Editorial: Disease biomarker analysis based on optical biosensing

Disease biomarker analysis has become a crucial tool for diagnosing and evaluating disease prognosis, especially with the increasing understanding of diseases at the molecular level. Abnormalities in various biomarkers can indicate diseased states, and can be used to rapidly and specifically detect and quantify diseases using optical biosensing techniques (Gao et al., 2023). Optical biosensing techniques have several advantages over traditional methods including higher sensitivity, specificity, and faster analysis times (Plikusiene and Ramanaviciene, 2023). It also allows for non-invasive sample collection. With advancements in optical biosensing technology, many medical conditions including cancers, infectious diseases, and autoimmune disorders can be accurately diagnosed and efficiently treated (Singh et al., 2023; Tang et al., 2023). The combination of optical biosensing with emerging technologies such as material science, optics, and electronics has further accelerated its development in biomarker analysis (Qureshi et al., 2022). Interdisciplinary collaboration between experts in fields such as physics, chemistry, bioengineering, and medicine has helped pave the way for novel optical biosensing technologies as well as improving existing ones. Continued interdisciplinary collaboration is essential in advancing the field of disease biomarker analysis based on optical biosensing. This exciting area of research holds great potential for the future of personalized and precision medicine, and will likely lead to more effective disease diagnoses and treatments (Duo et al., 2023)

BOTT: Box Only Transformer Tracker for 3D Object Tracking

Tracking 3D objects is an important task in autonomous driving. Classical Kalman Filtering based methods are still the most popular solutions. However, these methods require handcrafted designs in motion modeling and can not benefit from the growing data amounts. In this paper, Box Only Transformer Tracker (BOTT) is proposed to learn to link 3D boxes of the same object from the different frames, by taking all the 3D boxes in a time window as input. Specifically, transformer self-attention is applied to exchange information between all the boxes to learn global-informative box embeddings. The similarity between these learned embeddings can be used to link the boxes of the same object. BOTT can be used for both online and offline tracking modes seamlessly. Its simplicity enables us to significantly reduce engineering efforts required by traditional Kalman Filtering based methods. Experiments show BOTT achieves competitive performance on two largest 3D MOT benchmarks: 69.9 and 66.7 AMOTA on nuScenes validation and test splits, respectively, 56.45 and 59.57 MOTA L2 on Waymo Open Dataset validation and test splits, respectively. This work suggests that tracking 3D objects by learning features directly from 3D boxes using transformers is a simple yet effective way

Dynamic changes in transcripts during regeneration of the secondary vascular system in Populus tomentosa Carr. revealed by cDNA microarrays

<p>Abstract</p> <p>Background</p> <p>Wood is the end product of secondary vascular system development, which begins from the cambium. The wood formation process includes four major stages: cell expansion, secondary wall biosynthesis, lignification, and programmed cell death. Transcriptional profiling is a rapid way to screen for genes involved in these stages and their transitions, providing the basis for understanding the molecular mechanisms that control this process.</p> <p>Results</p> <p>In this study, cDNA microarrays were prepared from a subtracted cDNA library (cambium zone <it>versus </it>leaf) of Chinese white poplar (<it>Populus tomentosa </it>Carr.) and employed to analyze the transcriptional profiles during the regeneration of the secondary vascular system, a platform established in our previous study. Two hundred and seven genes showed transcript-level differences at the different regeneration stages. Dramatic transcriptional changes were observed at cambium initiation, cambium formation and differentiation, and xylem development, suggesting that these up- or downregulated genes play important roles in these stage transitions. Transcription factors such as AUX/IAA and PINHEAD, which were previously shown to be involved in meristem and vascular tissue differentiation, were strongly transcribed at the stages when cambial cells were initiated and underwent differentiation, whereas genes encoding MYB proteins and several small heat shock proteins were strongly transcribed at the stage when xylem development begins.</p> <p>Conclusion</p> <p>Employing this method, we observed dynamic changes in gene transcript levels at the key stages, including cambium initiation, cambium formation and differentiation, and xylem development, suggesting that these up- or downregulated genes are strongly involved in these stage transitions. Further studies of these genes could help elucidate their roles in wood formation.</p

