Sequence Of Segments Research Articles

Description of two new species of the genus Hemicycliophora (Nematoda: Hemicycliophoridae) from the USA

Summary Two species of sheath nematodes were studied using an integrative approach, combining morphological and molecular analyses. The first species was collected from an oak tree (Quercus sp.) imported from Texas into Florida, while the second one was obtained from the ornamental palm Phoenix sylvestris in a Florida nursery. These species were compared to a closely related species, Hemicaloosia uarki, which was originally described in Arkansas. Phylogenetic analyses using partial 18S rRNA, D2-D3 expansion segments of 28S rRNA, ITS rRNA, and COI gene sequences revealed that sheath nematodes from Texas and Florida represent two new species of the genus Hemicycliophora, which are described here as Hemicycliophora ornata sp. n. and H. silvestris sp. n., respectively. In the phylogenetic tree based on ITS rRNA gene sequences, Hemicaloosia uarki grouped within a clade of other Hemicycliophora species rather than within the Hemicaloosia species clade, indicating that this species belongs to the genus Hemicycliophora rather than to Hemicaloosia. Consequently, this species was reclassified as Hemicycliophora uarki comb. n. These findings confirmed that morphological characters traditionally used to distinguish Hemicaloosia species from those of Hemicycliophora are valid but insufficient for reliable identification when some life stages, such as males, are absent. The presence of males with straight spicules is a very useful character to validate the morphological identification of Hemicaloosia species. In absence of this life stage, phylogenetic analysis of gene sequences is the only reliable method to accurately differentiate sheath nematodes resembling Hemicaloosia from Hemicycliophora.

The Inhibitory Effect of Peptide Hydrolysate of Type I Collagen Derived from Pig Skin on Melanogenesis in B16F10 Melanoma Cells

Collagen peptides, as a natural source of peptides, possess multiple advantages such as anti-aging, anti-inflammatory properties, tissue repair, and the ability to inhibit melanin production. In this study, type I collagen extracted from pig skin was hydrolyzed with 1% and 3% hydrochloric acid, yielding collagen peptides CPH1 and CPH3. The melanin content and tyrosinase activity in B16F10 cells were compared via direct and paracrine action when CPH1 and CPH3 were used to interfere with melanogenesis. It was found that CPH3 significantly inhibited melanogenesis in B16F10 through the paracrine action involving HaCaT keratinocytes. The intracellular melanin content was measured at 65.23 ± 1.30%, and the mRNA levels of tyrosinase and microphthalmia transcription factor in cells were 55.77 ± 6.09% and 50.70 ± 8.18% of the negative control, respectively. Furthermore, pigment deposition assays in zebrafish showed that, at a concentration of 1.0 mg/mL, CPH3 significantly inhibited melanogenesis compared to the negative control. Finally, tyrosinase inhibitory peptides were identified from CPH3 through peptide segment sequence identification and molecular dynamics simulation. The peptides of Nona-AGPPGFPGA, Octa-APGPVGPA, and Octa-GLPGPPGP have a double effect on the inhibition of tyrosinase and melanin content in B16F10 cells.

A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant information. Depending on the target problem some modalities are more informative and thus relevant for decision-making. Identifying the optimal subset of modalities best suited to solve a particular task significantly reduces the complexity of acquisition without compromising performance. In this work, we propose a wrapper method for examining the importance of Magnetic Resonance Imaging (MRI) sequences for ischemic stroke lesion segmentation using a deep neural network trained for segmentation. Importance score for each modality is computed through a combinatorial dropout of input modalities at inference coupled with a systematic evaluation its impact on the model's performance. Experimental evaluation of the proposed method is performed on two publicly available datasets: (i) ISLES15 - comprising seven MRI sequences for 30 cases and (ii) ISLES22 - comprising of three MRI sequences for 250 cases. We identified DWI, Tmax and T1c as the optimal set of MRI sequences for core-penumbra delineation and Tmax as the optimal sequence for lesion segmentation in ISLES15 dataset. In ISLES22 dataset, DWI was identified as the optimal sequence for lesion segmentation. In addition to the exhaustive experimental validation, visually interpretable evidence for accuracy of the identified optimal subset is provided in the form of saliency maps.

