Differential Expression of MicroRNA-19b Promotes Proliferation of Cancer Stem Cells by Regulating the TSC1/mTOR Signaling Pathway in Multiple Myeloma

Background/Aims: MiR-19b has been reported to be involved in several malignancies, but its role in multiple myeloma (MM) is still unknown. The objective of this study was to explore the biological mechanism of miR-19b in the progression of MM. Methods: First, we performed real-time polymerase chain reaction (PCR) and Western blot to study the expression of miR-19b, tuberous sclerosis 1 (TSC1), and caspase-3 in different groups. MTT assay was performed to explore the effect of miR-19b on survival and apoptosis of cancer stem cells (CSCs). Computation analysis and luciferase assay were utilized to confirm the interaction between miR-19b and TSC1. Results: A total of 38 participants comprising 20 subjects with MM and 18 healthy subjects as normal controls were enrolled in our study. Real-time PCR showed dramatic upregulation of miR-19b, but TSC1 was evidently suppressed in the MM group. MiR-19b overexpression substantially promoted clonogenicity and cell viability, and further inhibited apoptosis of CSCs in vitro. Furthermore, miR-19b overexpression downregulated the expression of caspase-3, which induced apoptosis. Using in silico analysis, we identified that TSC1 might be a direct downstream target of miR-19b, and this was further confirmed by luciferase assay showing that miR-19b apparently reduced the luciferase activity of wild-type TSC1 3´-UTR, but not that of mutant TSC1 3´-UTR. There was also evident decrease in TSC1 mRNA and protein in CSCs following introduction of miR-19b. Interestingly, reintroduction of TSC1 abolished the miR-19b-induced proliferation promotion and apoptosis inhibition in CSCs. Conclusion: These findings collectively suggest that miR-19b promotes cell survival and suppresses apoptosis of MM CSCs via targeting TSC1 directly, indicating that miR-19b may serve as a potential and novel therapeutic target of MM based on miRNA expression

Smart Spårning för Edge-assisterad Objektdetektering : Djup Förstärkningsinlärning för Flermålsoptimering av Spårningsbaserad Detekteringsprocess

Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. Thus Object Tracking, which is a much lighter process, is introduced under the Tracking-By-Detection (TBD) paradigm to alleviate the computational overhead. Still, it is common that the configurations of the TBD remain unchanged, which would result in unnecessary computation and/or performance loss in many cases.\\ This Master's Thesis presents a novel approach for multi-objective optimization of the TBD process on precision and latency, with the platform being power-constrained devices. We propose a Deep Reinforcement Learning based scheduling architecture that selects appropriate TBD actions in video sequences to achieve the desired goals. Specifically, we develop a simulation environment providing Markovian state information as input for the scheduler neural network, justified options of TBD actions, and a scalarized reward function to combine the multiple objectives. Our results demonstrate that the trained policies can learn to utilize content information from the current and previous frames, thus optimally controlling the TBD process at each frame. The proposed approach outperforms the baselines that have fixed TBD configurations and recent research works, achieving the precision close to pure detection while keeping the latency much lower. Both tuneable configurations show positive and synergistic contribution to the optimization objectives. We also show that our policies are generalizable, with inference and action time of the scheduler having minimal latency overhead. This makes our scheduling design highly practical in real XR or similar applications on power-constrained devices.Att upptäcka generiska objekt är en viktig uppgift inom avkänning för tillämpningar som behöver förstå omgivningen, såsom eXtended Reality (XR) och navigering med drönare, bland annat. Algoritmer för objektdetektering är dock särskilt beräkningstunga när det gäller videoanalyser i realtid på resursbegränsade mobila enheter. Objektspårning, å andra sidan, är en lättare process som vanligtvis implementeras under Tracking-By-Detection (TBD)-paradigmet för att minska beräkningskostnaden. Det är dock vanligt att TBD-konfigurationerna förblir oförändrade, vilket leder till onödig beräkning och/eller prestandaförlust i många fall.\\ I detta examensarbete presenteras en ny metod för multiobjektiv optimering av TBD-processen med avseende på precision och latens på plattformar med begränsad prestanda. Vi föreslår en djup förstärkningsinlärningsbaserad schemaläggningsarkitektur som väljer lämpliga TBD-åtgärder för videosekvenser för att uppnå de önskade målen. Vi utvecklar specifikt en simulering som tillhandahåller Markovian state-information som indata för schemaläggaren, samt neurala nätverk, motiverade alternativ för TBD-åtgärder och en skalariserad belöningsfunktion för att kombinera de olika målen. Våra resultat visar att de tränade strategierna kan lära sig att använda innehållsinformation från aktuella och tidigare ramar för att optimalt styra TBD-processen för varje bild. Det föreslagna tillvägagångssättet är bättre än både de grundläggande metoderna med en fast TBD-konfiguration och nyare forskningsarbeten. Det uppnår en precision som ligger nära den rena detektionen samtidigt som latensen hålls mycket låg. Båda justerbara konfigurationerna bidrar positivt och synergistiskt till optimeringsmålen. Vi visar också att våra strategier är generaliserbara genom att dela upp träning och testning med en 50 %-ig uppdelning, vilket resulterar i minimal inferenslatens och schemaläggarens handlingslatens. Detta gör vår schemaläggningsdesign mycket praktisk i verkliga XR- eller liknande tillämpningar på enheter med begränsad strömförsörjning

