Fabrication and hardness of in-situ Al3Ti–Al2O3 composite

In this work, an in-situ Al3Ti–Al2O3 composite was optimally synthesized from raw powders via mechanical milling and conventional sintering processes. The strong influence of milling time on the promotion of the phase reaction between the initial TiO2 and Al materials was proven by using X-ray diffraction and surface morphology analysis. The obtained results showed that the milling process did not initiate any reaction between the raw TiO2 and Al materials. However, the milling process was important for creating a homogeneous powder mixture and refining the particle size of the powders. The Al3Ti–Al2O3 composites were completely formed after conventional sintering at 750°C for 30 min for a milling time of over 4 h. The highest obtained microhardness of the composite was approximately 130 HV, which was suggested to be related to the microstructure of the bulk composite specimen consisting of two main phases, the Al3Ti matrix and the Al2O3 particles dispersed in the matrix. A small portion of an unidentified phase, a Ti-rich compound, was found in the matrix together with a tiny fraction of AlTi3. We suggest that the optimal sintering process and mechanical milling are important key factors in fabricating bulk hardness Al3Ti–Al2O3 composite materials

Optical Response of DyN

We report measurements of the optical response of polycrystalline DyN thin films. The frequency-dependent complex refractive index in the near IR-visible-near UV was determined by fitting reflection/transmission spectra. In conjunction with resistivity measurements these identify DyN as a semiconductor with 1.2 eV optical gap. When doped by nitrogen vacancies it shows free carrier absorption and a blue-shifted gap associated with the Moss-Burstein effect. The refractive index of 2.0+/-0.1 depends only weakly on energy. Far infrared reflectivity data show a polar phonon of frequency 280 cm-1 and dielectric strength delta epsilon= 20

Establishing and validating noninvasive prenatal testing procedure for fetal aneuploidies in Vietnam

Noninvasive prenatal testing (NIPT) for fetal aneuploidies has been widely adopted in developed countries. Despite the sharp decrease in the cost of massively parallel sequencing, the technical know-how and skilled personnel are still one of the major limiting factors for applying this technology to NIPT in low-income settings. Here, we present the establishment and validation of our NIPT procedure called triSure for detection of fetal aneuploidies.We established the triSure algorithm based on the difference in proportion of fetal and maternal fragments from the target chromosome to all chromosomes. Our algorithm was validated using a published data set and an in-house data set obtained from high-risk pregnant women in Vietnam who have undergone amniotic testing. Several other aneuploidy calling methods were also applied to the same data set to benchmark triSure performance.The triSure algorithm showed similar accuracy to size-based method when comparing them using published data set. Using our in-house data set from 130 consecutive samples, we showed that triSure correctly identified the most samples (overall sensitivity and specificity of 0.983 and 0.986, respectively) compared to other methods tested including count-based, sized-based, RAPIDR and NIPTeR.We have demonstrated that our triSure NIPT procedure can be applied to pregnant women in low-income settings such as Vietnam, providing low-risk screening option to reduce the need for invasive diagnostic tests

Associations of Underlying Health Conditions With Anxiety and Depression Among Outpatients: Modification Effects of Suspected COVID-19 Symptoms, Health-Related and Preventive Behaviors

Objectives: We explored the association of underlying health conditions (UHC) with depression and anxiety, and examined the modification effects of suspected COVID-19 symptoms (S-COVID-19-S), health-related behaviors (HB), and preventive behaviors (PB).Methods: A cross-sectional study was conducted on 8,291 outpatients aged 18–85 years, in 18 hospitals and health centers across Vietnam from 14th February to May 31, 2020. We collected the data regarding participant's characteristics, UHC, HB, PB, depression, and anxiety.Results: People with UHC had higher odds of depression (OR = 2.11; p < 0.001) and anxiety (OR = 2.86; p < 0.001) than those without UHC. The odds of depression and anxiety were significantly higher for those with UHC and S-COVID-19-S (p < 0.001); and were significantly lower for those had UHC and interacted with “unchanged/more” physical activity (p < 0.001), or “unchanged/more” drinking (p < 0.001 for only anxiety), or “unchanged/healthier” eating (p < 0.001), and high PB score (p < 0.001), as compared to those without UHC and without S-COVID-19-S, “never/stopped/less” physical activity, drinking, “less healthy” eating, and low PB score, respectively.Conclusion: S-COVID-19-S worsen psychological health in patients with UHC. Physical activity, drinking, healthier eating, and high PB score were protective factors