Unlocking Potential Score Insights of Sentimental Analysis with a Deep Learning Revolutionizes

Online hate has emerged as a rapidly growing issue worldwide, often stemming from differences in opinion. It is crucial to use appropriate language and words on social media platforms, as inappropriate communication can negatively impact others. Consequently, detecting hate speech is of significant importance. While manual methods are commonly employed to identify hate and offensive content on social media, they are time-consuming, labor-intensive, and prone to errors. Therefore, AI-based approaches are increasingly being adopted for the effective classification of hate and offensive speech. The proposed model incorporates various text preprocessing techniques, such as removing extraneous elements like URLs, emojis, and blank spaces. Following preprocessing, tokenization is applied to break down the text into smaller components or tokens. The tokenization technique utilized in this study is TF-IDF (Term Frequency–Inverse Document Frequency). After tokenization, the model performs the classification of hate and offensive speech using the proposed BiLSTM-based SM-CJ (Scalable Multi-Channel Joint) framework. The BiLSTM-based SM-CJ model is particularly effective in detecting hate, offensive, and neutral tweets due to its ability to capture both forward and backward contexts within a given text. Detecting hate speech requires a comprehensive understanding of the text and the identification of patterns spanning across multiple words or phrases. To achieve this, the LSTM component of the BiLSTM model is designed to capture long-term dependencies by utilizing information from earlier parts of the text. The proposed SM-CJ framework further aligns the input sequence lengths fetched from the input layer, enabling the model to focus on specific segments of the input sequence that are most relevant for hate speech detection. This approach allows the model to accurately capture derogatory language, and subtle nuances present in hate speech. Finally, the performance of the proposed framework is evaluated using various metrics, including accuracy, recall, F1-score, and precision. The results are compared with state-of-the-art approaches, demonstrating the effectiveness of the proposed model. Doi: 10.28991/ESJ-2025-09-01-03 Full Text: PDF

Integrated 3D reservoir modelling for the unconventional reservoir prospects: a case study of Upper Cretaceous Brown Limestone reservoir, South Geisum field, Gulf of Suez, Egypt

The Gulf of Suez basin (GOS) hosts over eighty conventional oilfields spanning from the Pre-Cambrian to the Recent. This research focuses on a segment of the (GOS) sequence within the territories managed by PGM Company. It outlines a methodology for evaluating unconventional resource potential within the Brown Limestone interval of Upper Cretaceous age in this region. The Brown Limestone interval serves not only as a crucial source rock but also as a fractured reservoir widespread across many oilfields. Due to intricate reservoir architecture, diverse lateral variations in facies and the heterogeneity of the reservoir quality, uncertainties prevail. These challenges impede the optimization of reservoir exploitation throughout the GOS Basin. This study employs an integrated approach utilizing diverse datasets and techniques to ascertain the precise structural setting, property attributes of the Upper Cretaceous Brown Limestone and forecast its complex geometry in three-dimensional space within the study area. The study aims to assess the reservoir and trap heterogeneity under various scenarios to create a robust 3D static model dictating the structural and property complexities. The objective of the model is to enhance the precision of reserve estimates and provide crucial support for decision-making in the development planning of the South Geisum oilfield.