Smart Spårning för Edge-assisterad Objektdetektering : Djup Förstärkningsinlärning för Flermålsoptimering av Spårningsbaserad Detekteringsprocess

Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. Thus Object Tracking, which is a much lighter process, is introduced under the Tracking-By-Detection (TBD) paradigm to alleviate the computational overhead. Still, it is common that the configurations of the TBD remain unchanged, which would result in unnecessary computation and/or performance loss in many cases.\\ This Master's Thesis presents a novel approach for multi-objective optimization of the TBD process on precision and latency, with the platform being power-constrained devices. We propose a Deep Reinforcement Learning based scheduling architecture that selects appropriate TBD actions in video sequences to achieve the desired goals. Specifically, we develop a simulation environment providing Markovian state information as input for the scheduler neural network, justified options of TBD actions, and a scalarized reward function to combine the multiple objectives. Our results demonstrate that the trained policies can learn to utilize content information from the current and previous frames, thus optimally controlling the TBD process at each frame. The proposed approach outperforms the baselines that have fixed TBD configurations and recent research works, achieving the precision close to pure detection while keeping the latency much lower. Both tuneable configurations show positive and synergistic contribution to the optimization objectives. We also show that our policies are generalizable, with inference and action time of the scheduler having minimal latency overhead. This makes our scheduling design highly practical in real XR or similar applications on power-constrained devices.Att upptäcka generiska objekt är en viktig uppgift inom avkänning för tillämpningar som behöver förstå omgivningen, såsom eXtended Reality (XR) och navigering med drönare, bland annat. Algoritmer för objektdetektering är dock särskilt beräkningstunga när det gäller videoanalyser i realtid på resursbegränsade mobila enheter. Objektspårning, å andra sidan, är en lättare process som vanligtvis implementeras under Tracking-By-Detection (TBD)-paradigmet för att minska beräkningskostnaden. Det är dock vanligt att TBD-konfigurationerna förblir oförändrade, vilket leder till onödig beräkning och/eller prestandaförlust i många fall.\\ I detta examensarbete presenteras en ny metod för multiobjektiv optimering av TBD-processen med avseende på precision och latens på plattformar med begränsad prestanda. Vi föreslår en djup förstärkningsinlärningsbaserad schemaläggningsarkitektur som väljer lämpliga TBD-åtgärder för videosekvenser för att uppnå de önskade målen. Vi utvecklar specifikt en simulering som tillhandahåller Markovian state-information som indata för schemaläggaren, samt neurala nätverk, motiverade alternativ för TBD-åtgärder och en skalariserad belöningsfunktion för att kombinera de olika målen. Våra resultat visar att de tränade strategierna kan lära sig att använda innehållsinformation från aktuella och tidigare ramar för att optimalt styra TBD-processen för varje bild. Det föreslagna tillvägagångssättet är bättre än både de grundläggande metoderna med en fast TBD-konfiguration och nyare forskningsarbeten. Det uppnår en precision som ligger nära den rena detektionen samtidigt som latensen hålls mycket låg. Båda justerbara konfigurationerna bidrar positivt och synergistiskt till optimeringsmålen. Vi visar också att våra strategier är generaliserbara genom att dela upp träning och testning med en 50 %-ig uppdelning, vilket resulterar i minimal inferenslatens och schemaläggarens handlingslatens. Detta gör vår schemaläggningsdesign mycket praktisk i verkliga XR- eller liknande tillämpningar på enheter med begränsad strömförsörjning

