Potential of ozone technology for German cockroach (bBlattella germanica (L.)) management

German cockroaches (Blattella germanica (L.)) are important urban pests that adversely impact human health. Previous studies indicated that ozone technology (ozone gas) can be used as a management tool to control insect pests in grain storage facilities, stored foods and some finished products. The goal of my study was to broaden the applicability of ozone from traditional stored grain pests to B. germanica. The first part of my study determined the concentration-time (CT) relationship of ozone required to achieve 100% mortality in various life stages (adults, nymphs and eggs) ofB. germanica. Results showed that eggs were the most ozone-tolerant life stage and 100% mortality was only achieved after 24 h exposure at 480 ppm ozone (CT of 691200 ppm-min). Second part of my research determined the effects of sub-lethal ozone exposure on reproduction and population growth rate of B. germanica. In general, crosses that included ozone-treated females produced significantly fewer F1 nymphs. Exposure of both sexes to sub-lethal ozone environment caused significant population growth reduction in comparison to the other three pairings at 7, 11 and 15 weeks post exposure

Information-Theoretic Limits of Bistatic Integrated Sensing and Communication

The bistatic integrated sensing and communication (ISAC) system model avoids the strong self-interference in a monostatic ISAC system by employing a pair of physically separated sensing transceiver and maintaining the merit of co-designing radar sensing and communications on shared spectrum and hardware. Inspired by the appealing benefits of bistatic radar, we study bistatic ISAC, where a transmitter sends a message to a communication receiver and a sensing receiver at another location carries out a decoding-and-estimation(DnE) operation to obtain the state of the communication receiver. In this paper, both communication and sensing channels are modelled as state-dependent memoryless channels with independent and identically distributed time-varying state sequences. We consider a rate of reliable communication for the message at the communication receiver as communication metric. The objective of this model is to characterize the capacity-distortion region, i.e., the set of all the achievable rate while simultaneously allowing the sensing receiver to sense the state sequence with a given distortion threshold. In terms of the decoding degree on this message at the sensing receiver, we propose three achievable DnE strategies, the blind estimation, the partial-decoding-based estimation, and the full-decoding-based estimation, respectively. Based on the three strategies, we derive the three achievable rate-distortion regions. In addition, under the constraint of the degraded broadcast channel, i.e., the communication receiver is statistically stronger than the sensing receiver, and the partial-decoding-based estimation, we characterize the capacity region. Examples in both non-degraded and degraded cases are provided to compare the achievable rate-distortion regions under three DnE strategies and demonstrate the advantages of ISAC over independent communication and sensing.Comment: 40 pages, 7 figure

Integrated analysis identified novel miRNAs and mRNA in endometriosis

Objectives: Endometriosis is a common gynecological disease that seriously affects women’s health and quality of life. However, the pathogenesis of endometriosis remains uncertain. This study aims to find the key microRNAs (miRNAs) and mRNAs and further to elucidate the pathogenesis of endometriosis. Material and methods: Differentially expressed mRNAs (DEmRNAs) and the differentially expressed miRNAs (DEmiRNAs) were obtained by Gene Expression Omnibus (GEO) datasets integration analysis. Functional enrichment analysis of DEmRNAs and DEmRNAs targeted by DEmiRNAs was enforced using GeneCodis3. The DEmiRNA-DEmRNA interaction network was built using Cytoscape. The expression of candidate DEmRNA and DEmiRNA was verified using quantitative real time-polymerase chain reaction (QRT-PCR) and online datasets followed by diagnostic and immune cell infiltration analysis. Results: A total of 835 (327 down-regulated and 508 up-regulated) DEmRNAs and 39 (24 down-regulated and 15 up-regulated) DEmiRNAs were identified between ectopic endometria (EC) group and eutopic endometria (EU) group. DEmRNAs targeted by DEmiRNAs were markedly enriched in cell adhesion molecules, pathways in cancer, leukocyte transendothelial migration, cytokine-cytokine receptor interaction and MAPK signaling pathway. The DEmiRNA-DEmRNA interaction network of up-regulated miRNAs was consisted of 15 miRNAs and 188 corresponding mRNAs. For down-regulated miRNAs, the DEmiRNA-DEmRNA interaction network was consisted of 24 miRNAs and 305 corresponding mRNAs. QRT-PCR validation results of IRF6, PTGER3, NTRK2, hsa-miR-449a and hsa-miR-873-5p were in line with the GEO analysis result. RF6, PTGER3 and NTRK2 had a potential diagnostic value for endometriosis. In addition, the infiltration of macrophages M2 and NK cells activated was the most significantly increased and reduced in ectopic endometrial, respectively. Conclusions: These identified DEmRNAs and DEmiRNAs may be may be associated with the pathogenesis of endometriosis. The integrated analysis of miRNA and mRNA expression profiles may provide a new perspective for understanding the mechanisms of endometriosis and developing new treatments

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma.Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates.Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils.Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients

National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation

Multiplex LNA probe-based RAP assay for rapid and highly sensitive detection of rifampicin-resistant Mycobacterium tuberculosis

ObjectivesThe World Health Organization (WHO) Global tuberculosis Report 2021 stated that rifampicin-resistant tuberculosis (RR-TB) remains a major public health threat. However, the in-practice diagnostic techniques for RR-TB have a variety of limitations including longer time, lack of sensitivity, and undetectable low proportion of heterogeneous drug resistance.MethodsHere we developed a multiplex LNA probe-based RAP method (MLP-RAP) for more sensitive detection of multiple point mutations of the RR-TB and its heteroresistance. A total of 126 clinical isolates and 78 sputum samples collected from the National Tuberculosis Reference Laboratory, China CDC, were tested by MLP-RAP assay. In parallel, qPCR and Sanger sequencing of nested PCR product assay were also performed for comparison.ResultsThe sensitivity of the MLP-RAP assay could reach 5 copies/μl using recombinant plasmids, which is 20 times more sensitive than qPCR (100 copies/μl). In addition, the detection ability of rifampicin heteroresistance was 5%. The MLP-RAP assay had low requirements (boiling method) for nucleic acid extraction and the reaction could be completed within 1 h when placed in a fluorescent qPCR instrument. The result of the clinical evaluation showed that the MLP-RAP method could cover codons 516, 526, 531, and 533 with good specificity. 41 out of 78 boiled sputum samples were detected positive by MLP-RAP assay, which was further confirmed by Sanger sequencing of nested PCR product assay, on the contrary, qPCR was able to detect 32 samples only. Compared with Sanger sequencing of nested PCR product assay, both the specificity and sensitivity of the MLP-RAP assay were 100%.ConclusionMLP-RAP assay can detect RR-TB infection with high sensitivity and specificity, indicating that this assay has the prospect of being applied for rapid and sensitive RR-TB detection in general laboratories where fluorescent qPCR instrument is available

Structure of 136Sn and the Z = 50 magicity

The first 2+ excited state in the neutron-rich tin isotope 136Sn has been identified at 682(13) keV by measuring γ -rays in coincidence with the one proton removal channel from 137Sb. This value is higher than those known for heavier even-even N = 86 isotones, indicating the Z = 50 shell closure. It compares well to the first 2+ excited state of the lighter tin isotope 134Sn, which may suggest that the seniority scheme also holds for 136Sn. Our result confirms the trend of lower 2+ excitation energies of even-even tin isotopes beyond N = 82 compared to the known values in between the two doubly magic nuclei 100Sn and 132Sn. © The Author(s) 2014.published_or_final_versio

Substantial transition to clean household energy mix in rural China

The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements