Wide field of view optical phased array with a high-directionality antenna.

Light detection and ranging (LiDAR) is a widely utilized technology for extracting information from the outside world in fields such as automotive, robotics, and aerospace. Optical phased array (OPA) is a promising solution for LiDAR technology, although its application is limited by loss and alias-free steering range. In this paper, we propose a dual-layer antenna that achieves a peak directionality of over 92%, thereby mitigating antenna loss and enhancing power efficiency. Based on this antenna, we design and fabricate a 256-channel non-uniform OPA that achieves 150° alias-free steering.

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression.

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

Evaluating the value of p70s6k and mTOR signaling pathway in monitoring exercise-induced central fatigue in rats.

Exercise leads to muscle fatigue and decreased muscle strength in response to contraction activity, and besides, it causes central fatigue. In the current study, we evaluated the value of p70s6k and mTOR signaling pathways in monitoring exercise-induced central fatigue in rats. For this purpose, 12 male rats were divided into control (n=6) and intervention (n=6) groups. The intervention group performed five sessions of climbing a one-meter ladder with a weight hanging on the tail for eight weeks. The weekly load increase was based on the mice's body weight, so it reached 30% in the first week to 200% in the eighth week. In order to evaluate central fatigue, the sedation score system was used. Forty-eight hours after the last training session, a blood sample was prepared, the expression level of related proteins was measured by the ELISA method, and the one-way ANOVA method was used for statistical analysis. This study showed that central fatigue did not significantly affect the total mTOR protein content (F=0.720, P=0.421). However, the level of phosphorylated mTOR in the intervention group had a significant difference compared to the control group (F=684.893, P=0.001, Eta2=0.988). There was a significant effect for total p70S6K content (F=5.84, P=0.04, Eta2=0.42). Also, for phosphorylated p70S6K, there was a significant difference between the mentioned groups (F=7.262, P=0.027, Eta2=0.476). In General, it was shown in this study that central fatigue is directly related to the increase in p70S6K production and phosphorylation of p70S6K and mTOR. Therefore, these two proteins can probably be evaluated for monitoring exercise-induced central fatigue, although we need more evaluations.

Effects of sevoflurane exposure on apoptosis and cell cycle of peripheral blood lymphocytes, and immunologic function.

BACKGROUND: Waste anesthetic gases (WAGs) leaked from new-type halogenated inhalational anesthetics such as sevoflurane have been were reported to pose a risk for the health of operating room personnel. The effects of WAGs on peripheral blood lymphocytes, however, remain yet controversial. The present study was undertaken to examine the effects of occupational sevoflurane exposure on the peripheral blood lymphocytes of medical personnel who work in the operating room. METHODS: A cohort of 56 medical residents were divided into exposed group (n = 28) and control group (non-exposed group) (n = 28). Gas chromatography was used to measure the concentration of sevoflurane in the medical resident's breathing zone during surgeries under inhalation anesthesia in the exposure group. The gas collection lasted an hour. Peripheral blood lymphocytes were isolated from venous blood, and then apoptosis and cell cycle were analyzed by flow cytometry. EDTA-anticoagulated whole blood was harvested to analyze the lymphocyte subsets by flow cytometry. Immunoglobulins (IgA, IgM, IgG) were quantified by immunoturbidimetry. RESULTS: The average concentration of sevoflurane in the exposed group was 1.03 ppm with a range from 0.03 ppm to 2.24 ppm. No significant effects were found on the apoptosis rates or cell cycles of peripheral blood lymphocytes in the exposed group relative to the control group (P > 0.05). Similarly, there were no significant differences in the lymphocyte subsets or the levels of immunoglobulins (IgA, IgM, IgG) between the two groups (P > 0.05). CONCLUSIONS: Occupational exposure to low-level sevoflurane has no significant effect on the peripheral blood lymphocytes of operating room staff, but this conclusion needs to be confirmed by multicenter and long-term follow-up studies with large samples. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION: ChiCTR2000040772 , December 9, 2020 (Retrospective registration).

Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing.

Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.

Body fluids should be identified before estimating the time since deposition (TsD) in microbiome-based stain analyses for forensics.

Identification and the time since deposition (TsD) estimation of body fluid stains from a crime scene could provide valuable information for solving the cases and are always difficult for forensics. Microbial characteristics were considered as a promising biomarker to address the issues. However, changes in the microbiota may damage the specific characteristics of body fluids. Correspondingly, incorrect body fluid identification may result in inaccurate TsD estimation. The mutual influence is not well understood and limited the codetection. In the current study, saliva, semen, vaginal secretion, and menstrual blood samples were exposed to indoor conditions and collected at eight time points (from fresh to 30 days). High-throughput sequencing based on the 16S rRNA gene was performed to characterize the microbial communities. The results showed that a longer TsD could decrease the discrimination of different body fluid stains. However, the accuracies of identification still reached a quite high value even without knowing the TsD. Correspondingly, the mean absolute error (MAE) of TsD estimation significantly increased without distinguishing the types of body fluids. The predictive TsD of menstrual blood reached a quite low MAE (1.54 ± 0.39 d). In comparison, those of saliva (6.57 ± 1.17 d), semen (6.48 ± 1.33 d), and vaginal secretion (5.35 ± 1.11 d) needed to be further improved. The great effect of individual differences on these stains limited the TsD estimation accuracy. Overall, microbial characteristics allow for codetection of body fluid identification and TsD estimation, and body fluids should be identified before estimating TsD in microbiome-based stain analyses.IMPORTANCEEmerged evidences suggest microbial characteristics could be considered a promising tool for identification and time since deposition (TsD) estimation of body fluid stains. However, the two issues should be studied together due to a potential mutual influence. The current study provides the first evidence to understand the mutual influence and determines an optimal process for codetection of identification and TsD estimation for unknown stains for forensics. In addition, we involved aged stains into our study for identification of body fluid stains, rather than only using fresh stains like previous studies. This increased the predictive accuracy. We have preliminary verified that individual differences in microbiotas limited the predictive accuracy of TsD estimation for saliva, semen, and vaginal secretion. Microbial characteristics could provide an accurate TsD estimation for menstrual blood. Our study benefits the comprehensive understanding of microbiome-based stain analyses as an essential addition to previous studies.

Circadian metabolites for evaluating the timing of bloodstain deposition: A preliminary study.

Metabolites, as products of cellular metabolism, can provide a wealth of biological information and are less susceptible to degradation than other biomarkers due to their low molecular weight. Due to these properties, metabolites can be used as valuable biomarkers for forensic investigations. Knowing the timing of deposition of bloodstain could help to reconstruct crime scenes, draw conclusions about the time of the crime, and narrow down the circle of possible suspects. Previous studies have indicated that the concentration of some metabolites in blood is subject to circadian changes. However, the circadian metabolites of bloodstains have been still unclear. A total of sixty-four bloodstain samples were prepared under real conditions in three time categories (morning/noon (09:00 h ∼ 17:00 h), afternoon/evening (18:00 h ∼ 23:00 h) and night/early morning (24:00 h ∼ 08:00 h)). Fifty metabolites of bloodstains with significant differences were identified in the three time categories. Twenty-eight of these metabolites exhibited significant circadian changes. Finally, three independently contributing circadian metabolites were selected to build the logistic regression model, with an area under the curve of 0.91, 0.84 and 0.87 for the prediction of bloodstain deposition time in the morning/noon, afternoon/evening and night/early morning, respectively. The study indicated that circadian metabolites can be used for evaluating the timing of bloodstain deposition. This would provide a valuable perspective for analyzing the deposition time of biological traces in forensic investigations.

Estimation of bloodstain deposition time within a 24-h day-night cycle with rhythmic mRNA based on a machine learning algorithm.

