Log Values Research Articles

Plasma-Activated Tap Water Production and Its Application in Atomization Disinfection

Plasma-activated tap water (PATW) is a new technology for obtaining a high concentration of active aqueous plasma substances by discharging underwater. Commonly plasma-activated water (PAW) is realized by activating deionized water or distilled water, which has problems such as high cost, a small discharge area, and insufficient dissolution of active substances. This paper reports the development of a dielectric barrier discharge array to generate a high concentration of active aqueous plasma substances. The device can realize a uniform, stable, and large-area discharge in a large volume of tap water, and it has the advantages of low cost, high integration, and reusability. Using the device to treat 1000 mL of tap water for 1 h can reduce the pH of the tap water from 8.10 to 2.54, and the logarithmic value of killing E. coli is greater than 5.0. We sprayed PATW onto clothes to sterilize the bacteria when people were through the disinfection door and verified that the PATW had a good sterilization effect. The short-lived substances, singlet oxygen, and superoxide anion radicals played a key role in the sterilization process by PATW.

Assigning Viscosity Values in the Glass Softening Temperature Range

A new optical method for assigning glass viscosity values in the softening temperature range is presented. In this method, an irregular particle, a few millimeters in size, laying on an alumina plate, is heated up to temperature T, and then remains at this temperature. T should be within the softening temperature range of the glass. There are no external applied shear stresses, the only acting shear forces are those coming from the particle's own surface energy. At the fixed temperature T, the surface free energy of the sample decreases by viscous flow while its shape evolves from a polyhedron or irregular shape towards a spherical or rounded shape. This shape evolution is recorded using a photographic camera. From each image, the sample's roundness is determined, obtaining a characteristic time τ from the roundness against time. Simultaneously, using the available software, a value for the viscosity η was calculated, at temperature T, allowing for building sets of T, τ, η, namely three data values. Accordingly, if T, τ are considered as independent variables, a master function η = η (T, τ) can be built. Now, if we measure T, τ data on a glass of an unknown viscosity, the master function makes it possible to assign a η value. When incipient crystallization or liquid-liquid phase separations are present, effective viscosity values are obtained. This method requires a high temperature microscope, as well as tridimmensional samples with a few cubic millimeters of volume. Each isothermal τ determination can take from minutes to several hours. We tested the method with two glasses of known viscosity values: borosilicate glass (VG98) and alumimoborosilicate glass (SG7), both of which are used for radioactive waste immobilization and have assigned log(η) values between 6 and 7.3 with η in Pa s. The discrepancy between the log(η) values assigned here and those values fitted with a VFT function on the values measured for the SG7 and VG98 glasses were within ±14%.

Serum C1q/TNF-Related Protein 4 Levels are Associated with Nonalcoholic Fatty Liver Disease in Type 2 Diabetic Patients.

Introduction: There is a strong bidirectional relationship between nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), both of which can lead to an increase in harmful metabolism and cardiovascular risk. It was discovered that C1q/TNF-related protein 4 (CTRP4) regulates glucolipid metabolism and feeding behavior. However, the correlation between serum CTRP4 and NAFLD in T2DM patients is not yet fully understood. Methods: This study enrolled 188 T2DM participants who were separated into 2 distinct groups (NAFLD and non-NAFLD) according to abdominal ultrasound imaging results. The enzyme-linked immunosorbent assay was utilized to evaluate the levels of serum CTRP4. Clinical data and CTRP4 concentration were compared between the two groups. Linear and logistic regression analyses were performed to evaluate the correlation of serum CTRP4 levels with NAFLD risk in T2DM patients. Results: Compared with non-NAFLD, the concentration of CTRP4 was lower in NAFLD group (median 2.46 vs. 2.89, P < 0.001). The log(CTRP4) value was found to be negatively correlated with alanine aminotransferase, aspartate aminotransferase, body mass index (BMI), and waist circumference in a Pearson correlation analyses (r = -0.159, -0.156, -0.224, -0.268, all P < 0.05); besides, the trend χ2 test demonstrated that the prevalence of NAFLD rose as CTRP4 concentration decreased (P < 0.001). Regression analysis suggested that NAFLD served as an independent factor influencing log(CTRP4) independently (β-coefficient = -0.12, P = 0.011), even after adjusting for high-sensitivity C-reactive protein and white blood cells. Finally, the results of the logistic regression analysis demonstrated that BMI [odds ratio (OR) = 1.196, P = 0.028], triglyceride (OR = 2.744, P < 0.001), and CTRP4 (OR = 0.615, P = 0.032) were independently associated with NAFLD in T2DM. Conclusions: T2DM patients with NAFLD have lower CTRP4 serum concentrations than those without NAFLD. The risk of NAFLD in patients with T2DM is inversely correlated with serum CTRP4 levels.

Low cycle fatigue properties of porcine aorta — Pilot study

IntroductionBiomechanical rupture risk assessment of aortic tissues is commonly based on computed stress to measured uniaxial static strength comparison. Loading of the arterial wall, however, is cyclic; thus, the static strength may not be a proper limit value. This study investigates the low cycle fatigue of porcine aortic samples tested in a circumferential direction. Methods7 porcine descending aorta (both thoracic and abdominal) were harvested and 56 dogbone-shaped samples were prepared. Static strength was measured, the limit of engineering stress was chosen and then force controlled cyclic loading was performed up to 100,000 cycles. Efforts were made to obtain a sufficient number of points across the entire range of 0-100,000 cycles. Data were fitted by both linear and logarithmic law and extrapolated towards 1 cycle for validation against static strength/ultimate tension. Data dispersion was evaluated via normalised root mean square error. ResultsOut of 56 samples from 7pigs, 28 samples from 4 pigs were successfully tested. There was a strong negative correlation between applied stress/tension and number of cycles to failure. The fitting of both linear and logarithmic values resulted in a similar accuracy (R2=0.72 and 0.71 for stress and R2=0.62 and 0.7 for tension, respectively), while predicting static failure properties was more accurate by logarithmic law. NRMSE was lower for absolute values (20–21%) than for relative values (27–30%). ConclusionsAbsolute values of cyclic strength and tension are less dispersed than relative ones. Logarithmic fits are more robust in predicting static strength from cyclic data, while linear fits serve as a lower limit estimation.

The temperature dependency of Wolf–Rayet-type mass loss

Context. The mass loss of helium-burning stars, which are partially or completely stripped of their outer hydrogen envelope, is a catalyst of the cosmic matter cycle and decisive ingredient of massive star evolution. Yet, its theoretical fundament is only starting to emerge with major dependencies still to be uncovered. Aims. A temperature or radius dependence is usually not included in descriptions for the mass loss of classical Wolf-Rayet (cWR) stars, despite being crucial for other hot star wind domains. We thus aim to determine whether such a dependency will also be necessary for a comprehensive description of mass loss in the cWR regime. Methods. Sequences of dynamically consistent stellar atmosphere models were calculated with the hydrodynamic branch of the PoWR code along the temperature domain, using different choices for the luminosity, mass, and surface abundances. For the first time, we allowed nonmonotonic velocity fields when solving the hydrodynamic equation of motion. The resulting velocity structures were then interpolated for the comoving-frame radiative transfer, ensuring that the main wind characteristics were preserved. Results. We find a strong dependence of the mass-loss rate with the temperature of the critical/sonic point which mainly reflects the different radii and resulting gravitational accelerations. Moreover, we obtain a relation between the observed effective temperature and the transformed mass-loss rate Ṁt which seems to be largely independent of the underlying stellar parameters. The relation is shifted when different density contrasts are assumed for the wind clumping. Below a characteristic value of log (Ṁt [M⊙ yr−1]) −4.5, the slope of this relation changes and the winds become transparent for He II ionizing photons. Conclusions. The mass loss of cWR stars is a high-dimensional problem but also shows inherent scalings which can be used to obtain an approximation of the observed effective temperature. For a more realistic treatment of cWR stars and their mass loss in stellar evolution, we recommend the inclusion of a temperature dependency and ideally the calculation of hydrodynamic structure models.

Modeling knot features using branch scars from Mongolian oak (Quercus mongolica).

Wood quality is an important indicator for modern sawmills. Internal wood characteristics can be derived from their correlations with external appearances. In this study, we developed linear regression models to predict knot size from surface features of Mongolian oak (Quercus mongolica) using data collected from 53 trees. For this, manual measurements and X-ray computed tomography scanning technology was respectively used to obtain internal and external features of 1,297 knots. Our results showed that Mongolian oak knots were generally concentrated in the middle part of oak stems, with fewer knots observed at the top and base. The parameters of knot and scar showed significant correlations (P<0.01), where length and diameter of the corresponding external scar increase with increasing the length and diameter of a knot. The corresponding external scar can be used as an effective indicator to predict the internal value of oak logs. The accuracy of our constructed model is more than 95% when assessed against independent test samples. These models thus can be applied to improve the practical production of oak timber and reduce commercial loss caused by knots. These additional data can improve the estimation of the influence of knots on wood quality and provide a theoretical foundation for investigating the characteristics of hardwood knots.

Shielding Effectiveness of a PC Case Using Three-Axis Electro-optic Sensors

This paper presents a measurement method for the shielding effectiveness (SE) of a personal computer (PC) case using three-axis electrooptic sensors and a vector network analyzer (VNA). The SE is obtained by taking the difference between the logarithmic values of the ratios of transmitted power to the input power for conditions with and without the PC case. The SE measurements versus the incident angle and polarization of incident electromagnetic fields are performed in a fully anechoic chamber (FAC) in the frequency range of 1 GHz to 4 GHz. A computer simulation is conducted to compare the experimental and numerical results. Additionally, we measured the SE of the PC case in a reverberation chamber (RC). The SE values of the PC case for the three axes in the case of FAC and RC are within 4 dB. The results show that this SE measurement technique, using electro-optic sensors and a VNA, is useful for both FAC and RC.

Comparing the Effects and Mechanisms of Action of Permanganate, Chlorine, and Hydrogen Peroxide on the Membrane Integrity of Pseudanabaena sp. Cells

Preoxidation is usually used to remove cyanobacteria in drinking waterworks. However, the effects and mechanisms of action of oxidants on the membrane integrity of cyanobacterial cells are scarcely known, which may lead to cell damage and facile release of plenty of harmful metabolites. This study investigated the kinetics of Pseudanabaena cell lysis under separate exposure to potassium permanganate (KMnO4), sodium hypochlorite (NaClO), and hydrogen peroxide (H2O2). Mechanisms of interaction between them and the Pseudanabaena cell surface at the cellular and molecular levels were then defined. Results showed that cell lysis matched a two-stage kinetic model under KMnO4 or H2O2 exposure, and cells remained intact below a certain exposure threshold; in comparison, the log value of cell concentration decreased linearly with NaClO exposure. Pseudanabaena cell damage rates followed the order NaClO > KMnO4 > H2O2. Permanganate ions primarily reacted with cell-bound organic matter and then oxidized cell membrane, whereas neutral HClO produced by NaClO can permeate the membrane of algal cells and damage cell membrane quickly. As for H2O2, although H2O2 can gradually permeate the algal cell membrane, the reactivity between H2O2 and cell membranes was low. The algal cell membrane remained intact in the initial stage and then was damaged when H2O2 accumulation reached a certain level.

NIPAT as Non-Invasive Prenatal Paternity Testing Using a Panel of 861 SNVs.

