Multiple Variations Research Articles

Nusselt and Friction Factor Correlations for Gasketed Plate Heat Exchangers in Variable Inlet Conditions

Abstract Gasketed plate heat exchangers (GPHEs) are essential equipment in many industries due to their efficiency and compact design. They are particularly valued owing to their versatility, but GPHE designing and sizing are challenging due to divergences in Nusselt number and friction factor correlations. In addition to the influence of Reynolds and Prandtl numbers on the abovementioned correlations, recent studies have shown that GPHE performance is also affected by geometrical plate features, GPHE assembly accuracy quantified by the tightening distance, and inlet conditions in the branches. Despite the noticeable effort in the literature to develop more accurate GPHE correlations, no study to date has presented correlations affected by the plate thickness, tightening distance, and branches' inlet conditions (differences in the inlet pressures). Therefore, new correlations were created that filled this gap, including the influences of the chevron angle and the corrugation aspect ratio. With the aid of multiple linear regressions, the trends and large variations of our experimental databases were satisfactorily reproduced. The results have been compared to relevant literature.

The impact of surgical technique on the number of sentinel lymph nodes removed and its effect on complication rates.

Sentinel lymph node biopsy (SLNB) is a staging procedure used to guide treatment for patients with breast cancer. Multiple variations in the SLNB technique have been described. We questioned how technique impacts the number of sentinel lymph nodes (SLNs) removed and associated complications. Patients with breast cancer who were treated with lumpectomy and SLNB between 2018 and 2023 were analyzed. Patients were excluded if they had prior ipsilateral breast or axillary surgery or chest wall radiation,underwent neoadjuvant chemotherapy or endocrine therapy,or subsequently required ALND. Demographics, surgical technique, and operative and pathological data were collected. Complication rates were compared between more (4+) or fewer (1-3) SLNs removed. A total of 643 patients were included, with an average of 2.44 LNs removed (range 1-11). The overall complication rate was 19.8%, with a 4.4% lymphedema rate. The lymphedema rate was higher among patients who had more nodes removed. An average of 2.5 LNs were removed with dual mapping vs. 2.0 with technetium alone (p = 0.15). Breast massage had no effect on the number of SLNs removed (p = 0.12) but did impact blue dye uptake (p = 0.001). Surgical technique did not significantly impact the number of nodes removed. Removing more nodes was associated with a greater risk of lymphedema.

Exploring Pediatric Sutural Variations with 3D CT Imaging: A Retrospective Study at a Tertiary Hospital.

Sutural anatomy variation has long been a topic of debate among anatomists, paleontologists, and morphologists. While the exact reasons for the prevalence of this variance remains a topic of ongoing discussion, developmental and genetic factors are hypothesized to be the main reasons. Understanding the morphology and occurrence of normal sutural variations in pediatric patients is essential to making the right diagnosis, where a misinterpretation of a sutural bone may lead to an inaccurate assessment, completely misleading the diagnostic process. This is a retrospective study, we collected the data of the last 100 pediatric patients under the age of 29 months who underwent a 3D Skull CT for reasons of trauma. Our study, examining pediatric patients who underwent 3D CT imaging, consisted of 100 children, divided by gender as 52% females and 48%. The overall mean age of the cohort was 10.04 months. The anterior fontanelle was closed in 28% (n=28). The posterior fontanelle was closed in 84% with a mean age of 11.57 months (SD=6.24). Notably, all participants had patent coronal, lambdoid, and squamosal sutures (n=100, 100%) with a mean age of 10.04 months (SD = 6.8). The metopic suture was fused in 70% of the participants (n = 70). A metopic ridge was present in 22% of the participants. Wormian bones were present in 59% (n = 59) of the participants, the majority being on the lamboid suture. We also found other multiple cranial bone variations among our pediatric patients. Involving the spheno-parietal suture, spheno-frontal, posterior intraoccipital suture, frontozygomatic suture, and the mendosal suture. The current study provides valuable insights into the prevalence and characteristics of skull suture variations in the Saudi pediatric population under the age of one, based on 3D CT imaging data. Our findings indicate a notable occurrence of sutural bone variations, including the presence of Wormian bones and different patterns of suture patency.

Implementing A/B Testing and Hypothesis-driven Development for Product Performance Optimization

A/B testing and hypothesis-driven development (HDD) are two must-haves to maximize product performance. A/B testing refers to the ability to compare the multiple variations of a feature or design by measuring responses from users, while HDD is the systematic approach that forms and tests hypotheses through iterative cycles. This paper explores the relationship of methodologies involved and has a comprehensive analysis of theoretical foundations, historical development, and best practices on both. The study also has other major challenges, which include ethics issues, sample size, and balancing innovation with data-driven decisions. This research is actionable, providing insight for the practitioner who is interested in enhancing user engagement, conversion rates, and general success of a product, all by making use of the present trends and emerging technologies.

Understanding the variation in morphology and rhizome yield of Indian ginger (Zingiber officinale Rosc.) through distinctness, uniformity and stability (DUS) descriptors: A natural marker to enhance crop productivity

Ginger (Zingiber officinale) is a widely consumed and economically significant plant known for its medicinal and culinary properties. Understanding the diversity within ginger germplasm is vital for its successful use in breeding programs and conservation efforts. Morphological characterization plays a key role in identifying distinct varieties, ensuring uniform traits, protecting breeders' rights and conserving genetic diversity, all of which support commercial production and drive innovation in breeding. The present investigation was carried out at the Hybrid Rice Evaluation Centre, Gudalur, Nilgiris, with 32 genotypes to study the variability for identifying the best performing ginger genotypes. Genotypes were examined for 2 qualitative and 8 quantitative traits using Distinctness, Uniformity and Stability (DUS) parameters. Out of the traits assessed, plant height, number of leaves on the main stem and dry recovery were monomorphic, with no variation within the population. However, 4 characters, normal growth habit, shoot diameter, leaf length and leaf width, exhibited 2 distinct variations within the genotypes, indicating dimorphism. Additionally, 3 traits such as the number of tillers per clump, rhizome thickness and rhizome shapes showed multiple variations among the studied genotypes, illustrating polymorphism within the population. Principal component analysis was conducted on 7 principal components with eigenvalues of more than one accounted for 98.28 % of the total variability. Among these traits, leaf length, rhizome thickness and number of tillers demonstrated the highest variation, while the remaining traits showed lower variability. Based on PCA and cluster analysis, the genotypes Nadia, Manipur Local, Aswathy, Nagaland Local, Bhaise, ACC 578, Himachal Local, Rio De Janeiro, IISR Vajra, Humnabad Local, Hassan Local, Thalavadi Local and Chikkamagalore Local 2 were identified as the most diverse genotypes.

DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation.

Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence making it an indispensable tool for monitoring the disease and treatment response. Deep learning-based segmentation enables the precise delineation of cardiac structures including the myocardium, right ventricle, and left ventricle. The accurate segmentation of these structures helps in the diagnosis of heart failure, cardiac functional response to therapies, and understanding the state of the heart functions after treatment. The objective of this study is to develop a multiscale deep learning model to segment cardiac organs based on MRI imaging data. Good segmentation performance is difficult to achieve due to the complex nature of the cardiac structure, which includes a variety of chambers, arteries, and tissues. Furthermore, the human heart is also constantly beating, leading to motion artifacts that reduce image clarity and consistency. As a result, a multiscale method is explored to overcome various challenges in segmenting cardiac MRI images. This paper proposes DeSPPNet, a multiscale-based deep learning network. Its foundation follows encoder-decoder pair architecture that utilizes the Spatial Pyramid Pooling (SPP) layer to improve the performance of cardiac semantic segmentation. The SPP layer is designed to pool features from densely convolutional layers at different scales or sizes, which will be combined to maintain a set of spatial information. By processing features at different spatial resolutions, the multiscale densely connected layer in the form of the Pyramid Pooling Dense Module (PPDM) helps the network to capture both local and global context, preserving finer details of the cardiac structure while also capturing the broader context required to accurately segment larger cardiac structures. The PPDM is incorporated into the deeper layer of the encoder section of the deep learning network to allow it to recognize complex semantic features. An analysis of multiple PPDM placement scenarios and structural variations revealed that the 3-path PPDM, positioned at the encoder layer 5, yielded optimal segmentation performance, achieving dice, intersection over union (IoU), and accuracy scores of 0.859, 0.800, and 0.993, respectively. Different PPDM configurations produce a different effect on the network; as such, a shallower layer placement, like encoder layer 4, retains more spatial data that need more parallel paths to gather the optimal set of multiscale features. In contrast, deeper layers contain more informative features but at a lower spatial resolution, which reduces the number of parallel paths required to provide optimal multiscale context.

TML1 and TML2 synergistically regulate nodulation and affect arbuscular mycorrhiza in Medicago truncatula.

Two symbiotic processes, nodulation and arbuscular mycorrhiza, are primarily controlled by the plant's need for nitrogen (N) and phosphorus (P), respectively. Autoregulation of nodulation (AON) and autoregulation of mycorrhizal symbiosis (AOM) both negatively regulate their respective processes and share multiple components-plants that make too many nodules usually have higher arbuscular mycorrhiza (AM) fungal root colonization. The protein TML (TOO MUCH LOVE) was shown to function in roots to maintain susceptibly to rhizobial infection under low N conditions and control nodule number through AON in Lotus japonicus. Medicago truncatula has two sequence homologs: MtTML1 and MtTML2. We report the generation of stable single and double mutants harboring multiple allelic variations in MtTML1 and MtTML2 using CRISPR-Cas9 targeted mutagenesis and screening of a transposon mutagenesis library. Plants containing single mutations in MtTML1 or MtTML2 produced two to three times the nodules of wild-type plants, whereas plants containing mutations in both genes displayed a synergistic effect, forming 20× more nodules compared to wild-type plants. Examination of expression and heterozygote effects suggests that genetic compensation may play a role in the observed synergy. Plants with mutations in both TMLs only showed mild increases in AM fungal root colonization at later timepoints in our experiments, suggesting that these genes may also play a minor role in AM symbiosis regulation. The mutants created will be useful tools to dissect the mechanism of synergistic action of MtTML1 and MtTML2 in M. truncatula symbiosis with beneficial microbes.

Epistasis between genetic variations on MdMYB109 and MdHXK1 exerts a large effect on sugar content in apple fruit.

Many quantitative traits are controlled by multiple genetic variations with minor effects, making it challenging to resolve the underlying genetic network and to apply functional markers in breeding. Affected by up to a hundred quantitative trait loci (QTLs), fruit-soluble sugar content is one of the most complex quantitative traits in apple (Malus sp.). Here, QTLs for apple fruit sucrose and fructose content were identified via QTL mapping and bulked-segregant analysis sequencing (BSA-seq) using a population derived from a 'Jonathan' × 'Golden Delicious' cross. Allelic variations and non-allelic interactions were validated in the candidate genes within these defined QTL regions. Three single-nucleotide polymorphisms (SNPs) (SNP -326 C/T, SNP -705 A/G, and SNP -706 G/T) in the MdMYB109 promoter region affected the binding ability of the repressive transcription factor MdWRKY33, leading to increased MdMYB109 expression. MdMYB109 bound directly to the promoter of the sucrose transporter gene MdSUT2.2 and activated its expression, raising fruit sucrose content. A SNP (SNP1060 A/G) in the hexokinase gene MdHXK1 affected the phosphorylation of the transcription factor MdbHLH3, and phosphorylated MdbHLH3 interacted with MdMYB109 to co-activate MdSUT2.2 expression and increase fruit sucrose content. Adding the joint effects of the genotype combinations at the SNP markers based on the SNPs in MdMYB109 and MdHXK1 increased the prediction accuracy of a genomics-assisted prediction (GAP) model for total soluble solid content from 0.3758 to 0.5531. These results uncovered functional variations in MdMYB109 and MdHXK1 regulating apple fruit sucrose content. The updated GAP model with improved predictability can be used efficiently in apple breeding.

Multiple timescale variations in fronts in the Seto Inland Sea, Japan

Abstract. The Seto Inland Sea (SIS) is a critical semi-enclosed coastal sea in Japan, characterized by intricate coastlines and narrow straits that give rise to various fronts. Despite extensive research on tidal fronts, knowledge gaps persist regarding their spatiotemporal dynamics, particularly in certain poorly documented regions. Additionally, the understanding of thermohaline fronts, which emerge during winter, requires further investigation. We aimed to enhance our understanding of tidal and thermohaline fronts in the SIS by analyzing their dynamic processes, including intra-tidal and spring–neap tidal cycles, seasonal variations, and anomalous frontal variability. Using a gradient-based algorithm with an advanced contextual feature-preserving median filter, we processed the high-resolution sea surface temperature dataset to detect and quantify tidal and thermohaline fronts. Our analysis revealed the presence of numerous tidal fronts, predominantly influenced by the M2 tide, across the SIS, with substantial spatial variations in amplitude due to complex coastlines and narrow straits. Intra-tidal movements of tidal fronts corresponded to ebb and flood currents, while spring–neap tidal cycles and seasonal shifts influenced frontal positions and intensities. Additionally, thermohaline fronts were identified in certain regions during winter, characterized by large horizontal temperature and salinity gradients. This study enhances the understanding of tidal and thermohaline fronts in the SIS, emphasizing the importance of intra-tidal and wind-driven influences on frontal dynamics. However, limited observational coverage and resolution emphasize the need for further research to explore long-term temporal changes and better grasp the influences of ambient currents and wind patterns. Such insights are vital for effective coastal management and environmental monitoring in the SIS region.

Discrete air model for large scale rapid filling process contained entrapped air

In this paper, a discrete air model (DAM) is developed to capture the discontinuous characteristics of air at different locations during the rapid filling process in long-range, large-scale water pipeline. By introducing the continuity and momentum equations of air and combining them with the water control equation and the interface continuity equation, an improved model based on the uniform air is derived. The accuracy of the model is verified by comparing it with experimental data and the results of the original uniform air model (UAM). Subsequently, a long-range, large-scale pipeline was considered to investigate the dynamic properties of air in large systems, which had not been covered in previous studies. Additionally, the influence of air dynamic characteristics on initial air volume affected by different air lengths and various pipe diameters in large systems – is further studied. Results show that an increased pipe diameter expands the contact area of the air–water interface, often resulting in the UAM underestimating the maximum peak pressure. The propagation process of transient waves in air is divided into three stages: propagation stage with multiple variation, maximum value stage with interface propulsive, and stability stage with several fluctuations, which corresponds to the pressure fluctuation curve. This explains the occurrence of small fluctuations and peaks in the curve. Therefore, the peak pressure simulated by the proposed DAM offers a better understanding of wave behaviours.

A Novel Mathematical Approach for Inductor-Current Expressions Definition in Multilevel Dual-Active-Bridge Converters

The study of multilevel dual-active-bridge (DAB) converters has garnered significant attention in recent years thanks to their advantages with respect to the conventional two-level (2L) DAB; namely, its greater performance and its capability to operate at higher voltage. The analysis of the converter high-frequency inductor current (iL) is crucial, for instance, to compute its root mean square (RMS) value, required to estimate the conduction losses in the converter. The mathematical expression of iL is piecewise and multiple variations, i.e., modes, exist depending on the modulation parameter values. This increases the complexity of converter performance analytical study. Thus, a more practical and generalizable expression of iL current is desirable. This paper proposes novel compact analytic expressions for the instantaneous and RMS inductor current in the 2L-NL DAB converter, leveraging binary functions to define the piecewise intervals and to identify the mode as a function of the modulation parameter values. The proposed method paves the way for more simple and computationally efficient DAB performance optimization software tools that allow exploring any given converter structures and modulation strategies.

Chaotic-Based Shellcode Encryption: A New Strategy for Bypassing Antivirus Mechanisms

This study employed chaotic systems as an innovative approach for shellcode obfuscation to evade current antivirus detection methods. Standard AV solutions primarily rely on static signatures and heuristic analysis to identify malicious code. However, chaotic systems employ dynamic and unpredictable encryption methods, significantly obstructing detection efforts. The utilization of various chaotic maps for shellcode encryption facilitates the generation of multiple unique variations from the same functional code, each exhibiting distinct unpredictability due to the inherent nonlinearity and sensitivity of chaotic systems to initial conditions. The unpredictability of these situations poses a considerable challenge for antivirus software in recognizing consistent patterns, resulting in decreased detection rates. The findings from our experiments demonstrate that chaos-driven encryption methods significantly outperform traditional encryption techniques in terms of evading detection. This paper emphasizes the potential of chaos theory to enhance malware evasion strategies, offering a sophisticated approach to bypassing modern antivirus protections while ensuring the effectiveness of malicious payloads.

The Present Difficulties and Potential Benefits of Dopaminergic Agents in Parkinson's Disease Treatment

Parkinson's disease (PD) is a progressive neurological condition characterized by bradykinesia, rigidity, tremors, and impaired balance, among other motor impairments. The issue arises from dopaminergic neurons located in the spinal column of the brain. This research report examines the therapeutic potential of dopaminergic medications in the management of Parkinson's disease. The central concept of Parkinson's disease (PD) revolves around the notion that dopaminergic pathways exert significant influence over the regulation of movement. The book examines various dopaminergic medications, elucidating their mechanisms of action and the impact they exert on dopamine signaling. Examples of these medications include levodopa, dopamine agonists, and monoamine oxidase-B (MAO-B) inhibitors. Although dopaminergic medicines initially aid in the treatment of Parkinson's disease (PD), prolonged usage of these medications gives rise to several complications. Experiencing dyskinesias and motor fluctuations, characterized by episodes of involuntary movements and behaviors that are undesired, is a significant challenge. This study investigates the underlying causes of these difficulties and explores potential treatment options, including the use of controlled-release formulations and further therapy. The book discusses the non-motor symptoms of Parkinson's disease (PD), as well as the use of dopaminergic medications to treat mood disorders, autonomic dysfunction, and cognitive loss. Dopaminergic medications remain crucial in reducing motor symptoms and enhancing the quality of life for those with Parkinson's disease. Parkinson's disease (PD) is a complex condition with multiple distinct variations. In order to address its existing challenges and explore its potential implications for future Parkinson's disease medications, a comprehensive and effectively coordinated strategy is required.

Whole-genome analysis of stress resistance-related genes in Listeria monocytogenes

Listeria monocytogenes is a crucial foodborne pathogen with significant public health implications. This study analyzed whole-genome sequences (WGS) of L. monocytogenes strains from public databases, examining associations between resistance genes, lineage, strain type, isolation source, and geography. Results revealed that after eliminating duplicates and strains with incomplete WGS, a total of 316 strains were deemed suitable for subsequent analyses. Within these strains, lineages I and II were extensively distributed, predominantly isolated from clinical and food sources. 56.65% of these strains fell into seven major Clonal Complexes (CC), identified by Multilocus Sequence Typing (MLST), correlating significantly with isolation information. Analysis of 46 resistance-related genes showed a high consistency of resistance genes in the same type of strains, hinting at a potential causal chain of ‘food-food environment-evolution of L. monocytogenes’. Moreover, the standard strains exhibit similar gene carriage rates as the sample strains, with multiple variations observed in acid-resistance genes. In conclusion, through a comprehensive analysis of the L. monocytogenes genome sequences, this study deepens our understanding of the differences and associations between its lineage, strain typing, isolation sources, geographical distribution, and resistance genes, thereby providing a scientific basis for formulating more effective prevention and control strategies.

AI-Assisted Detection and Localization of Spinal Metastatic Lesions.

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions. Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection. The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection. The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

The Prognostic Significance of Plasma Beta2-Glycoprotein I Levels in Hepatocellular Carcinoma Patients.

Beta2-glycoprotein I (β2-GPI) is a plasma glycoprotein with multiple physiological functions, but its relationship with hepatocellular carcinoma (HCC) is still poorly understood. HCC is one of the most common forms of liver cancer and is a leading cause of cancer-related death worldwide. This study aimed to investigate the association between β2-GPI and liver cancer and further validate its potential as a biomarker for HCC. Thirty-six patients diagnosed with HCC at the Division of Gastroenterology and Hepatology, E-Da Hospital, Taiwan, were included in the study. The expression levels of β2-GPI in plasma specimens from patients with HCC were determined by enzyme immunoassay and analyzed in relation to clinicopathological variables using the Chi-square test or Fisher's exact test. The predictive significance of β2-GPI for both overall survival (OS) and disease-free survival (DFS) was assessed using Kaplan-Meier estimates, and the statistical significance of differences was evaluated through the log-rank test. Cox proportional hazards regression models were used to evaluate the association between OS/DFS time and clinicopathological characteristics. Results: Plasma β2-GPI levels were significantly lower in patients with HCC compared to non-cancer controls and significantly correlated with aspartate aminotransferase (AST) levels of HCC. High plasma β2-GPI levels were significantly associated with better OS and DFS in HCC patients. Furthermore, in multiple variates analyses, OS was found to be significantly better in HCC patients with higher plasma β2-GPI expression. Elevated levels of β2-GPI protein in the plasma of HCC patients were identified as an independent factor predictive of improved OS and DFS. Activating β2-GPI in individuals at high risk could serve as a promising way for mitigating the progression of HCC.

Learning Optimizer-Based Visual Analytics Method to Detect Targets in Autonomous Unmanned Aerial Vehicles

Visual analytics is vital for identifying targets by differentiating their structure and texture from unmanned aerial vehicles (UAVs). Object sensing disturbance and swift texture differentiation are tedious due to the UAV displacements. For improving the accuracy of target detection, this article introduces a learning optimizer-based visual analytics method. The proposed method assimilates deep learning and a gradient descent algorithm for feature differentiation and error minimization concurrently. The captured images are identified using multiple structural feature variations and are correlated with similar stored images. The features are extracted at different displacement and structural changing instances for leveraging accuracy. The learning process trains the similarity features during different differentiation factors. In the feature extraction, the minimum slope points are identified using a gradient descent algorithm by assigning random weights. As the differentiation increases using similar features, the minimum similarity value is detection. Postdetection, the weights are incremental and linear across different feature slopes. Therefore, the accuracy increases under varying displacement instances, preventing target misdetection. The gradient function is invariable between the minimum and maximum values for identifying high-precision features. This ensures optimal detection of different buildings and structures with high accuracy.

High-throughput phenotype-to-genotype testing of meningococcal carriage and disease isolates detects genetic determinants of disease-relevant phenotypic traits.

Genome-wide association studies (GWAS) with binary or single phenotype data have successfully identified disease-associated genotypes and determinants of antimicrobial resistance. We describe a novel phenotype-to-genotype approach for a major bacterial pathogen that involves simultaneously testing for associations among multiple disease-related phenotypes and linkages between phenotypic variation and genetic determinants. High-throughput assays quantified variation among 163 Neisseria meningitidis serogroup W ST-11 clonal complex isolates for 11 phenotypic traits. A comparison of carriage and two disease subgroups detected significant differences between groups for eight phenotypic traits. Candidate genotypic testing indicated that indels in csw, a capsular biosynthesis gene, were associated with reduced survival in antibody-depleted heat-inactivated serum. GWAS testing detected 341 significant genetic variants (3 single-nucleotide polymorphisms and 338 unitigs) across all traits except serum bactericidal antibody-depleted assays. Growth traits were associated with variants of capsular biosynthesis genes, carbonic anhydrase, and an iron-uptake system while adhesion-linked variation was in pilC2, marR, and mutS. Multiple phase variation states or combinatorial phasotypes were associated with significant differences in multiple phenotypes. Controlling for group effects through regression and recursive random forest approaches detected group-independent effects for nalP with biofilm formation and fetA with a growth trait. Through random forest testing, nine phenotypes were weakly predictive of MenW:cc11 sub-lineage, original or 2013, for disease isolates while three characteristics separated carriage and disease isolates with >80% accuracy. This study demonstrates the power of combining high-throughput phenotypic testing of pathogenically relevant isolate collections with genomics for identifying genetic determinants of specific disease-relevant phenotypes and the pathobiology of microbial pathogens.IMPORTANCENext-generation sequencing technologies have led to the creation of extensive microbial genome sequence databases for several bacterial pathogens. Mining of these databases is now imperative for unlocking the maximum benefits of these resources. We describe a high-throughput methodology for detecting associations between phenotypic variation in multiple disease-relevant traits and a range of genetic determinants for Neisseria meningitidis, a major causative agent of meningitis and septicemia. Phenotypic variation in 11 disease-related traits was determined for 163 isolates of the hypervirulent ST-11 lineage and linked to specific single-nucleotide polymorphisms, short sequence variants, and phase variation states. Application of machine learning algorithms to our data outputs identified combinatorial phenotypic traits and genetic variants predictive of a disease association. This approach overcomes the limitations of generic meta-data, such as disease versus carriage, and provides an avenue to explore the multi-faceted nature of bacterial disease, carriage, and transmissibility traits.

Poliface: A Multi-pose Synchronous Imaging System

To enhance the accuracy of face recognition technology in real-world scenarios, it is necessary to train deep learning models on datasets that contain a large number of labeled human face images under multiple poses, lighting, and accessory variations. In this paper, we introduce a novel acquisition system named the Poliface. This system can capture multiple high-resolution images simultaneously around the human head. We designed this system with a well-built aluminum structure, control electronic circuits, and high-performing in-house software. The results demonstrate the precise operation and exceptional stability of this system. Using this Poliface system, we have collected over 6 million photos, which can be used to train and evaluate facial recognition models, and exploited for three-dimensional (3D) virtual face reconstruction.

THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction

We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.