METTL8 links mt-tRNA m3C modification to the HIF1α/RTK/Akt axis to sustain GBM stemness and tumorigenicity.

Epitranscriptomic RNA modifications are crucial for the maintenance of glioma stem cells (GSCs), the most malignant cells in glioblastoma (GBM). 3-methylcytosine (m3C) is a new epitranscriptomic mark on RNAs and METTL8 represents an m3C writer that is dysregulated in cancer. Although METTL8 has an established function in mitochondrial tRNA (mt-tRNA) m3C modification, alternative splicing of METTL8 can also generate isoforms that localize to the nucleolus where they may regulate R-loop formation. The molecular basis for METTL8 dysregulation in GBM, and which METTL8 isoform(s) may influence GBM cell fate and malignancy remain elusive. Here, we investigated the role of METTL8 in regulating GBM stemness and tumorigenicity. In GSC, METTL8 is exclusively localized to the mitochondrial matrix where it installs m3C on mt-tRNAThr/Ser(UCN) for mitochondrial translation and respiration. High expression of METTL8 in GBM is attributed to histone variant H2AZ-mediated chromatin accessibility of HIF1α and portends inferior glioma patient outcome. METTL8 depletion impairs the ability of GSC to self-renew and differentiate, thus retarding tumor growth in an intracranial GBM xenograft model. Interestingly, METTL8 depletion decreases protein levels of HIF1α, which serves as a transcription factor for several receptor tyrosine kinase (RTK) genes, in GSC. Accordingly, METTL8 loss inactivates the RTK/Akt axis leading to heightened sensitivity to Akt inhibitor treatment. These mechanistic findings, along with the intimate link between METTL8 levels and the HIF1α/RTK/Akt axis in glioma patients, guided us to propose a HIF1α/Akt inhibitor combination which potently compromises GSC proliferation/self-renewal in vitro. Thus, METTL8 represents a new GBM dependency that is therapeutically targetable.

Hepatocyte-intrinsic SMN deficiency drives metabolic dysfunction and liver steatosis in spinal muscular atrophy.

Spinal Muscular Atrophy (SMA) is typically characterized as a motor neuron disease, but extra-neuronal phenotypes are present in almost every organ in severely affected patients and animal models. Extra-neuronal phenotypes were previously underappreciated as patients with severe SMA phenotypes usually died in infancy; however, with current treatments for motor neurons increasing patient lifespan, impaired function of peripheral organs may develop into significant future comorbidities and lead to new treatment-modified phenotypes. Fatty liver is seen in SMA animal models , but generalizability to patients and whether this is due to hepatocyte-intrinsic Survival Motor Neuron (SMN) protein deficiency and/or subsequent to skeletal muscle denervation is unknown. If liver pathology in SMA is SMN-dependent and hepatocyte-intrinsic, this suggests SMN repleting therapies must target extra-neuronal tissues and motor neurons for optimal patient outcome. Here we showed that fatty liver is present in SMA and that SMA patient-specific iHeps were susceptible to steatosis. Using proteomics, functional studies and CRISPR/Cas9 gene editing, we confirmed that fatty liver in SMA is a primary SMN-dependent hepatocyte-intrinsic liver defect associated with mitochondrial and other hepatic metabolism implications. These pathologies require monitoring and indicate need for systematic clinical surveillance and additional and/or combinatorial therapies to ensure continued SMA patient health.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

BACKGROUND: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. PURPOSE: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. STUDY TYPE: Retrospective. POPULATION: 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. FIELD STRENGTH/SEQUENCE: Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. ASSESSMENT: Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared. STATISTICAL TESTS: DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. RESULTS: The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%). DATA CONCLUSION: The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. TECHNICAL EFFICACY: Stage 2.

[Discussion on internal fixation of Hoffa fractures].

Hoffa fracture is an unstable intra-articular fracture with significant redisplacement tendency. It is easy to be missed diagnosis when accompanied by distal intercondylar or supracondylar fracture of femur. CT scan is the gold standard for the diagnosis of Hoffa fracture. The treatment principles are anatomic reduction of the articular surface, reliable internal fixation, and early functional activity. At present, the main treatment is arthroscopic screw fixation. During screw fixation, the tail cap of screw should be buried, resulting in non-healing iatrogenic injury of articular cartilage. In the early postoperative functional activity of knee joint, fracture block was repeatedly subjected to backward and upward shear force under the action of the tibial plateau, which is the main reason for the failure of internal fixation. Plate assisted screw fixation could increase local mechanical stability, but it still cannot avoid the defects of iatrogenic cartilage injury. At the same time, plate molding is required during the operation due to the absence of special anatomical plates, resulting in increased surgical trauma and time-consuming surgery. The ideal fixation method for Hoffa fracture should include:(1) Avoid iatrogenic injury of articular surface cartilage. (2) With the rear anti-shear barrier plate function.(3) The internal fixator is closer to the load interface, so as to obtain greater load and better fixed strength.

Altered intestinal barrier contributes to cognitive impairment in old mice with constipation after sevoflurane anesthesia.

Background: Elderly patients have a high risk of developing postoperative cognitive dysfunction (POCD). Gastrointestinal disorders, such as constipation, in the elderly population may be involved in the pathogenesis of neurological disorders by promoting inflammatory responses due to a 'leaky gut'. General anesthetic sevoflurane may impair gastrointestinal function in elderly patients to trigger neurological complications following surgery. Therefore, we hypothesized that elderly individuals with gastrointestinal dysfunction may be more vulnerable to sevoflurane and consequently develop POCD. Methods: Aged mice were randomly divided into four groups: control (CTRL), CTRL+sevoflurane (Sev), slow transit constipation (STC), and STC + Sev. Mice in the STC and STC + Sev groups were intra-gastrically administrated loperamide (3 mg/kg, twice a day for 7 days) to induce a slow transit constipation (STC) model determined with fecal water content and the time of first white fecal pellet, whereas mice in the other groups received the similar volume of saline. One week later, mice in the CTRL+Sev group and STC + Sev group received 2% sevoflurane for 2 h. The gut permeability evaluated with 4-kDa fluorescein isothiocyanate (FITC)-dextran, serum cytokines, microglia density, TLR4/NF-κB signaling expression, and POCD-like behavioral changes were determined accordingly. Results: The loperamide-induced STC mice had decreased fecal water content and prolonged time of first white fecal pellet. Sevoflurane exposure caused significantly increased gut permeability and serum cytokines, as well as the activation of microglia and the TLR4/NF-κB signaling pathway in the prefrontal cortex of the aged STC mice. Sevoflurane also caused cognitive impairment and emotional phenotype abnormality in aged STC mice. Conclusion: Aged STC mice were more vulnerable to sevoflurane anesthesia and consequently developed POCD-like behavioral changes. Our data suggest that gastrointestinal disorders including constipation may contribute to the development of POCD.

Prophylactic Epidural Blood Patch or Prophylactic Epidural Infusion of Hydroxyethyl Starch in Preventing Post-Dural Puncture Headache - A Retrospective Study.