In 1997, it was discovered that maternal plasma contains Cell-Free Fetal DNA (cffDNA). cffDNA has been investigated as a source of DNA for non-invasive prenatal testing for fetal pathologies, as well as for non-invasive paternity testing. While the advent of Next Generation Sequencing (NGS) led to the routine use of Non-Invasive Prenatal Screening (NIPT or NIPS), few data are available regarding the reliability and reproducibility of Non-Invasive Prenatal Paternity Testing (NIPPT or NIPAT). Here, we present a non-invasive prenatal paternity test (NIPAT) analyzing 861 Single Nucleotide Variants (SNV) from cffDNA through NGS technology. The test, validated on more than 900 meiosis samples, generated log(CPI)(Combined Paternity Index) values for designated fathers ranging from +34 to +85, whereas log(CPI) values calculated for unrelated individuals were below -150. This study suggests that NIPAT can be used with high accuracy in real cases.

Kinetics of SARS-CoV-2 neutralizing antibodies in Omicron breakthrough cases with inactivated vaccination: Role in inferring the history and duration of infection.

The quantitative level and kinetics of neutralizing antibodies (NAbs) in individuals with Omicron breakthrough infections may differ from those of vaccinated individuals without infection. Therefore, we aimed to evaluate the difference in NAb levels to distinguish the breakthrough cases from the post-immunized population to identify early infected person in an outbreak epidemic when nasal and/or pharyngeal swab nucleic acid real-time PCR results were negative. We collected 1077 serum samples from 877 individuals, including 189 with Omicron BA.2 breakthrough infection and 688 post-immunized participants. NAb titers were detected using the surrogate virus neutralization test, and were log(2)-transformed to normalize prior to analysis using Student's unpaired t-tests. Geometric mean titers (GMT) were calculated with 95% confidence intervals (CI). Linear regression models were used to identify factors associated with NAb levels. We further conducted ROC curve analysis to evaluate the NAbs' ability to identify breakthrough infected individuals in the vaccinated population. The breakthrough infection group had a consistently higher NAb levels than the post-immunized group according to time since the last vaccination. NAb titers in the breakthrough infection group were 6.4-fold higher than those in the post-immunized group (GMT: 40.72 AU/mL and 6.38 AU/mL, respectively; p<0.0001). In the breakthrough infection group, the NAbs in the convalescent phase were 10.9-fold higher than in the acute phase (GMT: 200.48 AU/mL and 18.46 AU/mL, respectively; p<0.0001). In addition, the time since infection, booster vaccination, and the time since last vaccination were associated with log(2)-transformed NAb levels in the breakthrough infection group. ROC curve analysis showed that ROC area was largest (0.728) when the cut-off value of log(2)-transformed NAb was 6, which indicated that NAb levels could identify breakthrough infected individuals in the vaccinated population. Our study demonstrates that the NAb titers of Omicron BA.2 variant breakthrough cases are higher than in the post-immunized group. The difference in NAb levels could be used to identify cases of breakthrough infection from the post-immunized population in an outbreak epidemic.

Interleukin response in patients treated with abbreviated laparotomy for severe intraabdominal sepsis

Background: Pro- and anti-inflammatory cytokines play an important role in abdominal sepsis. Studies suggest that the anti-inflammatory response is more detrimental to the patient with abdominal sepsis than the initial pro-inflammatory response. We therefore studied the serum levels of pro-inflammatory and anti-inflammatory interleukins in patients with abdominal sepsis treated by abbreviated laparotomy.&#x0D; Methods: We performed a prospective study of 42 patients treated by abbreviated laparotomy. The patients were divided into a high-risk (more than two procedures before abdominal closure) group (24 patients) and a low-risk (two or fewer procedures) group (18 patients). The differences and correlations between the serum levels of pro-inflammatory and anti-inflammatory cytokines on days one and ten after the initial procedure were assessed.&#x0D; Results: The mortality was significantly higher in the high-risk group (41.7% vs. 5.6% in the low-risk group; p = 0.012). IL-10 serum levels were significantly higher in the high-risk group (22.5 pg/mL (IQR 5.25)) compared to the low-risk group (12.15 pg/mL (IQR 6.725)) (p = 0.012). Age was significantly correlated with mortality (p = 0.007). The logarithmic value of IL-10 serum levels on day one (HR: 2.5; 95% CI: 1.109–5.638; p = 0.027) and the IL-10 cut-off value (HR: 3.816; 95% CI: 1.047–13.910; p = 0.042) were significantly correlated with worse disease course in multivariate analysis.&#x0D; Conclusions: Patients who exhibit a greater anti-inflammatory response on day one are at increased risk of a protracted course and higher mortality. IL-10 serum levels on day one after surgery predict a worse disease course in these patients and could be a useful marker of abdominal sepsis.

