Background: Next-generation sequencing (NGS) technologies have fostered an unprecedented proliferation of high-throughput sequencing projects and a concomitant development of novel algorithms for the assembly of short reads. However, numerous technical or computational challenges in de novo assembly still remain, although many new ideas and solutions have been suggested to tackle the challenges in both experimental and computational settings.

Results: In this review, we first briefly introduce some of the major challenges faced by NGS sequence assembly. Then, we analyze the characteristics of various sequencing platforms and their impact on assembly results. After that, we classify de novo assemblers according to their frameworks (overlap graph-based, de Bruijn graph-based and string graph-based), and introduce the characteristics of each assembly tool and their adaptation scene. Next, we introduce in detail the solutions to the main challenges of de novo assembly of next generation sequencing data, single-cell sequencing data and single molecule sequencing data. At last, we discuss the application of SMS long reads in solving problems encountered in NGS assembly.

Conclusions: This review not only gives an overview of the latest methods and developments in assembly algorithms, but also provides guidelines to determine the optimal assembly algorithm for a given input sequencing data type.

● Screened 8862 metal-organic frameworks for I₂ capture via molecular simulation.

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture, as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing. Starting from the computation-ready experimental (CoRE) metal-organic frameworks (MOFs) database, grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs. A ranking list of MOFs based on their iodine uptake capabilities was generated, with the Top 10 candidates identified and their respective adsorption sites visualized. Subsequently, machine learning was used to establish structure-property relationships to correlate MOFs’ various structural and chemical features with their corresponding performances in iodine capture, yielding interpretable common features and design rules for viable MOF adsorbents. The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine, and moreover, other volatile environmentally hazardous species.

● Online learning models accurately predict influent flow rate at wastewater plants.

Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants (WWTPs), as it is closely related to wastewater characteristics such as biochemical oxygen demand (BOD), total suspend solids (TSS), and pH. Previous studies have been conducted to predict influent flow rate, and it was proved that data-driven models are effective tools. However, most of these studies have focused on batch learning, which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly. Online learning, which has distinct advantages of dealing with stream data, large data set, and changing data pattern, has a potential to address this issue. In this study, the performance of conventional batch learning models Random Forest (RF), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), and their respective online learning models Adaptive Random Forest (aRF), Adaptive K-Nearest Neighbors (aKNN), and Adaptive Multi-Layer Perceptron (aMLP), were compared for predicting influent flow rate at two Canadian WWTPs. Online learning models achieved the highest R², the lowest MAPE, and the lowest RMSE compared to conventional batch learning models in all scenarios. The R² values on testing data set for 24-h ahead prediction of the aRF, aKNN, and aMLP at Plant A were 0.90, 0.73, and 0.87, respectively; these values at Plant B were 0.75, 0.78, and 0.56, respectively. The proposed online learning models are effective in making reliable predictions under changing data patterns, and they are efficient in dealing with continuous and large influent data streams. They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.

● Maldives’ unique natural and socioeconomic status cause waste management challenges.

Effective waste management is a major challenge for Small Island Developing States (SIDS) like Maldives due to limited land availability. Maldives exemplifies these issues as one of the most geographically dispersed countries, with a population unevenly distributed across numerous islands varying greatly in size and population density. This study provides an in-depth analysis of the unique waste management practices across different regions of Maldives in relation to its natural and socioeconomic context. Data shows Maldives has one of the highest population density and per capita waste generation among SIDS, despite its small land area and medium GDP per capita. Large disparities exist between the densely populated capital Male’ with only 5.8 km² area generating 63% of waste and the ~194 scattered outer islands with ad hoc waste management practices. Given Male’s dense population and high calorific waste, incineration could generate up to ~30 GW/a energy and even increase Maldives’ renewable energy supply by 200%. In contrast, decentralized anaerobic digestion presents an optimal solution for outer islands to reduce waste volume while providing over 40%–100% energy supply for daily cooking in local families. This timely study delivers valuable insights into designing context-specific waste-to-energy systems and integrated waste policies tailored to Maldives’ distinct regions. The framework presented can also guide other SIDS facing similar challenges as Maldives in establishing sustainable, ecologically sound waste management strategies.

