Fast Proliferation Research Articles

An algorithm for enhancement of audio content classification

Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.

An algorithm for enhancement of audio content classification

Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.

Metabolic versatility enables sulfur-oxidizers to dominate primary production in groundwater

High rates of CO2 fixation and the genetic potential of various groundwater microbes for autotrophic activity have shown that primary production is an important source of organic C in groundwater ecosystems. However, the contribution of specific chemolithoautotrophic groups such as S-oxidizing bacteria (SOB) to groundwater primary production and their adaptation strategies remain largely unknown. Here, we stimulated anoxic groundwater microcosms with reduced S and sampled the microbial community after 1, 3 and 6 weeks. Genome-resolved metaproteomics was combined with 50at-% 13CO2 stable isotope probing to follow the C flux through the microbial food web and infer traits expressed by active SOB in the groundwater microcosms. Already after 7 days, 90% of the total microbial biomass C in the microcosms was replaced by CO2-derived C, increasing to 97% at the end of incubation. Stable Isotope Cluster Analysis revealed active autotrophs, characterized by a uniform 13C-incorporation of 45% in their peptides, to dominate the microbial community throughout incubation. Mixo- and heterotrophs, characterized by 10 to 40% 13C-incorporation, utilized the primarily produced organic C. Interestingly, obligate autotrophs affiliated with Sulfuricella and Sulfuritalea contained traits enabling the storage of elemental S in globules to maintain primary production under energy limitation. Others related to Sulfurimonas seemed to rapidly utilize substrates for fast proliferation, and most autotrophs further maximized their energy yield via efficient denitrification and the potential for H2 oxidation. Mixotrophic SOB, belonging to Curvibacter or Polaromonas, enhanced metabolic flexibility by using organic compounds to satisfy their C requirements. Time series data spanning eight years further revealed that key taxa of our microcosms composed up to 15% of the microbial groundwater community, demonstrating their in-situ importance. This showed that SOB, by using different metabolic strategies, are able to account for high rates of primary production in groundwater, especially at sites limited to geogenic nutrient sources. The widespread presence of SOB with traits such as S storage, H2 oxidation, and organic C utilization in many aquatic habitats further suggested that metabolic versatility governs S-fueled primary production in the environment.

Fibroblasts are the most suitable cell source for regenerative medicine due to their high intracellular fibroblast growth factor 2 content

In our previous study, we found that dry-preserved multilayered fibroblast cell sheets promoted angiogenesis and wound healing in a mouse ulcer model by releasing high levels of intracellular fibroblast growth factor 2 (FGF2), hepatocyte growth factor (HGF), and vascular endothelial growth factor (VEGF), from dried cells. In the present study, to identify which cell types are suitable for human dry-preserved cell sheets (dry sheets), we compared the intracellular FGF2 levels in seven types of cells reported as cell sheets for clinical use or preclinical studies. FGF2 levels were high in mesenchymal cells, including human oral fibroblasts (HOFs) and human dermal fibroblasts (HDFs), human dental pulp stem cells (DPSCs), and human mesenchymal stem cells (MSCs); in contrast, FGF2 levels in human umbilical vascular endothelial cells (HUVECs), human skeletal muscle myoblasts (SkMMs), and human epidermal keratinocytes (HEKs) were remarkably low, approximately 25% those in fibroblasts. In addition, we prepared dry sheets from HOFs, DPSCs, and MSCs, and analyzed the growth factors released from each dry sheet upon rehydration. High levels of FGF2, HGF, and VEGF were detected in the eluate prepared by immersing each dry sheet. In particular, FGF2 and HGF were the most abundant in HOFs. An in vitro cell proliferation assay showed that these eluates significantly enhanced HUVEC proliferation compared to control cells. Furthermore, cells incubated with HOF eluate showed significantly higher cell proliferation than cells incubated with DPSC and MSC eluates. However, this proliferative response was significantly blocked by FGF2-neutralizing antibodies. These results demonstrate that growth factors released from human dry sheets have physiological activity and that this activity is mainly mediated by the effect of FGF2. Fibroblasts are ideal for the clinical application of dry-preserved cell sheets in humans owing to their high intracellular FGF2 content, fast cell proliferation, ease of handling, availability, and low culture costs, making them the most suitable cell source for regenerative medicine, with FGF2 release as the mechanism of action.

A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm

The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT.

A Lightweight Model-Based Evolutionary Consensus Protocol in Blockchain as a Service for IoT

Internet of Things (IoT) is experiencing fast proliferation with emerging trends in autonomy and local decision-making to avoid the explosive burden on network infrastructure between cloud and edge. Thereby, blockchain as a Service (BaaS) for IoT, as an emerging distributed services computing paradigm, has drawn intense attention due to its decentralization, auditability, and tamper-resistance. However, the primary challenge is to design a tailor-made consensus protocol that is applicable to BaaS for IoT. Existing consensus protocols generally focus on power-intensive environments, which is not feasible for power-constrained BaaS-enabled IoT systems. In this paper, to fully exploit BaaS’s superiority (e.g. to sharing data securely), we propose a lightweight model-based evolutionary consensus protocol called Proof of Evolutionary Model (PoEM) that can improve the quality of BaaS in IoT environments. Beyond existing rule-based consensus protocols, PoEM iteratively trains a machine learning model to achieve consensus. In this way, PoEM enhances consensus efficiency and enables low-performance IoT devices to be involved. Moreover, considering IoT environments’ dynamics, a novel mechanism is designed to manage nodes joining and exiting dynamically. Extensive analytical and experimental results show PoEM’s improved consensus efficiency and applicability in dynamic BaaS-based IoT environments while providing high-level security guarantees.

Computational Exploration of Functional and Structural Impact of Single Nucleotide Changes in DNMT3A Gene among Acute Myeloid Leukemia Patients

Acute myeloid leukemia (AML) is a blood cell malignancy of the myeloid line, characterized by fast proliferation of aberrant cells that build up in the bone marrow and blood, interfering with normal blood cell synthesis. DNMT3A is a DNA methyltransferase that plays a role in DNA methylation, an epigenetic modification associated with gene expression regulation. DNMT3A mutations are frequently found in AML and are associated with poor prognosis. Objective: To evaluate the impact of DNMT3A mutations on protein structure and function, specifically in the context of AML. Methods: SNPs of DNMT3A gene reported in AML (R882P, R882L, R882S, R882G, and R882C) were retrieved from National Centre for Biotechnology Information (NCBI) database and different in silico approaches were used to investigate how these mutations affect protein structure and function. Results: Prediction tools indicated that mutations are pathogenic affecting DNMT3A function and were found in evolutionarily conserved regions. Protein stability analysis showed that mutations reduce DNMT3A's structural stability, alter secondary structure of the protein, particularly helices, interacts with other proteins and reduce protein-protein affinity. RNA folding analysis revealed abnormal folding patterns caused by mutant, affecting protein translation. DNMT3A expression was reported to be considerably greater in AML compared to normal tissues, and mutations were associated with poor overall survival in AML patients. Methylation levels and post-translational modification sites of DNMT3A were also investigated. Conclusions: Overall, this research highlighted the negative impact of DNMT3A mutations on protein structure and function, emphasizing their importance in the development and prognosis of AML. Acute myeloid leukemia (AML) is a blood cell malignancy of the myeloid line, characterized by fast proliferation of aberrant cells that build up in the bone marrow and blood, interfering with normal blood cell synthesis. DNMT3A is a DNA methyltransferase that plays a role in DNA methylation, an epigenetic modification associated with gene expression regulation. DNMT3A mutations are frequently found in AML and are associated with poor prognosis. Objective: To evaluate the impact of DNMT3A mutations on protein structure and function, specifically in the context of AML. Methods: SNPs of DNMT3A gene reported in AML (R882P, R882L, R882S, R882G, and R882C) were retrieved from National Centre for Biotechnology Information (NCBI) database and different in silico approaches were used to investigate how these mutations affect protein structure and function. Results: Prediction tools indicated that mutations are pathogenic affecting DNMT3A function and were found in evolutionarily conserved regions. Protein stability analysis showed that mutations reduce DNMT3A's structural stability, alter secondary structure of the protein, particularly helices, interacts with other proteins and reduce protein-protein affinity. RNA folding analysis revealed abnormal folding patterns caused by mutant, affecting protein translation. DNMT3A expression was reported to be considerably greater in AML compared to normal tissues, and mutations were associated with poor overall survival in AML patients. Methylation levels and post-translational modification sites of DNMT3A were also investigated. Conclusions: Overall, this research highlighted the negative impact of DNMT3A mutations on protein structure and function, emphasizing their importance in the development and prognosis of AML.

Abstract 2047: Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Abstract Background: Glucose and glutamine are major carbon and energy sources that promote the fast proliferation of cancer cells. The Warburg effect, characterized by shifted flux ratio between aerobic and anaerobic respirations, is considered as a common metabolic reprogramming mechanism in cancer. The further discovery of glutaminolysis pathway elucidated the role of glutamate in providing energy and carbon source in cancer metabolism. Variations in the branches of the central metabolism of glucose and glutamate have also been observed. However, most of these observations were made on cell line or mouse systems, which cannot mimic the dysbalanced nutrient, redox, pH and oxygen levels in a real tumor microenvironment (TME), which may heavily shift in a TME and determines the metabolic phenotypes of a cancer. Analysis: In this study, we conducted a systematic evaluation of the metabolic reprogramming and characteristics via a computational analysis by using pan-cancer transcriptomics data of 11 cancer subtypes and 9 matched adjacent normal tissue types. We focused on the flux distribution and variations of the central energy metabolism and its key branches. We first reconstructed the central metabolism pathway in a subcellular resolution by including glycolytic pathway, production of lactate, TCA cycle, nucleic acids synthesis, glutaminolysis, glutaminate, glutamine and glutathione metabolism, and other amino acids synthesis. We applied our in-house developed scFEA method on TCGA pan-cancer transcriptomics data. Results: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of TCA cycle, i.e., Warburg effect in almost all the analyzed cancer types. However, increased lactate production and the second half of TCA cycle were only seen in certain cancer types. More interestingly, we did not see cancer tissues have highly shifted glutaminolysis compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes, (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls, and (3) the different shifts happened to tissue specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. Citation Format: Alex Lu, Haiqi Zhu, Kevin Hu, Grace Yang, Shaoyang Huang, Pengtao Dang, Sha Cao, Chi Zhang. Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2047.

Comparative assessment of algaecide performance on freshwater phytoplankton: Understanding differential sensitivities to frame cyanobacteria management

Cyanobacterial bloom represent a growing threat to global water security. With fast proliferation, they raise great concern due to potential health and socioeconomic concerns. Algaecides are commonly employed as a mitigative measure to suppress and manage cyanobacteria. However, recent research on algaecides has a limited phycological focus, concentrated predominately on cyanobacteria and chlorophytes. Without considering phycological diversity, generalizations crafted from these algaecide comparisons present a biased perpective. To limit the collateral impacts of algaecide interventions on phytoplankton communities it is critical to understand differential phycological sensitivities for establishing optimal dosage and tolerance thresholds. This research attempts to fill this knowledge gap and provide effective guidelines to frame cyanobacterial management. We investigate the effect of two common algaecides, copper sulfate (CuSO4) and hydrogen peroxide (H2O2), on four major phycological divisions (chlorophytes, cyanobacteria, diatoms, and mixotrophs). All phycological divisions exhibited greater sensitivity to copper sulfate, except chlorophytes. Mixotrophs and cyanobacteria displayed the highest sensitivity to both algaecides with the highest to lowest sensitivity being observed as follows: mixotrophs, cyanobacteria, diatoms, and chlorophytes. Our results suggest that H2O2 represents a comparable alternative to CuSO4 for cyanobacterial control. However, some eukaryotic divisions such as mixotrophs and diatoms mirrored cyanobacteria sensitivity, challenging the assumption that H2O2 is a selective cyanocide. Our findings suggest that optimizing algaecide treatments to suppress cyanobacteria while minimizing potential adverse effects on other phycological members is unattainable. An apparent trade-off between effective cyanobacterial management and conserving non-targeted phycological divisions is expected and should be a prime consideration of lake management.

Spontaneous nucleation and fast aggregate-dependent proliferation of α-synuclein aggregates within liquid condensates at neutral pH

The aggregation of α-synuclein into amyloid fibrils has been under scrutiny in recent years because of its association with Parkinson's disease. This process can be triggered by a lipid-dependent nucleation process, and the resulting aggregates can proliferate through secondary nucleation under acidic pH conditions. It has also been recently reported that the aggregation of α-synuclein may follow an alternative pathway, which takes place within dense liquid condensates formed through phase separation. The microscopic mechanism of this process, however, remains to be clarified. Here, we used fluorescence-based assays to enable a kinetic analysis of the microscopic steps underlying the aggregation process of α-synuclein within liquid condensates. Our analysis shows that at pH 7.4, this process starts with spontaneous primary nucleation followed by rapid aggregate-dependent proliferation. Our results thus reveal the microscopic mechanism of α-synuclein aggregation within condensates through the accurate quantification of the kinetic rate constants for the appearance and proliferation of α-synuclein aggregates at physiological pH.

Comparison of biological behavior of lacrimal gland adenoid cystic carcinoma with high-grade transformation cells.

To evaluate the differences between human lacrimal gland adenoid cystic carcinoma with high-grade transformation (LACC-HGT) primary cells cultured by high-grade transformation tissue and non-high-grade transformation (non-HGT) primary cells cultured by non-high-grade transformation tissue in proliferation, metastasis, drug susceptibility, and genes. LACC-HGT primary cells were established by tissue block culture, and the 4th to 10th generation primary cells were selected as research objects. The cells were preliminarily identified by immunofluorescent staining. The differences between non-HGT and LACC-HGT primary cells in terms of proliferation, metastasis, and drug susceptibility were compared by cell counting kit-8 (CCK-8) assay, wound healing, and drug sensitivity experiments. Differentially expressed genes were screened using mRNA array. Gene expression was analyzed using real-time quantitative polymerase chain reaction (RT-qPCR). LACC-HGT primary cells were successfully cultured by tissue block culture. Immunofluorescence staining results showed that cytokeratin (CK) and CK7 expression levels were positive in LACC-HGT primary cells. CCK-8 results showed that the proliferation ability of LACC-HGT cells was significantly higher than that of non-HGT cells. Wound healing experiment showed that the migration ability of LACC-HGT cells was significantly higher than that of non-HGT cells. LACC-HGT cells were also less sensitive to cisplatin and paclitaxel than non-HGT cells. Compared with non-HGT cells, 9566 differentially expressed genes were found in LACC-HGT primary cells, of which 5162 were up-regulated and 4404 were down-regulated. The expression of N-acetylneuraminate pyruvate lyase (NPL), MARVEL domain containing 3 (MARVELD3), syntabulin (SYBU), and allograft inflammatory factor 1 (AIF1) was higher in LACC-HGT cells than in non-HGT cells, whereas that of periostin (POSTN) was lower. LACC-HGT primary cells have faster proliferation, stronger migration ability, and poorer sensitivity to chemotherapy drugs than non-HGT primary cells. The expression of mRNAs in non-HGT and LACC-HGT primary cells are significantly different. These features are speculated to be the reasons why high-grade transformation tissues exhibit higher malignant degree and poorer prognosis than their counterparts.

Lactate in the tumor microenvironment: A rising star for targeted tumor therapy.

Metabolic reprogramming is one of fourteen hallmarks of tumor cells, among which aerobic glycolysis, often known as the "Warburg effect," is essential to the fast proliferation and aggressive metastasis of tumor cells. Lactate, on the other hand, as a ubiquitous molecule in the tumor microenvironment (TME), is generated primarily by tumor cells undergoing glycolysis. To prevent intracellular acidification, malignant cells often remove lactate along with H+, yet the acidification of TME is inevitable. Not only does the highly concentrated lactate within the TME serve as a substrate to supply energy to the malignant cells, but it also works as a signal to activate multiple pathways that enhance tumor metastasis and invasion, intratumoral angiogenesis, as well as immune escape. In this review, we aim to discuss the latest findings on lactate metabolism in tumor cells, particularly the capacity of extracellular lactate to influence cells in the tumor microenvironment. In addition, we examine current treatment techniques employing existing medications that target and interfere with lactate generation and transport in cancer therapy. New research shows that targeting lactate metabolism, lactate-regulated cells, and lactate action pathways are viable cancer therapy strategies.

