Pfp-fm: an accelerated FM-index.

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm .

Determining Protein Structures Using X-Ray Crystallography.

X-ray crystallography is a robust and widely used technique that facilitates the three-dimensional structure determination of proteins at an atomic scale. This methodology entails the growth of protein crystals under controlled conditions followed by their exposure to X-ray beams and the subsequent analysis of the resulting diffraction patterns via computational tools to determine the three-dimensional architecture of the protein. However, achieving high-resolution structures through X-ray crystallography can be quite challenging due to complexities associated with protein purity, crystallization efficiency, and crystal quality.In this chapter, we provide a detailed overview of the gene to structure determination pipeline used in X-ray crystallography, a crucial tool for understanding protein structures. The chapter covers the steps in protein crystallization, along with the processes of data collection, processing, structure determination, and refinement. The most commonly faced challenges throughout this procedure are also addressed. Finally, the importance of standardized protocols for reproducibility and accuracy is emphasized, as they are crucial for advancing the understanding of protein structure and function.

Special issue on software citation, indexing, and discoverability.

Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and citation, that presents evidence of increased citation rates when software artifacts are released, that provides guidance for registries and repositories to support software citation and findability, and that shows there are still barriers to improving and formalising software citation and publication practice. As the use of software increases further, driven by modern research methods, addressing the barriers to software citation and discoverability will encourage greater sharing and reuse of software, in turn enabling research progress.

scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing.

Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.

Multiple Object Tracking Without Pre-attentive Indexing.

Multiple object tracking (MOT) involves simultaneous tracking of a certain number of target objects amongst a larger set of objects as they all move unpredictably over time. The prevalent explanation for successful target tracking by humans in MOT involving visually identical objects is based on the Visual Indexing Theory. This assumes that each target is indexed by a pointer using a non-conceptual mechanism to maintain an object's identity even as its properties change over time. Thus, successful tracking requires successful indexing and the absence of identification errors. Identity maintenance and successful tracking are measured in terms of identification (ID) and tracking accuracy respectively, with higher accuracy indicating better identity maintenance or better tracking. Existing evidence suggests that humans have high tracking accuracy despite poor identification accuracy, suggesting that it might be possible to perform MOT without indexing. Our work adds to existing evidence for this position through two experiments, and presents a computational model of multiple object tracking that does not require indexes. Our empirical results show that identification accuracy is aligned with tracking accuracy in humans for tracking up to three, but is lower when tracking more objects. Our computational model of MOT without indexing accounts for several empirical tracking accuracy patterns shown in earlier studies, reproduces the dissociation between tracking and identification accuracy produced earlier in the literature as well as in our experiments, and makes several novel predictions.

[Predatory journals in Orthopedics and Traumatology]. / Revistas depredadoras en Ortopedia y Traumatología.

Predatory journals are distinguished from legitimate journals by their lack of adequate reviews and editorial processes, compromising the quality of published content. These journals do not conduct peer reviews or detect plagiarism, and accept manuscripts without requiring substantial modifications. Their near 100% acceptance rate is driven by profit motives, regardless of the content they publish. While they boast a prestigious editorial board composed of renowned researchers, in most cases, it is a facade aimed at impressing and attracting investigators. Furthermore, these journals lack appropriate ethical practices and are non-transparent in their editorial processes. Predatory journals have impacted multiple disciplines, including Orthopedics and Traumatology, and their presence remains unknown to many researchers, making them unwitting victims. Their strategy involves soliciting articles via email from authors who have published in legitimate journals, promising quick, easy, and inexpensive publication. The implications and negative consequences of predatory journals on the scientific community and researchers are numerous. The purpose of this work is to provide general information about these journals, specifically in the field of Orthopedics and Traumatology, offering guidelines to identify and avoid them, so that authors can make informed decisions when publishing their manuscripts and avoid falling into the hands of predatory journals or publishers.

Las revistas depredadoras se diferencian de las revistas legítimas por su falta de adecuadas revisiones y procesos editoriales, lo que compromete la calidad del contenido publicado. Estas revistas no llevan a cabo revisiones por pares ni realizan acciones que detecten y prevengan el plagio y aceptan manuscritos sin exigir modificaciones sustanciales. Su tasa de aceptación cercana al 100% se debe a su enfoque lucrativo, sin importarles el contenido que publican. Aunque presumen tener un comité editorial compuesto por investigadores destacados, en la mayoría de los casos es una simulación destinada a impresionar y atraer a los investigadores. Además, estas revistas carecen de prácticas éticas adecuadas y no son transparentes en sus procesos editoriales. Las revistas depredadoras han afectado a múltiples disciplinas, incluida la Ortopedia y Traumatología y su presencia es aún desconocida para muchos investigadores, lo que los convierte en víctimas sin saberlo. Su estrategia consiste en solicitar artículos por correo electrónico a autores que han publicado en revistas legítimas, prometiendo una publicación rápida, sencilla y económica. Las implicaciones y consecuencias negativas de las revistas depredadoras en la comunidad científica y los investigadores son numerosas. El propósito de este trabajo es proporcionar información general sobre estas revistas y específicamente en el campo de la Ortopedia y Traumatología, brindando pautas para identificarlas y evitarlas, para que los autores puedan tomar decisiones informadas al publicar sus manuscritos y evitar caer en manos de revistas o editoriales depredadoras.

Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository.

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system.

Blockchain-Based Method for Spatial Retrieval and Verification of Remote Sensing Images.

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain's notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems.

Identifying the best PCR enzyme for library amplification in NGS.

Background. PCR amplification is a necessary step in many next-generation sequencing (NGS) library preparation methods [1, 2]. Whilst many PCR enzymes are developed to amplify single targets efficiently, accurately and with specificity, few are developed to meet the challenges imposed by NGS PCR, namely unbiased amplification of a wide range of different sizes and GC content. As a result PCR amplification during NGS library prep often results in bias toward GC neutral and smaller fragments. As NGS has matured, optimized NGS library prep kits and polymerase formulations have emerged and in this study we have tested a wide selection of available enzymes for both short-read Illumina library preparation and long fragment amplification ahead of long-read sequencing.We tested over 20 different hi-fidelity PCR enzymes/NGS amplification mixes on a range of Illumina library templates of varying GC content and composition, and find that both yield and genome coverage uniformity characteristics of the commercially available enzymes varied dramatically. Three enzymes Quantabio RepliQa Hifi Toughmix, Watchmaker Library Amplification Hot Start Master Mix (2X) 'Equinox' and Takara Ex Premier were found to give a consistent performance, over all genomes, that mirrored closely that observed for PCR-free datasets. We also test a range of enzymes for long-read sequencing by amplifying size fractionated S. cerevisiae DNA of average size 21.6 and 13.4 kb, respectively.The enzymes of choice for short-read (Illumina) library fragment amplification are Quantabio RepliQa Hifi Toughmix, Watchmaker Library Amplification Hot Start Master Mix (2X) 'Equinox' and Takara Ex Premier, with RepliQa also being the best performing enzyme from the enzymes tested for long fragment amplification prior to long-read sequencing.

An efficient Burrows-Wheeler transform-based aligner for short read mapping.

Read mapping as the foundation of computational biology is a bottleneck task under the pressure of sequencing throughput explodes. In this work, we present an efficient Burrows-Wheeler transform-based aligner for next-generation sequencing (NGS) short read. Firstly, we propose a difference-aware classification strategy to assign specific reads to the computationally more economical search modes, and present some acceleration techniques, such as a seed pruning method based on the property of maximum coverage interval to reduce the redundant locating for candidate regions, redesigning LF calculation to support fast query. Then, we propose a heuristic verification to determine the best mapping from amounts of flanking sequences. Incorporated with low-distortion string embedding, most dissimilar sequences are filtered out cheaply, and the highly similar sequences left are just right for the wavefront alignment algorithm's preference. We provide a full spectrum benchmark with different read lengths, the results show that our method is 1.3-1.4 times faster than state-of-the-art Burrows-Wheeler transform-based methods (including bowtie2, bwa-MEM, and hisat2) over 101bp reads and has a speedup with 1.5-13 times faster over 750bp to 1000bp reads; meanwhile, our method has comparable memory usage and accuracy. However, hash-based methods (including Strobealign, Minimap2, and Accel-Align) are significantly faster, in part because Burrows-Wheeler transform-based methods calculate on the compressed space. The source code is available: https://github.com/Lilu-guo/Effaln.

