Astrocytes from the contused spinal cord inhibit oligodendrocyte differentiation of adult oligodendrocyte precursor cells by increasing the expression of bone morphogenetic proteins.

Promotion of remyelination is an important therapeutic strategy to facilitate functional recovery after traumatic spinal cord injury (SCI). Transplantation of neural stem cells (NSCs) or oligodendrocyte precursor cells (OPCs) has been used to enhance remyelination after SCI. However, the microenvironment in the injured spinal cord is inhibitory for oligodendrocyte (OL) differentiation of NSCs or OPCs. Identifying the signaling pathways that inhibit OL differentiation in the injured spinal cord could lead to new therapeutic strategies to enhance remyelination and functional recovery after SCI. In the present study, we show that reactive astrocytes from the injured rat spinal cord or their conditioned media inhibit OL differentiation of adult OPCs with concurrent promotion of astrocyte differentiation. The expression of bone morphogenetic proteins (BMP) is dramatically increased in the reactive astrocytes and their conditioned media. Importantly, blocking BMP activity by BMP receptor antagonist, noggin, reverse the effects of active astrocytes on OPC differentiation by increasing the differentiation of OL from OPCs while decreasing the generation of astrocytes. These data indicate that the upregulated bone morphogenetic proteins in the reactive astrocytes are major factors to inhibit OL differentiation of OPCs and to promote its astrocyte differentiation. These data suggest that manipulation of BMP signaling in the endogenous or grafted NSCs or OPCs may be a useful therapeutic strategy to increase their OL differentiation and remyelination and enhance functional recovery after SCI

Energy Cost of Reclining, Sitting, and Standing Activities in Chinese Adults

International Journal of Exercise Science 15(7): 1202-1211, 2022. The 2011 Compendium presents MET values for sedentary behaviors (SBs) and light-intensity physical activities (LIPAs). Some entries have estimated METs, others have multiple activities in a single entry, and newer activities are not in the Compendium. Accurate MET values are needed to increase the validity and generalizability of the Compendium. This study measured and analyzed SBs and LIPAs’ energy costs in reclining, sitting, standing postures, and fidgeting. Indirect calorimetry measured the energy costs (VO2, ml.kg-1.min-1) in 11 males and seven females (30.7 ± 7.6 y). Two groups of 9 participants each completed 17 randomly assigned activities (9 in group 1; 8 in group 2) for 5 minutes with a 2-minute rest between tasks. Standard METs were calculated as VO2 ml.kg-1.min-1/3.5 ml.kg-1.min-1. Results showed mean MET values for doing nothing (recline: 1.3, sit: 1.3. stand: 1.3); Watching TV on a mobile phone (recline: 1.3, sit: 1.3); Reading (recline; 1.5, sit: 1.0); Writing (recline: 1.5, sit: 1.3, stand: 1.3); Texting or viewing websites on a mobile phone (recline: 1.3, sit: 1.3, stand: 1.3); Fidgeting (sit hands only: 1.5, sit feet only: 1.8, stand hands and feet: 2.0); Typing (stand: 1.3). Measured vs. Compendium METs were the same for five SBs and LIPAs, higher for three SBs and LIPAs (by 0.2 METs), and lower for one SB (by 0.3 METs). In conclusion, the activities ranged from 1.0 to 2.0 METs, categorized as sedentary and light-intensity. Increasing the accuracy of Compendium MET values increases its utility for the correct classification of SB and LIPAs

Case Report: A novel FGFR1 fusion in acute B-lymphoblastic leukemia identified by RNA sequencing

8p11 myeloproliferative syndrome is a rare hematological malignancy with aggressive course caused by the various translocation of FGFR1. In this study, a novel FGFR1 fusion was identified by RNA sequencing in a 28-year-old male patient with acute B-lymphoblastic leukemia. The patient harbors an in-frame fusion between KIF5B exon 15 and FGFR1 exon 10. The FGFR1 fusion and its protein expression was validated by Sanger sequencing and Western blot. Meanwhile, cytogenetic analysis reported a normal karyotype and targeted DNA sequencing identified no driver mutations, respectively. Despite he achieved complete remission after induction regimen, a relapse occurred and he became refractory to chemotherapy, and salvage haploidentical hematopoietic stem cell transplantation failed to control the progressive disease. In conclusion, we present the first case of KIF5B-FGFR1 fusion in hematological malignancy. These findings extend the spectrum of translocation in 8p11 myeloproliferative syndrome, and demonstrate the great prospect of RNA sequencing in clinical practice again

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Tuning microtubule dynamics to enhance cancer therapy by modulating FER-mediated CRMP2 phosphorylation

Though used widely in cancer therapy, paclitaxel only elicits a response in a fraction of patients. A strong determinant of paclitaxel tumor response is the state of microtubule dynamic instability. However, whether the manipulation of this physiological process can be controlled to enhance paclitaxel response has not been tested. Here, we show a previously unrecognized role of the microtubule-associated protein CRMP2 in inducing microtubule bundling through its carboxy terminus. This activity is significantly decreased when the FER tyrosine kinase phosphorylates CRMP2 at Y479 and Y499. The crystal structures of wild-type CRMP2 and CRMP2-Y479E reveal how mimicking phosphorylation prevents tetramerization of CRMP2. Depletion of FER or reducing its catalytic activity using sub-therapeutic doses of inhibitors increases paclitaxel-induced microtubule stability and cytotoxicity in ovarian cancer cells and in vivo. This work provides a rationale for inhibiting FER-mediated CRMP2 phosphorylation to enhance paclitaxel on-target activity for cancer therapy

Long-term system load forecasting based on data-driven linear clustering method

Abstract In this paper, a data-driven linear clustering (DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling. Then optimal autoregressive integrated moving average (ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load. Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts. From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained