CARD11 regulates the thymic Treg development in an NF-κB-independent manner.

Introduction: CARD11 is a lymphoid lineage-specific scaffold protein regulating the NF-κB activation downstream of the antigen receptor signal pathway. Defective CARD11 function results in abnormal development and differentiation of lymphocytes, especially thymic regulatory T cells (Treg). Method: In this study, we used patients' samples together with transgenic mouse models carrying pathogenic CARD11 mutations from patients to explore their effects on Treg development. Immunoblotting and a GFP receptor assay were used to evaluate the activation effect of CARD11 mutants on NF-κB signaling. Then the suppressive function of Tregs carrying distinct CARD11 mutations was measured by in vitro suppression assay. Finally, we applied the retroviral transduced bone marrow chimeras to rescue the Treg development in an NF-κB independent manner. Results and discuss: We found CARD11 mutations causing hyper-activated NF-κB signals also gave rise to compromised Treg development in the thymus, similar to the phenotype in Card11 deficient mice. This observation challenges the previous view that CARD11 regulates Treg lineage dependent on the NF-kB activation. Mechanistic investigations reveal that the noncanonical function CARD11, which negatively regulates the AKT/ FOXO1 signal pathway, is responsible for regulating Treg generation. Moreover, primary immunodeficiency patients carrying CARD11 mutation, which autonomously activates NF-κB, also represented the reduced Treg population in their peripheral blood. Our results propose a new regulatory function of CARD11 and illuminate an NF-κB independent pathway for thymic Treg lineage commitment.

3D Printing Self-Healing and Self-Adhesive Elastomers for Wearable Electronics in Amphibious Environments.

To meet the demands of challenging usage scenarios, there is an increasing need for flexible electronic skins that can operate properly not only in terrestrial environments but also extend to complex aquatic conditions. In this study, we develop an elastomer by incorporating dynamic urea bonds and hydrogen bonds into the polydimethylsiloxane backbone, which exhibits excellent autonomous self-healing and reversible adhesive performance in both dry and wet environments. A multifunctional flexible sensor with excellent sensing stability, amphibious self-healing capacity, and amphibious self-adhesive performance is fabricated through solvent-free 3D printing. The sensor has a high sensing sensitivity (GF = 45.1) and a low strain response threshold (0.25%) and can be used to detect small human movements and physiological activities, such as muscle movement, joint movement, respiration, and heartbeat. The wireless wearable sensing system assembled by coupling this device with a bluetooth transmission system is suitable for monitoring strenuous human movement in amphibious environments, such as playing basketball, cycling, running (terrestrial environments), and swimming (aquatic environments). The design strategy provides insights into enhancing the self-healing and self-adhesive properties of soft materials and promises a prospective avenue for fabricating flexible electronic skin that can work properly in amphibious environments.

Who can benefit from postmastectomy radiotherapy among HR+/HER2- T1-2 N1M0 breast cancer patients? An explainable machine learning mortality prediction based approach.

Objective: The necessity of postmastectomy radiotherapy(PMRT) for patients with HR+/HER2 T1-2 N1M0 breast cancer remains controversial. We want to use explainable machine learning to learn the feature importance of the patients and identify the subgroup of the patients who may benefit from the PMRT. Additionally, develop tools to provide guidance to the doctors. Methods: In this study, we trained and validated 2 machine learning survival models: deep learning neural network and Cox proportional hazard model. The training dataset consisted of 35,347 patients with HR+/HER2- T1-2 N1M0 breast cancer who received mastectomies from the SEER database from 2013 to 2018. The performance of survival models were assessed using a concordance index (c-index).Then we did subgroup analysis to identify the subgroup who could benefit from PMRT. We also analyzed the global feature importance for the model and individual feature importance for individual survival prediction. Finally, we developed a Cloud-based recommendation system for PMRT to visualize the survival curve of each treatment plan and deployed it on the Internet. Results: A total of 35,347 patients were included in this study. We identified that radiotherapy improved the OS in patients with tumor size >14mm and age older than 54: 5-year OS rates of 91.9 versus 87.2% (radio vs. nonradio, P <0.001) and cohort with tumor size >14mm and grade worse than well-differentiated, 5-year OS rates of 90.8 versus 82.3% (radio vs. nonradio, P <0.001).The deep learning network performed more stably and accurately in predicting patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.776 vs 0.641) and in the external validation(C-index=0.769 vs 0.650).Besides, the deep learning model identified several key factors that significantly influence patient survival, including tumor size, examined regional nodes, age at 45-49 years old and positive regional nodes (PRN). Conclusion: Patients with tumor size >14mm and age older than 54 and cohort with tumor size >14mm and grade worse than well-differentiated could benefit from the PMRT. The deep learning network performed more stably and accurately in predicting patients survival than Cox proportional hazard model on the internal test. Besides, tumor size, examined regional nodes, age at 45-49 years old and PRN are the most significant factors to the overall survival (OS).

Identification of Multiple Novel Viruses in Fecal Samples of Black-Necked Cranes Using Viral Metagenomic Methods.

The black-necked crane is the only species of crane that lives in the high-altitude region of the Tibet Plateau. At present, there is little research on viral diseases of the black-necked crane (Grus nigricollis). In this study, a viral metagenomic approach was employed to investigate the fecal virome of black-necked cranes in Saga County, Shigatse City, Tibet, China. The identified virus families carried by black-necked cranes mainly include Genomoviridae, Parvoviridae, and Picornaviridae. The percentages of sequence reads belonging to these three virus families were 1.6%, 3.1%, and 93.7%, respectively. Among them, one genome was characterized as a novel species in the genus Grusopivirus of the family Picornaviridae, four new parvovirus genomes were obtained and classified into four different novel species within the genus Chaphamaparvovirus of the subfamily Hamaparvovirinae, and four novel genomovirus genomes were also acquired and identified as members of three different species, including Gemykroznavirus haeme1, Gemycircularvirus ptero6, and Gemycircularvirus ptero10. All of these viruses were firstly detected in fecal samples of black-necked cranes. This study provides valuable information for understanding the viral community composition in the digestive tract of black-necked cranes in Tibet, which can be used for monitoring, preventing, and treating potential viral diseases in black-necked cranes.

The clock gene Cryptochrome 1 is involved in the photoresponse of embryonic hatching behavior in Bombyx mori.

The hatching of insect eggs is a classic circadian behavior rhythm controlled by the biological clock. Its function is considered to impose a daily rhythm on the embryo, allowing it to hatch within a permissible time window. However, the molecular pathways through which the clock affects embryonic hatching behavior remain unclear. Here, we utilized a clock gene Cryptochrome1 (Cry1) knockout mutant to dissect the pathways by which the circadian clock affects embryonic hatching rhythm in the silkworm. In the Cry1 mutant, the embryo hatching rhythm was disrupted. Under the constant light or constant dark incubation conditions, mutant embryos lost their hatching rhythm, while wild-type embryos hatch exhibiting free-running rhythm. In the light-dark cycle (LD), the hatching rhythm of CRY1-deficient silkworms could not be entrained by the LD photoperiod during the incubation period. The messenger RNA levels and enzymatic activities of Cht and Hel in the mutant embryos were significantly reduced at circadian time 24 (CT24). Transcriptome analysis revealed significant differences in gene expression at CT24 between the Cry1 knockout mutant and the wild-type, with 2616 differentially expressed genes identified. The enriched Gene Ontology pathway includes enzyme activity, energy availability, and protein translation. Short neuropeptide F signaling was reduced in the CT24 embryonic brain of the mutant, the expression of the neuropeptide PTTH was also reduced and the rhythm was lost, which further affects ecdysteroid signaling. Our results suggested that the silkworm circadian clock affects neuropeptide-hormone signaling as well as physiological functions related to hatching, which may regulate the hatching rhythm.

Acetalized starch-based nanoparticles stabilized acid-sensitive Pickering emulsion as a potential antitumor drug carrier.

Pickering emulsions are attracting increased attention owing to their therapeutic applications. However, the slow-release property of Pickering emulsions and the in vivo solid particle accumulation caused by the solid particle stabilizer film limit their applications in therapeutic delivery. In this study, drug-loaded, acid-sensitive Pickering emulsions were prepared using acetal-modified starch-based nanoparticles as stabilizers. The acetalized starch-based nanoparticles (Ace-SNPs) not only act as a solid-particle emulsifier to stabilize Pickering emulsions but also exhibit acid sensitivity and degradability, conducive to the destabilization of Pickering emulsions to release the drug and reduce the effect of particle accumulation in an acidic therapeutic environment. In vitro drug release profiles show that 50 % of curcumin was released in 12 h in an acidic medium (pH 5.4), whereas only 14 % of curcumin was released in 12 h at higher pH (7.4), indicating that the Ace-SNP stabilized Pickering emulsion possess good acid-responsive release characteristics in acidic environments. Moreover, acetalized starch-based nanoparticles and their degradation products showed good biocompatibility, and the resulting curcumin-loaded Pickering emulsions exhibited significant anticancer activity. These features suggest that the acetalized starch-based nanoparticle-stabilized Pickering emulsion has the potential for application as an antitumor drug carrier to enhance therapeutic effects.

