Digital-intensive RFIC design techniques for transmitters in ISSCC 2023

Sub-6 GHz multi-standard wireless communication and millimeter-wave(mm-wave)wireless transmission provide users with multi-functionality and unprecedented wireless connectivity.With CMOS process scaling down,system-on-chip(SoC)implementation of wireless systems along with RFIC functionality is highly desirable for low cost and small form-factor.The digital transceiver architecture aligns with Moore’s law to provide compact die area,better interface to digital backend and higher efficiency due to the faster switching nature of core devices[1−28].

Purification and characterization of keratinase from a new Bacillus subtilis strain

The aim of this study was to purify and characterize a keratinase produced by a new isolated Bacillus subtilis KD-N2 strain. The keratinase produced by the isolate was purified using ammonium sulphate precipitation, Sephadex G-75 and DEAE (diethylaminoethyl)-Sepharose chromatographic techniques. The purified enzyme was shown to have a molecular mass of 30.5 kDa, as determined by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis. The optimum pH at 50 °C was 8.5 and the optimum temperature at pH 8.5 was 55 °C. The keratinase was partially inactivated by some metal ions, organic solvents and serine protease inhibitor phenylmethanesulfonyl fluoride (PMSF). Sodium dodecyl sulfate (SDS) and ethylene diamine tetraacetic acid (EDTA) had positive effect on the keratinase activity. Reducing agents including dithiothreitol (DTT), mercaptoethanol, L-cysteine, sodium sulphite, as well as chemicals of SDS, ammonium sulfamate and dimethylsulfoxide (DMSO) stimulated the enzyme activity upon a feather meal substrate. Besides feather keratin, the enzyme is active upon the soluble proteins ovalbumin, bovine serum albumin (BSA), casein and insoluble ones as sheep wool and human hair. Calf hair, silk and collagen could not be hydrolyzed by the keratinase.

SARS-CoV-2 pathogenesis in the gastrointestinal tract mediated by Spike-induced intestinal inflammation

Dear Editor,The pathogenesis of COVID-19 in the gastrointestinal(GI)tract is well-documented[1].However,due to a lack of simple animal models,the pathogenic mechanisms of SARS-CoV-2 in the GI tract is not well established.Currently,common animal models for COVID-19 include mice,rats,hamsters,ferrets and non-human primates.

Infuence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year’s longitudinal surveillance data:a spatio-temporal modelling study

Background Urbanization greatly afects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases.Schistosomiasis,a common parasitic disease transmitted by the snail Oncomelania hupensis,is mainly found in areas with population aggregations along rivers and lakes where snails live.Previous studies have suggested that factors related to urbanization may infuence the infection risk of schistosomiasis,but this association remains unclear.This study aimed to analyse the efect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.Methods County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected.The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite(NPP-VIIRS).The geographically and temporally weighted regression model(GTWR)was used to quantify the infuence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled.The regression coefcient of urbanization was tested for signifcance(α=0.05),and the infuence of urbanization on schistosomiasis infection risk was analysed over time and across space based on signifcant regression coefcients.Variables studied included climate,soil,vegetation,hydrology and topography.Results The mean regression coefcient for urbanization(0.167)is second only to the leached soil area(0.300),which shows that the urbanization is the most important infuence factors for schistosomiasis infection risk besides leached soil area.The other important variables are distance to the nearest water source(0.165),mean minimum temperature(0.130),broadleaf forest area(0.105),amount of precipitation(0.073),surface temperature(0.066),soil bulk density(0.037)and grassland area(0.031).The infuence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year.During the study period,the signifcant coefcient of urbanization level increased from−0.205 to−0.131.Conclusions The infuence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous.The urbanization does reduce the risk of schistosomiasis infection to some extend,but the strength of this infuence decreases with increasing urbanization.Additionally,the efect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors.This study provides scientifc basis for understanding the infuence of urbanization on schistosomiasis,and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Preparation of high-affinity rabbit monoclonal antibodies for ciprofloxacin and development of an indirect competitive ELISA for residues in milk

A convenient competitive enzyme-linked immunosorbent assay(ELISA) for ciprofloxacin(CPFX) was developed by using rabbit monoclonal antibodies(RabMAbs) against a hapten-protein conjugate of CPFX-bovine serum albumin(BSA).The indirect competitive ELISA of CPFX had a concentration at 50% inhibition(IC50) of 1.47 ng/ml and a limit of detection(LOD) of 0.095 ng/ml.The mAb exhibited some cross-reactivity,however,not so high with enrofloxacin(28.8%),ofloxacin(13.1%),norfloxacin(11.0%),fleroxacin(22.6%),and pefloxacin(20.4%).And it showed almost no cross-reactivity with other antibiotics or sulfonamides evaluated in this study.The competitive ELISA kit developed here could be used as a screening tool to detect and control illegal addition of CPFX in food products.This kit had been applied to milk detection and the recovery rates from samples spiked by CPFX were in a range of 63.02%-84.60%,with coefficients of variation of less than 12.2%.

Volatile fingerprints and biomarkers of Chinese fragrant and non-fragrant japonica rice before and after cooking obtained by untargeted GC/MS-based metabolomics

China has a rich resource of fragrant japonica rice that is gaining increasing consumers’ interests.In the present study,an untargeted gas chromatography-mass spectrometry-based metabolomics was employed to investigate the volatile profile of Chinese fragrant and non-fragrant japonica varieties.Partial least square discriminative analysis (PLS-DA) along with univariate analysis was taken to seek specific markers in raw and cooked Chinese japonica rice distinguishing fragrant and non-fragrant varieties.Volatile profile of raw Chinese japonica rice dominated by alkanes was completely different from that of cooked rice predominated by aldehydes.Thirteen and eleven volatile compounds were selected as the potential discriminative markers in raw and cooked Chinese japonica rice,respectively.Only 2-acetylpyrrole and decanal served as the specific marker in both raw and cooked Chinese japonica rice.Fragrant Chinese japonica rice varieties contained more 2-acetylpyrrole,(E )-2-octenal,nonanal,1-tetradecene,decanal and (E )-2-decenal in raw rice.While fragrant Chinese japonica rice varieties contained more 2-acetyl-1-pyrroline,2-acetylpyrrole,1-hexanol,decanal and indole in cooked rice.The results provided an in-depth understanding of volatile variation in raw and cooked form of aromatic and non-aromatic cultivars and will benefit rice adulteration identification and marker-assisted flavor breeding in the future.

EyeHealer:A large-scale anterior eye segment dataset with eye structure and lesion annotations

Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(e.g.blood or inflammation),and lens diseases.The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care.With research on artificial intelligence progressing in recent years,deep learning models have shown their superiority in image classification and segmentation.The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise;however,such data are relatively scarce in the domain of medicine.Herein,the authors developed a new medical image annotation system,called EyeHealer.It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level.Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation.The results showed that semantic segmentation models outperformed medical segmentation models.This paper describes the establishment of the system for automated classification and segmentation tasks.The dataset will be made publicly available to encourage future research in this area.

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics:a model-based study

Background:The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019(COVID-19)pandemic complicated to predict and posed a severe challenge to the Beijing 2022Winter Olympics and Winter Paralympics held in February and March 2022.Methods:During the preparations for the Beijing 2022 Winter Olympics,we established a dynamic model with pulsedetection and isolation efect to evaluate the efect of epidemic prevention and control measures such as entry policies,contact reduction,nucleic acid testing,tracking,isolation,and health monitoring in a closed-loop managementenvironment,by simulating the transmission dynamics in assumed scenarios.We also compared the importance ofeach parameter in the combination of intervention measures through sensitivity analysis.Results:At the assumed baseline levels,the peak of the epidemic reached on the 57th day.During the simulationperiod(100 days),13,382 people infected COVID-19.The mean and peak values of hospitalized cases were 2650and 6746,respectively.The simulation and sensitivity analysis showed that:(1)the most important measures to stopCOVID-19 transmission during the event were daily nucleic acid testing,reducing contact among people,and dailyhealth monitoring,with cumulative infections at 0.04%,0.14%,and 14.92%of baseline levels,respectively(2)strictlyimplementing the entry policy and reducing the number of cases entering the closed-loop system could delay thepeak of the epidemic by 9 days and provide time for medical resources to be mobilized;(3)the risk of environmentaltransmission was low.Conclusions:Comprehensive measures under certain scenarios such as reducing contact,nucleic acid testing,health monitoring,and timely tracking and isolation could efectively prevent virus transmission.Our research resultsprovided an important reference for formulating prevention and control measures during the Winter Olympics,andno epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.

Significant reduction of humoral response to SARS-CoV-2 4 months after the diagnosis of COVID-19

Editor’s note A commentary on“Humoral immune response to SARS-CoV-2 in Iceland”.Humoral response to COVID-19 disease,including IgM and IgG antibodies against SARS-CoV-2 virus,is well documented.1,2 Neutralizing activities against SARS-CoV-2 virus have also been confirmed in patients who have recovered from COVID-19.2 These observations form the basis for the assumption that SARS-CoV-2 vaccines can be developed through stimulation of the host immune system to generate neutralizing antibodies to SARS-CoV-2 virus.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos

Background:In vitro fertilization(IVF)has emerged as a transformative solution for infertility.However,achieving favorable live-birth outcomes remains challenging.Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods,including static images and temporal videos.However,traditional embryo selection methods,primarily reliant on visual inspection of morphology,exhibit variability and are contingent on the experience of practitioners.Therefore,an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable.Methods:We employed artificial intelligence(AI)for embryo morphological grading,blastocyst embryo selection,aneuploidy prediction,and final live-birth outcome prediction.We developed and validated the AI models using multitask learning for embryo morphological assessment,including pronucleus type on day 1 and the number of blastomeres,asymmetry,and fragmentation of blastomeres on day 3,using 19,201 embryo photographs from 8271 patients.A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5,and predict live-birth outcomes.Additionally,a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing(PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results:These two approaches enabled us to automatically assess the implantation potential.By combining embryo and maternal metrics in an ensemble AI model,we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists(46.1%vs.30.7%on day 3,55.0%vs.40.7%on day 5).Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians(area under the curve:0.769,95%confidence interval:0.709-0.820).These findings could potentially provide a noninvasive,high-throughput,and low-cost screening tool to facilitate embryo selection and achieve better outcomes.Conclusions:Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction,highlighting its potential as a noninvasive,efficient,and cost-effective tool for improved embryo selection and enhanced IVF outcomes.The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.