Estimating the time that bloodstains are left at a crime scene can provide invaluable evidence for law enforcement investigations, including determining the time of the crime, linking the perpetrator to the crime scene, narrowing the pool of possible suspects, and verifying witness statements. There have been some attempts to estimate the time since deposition of bloodstains, i.e., how much time has passed since the bloodstain was left at a crime scene. However, most studies focus on the time interval of days. As far as we know, previous study have been conducted to estimate the deposition time of blood within a 24-h day-night cycle. To date, there is a lack of studies on whether rhythmic mRNA of blood is suitable for bloodstain samples. In this study, we estimated the bloodstain deposition time within a 24-h day-night cycle based on the expression of messenger RNAs (mRNAs) by real-time quantitative polymerase chain reaction. Bloodstain samples were prepared from eight individuals at eight time points under real and uncontrolled conditions. Four mRNAs expressed rhythmically and were used to construct a regression model using the k-nearest neighbor (KNN) algorithm, resulting in a mean absolute error of 3.92 h. Overall, using the rhythmic mRNAs, a machine learning model was developed which has allowed us to predict the deposition time of bloodstains within the 24-h day-night cycle in East Asian populations. This study demonstrates that mRNA biomarkers can be used to estimate the bloodstain deposition time within a 24-h period. Furthermore, rhythmic mRNA biomarkers provide a potential method and perspective for estimating the deposition time of forensic traces in forensic investigation. Case samples in forensic analysis are usually limited or degraded, so the stability and sensitivity of rhythmic biomarkers need to be further investigated.

The occurrence of lupus nephritis is regulated by USP7-mediated JMJD3 stabilization.

Ubiquitin-specific peptidases7 (USP7) participates in the regulation of various metabolic and immune disorders. However, the role of USP7 in lupus nephritis (LN) remains unknown. The current study set out to elucidate the regulatory role of USP7 in LN together with JMJD3 and NF-κB. SLE MRL/LPR mice and mouse glomerular mesangial cells SV40 MES 13 cells were employed for in vivo or vitro experiments. USP7, JMJD3 and NF-κB expression in MRL/LPR mice were detected, followed by investigation of their functions in the proliferation of mesangial cells and mesangial matrix. Subsequently, the interaction among USP7, JMJD3 and NF-κB was determined by means of ChIP and co-immunoprecipitation assay. The results indicated that USP7, JMJD3, p-NF-κB p65 were all highly-expressed in MRL/LPR mice. USP7 promoted the proliferation of mesangial cells and mesangial matrix, and stabilized the JMJD3 protein via deubiquitination in SV40 MES 13 cells. Meanwhile, silencing of JMJD3 inhibited the promotive effect of USP7 on the proliferation of mesangial cells and mesangial matrix. Furthermore, JMJD3 increased the expression of NF-κB p65 through demethylation, whereas silencing JMJD3 alleviated the proliferation of mesangial cells and mesangial matrix. Lastly, NF-κB p65 was proved to aggravate LN pathogenesis. Altogether, our findings highlighted that USP7 promoted the occurrence of LN by regulating the NF-κB p65 signaling pathway via stabilization of JMJD3.

LncRNA-BLACAT1 Facilitates Proliferation, Migration and Aerobic Glycolysis of Pancreatic Cancer Cells by Repressing CDKN1C via EZH2-Induced H3K27me3.

OBJECTIVE: To investigate the role of lncRNA-BLACAT1 in promoting H3K27 trimethylation of CDKN1C gene by recruiting EZH2 to regulate CCNE on glycolysis and mitochondrial oxidative phosphorylation of pancreatic cancer (PC) cells. METHODS: Following bioinformatic prediction, EZH2 and BLACAT1 in PC cells were interfered, and cells proliferation, migration and invasion in each group were detected. Western blotting detected the expression of key proteins of mitochondrial complex. The sub-cellular localization of BLACAT1 was tested, followed by testing the binding of CDKN1C and BLACAT1 with EZH2, followed by in vivo verification. RESULTS: Based on bioinformatic prediction, EZH2 and BLACAT1 were highly expressed in PC, while CDKN1C was lowly expressed (all P < 0.05). Interference with EZH2 and BLACAT1 inhibited cell proliferation, migration and aerobic glycolysis, and promoted mitochondrial oxidative phosphorylation (all P < 0.05). BLACAT1 promoted H3K27 trimethylation of CDKN1C through recruiting EZH2 (all P < 0.05). In vivo results showed that BLACAT1 interference inhibited tumor formation (all P < 0.05). CONCLUSION: Interference with BLACAT1 inhibits H3K27 trimethylation of CDKN1C gene by blocking EZH2 recruitment to promote CDKN1C expression and inhibit CCNE expression, thus suppressing PC cell proliferation, migration and aerobic glycolysis, and promoting mitochondrial oxidative phosphorylation.

Silencing Filamin A Inhibits the Invasion and Migration of Breast Cancer Cells by Up-regulating 14-3-3σ.

Filamin A and 14-3-3-σ are closely associated with the development of breast cancer. However, the exact relationship between them is still unknown. The present study aimed to examine the interaction of filamin A with 14-3-3-σ in the invasion and migration of breast cancer. RNA interference technology was employed to silence filamin A in MDA-MB-231 cells. Real-time PCR and Western blotting were used to detect the expression of filamin A and 14-3-3-σ at mRNA and protein levels, respectively. Double immunofluorescence was applied to show their colocalization morphologically. Wound healing assay and Trans-well assay were used to testify the migration and invasion of MDA-MB-231 cells in filamin A-silenced cells. The results showed that silencing filamin A significantly increased the mRNA and protein levels of 14-3-3σ. In addition, double immunofluorescence displayed that filamin A and 14-3-3σ were predominantly colocalized in the cytoplasm of MDA-MB-231 cells. Silencing filamin A led to the enhanced fluorescence of 14-3-3σ. Furthermore, cell functional experiments showed that silencing filamin A inhibited the migration and invasion of MDA-MB-231 cells in vitro. In conclusion, silencing filamin A may inhibit the invasion and migration of breast cancer cells by upregulating 14-3-3σ.

Correlation of epidermal growth factor receptor (EGFR), androgen receptor (AR) and 14-3-3 sigma expression in breast cancer.

Epidermal growth factor receptor (EGFR), androgen receptor (AR) and 14-3-3 sigma have been reported to be implicated in breast tumorigenesis. Their correlations, however, remain elusive in this condition. In order to examine the correlation of EGFR, AR and 14-3-3 sigma in breast cancer, and analyze their relationships with molecular subtypes of breast cancer and their impacts on overall survival, we immunohistochemistrically detected EGFR, AR and 14-3-3 sigma expression in 139 cases of breast cancer. We found that EGFR expression was negatively correlated with AR (r=-0.223, P=0.008) and positively with 14-3-3 sigma expression (r=0.181, P=0.033). There were significant differences in EGFR and AR expression between different molecular subtypes (P=0.000 and P=0.000 respectively). Kaplan-Meier cumulative survival analysis showed that none of the three biomarkers had significant impacts on overall survival of breast cancer patients (P=0.315, P=0.709, P=0.789 respectively). Univariate survival analysis revealed that tumor size (P=0.044), lymph node status (P=0.006) and clinical stage (P=0.008) were significantly associated with overall survival. Multivariate analysis demonstrated that lymph node status was the only statistically significant independent prognostic factor for overall survival [P=0.006, exp (B) =1.511, CI (1.124-2.032)]. In conclusion, EGFR expression is negatively correlated with AR and positively with 14-3-3 sigma expression in breast cancer. Furthermore, there are significant differences in EGFR and AR expression between various molecular subtypes of breast cancer. Lastly, EGFR, AR and 14-3-3 sigma have no significant impacts on overall survival of breast cancer patients.