Large language models: fast proliferation and budding international competition

The capabilities of large language models have improved significantly in recent years and have come to broader public attention with the release of OpenAI’s ChatGPT in November 2022. The sudden general interest in these models includes attention from potentially malicious actors, who may seek to misuse them, and attention from policymakers, who are now increasingly invested in national competition over language-model development and securing access to the massive amount of computing power needed to support innovation in this domain.

MiRNA Profiling and Its Potential Roles in Rapid Growth of Velvet Antler in Gansu Red Deer (Cervus elaphus kansuensis)

A significant variety of cell growth factors are involved in the regulation of antler growth, and the fast proliferation and differentiation of various tissue cells occur during the yearly regeneration of deer antlers. The unique development process of velvet antlers has potential application value in many fields of biomedical research. Among them, the nature of cartilage tissue and the rapid growth and development process make deer antler a model for studying cartilage tissue development or rapid repair of damage. However, the molecular mechanisms underlying the rapid growth of antlers are still not well studied. MicroRNAs are ubiquitous in animals and have a wide range of biological functions. In this study, we used high-throughput sequencing technology to analyze the miRNA expression patterns of antler growth centers at three distinct growth phases, 30, 60, and 90 days following the abscission of the antler base, in order to determine the regulatory function of miRNA on the rapid growth of antlers. Then, we identified the miRNAs that were differentially expressed at various growth stages and annotated the functions of their target genes. The results showed that 4319, 4640, and 4520 miRNAs were found in antler growth centers during the three growth periods. To further identify the essential miRNAs that could regulate fast antler development, five differentially expressed miRNAs (DEMs) were screened, and the functions of their target genes were annotated. The results of KEGG pathway annotation revealed that the target genes of the five DEMs were significantly annotated to the "Wnt signaling pathway", "PI3K-Akt signaling pathway", "MAPK signaling pathway", and "TGF-β signaling pathway", which were associated with the rapid growth of velvet antlers. Therefore, the five chosen miRNAs, particularly ppy-miR-1, mmu-miR-200b-3p, and novel miR-94, may play crucial roles in rapid antler growth in summer.

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture.

Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21st century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment.

FLI1 accelerates leukemogenesis through transcriptional regulation of pyruvate kinase-L/R and other glycolytic genes.

In cancer cells, multiple oncogenes and tumor suppressors control glycolysis to sustain rapid proliferation. The ETS-related transcription factor Fli1 plays a critical role in the induction and progression of leukemia, yet, the underlying mechanism of this oncogenic event is still not fully understood. In this study, RNAseq analysis of FLI1-depleted human leukemic cells revealed transcriptional suppression of the PKLR gene and activation of multiple glycolytic genes, such as PKM1/2. Pharmacological inhibition of glycolysis by PKM2 inhibitor, Shikonin, significantly suppressed leukemic cell proliferation. FLI1 directly binds to the PKLR promoter, leading to the suppression of this inhibitor of glycolysis. In accordance, shRNA-mediated depletion of PKLR in leukemic HEL cells expressing high levels of FLI1 accelerated leukemia proliferation, pointing for the first time to its tumor suppressor function. PKLR knockdown also led to downregulation of the erythroid markers EPOR, HBA1, and HBA2 and suppression of erythroid differentiation. Interestingly, silencing of PKLR in HEL cells significantly increased FLI1 expression, which was associated with faster proliferation in culture. In FLI1-expressing leukemic cells, lower PKLR expression was associated with higher expression of PKM1 and PKM2, which promote aerobic glycolysis. Finally, injection of pyruvate, a known inhibitor of glycolysis, into leukemia mice significantly suppressed leukemogenesis. These results demonstrate that FLI1 promotes leukemia in part by inducing glycolysis, implicates PKLR in erythroid differentiation, and suggests that targeting glycolysis may be an attractive therapeutic strategy for cancers driven by FLI1 overexpression.

RUNAT-BI: A Ruthenium(III) Complex as a Selective Anti-Tumor Drug Candidate against Highly Aggressive Cancer Cell Lines.

Ruthenium compounds have demonstrated promising activity in different cancer types, overcoming several limitations of platinum-based drugs, yet their global structure-activity is still under debate. We analyzed the activity of Runat-BI, a racemic Ru(III) compound, and of one of its isomers in eight tumor cell lines of breast, colon and gastric cancer as well as in a non-tumoral control. Runat-BI was prepared with 2,2'-biimidazole and dissolved in polyethylene glycol. We performed assays of time- and dose-dependent viability, migration, proliferation, and expression of pro- and antiapoptotic genes. Moreover, we studied the growth rate and cell doubling time to correlate it with the apoptotic effect of Runat-BI. As a racemic mixture, Runat-BI caused a significant reduction in the viability and migration of three cancer cell lines from colon, gastric and breast cancer, all of which displayed fast proliferation rates. This compound also demonstrated selectivity between tumor and non-tumor lines and increased proapoptotic gene expression. However, the isolated isomer did not show any effect. Racemic Runat-BI is a potential drug candidate for treatment of highly aggressive tumors. Further studies should be addressed at evaluating the role of the other isomer, for a more precise understanding of its antitumoral potential and mechanism of action.

Slow growing behavior in African trypanosomes during adipose tissue colonization.

When Trypanosoma brucei parasites, the causative agent of sleeping sickness, colonize the adipose tissue, they rewire gene expression. Whether this adaptation affects population behavior and disease treatment remained unknown. By using a mathematical model, we estimate that the population of adipose tissue forms (ATFs) proliferates slower than blood parasites. Analysis of the ATFs proteome, measurement of protein synthesis and proliferation rates confirm that the ATFs divide on average every 12 h, instead of 6 h in the blood. Importantly, the population of ATFs is heterogeneous with parasites doubling times ranging between 5 h and 35 h. Slow-proliferating parasites remain capable of reverting to the fast proliferation profile in blood conditions. Intravital imaging shows that ATFs are refractory to drug treatment. We propose that in adipose tissue, a subpopulation of T. brucei parasites acquire a slow growing behavior, which contributes to disease chronicity and treatment failure.

Toward Optimal Deployment for Full-View Point Coverage in Camera Sensor Networks

Recent years have witnessed the fast proliferation of camera sensors networks (CSNs) in numerous Internet of Things (IoT) applications. In order to a capture distinct image of targets from interesting directions, we leverage a special type of coverage called full-view coverage. Full-view coverage guarantees to obtain the images of a point from every direction, whereas it demands much more sensors than a conventional coverage. To this end, we investigate the problem of deploying the minimum number of rotatable camera sensors to achieve the full-view coverage of a set of target points, namely, optimal deployment for the full-view point coverage (OFP) problem. In this work, camera sensors are capable of rotating freely with infinite orientations, thus not only the deployment locations but also the orientations for each camera sensor are required to be optimized. To tackle this challenging problem, we first prove that the OFP problem is NP-hard. Then, we propose two approximation algorithms—iterative screening algorithm (ISA) and improved ISA (IISA) to solve the OFP. We further perform extensive simulations and conduct physical testings to demonstrate the superiority and effectiveness of our proposed solutions. Experimental results show that IISA can generally reduce the total number of required camera sensors by more than 20% compared with the state-of-the-art work.

FedTwin: Blockchain-Enabled Adaptive Asynchronous Federated Learning for Digital Twin Networks

The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTNs). The DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTNs from further application, including centralized processing, data falsification, privacy leakage, lack of incentive mechanism, and so on. To make DTN better meet the ever changing demands, we propose a novel block-chain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTNs. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTNs. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters, while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a rollback mechanism to redact the falsified global models. With this article, we aim to provide insights to forthcoming researchers and readers in this under-explored domain.