The formation of Jupiter's diluted core by a giant impact.

The Juno mission1 has provided an accurate determination of Jupiter's gravitational field2, which has been used to obtain information about the planet's composition and internal structure. Several models of Jupiter's structure that fit the probe's data suggest that the planet has a diluted core, with a total heavy-element mass ranging from ten to a few tens of Earth masses (about 5 to 15 per cent of the Jovian mass), and that  heavy elements (elements other than hydrogen and helium) are distributed within a region extending to nearly half of Jupiter's radius3,4. Planet-formation models indicate that most heavy elements are accreted during the early stages of a planet's formation to create a relatively compact core5-7 and that almost no solids are accreted during subsequent runaway gas accretion8-10. Jupiter's diluted core, combined with its possible high heavy-element enrichment, thus challenges standard planet-formation theory. A possible explanation is erosion of the initially compact heavy-element core, but the efficiency of such erosion is uncertain and depends on both the immiscibility of heavy materials in metallic hydrogen and on convective mixing as the planet evolves11,12. Another mechanism that can explain this structure is planetesimal enrichment and vaporization13-15 during the formation process, although relevant models typically cannot produce an extended diluted core. Here we show that a sufficiently energetic head-on collision (giant impact) between a large planetary embryo and the proto-Jupiter could have shattered its primordial compact core and mixed the heavy elements with the inner envelope. Models of such a scenario lead to an internal structure that is consistent with a diluted core, persisting over billions of years. We suggest that collisions were common in the young Solar system and that a similar event may have also occurred for Saturn, contributing to the structural differences between Jupiter and Saturn16-18.

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation.

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.

Transfer of the high-temperature adult-plant stripe rust resistance gene Yr62 in four Chinese wheat cultivars.

Wheat stripe rust is one of the diseases that seriously affect wheat production worldwide. Breeding resistant cultivars is an effective way to control this disease. The wheat stripe rust resistance gene Yr62 has high-temperature adult-plant resistance (HTAP). In this study, PI 660,060, a single Yr62 gene line, was crossed with four Chinese wheat cultivars, LunXuan987 (LX987), Bainongaikang58 (AK58), ZhengMai9023 (ZM9023), and HanMai6172 (H6172). F1 seeds of four cross combinations were planted and self-crossed to develop the advance generations in the field. The seeds of each cross were mixed harvested and about 2400 to 3000 seeds were sown in each generation for F1 to F4 to maintain the maximum possible genotypes. Forty-five lines were selected and evaluated for resistance to stripe rust and agronomic traits, including plant height, number of grains per spike, and tiller number, in F5 and F6. Then, 33 lines with good agronomic traits and high disease resistance were developed to F9 generation. SSR markers Xgwm251 and Xgwm192 flank linked with the Yr62 were used to detect the presence of Yr62 in these 33 F9 lines. Of these, 22 lines were confirmed with the resistance gene Yr62. Finally, nine lines with good agronomic traits and disease resistance were successfully selected. The selected wheat lines in this study provide material support for the future breeding of wheat for stripe rust resistance. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01393-1.

Time course of right-hemisphere recruitment during word production following left-hemisphere damage: A single case of young stroke.

Our understanding of post-stroke language function is largely based on older age groups, who show increasing age-related brain pathology and neural reorganisation. To illustrate language outcomes in the young-adult brain, we present the case of J., a 23-year-old woman with chronic aphasia from a left-hemisphere stroke affecting the temporal lobe. Diffusion MRI-based tractography indicated that J.'s language-relevant white-matter structures were severely damaged. Employing magnetoencephalography (MEG), we explored J.'s conceptual preparation and word planning abilities using context-driven and bare picture-naming tasks. These revealed naming deficits, manifesting as word-finding difficulties and semantic paraphasias about half of the time. Naming was however facilitated by semantically constraining lead-in sentences. Altogether, this pattern indicates disrupted lexical-semantic and phonological retrieval abilities. MEG revealed that J.'s conceptual and naming-related neural responses were supported by the right hemisphere, compared to the typical left-lateralised brain response of a matched control. Differential recruitment of right-hemisphere structures (330-440 ms post-picture onset) was found concurrently during successful naming (right mid-to-posterior temporal lobe) and word-finding attempts (right inferior frontal gyrus). Disconnection of the temporal lobes via corpus callosum was not critical for recruitment of the right hemisphere in visually guided naming, possibly due to neural activity right lateralising from the outset. Although J.'s right hemisphere responded in a timely manner during word planning, its lexical and phonological retrieval abilities remained modest.

Treatment of thoracolumbar fractures by temporary posterior instrumentation with selective fusion schemes.

OBJECTIVE: This retrospective study investigated the clinical and radiographic outcomes following temporary transpedicular posterior instrumentation between two cohorts of patients with thoracolumbar fractures (TLF) who underwent selective or bi-segments intervertebral articular process fusion. METHODS: Patients with TLF who underwent the temporary posterior fixation with selective fusion (Group SF), or bi-segments fusion (Group BF) were studied. Superior intervertebral articular process and interlaminar fusion were performed in Group SF, whereas in Group BF, the patients underwent bi-segments fusion in both superior and inferior articular processes, as well as interlaminar fusion. We measured the distal and proximal intervertebral mobility, regional kyphotic angle, and vertebral height before and after surgery in both groups. Greenough Low-Back Outcome Score was used to assess the clinical outcomes. RESULTS: Sixty-five patients with TLF from T12 to L2 fractures were enrolled in the study period: 33 patients in the Group SF and 32 patients in the Group BF. All the patients experienced fracture healing (mean follow-up time: 19.7 months). The mean postoperative functional outcomes were 65.0 ± 2.0 points for the Low-Back Outcome Score in the Group SF and 65.2 ± 1.8 for the Group BF. A progressive regional kyphotic angle was observed with time regardless of fusion but was not significantly different between the two groups. There was a statistical difference between unfused inferior proximal adjacent and inferior distal adjacent segment regardless of fracture segments. CONCLUSIONS: The strategy of selective fusion is reported to be useful for the treatment of patients with TLF. The motion in the un-fused and adjacent segment could be better regained after instrumentation removal in the selective fusion group. LEVEL OF EVIDENCE: Level 3.

Language Neuroplasticity in Brain Tumor Patients Revealed by Magnetoencephalography.

Little is known about language impairment in brain tumor patients, especially in the presurgical phase. Impairment in this population may be missed because standardized tests fail to capture mild deficits. Additionally, neuroplasticity may also contribute to minimizing language impairments. We examined 14 presurgical patients with brain tumors in the language-dominant hemisphere using magnetoencephalography (MEG) while they performed a demanding picture-word interference task, that is, participants name pictures while ignoring distractor words. Brain tumor patients had behavioral picture-naming effects typically observed in healthy controls. The MEG responses also showed the expected pattern in its timing and amplitude modulation typical of controls, but with an altered spatial distribution of right hemisphere sources, in contrast to the classic left hemisphere source found in healthy individuals. This finding supports tumor-induced neural reorganization of language before surgery. Crucially, the use of electrophysiology allowed us to show the "same" neuronal response in terms of its timing and amplitude modulation in the right hemisphere, supporting the hypothesis that the processes performed by the right hemisphere following reorganization are similar in nature to those (previously) performed by the left hemisphere. We also identified one participant with a fast-growing tumor affecting large parts of critical language areas and underlying ventral and dorsal white matter tracts who showed a deviant pattern in behavior and in the MEG event-related responses. In conclusion, our results attest to the validity of using a demanding picture-naming task in presurgical patients and provide evidence for neuroplasticity, with the right hemisphere performing similar computations as the left hemisphere typically performs.

Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems.

The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being sure that their decisions are the right ones. Previously to this work, genetic algorithms were able to calculate the state of alignment of industrial processes that were measured through certain key performance indicators (KPIs) to ensure that the leaders of the Industry 4.0 make decisions that are aligned with the strategic objectives of the organization. However, the computational cost of these algorithms increases exponentially with the number of KPIs. That is why this work makes use of the principles of quantum computing to present the strategic design of organizations from a novel point of view: Quantum Strategic Organizational Design (QSOD). The effectiveness of the application of these principles is shown with a real case study, in which the computing time is reduced from hundreds of hours to seconds. This has very powerful practical applications for industry leaders, since, with this new approach, they can potentially allow a better understanding of the complex processes underlying the strategic design of organizations and, above all, make decisions in real-time.

Healthy Operator 4.0: A Human Cyber-Physical System Architecture for Smart Workplaces.

Recent advances in technology have empowered the widespread application of cyber-physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to Vhuman-cyber-physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits.