Predicting Drug-Induced Liver Injury using Convolutional Neural Network and Molecular Fingerprint-embedded features

© 2020 American Chemical Society. As a critical issue in drug development and postmarketing safety surveillance, drug-induced liver injury (DILI) leads to failures in clinical trials as well as retractions of on-market approved drugs. Therefore, it is important to identify DILI compounds in the early-stages through in silico and in vivo studies. It is difficult using conventional safety testing methods, since the predictive power of most of the existing frameworks is insufficiently effective to address this pharmacological issue. In our study, we employ a natural language processing (NLP) inspired computational framework using convolutional neural networks and molecular fingerprint-embedded features. Our development set and independent test set have 1597 and 322 compounds, respectively. These samples were collected from previous studies and matched with established chemical databases for structural validity. Our study comes up with an average accuracy of 0.89, Matthews's correlation coefficient (MCC) of 0.80, and an AUC of 0.96. Our results show a significant improvement in the AUC values compared to the recent best model with a boost of 6.67%, from 0.90 to 0.96. Also, based on our findings, molecular fingerprint-embedded featurizer is an effective molecular representation for future biological and biochemical studies besides the application of classic molecular fingerprints

Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features

© 2020 American Chemical Society. As a critical issue in drug development and postmarketing safety surveillance, drug-induced liver injury (DILI) leads to failures in clinical trials as well as retractions of on-market approved drugs. Therefore, it is important to identify DILI compounds in the early-stages through in silico and in vivo studies. It is difficult using conventional safety testing methods, since the predictive power of most of the existing frameworks is insufficiently effective to address this pharmacological issue. In our study, we employ a natural language processing (NLP) inspired computational framework using convolutional neural networks and molecular fingerprint-embedded features. Our development set and independent test set have 1597 and 322 compounds, respectively. These samples were collected from previous studies and matched with established chemical databases for structural validity. Our study comes up with an average accuracy of 0.89, Matthews's correlation coefficient (MCC) of 0.80, and an AUC of 0.96. Our results show a significant improvement in the AUC values compared to the recent best model with a boost of 6.67%, from 0.90 to 0.96. Also, based on our findings, molecular fingerprint-embedded featurizer is an effective molecular representation for future biological and biochemical studies besides the application of classic molecular fingerprints

Excellent Fireproof Characteristics and High Thermal Stability of Rice Husk-Filled Polyurethane with Halogen-Free Flame Retardant

The thermal stabilities, flame retardancies, and physico-mechanical properties of rice husk-reinforced polyurethane (PU–RH) foams with and without flame retardants (FRs) were evaluated. Their flammability performances were studied by UL94, LOI, and cone calorimetry tests. The obtained results combined with FTIR, TGA, SEM, and XPS characterizations were used to evaluate the fire behaviors of the PU–RH samples. The PU–RH samples with a quite low loading (7 wt%) of aluminum diethylphosphinate (OP) and 32 wt% loading of aluminum hydroxide (ATH) had high thermal stabilities, excellent flame retardancies, UL94 V-0 ratings, and LOIs of 22%–23%. PU–RH did not pass the UL94 HB standard test and completely burned to the holder clamp with a low LOI (19%). The cone calorimetry results indicated that the fireproof characteristics of the PU foam composites were considerably improved by the addition of the FRs. The proposed flame retardancy mechanism and cone calorimetry results are consistent. The comprehensive FTIR spectroscopy, TG, SEM, and XPS analyses revealed that the addition of ATH generated white solid particles, which dispersed and covered the residue surface. The pyrolysis products of OP would self-condense or react with other volatiles generated by the decomposition of PU–RH to form stable, continuous, and thick phosphorus/aluminum-rich residual chars inhibiting the transfer of heat and oxygen. The PU–RH samples with and without the FRs exhibited the normal isothermal sorption hysteresis effect at relative humidities higher than 20%. At lower values, during the desorption, this effect was not observed, probably because of the biodegradation of organic components in the RH. The findings of this study not only contribute to the improvement in combustibility of PU–RH composites and reduce the smoke or toxic fume generation, but also solve the problem of RHs, which are abundant waste resources of agriculture materials leading to the waste disposal management problems