Monitoring of Land Desertification Changes in Urat Front Banner from 2010 to 2020 Based on Remote Sensing Data

Monitoring the spatio-temporal dynamics of desertification is critical for desertification control. Using the Urat front flag as the study area, Landsat remote sensing images between 2010 and 2020 were selected as data sources, along with MOD17A3H as auxiliary data. Additionally, RS and GIS theories and methods were used to establish an Albedo–NDVI feature space based on the normalized difference vegetation index (NDVI) and land surface albedo. The desertification difference index (DDI) was developed to investigate the dynamic change and factors contributing to desertification in the Urat front banner. The results show that: ① the Albedo–NDVI feature space method is effective and precise at extracting and classifying desertification information, which is beneficial for quantitative analysis and monitoring of desertification; ② from 2010 to 2020, the spatial distribution of desertification degree in the Urat front banner gradually decreased from south to north; ③ throughout the study period, the area of moderate desertification land increased the most, at an annual rate of 8.2%, while the area of extremely serious desertification land decreased significantly, at an annual rate of 9.2%, indicating that desertification degree improved during the study period; ④ the transformation of desertification types in Urat former banner is mainly from very severe to moderate, from severe to undeserted, and from mild to undeserted, with respective areas of 22.5045 km2, 44.0478 km2, and 319.2160 km2. Over a 10-year period, the desertification restoration areas in the study area ranged from extremely serious desertification to moderate desertification, from serious desertification to non-desertification, and from weak desertification to non-desertification, while the desertification aggravation areas ranged mainly from serious desertification to moderate desertification; ⑤ NPP dynamic changes in vegetation demonstrated a zonal increase in distribution from west to east, and significant progress was made in desertification control. The change in desertification has accelerated significantly over the last decade. Climate change and irresponsible human activities have exacerbated desertification in the eastern part of the study area

Monitoring of Land Desertification Changes in Urat Front Banner from 2010 to 2020 Based on Remote Sensing Data

Monitoring the spatio-temporal dynamics of desertification is critical for desertification control. Using the Urat front flag as the study area, Landsat remote sensing images between 2010 and 2020 were selected as data sources, along with MOD17A3H as auxiliary data. Additionally, RS and GIS theories and methods were used to establish an Albedo–NDVI feature space based on the normalized difference vegetation index (NDVI) and land surface albedo. The desertification difference index (DDI) was developed to investigate the dynamic change and factors contributing to desertification in the Urat front banner. The results show that: ① the Albedo–NDVI feature space method is effective and precise at extracting and classifying desertification information, which is beneficial for quantitative analysis and monitoring of desertification; ② from 2010 to 2020, the spatial distribution of desertification degree in the Urat front banner gradually decreased from south to north; ③ throughout the study period, the area of moderate desertification land increased the most, at an annual rate of 8.2%, while the area of extremely serious desertification land decreased significantly, at an annual rate of 9.2%, indicating that desertification degree improved during the study period; ④ the transformation of desertification types in Urat former banner is mainly from very severe to moderate, from severe to undeserted, and from mild to undeserted, with respective areas of 22.5045 km2, 44.0478 km2, and 319.2160 km2. Over a 10-year period, the desertification restoration areas in the study area ranged from extremely serious desertification to moderate desertification, from serious desertification to non-desertification, and from weak desertification to non-desertification, while the desertification aggravation areas ranged mainly from serious desertification to moderate desertification; ⑤ NPP dynamic changes in vegetation demonstrated a zonal increase in distribution from west to east, and significant progress was made in desertification control. The change in desertification has accelerated significantly over the last decade. Climate change and irresponsible human activities have exacerbated desertification in the eastern part of the study area

Genetic analysis of 42 Y-STR loci in Han and Manchu populations from the three northeastern provinces in China

Abstract Background Y-STR polymorphisms are useful in tracing genealogy and understanding human origins and migration history. This study aimed to fill a knowledge gap in the genetic diversity, structure, and haplogroup distribution of the Han and Manchu populations from the three northeastern provinces in China (Liaoning, Jilin, and Heilongjiang). Methods A total of 1,048 blood samples were collected from unrelated males residing in Dalian. Genotyping was performed using the AGCU Y37 + 5 Amplification Kit, and the genotype data were analyzed to determine allele and haplotype frequencies, genetic and haplotype diversity, discrimination capacity, and haplotype match probability. Population pairwise genetic distances (F st ) were calculated to compare the genetic relationships among Han and Manchu populations from Northeast China and other 23 populations using 27 Yfiler Plus loci set. Multi-dimensional scaling and phylogenetic analysis were employed to visualize the genetic relationships among the 27 populations. Moreover, haplogroups were predicted based on 27 Yfiler Plus loci set. Results The Han populations from Northeast China exhibited genetic affinities with both Han populations from the Central Plain and the Sichuan Qiang population, despite considerable geographical distances. Conversely, the Manchu population displayed a relatively large genetic distance from other populations. The haplogroup analysis revealed the prevalence of haplogroups E1b1b, O1b, O2, and Q in the studied populations, with variations observed among different ethnic groups. Conclusion The study contributes to our understanding of genetic diversity and history of the Han and Manchu populations in Northeast China, the genetic relationships between populations, and the intricate processes of migration, intermarriage, and cultural integration that have shaped the region’s genetic landscape

Effect of hindered phenolic antioxidants on crosslinking characteristics of low-density polyethylene initiated by peroxide

Hindered phenolic antioxidants are the most prevalent antioxidants used in crosslinked polyethylene (XLPE) insulating materials of high-voltage (HV) cables. Doping antioxidants is conducive to delaying the low-temperature pre-crosslinking, but decreases final crosslinking degree. In order to comprehensively reveal the effect of hindered phenolic antioxidants on the crosslinking characteristics of XLPE insulating materials, four kinds of insulating materials doped with different hindered phenolic antioxidants are studied by experiment and simulation methods. The results demonstrate that antioxidant 300 is better for delaying low-temperature pre-crosslinking of insulating materials, but negative for maintaining high crosslinking efficiency and degree. Although insulating materials with antioxidant 1010 have higher crosslinking degree, it is poor to delay the low-temperature pre-crosslinking. Notably, antioxidant 245 with phenolic hydroxyl of lower bond dissociation energies can delay the low-temperature pre-crosslinking to some extent, meanwhile, crosslinking efficiency and degree of XLPE are still higher. Notably, the crosslinking characteristics of XLPE insulating materials are closely related to molecular structure of antioxidants, including functional group and ortho-substituents. Comprehensively considering the demands of delaying low-temperature pre-crosslinking and maintaining high crosslinking degree of insulating materials, antioxidant 245 is the better choice. This study provides a theoretical guidance for the choice of hindered phenolic antioxidants used for HV cable insulating materials

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents