Highly enantioselective production of a chiral intermediate of sitagliptin by a novel isolate of Pseudomonas pseudoalcaligenes.

OBJECTIVE: To produce (S)-3-hydroxy-1-(3-(trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-a]pyrazin-7(8H)-yl]-4-(2,4,5-trifluorophenyl)butan-1-one (S)-1 from 4-oxo-4-[3-(trifluoromethyl)-5,6-dihydro [1,2,4]triazolo[4,3-a]pyrazin-7(8H)-yl)-1-(2,4,5-trifluorophenyl)butan-2-one (2) by microbial bioreduction. RESULTS: A new isolate of Pseudomonas pseudoalcaligenes reduced enantioselectively prochiral ketone 2 to chiral alcohol (S)-1. Whole cells of the bacterium were tolerant towards 20 % (v/v) DMSO and 10 g 2/l. Under the optimal conditions, the preparative-scale bioreduction yielded (S)-1 at 90 % yield and >99 % ee. Cells could be re-used with the yield and ee of product being 45 % and >99 %, respectively, after five cycles. CONCLUSION: Bioreduction using whole cells of P. pseudoalcaligenes is an attractive approach to produce (S)-1, as a chiral intermediate of the anti-diabetic drug, sitagliptin.

Serum brain-derived neurotrophic factor concentration is different between autism spectrum disorders and intellectual disability children and adolescents.

PURPOSE: Recent studies showed that mature brain-derived neurotrophic factor (mBDNF) and its precursor proBDNF are associated with autism spectrum disorders (ASD). Whether their levels are different between ASD and intellectual disability (ID) subjects is not clear. The aim of this study is to compare the serum mBDNF and proBDNF concentration, and mBNDF/proBDNF ratio in ASD and ID volunteers. METHODS: Children and adolescents with ASD or ID between the ages of 4 and 22 were recruited in Tianjin, China. Serum mBDNF and proBDNF level were tested and Wechsler Preschool and Primary Scale of Intelligence (WPPSI), Wechsler Intelligence Scale for Children (WISC), and Childhood Autism Rating Scale (CARS) evaluations were conducted. RESULTS: Serum mBDNF concentration and the ratio of mBDNF to proBDNF was higher in ASD subjects than that in ID subjects (P = 0.035 and P < 0.001, respectively), while serum proBDNF of ASD participants was lower compared to that of ID participants (P < 0.001). CARS score was positively correlated with serum mBDNF level (r = 0.33, P = 0.004) and m/p ratio (r = 0.39, P < 0.001), and negatively correlated with serum proBDNF level (r = -0.39, <0.001) after adjusting for age and IQ. The AUC of mBDNF, proBDNF, and m/p ratio were 0.741, 0.790, and 0.854, respectively, after adjusted for age and IQ. CONCLUSION: Serum mBDNF, proBDNF and m/p ratio were different between ASD and ID group. The three biomarkers displayed good diagnostic values for classification of ASD and ID subjects.

Associations of maternal sleep trajectories during pregnancy and adverse perinatal outcomes: a prospective cohort study.

OBJECTIVE: Sleep problems are common in pregnant women and sleep is altered during pregnancy. However, the associations between sleep trajectory patterns and adverse maternal and neonatal outcomes are unclear. The current study aims to identify sleep trajectory patterns and explore their associations with adverse perinatal outcomes in a prospective cohort study. METHODS: Pregnant women (N = 232) completed the Pittsburgh Sleep Quality Index each trimester during pregnancy in Tianjin, China. Perinatal outcomes were extracted from the hospital delivery records. Latent class growth analysis (LCGA) described the trajectories of sleep timing, duration, and efficiency. Multivariable linear regression and multivariable logistic regression were employed to evaluate associations between sleep trajectory patterns and perinatal outcomes. RESULTS: Trajectories were identified for bedtime (early, 49.1%; delaying, 50.9%), wake-up time (early, 82.8% of the sample; late, 17.2%), duration (short, 5.2%; adequate 78.0%; excessive, 16.8%), and efficiency (high, 88.4%; decreasing, 11.6%). Compared with women in more optimal sleep groups, those in the late wake-up, excessive duration, and decreasing efficiency groups had babies with shorter birth lengths (ß range, -0.50 to -0.28, p < 0.05). Moreover, women in the decreasing efficiency group had babies with lower birth weight (ß, -0.44; p < 0.05). Women in the delaying bedtime group had greater odds of preterm delivery (OR, 4.57; p < 0.05), while those in the decreasing efficiency group had greater odds of cesarean section (OR, 3.12; p < 0.05). CONCLUSIONS: Less optimal sleep trajectory patterns during pregnancy are associated with perinatal outcomes. Therefore, early assessment of maternal sleep during pregnancy is significant for identifying at-risk women and initiating interventions to reduce perinatal outcomes.

Classification of Epileptic and Psychogenic Non-Epileptic Seizures Using Electroencephalography and Electrocardiography.

Patients with psychogenic non-epileptic seizures (PNES) may exhibit similar clinical features to patients with epileptic seizures (ES). Misdiagnosis of PNES and ES can lead to inappropriate treatment and significant morbidity. This study investigates the use of machine learning techniques for classification of PNES and ES based on electroencephalography (EEG) and electrocardiography (ECG) data. Video-EEG-ECG of 150 ES events from 16 patients and 96 PNES from 10 patients were analysed. Four preictal periods (time before event onset) in EEG and ECG data were selected for each PNES and ES event (60-45 min, 45-30 min, 30-15 min, 15-0 min). Time-domain features were extracted from each preictal data segment in 17 EEG channels and 1 ECG channel. The classification performance using k-nearest neighbour, decision tree, random forest, naive Bayes, and support vector machine classifiers were evaluated. The results showed the highest classification accuracy was 87.83% using the random forest on 15-0 min preictal period of EEG and ECG data. The performance was significantly higher using 15-0 min preictal period data than 30-15 min, 45-30 min, and 60-45 min preictal periods ( [Formula: see text]). The classification accuracy was improved from 86.37% to 87.83% by combining ECG data with EEG data ( [Formula: see text]). The study provided an automated classification algorithm for PNES and ES events using machine learning techniques on preictal EEG and ECG data.

Effect of Circadian Distribution of Energy and Macronutrients on Gestational Weight Gain in Chinese Pregnant Women.

Gestational weight gain (GWG) may be affected by the timing of dietary intake. Previous studies have reported contradictory findings, possibly due to inconsistent characterizations of meal timing. We conducted a birth cohort study in Tianjin to determine the effect of daily energy and macronutrient distribution in mid and late pregnancy on GWG. Dietary intake information in the second and third trimesters used three 24-h dietary recalls, and meal timing was defined in relation to sleep/wake timing. The adequacy of GWG was assessed using recommendations from the Institute of Medicine guidelines. Pregnant women who had a relatively high average energy and macronutrient distribution in the late afternoon-early evening time window exhibited a greater GWG rate and a greater total GWG than that in morning time window during the third trimester (ß = 0.707; ß = 0.316). Carbohydrate intake in the morning of the second and third trimesters (ß = 0.005; ß = 0.008) was positively associated with GWG rates. Morning carbohydrate intake in the second trimester was also positively associated with total GWG (ß = 0.004). Fat intake in the morning of the third trimester (ß = 0.051; ß = 0.020) was positively associated with the GWG rates and total GWG. Excessive GWG of Chinese pregnant women was related closely to eating behavior focused on the late afternoon-early evening and carbohydrate and fat intake in the morning during the second and third trimesters.

Exposure to phthalates and their alternatives in relation to biomarkers of inflammation and oxidative stress in adults: evidence from NHANES 2017-2018.

Phthalates and their alternatives are considered significant environmental risk factors that potentially influence inflammation and oxidative stress. However, their impact on biomarkers of inflammation and oxidative stress was inconsistent. This study aimed to explore the associations between phthalates and high-sensitivity C-reactive protein (hsCRP), gamma-glutamyl transferase (GGT), and white blood cell (WBC) counts, employing both univariate exposure and multivariate co-exposure models. For this analysis, a total of 1619 individuals aged 18 years and above, sourced from the National Health and Nutrition Examination Survey (NHANES) conducted between 2017 and 2018, were selected as subjects. We explored the associations between hsCRP, GGT, and WBC counts and eighteen different phthalate metabolites. Multiple linear regression analysis revealed significant associations between both MCNP and MEHP and hsCRP. We observed negative correlations of MCOP, MCPP, MHBP, and MONP with GGT. Conversely, MEHHP and MEHHTP exhibited positive correlations with GGT. Furthermore, MECPTP and MEHHTP showed positive correlations with WBC. Notably, we identified a non-linear relationship between phthalates and inflammation and oxidative stress markers. The Bayesian kernel machine regression (BKMR) analysis demonstrated a negative joint effect of the phthalates mixture on GGT, particularly at lower concentrations. The BKMR model also found that MEOHP and MHiBP were negatively associated with GGT. In contrast, MEHHP showed a significant positive association with GGT. Moderating effect analysis suggested that dietary inflammatory index (DII), income-to-poverty ratio (PIR), age, BMI, and physical activity influenced the association between phthalates and inflammation and oxidative stress. These findings contribute to a deeper understanding of the relationships between phthalates and inflammation and oxidative stress.

BMI-specific inflammatory response to phthalate exposure in early pregnancy: findings from the TMCHESC study.

Studies that have evaluated associations between phthalate metabolites and inflammation have reported inconsistent results among pregnant women, and it is unclear how body mass index (BMI) affects such relationships. Therefore, the present study aimed to examine the association between urinary phthalate metabolite concentrations and the levels of inflammatory biomarkers in the general circulation among 394 pregnant women selected from the Tianjin Maternal and Child Health Education and Service Cohort (TMCHESC) and to determine the role that BMI plays in the relationship. The concentrations of eight inflammatory biomarkers and three phthalate metabolites were measured in serum and urine samples, respectively. Multivariable linear modeling was conducted to examine the association between each phthalate and inflammatory biomarker while controlling for potential confounding factors in BMI-stratified subgroups. Restricted cubic splines were also utilised to explore potential non-linear relationships. In the high-BMI group, positive associations were observed between the levels of mono-n-butyl phthalate (MBP) and interleukin 1 beta (IL-1ß) (ß = 0.192; 95% CI: 0.033, 0.351), monoethyl phthalate (MEP), and C-reaction protein (CRP) (ß = 0.129; 95% CI 0.024, 0.233), and mono-ethylhexyl phthalate (MEHP) and interleukin 6 (IL-6) (ß = 0.146; 95% CI 0.016, 0.277). Restricted cubic spline models also revealed non-linear associations between the levels of MBP and interleukins 10 and 17A (IL-10 and IL-17A) and between MEP and interleukin 8 (IL-8) and tumor necrosis factor alpha (TNF-α) in pregnant women. These results suggest that phthalate exposure plays a potential role in promoting inflammation in the high-BMI group. While the precise mechanisms underlying the proinflammatory effects of phthalates are not fully understood, these findings suggest that BMI may play a role.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study.

BACKGROUND: Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. METHOD: Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. FINDINGS: The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. INTERPRETATION: The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. FUNDING: This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Investigation on Recrystallization Channel for Vertical C-Shaped-Channel Nanosheet FETs by Laser Annealing.

Transistor scaling has become increasingly difficult in the dynamic random access memory (DRAM). However, vertical devices will be good candidates for 4F2 DRAM cell transistors (F = pitch/2). Most vertical devices are facing some technical challenges. For example, the gate length cannot be precisely controlled, and the gate and the source/drain of the device cannot be aligned. Recrystallization-based vertical C-shaped-channel nanosheet field-effect transistors (RC-VCNFETs) were fabricated. The critical process modules of the RC-VCNFETs were developed as well. The RC-VCNFET with a self-aligned gate structure has excellent device performance, and its subthreshold swing (SS) is 62.91 mV/dec. Drain-induced barrier lowering (DIBL) is 6.16 mV/V.

High-Quality Recrystallization of Amorphous Silicon on Si (100) Induced via Laser Annealing at the Nanoscale.

At sub-3 nm nodes, the scaling of lateral devices represented by a fin field-effect transistor (FinFET) and gate-all-around field effect transistors (GAAFET) faces increasing technical challenges. At the same time, the development of vertical devices in the three-dimensional direction has excellent potential for scaling. However, existing vertical devices face two technical challenges: "self-alignment of gate and channel" and "precise gate length control". A recrystallization-based vertical C-shaped-channel nanosheet field effect transistor (RC-VCNFET) was proposed, and related process modules were developed. The vertical nanosheet with an "exposed top" structure was successfully fabricated. Moreover, through physical characterization methods such as scanning electron microscopy (SEM), atomic force microscopy (AFM), conductive atomic force microscopy (C-AFM) and transmission electron microscopy (TEM), the influencing factors of the crystal structure of the vertical nanosheet were analyzed. This lays the foundation for fabricating high-performance and low-cost RC-VCNFETs devices in the future.