Spatio-Temporal Attention for Text-Video Retrieval

Text-video retrieval, a fundamental task for associating textual descriptions with video content, has become increasingly important in the video domain. Most existing methods focus on the single-modality features only considering the knowledge within individual video or text modalities, often neglecting cross-modal interactions. However, a text description corresponds to a specific spatio-temporal content within a video, involving a certain segment of a frame sequence and distinct sub-regions within these frames. Therefore, we focus on the text-conditioned video features to bridge the modality gap. In this paper, we propose Spatio-Temporal Attention for video-text retrieval, termed STAttn, which utilizes textual information to focus on the spatio-temporal video content. Our final text-conditioned video features are generated from the text-related video frames and the text-related regions within these frames. First, we propose the Spatial Text-Attention Module (STAM) to learn the spatial information within video frames. STAM introduces the text-related salient patches to capture more fine-grained details. Second, we propose the Temporal Text-Attention Module (TTAM) to learn the temporal relationships between video frames. Temporal Triplet loss is proposed in TTAM to enhance the attention towards the text-related frames. Thus, the two modules learn the text-related spatio-temporal content from both intra-frame and inter-frame aspects. Extensive experiments on three benchmark datasets, MSRVTT, ActivityNet, and DiDeMo, demonstrate that our STAttn outperforms state-of-the-art methods.

Solar-Active Carbon Nitride Film Integrated by Free Radical Copolymerization for Photocatalytic Indoor Air Purification.

The current lack of stable, scalable, and efficient coating technology dramatically limits the exploitation of solar-driven graphitic carbon nitride (CN) photocatalysts. Herein, a unique, efficient, and scalable method is reported to immobilize CN powder on various substrates ranging from Fluorine tin oxide (FTO), glass, Plexiglas, Al foil, Ti foil, and Granite stone, to even wood. The film shows an outstanding thickness of 212µm, which is the highest value ever reported. The formation process is ascribed to free radical copolymerization between the tri-s-triazine backbone and polyacrylamide, followed by cross-linking. The smooth, non-oxidizable, and well-defined continuous coating exhibits excellent adherence and durability. The distinctive sequence segments preserve the light transition within the film while enhancing the optical absorption in the solar spectrum. Under visible light illumination, the film shows outstanding photodegradation performance toward air pollutants, whether for gaseous acetaldehyde (Act) or toluene (Tol). This method is a great step forward that can open new opportunities for the commercial applications of CN powder.

Peptide‐Grafted Waterborne Polyurethane With Enhanced Biocompatibility and Mechanical Properties for Biomedical Applications

ABSTRACTThe incorporation of fillers into waterborne polyurethane (WPU) can endow the composites with improved physicochemical and physiological properties for biomedical applications. Silk fibroin possesses excellent mechanical properties and biocompatibility owing to its unique hierarchical structure comprising β‐sheet crystallites and amorphous matrix. To mimic its structure, a crystalline tetrapeptide Gly‐Ala‐Gly‐Ala (GAGA) derived from the repetitive segments of the primary sequence of fibroins was grafted onto the WPU backbone. The tetrapeptides aggregated via additional hydrogen bonds to form crystalline domains, which served as physical crosslinking points to significantly improve the mechanical properties of WPU. Compared with the pristine WPU, the modification enhanced the maximum tensile strength (from 13.1 to 25.4 MPa) and hydrophilicity (the water contact angle from 80.6° to 38.8°). In addition, the modified WPU with the optimal proportion of the tetrapeptide remained structurally stable under physiological environments and showed improved biocompatibility including good cell and protein adhesion, noncytotoxicity, and nonhemolysis. The excellent performance confirms the effectiveness of peptides in WPU modification and sheds light on the potential of modified WPU in biomedical applications.

DNA promoter task-oriented dictionary mining and prediction model based on natural language technology

Promoters are essential DNA sequences that initiate transcription and regulate gene expression. Precisely identifying promoter sites is crucial for deciphering gene expression patterns and the roles of gene regulatory networks. Recent advancements in bioinformatics have leveraged deep learning and natural language processing (NLP) to enhance promoter prediction accuracy. Techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and BERT models have been particularly impactful. However, current approaches often rely on arbitrary DNA sequence segmentation during BERT pre-training, which may not yield optimal results. To overcome this limitation, this article introduces a novel DNA sequence segmentation method. This approach develops a more refined dictionary for DNA sequences, utilizes it for BERT pre-training, and employs an Inception neural network as the foundational model. This BERT-Inception architecture captures information across multiple granularities. Experimental results show that the model improves the performance of several downstream tasks and introduces deep learning interpretability, providing new perspectives for interpreting and understanding DNA sequence information. The detailed source code is available at https://github.com/katouMegumiH/Promoter_BERT.

Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism.

The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mechanism is utilized for regression which extracts temporal and spatial complex features. Deep CNN extracts features impacting energy consumption whereas Bi-LSTM with attention layer finds suitability for regression as it is capable of modelling irregular trends in the time-series components, where the attention mechanism is implemented to enhance the decoder's ability to selectively focus on the most relevant segments of the input sequence. This is achieved through a weighted integration of all encoded input trajectories, allowing the model to dynamically emphasize the vectors that carry the highest significance for accurate predictions. Based on regression outcomes from analysis taken in hourly, daily and monthly time intervals, enhanced prediction accuracy is estimated through evaluation metrics such as MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) which determines the efficacy of the system, where Specifically, the proposed model achieves MSE of 0.123, MAE of 0.22, and MAPE of 324.12. Furthermore, this model demonstrates a training time of 692.12s and a prediction time of just 1.87s. Therefore, present research highlights the critical need for accurate energy consumption prediction in households, driven by the increasing reliance on electrical appliances in daily life.

Orbits of families of discrete dynamical systems evolving in the natural numbers

In this paper, we give a class of one-dimensional discrete dynamical systems with state space N+. This class of systems is defined by two parameters: one of them sets the number of nearest neighbors that determine the rule of evolution, and the other parameter determines a segment of natural numbers Λ={1,2,…,b}. In particular, we investigate the behavior of a class of one-dimensional maps where an integer moves to an other integer given by the sum of the nearest neighbors minus a multiple of b∈N+. We find the coexistence of fixed points and periodic cycles. Two single parameter families of maps are introduced and their dynamics in the segment of natural sequence Λ. Furthermore, an order of the numbers of the set Λ−b is given by these families. Last, we present a connection of the N+ generated by the orbits of a particular case.

Molecular detection of a novel mutation in the TPO gene associated with congenital hypothyroidism in a cat: Case report

Objective: The objective of this study was to analyze the sequence of different fragments of the thyroperoxidase (TPO) gene from a cat diagnosed with congenital hypothyroidism (CH). Materials and Methods: The feline was diagnosed due to high serum concentrations of thy¬roid-stimulating hormone and low T4. The analysis of sequences containing mutations in the TPO gene from dogs with CH allowed for the prediction of mutation sites within the gene in an affected cat. In addition, the design of a polymerase chain reaction-based test allowed the amplification and sequencing of these gene segments. In addition, after the death of the patient, a necropsy and histopathology were performed, looking for macroscopic and microscopic alterations of affected organs. Results: The necropsy examination showed megacolon, cardiac concentric left ventricular hyper¬trophy, and bilateral enlargement of the thyroid gland. The histopathology of the thyroid showed follicular hypoplasia and low colloid production. gDNA analysis allowed the detection of mutation in the TPO gene, which corresponded to one transition in the nucleotide 12.542 (A > G) and het¬erozygous variations located in the nucleotide 14.627 (G/A) and in the nucleotide 30.713 (G/C). Conclusion: Due to the presence of these polymorphisms, it is suspected that one monoallelic expression of mutant alleles is present. More studies that allow an understanding of the role of the heterozygous in this pathology are required, as well as the role of gene mutations related to CH in cats. On the other hand, the data from the present study serve as the base for the development of a molecular test that allows a fast and accurate diagnosis of HC in cats.

Data storage based on the absence of nucleotides using a bacteriophage abortive infection system reverse transcriptase.

