sTREM2 Mediates the Correlation Between BIN1 Gene Polymorphism and Tau Pathology in Alzheimer's Disease.

Background: Bridging integrator 1 (BIN1) gene polymorphism has been reported to play a role in the pathological processes of Alzheimer's disease (AD). Objective: To explore the association of BIN1 loci with neuroinflammation and AD pathology. Methods: Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 495) was the discovery cohort, and Chinese Alzheimer's Biomarker and LifestylE (CABLE, N = 619) study was used to replicate the results. Two BIN1 gene polymorphism (rs7561528 and rs744373) were included in the analysis. Multiple linear regression model and causal mediation analysis conducted through 10,000 bootstrapped iterations were used to examine the BIN1 loci relationship with cerebrospinal fluid (CSF) AD biomarkers and alternative biomarker of microglial activation microglia-soluble triggering receptor expressed on myeloid cells 2 (sTREM2). Results: In ADNI database, we found a significant association between BIN1 loci (rs7561528 and rs744373) and levels of CSF phosphorylated-tau (P-tau) (pc = 0.017; 0.010, respectively) and total-tau (T-tau) (pc = 0.011; 0.013, respectively). The BIN1 loci were also correlated with CSF sTREM2 levels (pc = 0.010; 0.008, respectively). Mediation analysis demonstrated that CSF sTREM2 partially mediated the association of BIN1 loci with P-tau (Proportion of rs7561528 : 20.8%; Proportion of rs744373 : 24.8%) and T-tau (Proportion of rs7561528 : 36.5%; Proportion of rs744373 : 43.9%). The analysis in CABLE study replicated the mediation role of rs7561528. Conclusions: This study demonstrated the correlation between BIN1 loci and CSF AD biomarkers as well as microglia biomarkers. Additionally, the link between BIN1 loci and tau pathology was partially mediated by CSF sTREM2.

Phosphorylation of the transcription factor SlBIML1 by SlBIN2 kinases delays flowering in tomato.

Brassinosteroids (BRs) are well known for their important role in the regulation of plant growth and development. Plants with deficiency in BR signaling show delayed plant development and exhibit late flowering phenotypes. However, the precise mechanisms involved in this process require investigation. In this study, we cloned homologs of BRASSINOSTEROID INSENSITIVE 2 (SlBIN2), the GSK3-like protein kinase in tomato (Solanum lycopersicum). We characterized growth-related processes and phenotypic changes in the transgenic lines and found that SlBIN2s transgenic lines have delayed development and slow growing phenotypes. SlBIN2s work redundantly to negatively regulate BR signaling in tomato. Furthermore, the transcription factor SlBIN2.1-INTERACTING MYB-LIKE 1 (SlBIML1) was identified as a downstream substrate of SlBIN2s that SlBIN2s interact with and phosphorylate to synergistically regulate tomato developmental processes. Specifically, SlBIN2s modulated protein stability of SlBIML1 by phosphorylating multiple amino acid residues, including the sites Thr266 and Thr280. This study reveals a branch of the BR signaling pathway that regulates the vegetative growth phase and delays floral transition in tomato without the feedback affecting BR signaling. This information enriches our understanding of the downstream transduction pathway of BR signaling and provides potential targets for adjusting tomato flowering time.

Powerful QTL mapping and favorable allele mining in an all-in-one population: a case study of heading date.

The multiparent advanced generation intercross (MAGIC) population is characterized with great potentials in power and resolution of quantitative trait locus (QTL) mapping, but single nucleotide polymorphism (SNP)-based GWAS does not fully reach its potential. In this study, a MAGIC population of 1021 lines was developed from four Xian and four Geng varieties from five subgroups of rice. A total of 44 000 genes showed functional polymorphisms among eight parents, including frameshift variations or premature stop codon variations, which provides the potential to map almost all genes of the MAGIC population. Principal component analysis results showed that the MAGIC population had a weak population structure. A high-density bin map of 24 414 bins was constructed. Segregation distortion occurred in the regions possessing the genes underlying genetic incompatibility and gamete development. SNP-based association analysis and bin-based linkage analysis identified 25 significant loci and 47 QTLs for heading date, including 14 known heading date genes. The mapping resolution of genes is dependent on genetic effects with offset distances of <55 kb for major effect genes and <123 kb for moderate effect genes. Four causal variants and noncoding structure variants were identified to be associated with heading date. Three to four types of alleles with strong, intermediate, weak, and no genetic effects were identified from eight parents, providing flexibility for the improvement of rice heading date. In most cases, japonica rice carries weak alleles, and indica rice carries strong alleles and nonfunctional alleles. These results confirm that the MAGIC population provides the exceptional opportunity to detect QTLs, and its use is encouraged for mapping genes and mining favorable alleles for breeding.

BIN1 deficiency enhances ULK3-dependent autophagic flux and reduces dendritic size in mouse hippocampal neurons.

Genome-wide association studies identified variants around the BIN1 (bridging integrator 1) gene locus as prominent risk factors for late-onset Alzheimer disease. In the present study, we decreased the expression of BIN1 in mouse hippocampal neurons to investigate its neuronal function. Bin1 knockdown via RNAi reduced the dendritic arbor size in primary cultured hippocampal neurons as well as in mature Cornu Ammonis 1 excitatory neurons. The AAV-mediated Bin1 RNAi knockdown also generated a significant regional volume loss around the injection sites at the organ level, as revealed by 7-Tesla structural magnetic resonance imaging, and an impaired spatial reference memory performance in the Barnes maze test. Unexpectedly, Bin1 knockdown led to concurrent activation of both macroautophagy/autophagy and MTOR (mechanistic target of rapamycin kinase) complex 1 (MTORC1). Autophagy inhibition with the lysosome inhibitor chloroquine effectively mitigated the Bin1 knockdown-induced dendritic regression. The subsequent molecular studydemonstrated that increased expression of ULK3 (unc-51 like kinase 3), which is MTOR-insensitive, supported autophagosome formation in BIN1 deficiency. Reducing ULK3 activity with SU6668, a receptor tyrosine kinase inhibitor, or decreasing neuronal ULK3 expression through AAV-mediated RNAi, significantly attenuated Bin1 knockdown-induced hippocampal volume loss and spatial memory decline. In Alzheimer disease patients, the major neuronal isoform of BIN1 is specifically reduced. Our work suggests this reduction is probably an important molecular event that increases the autophagy level, which might subsequently promote brain atrophy and cognitive impairment through reducing dendritic structures, and ULK3 is a potential interventional target for relieving these detrimental effects.Abbreviations: AV: adeno-associated virus; Aß: amyloid-ß; ACTB: actin, beta; AD: Alzheimer disease; Aduk: Another Drosophila Unc-51-like kinase; AKT1: thymoma viral proto-oncogene 1; AMPK: AMP-activated protein kinase; AP: autophagosome; BafA1: bafilomycin A1; BDNF: brain derived neurotrophic factor; BIN1: bridging integrator 1; BIN1-iso1: BIN1, isoform 1; CA1: cornu Ammonis 1; CA3: cornu Ammonis 3; CLAP: clathrin and adapter binding; CQ: chloroquine; DMEM: Dulbecco's modified Eagle medium; EGFP: enhanced green fluorescent protein; GWAS: genome-wide association study; MAP1LC3B/LC3B: microtubule-associated protein 1 light chain 3 beta; MRI: magnetic resonance imaging; MTOR; mechanistic target of rapamycin kinase; MTORC1: MTOR complex 1; PET: positron emission tomography; qRT-PCR: real-time quantitative reverse transcription PCR; ROS: reactive oxygen species; RPS6KB1: ribosomal protein S6 kinase B1; TFEB: transcription factor EB; ULK1: unc-51 like kinase 1; ULK3: unc-51 like kinase 3.

Scavenger Receptor C1 Mediates Toxicity of Binary Toxin from Lysinibacillus sphaericus to Ag55 Cells.

Lysinibacillus sphaericus harboring Binary (BinA and BinB) toxins is highly toxic against Anopheles and Culex mosquito larvae. The Anopheles Ag55 cell line is a suitable model for investigating the mode of Bin toxin action. Based on the low-levels of α-glycosidase Agm3 mRNA in Ag55 cells and the absence of detectable Agm3 proteins, we hypothesized that a scavenger receptor could be mediating Bin cytotoxicity. Preliminary RNA interference knockdown of the expressed scavenger receptors, combined with Bin cytotoxicity assays, was conducted. The scavenger Receptor C1 (SCRC1) became the focus of this study, as a putative receptor for Bin toxins in Ag55 cells, and SCRBQ2 was selected as a negative control. Open reading frames encoding SCRC1 and SCRBQ2 were cloned and expressed in vitro, and polyclonal antibodies were prepared for immunological analyses. The RNAi silencing of SCRC1 and SCRBQ2 resulted in the successful knockdown of both SCRC1 and SCRBQ2 transcripts and protein levels. The cytolytic toxicity of Bin against Ag55 cells was severely reduced after the SCRC1-RNAi treatment. The phagocytic receptor protein SCRC1 mediates endocytosis of the Bin toxin into Ag55 cells, thereby facilitating its internal cytological activity. The results support a mechanism of the Bin toxin entering Ag55 cells, possibly via SCRC1-mediated endocytosis, and encourage investigations into how Bin is transferred from its bound form on the midgut epithelial cells into the epithelial endocytic system.

Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin-Packing Optimization.

This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic algorithms for bin-packing, an extreme point priority sorting method is proposed to sort the generated extreme points according to their waste spaces to improve space utilization. In addition, a 3D grid representation of the space status of the container is used, and some partial support constraints are proposed to increase the possibilities for stacking objects and enhance overall space utilization. In the PPO algorithm, some heuristic algorithms are integrated, and the reward function and the action space of the policy network are designed so that the proposed method can effectively complete the online 3D bin-packing task. Some experimental results illustrate that the proposed method has good results in achieving online 3D bin-packing tasks in some simulation environments. In addition, an environment with image vision is constructed to show that the proposed method indeed enables an actual robot manipulator to successfully and effectively complete the bin-packing task in a real environment.

ATBS1-INTERACTING FACTOR 2 Positively Regulates Freezing Tolerance via INDUCER OF CBF EXPRESSION 1/C-REPEAT BINDING FACTOR-Induced Cold Acclimation Pathway.

The INDUCER OF CBF EXPRESSION 1/C-REPEAT BINDING FACTOR (ICE1/CBF) pathway plays a crucial role in plant responses to cold stress, impacting growth and development. Here, we demonstrated that ATBS1-INTERACTING FACTOR 2 (AIF2), a non-DNA-binding basic helix-loop-helix transcription factor, positively regulates freezing tolerance through the ICE1/CBF-induced cold tolerance pathway in Arabidopsis. Cold stress transcriptionally upregulated AIF2 expression and induced AIF2 phosphorylation, thereby stabilizing the AIF2 protein during early stages of cold acclimation. The AIF2 loss-of-function mutant, aif2-1, exhibited heightened sensitivity to freezing before and after cold acclimation. In contrast, ectopic expression of AIF2, but not the C-terminal-deleted AIF2 variant, restored freezing tolerance. AIF2 enhanced ICE1 stability during cold acclimation and promoted the transcriptional expression of CBFs and downstream cold-responsive genes, ultimately enhancing plant tolerance to freezing stress. MITOGEN-ACTIVATED PROTEIN KINASES 3 and 6 (MPK3/6), known negative regulators of freezing tolerance, interacted with and phosphorylated AIF2, subjecting it to protein degradation. Furthermore, transient co-expression of MPK3/6 with AIF2 and ICE1 downregulated AIF2/ICE1-induced transactivation of CBF2 expression. AIF2 interacted preferentially with BIN2 and MPK3/6 during the early and later stages of cold acclimation, respectively, thereby differentially regulating AIF2 activity in a cold acclimation time-dependent manner. Moreover, AIF2 acted additively in a gain-of-function mutant of BRASSINAZOLE-RESISTANT 1 (BZR1; bzr1-1D) and a triple knockout mutant of BRASSINOSTEROID-INSENSITIVE 2 (BIN2) and its homologs (bin2bil1bil2) to induce CBFs-mediated freezing tolerance. This suggests that cold-induced AIF2 coordinates freezing tolerance along with BZR1 and BIN2, key positive and negative components, respectively, of brassinosteroid signaling pathways.

Uncovering the BIN1-SH3 interactome underpinning centronuclear myopathy.