Mussel-inspired polymeric coatings with the antifouling efficacy controlled by topologies.

Block copolymers with different topologies (linear, loop, 3-armed and 4-armed polymers) containing poly(N-vinylpyrrrolidone) (PVP) antifouling blocks and terminal poly(dopamine-acrylamide) (PDAA) anchoring blocks were synthesized. These polymers can form a robust antifouling nanolayer on various surfaces. The morphologies of the polymer-modified surfaces are strongly dependent on the topologies of the polymers: with the increase of arm numbers, the morphology evolves from the smooth surface to the nanoscale coarse surface. As a result, the hydrophilicity of the coatings increases with the increase of degree of nanoscale roughness, and the 4-armed block copolymer forms a superhydrophilic surface with a water contact angle (WCA) as low as 8.7°. Accordingly, the linear diblock copolymer exhibits the worst antifouling efficiency, while the 4-armed polymer exhibits the best antifouling efficiency. This is the first example systematically showing that the antifouling efficacy could be adjusted simply by the topology of the coatings. Cell viability studies revealed that all of the copolymers exhibit excellent cytocompatibility. These biocompatible polymers with narrowly distributed molecular weight might find niches for antifouling applications in various areas such as anti-protein absorption, anti-bacterial and anti-marine fouling.

Exploring associations between urine levels of phthalates and autism spectrum disorder symptoms: a case-control study in Tianjin, China.

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders. The etiology of autism remains unclear, but a growing body of evidence indicates that phthalates play a role in its pathogenesis. The aim of this study was to compare the urine levels of phthalates in children with ASD and healthy children. We also explored whether phthalates have an effect on ASD symptoms. The participants in this study included 101 children with ASD (79 boys and 22 girls) and 101 sex- and age-matched controls. The levels of phthalates were analyzed by gas chromatography-mass spectrometry (GC-MS). We detected significant differences in monoethyl phthalate (MEP) levels between the severe ASD and control groups (p < 0.05). Mono-n-butyl phthalate (MBP) concentration was positively correlated with language skill impairment in ASD (ß: 0.387, p = 0.041). MEP levels were associated with the CARS "Imitation" score in all children (OR: 1.470). MBP levels were associated with the "Nonverbal Communication" score among boys (OR: 1.233), and MEP levels were associated with the "Nonverbal Communication" score among girls (OR: 2.648). MEP levels were related to the CARS total score after adjustment for sex (ß: 1.524, p = 0.047). Compared with the reference mono(2-ethylhexyl) phthalate (MEHP) group, children with ASD in the medium-exposure group had an OR of 3.370 for aggravating ASD severity. These results suggested that increased exposure to phthalates contributes to more ASD symptoms and that there are potentially sex-specific associations. These findings warrant further confirmation.

Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG.

Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.

Nanometer-Thick ZnO/SnO2 Heterostructures Grown on Alumina for H2S Sensing.

Designing heterostructure materials at the nanoscale is a well-known method to enhance gas sensing performance. In this study, a mixed solution of zinc chloride and tin (II) chloride dihydrate, dissolved in ethanol solvent, was used as the initial precursor for depositing the sensing layer on alumina substrates using the ultrasonic spray pyrolysis (USP) method. Several ZnO/SnO2 heterostructures were grown by applying different ratios in the initial precursors. These heterostructures were used as active materials for the sensing of H2S gas molecules. The results revealed that an increase in the zinc chloride in the USP precursor alters the H2S sensitivity of the sensor. The optimal working temperature was found to be 450 °C. The sensor, containing 5:1 (ZnCl2: SnCl2·2H2O) ratio in the USP precursor, demonstrates a higher response than the pure SnO2 (∼95 times) sample and other heterostructures. Later, the selectivity of the ZnO/SnO2 heterostructures toward 5 ppm NO2, 200 ppm methanol, and 100 ppm of CH4, acetone, and ethanol was also examined. The gas sensing mechanism of the ZnO/SnO2 was analyzed and the remarkably enhanced gas-sensing performance was mainly attributed to the heterostructure formation between ZnO and SnO2. The synthesized materials were also analyzed by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray, transmission electron microscopy, and X-ray photoelectron spectra to investigate the material distribution, grain size, and material quality of ZnO/SnO2 heterostructures.

Association of urinary polycyclic aromatic hydrocarbon metabolites with symptoms among autistic children: A case-control study in Tianjin, China.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants that are associated with altered neurodevelopment and various neurodevelopmental disorders. However, studies evaluating internal biomarkers of PAH exposure in reference to the severity of autism spectrum disorder (ASD) symptomology and autistic behaviors are scarce. Hence, we conducted a case-control study evaluating 12 urinary hydroxylated PAH metabolites (i.e., hydroxy-PAHs) in 101 children with autism and 101 neurotypical children, matching according to sex and age in a 1:1 ratio. In children with ASD, the severity of symptomology and autistic behaviors were assessed using the child autism rating scale (CARS) and the autism behavior checklist (ABC). We found that urinary levels of nine of the hydroxy-PAHs were statistically significantly higher in the ASD group, with the exception of 2-hydroxynaphthalene (2-OHNap) and 4-hydroxyphenanthrene (4-OHPhe). Moreover, urinary hydroxy-PAH levels were associated with ASD risk, with odds ratios ranging from 1.86 to 17.19. Exposures to 1-hydroxynaphthalene (1-OHNap, ß = 3.32), hydroxyphenanthrenes (1/2/3 + 9-OHPhes, ß = 3.41-5.12), 1-hydroxypyrene (1-OH-Pyr; ß = 3.91), 2-hydroxybenzofuran (2-OHDBF; ß = 3.93), and ∑OH-PAHs (ß = 4.67) were positively associated with CARS scores after adjusting for covariates (all p < 0.05). When applying the ABC scale, 1-OHPyr levels were positively associated with ABC total scores (ß = 18.54), with the strongest associations evidenced in regard to the social relatedness (ß = 6.51) and language domains (ß = 6.51) (all p < 0.05). Bayesian kernel machine regression (BKMR) showed consistent positive exposure responses for 1-OHNap, 1-OHPhe, and 3 + 9-OHPhe levels in regard to CARS scores, and for 1-OHPyr levels in regard to ABC total scores. Our findings suggest that children with ASD may have higher urinary levels of hydroxy-PAHs, and that these biomarker levels are associated with an increased odds of ASD, an increased severity of autism symptomology, and increased autistic behaviors in children with autism. LAY SUMMARY: We conducted an epidemiologic study evaluating the associations of urinary hydroxy-PAH levels with autism spectrum disorder (ASD), autism symptomology, and autistic behaviors. We found that urinary hydroxy-PAHs were statistically significantly associated with ASD. We note strong statistically significant associations between 1-OHNap, 1-OHPhe, and 3 + 9-OHPhe levels and increased severity of autism symptomology, as well as a strong statistically significant association between 1-OHPyr levels and behavioral characteristics within the social and linguistic domains. This work, if confirmed, will contribute to the future development of diagnostics for children with mild autism, as well as to environmental measures to promote the health and wellbeing of children with autism spectrum disorders.

Relationship between the Dietary Inflammatory Index Score and Cytokine Levels in Chinese Pregnant Women during the Second and Third Trimesters.

The impact of dietary inflammatory potential on serum cytokine concentrations in second and third trimesters of Chinese pregnant women is not clear. A total of 175 pregnant women from the Tianjin Maternal and Child Health Education and Service Cohort (TMCHESC) were included. The dietary inflammatory index (DII) was calculated based on 24-h food records. Serum tumor necrosis factor-α (TNF-α), interleukin 1ß (IL-1ß), IL-6, IL-8, IL-10, C-reactive protein (CRP), and monocyte chemoattractant protein-1 (MCP-1) levels in the second and third trimesters were measured. The mean DII scores (mean ± SD) were -0.07 ± 1.65 and 0.06 ± 1.65 in the second and third trimesters, respectively. In the third trimester, IL-1ß (p = 0.039) and MCP-1 (p = 0.035) levels decreased and then increased with increasing DII scores. IL-10 concentrations decreased in pregnant women whose DII scores increased between the second and third trimesters (p = 0.011). Thiamin and vitamin C were negatively correlated with MCP-1 (ß = -0.879, and ß = -0.003) and IL-6 (ß = -0.602, and ß = -0.002) levels in the third trimester. In conclusion, the DII score had a U-shaped association with cytokine levels during the third trimester. Changes in DII scores between the second and third trimesters of pregnancy were correlated with cytokine levels during the third trimester.

