Evolutionary genetics of pulmonary anatomical adaptations in deep-diving cetaceans.

BACKGROUND: Cetaceans, having experienced prolonged adaptation to aquatic environments, have undergone evolutionary changes in their respiratory systems. This process of evolution has resulted in the emergence of distinctive phenotypic traits, notably the abundance of elastic fibers and thickened alveolar walls in their lungs, which may facilitate alveolar collapse during diving. This structure helps selective exchange of oxygen and carbon dioxide, while minimizing nitrogen exchange, thereby reducing the risk of DCS. Nevertheless, the scientific inquiry into the mechanisms through which these unique phenotypic characteristics govern the diving behavior of marine mammals, including cetaceans, remains unresolved. RESULTS: This study entails an evolutionary analysis of 42 genes associated with pulmonary fibrosis across 45 mammalian species. Twenty-one genes in cetaceans exhibited accelerated evolution, featuring specific amino acid substitutions in 14 of them. Primarily linked to the development of the respiratory system and lung morphological construction, these genes play a crucial role. Moreover, among marine mammals, we identified eight genes undergoing positive selection, and the evolutionary rates of three genes significantly correlated with diving depth. Specifically, the SFTPC gene exhibited convergent amino acid substitutions. Through in vitro cellular experiments, we illustrated that convergent amino acid site mutations in SFTPC contribute positively to pulmonary fibrosis in marine mammals, and the presence of this phenotype can induce deep alveolar collapse during diving, thereby reducing the risk of DCS during diving. CONCLUSIONS: The study unveils pivotal genetic signals in cetaceans and other marine mammals, arising through evolution. These genetic signals may influence lung characteristics in marine mammals and have been linked to a reduced risk of developing DCS. Moreover, the research serves as a valuable reference for delving deeper into human diving physiology.

Rice PIFs: Critical regulators in rice development and stress response.

Phytochrome-interacting factors (PIFs) belong to a subfamily of the basic helix-loop-helix (bHLH) family of transcription factors, which serve as a "hub" for development and growth of plants. They have the capability to regulate the expression of many downstream genes, integrate multiple signaling pathways, and act as a signaling center within the cell. In rice (Oryza sativa), the PIF family genes, known as OsPILs, play a crucial part in many different aspects. OsPILs play a crucial role in regulating various aspects of photomorphogenesis, skotomorphogenesis, plant growth, and development in rice. These vital processes include chlorophyll synthesis, plant gravitropism, plant height, flowering, and response to abiotic stress factors such as low temperature, drought, and high salt. Additionally, OsPILs are involved in controlling several important agronomic traits in rice. Some OsPILs members coordinate with each other to function. This review summarizes and prospects the latest research progress on the biological functions of OsPILs transcription factors and provides a reference for further exploring the functions and mechanism of OsPILs.

A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.

To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidic chip to simultaneously capture neural activity and body movement in small freely behaving Drosophila larva. We develop a transfer learning based method to simultaneously track the continuously changing body posture and activity of neurons that move together using a sub-region tracking network with a precise landmark estimation network for the inference of target landmark trajectory. Based on the tracking of each labelled neuron, the activity of the neuron indicated by fluorescent intensity is calculated. For each video, annotation of only 20 frames in a video is sufficient to yield human-level accuracy for all other frames. The validity of this method is further confirmed by reproducing the activity pattern of PMSIs (period-positive median segmental interneurons) and larval movement as previously reported. Using this method, we disclosed the correlation between larval movement and left-right asymmetry in activity of a group of unidentified neurons labelled by R52H01-Gal4 and further confirmed the roles of these neurons in bilateral balance of body contraction during larval crawling by genetic inhibition of these neurons. Our method provides a new tool for accurate extraction of neural activities and movement of freely behaving small-size transparent animals.

A generalized QUCESOP method with evaluating CEST peak overlap.

The overlapping peaks of the target chemical exchange saturation transfer (CEST) solutes and other unknown CEST solutes affect the quantification results and accuracy of the chemical exchange parameters-the fractional concentration, f b , exchange rate, k b , and transverse relaxation rate, R 2 b -for the target solutes. However, to date, no method has been established for assessing the overlapping peaks. This study aimed to develop a method for quantifying the f b , k b , and R 2 b values of a specific CEST solute, as well as assessing the overlap between the CEST peaks of the specific solute(s) and other unknown solutes. A simplified R 1 ρ model was proposed, assuming linear approximation of the other solutes' contributions to R 1 ρ . A CEST data acquisition scheme was applied with various saturation offsets and saturation powers. In addition to fitting the f b , k b , and R 2 b values of the specific solute, the overlapping condition was evaluated based on the root mean square error (RMSE) between the trajectories of the acquired and synthesized data. Single-solute and multi-solute phantoms with various phosphocreatine (PCr) concentrations and pH values were used to calculate the f b and k b of PCr and the corresponding RMSE. The feasibility of RMSE for evaluating the overlapping condition, and the accurate fitting of f b and k b in weak overlapping conditions, were verified. Furthermore, the method was employed to quantify the nuclear Overhauser effect signal in rat brains and the PCr signal in rat skeletal muscles, providing results that were consistent with those reported in previous studies. In summary, the proposed approach can be applied to evaluate the overlapping condition of CEST peaks and quantify the f b , k b , and R 2 b values of specific solutes, if the weak overlapping condition is satisfied.

The scaffold protein AXIN1: gene ontology, signal network, and physiological function.

AXIN1, has been initially identified as a prominent antagonist within the WNT/ß-catenin signaling pathway, and subsequently unveiled its integral involvement across a diverse spectrum of signaling cascades. These encompass the WNT/ß-catenin, Hippo, TGFß, AMPK, mTOR, MAPK, and antioxidant signaling pathways. The versatile engagement of AXIN1 underscores its pivotal role in the modulation of developmental biological signaling, maintenance of metabolic homeostasis, and coordination of cellular stress responses. The multifaceted functionalities of AXIN1 render it as a compelling candidate for targeted intervention in the realms of degenerative pathologies, systemic metabolic disorders, cancer therapeutics, and anti-aging strategies. This review provides an intricate exploration of the mechanisms governing mammalian AXIN1 gene expression and protein turnover since its initial discovery, while also elucidating its significance in the regulation of signaling pathways, tissue development, and carcinogenesis. Furthermore, we have introduced the innovative concept of the AXIN1-Associated Phosphokinase Complex (AAPC), where the scaffold protein AXIN1 assumes a pivotal role in orchestrating site-specific phosphorylation modifications through interactions with various phosphokinases and their respective substrates.

Gastric emptying in newly diagnosed, treatment-naïve Han Chinese with type 2 diabetes and the impact of 4-week insulin pump therapy.

AIM: To evaluate gastric emptying (GE) and the glycaemic response to a 75-g oral glucose load in newly diagnosed, treatment-naïve Han Chinese with type 2 diabetes (T2D) before insulin pump therapy, after 4 weeks of insulin pump therapy, and 12-15 months after insulin pump therapy. MATERIALS AND METHODS: Twenty participants with T2D (baseline glycated haemoglobin [± SD] 10.7% [± 1.2%] 93 [± 10] mmol/mol) ingested a 75-g glucose drink containing 150 mg 13C-acetate, to determine the gastric half-emptying time, and underwent assessment of plasma glucose and serum insulin, C-peptide and glucagon-like peptide-1 (GLP-1) over 180 min before and after 4 weeks of insulin pump therapy (discontinued for 48 h before re-assessment). Data were compared to those in 19 healthy participants matched for sex and age. After 12-15 months, GE was re-measured in 14 of the T2D participants. RESULTS: At baseline, participants with T2D exhibited substantially augmented fasting and post-glucose glycaemia, diminished insulin secretion, and more rapid GE (p < 0.05 each), but comparable GLP-1, compared to healthy participants. Following insulin pump therapy, insulin secretion increased, GLP-1 secretion was attenuated, fasting and post-glucose glycaemia were lower, and GE was slowed (p < 0.05 each). The slowing of GE in T2D participants was sustained over 12-15 months of follow-up. CONCLUSIONS: In newly diagnosed Han Chinese with T2D, GE is often accelerated despite poor glycaemic control and is slowed by short-term insulin pump therapy. The effect on GE is maintained for at least 12 months.

Gastric emptying is slower in women than men with type 2 diabetes and impacts on postprandial glycaemia.

AIM: To evaluate sex differences in gastric emptying and the glycaemic response to a glucose drink and a high carbohydrate meal in type 2 diabetes (T2D). METHODS: In cohort 1, 70 newly diagnosed, treatment-naïve Chinese patients with T2D (44 men) recruited from a diabetes outpatient clinic ingested a 75-g glucose drink containing 150 mg 13C-acetate. In cohort 2, 101 Australian patients with T2D (67 male) recruited from the community, managed by diet and/or metformin monotherapy, ingested a semi-solid mashed potato meal, labelled with 100 µl 13C-octanoic acid. Breath samples were collected over 3 and 4 h, respectively, for assessment of gastric emptying, and venous blood was sampled for evaluation of glycaemia (with and without adjustment for each participant's estimated total blood volume). RESULTS: Gastric emptying was slower in female than male subjects in both cohorts (both p < .01). Multiple linear regression analyses revealed that gastric emptying was independently associated with sex (both p < .05). Without adjustment for blood volume, the glycaemic responses to oral glucose and the mixed meal were greater in female subjects (both p < .001). However, after adjustment for blood volume, the glycaemic responses were greater in men (both p < .05). CONCLUSIONS: Gastric emptying is slower in women than men with T2D, associated with a reduced blood volume-adjusted glycaemic response to oral glucose and a mixed meal in women. These observations highlight the sex difference in postprandial glucose handling, which is relevant to the personalized management of postprandial glycaemia in T2D.

Follicular fluid derived exosomal miR-4449 regulates cell proliferation and oxidative stress by targeting KEAP1 in human granulosa cell lines KGN and COV434.

Polycystic ovary syndrome (PCOS) is characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovary morphology, affecting more and more women of reproductive age. Our study aimed to explore the molecular mechanism and effect of exosomal miR-4449 on granulosa cells (GCs). Two immortalized human ovarian granulosa cells (KGN and COV434 cells) were used for in vitro functional studies. Our study found that follicular fluid (FF) derived exosomal miR-4449 was significantly decreased in women with PCOS compared with the control patients. And exosomal miR-4449 could alleviate GCs oxidative stress (OS) and promote GCs proliferation, while the opposite trend was observed after inhibiting the expression of miR-4449. In addition, we demonstrated that Kelch-like ECH-associated protein 1(KEAP1) was a direct target of miR-4449 through dual-luciferase reporter assay, and the expression patterns of KEAP1 and miR-4449 in PCOS FF-derived exosomes were exactly opposite. In addition, KEAP1/NRF2 signaling pathway may play an important role in GCs proliferation and OS. Our results demonstrated that the decreased FF-derived exosomal miR-4449 expression in PCOS might aggravate the OS of GCs and inhibit GCs proliferation via KEAP1/NRF2 signaling pathway. Exosomal miR-4449 might be a potential biomarker for the diagnosis of PCOS. Our study contributes to a new understanding of the pathogenesis of PCOS.

Renal toxicity of Aconitum plants? A study based on a new mass spectrometry scanning strategy and computer virtual screening.

BACKGROUND: Radix Aconiti Lateralis (Fuzi), a mono-herbal preparation of Aconitum herbs in the genus Aconitum, is commonly used in traditional Chinese medicine (TCM) to treat critical illnesses. The curative effect of Fuzi is remarkable. However, the toxic effects of Fuzi are still a key clinical focus, and the substances inducing nephrotoxicity are still unclear. Therefore, this study proposes a research model combining "in vitro and in vivo component mining-virtual multi-target screening-active component prediction-literature verification" to screen potential nephrotoxic substances rapidly. METHOD: The UHPLC-Q-Exactive-Orbitrap MS analysis method was used for the correlation analysis of Fuzi's in vitro-in vivo chemical substance groups. On this basis, the key targets of nephrotoxicity were screened by combining online disease databases and a protein-protein interaction (PPI) network. The computer screening technique was used to verify the binding mode and affinity of Fuzi's components with nephrotoxic targets. Finally, the potential material basis of Fuzi-induced nephrotoxicity was screened. RESULTS: Eighty-one Fuzi components were identified. Among them, 35 components were absorbed into the blood. Based on the network biology method, 21 important chemical components and three potential key targets were screened. Computer virtual screening revealed that mesaconine, benzoylaconine, aconitine, deoxyaconitine, hypaconitine, benzoylhypaconine, benzoylmesaconine, and hypaconitine may be potential nephrotoxic substances of Fuzi. CONCLUSIONS: Fuzi may interact with multiple components and targets in the process of inducing nephrotoxicity. In the future, experiments can be designed to explore further. This study provides a reference for screening Fuzi nephrotoxic components and has certain significance for the safe use of Fuzi.

Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography.

In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT.

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography.

This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

Integrated transcriptomics and metabolomics analyses to investigate the anticancer mechanisms of cinobufagin against liver cancer through interfering with lipid, amino acid, carbohydrate, and nucleotide metabolism.

Liver cancer has characteristics of high morbidity, high mortality, and poor prognosis. Metabolic reprogramming is a prominent characteristic of tumors and plays a key role in promoting tumorigenesis. The metabolic process of liver cancer cells has undergone many significant changes including abnormal active glycolysis, enhanced de novo synthesis of fatty acids, and hyperactive metabolism of amino acids and nucleotides. Targeting metabolic reprogramming through regulation of anomalously expressed key metabolic enzymes and signaling molecules is considered to be an important strategy for liver cancer treatment. Multi-omics association analyses currently facilitate precise diagnosis, personalized clinical therapy, and revelation of mechanisms of drug action. Cinobufagin, as the major anti-tumor active ingredient of Chansu, the famous chinese medicine used in clinic for cancer treatment, has been reported to exert anticancer effects through many different kinds of mechanisms, but the effects of cinobufagin on metabolic reprogramming of cancer cells still remain unclear. In our study, we identify that cinobufagin exhibits anti-hepatoma effects through interfering with metabolic reprogramming (lipid, amino acid, carbohydrate, and nucleotide metabolism) based on integrated transcriptomics and metabolomics analyses. Furthermore, the results of integrated multi-omics analyses enrich various core regulatory mechanisms of anti-tumor effects of cinobufagin which are associated with metabolic pathway. In addition, some verifications of the enriched mechanisms related to intervention of lipid and carbohydrate metabolism in response to cinobufagin are also performed. This work will promote the innovation of the research model of TCM, and lay a solid theoretical foundation for the clinical application of cinobufagin and Chansu.

Adaptive behaviors of Drosophila larvae on slippery surfaces.

Friction is ubiquitous but an essential force for insects during locomotion. Insects use dedicated bio-mechanical systems such as adhesive pads to modulate the intensity of friction, providing a stable grip with touching substrates for locomotion. However, how to uncover behavioral adaptation and regulatory neural circuits of friction modification is still largely understood. In this study, we devised a novel behavior paradigm to investigate adaptive behavioral alternation of Drosophila larvae under low-friction surfaces. We found a tail looseness phenotype similar to slipping behavior in humans, as a primary indicator to assess the degree of slipping. We found a gradual reduction on slipping level in wild-type larvae after successive larval crawling, coupled with incremental tail contraction, displacement, and speed acceleration. Meanwhile, we also found a strong correlation between tail looseness index and length of contraction, suggesting that lengthening tail contraction may contribute to enlarging the contact area with the tube. Moreover, we found a delayed adaptation in rut mutant larvae, inferring that neural plasticity may participate in slipping adaptation. In conclusion, our paradigm can be easily and reliably replicated, providing a feasible pathway to uncover the behavioral principle and neural mechanism of acclimation of Drosophila larvae to low-friction conditions.

Potential heterogeneity of urban ecological resilience and urbanization in multiple urban agglomerations from a landscape perspective.

Rapid urbanization has reduced the capacity of cities to mitigate and withstand disasters. Strengthening urban ecological resilience (ER) is important for improving urban self-organization. Geographical characteristics and developmental status of different cities lead to a more complex relationship between urbanization and ER. Using the three major urban agglomerations in China, we constructed a new framework for assessing the ER from a landscape and ecological processes perspective, and analyzed the driving heterogeneity of urbanization on ER. The results indicated that the ER of Yangtze River Delta (YRD) and Pearl River Delta (PRD) decreased continuously from 2000 to 2018, while the ER of Beijing-Tianjin-Hebei (BTH) decreased from 2000 to 2010, and then increased from 2010 to 2018. The resilience level of PRD was significantly lower than those of BTH and YRD. The urbanization process had a negative impact on ER, and the contribution of urbanization factors to ER varied significantly across cities, and population factors have the most direct influence. Curve fitting analysis further deepened our understanding of heterogeneity, investigating from the perspective of landscape and driving factors, and suggesting improvement measures. This study can deepen the understanding of the impact of urbanization on resilience and provide scientific guidance for achieving regional sustainability.

Role of Penicillin-Binding Protein 1b in the Biofilm Inhibitory Efficacy of Ceftazidime Against Escherichia coli.

Penicillin-binding proteins (PBPs) play an important role in bacterial biofilm formation and are the targets of ß-lactam antibiotics. This study aimed to investigate the effect of the ß-lactam antibiotic ceftazidime (CAZ) at subminimal inhibitory concentration (sub-MIC) on the biofilm formation of Escherichia coli by targeting PBPs. In this study, PBP1a (encoded by mrcA), PBP1b (encoded by mrcB) and PBP3 (encoded by ftsI), which have high affinity for CAZ, were deleted from the E. coli strain. The mrcB mutant showed lower adhesion, biofilm formation and swimming motility, whereas the knockout of mrcA or ftsI had no obvious influence on the biofilm-associated indicators mentioned above. After treatment with sub-MIC of CAZ, the adhesion, biofilm formation and swimming motility of the mrcB-mutant strain were not different or were slightly reduced compared with those of the untreated group. However, sub-MIC of CAZ still significantly inhibited these biofilm-associated indicators in mrcA- and ftsI-mutant strains. In addition, consistent with the bacterial motility results, the deletion of the mrcB gene reduced the flagellar numbers and the expression of flagellar structural genes, but flagellum-related indicators in the mrcB-mutant strain treated with CAZ were similar to those in the untreated group. Bioinformatic analysis showed that CAZ binds to Lys287, Lys274, Glu281, and Arg286 in PBP1b. Taken together, these results suggest that CAZ reduced flagellar synthesis and bacterial motility by binding with PBP1b and thereby inhibited the adhesion and biofilm formation of E. coli.

Metformin leads to accumulation of reactive oxygen species by inhibiting the NFE2L1 expression in human hepatocellular carcinoma cells.

Metformin, as the first-line drug for the treatment of type 2 diabetes mellitus, has been shown to possess a capability to activate or inhibit the production of reactive oxygen species (ROS) in different ways. However, the detailed mechanisms of the opposite effect are poorly understood. Here we provide evidence that metformin induces accumulation of ROS by inhibiting the expression of a core antioxidant transcription factor nuclear factor erythroid 2 like 1 (NFE2L1/Nrf1) in human hepatocellular carcinoma HepG2 cells. In the present study, we originally found that the increased ROS induced by metformin was blunted in NFE2L1 knockdown cell line. Furtherly by examining the effects of metformin on endogenous and exogenous NFE2L1, we also found metformin could not only inhibit the transcription of NFE2L1 gene, but also promote the degradation of NFE2L1 protein at the post-transcriptional level, whereas this effect can be reversed by high glucose. The inhibitory effect of metformin on NFE2L1 was investigated to occur through the N-terminal domain (NTD) of NFE2L1 protein, and its downregulation by metformin was in an AMP-activated protein kinase (AMPK)-independent manner. But the activation of AMPK signaling pathway by metformin in NFE2L1 knockdown HepG2 cells is reversed, indicating that NFE2L1 may be an important regulator of AMPK signal. Altogether, this work provides a better understanding of the relationship between metformin and oxidative stress, and hence contributes to translational study of metformin through its hypoglycemic and tumor suppressive effects.

A network pharmacology approach to investigate the anticancer mechanism of cinobufagin against hepatocellular carcinoma via downregulation of EGFR-CDK2 signaling.

Hepatocellular carcinoma (HCC) is one of the deadliest cancers with high mortality and poor prognosis, and the investigation on new approaches and effective drugs for HCC therapy is of great significance. In our study, we demonstrate that treatment with cinobufagin, a natural compound isolated from traditional chinese medicine Chansu, reduces proliferation and the colony formation capacity of the human hepatoma cells in vitro, in addition, cinobufagin induces mitotic arrest in human hepatoma cells. The results of a network pharmacology-based analysis show that EGFR, MAPK1, PTK2, CDK2, MAPK3, ESR1, CDK1, PRKCA, AR, and CSNK2A1 are the key targets involved in the anti-tumor activities of cinobufagin, additionally, several signaling pathways such as proteoglycans in cancer, pathways in cancer, HIF-1 signaling pathway, VEGF signaling pathway, ErbB signaling pathway, and PI3K-AKT signaling pathway are identified as the potential pathways involved in the inhibitory effects of cinobufagin against HCC. Furthermore, at the molecular level, we find that cinobufagin decreases EGFR expression and CDK2 activity in human hepatoma cells. Inhibition of EGFR or CDK2 expression could not only suppress the growth of tumor cells but also enhance the inhibitory effects of cinobufagin on the proliferative potential of human hepatoma cells. We also demonstrate that EGFR positively regulates CDK2 expression. Furthermore, EGFR inhibitor gefitinib or CDK2 inhibitor CVT-313 synergistically enhances anticancer effects of cinobufagin in human hepatoma cells. Taken together, these findings indicate that cinobufagin may exert antitumor effects by suppressing EGFR-CDK2 signaling, and our study suggests that cinobufagin may be a novel, promising anticancer agent for the treatment of HCC.

Computer-aided design of PVR mutants with enhanced binding affinity to TIGIT.

BACKGROUND: TIGIT, as a novel immune checkpoint molecule involved in T cell and NK cell anergy, could induce the immune tolerance and escape through binding with its ligand PVR. Blockade of TIGIT/PVR is considered as a promising strategy in cancer immunotherapy. However, to facilitate the design of inhibitors targeting TIGIT/PVR, the structural characteristics and binding mechanism still need to be further studied. METHODS: In this study, molecular dynamics (MD) simulations and in silico mutagenesis were used to analyze the interaction between TIGIT and its ligand PVR. Then, PVR mutants were designed and their activities were determined by using TIGIT overexpressed Jurkat cells. RESULTS: The results suggested that the loops of PVR (CC' loop, C'C″ loop, and FG loop) underwent a large intra-molecular rearrangement, and more hydrogen bond crosslinking between PVR and TIGIT were formed during MD simulations. The potential residues for PVR to interact with TIGIT were identified and utilized to predict high affinity PVR mutants. Through the biological activity evaluation, four PVR mutants (PVRS72W, PVRS72R, PVRG131V and PVRS132Q) with enhanced affinity to TIGIT were discovered, which could elicit more potent inhibitory effects compared with the wild type PVR. CONCLUSIONS: The MD simulations analysis provided new insights into the TIGIT/PVR interaction model, and the identified PVR mutants (PVRS72W, PVRS72R, PVRG131V and PVRS132Q) could serve as new candidates for immunotherapy to block TIGIT/PVR. Video Abstract.

The high expression of miR-564 in patients with systemic lupus erythematosus promotes differentiation and maturation of DC cells by negatively regulating TP53 expression in vitro.

BACKGROUND: miRNA is involved in the occurrence and progression of systemic lupus erythematosus (SLE), but the regulatory effect of miRNA on dendritic cells in SLE patients is still unclear. MATERIAL AND METHODS: Bioinformatics methods were used to analyze the differentially expressed miRNA and its target genes in SLE patients. In vitro experiments were conducted to explore the effects and mechanisms of differentially expressed miRNAs in SLE patients on the differentiation and maturation of monocyte-derived dendritic cells. RESULTS: Bioinformatics analysis showed that miR-564 was up-regulated in SLE patients, and TP53 was the core target gene of miR-564. The expression level of miR-564 showed a rising trend during the differentiation and maturation of monocytes into Mo-DC cells. The differentiation, maturation and proliferation of Mo-DC cells were significantly inhibited by transfection with miR-564 antagomir. The expression of TP53 is negatively regulated by miR-564. In rescue experiments, the proliferation and migration of DC cells were significantly restored by co-transfection of miR-564 antagomir and TP53 si-RNA. CONCLUSION: Highly expressed miR-564 promotes the maturation, proliferation of Mo-DC cells by negatively regulating the expression of TP53.

Counting Crowds with Perspective Distortion Correction via Adaptive Learning.

The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF), and the results show that our model has a good performance.