Bactericidal effects of a low-temperature acidic electrolyzed water on quantitative suspension, packaging and contact surface in food cold chain

The research and development of high-efficiency low-temperature disinfection technology in all aspects of cold-chain food has begun to receive attention. This paper aims to develop low temperature acidic electrolyzed water (LT-AEW) disinfectant. The anti-freezing temperature, disinfection efficiency and practical application effect were evaluated in detail. The results had shown that the freezing point of AEW can be reduced to −18 °C by adding 25% CaCl2, 20% NaCl, 30% ethylene glycol (EG) and 35% propylene glycol (PG). At the low temperature (−1, −10 and −18 °C), the bactericidal efficiencies of all EG-acidic electrolyzed waters (EG-AEWs) with different EG contents were close. The reason is that although the low temperature environment will reduce the sterilization efficiency of EG-AEW, the addition of EG can promote the increase of HClO content in EG-AEW, thus increasing the sterilization efficiency. As long as the HClO content reached 35.2, 50.8 and 64.3 mg L−1, the killing logarithm (KL) value had achieved 4.50, 4.49 and 5.07 log10(CFU mL−1). Finally, the KL values of EG-AEW against Escherichia coli on the corrugated board and stainless-steel board both exceeded 3 log10(CFU mL−1) at the low temperature, which indicates that EG-AEW can be applied in food cold chain.

Impact of Hormone Replacement Therapy on the Overall Survival and Progression Free Survival of Ovarian Cancer Patients: A Systematic Review and Meta-Analysis.

Background: Frequently, patients treated for Ovarian Cancer (OC) undergo menopause with subsequent symptoms. This review scrutinised the impact of Hormone Replacement Therapy (HRT) on the Overall Survival (OS) and Progression-Free Survival (PFS) of patients diagnosed with OC. Methods: A systematic literature search was conducted in the most popular English databases. Inclusion and exclusion criteria were applied to select publications that evaluate OS and PFS in these patients. End-point analysis targeted values of log(HR) and its Standard Error (SE). Results: Up to 1 September 2022, 11 studies were included in the qualitative synthesis. Eight publications, totalling 4191 patients, were included in the meta-analyses. Eight studies were considered for the OS analysis and pooled an HR of 0.66 with respective 95% CI between 0.57 and 0.76, with a p-value < 0.00001 at a Z value of 5.7, in favour of the HRT group. Results for PFS showed an overall HR of 0.73 in favour of the HRT group; CI between 0.57 and 0.95, p = 0.02 at a Z value of 2.36. Further subgroup analyses highlighted the non-inferiority of this treatment. Conclusions: Patients treated for OC that receive HRT for menopausal symptoms after various treatments appeared to have better OS than never-users.