● H₂O₂ quenching rates by Cl/S-based chemicals were measured

Residual H₂O₂ from UV/H₂O₂ treatment can be quenched by thiosulfate, bisulfite, and chlorine, but the kinetics of these reactions have not been reported under the full range of practical conditions. In this study, the rates of H₂O₂ quenching by these compounds were compared in different water matrices, temperatures, pH, and when using different forms of bisulfite and chlorine. In general, it was confirmed that thiosulfate would be too slow to serve as a quenching agent in most practical scenarios. At pH 7–8.5, chlorine tends to quench H₂O₂ more than 20 times faster than bisulfite in the various conditions tested. An important observation was that in lightly-buffered water (e.g., alkalinity of 20 mg/L as CaCO₃), the form of chlorine can have a large impact on quenching rate, with gaseous chlorine slowing the reaction due to its lowering of the pH, and hypochlorite having the opposite effect. These impacts will become less significant when water buffer capacity (i.e., alkalinity) increases (e.g., to 80 mg/L as CaCO₃). In addition, water temperature should be considered as the time required to quench H₂O₂ by chlorine at 4 °C is up to 3 times longer than at 20 °C.

Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field.

● A machine learning approach was applied to predict free chlorine residuals.

Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease caused by pathogens that may be present in source water. An important index of disinfection efficiency is the free chlorine residual (FCR), a regulated disinfection parameter in the US that indirectly measures disinfectant power for prevention of microbial recontamination during DWT and distribution. This work demonstrates how machine learning (ML) can be implemented to improve FCR forecasting when supplied with water quality data from a real, full-scale chlorine disinfection system in Georgia, USA. More precisely, a gradient-boosting ML method (CatBoost) was developed from a full year of DWT plant-generated chlorine disinfection data, including water quality parameters (e.g., temperature, turbidity, pH) and operational process data (e.g., flowrates), to predict FCR. Four gradient-boosting models were implemented, with the highest performance achieving a coefficient of determination, R², of 0.937. Values that provide explanations using Shapley’s additive method were used to interpret the model’s results, uncovering that standard DWT operating parameters, although non-intuitive and theoretically non-causal, vastly improved prediction performance. These results provide a base case for data-driven DWT disinfection supervision and suggest process monitoring methods to provide better information to plant operators for implementation of safe chlorine dosing to maintain optimum FCR.

● The Cd(II) adsorption capacity followed the order of PA > PLA > PP.

It has been demonstrated that microplastics (MPs) can accumulate heavy metals from the environment and transfer them into organisms via the food chain. However, adsorption and desorption capacities for biodegradable MPs relative to those for conventional MPs remain poorly understood. In this study, cadmium (Cd(II)) adsorption and desorption characteristics of polylactic acid (PLA), a typical biodegradable MP, were investigated. Two conventional MPs, i.e., polypropylene (PP) and polyamide (PA) were used for comparison. The maximum Cd(II) adsorption capacities of the MPs studied in the adsorption experiments decreased in the order PA (0.96 ± 0.07 mg/g) > PLA (0.64 ± 0.04 mg/g) > PP (0.22 ± 0.03 mg/g). The Pseudo-second-order kinetic model and Freundlich isothermal model described the Cd(II) adsorption behaviors of PLA MPs well. X-ray photoelectron spectroscopy and two-dimensional Fourier transform infrared correlation spectroscopy analysis indicated that oxygen functional groups were the major and preferential binding sites of PLA MPs, which contributed to their high Cd(II) adsorption capacities. Simulated gastric and intestinal fluids both significantly enhanced the desorption capacities of the examined MPs. Notably, degradation of the PLA MPs during in vitro human digestion made the Cd(II) on the PLA MPs more bioaccessible (19% in the gastric phase and 62% in the intestinal phase) than Cd(II) on the PP and PA MPs. These results indicate the remarkable capacities of biodegradable MPs to accumulate Cd(II) and transfer it to the digestive system and show that biodegradable MPs might pose more severe threats to human health than conventional nonbiodegradable MPs.

● Stable and efficient U extraction with electrical energy production was achieved.

The extraction of uranium (U) from U-bearing wastewater is of paramount importance for mitigating negative environmental impacts and recovering U resources. Microbial reduction of soluble hexavalent uranium (U(VI)) to insoluble tetravalent uranium (U(IV)) holds immense potential for this purpose, but its practical application has been impeded by the challenges associated with managing U-bacterial mixtures and the biotoxicity of U. To address these challenges, we present a novel spontaneous microbial electrochemical (SMEC) method that spatially decoupled the microbial oxidation reaction and the U(VI) reduction reaction. Our results demonstrated stable and efficient U extraction with net electrical energy production, which was achieved with both synthetic and real wastewater. U(VI) removal occurred via diffusion-controlled U(VI)-to-U(IV) reduction-precipitation at the cathode, and the U^IVO₂ deposited on the surface of the cathode contributed to the stability and durability of the abiotic U(VI) reduction. Metagenomic sequencing revealed the formation of efficient electroactive communities on the anodic biofilm and enrichment of the key functional genes and metabolic pathways involved in electron transfer, energy metabolism, the TCA cycle, and acetate metabolism, which indicated the ectopic reduction of U(VI) at the cathode. Our study represents a significant advancement in the cost-effective recovery of U from U(VI)-bearing wastewater and may open a new avenue for sustainable uranium extraction.

● An optical metallurgy is proposed to directly generate Zn⁰ from ZnS using laser.

In response to the goal of net-zero emissions proposed by Intergovernmental Panel on Climate Change, Chinese government has pledged that carbon emissions will peak by 2030, and achieve carbon neutrality by 2060. However, the high carbon energy structure of traditional industries has aggravated environmental problems, such as greenhouse effect and air pollution. The goal of carbon neutrality will be difficult to achieve without the development of disruptive theories and technologies. The electrolytic zinc industry requires high-temperature roasting at ~1000 °C, generating large amounts of greenhouse gases and SO₂. High concentrations of sulfuric acid (200 g/L) are subsequently used for electrolysis, and each ton of zinc produced generates 50 kg of anode slime with lead content of up to 16%, as well as 0.35 m³ of wastewater containing zinc and lead. To solve these problems, an optical metallurgy method is proposed in this study. The proposed method uses laser-induced photoreduction to decompose ZnS and reduce metal ions to metal. Results indicate that Zn⁰ and S₈ can be detected on the surface of ZnS at a specific wavelength and laser fluence. The generation mechanism of Zn⁰ is such that laser induces an electronic transition that breaks ionic bond in ZnS, resulting in its decomposition and photoreduction to Zn⁰ under an inert argon gas atmosphere. This method does not reduce other metals in the mineral since it does not use high-temperature roasting, providing a new way of producing high-purity metal without greenhouse gas emissions and heavy metal pollution caused by traditional zinc electrolysis.

● A conductive and stable polyphenylene/CNT membrane was fabricated.

Nanocarbon-based conductive membranes, especially carbon nanotube (CNT)-based membranes, have tremendous potential for wastewater treatment and water purification because of their excellent water permeability and selectivity, as well as their electrochemically enhanced performance (e.g., improved antifouling ability). However, it remains challenging to prepare CNT membranes with high structural stability and high electrical conductivity. In this study, a highly electroconductive and structurally stable polyphenylene/CNT (PP/CNT) composite membrane was prepared by electropolymerizing biphenyl on a CNT hollow fiber membrane. The PP/CNT membrane showed 3.4 and 5.0 times higher electrical conductivity than pure CNT and poly(vinyl alcohol)/CNT (PVA/CNT) membranes, respectively. The structural stability of the membrane was superior to that of the pure CNT membrane and comparable to that of the PVA/CNT membrane. The membrane fouling was significantly alleviated under an electrical assistance of −2.0 V, with a flux loss of only 11.7% after 5 h filtration of humic acid, which is significantly lower than those of PP/CNT membranes without electro-assistance (56.8%) and commercial polyvinylidene fluoride (PVDF) membranes (64.1%). Additionally, the rejection of negatively charged pollutants (humic acid and sodium alginate) was improved by the enhanced electrostatic repulsion. After four consecutive filtration-cleaning cycle tests, the flux recovery rate after backwashing reached 97.2%, which was much higher than those of electricity-free PP/CNT membranes (67.0%) and commercial PVDF membranes (61.1%). This study offers insights into the preparation of stable conductive membranes for membrane fouling control in potential water treatment applications.