DNA molecules are a promising data storage medium for the future; however, effective de novo synthesis of DNA using an enzyme that catalyzes the polymerization of natural nucleoside triphosphates in a user-defined manner, without the need for multiple injections of polymerase, remains a challenge. In the present study, we demonstrated that the bacteriophage abortive infection system reverse transcriptase AbiK from Lactococcus lactis facilitates such an approach. We employed surface plasmon resonance to monitor the polymerization of the DNA strand with a user-defined sequence of multiple segments through a sequential buffer exchange process. Using this method, we synthesized synthetic DNA with segments of random length and a sequence consisting of only three of the four natural nucleotides. The information is encoded using the absence of one nucleotide in each segment. We demonstrated that synthetic DNA can be stored on the chip, and when the DNA is released from the chip, the second strand can be synthesized and read by sequencing. Our setup facilitates a writing speed of one nucleotide in less than 1 s and holds enormous potential for synthesizing DNA for data storage.

Pulse Sequence Segmentation for Multifunction Radar Using Multiple Reconnaissance Receivers

Pulse Sequence Segmentation for Multifunction Radar Using Multiple Reconnaissance Receivers

Establishment of minigenomes for infectious bursal disease virus

Minigenomes (MGs) have greatly advanced research on the viral life cycle, including viral replication and transcription, virus‒host interactions, and the discovery of antivirals against RNA viruses. However, an MG for infectious bursal disease virus (IBDV) has not been well established. Here, we describe the development of IBDV MG, in which the entire coding sequences of viral genomic segments A and B are replaced with Renilla luciferase (Rluc) or enhanced green fluorescent protein (EGFP) reporter genes. Under the control of the RNA polymerase I promoter, the translation of IBDV MG is controlled by the viral proteins VP1 and VP3. Interestingly, IBDV B MG shows greater activity than does IBDV A MG. Moreover, the sense IBDV B MG was expressed at a higher level than the antisense IBDV B MG. In agreement with our previous findings, the translation of IBDV B MG controlled by VP1 and VP3 is independent of the cellular translation machinery components eukaryotic initiation factor (eIF)4E and eIF4G, but intact VP1 polymerase activity, VP3 dsRNA-binding activity, and the interaction between VP1 and VP3 are indispensable for both sense and antisense IBDV B MG activity. In addition, ribavirin, which inhibits IBDV replication, inhibits IBDV B MG activity in a dose-dependent manner. Collectively, the IBDV MG established in this study provides a powerful tool to investigate IBDV intracellular replication and transcription and virus‒host interactions and facilitates high-throughput screening for the identification of IBDV antivirals.

Severe fever with thrombocytopenia syndrome virus was found in Northern Jiangxi Province, China

IntroductionSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. SFTS monitoring has been carried out since 2010 in mainland China. In recent years, human infection with SFTS virus (SFTSV) has frequently been detected in Jiujiang of Jiangxi Province, Central China.MethodsSera of SFTS surveillance cases and samples collected from humans, animals and ticks surrounding the cases were used to detect SFTSV RNA by real-time RT-PCR. SFTSV-positive samples were further subjected to sequencing and analysis of the S, M, and L segments of SFTSV.ResultsFour patients were positive for SFTSV infection. However, the subjects like humans, animals and ticks around the cases were all detected as negative for the virus infection. Phylogenetic analysis of the partial segments revealed that SFTSVs in three patients from Jiujiang were clustered with genotype C4 and C5 on the L and M segment-established phylogenetic tree; however, phylogenetic analysis of the S segment showed that the strains were grouped into genotype J1. These results suggested that the S, M, and L segments of these strains underwent segmental reassortment, which was later supported by recombination signal detection. The reassortment event was detected by at least four methods with a significance level of p value <0.05. In addition, the RDP recombination consensus score (RDPRCS) was greater than 0.40. To avoid poor tree topology support with the partial S, M, and L segments, we further performed analysis of the complete genome of SFTSV. The full-length L, M, and S segment sequences of the strains were consistently clustered into two genotypes, namely the genotype C5 and the genotype C4. The strains belonging to genotype C5 were detected for recombination signal with moderate confidence by all the six methods, with a significance level of p value <0.05 and an RDPRCS of 0.41. The recombination event might have occurred between the minor parent (2011YPQ11, C3/C3/C3) and major parent (SPL053A, J1/ J1/ J1). However, there was no genetic recombination detected in the strains belonging to genotype C4. The event was detected by only two methods with a significance level of p value <0.05.DiscussionTwo genotypes of SFTSVs were identified in Jiujiang City, Jiangxi Province of Central China, and they were genotype C5 and genotype C4. The genotype C5 underwent genetic recombination in this region, with the minor parent of the strain 2011YPQ11 and the major parent of the strain SPL053A.

