A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.

C, N dual-doped ZnO nanofoams: a potential antimicrobial agent, an efficient visible light photocatalyst and SXAS studies.

It is crucial to develop an environmentally friendly and low-cost method to treat industrial effluent that contains soluble dyes and microbes. Most of the photocatalysts have been studied using an external light source that increases the cost of the purification process of effluent. This study focuses on developing efficient solar photocatalytic nanofoams. The controlled growth of ZnO nanofoams (CNZ nanofoams) in a simple method of thermal oxidation using a soft template is reported. Prepared nanofoams are characterized using X-ray diffraction, scanning electon microscopy and synchrotron soft X-ray absorption spectroscopy. By photocatalysis studies under direct sunlight it was found that within 120 min CNZ nanofoams degraded 99% of the dye. In addition, antimicrobial studies of multi-drug-resistant E. Fergusonii isolated from wastewater was carried out. These antimicrobial results showed a good inhibition zone, indicating that prepared nanofoams are both an effective solar photocatalyst and an antimicrobial agent.

Biomonitoring of airborne platinum group elements in urban traffic police officers.

In the present study, an attempt was made to study the levels of platinum (Pt), palladium (Pd), and rhodium (Rh) in respirable suspended particulate matter samples and respective blood samples of occupationally exposed traffic personnel in selected sites of Hyderabad city. The maximum concentration of platinum group elements in air dust samples of Hyderabad city were as follows: Pt = 1,416 µg/m(3), Pd = 1,024 µg/m(3), and Rh = 1,352 µg/m(3). The blood samples of occupationally exposed personnel of Hyderabad city showed Pt as high as 6.65, Pd as high as 2.15, and Rh as high as 4.95 µg/l. The results showed an important aspect of bioaccumulation tendency of these metals with increase in age and years of occupational exposure.

Iterative method of baffle design for modified Ritchey-Chretien telescope.

We developed a baffle design method based on a combination of the results of optical design software and analytical relations formulated herein. The method finds the exact solution for baffle parameters of a modified Ritchey-Chretien telescope by iteratively solving the analytical relations using the actual ray coordinates of the telescope computed with the aid of optical design software. The baffle system so designed not only blocks the direct rays of stray light reaching the image plane but also provides minimum obscuration to imaging light. Based on the iterative method, we proposed a baffle design approach for a rectangular-image-format telescope.

Evaluation of the potential of various aquatic eco-systems in harnessing bioelectricity through benthic fuel cell: effect of electrode assembly and water characteristics.

Six different types of ecological water bodies were evaluated to assess their potential to generate bioelectricity using benthic type fuel cell assemblies. Experiments were designed with various combinations of electrode assemblies, surface area of anode and anodic materials. Among the 32 experiments conducted, nine combinations evidenced stable electron-discharge/current. Nature, flow conditions and characteristics of water bodies showed significant influence on the power generation apart from electrode assemblies, surface area of anode and anodic material. Stagnant water bodies showed comparatively higher power output than the running water bodies. Placement of cathode on algal mat (as bio-cathode) documented several folds increment in power output. Electron-discharge started at 1000 Omega resistance in polluted water bodies (Nacaharam cheruvu, Hussain Sagar lake Musi river), whereas, in relatively less polluted water bodies (Uppal pond/stream, Godavari river) electron-discharge was observed at low resistances (500/750 Omega).