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Abstract Pollution by toxic heavy metals creates a significant impact on the biotic community of the ecosystem. Nowadays, a solution to this problem is an eco-friendly approach like phytoremediation, in which plants are used to ameliorate heavy metals. In addition, various amendments are used to enhance the potential of heavy metal phytoremediation. Symbiotic microorganisms such as phosphate-solubilizing bacteria (PSB), endophytes, mycorrhiza and plant growth-promoting rhizobacteria (PGPR) play a significant role in the improvement of heavy metal phytoremediation potential along with promoting the growth of plants that are grown in contaminated environments. Various chemical chelators (Indole 3-acetic acid, ethylene diamine tetra acetic acid, ethylene glycol tetra acetic acid, ethylenediamine-N, N-disuccinic acid and nitrilotri-acetic acid) and their combined action with other agents also contribute to heavy metal phytoremediation enhancement. With modern techniques, transgenic plants and microorganisms are developed to open up an alternative strategy for phytoremediation. Genomics, proteomics, transcriptomics and metabolomics are widely used novel approaches to develop competent phytoremediators. This review accounts for the synergistic interactions of the ameliorating agent’s role in enhancing heavy metal phytoremediation, intending to highlight the importance of these various approaches in reducing heavy metal pollution.

Introduction: Neurological disorders are the leading causes of death and disability globally. In low-middle-income countries such as India, there is a wide treatment gap for neurological disorders. To address this gap, an initiative known as the Karnataka Brain Health Initiative (KaBHI) was developed in collaboration with the public health system of the state of Karnataka in India. Method: A structured hybrid training program was conducted for 120 medical doctors working in the Primary Health Centers (PHCs) in the public health system in three districts of Karnataka. Pre- and post-training evaluation was conducted to assess physicians’ knowledge and skills for evaluating patients with common neurological conditions. Results: Of 120 PHC doctors, 111 (92%) scored below the passing score of 50 points out of 100 before the training program. After the training, all trainees scored above this cut-off. The study found a significant improvement in knowledge and skills after the training (before training: 40.48 ± 7.92, after training: 66.28 ± 8.98 (P < 0.001) mean difference = 25.81), and was comparable among the PHC doctors in all three districts. Conclusions: Our study shows the feasibility of training primary care doctors in the public health system to manage selected common neurological disorders. The training program can potentially help to reduce the treatment gap for neurological disorders in India.

Plant peroxidases are a family of related proteins possessing highly conserved domains. Degenerate oligonucleotide primers based on these conserved domains can be used to amplify DNA sequences coding for peroxidases from plants with unsequenced genomes. Polymorphisms in peroxidase genes among buffalograss [Buchloe dactyloides (Nutt.) Engelm.] genotypes and eight other grasses were evaluated, and potential evolutionary relationships were deduced using this approach. Fourteen peroxidase specific primers with alternative forward and reverse primers using 34 rice peroxidase cDNAs were designed based on conserved motifs of this gene family. Targeted polymerase chain reaction (PCR) amplification of genomic DNA from 28 buffalograss, 4 C4, and 4 C3 grass genotypes yielded polymorphisms, differentiating diploids from polyploids within buffalograss and C3 and C4 grass species from each other. A total of 11 peroxidase gene fragments, 7 belonging to buffalograss and 4 to the other grass species, were sequenced. Five of these sequences were clustered with rice (Oryza sativa L.) ascorbate peroxidase known to have chloroplast origin. These results demonstrate that primers targeting the peroxidase gene family can be used to study genotypic diversity and evolutionary relationships on an intraspecific and interspecific basis. The PCR-based peroxidase markers may also have potential for linkage mapping and differential gene expression studies in grasses.

Abstract Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

Inflammatory bowel disease (IBD) has become alarmingly prevalent in the last two decades affecting 6.8 million people worldwide with a starkly high relapse rate of 40% within 1 year of remission. Existing visual endoscopy techniques rely on subjective assessment of images that are error-prone and insufficient indicators of early-stage IBD, rendering them unsuitable for frequent and quantitative monitoring of gastrointestinal health necessary for detecting regular relapses in IBD patients. To address these limitations, we have implemented a miniaturized smart capsule (2.2 cm × 11 mm) that allows monitoring reactive oxygen species (ROS) levels as a biomarker of inflammation for quantitative and frequent profiling of inflammatory lesions throughout the gastrointestinal tract. The capsule is composed of a pH and oxidation reduction potential (ORP) sensor to track the capsule's location and ROS levels throughout the gastrointestinal tract, respectively, and an optimized electronic interface for wireless sensing and data communication. The designed sensors provided a linear and stable performance within the physiologically relevant range of the GI tract (pH: 1–8 and ORP: −500 to +500 mV). Additionally, systematic design optimization of the wireless interface electronics offered an efficient sampling rate of 10 ms for long-running measurements up to 48 h for a complete evaluation of the entire gastrointestinal tract. As a proof-of-concept, the capsule the capsule's performance in detecting inflammation risks was validated by conducting tests on in vitro cell culture conditions, simulating healthy and inflamed gut-like environments. The capsule presented here achieves a new milestone in addressing the emerging need for smart ingestible electronics for better diagnosis and treatment of digestive diseases.

Abstract For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system’s complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their credibility, a technique originally developed for “truth discovery” in social sensing applications. We use the Maximum Likelihood Estimation to estimate the true signal and the credibility index of the sensors over time. Using the estimated signal, we develop an on-the-fly drift-correction method to make unreliable sensors reliable by correcting any systematic drifts during operation. Our approach can determine solution pH within 0.09 pH for more than three months by detecting and correcting the gradual drift of pH sensors as a function of gamma-ray irradiation. In the field study, we validate our method by measuring nitrate levels in an agricultural field onsite over 22 days within 0.06 mM of a high-precision laboratory-based sensor. We theoretically demonstrate and numerically validate that our approach can estimate the true signal even when the majority (~ 80%) of the sensors are unreliable. Moreover, by restricting wireless transmission to high-credible sensors, we achieve near-perfect information transfer at a fraction of the energy cost. The high-precision sensing with low-cost sensors at reduced transmission cost will pave the way for pervasive in-field sensing with electrochemical sensors. The approach is general and can improve the accuracy of any field-deployed sensors undergoing drift and degradation during operation.

Abstract The development of flexible hybrid electronics (FHEs) with high‐throughput integration of electrical components onto digitally printed circuits has a wide range of applications, such as asset tracking, wearable electronics, and structural health monitoring. However, one of the major challenges with FHEs is the process of soldering the electrical components onto a printed circuit while having minimal thermal damage to the printed traces and their temperature‐sensitive polymeric substrates. Here, the possibility of utilizing near‐infrared (NIR) technology as a nondestructive photonic approach for rapid soldering and mounting electrical components onto printed circuits while keeping the polymer substrate at a relatively low temperature during the soldering process is investigated. Results of this systematic study show that FHEs prepared with the optimized NIR processing conditions produce the desired reflow of solder with effective electrical connection and metallic bonding of electrical components onto the conductive traces with excellent mechanical stability (no failure even after 1000 cycles of bending). Furthermore, using this technique and as a proof of concept, the fabrication of a wearable FHE device that provides a remote assessment of the wound exudate absorption in dressings and notifies caregivers of the appropriate time to change the dressing is demonstrated.

Abstract Precision Agriculture (PA) is an integral component of the contemporary agricultural revolution that focuses on enhancing food productivity in proportion to the increasing global population while minimizing resource waste. While the recent advancements in PA, such as the integration of IoT (Internet of Things) sensors, have significantly improved the surveillance of field conditions to achieve high yields, the presence of batteries and electronic chips makes them expensive and non-biodegradable. To address these limitations, for the first time, we have developed a fully Degradable Intelligent Radio Transmitting Sensor (DIRTS) that allows remote sensing of subsoil volumetric water using drone-assisted wireless monitoring. The device consists of a simple miniaturized resonating antenna encapsulated in a biodegradable polymer material such that the resonant frequency of the device is dependent on the dielectric properties of the soil surrounding the encapsulated structure. The simple structure of DIRTS enables scalable additive manufacturing processes using cost-effective, biodegradable materials to fabricate them in a miniaturized size, thereby facilitating their automated distribution in the soil. As a proof-of-concept, we present the use of DIRTS in lab and field conditions where the sensors demonstrate the capability to detect volumetric water content within the range of 3.7–23.5% with a minimum sensitivity of 9.07 MHz/%. Remote sensing of DIRTS can be achieved from an elevation of 40 cm using drones to provide comparable performance to lab measurements. A systematic biodegradation study reveals that DIRTS can provide stable readings within the expected duration of 1 year with less than 4% change in sensitivity before signs of degradation. DIRTS provides a new steppingstone toward advancing precision agriculture while minimizing the environmental footprint.

Background: Infantile colic is characterized by prolonged periods of inconsolable, incessant crying and persistent fussing in an otherwise healthy infant. It is a self-limiting condition, but causes significant stress to mothers. AIM: To observe the role of Lactobacillus reuteriDSM 17938 in reducing crying time in colicky infants in routine clinical practice. Methods: This was a prospective observational multicentric clinic-based study. Each practitioner included approximately 30 infants < 5 months of age with infantile colic who were prescribed L. reuteri DSM 17938 for a period of 21 days. There were four physical consultations and two telephonic consultations. The parents were given a daily diary to record the duration of crying and fussing episodes and a questionnaire was administered during the consultations. Results: A total of 120 infants with a mean age of 56.9 ± 34.2 days were included in this 28-day study. The mean crying time as reported by the parents in the subject diary reduced from 248.2 ± 101.2 min, 95% CI: 229.45, 266.94 at baseline to 45.6 ± 79.1 min 95% CI: 31.02, 60.31 at study end (P < 0.01). The clinical response (defined as reduction of 50% in crying time) was observed in 85% of subjects at study end. The fussiness and parental perception of colic recorded during the consultations were reduced by 66% and 72%, respectively, at study end. The maternal depression scores were reduced to 63% at study end. Conclusion: L. reuteri DSM 17938 was associated with a significant reduction in crying time in colicky infants, and showed improvement in maternal depression.

Index modulation assisted OFDM (OFDM-IM) has become a popular technique in wireless communications. In this paper, we proposes a novel multilayer framework for OFDM-IM. Being quite generic in its nature, the framework can be amalgamated with most of the OFDM-IM variants. For coherent OFDM-IM, we propose three different schemes; 1) multilayer index modulation (MIM); 2) dual mode MIM; and 3) compressive sampling aided MIM. The schemes strikes a compelling tradeoff between the BER, energy efficiency and throughput. Finally, we extend this multilayer framework to non-coherent index modulation, where the structure provides an improved BER performance compared to its single layer counterpart, albeit this is achieved at the cost of reduced energy efficiency. The performance analysis of the proposed scheme is confirmed by our simulation results.