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The traditional underground power cable fault positioning method relies on subjective parameter selection and noise resistance is poor. It cannot meet the accurate fault positioning requirements of underground power cable under strong noise background. In order to solve the above problems, a fault positioning method of underground power cable based on salp swarm algorithm (SSA) optimizing variational mode decomposition (VMD) combined with novel Teager energy operator (NTEO) is proposed. In response to the problem of modal aliasing, over decomposition, and under decomposition in signal decomposition of VMD, SSA is used to optimize the modal number K and penalty factor α of VMD parameters using fuzzy entropy as the fitness function. The intrinsic modal function that better reflects the fault feature information is obtained. NTEO is used to calibrate the first wave head to obtain the arrival time of the wave heads at both ends. The fault position is determined based on the dual end distance measurement method. PSCAD/EMTDC is used for underground power cable fault simulation. It simulates underground fault signals with strong background noise. The results show the following points. ① After adding 9 dB and 12 dB noise to the ideal current signal, the signal-to-noise ratio of SSA-VMD is the lowest, and the Pearson correlation coefficient is the highest. It indicates that SSA-VMD can effectively preserve the characteristic information of the signal while minimizing noise. ② Under different transition resistances, the positioning precision of SSA-VMD-NTEO is relatively high. ③ Under different fault phase angles, although SSA-VMD-NTEO may have different sampling points, the positioning position remains unchanged and still maintains high precision. ④ SSA−VMD-NTEO can ensure high positioning precision at different fault distances. ⑤ Under high underground noise and a sampling frequency of 10 MHz, SSA-VMD-NTEO has significant advantages in positioning precision compared to wavelet modulus maximum and VMD+NTEO methods.

PD‐1/PD‐L1 inhibitors have emerged as standard treatments for advanced solid tumors; however, challenges such as a low overall response rate and systemic side effects impede their implementation. Hypoxia drives the remodeling of the tumor microenvironment, which is a leading reason for the failure of immunotherapies. Despite some reported strategies to alleviate hypoxia, their individual limitations constrain further improvements. Herein, a novel two‐pronged strategy is presented to efficiently address hypoxia by simultaneously adopting atovaquone (ATO, inhibiting oxygen consumption) and oxyhemoglobin (HbO2, directly supplementing oxygen) within a multifunctional aggregate termed NPs‐aPD‐1/HbO2/ATO. In addition to eliminating hypoxia with these two components, this smart aggregate also includes albumin and an ROS‐responsive cross‐linker as a controlled release scaffold, along with PD‐1 antibody (aPD‐1) for immunotherapy. Intriguingly, NPs‐aPD‐1/HbO2/ATO demonstrates exceptional tumor targeting in vivo, exhibiting ≈4.2 fold higher accumulation in tumors than in the liver. Consequently, this aggregate not only effectively mitigates hypoxia and significantly assists aPD‐1 immunotherapy but also simultaneously resolves the targeting and systemic toxicity issues associated with individual administration of each component. This study proposes substantial implications for drug‐targeted delivery, addressing tumor hypoxia and advancing immunotherapy, providing valuable insights for advancing cancer treatment strategies. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types. [ABSTRACT FROM AUTHOR]

Low-profile electrolytic copper foils were prepared by direct-current electrodeposition with only gelatin as an additive. The effects of some process conditions such as surface roughness of titanium substrate (i.e. cathode), current density, mass concentrations of sulfuric acid and copper ions, and electrolyte temperature on the surface morphology and roughness of the electrodeposited copper foil were studied. The optimal process conditions were determined as follows: surface roughness (Rz) of titanium substrate 1-2 μm, current density 4.0 A/dm², sulfuric acid 100 g/L, copper ions 60 g/L, and electrolyte temperature 37 ². The copper foil electrodeposited under the optimized conditions exhibited good surface morphology with a roughness (Rz) of lower than 1.8 μm. [ABSTRACT FROM AUTHOR]

Background: Thyrotropin receptor (TSHR) plays a central role in maintaining thyroid function and TSHR impairment causes hypothyroidism, which is often associated with metabolic disarrangement. The most common type of hypothyroidism is autoimmune disease-related and the mechanism, particularly with respect to the role of microRNAs (miRNAs), has not been delineated. Methods: Serum from 30 patients with subclinical hypothyroidism (SCH) and 30 healthy individuals were collected and exosomal miR-146a (exo-miR-146a) was examined, followed by extensive mechanistic investigation using various molecular and cellular experimental approaches and genetic-knockout mouse models. Results: Our clinical investigation showed that exo-miR-146a was systemically elevated in the serum of patients with SCH (p = 0.04) compared with healthy individuals, prompting us to investigate the biological effects of miR-146a in cells. We found that miR-146a could target and down-regulate neuron-glial antigen 2 (Ng2), with consequent down-regulation of TSHR. We next generated a thyroid-specific Ng2 knockout (Thy-Ng2−/−) mouse model and found a significant down-regulation of TSHR in Thy-Ng2−/− mice, accompanied by the development of hypothyroidism and metabolic disorders. We further found that a decrease in NG2 resulted in decreased receptor tyrosine kinase-linked downstream signaling and down-regulation of c-Myc, consequently resulting in up-regulation of miR-142 and miR-146a in thyroid cells. Up-regulated miR-142 targeted the 3′-untranslated region (UTR) of TSHR messenger RNA (mRNA) and post-transcriptionally down-regulated TSHR, explaining the development of hypothyroidism above. Local up-regulation of miR-146a in thyroid cells augments the earlier cited processes initiated by systemically elevated miR-146a, thereby forming a feedback loop to propel the development and progression of hypothyroidism. Conclusions: This study has uncovered a self-augmenting molecular loop initiated by elevated exo-miR-146a to suppress TSHR through targeting and down-regulating NG2, thereby initiating and propelling the development and progression of hypothyroidism. [ABSTRACT FROM AUTHOR]