BACKGROUND: Post-dural puncture headache (PDPH) is particularly likely to happen in patients under obstetric care due to an unintentional dural puncture (UDP). There is as yet no ideal strategy for preventing UDP-induced PDPH. OBJECTIVES: The primary objective of this study was to assess whether a prophylactic epidural blood patch (EBP) or prophylactic epidural infusion of hydroxyethyl starch (HES) is effective in preventing PDPH for parturients with UDP compared with conservative treatments. STUDY DESIGN: Retrospective analysis from a single center's inpatient data. SETTING: Department of Anesthesiology at a single center. METHODS: A retrospective study was conducted of a single center's inpatient data from January 2017 through March 2020. The study included parturients with UDP during neuraxial anesthesia. The interventions of UDP included conservative treatment, prophylactic EBP, and prophylactic epidural infusion of HES. The incidence of PDPH, the use of intravenous aminophylline, therapeutic EBP, symptom onset, duration of headache, and duration of hospital stay were compared. RESULTS: A total of 85 patients were analyzed. The incidences of PDPH were 84%, 52.6% and 54.5% with conservative, prophylactic EBP, and prophylactic epidural HES treatments, respectively. Compared with the conservative treatment, prophylactic EBP and prophylactic epidural HES treatment significantly reduced the incidence of PDPH (P < 0.05). No significant difference was found between the prophylactic EBP and prophylactic epidural HES groups. Compared with the conservative treatment group, therapeutic EBP was significantly less used in the prophylactic EBP and prophylactic epidural HES groups (P < 0.05). Prophylactic EBP shortened the length of hospital stay of parturients with UDP (P < 0.05) while prophylactic epidural HES showed no statistical difference compared with conservative treatment. No severe complications, such as central nervous system and puncture site infection or nerve injury, were found in those patients. LIMITATIONS: Retrospective nature and single center data with a relatively small sample size. CONCLUSIONS: Prophylactic management with EBP and epidural infusion of HES has an effect in preventing the occurrence of PDPH; prophylactic EBP significantly shortened hospital stay length in parturients with UDP. KEY WORDS: Unintentional dural puncture, epidural blood patch, hydroxyethyl starch, post-dural puncture headache, parturient.

Morphology, sucrose metabolism and gene network reveal the molecular mechanism of seed fiber development in poplar.

Poplar is an important tree species for ecological protection, wood production, bioenergy and urban greening; it has been widely planted worldwide. However, the catkin fibers produced by female poplars can cause environmental pollution and safety hazards during spring. This study focused on Populus tomentosa, and revealed the sucrose metabolism regulatory mechanism of catkin fibers development from morphological, physiological and molecular aspects. Paraffin section suggested that poplar catkin fibers were not seed hairs and produced from the epidermal cells of funicle and placenta. Sucrose degradation via invertase and sucrose synthase played the dominant role during poplar catkin fibers development. The expression patterns revealed that sucrose metabolism-related genes played important roles during catkin fibers development. Y1H analysis indicated that there was a potential interaction between sucrose synthase 2 (PtoSUS2)/vacuolar invertase 3 (PtoVIN3) and trichome-regulating MYB transcription factors in poplar. Finally, the two key genes, PtoSUS2 and PtoVIN3, had roles in Arabidopsis trichome density, indicating that sucrose metabolism is important in poplar catkin fibers development. This study is not only helpful for clarifying the mechanism of sucrose regulation during trichome development in perennial woody plants, but also establishes a foundation to solve poplar catkin fibers pollution through genetic engineering methods.

CAMK2D serves as a molecular scaffold for RNF8-MAD2 complex to induce mitotic checkpoint in glioma.

MAD2 is a spindle assembly checkpoint protein that participates in the formation of mitotic checkpoint complex, which blocks mitotic progression. RNF8, an established DNA damage response protein, has been implicated in mitotic checkpoint regulation but its exact role remains poorly understood. Here, RNF8 proximity proteomics uncovered a role of RNF8-MAD2 in generating the mitotic checkpoint signal. Specifically, RNF8 competes with a small pool of p31comet for binding to the closed conformer of MAD2 via its RING domain, while CAMK2D serves as a molecular scaffold to concentrate the RNF8-MAD2 complex via transient/weak interactions between its p-Thr287 and RNF8's FHA domain. Accordingly, RNF8 overexpression impairs glioma stem cell (GSC) mitotic progression in a FHA- and RING-dependent manner. Importantly, low RNF8 expression correlates with inferior glioma outcome and RNF8 overexpression impedes GSC tumorigenicity. Last, we identify PLK1 inhibitor that mimics RNF8 overexpression using a chemical biology approach, and demonstrate a PLK1/HSP90 inhibitor combination that synergistically reduces GSC proliferation and stemness. Thus, our study has unveiled a previously unrecognized CAMK2D-RNF8-MAD2 complex in regulating mitotic checkpoint with relevance to gliomas, which is therapeutically targetable.

Comparison of the postoperative in-hospital versus at-home recovery quality and circadian rhythm status in preschool kids after adenotonsillectomy: a cohort and observational study.

Background: At present, minimally invasive surgery is often used in paediatric patients as a day surgery to promote rapid post-operative recovery. Obstructive Sleep Apnea Syndrome (OSAS) Patients recovery in the hospital or at home after surgery may differ in terms of recovery quality and circadian rhythm status because of sleep disruption; however, this remains unknown. Pediatric patients usually unable to explain their feelings effectively, and objective indicators to measure recovery situation in different environments are promising. This study was conducted to compare the impact of in-hospital and at-home postoperative recovery quality (primary outcome) and circadian rhythm (as measured via the salivary melatonin level) (secondary outcome) in preschool-age patients. Methods: This was a cohort, non-randomized and exploratory observational study. A total of 61 children aged 4 to 6 years who were scheduled to receive adenotonsillectomy were recruited and assigned to recover either in the hospital (Hospital group) or at home (Home group) after surgery. There were no differences in the patient characteristics and perioperative variables between the Hospital and Home groups at baseline. They received the treatment and anesthesia in the same way. The patients' preoperative and up to 28 days post-surgery OSA-18 questionnaires were harvested. Moreover, their pre- and post-surgery salivary melatonin concentrations, body temperature, three-night postoperative sleep diaries, pain scales, emergence agitation, and other adverse effects were recorded. Results: There were no significant differences in the postoperative recovery quality, as assessed by the OSA-18 questionnaire, body temperature, sleep quality, pain scales, and other adverse events (such as respiratory depression, sinus bradycardia, sinus tachycardia, hypertension, hypotension, nausea, and vomiting) between the two groups. The preoperative morning saliva melatonin secretion was decreased in both groups on the first postoperative morning (P<0.05), while a significantly greater decrease was found in the Home group on postoperative day 1 (P<0.05) and day 2 (P<0.05). Conclusions: The postoperative recovery quality of preschool kids in the hospital is as good as at home based on OSA-18 evaluation scale. However, the clinical importance of the significant decrease in morning saliva melatonin levels with at-home postoperative recovery remains unknown and warrants further study.

PTA-Det: Point Transformer Associating Point Cloud and Image for 3D Object Detection.

In autonomous driving, 3D object detection based on multi-modal data has become an indispensable perceptual approach when facing complex environments around the vehicle. During multi-modal detection, LiDAR and a camera are simultaneously applied for capturing and modeling. However, due to the intrinsic discrepancies between the LiDAR point and camera image, the fusion of the data for object detection encounters a series of problems, which results in most multi-modal detection methods performing worse than LiDAR-only methods. In this investigation, we propose a method named PTA-Det to improve the performance of multi-modal detection. Accompanied by PTA-Det, a Pseudo Point Cloud Generation Network is proposed, which can represent the textural and semantic features of keypoints in the image by pseudo points. Thereafter, through a transformer-based Point Fusion Transition (PFT) module, the features of LiDAR points and pseudo points from an image can be deeply fused under a unified point-based form. The combination of these modules can overcome the main obstacle of cross-modal feature fusion and achieves a complementary and discriminative representation for proposal generation. Extensive experiments on KITTI dataset support the effectiveness of PTA-Det, achieving a mAP (mean average precision) of 77.88% on the car category with relatively few LiDAR input points.

Flow Cytometry with Anti-Diffraction Light Sheet (ADLS) by Spatial Light Modulation.

Flow cytometry is a widespread and powerful technique whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. Herein, we propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel-Gaussian beam array, the light sheet is 12 µm wide, 12 µm high, and has a thickness of ~0.8 µm. The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O (100 nm) to 10 µm (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz, which provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds.

Domain-specific p53 mutants activate EGFR by distinct mechanisms exposing tissue-independent therapeutic vulnerabilities.

Mis-sense mutations affecting TP53 promote carcinogenesis both by inactivating tumor suppression, and by conferring pro-carcinogenic activities. We report here that p53 DNA-binding domain (DBD) and transactivation domain (TAD) mis-sense mutants unexpectedly activate pro-carcinogenic epidermal growth factor receptor (EGFR) signaling via distinct, previously unrecognized molecular mechanisms. DBD- and TAD-specific TP53 mutants exhibited different cellular localization and induced distinct gene expression profiles. In multiple tissues, EGFR is stabilized by TAD and DBD mutants in the cytosolic and nuclear compartments respectively. TAD mutants promote EGFR-mediated signaling by enhancing EGFR interaction with AKT via DDX31 in the cytosol. Conversely, DBD mutants maintain EGFR activity in the nucleus, by blocking EGFR interaction with the phosphatase SHP1, triggering c-Myc and Cyclin D1 upregulation. Our findings suggest that p53 mutants carrying gain-of-function, mis-sense mutations affecting two different domains form new protein complexes that promote carcinogenesis by enhancing EGFR signaling via distinctive mechanisms, exposing clinically relevant therapeutic vulnerabilities.

Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative.

Background: The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis. Methods: Data used in this work were obtained from the osteoarthritis initiative (OAI). In this study, iROA was defined as a knee with a baseline Kellgren-Lawrence grade (KLG) of 0 or 1 that further progressed to KLG ≥2 during the follow-up visit. Intermediate-weighted turbo spin-echo knee MR images at the time of iROA diagnosis and 1 year prior were obtained. Five clinical characteristics-age, sex, body mass index, knee injury history, and knee surgery history-were obtained. A total of 604 knees were selected and matched (302 cases and 302 controls). A U-Net segmentation model was independently trained to automatically segment the IPFP. The prediction models were established in the training set (60%). Three main models were generated using (I) clinical characteristics; (II) radiomic features; (III) combined (clinical plus radiomic) features. Model performance was evaluated in an independent testing set (remaining 40%) using the area under the curve (AUC). Two secondary models were also generated using Hoffa-synovitis scores and clinical characteristics. Results: The comparison between the automated and manual segmentations of the IPFP achieved a Dice coefficient of 0.900 (95% CI: 0.891-0.908), which was comparable to that of experienced radiologists. The radiomic features model and the combined model yielded superior AUCs of 0.700 (95% CI: 0.630-0.763) and 0.702 (95% CI: 0.635-0.763), respectively. The DeLong test found no statistically significant difference between the receiver operating curves of the radiomic and combined models (P=0.831); however, both models outperformed the clinical model (P=0.014 and 0.004, respectively). Conclusions: Our results demonstrated that radiomic features of the IPFP are predictive of iROA 1 year prior to the diagnosis, suggesting that IPFP radiomic features can serve as an early quantitative prediction biomarker of iROA.

Artificial Intelligence (AI) for Detection and Localization of Unobturated Second Mesial Buccal (MB2) Canals in Cone-Beam Computed Tomography (CBCT).

The aim of this study was to develop a deep learning model to automatically detect and segment unobturated mesial buccal 2 (MB2) canals on endodontically obturated maxillary molars depicted in CBCT studies. Fifty-seven deidentified CBCT studies of maxillary molars with clinically confirmed unobturated MB2 canals were retrieved from a dental institution radiology database. One-hundred and two maxillary molar roots with and without unobturated MB2 canals were segmented using ITK-SNAP. The data were split into training and testing samples designated to train and evaluate the performance, respectively, of a convolutional neural network (CNN), U-Net. The detection performance revealed a sensitivity of 0.8, a specificity of 1, a high PPV of 1, and a NPV of 0.83 for the testing set, along with an accuracy of 0.9. The segmentation performance of unobturated MB2 canals, assessed using the custom metric, rendered a mean value of 0.3018 for the testing set. The current AI algorithm has the potential to identify obturated and unobturated canals in endodontically treated teeth. However, the AI algorithm is still somewhat affected by metallic artifacts, variations in canal calcifications, and the applied configuration. Thus, further development is needed to improve the algorithm and validate the accuracy using external validation data sets.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility.

Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures. Three datasets of volunteers from two clinical trials were included. Breast MR images were acquired on 3 T Siemens Biograph mMR, Prisma, and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique. Two whole-breast segmentation strategies, utilizing image registration and 3D U-Net, were developed. Manual segmentation was performed. A task-based analysis was performed: a previously developed MR-based BD measure, MagDensity, was calculated and assessed using automated and manual segmentation. The mean squared error (MSE) and intraclass correlation coefficient (ICC) between MagDensity were evaluated using the manual segmentation as a reference. The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures (Δ2-1), MSE, and ICC. The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation, with ICCs of 0.986 (95%CI: 0.974-0.993) and 0.983 (95%CI: 0.961-0.992), respectively. For test-retest analysis, MagDensity derived using the registration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993 (95%CI: 0.982-0.997) when compared to other segmentation methods. In conclusion, the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD. Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment, with the registration exhibiting superior performance for highly reproducible BD measurements.

Phosphosites of the yeast centrosome component Spc110 contribute to cell cycle progression and mitotic exit.

Spc110 is an essential component of the spindle pole body (SPB), the yeast equivalent of the centrosome, that recruits the γ-tubulin complex to the nuclear side of the SPB to produce the microtubules that form the mitotic spindle. Here, we identified phosphosites S11 and S36 in maternally originated Spc110 and explored their functions in vivo. Yeast expressing non-phosphorylatable Spc110S11A had a distinct spindle phenotype characterised by higher levels of α-tubulin, which was frequently asymmetrically distributed between the two SPBs. Furthermore, expression of the double mutant Spc110S11AS36A had a delayed cell cycle progression. Specifically, the final steps of mitosis were delayed in Spc110S11AS36A cells, including expression and degradation of the mitotic cyclin Clb2, disassembling the mitotic spindle and re-localizing Cdc14 to the nucleoli, resulting in late mitotic exit and entry in G1. Thus, we propose that Spc110 phosphorylation at S11 and S36 is required to regulate timely cell cycle progression in budding yeast. This article has an associated First Person interview with the first author of the paper.

Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network.

To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network's features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.

Mixing and Flow Transition in an Optimized Electrokinetic Turbulent Micromixer.

Micromixer is a key element in a lab on a chip for broad applications in the analysis and measurement of chemistry and engineering. Previous investigations reported that electrokinetic (EK) turbulence could be realized in a "Y" type micromixer with a cross-sectional dimension of 100 µm order. Although the ultrafast turbulent mixing can be generated at a bulk flow Reynolds number on the order of unity, the micromixer has not been optimized. In this investigation, we systematically investigated the influence of electric field intensity, AC frequency, electric conductivity ratio, and channel width at the entrance on the mixing effect and transition electric Rayleigh number in the "Y" type electrokinetic turbulent micromixer. It is found that the optimal mixing is realized in a 350 µm wide micromixer, under 100 kHz and 1.14 × 105 V/m AC electric field, with an electric conductivity ratio of 1:3000. Under these conditions, a degree of mixedness of 0.93 can be achieved at 84 µm from the entrance and 100 ms. A further investigation of the critical electric field and the critical electric Rayleigh number indicates that the most unstable condition of EK flow instability is inconsistent with that of the optimal mixing in EK turbulence. To predict the evolution of EK flow under high Raσ and guide the design of EK turbulent micromixers, it is necessary to apply a computational turbulence model instead of linear instability analysis.

ZAKα-driven ribotoxic stress response activates the human NLRP1 inflammasome.

Human NLRP1 (NACHT, LRR, and PYD domain-containing protein 1) is an innate immune sensor predominantly expressed in the skin and airway epithelium. Here, we report that human NLRP1 senses the ultraviolet B (UVB)- and toxin-induced ribotoxic stress response (RSR). Biochemically, RSR leads to the direct hyperphosphorylation of a human-specific disordered linker region of NLRP1 (NLRP1DR) by MAP3K20/ZAKα kinase and its downstream effector, p38. Mutating a single ZAKα phosphorylation site in NLRP1DR abrogates UVB- and ribotoxin-driven pyroptosis in human keratinocytes. Moreover, fusing NLRP1DR to CARD8, which is insensitive to RSR by itself, creates a minimal inflammasome sensor for UVB and ribotoxins. These results provide insight into UVB sensing by human skin keratinocytes, identify several ribotoxins as NLRP1 agonists, and establish inflammasome-driven pyroptosis as an integral component of the RSR.