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Wastewater treatment plant (WWTP) reduces environmental load by improving the quality of effluent sewage water. A wastewater treatment plant during its operation, consumes energy for circulation and aeration of water, utilise chemicals such as anti-foaming agents, coagulants and flocculants and release gaseous emissions and solid waste (sludge). It is necessary to consider the environmental impacts of all these activities realistically. Life cycle assessment (LCA) is used to calculate the potential environmental impact associated with the operation of WWTP. In this study, LCA was carried out for a WWTP located in Jaipur in the state of Rajasthan, India. CML 2001 method was used for the life cycle impact assessment using GaBi 8.0 software. The objective of this study is to quantify the environmental impact associated with the operation of a WWTP and to identify the environmental hotspots among different processes within the WWTP. The LCA result of the WWTP reveals that the sequencing batch reactor process 1 and 2 have the highest environmental impacts within the WWTP as this process consumes the highest amount of electricity whereas the pretreatment process has the least environmental impact.

Abstract Current methods for therapeutic drug monitoring (TDM) have a long turnaround time as they involve collecting patients' blood samples followed by transferring the samples to medical laboratories where sample processing and analysis are performed. To enable real‐time and minimally invasive TDM, a microneedle (MN) biosensor to monitor the levels of two important antibiotics, vancomycin (VAN) and gentamicin (GEN) is developed. The MN biosensor is composed of a hydrogel MN (HMN), and an aptamer‐functionalized flexible (Flex) electrode, named HMN‐Flex. The HMN extracts dermal interstitial fluid (ISF) and transfers it to the Flex electrode where sensing of the target antibiotics happens. The HMN‐Flex performance is validated ex vivo using skin models as well as in vivo in live rat animal models. Data is leveraged from the HMN‐Flex system to construct pharmacokinetic profiles for VAN and GEN and compare these profiles with conventional blood‐based measurements. Additionally, to track pH and monitor patient's response during antibiotic treatment, an HMN is developed that employs a colorimetric method to detect changes in the pH, named HMN‐pH assay, whose performance has been validated both in vitro and in vivo. Further, multiplexed antibiotic and pH detection is achieved by simultaneously employing the HMN‐pH and HMN‐Flex on live animals.

Abstract Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

Objective To assess the impact of scaling up of kangaroo mother care (KMC) on neonatal mortality among babies born with birth weight

We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network's hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world.

Different sleep stages have been shown to be vital for a variety of brain functions, including learning, memory, and skill consolidation. However, our understanding of neural dynamics during sleep and the role of prominent LFP frequency bands remain incomplete. To elucidate such dynamics and differences between behavioral states we collected multichannel LFP and spike data in primary motor cortex of unconstrained macaques for up to 24 h using a head-fixed brain-computer interface (Neurochip3). Each 8-s bin of time was classified into awake-moving (Move), awake-resting (Rest), REM sleep (REM), or non-REM sleep (NREM) by using dimensionality reduction and clustering on the average spectral density and the acceleration of the head. LFP power showed high delta during NREM, high theta during REM, and high beta when the animal was awake. Cross-frequency phase-amplitude coupling typically showed higher coupling during NREM between all pairs of frequency bands. Two notable exceptions were high delta-high gamma and theta-high gamma coupling during Move, and high theta-beta coupling during REM. Single units showed decreased firing rate during NREM, though with increased short ISIs compared to other states. Spike-LFP synchrony showed high delta synchrony during Move, and higher coupling with all other frequency bands during NREM. These results altogether reveal potential roles and functions of different LFP bands that have previously been unexplored.

A Bayesian framework based on partially observable Markov decision processes (POMDPs) not only predicts subjects’ confidence in a perceptual decision making task but also explains well-known discrepancies between confidence and choice accuracy as arising from incomplete knowledge of the environment.

Traditionally, recording from and stimulating the brain with high spatial and temporal resolution required invasive means. However, recently, the technical capabilities of less invasive and non-invasive neuro-interfacing technology have been dramatically improving, and laboratories and funders aim to further improve these capabilities. These technologies can facilitate functions such as multi-person communication, mood regulation and memory recall. We consider a potential future where the less invasive technology is in high demand. Will this demand match that the current-day demand for a smartphone? Here, we draw upon existing research to project which particular neuroethics issues may arise in this potential future and what preparatory steps may be taken to address these issues.

Background: alcohol may have additional neurotoxic ill-effects in patients with alcohol related cirrhosis apart from hepatic encephalopathy. We aimed to evaluate minimal hepatic encephalopathy (MHE) with Psychometric Hepatic Encephalopathy (PHES) score and Critical Flicker Frequency (CFF) in alcohol (ALD) and non-alcoholic steatohepatitis related (NASH) related cirrhosis. Methods: 398 patients were screened between March 2016 and December 2018; of which 71 patients were included in ALD group and 69 in NASH group. All included patients underwent psychometric tests which included number connection test A and B (NCT-A and NCT-B), serial dot test (SDT), digit symbol test (DST), line tracing test (LTT) and CFF. MHE was diagnosed when their PHES was

Introduction The COVID-19 pandemic is disrupting health systems globally. Maternity care disruptions have been surveyed, but not those related to vulnerable small newborns. We aimed to survey reported disruptions to small and sick newborn care worldwide and undertake thematic analysis of healthcare providers’ experiences and proposed mitigation strategies.Methods Using a widely disseminated online survey in three languages, we reached out to neonatal healthcare providers. We collected data on COVID-19 preparedness, effects on health personnel and on newborn care services, including kangaroo mother care (KMC), as well as disruptors and solutions.Results We analysed 1120 responses from 62 countries, mainly low and middle-income countries (LMICs). Preparedness for COVID-19 was suboptimal in terms of guidelines and availability of personal protective equipment. One-third reported routine testing of all pregnant women, but 13% had no testing capacity at all. More than 85% of health personnel feared for their own health and 89% had increased stress. Newborn care practices were disrupted both due to reduced care-seeking and a compromised workforce. More than half reported that evidence-based interventions such as KMC were discontinued or discouraged. Separation of the mother–baby dyad was reported for both COVID-positive mothers (50%) and those with unknown status (16%). Follow-up care was disrupted primarily due to families’ fear of visiting hospitals (~73%).Conclusion Newborn care providers are stressed and there is lack clarity and guidelines regarding care of small newborns during the pandemic. There is an urgent need to protect life-saving interventions, such as KMC, threatened by the pandemic, and to be ready to recover and build back better.

In the ongoing COVID-19 pandemic, public health experts have produced guidelines to limit the spread of the coronavirus, but individuals do not always comply with experts' recommendations. Here, we tested whether a specific psychological belief-identification with all humanity-predicts cooperation with public health guidelines as well as helpful behavior during the COVID-19 pandemic. We hypothesized that peoples' endorsement of this belief-their relative perception of a connection and moral commitment to other humans-would predict their tendencies to adopt World Health Organization (WHO) guidelines and to help others. To assess this, we conducted a global online study (N = 2537 participants) of four WHO-recommended health behaviors and four pandemic-related moral dilemmas that we constructed to be relevant to helping others at a potential cost to oneself. We used generalized linear mixed models (GLMM) that included 10 predictor variables (demographic, contextual, and psychological) for each of five outcome measures (a WHO cooperative health behavior score, plus responses to each of our four moral, helping dilemmas). Identification with all humanity was the most consistent and consequential predictor of individuals' cooperative health behavior and helpful responding. Analyses showed that the identification with all humanity significantly predicted each of the five outcomes while controlling for the other variables (Prange < 10-22 to < 0.009). The mean effect size of the identification with all humanity predictor on these outcomes was more than twice as large as the effect sizes of other predictors. Identification with all humanity is a psychological construct that, through targeted interventions, may help scientists and policymakers to better understand and promote cooperative health behavior and help-oriented concern for others during the current pandemic as well as in future humanitarian crises.

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant’s ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.