PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage

Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937

ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions

Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis, as well as reducing the workload of doctors. However, the absence of publicly available endometrial cancer image datasets restricts the application of computer-assisted diagnostic techniques.In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with a total of 7159 images in multiple formats. In order to prove the effectiveness of segmentation methods on ECPC-IDS, five classical deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with a total of 3579 images and XML files with annotation information. Six deep learning methods are selected for experiments on the detection task.This study conduct extensive experiments using deep learning-based semantic segmentation and object detection methods to demonstrate the differences between various methods on ECPC-IDS. As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection. ECPC-IDS can aid researchers in exploring new algorithms to enhance computer-assisted technology, benefiting both clinical doctors and patients greatly.Comment: 14 pages,6 figure

AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation

Methods: In this study, a benchmark \emph{Abdominal Adipose Tissue CT Image Dataset} (AATTCT-IDS) containing 300 subjects is prepared and published. AATTCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3,213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATTCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. Results: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATTCT-IDS reveals three adipose distributions in the subject population. Conclusion: AATTCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: \url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}.Comment: 17 pages, 7 figure

Fast folding kinetics of protein elements

The question of how protein sequences can efficiently achieve their native state is one of the most intriguing problems in structural biology. Understanding the folding mechanism of small protein motifs, such as protein secondary structural elements, will provide the needed insights for understanding the folding of more complex protein structures. β-Hairpin is one of the smallest secondary structural elements in proteins. However, the folding mechanism of this simple structural unit still remains elusive. Herein, I investigated the structural stability and folding kinetics of a series of β-hairpins using static infrared and circular dichroism spectroscopies and a laser-induced temperature jump method. The results support a β-hairpin folding mechanism wherein the rate-limiting event corresponds to the formation of the turn. Therefore, a stronger turn-promoting sequence increases the stability of a β-hairpin primarily by increasing its folding rate, whereas a stronger hydrophobic cluster increases the stability of a β-hairpin primarily by decreasing its unfolding rate. The folding kinetics of a super secondary structure motif, α-helical hairpin Z34C, was also studied. Our results showed that Z34C folds on the μs timescale, and indicated a folding mechanism wherein the rate-limiting step corresponds to the formation of the reverse turn. The hydrophobic cluster and the disulfide bond appear to largely stabilize the native state but not the folding transition state. In addition, I have studied how charge-charge interactions affect the folding kinetics of monomeric α-helical structures. While previous molecular dynamics simulation suggests that the formation of salt bridges can speed up the folding process, our results indicate that the formation of salt bridge slows down folding, due probably to the formation and unfavorable burial of nonnative salt-bridges in the hydrophobic environment during the early folding step and the energetic cost to break those nonnative salt-bridges in the subsequent folding process to form the native structure. Finally, I have investigated the aggregation behaviors of two trpzip β-hairpins in this thesis. The effects of concentration, pH and temperature on aggregation were examined experimentally. The apparent difference in aggregation behavior between the trpzip β-hairpins reveals that the aggregation process is sensitive to β-turn sequence and overall peptide stability

Vibrational Approach to the Dynamics and Structure of Protein Amyloids

Amyloid diseases, including neurodegenerative diseases such as Alzheimer’s and Parkinson’s, are linked to a poorly understood progression of protein misfolding and aggregation events that culminate in tissue-selective deposition and human pathology. Elucidation of the mechanistic details of protein aggregation and the structural features of the aggregates is critical for a comprehensive understanding of the mechanisms of protein oligomerization and fibrillization. Vibrational spectroscopies, such as Fourier transform infrared (FTIR) and Raman, are powerful tools that are sensitive to the secondary structure of proteins and have been widely used to investigate protein misfolding and aggregation. We address the application of the vibrational approaches in recent studies of conformational dynamics and structural characteristics of protein oligomers and amyloid fibrils. In particular, introduction of isotope labelled carbonyl into a peptide backbone, and incorporation of the extrinsic unnatural amino acids with vibrational moieties on the side chain, have greatly expanded the ability of vibrational spectroscopy to obtain site-specific structural and dynamic information. The applications of these methods in recent studies of protein aggregation are also reviewed

Aggregation Gatekeeper and Controlled Assembly of Trpzip β‑Hairpins

Protein and peptide aggregation is an important issue both <i>in vivo</i> and <i>in vitro</i>. Herein, we examine the aggregation behaviors of two well-studied β-hairpins, Trpzip1 and Trpzip2. Previous studies suggested that Trpzip2 remains monomeric up to a concentration of ∼15 mM whereas Trpzip1 readily aggregates at micromolar concentrations at acidic or neutral pH. This disparity is puzzling considering that these two peptides differ only in their turn sequences (i.e., GN vs NG). We hypothesize that these peptides can aggregate from their folded states via native edge-to-edge interactions and that the Lys8 residue in Trpzip2 is a more effective aggregation gatekeeper, because of a more favorable orientation. In support of this hypothesis, we find that increasing the pH to 13 or replacing Lys8 with a hydrophobic and photolabile Lys analogue, Lys­(nvoc), leads to a significant increase in the aggregation propensity of Trpzip2, and that the aggregation of this Trpzip2 mutant can be reversed upon restoring the native Lys side chain via photocleavage of the nvoc moiety. In addition, we find that while both Trpzip1 and Trpzip2 form parallel β-sheet aggregates, the Lys­(nvoc) Trpzip2 mutant forms antiparallel β-sheets and more stable fibrils. Taken together, these findings provide another example showing how sensitive peptide and protein aggregation is to minor sequence variation and that it is possible to use a photolabile non-natural amino acid, such as Lys­(nvoc), to tune the rate of peptide aggregation and to control fibrillar structure

High-energy square-wave pulses generated in a 1/1.5-µm dual-band mode-locked fiber laser

The generation of square-wave pulses in a 1/1.5-µm dual-band mode-locked fiber laser is experimentally demonstrated. The laser is based upon a peculiar “figure-θ” architecture that exploits a single active fiber to realize dual-band operation. High-energy square-wave pulses are simultaneously generated in both the 1-µm and the 1.5-µm spectral band using the laser. The 1-µm pulse maintains wave-breaking-free operation during the increase of the pump power and finally achieves energy as high as 88.6 nJ, while the 1.5-µm pulse achieves energy up to 1.5 µJ before it ultimately collapses into second-order mode locking. To the best of our knowledge, this is the first report on the formation of square-wave pulses in dual-band mode-locked fiber lasers

Effects of Charged Cholesterol Derivatives on Aβ40 Amyloid Formation

Understanding of the mechanistic progess of amyloid-β peptide (Aβ) aggregation is critical for elucidating the underlying pathogenesis of Alzheimer’s disease (AD). Herein, we report for the first time the effects of two cholesterol derivatives, negatively charged cholesterol sulfate (cholesterol-SO₄) and positively charged 3β-[<i>N</i>-(dimethyl­amino­ethane)­carbamoyl]-cholesterol (DC-cholesterol), on the fibrillization of Aβ40. Our results demonstrate that both of the nonvesicular forms of cholesterol-SO₄ and DC-cholesterol moderately accelerate the aggregation rate of Aβ40. This effect is similar to that observed for unmodified cholesterol, indicating the importance of hydrophobic interactions in binding of Aβ40 to these steroid molecules. Furthermore, we show that the vesicles formed at higher concentrations of anionic cholesterol-SO₄ facilitate Aβ40 aggregation rate markedly. In contrast, the cationic DC-cholesterol vesicles show the ability to inhibit Aβ40 fibril formation under appropriate experimental conditions. The results suggest that the electrostatic interactions between Aβ40 and the charged vesicles can be of great importance in regulating Aβ40–vesicle interaction. Our results also indicate that the structural properties of the aggregates of the cholesterol derivatives, including the surface charge and the size of the vesicles, are critical in regulating the effects of these vesicles on Aβ40 aggregation kinetics