Diffusion Kurtosis Imaging provide complementary information over structural MRI in the assessment of hippocampal subfields in patients with cognitive decline

AbstractBackgroundHippocampal atrophy is an established biomarker of neurodegeneration in Alzheimer's disease, affecting specific subfields (De Flores, La Joie and Chételat, 2015). In this study, we used 7T MRI and advanced diffusion MRI (dMRI) to investigate the relationship between hippocampal subfield volumes and microstructure and assess their sensitivity to cognitive impairment.MethodSeventeen cognitively impaired (CI; age: 69±8, M/F: 12/5, MMSE: 28) and 22 cognitively unimpaired subjects (CU; age: 62±10, M/F: 6/16) were recruited in the context of the COSCODE project (Ribaldi et al., 2021). MRI was performed at 7 Tesla, including a high resolution T2w FSE sequence for hippocampal segmentation and multi‐shell diffusion MRI sequence for kurtosis tensor imaging (DKI, Jensen and Helpern, 2010) fitting. Hippocampal subfields were segmented using Automated Segmentation of Hippocampal Subfields (ASHS) package. Microstructure analysis included DTI and DKI (Figure 1). Mean values for each diffusion metric were calculated in subfields of the ASHS atlas (Xie et al. 2016). Subfield volumes were normalised by the total intracranial volume while dMRI metrics were residualized by age via a linear regression trained only on CU and then correlated to the volumetric data across within each group (statistical significance: p<0.05). Lasso logistic regression was used to identify the metrics that better discriminated CI from CU.ResultFew hippocampal subfields’ volumes correlated with dMRI metrics (Figure 2). In the CU group, DG volume showed negative associations with diffusion metrics, primarily RD (r:‐0.59, p=0.007), MD(r:‐0.56, p=0.012), MK(r:‐0.46, p=0.043) and AK(r:‐0.62, p=0.005). In the CI group, negative correlations between BA35 (r:‐0.70, p=0.008) and CA3 (r:‐0.57, p=0.041) volumes were found with AK. When discriminating CI from CU, the logistic regression found the ERC volume as the most important metric, followed by the MK of the DG, and AK of CA2 (Figure 3).ConclusionThe disconnection between dMRI metrics and volumetric measures and the selection of DKI metrics in discriminating CI from CU participants suggest that DKI metrics may act as complementary biomarkers to subfield volumes for early detection of cognitive impairment.

Feasibility of interleaved multislice averaged magnetization inversion-recovery acquisitions of the spinal cord.