Deep learning for the detection of semantic features in tree X-ray CT scans

According to the industry, the value of wood logs is heavily influenced by their internal structure, particularly the distribution of knots within the trees. Nowadays, CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees. Knowing where the tree semantic features, especially knots, contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products. However, this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline, knot localization and log contour, in a robust and efficient manner. In this paper, we propose an effective methodology based on deep learning for performing these different tasks by processing CT-scanner images with deep convolutional neural networks. To meet this objective, three end-to-end trainable pipelines are proposed. The first pipeline is focused on centers detection using CNNs architecture with a regression head, the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture. The different architectures are tested on several tree species. With these experiments, we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness. The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

An ultrasensitive bacterial imprinted electrochemical sensor for the determination of Lactobacillus rhamnosus GG

An ultrasensitive label-free electrochemical sensor based on a homemade imprinted polypyrrole (PPy) polymer film was prepared to achieve quantitative determination of Lactobacillus rhamnosus GG (LGG). The LGG-imprinted polymer (LIP) film was deposited on a portable screen-printed electrode (SPE) via electropolymerization, which constituted an independent integrated system. The main preparation parameters of the LIP sensor were investigated to obtain optimal performance. Under optimized conditions, the peak current response of the LIP sensor showed a linear relationship with the logarithmic value of LGG concentration in the range from 101 to 109 CFU mL−1 and a detection limit of 5 CFU mL−1. The proposed LIP sensor has achieved efficient, ultrasensitive, highly selective, and cost-effective detection of LGG and can be further developed for practical applications in the quality inspection and development of probiotic products.

Prediction of the COVID-19 transmission: a case study of Pakistan.

The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We've used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum logL and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.

Altered Low Frequency Heart Rate Variability Associated with Agoraphobia in Panic Disorder: A Retrospective Study.

This study aimed to compare the clinical features of panic disorder (PD) with comorbid agoraphobia to those of PD alone. We focused on autonomic nervous system (ANS) alterations reflected in heart rate variability (HRV) and executive function deficits reflected in the Stroop test. We retrospectively compared psychometric features, Stroop test results, and resting-state HRV across three groups: a subclinical group with anxiety attack history, a PD group without agoraphobia, and a PD group with agoraphobia. The subclinical group included 10 male and 34 female, the PD without agoraphobia group included 17 male and 19 female, and the PD with agoraphobia group included 11 male and 18 female. The PD with agoraphobia group had higher Symptom Checklist-95 scores than the other groups. Both PD groups had longer reaction times in the Stroop test than the subclinical group. There were no significant differences in HRV parameters between the PD groups with and without agoraphobia. Compared with the subclinical group, the PD with agoraphobia group showed significantly lower values of the natural logarithm of low-frequency HRV. Our results do not support that executive function deficits and ANS alterations are more pronounced with comorbid agoraphobia among PD groups. However, PD with agoraphobia patients showed more complex and severe clinical symptoms in their self-reports. Compared with the subclinical group, PD patients with agoraphobia showed specific features in the natural logarithm of low-frequency HRV. Our findings suggest that agoraphobia comorbidity should be considered when evaluating or treating patients with PD.

Moderating Effect of Tactical Asset Allocation on the Risk-Return Relationship in the Nigerian Stock Market

Before making investment decisions, the investor must find out the risk-return characteristics of investments with the help of investment strategies. These investment strategies, including drawdown, tactical asset allocation and short selling, are used by investors and fund managers to improve portfolio performance by maximizing returns and minimizing risks. This study aims to examine the moderating effect of tactical asset allocation on the risk-return relationship in the Nigerian stock market. The study sample is 90 stocks that are consistently traded in the Nigerian stock market within the period of sixteen years on a monthly basis. The study employed time series data which were extracted from the NGX website, annual financial statement, and CBN statistical bulletin. This study's data collection and analysis framework was based on the methodology of Fama and French (2015) using FamaMacbeth's two-step regression. The study used the difference between the logarithmic present value of the price and the previous value of the price to compute the return. Evidence from the result revealed that tactical asset allocation is not significantly priced under the CoFF5F and HMFF5F in the Nigerian stock market. Also, introducing tactical asset allocation as the moderating variable does not improve the relationship between risk and return in the Nigerian stock market. In light of this, the study concluded that tactical asset allocation could not be used to improve the relationship between risk and return through the minimization of risk and maximization of return in the Nigerian stock market. The study recommends that other investment strategies, such as drawdown, short selling etc., can be used by investors to improve the relationship between risk and return in the Nigerian stock market.

