ALKBH5 is a prognostic factor and promotes the angiogenesis of glioblastoma

Despite numerous reports indicating the significant impact of RNA modification on malignant glioblastoma (GBM) cell behaviors such as proliferation, invasion and therapy efficacy, its specific involvement in glioblastoma (GBM) angiogenesis is remains unclear and is currently under investigation. In this study, we aimed to investigate the relevance between RNA modification regulators and GBM angiogenesis. Our study employed bioinformatic analyses, including Gene Set Enrichment Analysis (GSEA), differential expression analysis, and Kaplan-Meier survival analysis, to identify regulators of angiogenesis-associated RNA modification (RM). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to identify the enrichment of angiogenesis associated signatures in ALKBH5-high expression GBMs. We also utilized Western blot to verify the upregulation of ALKBH5 in clinical GBM samples. By a series of in vitro and in vivo assays, including plasmid transfection, wound healing, transwell invasion test, tube formation, RT-qPCR, ELISA assays and xenograft mice model, we validated the angiogenesis regulation ability of ALKBH5 in GBM. The N6-methyladenosine (m6A) modification "erase" ALKBH5 emerged as a candidate regulator associated with angiogenesis, demonstrating elevated expression and robust prognostic predictive ability in GBM patients. We also revealed enrichment of vasculature development biological process in GBMs with high ALKBH5 expression. Subsequently, we validated the elevated the expression of ALKBH5 in clinical GBM and paired adjacent tissues through western blot. Additionally, we knocked down the expression of ALKBH5 using sh-RNAs in U87 GBM cells to access the angiogenesis induction ability in U87 cells. In vitro experiments, Human Umbilical Vein Endothelial Cells (HUVECs) were used to perform wound healing, transwell migration and tube formation analysis, results indicated that ALKBH5 knock-down of U87 cells could decrease the pro-angiogenesis ability of U87 GBM cells. Further validation of our bioinformatic findings confirmed that ALKBH5 knockdown impaired VEGFA secretion in both in vitro and in vivo settings in U87 cells. These results comprehensively affirm the crucial role of ALKBH5 in regulating GBM-induced angiogenesis, both in vitro and in vivo. ALKBH5 not only emerges as a promising prognostic factor for GBM patients, but also plays a pivotal role in sustaining GBM progression by promoting angiogenesis.

Properties of Alkali-Activated Slag Cement Activated by Weakly Alkaline Activator

Sodium sulfate (Na₂SO₄) and sodium carbonate (Na₂CO₃) are weakly alkaline activators. Alkali-activated slag (AAS) cement prepared with them shows the special advantages of long setting time and low shrinkage, but it shows slow development of mechanical properties. In the paper, Na₂SO₄ and Na₂CO₃ were used as activators and compounded with reactive magnesium oxide (MgO) and calcium hydroxide (Ca(OH)₂) to optimize the setting time and mechanical properties. The hydration products and microscopic morphology were also studied using XRD, SEM, and EDS. Furthermore, the production cost and environmental benefits were compared and analyzed. The results show that Ca(OH)₂ is the main influencing factor for setting time. It reacts preferentially with Na₂CO₃ to form CaCO₃, which makes AAS paste lose plasticity rapidly and shortens the setting time, and then produces strength. Na₂SO₄ and Na₂CO₃ are the main influencing factors for flexural and compressive strength, respectively. Suitably high content is beneficial to promote the development of mechanical strength. The interaction of Na₂CO₃ and Ca(OH)₂ shows a great effect on the initial setting time. High content of reactive MgO can shorten the setting time and increase the mechanical strength at 28 days. There are more crystal phases in hydration products. Considering the setting time and mechanical properties, the composition of activators are: 7% Na₂SO₄, 4% Na₂CO₃, 3-5% Ca(OH)₂, and 2-4% reactive MgO. Compared with ordinary Portland cement (OPC) and AAS cement activated by sodium hydroxide (NaOH, NH) and water glass (WG) with the same alkali equivalent, the production cost and energy consumption are greatly reduced. Compared with P·O 42.5 of OPC, CO₂ emission is reduced by 78.1%. AAS cement activated by weakly alkaline activators shows excellent environmental and economic benefits and good mechanical properties.