Molecular detection and characterization of three novel parvoviruses belonging to two different subfamilies in zoo birds.

Birds carry a large number of viruses that may cause diseases in animals or humans. At present, information about the virome of zoo birds is limited. In this study, using viral metagenomics, we investigated the fecal virome of zoo birds collected from a zoo in Nanjing, Jiangsu Province, China. Three novel parvoviruses were obtained and characterized. The genomes of the three viruses are 5,909, 4,411, and 4,233 nt in length, respectively, and contain four or five ORFs. Phylogenetic analysis showed that these three novel parvoviruses clustered with other strains and formed three different clades. Pairwise comparison of NS1 amino acid sequences showed that Bir-01-1 shared 44.30-74.92% aa sequence identity with other parvoviruses belonging to the genus Aveparvovirus, while Bir-03-1 and Bir-04-1 shared less than 66.87% and 53.09% aa sequence identity, respectively, with other parvoviruses belonging to the genus Chaphamaparvovirus. Each of these three viruses was identified as a member of a novel species based on the species demarcation criteria for parvoviruses. These findings broaden our knowledge of the genetic diversity of parvoviruses and provide epidemiological data regarding potential outbreaks of parvovirus disease in birds.

Molecular detection and characterization of three novel parvoviruses belonging to two different subfamilies in zoo birds.

Birds carry a large number of viruses that may cause diseases in animals or human. At present, virome of zoo birds are limited. In this study, using viral metagenomics method, we investigated the feces virome of zoo birds collected from a zoo of Nanjing, Jiangsu Province, China. Three novel parvoviruses were obtained and characterized. The genome of the three viruses are 5,909 bp, 4,411 bp and 4,233 bp in length respectively which encoded four or five ORFs. Phylogenetic analysis indicated that these three novel parvoviruses clustered with other strains formed three different clades. Pairwise comparison of NS1 amino acid sequences showed that Bir-01-1 shared 44.30%-74.92% aa sequence identity with other parvoviruses belonging to the genus Aveparvovirus, while Bir-03-1 and Bir-04-1 had lower than 66.87% and 53.09% aa sequence identity with other parvoviruses belonging to the genus Chaphamaparvovirus. These three viruses were identified as three novel species of the genus Aveparvovirus and Chaphamaparvovirus respectively basing on the species demarcation criteria of parvovirus. Our findings broaden the knowledge of the genetic diversity of parvovirus and provide epidemiological data for the outbreak of potential bird’s parvovirus disease.

Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC.

Objective: To compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model. Methods: In this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People's Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet. Results: A total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser. Conclusion: Deep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning.

The quasi-periodic signals in the earth system could be the predictability source for sub-seasonal to seasonal (S2S) climate prediction because of the connections among the lead-lag time of those signals. The Madden-Julian Oscillation (MJO) is a typical quasi-periodic signal, which is the dominant S2S variability in the tropics. Besides, significantly periodic features in terms of both intensity and location are identified in 10-40 days for the concurrent variation of the subtropical and polar jet streams over Asia in this study. So far, those signals contribute less and are not fully applied to the S2S prediction. The deep learning (DL) approach, especially the long-short term memory (LSTM) networks, has the ability to take advantage of the information at the previous time to improve the prediction after then. This study presents the application of the DL in the postprocessing of S2S prediction using quasi-periodic signals predicted by the operational model to improve the prediction of minimum 2-m air temperature over Asia. With the help of deep learning, it finds the best weights for the ensemble predictions, and the quasi-periodic signals in the atmosphere can further benefit the S2S operational prediction.

Does Diabetes Influence Adherence in Glaucoma Patients? / Beeinflusst Diabetes mellitus die Adhärenz von Glaukompatienten?

With chronic diseases, patient adherence plays a crucial role in delaying disease progression and in determining the success of therapy. Problems arise not only from low medication adherence, but also non-adherence to recommended follow-up examinations. Obtaining an accurate estimate of adherence is difficult, especially in glaucoma patients, due to the fact that most antihypertensive drugs are administered in the form of eye drops. There is great variability in the published adherence values for glaucoma patients. Most studies report an average medication adherence of approximately 70%, with around 50% of patients having good adherence (at least 80% of medication administered as planned). Furthermore, 6.8 - 31.4% of the eye drops do not end up in the patient's eye, which means there is even less active ingredient to achieve a therapeutic effect. Glaucoma patients also show low persistence and adherence to follow-up appointments. Since diabetes increases the risk for POAG and secondary glaucoma and given that diabetics have particularly low adherence, the question arose whether a diabetes diagnosis is associated with reduced adherence in glaucoma patients. Previous studies found no significant association between diabetes and reduced adherence in glaucoma patients, although a significant impact of elevated HbA1c on adherence in glaucoma patients was found in one study. However, this connection still needs to be examined more closely in studies with larger samples.

Temperature sensitivity of soil enzyme kinetics under N and P fertilization in an alpine grassland, China.

Soil nutrient cycling can be best studied by supplementing the soil with N and P fertilizers. Soil enzyme kinetic parameters (Vmax and Km) can be used to reflect the maximum reaction rates and affinities of soil enzymes. However, how N and P fertilizers affect the temperature sensitivity of soil enzyme kinetics is poorly understood. Therefore, our study investigated the response of soil enzyme kinetic temperature sensitivity relevant to C, N, and P cycles based on a 9-year fertilization (N and P) experiment performed in an alpine grassland on the Qinghai-Tibetan Plateau in China. Our results showed the following: N and P addition positively affected the Km of ß-glucosidase (BG); P and NP interaction significantly increased the Km of phosphatase (AP), indicating that N and P addition significantly negatively affected the substrate affinity of soil enzymes. The temperature sensitivity of Michaelis-Menten kinetics was different for different enzymes. N and P fertilization decreased the temperature sensitivity of Km of BG but increased the temperature sensitivity of the Km of N-acetyl-glucosaminidase (NAG) and AP. In our study, P and NP fertilization increased the temperature sensitivity and activation energy of the Vmax of BG, indicating that P elements promoted the secretion of more extracellular enzymes by soil microbes to cope with temperature changes. The enzymes involved in the soil N and P cycle responded to the exogenous N and P through increases and decreases in the temperature sensitivity of the Km and Vmax, respectively. This study is crucial for investigating the impact of nutrient input on soil ecosystem functions under future climate warming conditions.

ZMYND8 mediated liquid condensates spatiotemporally decommission the latent super-enhancers during macrophage polarization.

Super-enhancers (SEs) govern macrophage polarization and function. However, the mechanism underlying the signal-dependent latent SEs remodeling in macrophages remains largely undefined. Here we show that the epigenetic reader ZMYND8 forms liquid compartments with NF-κB/p65 to silence latent SEs and restrict macrophage-mediated inflammation. Mechanistically, the fusion of ZMYND8 and p65 liquid condensates is reinforced by signal-induced acetylation of p65. Then acetylated p65 guides the ZMYND8 redistribution onto latent SEs de novo generated in polarized macrophages, and consequently, recruit LSD1 to decommission latent SEs. The liquidity characteristic of ZMYND8 is critical for its regulatory effect since mutations coagulating ZMYND8 into solid compartments disable the translocation of ZMYND8 and its suppressive function. Thereby, ZMYND8 serves as a molecular rheostat to switch off latent SEs and control the magnitude of the immune response. Meanwhile, we propose a phase separation model by which the latent SEs are fine-tuned in a spatiotemporal manner.

TRIM24 facilitates antiviral immunity through mediating K63-linked TRAF3 ubiquitination.

Ubiquitination is an essential mechanism in the control of antiviral immunity upon virus infection. Here, we identify a series of ubiquitination-modulating enzymes that are modulated by vesicular stomatitis virus (VSV). Notably, TRIM24 is down-regulated through direct transcriptional suppression induced by VSV-activated IRF3. Reducing or ablating TRIM24 compromises type I IFN (IFN-I) induction upon RNA virus infection and thus renders mice more sensitive to VSV infection. Mechanistically, VSV infection induces abundant TRIM24 translocation to mitochondria, where TRIM24 binds with TRAF3 and directly mediates K63-linked TRAF3 ubiquitination at K429/K436. This modification of TRAF3 enables its association with MAVS and TBK1, which consequently activates downstream antiviral signaling. Together, these findings establish TRIM24 as a critical positive regulator in controlling the activation of antiviral signaling and describe a previously unknown mechanism of TRIM24 function.