Quantitative detection of cassava common mosaic virus for health certification of cassava (Manihot esculenta Crantz) germplasm using qPCR analysis.

Cassava (Manihot esculenta Crantz) is a crop of global economic and food safety importance, used for human consumption and in various industrial applications. The genebank of the Genetic Resources Program of the Alliance of Bioversity International and CIAT currently holds the world's largest cassava collection, with 5965 in vitro accessions from 28 countries. Managing this extensive collection involves indexing quarantine pathogens as a phytosanitary certification requirement for safely distributing cassava germplasm. The study therefore aimed to optimize a quantitative diagnostic protocol to detect cassava common mosaic virus (CsCMV) using quantitative PCR (qPCR) as a better alternative to other molecular techniques. This was done through designing primers and a probe in the RdRP region of CsCMV, and optimizing the qPCR conditions of the diagnostic protocol using primer concentration assays, and reaction amplification conditions such as volume and reaction time. We also evaluated the qPCR protocol by comparing the results of 140 cassava accession evaluations using three diagnostic methodologies (DAS-ELISA, end-point PCR, and qPCR) for CsCMV. Our protocol established that qPCR technique analysis is ten-times more sensitive in detecting CsCMV compared to end-point PCR, showing a maximum detection level of 77.97 copies/µL of plasmid, with 76 min of reaction time. The comparison allowed us to verify the level of CsCMV detection through the techniques evaluated, concluding that qPCR was more sensitive and allowed the quantification of viral concentration. The optimized qPCR protocol will be used to accelerate diagnostic screening of cassava germplasm for the presence or absence of CsCMV to ensure safe movement and distribution of disease-free germplasm.

Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy.

A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.

txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility.

We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.

A randomized optimal k-mer indexing approach for efficient parallel genome sequence compression.

Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h.

A switch in letters leads to the "creation" of Eszett (ß)-catenin rather than beta (ß)-catenin.

A simple replacement-most likely due to a linguistic error-of the Greek letter beta (ß) by the German letter Eszett (ß), has led to the introduction of an error in a body of literature related to beta (ß)-catenin, resulting in the "creation" of a non-existent compound and false positive, Eszett (ß)-catenin. A search on 9 December 2023 in PubMed for ß-catenin and ß-catenin revealed 395 and 45,919 results, respectively. The 25 results of ß-catenin published in 2023 were examined in more detail to appreciate the location(s) in each paper where this error had occurred. While the scientific findings of these studies do not seem to be impacted by this error, authors and editors would need to weigh the need or benefit of correcting this error.

Internal Medicine Resident Addiction Training at the Veteran's Health Administration: A Qualitative Evaluation of Site Directors' Response to the 2022 ACGME Requirements.

BACKGROUND: Substance use disorders (SUDs) are prevalent in the USA yet remain dramatically undertreated. To address this care gap, the Accreditation Council for Graduate Medical Education (ACGME) approved revisions to the Program Requirements for Graduate Medical Education (GME) in Internal Medicine, effective July 1, 2022, requiring addiction medicine training for all internal medicine (IM) residents. The Veterans Health Administration (VHA) is a clinical training site for many academic institutions that sponsor IM residencies. This focus group project evaluated VHA IM residency site directors' perspectives about providing addiction medical education within VHA IM training sites. OBJECTIVE: To better understand the current state, barriers to, and facilitators of IM resident addiction medicine training at VHA sites. DESIGN: This was a qualitative evaluation based on semi-structured video-based focus groups. PARTICIPANTS: Participants were VHA IM site directors based at a VHA hospital or clinic throughout the USA. APPROACH: Focus groups were conducted using a semi-structured group interview guide. Two investigators coded each focus group independently, then met to create a final adjudicated coding scheme. Thematic analysis was used to identify key themes. KEY RESULTS: Forty-three participants from 38 VHA sites participated in four focus groups (average size: 11 participants). Six themes were identified within four pre-defined categories. Current state of training: most VHA sites offered no formal training in addiction medicine for IM residents. Barriers: addiction experts are often located outside of IM settings, and ACGME requirements were non-specific. Facilitators: clinical champions help support addiction training. Desired next steps: participants desired incentives to train or hire local champions and a pre-packaged didactic curriculum. CONCLUSIONS: Developing competent clinical champions and leveraging VHA addiction specialists from non-IM settings would create more addiction training opportunities for IM trainees at VHA sites. These insights can likely be applied to IM training at non-VHA sites.

Next Generation Sequencing, and Development of a Pipeline as a Tool for the Detection and Discovery of Citrus Pathogens to Facilitate Safer Germplasm Exchange.

Citrus is affected by many diseases, and hence, the movement of citrus propagative materials is highly regulated in the USA. Currently used regulatory pathogen detection methods include biological and laboratory-based technologies, which are time-consuming, expensive, and have many limitations. There is an urgent need to develop alternate, rapid, economical, and reliable testing methods for safe germplasm exchange. Citrus huanglongbing (HLB) has devastated citrus industries leading to an increased need for germplasm exchanges between citrus growing regions for evaluating many potentially valuable hybrids for both HLB resistance and multilocational performance. In the present study, Next-Generation Sequencing (NGS) methods were used to sequence the transcriptomes of 21 test samples, including 15 well-characterized pathogen-positive plants. A workflow was designed in the CLC Genomics Workbench software, v 21.0.5 for bioinformatics analysis of the sequence data for the detection of pathogens. NGS was rapid and found to be a valuable technique for the detection of viral and bacterial pathogens, and for the discovery of new citrus viruses, complementary to the existing array of biological and laboratory assays. Using NGS methods, we detected beet western yellows virus, a newly reported citrus virus, and a variant of the citrus yellow vein-associated virus associated with the "fatal yellows" disease.

Development of Polyclonal Antibodies-Based Serological Method for the Detection of Calanthe Mild Mosaic Virus and Application in Virus Certification Programme.

Calanthe mild mosaic virus (CalMMV) infecting orchids is an important potyvirus which is known to cause mild leaf mosaic and flower colour-breaking symptoms in Calanthe and other orchid plants. The present study reports the production of polyclonal antibodies against CalMMV using bacterially expressed recombinant coat protein as immunogen, which in turn would be useful in routine indexing and screening of orchid germplasm. The coat protein (CP) gene (~ 807 bp) of CalMMV isolated from infected orchid sample was cloned in expression vector, pET-28a ( +) that yielded ~ 31 kDa fusion protein with Histidine tag (His6BP). The expression of fusion CP was confirmed through SDS-PAGE and Western blotting. The His6BP-CalMMV-CP obtained in soluble state after purification was used to immunize New Zealand white rabbit for the production of polyclonal antibodies (PAb). The PAb produced against the purified fusion protein successfully detected CAlMMV in the orchid samples at a dilution of 1:2000 in direct antigen-coated enzyme-linked immunosorbent assay (DAC-ELISA). This study presents the first report of Histidine tag (His6BP) fusion CalMMV-CP-based antibody production and its successful application in the identification of the virus in orchid plants. Outcome of this study will be helpful in routine certification programmes, screening of orchid germplasm and production of CalMMV-free planting materials of orchids.

Multi-level index construction method based on master-slave blockchains.

Master-slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master-slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master-slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master-slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%.