● Urban water systems are challenged by climate change.

Urban water systems are facing various challenges against climate change, impacting cities’ security and their sustainable development. Specifically, there are three major challenges: submersion risk of coastal cities as glaciers melt and sea level rises, more and severe urban flooding caused by extreme weather like intensified storm surge and heavy precipitation, and regional water resource patterns challenged by alteration of spatial distribution of precipitation. Regarding this, two strategies including proactive adaptation and positive mitigation were proposed in this article to realize the reconstruction and optimization of urban water systems, to enhance their resilience, and eventually increase their adaptability and coping ability to climate change. The proactive adaptation strategy consists of 1) construction of sponge cities to accommodate the increased regular rainfall and to balance the alterations of spatial redistribution of precipitation; 2) reconstruction of excess stormwater discharge and detention system to increase capability for extreme precipitation events based on flood risk assessment under future climate change; 3) deployment of forward-looking, ecological, and integrated measures to improve coastal protection capability against inundation risks caused by climate change and sea level rise. The positive mitigation strategy is to employ the systematic concept in planning and design and to adopt advanced applicable energy-saving technologies, processes, and management practices, aiming at reduction in flux of urban water systems, reinforcement in energy conservation and carbon reduction in both water supply systems and wastewater treatment systems, and thus a reduction of greenhouse gas emission from urban water systems.

● Express energy use and carbon emissions in understandable numbers.

There is a global need to reduce greenhouse gas emissions to limit the extent of climate change. A better understanding of how our own activities and lifestyle influence our energy use and carbon emissions can help us enable changes in activities that can lead to reductions in carbon emissions. Here we discuss an approach based on examining carbon emissions from the perspective of the unit C, where 1 C is the CO₂ from food a person would on average eat every day. This approach shows that total CO₂ emissions in China, normalized by the population, is 22.5 C while carbon emissions for a person in the US is 43.9 C. A better appreciation of our own energy use can be obtained by calculating carbon emissions from our own activities in units of C, for example for driving a car gasoline or electric vehicle a certain number of kilometers, using electricity for our homes, and eating different foods. With this information, we can see how our carbon emissions compare to national averages in different countries and make decisions that could lower our personal CO₂ emissions.

It has been known that, the novel coronavirus, 2019-nCoV, which is considered similar to SARS-CoV, invades human cells via the receptor angiotensin converting enzyme II (ACE2). Moreover, lung cells that have ACE2 expression may be the main target cells during 2019-nCoV infection. However, some patients also exhibit non-respiratory symptoms, such as kidney failure, implying that 2019-nCoV could also invade other organs. To construct a risk map of different human organs, we analyzed the single-cell RNA sequencing (scRNA-seq) datasets derived from major human physiological systems, including the respiratory, cardiovascular, digestive, and urinary systems. Through scRNA-seq data analyses, we identified the organs at risk, such as lung, heart, esophagus, kidney, bladder, and ileum, and located specific cell types (i.e., type II alveolar cells (AT2), myocardial cells, proximal tubule cells of the kidney, ileum and esophagus epithelial cells, and bladder urothelial cells), which are vulnerable to 2019-nCoV infection. Based on the findings, we constructed a risk map indicating the vulnerability of different organs to 2019-nCoV infection. This study may provide potential clues for further investigation of the pathogenesis and route of 2019-nCoV infection.