Analysis of high risk factors of pathological escalation after LEEP in CIN2 diagnosed by colposcopic biopsy

Objective To investigate the risk factors associated with pathological escalation to cervical intraepithelial neoplasia grade 2 （CIN2） or above （CIN2+） in patients with CIN2 confirmed by colposcopic biopsy， aiming to provide evidence for the stratified management of CIN2 patients. Methods Clinical data of 210 patients who underwent LEEP surgery after pathological diagnosis of cervical CIN2 by colposcopic biopsy were retrospectively analyzed. Pathological diagnosis of patients before and after LEEP surgery was observed. The relationship between pathological escalation after LEEP， and age，results of primary liquid-based thin-layer cytology （TCT）， typing of high-risk human papillomavirus （HPV）， the number of affected quadrants of lesions under colposcopy， the proportion of visible lesion area to cervical surface area， the longest linear length of the lesion， the type of transformation zone （TZ）， whether the lesion was involved with glands and the characteristics of colposcopic images was assessed by univariate and multivariate Logistic regression analyses. Results Among 210 cases of cervical CIN2 diagnosed by colposcopic biopsy， 37 cases （17.6%） were pathologically diagnosed with CIN2+ after LEEP， and 1 case was diagnosed with cervical squamous cell carcinoma stage IB1. Univariate analysis showed that pathological escalation after LEEP was associated with the age of patients， the number of affected quadrants of lesions under colposcopy， the proportion of visible lesion area to cervical surface area， the longest linear length of visible lesions， the type of transformation zone，and the characteristics of colposcoic images （all P &lt; 0.05）. Multivariate analysis showed that the number （&gt;1） of affected quadrants of lesions under colposcopy，the proportion （≥1/3） of visible lesion area to cervical surface area， TZ3 type and the characteristics （≥2） of colposcopic images were the high-risk factors for pathological escalation after LEEP （all P &lt; 0.05）. For patients aged 26-50 years， the proportion （≥1/3） of lesion area to cervical surface area， TZ3 type and the characteristics （≥2） of colposcopic images were the high-risk factors for pathological escalation after LEEP （all P &lt; 0.05）. Conclusions Colposcopic biopsy may miss the diagnosis of CIN2+ in patients diagnosed with CIN2. The risk of missing the diagnosis of CIN2+ is increased with the increase of the proportion of visible lesion area to cervical surface area （&gt;1/3）， the invisibility of the squamous-column junction under colposcopy，and the proportion of grade 2 signs in colposcopic images （≥2）. For patients with CIN2 who are willing to have children，if they have the above high-risk factors，it is recommended to carefully deliver follow-up observation

Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering

Multi-modal keyphrase generation aims to produce a set of keyphrases that represent the core points of the input text-image pair. In this regard, dominant methods mainly focus on multi-modal fusion for keyphrase generation. Nevertheless, there are still two main drawbacks: 1) only a limited number of sources, such as image captions, can be utilized to provide auxiliary information. However, they may not be sufficient for the subsequent keyphrase generation. 2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model. To address these limitations, in this paper, we propose a novel multi-modal keyphrase generation model, which not only enriches the model input with external knowledge, but also effectively filters image noise. First, we introduce external visual entities of the image as the supplementary input to the model, which benefits the cross-modal semantic alignment for keyphrase generation. Second, we simultaneously calculate an image-text matching score and image region-text correlation scores to perform multi-granularity image noise filtering. Particularly, we introduce the correlation scores between image regions and ground-truth keyphrases to refine the calculation of the previously-mentioned correlation scores. To demonstrate the effectiveness of our model, we conduct several groups of experiments on the benchmark dataset. Experimental results and in-depth analyses show that our model achieves the state-of-the-art performance. Our code is available on https://github.com/DeepLearnXMU/MM-MKP.Comment: Accepted In Proceedings of the 31st ACM International Conference on Multimedia (MM' 23