Dual-Step Selective Homoepitaxy of Ge with Low Defect Density and Modulated Strain Based on Optimized Ge/Si Virtual Substrate.

In this manuscript, a novel dual-step selective epitaxy growth (SEG) of Ge was proposed to significantly decrease the defect density and to create fully strained relaxed Ge on a Si substrate. With the single-step SEG of Ge, the threading defect density (TDD) was successfully decreased from 2.9 × 107 cm-2 in a globally grown Ge layer to 3.2 × 105 cm-2 for a single-step SEG and to 2.84 × 105 cm-2 for the dual-step SEG of the Ge layer. This means that by introducing a single SEG step, the defect density could be reduced by two orders of magnitude, but this reduction could be further decreased by only 11.3% by introducing the second SEG step. The final root mean square (RMS) of the surface roughness was 0.64 nm. The strain has also been modulated along the cross-section of the sample. Tensile strain appears in the first global Ge layer, compressive strain in the single-step Ge layer and fully strain relaxation in the dual-step Ge layer. The material characterization was locally performed at different points by high resolution transmission electron microscopy, while it was globally performed by high resolution X-ray diffraction and photoluminescence.

Diagnostic model for predicting hyperuricemia based on alterations of the gut microbiome in individuals with different serum uric acid levels.

Background: We aimed to assess the differences in the gut microbiome among participants with different uric acid levels (hyperuricemia [HUA] patients, low serum uric acid [LSU] patients, and controls with normal levels) and to develop a model to predict HUA based on microbial biomarkers. Methods: We sequenced the V3-V4 variable region of the 16S rDNA gene in 168 fecal samples from HUA patients (n=50), LSU patients (n=61), and controls (n=57). We then analyzed the differences in the gut microbiome between these groups. To identify gut microbial biomarkers, the 107 HUA patients and controls were randomly divided (2:1) into development and validation groups and 10-fold cross-validation of a random forest model was performed. We then established three diagnostic models: a clinical model, microbial biomarker model, and combined model. Results: The gut microbial α diversity, in terms of the Shannon and Simpson indices, was decreased in LSU and HUA patients compared to controls, but only the decreases in the HUA group were significant (P=0.0029 and P=0.013, respectively). The phylum Proteobacteria (P<0.001) and genus Bacteroides (P=0.02) were significantly increased in HUA patients compared to controls, while the genus Ruminococcaceae_Ruminococcus was decreased (P=0.02). Twelve microbial biomarkers were identified. The area under the curve (AUC) for these biomarkers in the development group was 84.9% (P<0.001). Notably, an AUC of 89.1% (P<0.001) was achieved by combining the microbial biomarkers and clinical factors. Conclusions: The combined model is a reliable tool for predicting HUA and could be used to assist in the clinical evaluation of patients and prevention of HUA.

A Polarization-Switching, Charge-Trapping, Modulated Arithmetic Logic Unit for In-Memory Computing Based on Ferroelectric Fin Field-Effect Transistors.

Nonvolatile logic devices are crucial for the development of logic-in-memory (LiM) technology to build the next-generation non-von Neumann computing architecture. Ferroelectric field-effect transistors (Fe FET) are one of the most promising candidates for LiMs because of high compatibility with mainstream silicon-based complementary metal-oxide semiconductor processes, nonvolatile memory, and low power consumption. However, because of the unipolar characteristics of a Fe FET, a nonlinear XOR or XNOR logic gate function is difficult to realize with a single device. In addition, because single Fe polarization switch modulation is available in the devices, a reconfigurable logic gate usually needs multiple devices to construct and realize fewer logic functions. Here, we introduced polarization-switching (PS) and charge-trapping (CT) effects in a single Fe FET and fabricated a multi-field-effect transistor with bipolar-like characteristics based on advanced 10 nm node fin field-effect transistors (PS-CT FinFET) with 9 nm thick Hf0.5Zr0.5O2 films. The special hybrid effects of charge-trapping and polarization-switching enabled eight Boolean logic functions with a single PS-CT FinFET and 16 Boolean logic functions with two complementary PS-CT FinFETs were obtained with three operations. Furthermore, reconfigurable full 1 bit adder and subtractor functions were demonstrated by connecting only two n-type and two p-type PS-CT FinFET devices, indicating that the technology was promising for LiM applications.

Seizure Forecasting Using Long-Term Electroencephalography and Electrocardiogram Data.

Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.