A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in PET/CT images can help reduce doctors' workload, thereby improving diagnostic quality. However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions. To address these issues, this paper proposes a localization-to-segmentation framework (L2SNet) for precise tumor segmentation. L2SNet first localizes the possible lesions in the lesion localization phase and then uses the location cues to shape the segmentation results in the lesion segmentation phase. To further improve the segmentation performance of L2SNet, we design an adaptive threshold scheme that takes the segmentation results of the two phases into consideration. The experiments with the MICCAI 2023 Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show that our method achieved a competitive result and was ranked in the top 7 methods on the preliminary test set. Our work is available at: https://github.com/MedCAI/L2SNet.Comment: 7 pages,3 figure

An on-chip tunable micro-disk laser fabricated on Er3+ doped lithium niobate on insulator (LNOI)

We demonstrate a C-band wavelength-tunable microlaser with an Er3+ doped high quality (~1.02x10^6) lithium niobate microdisk resonator. With a 976 nm continuous-wave pump laser, lasing action can be observed at a pump power threshold as low as ~250 {\mu}W at room temperature. Furthermore, the microdisk laser wavelength can be tuned by varying the pump laser power, showing a tuning efficiency of ~-17.03 pm/mW at low pump power blow 13 mW, and 10.58 pm/mW at high pump power above 13 mW

A tau fragment links depressive-like behaviors and cognitive declines in Alzheimer’s disease mouse models through attenuating mitochondrial function

IntroductionAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease characterized by extracellular senile plaques including amyloid-β peptides and intracellular neurofibrillary tangles consisting of abnormal Tau. Depression is one of the most common neuropsychiatric symptoms in AD, and clinical evidence demonstrates that depressive symptoms accelerate the cognitive deficit of AD patients. However, the underlying molecular mechanisms of depressive symptoms present in the process of AD remain unclear.MethodsDepressive-like behaviors and cognitive decline in hTau mice were induced by chronic restraint stress (CRS). Computational prediction and molecular experiments supported that an asparagine endopeptidase (AEP)-derived Tau fragment, Tau N368 interacts with peroxisome proliferator-activated receptor delta (PPAR-δ). Further behavioral studies investigated the role of Tau N368-PPAR-δ interaction in depressive-like behaviors and cognitive declines of AD models exposed to CRS.ResultsWe found that mitochondrial dysfunction was positively associated with depressive-like behaviors and cognitive deficits in hTau mice. Chronic stress increased Tau N368 and promoted the interaction of Tau N368 with PPAR-δ, repressing PPAR-δ–mediated transactivation in the hippocampus of mice. Then we predicted and identified the binding sites of PPAR-δ. Finally, inhibition of AEP, clearance of Tau N368 and pharmacological activation of PPAR-δ effectively alleviated CRS-induced depressive-like behaviors and cognitive decline in mice.ConclusionThese results demonstrate that Tau N368 in the hippocampus impairs mitochondrial function by suppressing PPAR-δ, facilitating the occurrence of depressive-like behaviors and cognitive decline. Therefore, our findings may provide new mechanistic insight in the pathophysiology of depression-like phenotype in mouse models of Alzheimer’s disease

Insect-Specific microRNA Involved in the Development of the Silkworm Bombyx mori

MicroRNAs (miRNAs) are endogenous non-coding genes that participate in post-transcription regulation by either degrading mRNA or blocking its translation. It is considered to be very important in regulating insect development and metamorphosis. We conducted a large-scale screening for miRNA genes in the silkworm Bombyx mori using sequence-by-synthesis (SBS) deep sequencing of mixed RNAs from egg, larval, pupal, and adult stages. Of 2,227,930 SBS tags, 1,144,485 ranged from 17 to 25 nt, corresponding to 256,604 unique tags. Among these non-redundant tags, 95,184 were matched to the silkworm genome. We identified 3,750 miRNA candidate genes using a computational pipeline combining RNAfold and TripletSVM algorithms. We confirmed 354 miRNA genes using miRNA microarrays and then performed expression profile analysis on these miRNAs for all developmental stages. While 106 miRNAs were expressed in all stages, 248 miRNAs were egg- and pupa-specific, suggesting that insect miRNAs play a significant role in embryogenesis and metamorphosis. We selected eight miRNAs for quantitative RT-PCR analysis; six of these were consistent with our microarray results. In addition, we searched for orthologous miRNA genes in mammals, a nematode, and other insects and found that most silkworm miRNAs are conserved in insects, whereas only a small number of silkworm miRNAs has orthologs in mammals and the nematode. These results suggest that there are many miRNAs unique to insects

Research on Optimization Method for Fault-Tolerant Integration of Real-Time Dual-Computer Embedded Systems

This paper addresses the fault-tolerant performance of real-time dual-computer embedded systems. The article first emphasizes the importance of real-time and reliability in various fields and points out that improving fault-tolerant performance is a crucial topic. Based on the Markov chain algorithm, the study optimizes the fault-tolerant integration method for real-time dual-computer embedded systems. By constructing a model of Markov algorithm and using the deadline of the task as a benchmark, the passage and transfer probabilities of faults are calculated. The article also provides algorithmic proofs of fault-tolerant control of Markovian jump systems and calculates their stability levels. The results show that the fault passage rate of the system increases as the number of complex tasks increases, e.g., when the number of complex tasks is 4, the passage rate can reach 90%. In addition, in the scheduling test, it was found that the schedulability of the system increases with the increase in the number of processors. When the number of processors reaches 5, the system's schedulability is 43%. In conclusion, the system fault tolerance optimization method based on Markov algorithm proposed in the article can effectively improve the reliability and fault tolerance of the system