Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of the MRIs into different organs and tissues would be very beneficial as it would allow more accurate measurements, which are essential for accurate diagnosis and effective treatment planning. Specifically, segmenting bones in MRI would allow for more quantitative assessments of musculoskeletal conditions, while such assessments are largely absent in current radiological practice. The difficulty of bone MRI segmentation is illustrated by the fact that limited algorithms are publicly available, and those contained in the literature typically address a specific anatomic area. In our study, we propose a versatile, publicly available deep learning model for bone segmentation in MRI at multiple standard MRI locations. The proposed model can operate in two modes: fully automated segmentation and prompt-based segmentation. Our contributions include (1) collecting and annotating a new MRI dataset across various MRI protocols, encompassing 320 annotated volumes and more than 10k annotated slices across diverse anatomic regions; (2) investigating several standard network architectures and strategies for automated segmentation; (3) introducing SegmentAnyBone, an innovative foundation model-based approach that extends the Segment Anything Model (SAM); (4) comparative analysis of our algorithm and previous approaches; and (5) generalization analysis of our algorithm across different anatomical locations and MRI sequences, as well as three external datasets. We publicly release our model at Github Code.

Temporal action detection is one of the most important and challenging tasks in video analysis. Due to its wide application prospects, it has received extensive attention in recent years. With the development of deep learning, great progress has been made in temporal behavior detection, but there are still many difficulties to be solved, such as accurate proposal generation and high computational cost. In this paper, deep learning-based temporal action detection methods are classified according to full supervision and weak supervision, and then the representative models of the two methods are summarized in detail, and the ideas, advantages and disadvantages of different models and the evolution between different models are analyzed. At the same time, the performance of different models on mainstream datasets is compared. The mainstream dataset and evaluation index used in temporal action detection are introduced in detail, and the calculation method of evaluation index is also elaborated. Finally, through in-depth analysis, the possible future research directions of temporal action detection and the whole review are summarized.

Biological Sequence alignment is a fundamental application in bioinformatics. It can be used to identify functionally conserved sequences and find evolutionary relationships between species. To compare entire genomes from different species, biologists increasingly need alignment methods that are efficient enough to handle long sequences, and accurate enough to correctly align the conserved biological features between distant species. Global alignments are important because they reveal the shared order of biological features in the compared species, and produce a more accurate alignment at the base-pair level when the features are in the same order. The best known global alignment algorithm is Needleman-Wunsch, later, BitPAl, a bit parallel algorithm for general, integer scoring global algorithm, provides a new implementation of Needleman-Wunsch algorithm (BitNW). Compared with original Needleman-Wunsch algorithm, BitNW is significantly faster by exploiting bit parallelism. A number of parallel strategies have been proposed to accelerate exact alignment methods. However, most of them failed to align long biological sequences due to quadratic time complexity. In this paper, we propose SLPal, a fast bit-parallel algorithm for accelerating long DNA sequence comparison on Intel manycore and multi-core architectures. In order to fully exploit the computing power of many cores and the 512-bit vector processing units (VPUs), we use a two-level parallelism scheme: coarsegrained thread level and fine-grained VPU level approaches. In thread level, the alignment scoring matrix will be split into small tiles and multiple threads will process these small tiles currently by using Intel TBB library. In the VPU level, the computing kernels are implemented using the Single Instruction Multiple Data (SIMD) instructions, thus, 16 independent integers reside in a 512-bit vector register can be processed simultaneously. The evaluation reveals that our algorithm achieves a stable performance for all benchmark data and yields a performance of up to 511.7 (617.2) GCUPS on a server with single Xeon Phi 7210 processor (dual Xeon Gold 614820-core processors). Furthermore, our test shows that SLPal can align two sequences with about 5 million bps in 50 seconds on our server equipped with dual Xeon Gold 6148 CPUs.

MOTIVATION: Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. RESULTS: BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4. AVAILABILITY AND IMPLEMENTATION: BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.