Calcyclin-binding protein-promoted degradation of MdFRUCTOKINASE2 regulates sugar homeostasis in apple

Fructokinase (FRK) activates fructose through phosphorylation, which sends the activated fructose into primary metabolism and regulates fructose signaling capabilities in plants. The apple (Malus × domestica) FRK gene MdFRK2 shows especially high affinity to fructose, and its overexpression decreases fructose levels in the leaves of young plants. However, in the current study of mature plants, fruits of transgenic apple trees overexpressing MdFRK2 accumulated a higher level of fructose than wild-type (WT) fruits (at both young and mature stages). Transgenic apple trees with high mRNA MdFRK2 expression showed no significant differences in MdFRK2 protein abundance or FRK enzyme activity compared to WT in mature leaves, young fruits, and mature fruits. Immunoprecipitation-mass spectrometry analysis identified an skp1, cullin, F-box (SCF) E3 ubiquitin ligase, calcyclin-binding protein (CacyBP), that interacted with MdFRK2. RNA-sequencing analysis provided evidence for ubiquitin-mediated post-transcriptional regulation of MdFRK2 protein for the maintenance of fructose homeostasis in mature leaves and fruits. Further analyses suggested an MdCacyBP-MdFRK2 regulatory module, in which MdCacyBP interacts with and ubiquitinates MdFRK2 to facilitate its degradation by the 26S proteasome, thus decreasing the FRK enzyme activity to elevate fructose concentration in transgenic apple trees. This result uncovered an important mechanism underlying plant fructose homeostasis in different organs through regulating the MdFRK2 protein level via ubiquitination and degradation. Our study provides usable data for the future improvement of apple flavor and expands our understanding of the molecular mechanisms underlying plant fructose content and signaling regulation.

Enabling efficient traceable and revocable time-based data sharing in smart city

With the assistance of emerging techniques, such as cloud computing, fog computing and Internet of Things (IoT), smart city is developing rapidly into a novel and well-accepted service pattern these days. The trend also facilitates numerous relevant applications, e.g., smart health care, smart office, smart campus, etc., and drives the urgent demand for data sharing. However, this brings many concerns on data security as there is more private and sensitive information contained in the data of smart city applications. It may incur disastrous consequences if the shared data are illegally accessed, which necessitates an efficient data access control scheme for data sharing in smart city applications with resource-poor user terminals. To this end, we proposes an efficient traceable and revocable time-based CP-ABE (TR-TABE) scheme which can achieve time-based and fine-grained data access control over large attribute universe for data sharing in large-scale smart city applications. To trace and punish the malicious users that intentionally leak their keys to pursue illicit profits, we design an efficient user tracing and revocation mechanism with forward and backward security. For efficiency improvement, we integrate outsourced decryption and verify the correctness of its result. The proposed scheme is proved secure with formal security proof and is demonstrated to be practical for data sharing in smart city applications with extensive performance evaluation.

Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network

Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively.

Altered Interactions Among Resting-State Networks in Individuals with Obesity

OBJECTIVE

The aim of this study was to investigate alterations in functional connectivity (FC) within and interactions between resting-state networks involved in salience, executive control, and interoception in participants with obesity (OB).

METHODS

Using resting-state functional magnetic resonance imaging with independent component analysis and FC, alterations within and interactions between resting-state networks in 35 OB and 35 normal-weight controls (NW) were investigated.

RESULTS

Compared with NW, OB showed reduced FC strength in the ventromedial prefrontal cortex and posterior cingulate cortex/precuneus within the default-mode network, dorsal anterior cingulate cortex within the salience network (SN), bilateral dorsolateral prefrontal cortex-angular gyrus within the frontoparietal network (FPN), and increased FC strength in the insula (INS) (P_{familywise error}  < 0.0125). The dorsal anterior cingulate cortex FC strength was negatively correlated with craving for food cues, left dorsolateral prefrontal cortex FC strength was negatively correlated with Yale Food Addiction Scale scores, and right INS FC strength was positively correlated with craving for high-calorie food cues. Compared with NW, OB also showed increased FC between the SN and FPN driven by altered FC of bilateral INS and anterior cingulate cortex-angular gyrus.

CONCLUSIONS

Alterations in FC within and interactions between the SN, default-mode network, and FPN might contribute to the high incentive value of food (craving), lack of control of overeating (compulsive overeating), and increased awareness of hunger (impaired interoception) in OB.

A computational method for prediction of matrix proteins in endogenous retroviruses

Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in group-specific antigen genes (gag) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far. No computational method to predict the exact start and end coordinates of MAs in gags has been proposed yet. In this paper, a computational method to identify MAs in ERVs is proposed. A divide and conquer technique was designed and applied to the conventional prediction model to acquire better results when dealing with gene sequences with various lengths. Initiation sites and termination sites were predicted separately and then combined according to their intervals. Three different algorithms were applied and compared: weighted support vector machine (WSVM), weighted extreme learning machine (WELM) and random forest (RF). G − mean (geometric mean of sensitivity and specificity) values of initiation sites and termination sites under 5-fold cross validation generated by random forest models are 0.9869 and 0.9755 respectively, highest among the algorithms applied. Our prediction models combine RF & WSVM algorithms to achieve the best prediction results. 98.4% of all the collected ERV sequences with complete MAs (125 in total) could be predicted exactly correct by the models. 94,671 HERV sequences from 118 families were scanned by the model, 104 new putative MAs were predicted in human chromosomes. Distributions of the putative MAs and optimizations of model parameters were also analyzed. The usage of our predicting method was also expanded to other retroviruses and satisfying results were acquired.