Truncation of the protein-protein interaction SH3 domain of the membrane remodeling Bridging Integrator 1 (BIN1, Amphiphysin 2) protein leads to centronuclear myopathy. Here, we assessed the impact of a set of naturally observed, previously uncharacterized BIN1 SH3 domain variants using conventional in vitro and cell-based assays monitoring the BIN1 interaction with dynamin 2 (DNM2) and identified potentially harmful ones that can be also tentatively connected to neuromuscular disorders. However, SH3 domains are typically promiscuous and it is expected that other, so far unknown partners of BIN1 exist besides DNM2, that also participate in the development of centronuclear myopathy. In order to shed light on these other relevant interaction partners and to get a holistic picture of the pathomechanism behind BIN1 SH3 domain variants, we used affinity interactomics. We identified hundreds of new BIN1 interaction partners proteome-wide, among which many appear to participate in cell division, suggesting a critical role of BIN1 in the regulation of mitosis. Finally, we show that the identified BIN1 mutations indeed cause proteome-wide affinity perturbation, signifying the importance of employing unbiased affinity interactomic approaches.

Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy.

BACKGROUND: Multivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model. RESULTS: We describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value. SIGNIFICANCE: Robust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.

CEPB dataset: a photorealistic dataset to foster the research on bin picking in cluttered environments.

Several datasets have been proposed in the literature, focusing on object detection and pose estimation. The majority of them are interested in recognizing isolated objects or the pose of objects in well-organized scenarios. This work introduces a novel dataset that aims to stress vision algorithms in the difficult task of object detection and pose estimation in highly cluttered scenes concerning the specific case of bin picking for the Cluttered Environment Picking Benchmark (CEPB). The dataset provides about 1.5M virtually generated photo-realistic images (RGB + depth + normals + segmentation) of 50K annotated cluttered scenes mixing rigid, soft, and deformable objects of varying sizes used in existing robotic picking benchmarks together with their 3D models (40 objects). Such images include three different camera positions, three light conditions, and multiple High Dynamic Range Imaging (HDRI) maps for domain randomization purposes. The annotations contain the 2D and 3D bounding boxes of the involved objects, the centroids' poses (translation + quaternion), and the visibility percentage of the objects' surfaces. Nearly 10K separated object images are presented to perform simple tests and compare them with more complex cluttered scenarios tests. A baseline performed with the DOPE neural network is reported to highlight the challenges introduced by the novel dataset.

Improvement of a pharmaceutical powder mixing process in a tote blender via DEM simulations.

An industrial-scale pharmaceutical powder blending process was studied via discrete element method (DEM) simulations. A DEM model of two active pharmaceutical ingredient (API) components and a combined excipient component was calibrated by matching the simulated response in a dynamic angle of repose tester to the experimentally observed response. A simulation of the 25-minute bin blending process predicted inhomogeneous API distributions along the rotation axis of the blending container. These concentration differences were confirmed experimentally in a production-scale mixing trial using high-performance liquid chromatography analysis of samples from various locations in the bin. Several strategies to improve the blend homogeneity were then studied using DEM simulations. Reversing the direction of rotation of the blender every minute was found to negligibly improve the blending performance. Introducing a baffle into the lid at a 45° angle to the rotation axis sped up the axial mixing and resulted in a better final blend uniformity. Alternatively, rotating the blending container 90° around the vertical axis five minutes prior to the process end was predicted to reduce axial segregation tendencies.

The Relation Between Theoretical and Practical Exams for Health Sciences Students at King Saud Bin Abdulaziz University for Health Sciences- Jeddah.

Introduction: A comprehensive approach to assessment is essential to ensure that all students' learning competencies are measured accurately. Therefore, multiple methods of assessment have been developed to address this matter. This Study aims to assess the correlation between health sciences students' performance on theoretical and practical exams. Methods: A correlational study design was conducted. The academic performance of 352 students across theoretical/practical courses was tested. SPSS version 29.0 was used for analysis. Spearman's rho correlation (Rs), Wilcoxon, and Mann Whitney were computed at p<0.05. Results: The theoretical performance was strongly correlated with the practical performance of all programs pooled together (Rs (352) = 0.67, p<0.001). Also, there was a strong correlation between theoretical and practical performance for male students (Rs (181) = 0.72, p<0.001), while a moderate correlation for female students (Rs (171) = 0.53, p<0.001). Mann-Whitney test revealed significant mean performance difference by gender both at theoretical (U = 9284, p<0.0001) and practical (U = 11,373, p < 0.0001) levels. Conclusion: There were significant correlations between theoretical knowledge and practical skills across the selected four programs.; The mean student's performance was better in the practical skills than in the theoretical knowledge assessment, and female students surpassed male students in both practical and theoretical assessments in the four programs offered to both genders.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with respect to their features. Our approach uses a novelty search algorithm with a linearly weighted fitness function that balances novelty and performance to generate a large set of diverse and discriminatory instances in a single run of the algorithm. We consider two definitions of novelty: (1) with respect to discriminatory performance within a portfolio of solvers; (2) with respect to the features of the evolved instances. We evaluate the proposed method with respect to its ability to generate diverse and discriminatory instances in two domains (knapsack and bin-packing), comparing to another well-known quality diversity method, Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and an evolutionary algorithm that only evolves for discriminatory behaviour. The results demonstrate that the novelty search method outperforms its competitors in terms of coverage of the space and its ability to generate instances that are diverse regarding the relative size of the "performance gap" between the target solver and the remaining solvers in the portfolio. Moreover, for the Knapsack domain, we also show that we are able to generate novel instances in regions of an instance space not covered by existing benchmarks using a portfolio of state-of-the-art solvers. Finally, we demonstrate that the method is robust to different portfolios of solvers (stochastic approaches, deterministic heuristics and state-of-the-art methods), thereby providing further evidence of its generality.

FPGA-based fast bin-ratio spiking ensemble network for radioisotope identification.

In this work, we demonstrate the training, conversion, and implementation flow of an FPGA-based bin-ratio ensemble spiking neural network applied for radioisotope identification. The combination of techniques including learned step quantisation (LSQ) and pruning facilitated the implementation by compressing the network's parameters down to 30% yet retaining the accuracy of 97.04% with an accuracy loss of less than 1%. Meanwhile, the proposed ensemble network of 20 3-layer spiking neural networks (SNNs), which incorporates 1160 spiking neurons, only needs 334 µs for a single inference with the given clock frequency of 100 MHz. Under such optimisation, this FPGA implementation in an Artix-7 board consumes 157 µJ per inference by estimation.

Delimiting Species with Single-Locus DNA Sequences.

DNA sequences are increasingly used for large-scale biodiversity inventories. Because these genetic data avoid the time-consuming initial sorting of specimens based on their phenotypic attributes, they have been recently incorporated into taxonomic workflows for overlooked and diverse taxa. Major statistical developments have accompanied this new practice, and several models have been proposed to delimit species with single-locus DNA sequences. However, proposed approaches to date make different assumptions regarding taxon lineage history, leading to strong discordance whenever comparisons are made among methods. Distance-based methods, such as Automatic Barcode Gap Discovery (ABGD) and Assemble Species by Automatic Partitioning (ASAP), rely on the detection of a barcode gap (i.e., the lack of overlap in the distributions of intraspecific and interspecific genetic distances) and the associated threshold in genetic distances. Network-based methods, as exemplified by the REfined Single Linkage (RESL) algorithm for the generation of Barcode Index Numbers (BINs), use connectivity statistics to hierarchically cluster-related haplotypes into molecular operational taxonomic units (MOTUs) which serve as species proxies. Tree-based methods, including Poisson Tree Processes (PTP) and the General Mixed Yule Coalescent (GMYC), fit statistical models to phylogenetic trees by maximum likelihood or Bayesian frameworks.Multiple webservers and stand-alone versions of these methods are now available, complicating decision-making regarding the most appropriate approach to use for a given taxon of interest. For instance, tree-based methods require an initial phylogenetic reconstruction, and multiple options are now available for this purpose such as RAxML and BEAST. Across all examined species delimitation methods, judicious parameter setting is paramount, as different model parameterizations can lead to differing conclusions. The objective of this chapter is to guide users step-by-step through all the procedures involved for each of these methods, while aggregating all necessary information required to conduct these analyses. The "Materials" section details how to prepare and format input files, including options to align sequences and conduct tree reconstruction with Maximum Likelihood and Bayesian inference. The Methods section presents the procedure and options available to conduct species delimitation analyses, including distance-, network-, and tree-based models. Finally, limits and future developments are discussed in the Notes section. Most importantly, species delimitation methods discussed herein are categorized based on five indicators: reliability, availability, scalability, understandability, and usability, all of which are fundamental properties needed for any approach to gain unanimous adoption within the DNA barcoding community moving forward.

Vulnerability of Thermal Energy Storage Lining Material to Erosion Induced by Particulate Flow in Concentrated Solar Power Tower Systems.

Researchers from all around the world have been paying close attention to particle-based power tower technologies. On the King Saud University campus in the Kingdom of Saudi Arabia, the first integrated gas turbine-solar particle heating hybrid system has been realized. In this study, two different types of experiments were carried out to examine how susceptible prospective liner materials for thermal energy storage tanks were to erosion. An accelerated direct-impact test with high particulate temperature was the first experiment. A low-velocity mass-flow test was the second experiment, and it closely mimicked the flow circumstances in a real thermal energy storage tank. The tests were conducted on bare insulating fire bricks (IFBs) and IFBs coated with Tuffcrete 47, Matrigun 25 ACX, and Tuffcrete 60 M. The latter three lining materials were high-temperature-resilient materials made by Allied Mineral Products Inc. (AMP) (Columbus, OH, USA). The results showed that although IFBs coated with AMP materials worked well in this test, the accelerated direct-impact test significantly reduced the bulk of the bare IFB. As a result, lining substances must be added to the surface of IFBs to increase their strength and protection because they cannot be used in situations where particles directly impact their surface. On the other hand, the findings of the 60 h cold-particle mass-flow test revealed that the IFBs were not significantly eroded. Additionally, it was discovered that the degree of erosion on the samples of bare IFB was unaffected by the height of the particle bed.

Filtering Organized 3D Point Clouds for Bin Picking Applications.

In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.

Detailed DNA barcoding of mayflies in a small European country proved how far we are from having comprehensive barcode reference libraries.

Mayflies (Ephemeroptera) are among the crucial water and habitat quality bioindicators. However, despite their intensive long-term use in various studies, more reliable mayfly DNA barcode data have been produced in a negligible number of countries, and only ~40% of European species had been barcoded with less than 50% of families covered. Despite being carried out in a small area, our study presents the second-most species-rich DNA reference library of mayflies from Europe and the first comprehensive view from an important biodiversity hotspot such as the Western Carpathians. Within 1153 sequences, 76 morphologically determined species were recorded and added to the Barcode of Life Data System (BOLD) database. All obtained sequences were assigned to 97 BINs, 11 of which were unique and three represented species never barcoded before. Sequences of 16 species with high intraspecific variability were divided into 40 BINs, confirming the presence of cryptic lineages. Due to the low interspecific divergence and the non-existing barcoding gap, sequences of six species were assigned to three shared BINs. Delimitation analyses resulted in 79 and 107 putative species respectively. Bayesian and maximum-likelihood phylogenies confirmed the monophyly of almost all species and complexes of cryptic taxa and proved that DNA barcoding distinguishes almost all studied mayfly species. We have shown that it is still sufficient to thoroughly investigate the fauna of a small but geographically important area to enrich global databases greatly. In particular, the insights gained here transcend the local context and may have broader implications for advancing barcoding efforts.

Amphiphysin-2 (BIN1) functions and defects in cardiac and skeletal muscle.

Amphiphysin-2 is a ubiquitously expressed protein also known as bridging integrator 1 (BIN1), playing a critical role in membrane remodeling, trafficking, and cytoskeleton dynamics in a wide range of tissues. Mutations in the gene encoding BIN1 cause centronuclear myopathies (CNM), and recent evidence has implicated BIN1 in heart failure, underlining its crucial role in both skeletal and cardiac muscle. Furthermore, altered expression of BIN1 is linked to an increased risk of late-onset Alzheimer's disease and several types of cancer, including breast, colon, prostate, and lung cancers. Recently, the first proof-of-concept for potential therapeutic strategies modulating BIN1 were obtained for muscle diseases. In this review article, we discuss the similarities and differences in BIN1's functions in cardiac and skeletal muscle, along with its associated diseases and potential therapies.