To establish an interleaved multislice variant of the averaged magnetization inversion-recovery acquisitions (AMIRA) approach for 2D spinal cord imaging with increased acquisition efficiency compared with the conventional 2D single-slice approach(es), and to determine essential prerequisites for a working interleaved multislice AMIRA approach in practice. The general AMIRA concept is based on an inversion recovery-prepared, segmented, and time-limited cine balanced SSFP sequence, generating images of different contrast. For AMIRA imaging of multiple, independent slices in a 2D interleaved fashion, a slice loop within the acquisition loops was programmed. The former non-selective inversions were replaced with slice-selective inversions with user-definable slice thickness. The thickness of the slice-selective inversion in 2D interleaved multislice AMIRA should be doubled compared with the manufacturer's standard setting to avoid an increased sensitivity to flow and pulsation effects particularly in the CSF. However, this solution also limits its practical applicability, as slices located at directly adjacent vertebrae cannot be imaged together. Successful interleaved two-slice AMIRA imaging for a "reference" in vivo protocol with 0.50 × 0.50 mm2 in-plane resolution and 8-mm slice thickness is demonstrated, therefore halving its acquisition time per slice from 3 min down to 1.5 min. The investigated 2D interleaved two-slice AMIRA variant facilitates spinal cord imaging that maintains similar contrast and the same resolution as the conventional 2D single-slice AMIRA approach, but does so with a halved acquisition time.

Real-time multislice MR-thermometry of the prostate: Assessment of feasibility, accuracy and sources of biases in patients.

The primary purpose of this study was to evaluate the accuracy of an MR-thermometry sequence for monitoring prostate temperature. The secondary purposes were to analyze clinical and technical factors that may affect accuracy and testing the method in a realistic setting, with MR-guided Laser ablation on an ex vivo muscle sample. An ex vivo muscle sample was subjected to Laser ablation while using a two-dimensional multislice segmented echo planar imaging sequence for MR thermometry. The MR thermometry measurements were compared with invasive sensor temperature readings to assess accuracy. Subsequently, 56 men with a median age of 70 years (age range: 53-84 years) who underwent prostate MRI examinations at 1.5- (n = 27) or 3 T (n = 24) were prospectively included. For each patient, the proportion of 'noisy voxels' (i.e., those with a temporal standard deviation of temperature [SD(T)] > 2 °C) in the prostate was calculated. The impact of clinical and technical factors on the proportion of noisy voxels was also examined. MR-thermometry showed excellent correlation with invasive sensors during MR-guided Laser ablation on the ex vivo muscle sample. The median proportion of noisy voxels per patient in the entire cohort was 1 % (Q1, 0.2; Q3, 4.9; range: 0-90.4). No significant differences in median proportion of noisy voxels were observed between examinations performed at 1.5 T and those at 3 T (P = 0.89 before and after adjustment). No clinical or technical factors significantly influenced the proportion of noisy voxels. Two-dimensional real time multislice MR-thermometry is feasible and accurate for monitoring prostate temperature in patients.

National and Regional Molecular Epidemiology of HIV-1 - China, 2004-2023.

The genetic diversity of human immunodeficiency virus (HIV)-1 subtypes significantly influences the effectiveness of diagnostic tools, antiretroviral therapy (ART), and vaccine development. This study aimed to assess the regional and national prevalence of HIV-1 subtypes and recombinants in China between 2004 and 2023 using pol gene segment analysis. We analyzed annual HIV/AIDS reports and pol gene segment sequences from all Chinese provinces between 2004 and 2023. The distribution of HIV-1 subtypes and recombinants across China and within its regions was estimated by multiplying the proportion of each subtype, circulating recombinant form (CRF), and unique recombinant form (URF) in each province by the corresponding number of reported HIV infections. Analysis of 94,476 pol gene segments from 31 provinces revealed that CRF01_AE strain accounted for 32.1% of HIV-1 infections during 2004-2023, while CRF07_BC lineage represented 39.1%. CRF08_BC strain contributed 9.2%, followed by subtype B (8.7%) and CRF55_01B (2.4%). Other CRFs collectively comprised 6.0% of infections, while URFs and other subtypes accounted for 1.3% and 1.1%, respectively. The study revealed significant regional variations and temporal changes in the proportions of HIV-1 CRFs, subtypes, and URFs across China, emphasizing the importance of continued surveillance of strain distribution patterns.