results show that the parameter of gauge zero value is 1434.829 mm with a standard deviation of 1.4 µm. The parallelism of the two upper working surfaces is 1.1 µm, and the parallelism of the two inner working surfaces is 4 µm. Finally, the uncertainty evaluation of zero-calibration gauge is completed. The measurement uncertainty of gauge zero value is 12 µm and the measurement uncertainty of height difference is 6 µm.

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Diabetic nephropathy (DN) and diabetic retinopathy (DR), as microvascular complications of diabetes mellitus, are currently the leading causes of end-stage renal disease (ESRD) and blindness, respectively, in the adult working population, and they are major public health problems with social and economic burdens. The parallelism between the two in the process of occurrence and development manifests in the high overlap of disease-causing risk factors and pathogenesis, high rates of comorbidity, mutually predictive effects, and partial concordance in the clinical use of medications. However, since the two organs, the eye and the kidney, have their unique internal environment and physiological processes, each with specific influencing molecules, and the target organs have non-parallelism due to different pathological changes and responses to various influencing factors, this article provides an overview of the parallelism and non-parallelism between DN and DR to further recognize the commonalities and differences between the two diseases and provide references for early diagnosis, clinical guidance on the use of medication, and the development of new drugs.

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Poetic diction routinely involves two complementary classes of features: (i) parallelisms, i.e. repetitive patterns (rhyme, metre, alliteration, etc.) that enhance the predictability of upcoming words, and (ii) poetic deviations that challenge standard expectations/predictions regarding regular word form and order. The present study investigated how these two prediction-modulating fundamentals of poetic diction affect the cognitive processing and aesthetic evaluation of poems, humoristic couplets and proverbs. We developed quantitative measures of these two groups of text features. Across the three text genres, higher deviation scores reduced both comprehensibility and aesthetic liking whereas higher parallelism scores enhanced these. The positive effects of parallelism are significantly stronger than the concurrent negative effects of the features of deviation. These results are in accord with the hypothesis that art reception involves an interplay of prediction errors and prediction error minimization, with the latter paving the way for processing fluency and aesthetic liking. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

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Parallel clines across environmental gradients can be strong evidence of adaptation. House mice (Mus musculus domesticus) were introduced to the Americas by European colonizers and are now widely distributed from Tierra del Fuego to Alaska. Multiple aspects of climate, such as temperature, vary predictably across latitude in the Americas. Past studies of North American populations across latitudinal gradients provided evidence of environmental adaptation in traits related to body size, metabolism, and behavior and identified candidate genes using selection scans. Here, we investigate genomic signals of environmental adaptation on a second continent, South America, and ask whether there is evidence of parallel adaptation across multiple latitudinal transects in the Americas. We first identified loci across the genome showing signatures of selection related to climatic variation in mice sampled across a latitudinal transect in South America, accounting for neutral population structure. Consistent with previous results, most candidate SNPs were in regulatory regions. Genes containing the most extreme outliers relate to traits such as body weight or size, metabolism, immunity, fat, and development or function of the eye as well as traits associated with the cardiovascular and renal systems. We then combined these results with published results from two transects in North America. While most candidate genes were unique to individual transects, we found significant overlap among candidate genes identified independently in the three transects, providing strong evidence of parallel adaptation and identifying genes that likely underlie recent environmental adaptation in house mice across North and South America.

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BACKGROUND: A general view in the study of pollination syndromes is that floral traits usually represent convergent floral adaptations to specific functional pollinator groups. However, the definition of convergence is elusive and contradictory in the literature. Is convergence the independent evolution of either the same trait or similar traits with the same function? A review of the concept of convergence in developmental biology and phylogenetic systematics may shed new light in studies of pollination syndromes. SCOPE: The aims of this article are (1) to explore the notion of convergence and other concepts (analogy, homoplasy and parallelism) within the theory and practice of developmental evolution and phylogenetic systematics; (2) to modify the definitions of syndromes in order to embrace the concepts of analogy and convergence; (3) to revisit the bat pollination syndrome in the context of angiosperm phylogeny, with focus on the showy 'petaloid' organs associated with the syndrome; (4) to revisit the genetic-developmental basis of flower colour; (5) to raise evolutionary hypotheses of floral evolution associated with the bat pollination syndrome; and (6) to highlight some of the current frontiers of research on the origin and evolution of flowers and its impact on pollination syndrome studies in the 21st century. CONCLUSIONS: The inclusion of the concepts of analogy and convergence within the concept of syndromes will constitute a new agenda of inquiry that integrates floral biology, phylogenetic systematics and developmental biology. Phyllostomid and pteropodid bat pollination syndrome traits in eudicots and monocots represent cases of analogous and convergent evolution. Pollination syndromes are a multivariate concept intrinsically related to the understanding of flower organogenesis and evolution. The formulation of hypotheses of pollination syndromes must consider the phylogenetic levels of universality for both plant and animal taxa, flower development, genetics, homology and evolution, and a clear definition of evolutionary concepts, including analogy, convergence, homoplasy and parallelism.

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IMPORTANCE: Biofilm formation is a vital factor for the survival and adaptation of bacteria in diverse environmental niches. Experimental evolution combined with the advancement of whole-population genome sequencing provides us a powerful tool to understand the genomic dynamic of evolutionary adaptation to different environments, such as during biofilm development. Previous studies described the genetic and phenotypic changes of selected clones from experimentally evolved Bacillus thuringiensis and Bacillus subtilis that were adapted under abiotic and biotic biofilm conditions. However, the full understanding of the dynamic evolutionary landscapes was lacking. Furthermore, the differences and similarities of adaptive mechanisms in B. thuringiensis and B. subtilis were not identified. To overcome these limitations, we performed longitudinal whole-population genome sequencing to study the underlying genetic dynamics at high resolution. Our study provides the first comprehensive mutational landscape of two bacterial species' biofilms that is adapted to an abiotic and biotic surface.

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