Transcription factors MhDREB2A/MhZAT10 play a role in drought and cold stress response crosstalk in apple.

Drought and cold stresses seriously affect tree growth and fruit yield during apple (Malus domestica) production, with combined stress causing injury such as shoot shriveling. However, the molecular mechanism underlying crosstalk between responses to drought and cold stress remains to be clarified. In this study, we characterized the zinc finger transcription factor ZINC FINGER OF ARABIDOPSIS THALIANA 10 (ZAT10) through comparative analysis of shoot-shriveling tolerance between tolerant and sensitive apple rootstocks. MhZAT10 responded to both drought and cold stresses. Heterologous expression of MhZAT10 in the sensitive rootstock 'G935' from domesticated apple (Malus domestica) promoted shoot-shriveling tolerance, while silencing of MhZAT10 expression in the tolerant rootstock 'SH6' of Malus honanensis reduced stress tolerance. We determined that the apple transcription factor DEHYDRATION RESPONSE ELEMENT-BINDING PROTEIN 2A (DREB2A) is a direct regulator activating the expression of MhZAT10 in response to drought stress. Apple plants overexpressing both MhDREB2A and MhZAT10 genes exhibited enhanced tolerance to drought and cold stress, while plants overexpressing MhDREB2A but with silenced expression of MhZAT10 showed reduced tolerance, suggesting a critical role of MhDREB2A-MhZAT10 in the crosstalk between drought and cold stress responses. We further identified drought-tolerant MhWRKY31 and cold-tolerant MhMYB88 and MhMYB124 as downstream regulatory target genes of MhZAT10. Our findings reveal a MhDREB2A-MhZAT10 module involved in crosstalk between drought and cold stress responses, which may have applications in apple rootstock breeding programs aimed at developing shoot-shriveling tolerance.

Evaluation of gadolinium deposition in cortical bone using three-dimensional ultrashort echo time quantitative susceptibility mapping: A preliminary study.

The aim of the current study was to investigate the feasibility of three-dimensional ultrashort echo time quantitative susceptibility mapping (3D UTE-QSM) for the assessment of gadolinium (Gd) deposition in cortical bone. To this end, 40 tibial bovine cortical bone specimens were divided into five groups then soaked in phosphate-buffered saline (PBS) solutions with five different Gd concentrations of 0, 0.4, 0.8, 1.2, and 1.6 mmol/L for 48 h. Additionally, eight rabbits were randomly allocated into three groups, consisting of a normal-dose macrocyclic gadolinium-based contrast agent (GBCA) group (n = 3), a high-dose macrocyclic GBCA group (n = 3), and a control group (n = 2). All bovine and rabbit tibial bone samples underwent magnetic resonance imaging (MRI) on a 3-T clinical MR system. A 3D UTE-Cones sequence was utilized to acquire images with five different echo times (i.e., 0.032, 0.2, 0.4, 0.8, and 1.2 ms). The UTE images were subsequently processed with the morphology-enabled dipole inversion algorithm to yield a susceptibility map. The average susceptibility was calculated in three regions of interest in the middle of each specimen, and the Pearson's correlation between the estimated susceptibility and Gd concentration was calculated. The bone samples soaked in PBS with higher Gd concentrations exhibited elevated susceptibility values. A mean susceptibility value of -2.47 ± 0.23 ppm was observed for bovine bone soaked in regular PBS, while the mean QSM value increased to -1.75 ± 0.24 ppm for bone soaked in PBS with the highest Gd concentration of 1.6 mmol/L. A strong positive correlation was observed between Gd concentrations and QSM values. The mean susceptibility values of rabbit tibial specimens in the control group, normal-dose GBCA group, and high-dose GBCA group were -4.11 ± 1.52, -3.85 ± 1.33, and -3.39 ± 1.35 ppm, respectively. In conclusion, a significant linear correlation between Gd in cortical bone and QSM values was observed. The preliminary results suggest that 3D UTE-QSM may provide sensitive noninvasive assessment of Gd deposition in cortical bone.

Lipidomic signatures in patients with early-onset and late-onset Preeclampsia.

BACKGROUND: Preeclampsia is a pregnancy-specific clinical syndrome and can be subdivided into early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) according to the gestational age of delivery. Patients with preeclampsia have aberrant lipid metabolism. This study aims to compare serum lipid profiles of normal pregnant women with EOPE or LOPE and screening potential biomarkers to diagnose EOPE or LOPE. METHODS: Twenty normal pregnant controls (NC), 19 EOPE, and 19 LOPE were recruited in this study. Untargeted lipidomics based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to compare their serum lipid profiles. RESULTS: The lipid metabolism profiles significantly differ among the NC, EOPE, and LOPE. Compared to the NC, there were 256 and 275 distinct lipids in the EOPE and LOPE, respectively. Furthermore, there were 42 different lipids between the LOPE and EOPE, of which eight were significantly associated with fetal birth weight and maternal urine protein. The five lipids that both differed in the EOPE and LOPE were DGTS (16:3/16:3), LPC (20:3), LPC (22:6), LPE (22:6), PC (18:5e/4:0), and a combination of them were a potential biomarker for predicting EOPE or LOPE. The receiver operating characteristic analysis revealed that the diagnostic power of the combination for distinguishing the EOPE from the NC and for distinguishing the LOPE from the NC can reach 1.000 and 0.992, respectively. The association between the lipid modules and clinical characteristics of EOPE and LOPE was investigated by the weighted gene co-expression network analysis (WGCNA). The results demonstrated that the main different metabolism pathway between the EOPE and LOPE was enriched in glycerophospholipid metabolism. CONCLUSIONS: Lipid metabolism disorders may be a potential mechanism of the pathogenesis of preeclampsia. Lipid metabolites have the potential to serve as biomarkers in patients with EOPE or LOPE. Furthermore, lipid metabolites correlate with clinical severity indicators for patients with EOPE and LOPE, including fetal birth weight and maternal urine protein levels.

Alterations in gut microbiota contribute to cognitive deficits induced by chronic infection of Toxoplasma gondii.

Chronic infection with Toxoplasma gondii (T. gondii) emerges as a risk factor for neurodegenerative diseases in animals and humans. However, the underlying mechanisms are largely unknown. We aimed to investigate whether gut microbiota and its metabolites play a role in T. gondii-induced cognitive deficits. We found that T. gondii infection induced cognitive deficits in mice, which was characterized by synaptic ultrastructure impairment and neuroinflammation in the hippocampus. Moreover, the infection led to gut microbiota dysbiosis, barrier integrity impairment, and inflammation in the colon. Interestingly, broad-spectrum antibiotic ablation of gut microbiota attenuated the adverse effects of the parasitic infection on the cognitive function in mice; cognitive deficits and hippocampal pathological changes were transferred from the infected mice to control mice by fecal microbiota transplantation. In addition, the abundance of butyrate-producing bacteria and the production of serum butyrate were decreased in infected mice. Interestingly, dietary supplementation of butyrate ameliorated T. gondii-induced cognitive impairment in mice. Notably, compared to the healthy controls, decreased butyrate production was observed in the serum of human subjects with high levels of anti-T. gondii IgG. Overall, this study demonstrates that gut microbiota is a key regulator of T. gondii-induced cognitive impairment.

Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study.

PURPOSE: This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)-based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients. METHODS: We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient's T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles. RESULTS: TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 - 0.923; p = 0.025). CONCLUSION: MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.

Facial adult female acne in China: An analysis based on artificial intelligence over one million.

BACKGROUND: To further clarify the acne profile of Chinese adult women, we included 1,156,703 adult women. An artificial intelligence algorithm was used to analyze images taken by high-resolution mobile phones to further explore acne levels in Chinese adult women. METHOD: In this study, we assessed the severity of acne by evaluating patients' selfies through a smartphone application. Furthermore, we gathered basic user information through a questionnaire, including details such as age, gender, skin sensitivity, and dietary habits. RESULTS: This study showed a gradual decrease in acne severity from the age of 25 years. A trough was reached between the ages of 40 and 44, followed by a gradual increase in acne severity. In terms of skin problems and acne severity, we have found that oily skin, hypersensitive skin, frequent makeup application and unhealthy dietary habits can affect the severity of acne. For environment and acne severity, we observed that developed city levels, cold seasons and high altitude and strong radiation affect acne severity in adult women. For the results of the AI analyses, the severity of blackheads, pores, dark circles and skin roughness were positively associated with acne severity in adult women. CONCLUSIONS: AI analysis of high-res phone images in Chinese adult women reveals acne severity trends. Severity decreases after 25, hits a low at 40-44, then gradually rises. Skin type, sensitivity, makeup, diet, urbanization, seasons, altitude, and radiation impact acne. Blackheads, pores, dark circles, and skin roughness are linked to acne severity. These findings inform personalized skincare and public health strategies for adult women.

Design, Synthesis and Bioactivities of Novel Pyridyl Containing Pyrazole Oxime Ether Derivatives.

In this research, with an aim to develop novel pyrazole oxime ether derivatives possessing potential biological activity, thirty-two pyrazole oxime ethers, including a substituted pyridine ring, have been synthesized and structurally identified through 1H NMR, 13C NMR, and HRMS. Bioassay data indicated that most of these compounds owned strong insecticidal properties against Mythimna separata, Tetranychus cinnabarinus, Plutella xylostella, and Aphis medicaginis at a dosage of 500 µg/mL, and some title compounds were active towards Nilaparvata lugens at 500 µg/mL. Furthermore, some of the designed compounds had potent insecticidal effects against M. separata, T. cinnabarinus, or A. medicaginis at 100 µg/mL, with the mortalities of compounds 8a, 8c, 8d, 8e, 8f, 8g, 8o, 8s, 8v, 8x, and 8z against A. medicaginis, in particular, all reaching 100%. Even when the dosage was lowered to 20 µg/mL, compound 8s also expressed 50% insecticidal activity against M. separata, and compounds 8a, 8e, 8f, 8o, 8v, and 8x displayed more than 60% inhibition rates against A. medicaginis. The current results provided a significant basis for the rational design of biologically active pyrazole oxime ethers in future.

Peritumoral Radiomics for Identification of Telomerase Reverse Transcriptase Promoter Mutation in Patients With Glioblastoma Based on Preoperative MRI.

Purpose: To evaluate the value of intra- and peritumoral deep learning (DL) features based on multi-parametric magnetic resonance imaging (MRI) for identifying telomerase reverse transcriptase (TERT) promoter mutation in glioblastoma (GBM). Methods: In this study, we included 229 patients with GBM who underwent preoperative MRI in two hospitals between November 2016 and September 2022. We used four 2D Convolutional Neural Networks (GoogLeNet, DenseNet121, VGG16, and MobileNetV3-Large) to extract intra- and peritumoral DL features. The Mann-Whitney U test, Pearson correlation analysis, least absolute shrinkage and selection operator, and logistic regression analysis were used for feature selection and construction of DL radiomics (DLR) signatures in different regions. These multi-parametric and multi-region signatures were combined to identify TERT promoter mutation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effects of the signatures. Results: The signatures based on the DL features from the peritumoral regions with expansion distances of 2 mm, 8 mm, and 10 mm using the GoogLeNet architecture correlated with the optimal AUC values (test set: .823, .753, and .768) in the T2-weighted, T1-weighted contrast-enhanced, and T1-weighted images. Using the stacking fusion method, DLR with multi-parameter and multi-region fusion achieved the best discrimination with AUC values of .948 and .902 in the training and test sets, respectively. Conclusions: The radiomics model based on the fusion of multi-parameter MRI intra- and peritumoral DLR signatures may help to identify TERT promoter mutation in patients with GBM.

Spatiotemporal patterns and evolutionary trends of eco-environmental quality in arid regions of Northwest China.

The arid regions of northwest China suffer from water shortages, low land quality, and a fragile ecological environment, while social and economic development has increased the ecological and environmental load. The spatiotemporal pattern and evolutionary trend of ecological environmental quality were investigated by constructing a remote sensing-based ecological environmental index (EQI) evaluation model incorporating four indicators: drought index (DI), soil erosion index (SEI), greenness index (GI), and carbon exchange index (CEI). The study found that between 2001 and 2020, the DI, the SEI, and the CEI in the northwest arid region exhibited a downward trend with reduction rates of - 3e-05, -0.0006, and -0.0018, respectively. However, the GI demonstrated an upward trend, with a growth rate of 0.002. The average EQI in 2020 was 0.315, indicating a fair grade, with only 11.56% falling above the medium level. A general increasing trend was observed throughout the study period in EQI, with an incremental rate of 0.0002. Areas with future improvements in EQI accounted for 57.547% and were principally located in the eastern part of Inner Mongolia, Qinghai, and the northern and southern portions of Xinjiang. Notably, land use was significantly correlated with EQI (p < 0.01), with a hierarchy of effects that ran: forest land (0.678) > cultivated land (0.422) > grassland (0.382) > wasteland (0.138). The highly robust findings presented here offer innovative methods for ecological and environmental monitoring in the arid region of the northwest, with potential implications at an international scale.

A newly isolated bacteriophage vB8388 and its synergistic effect with aminoglycosides against multi-drug resistant Klebsiella oxytoca strain FK-8388.

The bacteriophage vB8388 can lyse multi-drug resistant Klebsiella oxytoca strain FK-8388 and maintain stability in a wide range of temperatures (from 4 °C to 80 °C) and pHs (3-11). Bioinformatics analysis showed that vB8388 is a linear double-stranded DNA virus that is 39,750 long with 50.65% G + C content and 44 putative open reading frames (ORFs). Phage vB8388 belongs to the family Autographviridae and possesses a non-contractile tail. The latency period of vB8388 was approximately 20 min. The combination of phage vB8388 and gentamicin, amikacin, or tobramycin could effectively inhibit the growth of K. oxytoca strain FK-8388, with a decrease of more than 4 log units within 12 h in vitro. Phage vB8388 showed a strong synergistic effect with gentamicin that could enhance the anti-biofilm effect of vB8388. The phage + gentamicin combination also showed synergy in vivo in the larval infection model of Galleria mellonella. In conclusion, the findings of this study suggest the potential of phage + antibiotic combination therapy to be used as an alternative therapeutic approach for treating infectious diseases caused by multidrug-resistant bacteria.

Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI.

BACKGROUND: Studies have shown that magnetic resonance imaging (MRI)-based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status in patients with glioblastoma (GBM) remains unclear. PURPOSE: To evaluate the value of deep learning (DL) in multiparametric MRI-based radiomics in identifying TERT promoter mutations in patients with GBM preoperatively. STUDY TYPE: Retrospective. POPULATION: A total of 274 patients with isocitrate dehydrogenase-wildtype GBM were included in the study. The training and external validation cohorts included 156 (54.3 ± 12.7 years; 96 males) and 118 (54 .2 ± 13.4 years; 73 males) patients, respectively. FIELD STRENGTH/SEQUENCE: Axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1CE), T1-weighted spin-echo inversion recovery sequence (T1WI), and T2-weighted spin-echo inversion recovery sequence (T2WI) on 1.5-T and 3.0-T scanners were used in this study. ASSESSMENT: Overall tumor area regions (the tumor core and edema) were segmented, and the radiomics and DL features were extracted from preprocessed multiparameter preoperative brain MRI images-T1WI, T1CE, and T2WI. A model based on the DLR signature, clinical signature, and clinical DLR (CDLR) nomogram was developed and validated to identify TERT promoter mutation status. STATISTICAL TESTS: The Mann-Whitney U test, Pearson test, least absolute shrinkage and selection operator, and logistic regression analysis were applied for feature selection and construction of radiomics and DL signatures. Results were considered statistically significant at P-value <0.05. RESULTS: The DLR signature showed the best discriminative power for predicting TERT promoter mutations, yielding an AUC of 0.990 and 0.890 in the training and external validation cohorts, respectively. Furthermore, the DLR signature outperformed CDLR nomogram (P = 0.670) and significantly outperformed clinical models in the validation cohort. DATA CONCLUSION: The multiparameter MRI-based DLR signature exhibited a promising performance for the assessment of TERT promoter mutations in patients with GBM, which could provide information for individualized treatment. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Artificial intelligence analysis of over a million Chinese men and women reveals level of dark circle in the facial skin aging process.

BACKGROUND: To better compare the progression of dark circles and the aging process in Chinese skin. A total of 100 589 Chinese males and 1 838 997 Chinese females aged 18 to 85, without facial skin conditions, and who had access to a smartphone with a high-resolution camera all took selfies. METHOD: Using a smartphone application with a built-in artificial intelligence algorithm, facial skin diagnostic evaluated the selfies and score the severity of the dark circles with four other facial indicators (including skin type, Pores, Acne vulgaris, and Blackheads). Basic information was collected with online questionnaire, including their age, gender, skin sensitivity, and dietary habits. RESULTS: In users between the age of 18 and 59, the prevalence of comprehensive, pigmented, and structural type of dark circles all rose with age. However, between the age of 60 and 85, the intensity of all types of dark circles diminished. Besides, vascular dark circles progressively worsen from the age of 18 to their peak at 39, and then gradually decline with age. Females typically have more pronounced black circles under their eyes than males in China. Bad eating habits, urbanization, regular cosmetics use, and sensitive skin positively correlate with severe dark circles. Vascular, comprehensive dark circles were worse in spring. Both pigmented and structural dark circles were worse in the summer. The results indicated that the intensity of dark circles was influenced by oily skin, wide pores, severe blackheads, and severe acne. CONCLUSIONS: Chinese men and women differed noticeably in the prevalence of each face aging indicator and the appearance of aging dark circles. Selfies could be automatically graded and examined by artificial intelligence, which is a quick and private method for quantifying signs of facial aging and identifying major problems for different populations. Artificial intelligence would assist in the development of individualized preventive and therapeutic interventions.

Molecular detection and species identification of Plasmodium spp. infection among imported cases in Eastern China, 2012-2018.

Due to the increasing number of returnees from malaria endemic areas, imported malaria has become a public health challenge in China. To better understand the characteristics of imported Plasmodium species and adjust appropriate strategies for malaria prevention and control in Eastern China, we conducted molecular detection and species identification on 1282 imported malaria cases in Shandong Province between 2012 and 2018. The findings showed that P. falciparum was predominant, particularly in cases imported from Africa. P. vivax was the dominant species imported from Asian countries. Additionally, imported P. ovale and P. malariae emerged in the province. Further surveillance and control of imported malaria among returnees from Africa and Southeast Asia is needed to be strengthened in Eastern China.

An analytical model for simulating the rainfall-interception-infiltration-runoff process with non-uniform rainfall.

The rainfall runoff process is a critical factor in determining the transport of surface materials. Simulating the surface runoff process is fundamental to accurately characterize soil erosion and soil nutrient loss. This research aims to develop a comprehensive simulation model for rainfall-interception-infiltration-runoff under vegetation cover conditions. The model integrates three key components: a vegetation interception model, Philip's infiltration model, and a kinematic wave model. By combining these models, an analytical solution is derived to simulate slope runoff considering vegetation interception and infiltration during non-constant rainfall events. To validate the reliability of the analytical solution, a numerical solution was obtained using the Pressimann Box scheme method and compared with the analytical results. The comparison confirms the accuracy and robustness of the analytical solution (R2 = 0.984, RMSE = 0.0049 cm/min, NS = 0.969). Moreover, this study investigates the influence of two significant parameters, Intm and k, on the production flow process. The analysis reveals that both parameters exert a significant impact on the timing of production initiation and the magnitude of runoff. Specifically, Intm exhibits a positive correlation with runoff intensity, while k displays a negative correlation. This research introduces a novel simulation method that enhances our understanding and modeling of rainfall production and convergence under complex slope conditions. The proposed model provides valuable insights into rainfall-runoff dynamics, particularly in scenarios characterized by varying rainfall patterns and vegetation cover. Overall, this study contributes to advancing the field of hydrological modeling and offers a practical approach for quantifying soil erosion and nutrient loss under different environmental conditions.

Methylome and transcriptome analyses of three different degrees of albinism in apple seedlings.

BACKGROUND: Leaf colour mutations are universally expressed at the seedling stage and are ideal materials for exploring the chlorophyll biosynthesis pathway, carotenoid metabolism and the flavonoid biosynthesis pathway in plants. RESULTS: In this research, we analysed the different degrees of albinism in apple (Malus domestica) seedlings, including white-leaf mutants (WM), piebald leaf mutants (PM), light-green leaf mutants (LM) and normal leaves (NL) using bisulfite sequencing (BS-seq) and RNA sequencing (RNA-seq). There were 61,755, 79,824, and 74,899 differentially methylated regions (DMRs) and 7566, 3660, and 3546 differentially expressed genes (DEGs) identified in the WM/NL, PM/NL and LM/NL comparisons, respectively. CONCLUSION: The analysis of the methylome and transcriptome showed that 9 DMR-associated DEGs were involved in the carotenoid metabolism and flavonoid biosynthesis pathway. The expression of different transcription factors (TFs) may also influence the chlorophyll biosynthesis pathway, carotenoid metabolism and the flavonoid biosynthesis pathway in apple leaf mutants. This study provides a new method for understanding the differences in the formation of apple seedlings with different degrees of albinism.

Synergistic effect of the novel ß-lactamase inhibitor BLI-489 combined with imipenem or meropenem against diverse carbapenemase-producing carbapenem-resistant Enterobacterales.

OBJECTIVES: To investigate the antibacterial activity of the novel ß-lactamase inhibitor BLI-489 combined with imipenem or meropenem against diverse carbapenemase-producing carbapenem-resistant Enterobacterales (CRE) in vivo and in vitro. METHODS: Twenty-five CRE strains, including Klebsiella pneumoniae (n = 10), Escherichia coli (n = 6) and Enterobacter cloacae (n = 9), were used in chequerboard assays to evaluate the synergistic effect of BLI-489 combined with imipenem or meropenem. A cytotoxicity test was used to detect the toxicity of BLI-489 monotherapy or combination therapy. Three isolates producing class A, B and D carbapenemases, respectively, were selected to further confirm the synergistic effect in vitro by time-kill assays and in vivo by the Galleria mellonella infection model. RESULTS: Chequerboard assays demonstrated that BLI-489 combined with imipenem had a synergistic effect on 7/10, 7/9 and 5/6 of carbapenem-resistant K. pneumoniae, E. cloacae and E. coli, respectively, while BLI-489 and meropenem had a synergistic effect on 8/10, 9/9 and 6/6 of the isolates, respectively. No cytotoxicity was observed when BLI-489 was used alone or in combination with imipenem or meropenem at the test concentrations. In the time-kill assays, combination therapy had a synergistic effect on DC5114 carrying blaKPC-2, FK8401 carrying blaNDM-5 and CG996 carrying blaOXA-23. The synergistic effect in vivo was confirmed by the G. mellonella infection model. CONCLUSIONS: The novel ß-lactamase inhibitor BLI-489 possesses a synergistic effect against diverse carbapenemase-producing CRE combined with imipenem or meropenem.

Near-Field Microwave Scattering Formulation by A Deep Learning Method.

A deep learning method is applied to modelling electromagnetic (EM) scattering for microwave breast imaging (MBI). The neural network (NN) accepts 2D dielectric breast maps at 3 GHz and produces scattered-field data on an antenna array composed of 24 transmitters and 24 receivers. The NN was trained by 18,000 synthetic digital breast phantoms generated by generative adversarial network (GAN), and the scattered-field data pre-calculated by method of moments (MOM). Validation was performed by comparing the 2,000 NN-produced datasets isolated from the training data with the data computed by MOM. Finally, data generated by NN and MOM were used for image reconstruction. The reconstruction demonstrated that errors caused by NN would not significantly affect the image result. But the computational speed of NN was nearly 104 times faster than the MOM, indicating that deep learning has the potential to be considered as a fast tool for EM scattering computation.

Dielectric Breast Phantoms by Generative Adversarial Network.

In order to conduct the research of machine-learning (ML) based microwave breast imaging (MBI), a large number of digital dielectric breast phantoms that can be used as training data (ground truth) are required but are difficult to be achieved from practice. Although a few dielectric breast phantoms have been developed for research purpose, the number and the diversity are limited and is far inadequate to develop a robust ML algorithm for MBI. This paper presents a neural network method to generate 2D virtual breast phantoms that are similar to the real ones, which can be used to develop ML-based MBI in the future. The generated phantoms are similar but are different from those used in training. Each phantom consists of several images with each representing the distribution of a dielectric parameter in the breast map. Statistical analysis was performed over 10,000 generated phantoms to investigate the performance of the generative network. With the generative network, one may generate unlimited number of breast images with more variations, so the ML-based MBI will be more ready to deploy.

Persistence of Salmonella Typhimurium and antibiotic resistance genes in different types of soil influenced by flooding and soil properties.

Salmonella is a zoonotic foodborne bacterial pathogen that can seriously harm health. Persistence of Salmonella and antibiotic resistance genes (ARGs) in different types of soil under flooding and natural conditions are rare explored. This study investigated the dynamic changes of the Salmonella, ARGs and bacterial communities in three types of soils applied with pig manure in lab scale. Abundance of the Salmonella Typhimurium in soils reduced to the detection limit varied from 40 to 180 days, most of the Salmonella did not survive in soil for more than 90 days. Flooding and soil texture (content of sand) promote the decline rate of Salmonella. No Salmonella was found have acquired resistance gene from the soil or manure after 90 days. 64 ARGs and 11 MGEs were quantified, abundance of these genes and risky ARGs both gradually decline along with the extension of time. Most of the extrinsic ARGs cannot colonize in soil, cellular protection and antibiotic deactivation were their main resistance mechanism. Multidrug resistance and efflux pump were the dominant class and mechanism of soil intrinsic ARGs. Flooding can affect the ARGs profiles by reducing the types of extrinsic ARGs invaded into soil and inhibit the proliferation of intrinsic genes. Soil sand content, soil moisture and nutrition concentrations had significant direct effect on the abundance or profile of ARGs. Soil bacterial community structures also changed along with the extension of time and affected by flooding. Network analyses between ARGs and bacteria taxa revealed that Actinobacteria and Myxococcia were the main hosts of intrinsic ARGs, some taxa may play a role in inhibiting extrinsic ARGs colonization in the soils. These findings unveil that saturate soil with water may play a positive role in reducing potential risk of Salmonella and ARGs in the farmland environment.

Three new polyketide derivatives of the endophytic fungus Aspergillus cristatus 2H1.

Three new polyketide derivatives, 2-ethoxycarbonyl-endocrocin (1), 6-methoxy-2-ethoxycarbonyl-endocrocin (2) and pannorin C (3), along with sixteen known compounds (4-19) were isolated from a plant endophytic fungus Aspergillus cristatus 2H1. Their structures were elucidated by 1D/2D NMR and HR-ESI-MS data analysis. Compound 3 showed weak antibacterial activity against Staphylococcus aureus (MIC 20 µg/ml). Compounds 14 and 15 showed effective cytotoxicity on human melanoma A375 cells (IC50 4.13 µM for 14, 3.39 µM for 15).