Cytotoxic and genotoxic effects of nateglinide on human ovarian, prostate, and colon cancer cell lines

Objective: Nateglinide, an oral anti-diabetic medication used to treat type 2 diabetes, activates ATP-dependent potassium channels in pancreatic beta cells and induces insulin secretion. Numerous antidiabetic medicines, particularly metformin, are known to drastically reduce the viability of cancer cells. This study examined the effects of nateglinide on the DNA and viability of human ovarian (A2780), prostate (LNCaP), and colon (Caco-2) cancer cells. Materials and methods: Initially in the study, 1, 10, 100, and 1000 µM doses of nateglinide were administered for 24 hours to A2780, LNCaP, and Caco-2 cells. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test was used to measure cell viability. Using Graphpad Prism 8, the inhibitory logarithmic concentration values (LogIC50) of nateglinide in A2780, LNCaP, and Caco-2 cells were computed based on the results of the MTT experiment. These doses were applied to A2780, LNCaP, and Caco-2 cells for the Comet assay. The Bonferroni-corrected Mann–Whitney U test was used to compare groups, and a value of p&lt;0.05 was considered statistically significant. Results: In A2780 and LNCaP cell lines, only 1000 μM nateglinide concentration decreased cell viability (p&lt;0.05), whereas in Caco-2 cells, all concentrations except 1 μM reduced cell viability (p&lt;0.05). The Comet assay indicated that nateglinide produced DNA damage by increasing the tail lengths and tail moments of A2780, LNCaP, and Caco-2 cells (p&lt;0.05) and reducing the head diameters (p&lt;0.05). Conclusion: According to the findings of this study, nateglinide has cytotoxic effects on human ovarian, prostate and colon cancer cell lines and may possess anticancer properties.

Regression-decision-tree based parameter optimization of measurement-device-independent quantum key distribution

The parameter configuration of quantum key distribution (QKD) has a great effect on the communication effect, and in the practical application of the QKD network in the future, it is necessary to quickly realize the parameter configuration optimization of the asymmetric channel measurement-device-independent QKD according to the communication state, so as to ensure the good communication effect of the mobile users, which is an inevitable requirement for real-time quantum communication. Aiming at the problem that the traditional QKD parameter optimization configuration scheme cannot guarantee real-time, in this paper we propose to apply the supervised machine learning algorithm to the QKD parameter optimization configuration, and predict the optimal parameters of TF-QKD and MDI-QKD under different conditions through the machine learning model. First, we delineate the range of system parameters and evenly spaced (linear or logarithmic) values through experimental experience, and then use the traditional local search algorithm (LSA) to obtain the optimal parameters and take them as the optimal parameters in this work. Finally, we train various machine learning models based on the above data and compare their performances. We compare the supervised regression learning models such as neural network, K-nearest neighbors, random forest, gradient tree boosting and classification and regression tree (CART), and the results show that the CART decision tree model has the best performance in the regression evaluation index, and the average value of the key rate (of the prediction parameters) and the optimal key rate ratio is about 0.995, which can meet the communication needs in the actual environment. At the same time, the CART decision tree model shows good environmental robustness in the residual analysis of asymmetric QKD protocol. In addition, compared with the traditional scheme, the new scheme based on CART decision tree greatly improves the real-time performance of computing, shortening the single prediction time of the optimal parameters of different environments to the microsecond level, which well meets the real-time communication needs of the communicator in the movable state. This work mainly focuses on the parameter optimization of discrete variable QKD (DV-QKD). In recent years, the continuous variable QKD (CV-QKD) has developed also rapidly. At the end of the paper, we briefly introduce academic attempts of applying machine learning to the parameter optimization of CV-QKD system, and discuss the applicability of the scheme in CV-QKD system.