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology.

Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture.

Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies.

BACKGROUND: Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. OBJECTIVE: This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. METHODS: The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA's distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. RESULTS: A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. CONCLUSIONS: DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.

Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits.

Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people's comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain.

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners' brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0.

According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a method to connect people to CPS. Deep learning has shown surpassing performance in HAR. Data preprocessing is an important part of deep learning projects and takes up a large part of the whole analytical pipeline. Data segmentation and data transformation are two critical steps of data preprocessing. This study analyzes the impact of segmentation methods on deep learning model performance, and compares four data transformation approaches. An experiment with HAR based on acceleration data from multiple wearable devices was conducted. The multichannel method, which treats the data for the three axes as three overlapped color channels, produced the best performance. The highest overall recognition accuracy achieved was 97.20% for eight daily activities, based on the data from seven wearable sensors, which outperformed most of the other machine learning techniques. Moreover, the multichannel approach was applied to three public datasets and produced satisfying results for multi-source acceleration data. The proposed method can help better analyze workers’ activities and help to integrate people into CPS.

The development of intelligent healthcare in China.

BACKGROUND: Intelligent healthcare (IH) is proposed with the fast application of Internet of Things technology in the healthcare area in recent years. It is considered as an expansion of e-health and telemedicine. As the largest developing country, China is investing large amounts of resources to push forward the development of IH. It is one of the centerpieces of the country's New Healthcare Reform, and great expectation is placed on it to help solve the conflict between limited healthcare resources and the large patient population. MATERIALS AND METHODS: Essential policies, milestones, standards, and specifications from the Chinese government since the 1990s were reviewed to show the brief development history of IH in China. Some typical cases and products have been analyzed to present the current situation. The main problems and future development directions have been summarized. CONCLUSIONS: The IH industry in China has great potential and is growing very fast, but a lot of challenges also exist. In the future both government support and the active participation of nongovernmental capital are needed to push forward the whole industry.

Acellular spinal cord scaffold seeded with bone marrow stromal cells protects tissue and promotes functional recovery in spinal cord-injured rats.

Therapy using scaffolds seeded with stem cells plays an important role in repair of spinal cord injury (SCI), with the transplanted cells differentiating into nerve cells to replace the lost tissue while releasing neurotrophic factors that contribute to repair following SCI and enhance the function of the damaged nervous system. The present study investigated the ability to extend the survival time of bone marrow stromal cells (BMSCs) to restore the damaged spinal cord and improve functional recovery by grafting acellular spinal cord (ASC) scaffold seeded or not with BMSCs in a rat model of acute hemisected SCI. BBB scores revealed that treatment with BMSCs seeded into ASC scaffold led to an obvious improvement in motor function recovery compared with treatment with ASC scaffold alone or untreated controls. This improvement was evident at 2 and 8 weeks after surgery (P < 0.05). When BMSCs labeled with 5-bromodeoxyuridine were implanted together with ASC scaffold into the injured sites, they differentiated into glial cells, and some BMSCs could be observed within the graft by immunofluorescent staining at 8 weeks after implantation. Evaluation of caspase-3 activation suggested that the graft group was able to reduce apoptosis compared with SCI alone at 8 weeks after operation (P < 0.05). This study suggests that ASC scaffolds have the ability to enhance BMSC survival and improve differentiation and could also reduce native damaged nerve tissue apoptosis, thus protecting host tissue as well as improving functional recovery after implantation.

A Biological Immunity-Based Neuro Prototype for Few-Shot Anomaly Detection with Character Embedding.

Anomaly detection has wide applications to help people recognize false, intrusion, flaw, equipment failure, etc. In most practical scenarios, the amount of the annotated data and the trusted labels is low, resulting in poor performance of the detection. In this paper, we focus on the anomaly detection for the text type data and propose a detection network based on biological immunity for few-shot detection, by imitating the working mechanism of the immune system of biological organisms. This network enabling the protected system to distinguish the aggressive behavior of "nonself" from the legitimate behavior of "self" by embedding characters. First, it constructs episodic task sets and extracts data representations at the character level. Then, in the pretraining phase, Word2Vec is used to embed the representations. In the meta-learning phase, a dynamic prototype containing encoder, routing, and relation is designed to identify the data traffic. Compare to the mean-based prototype, the proposed prototype applies a dynamic routing algorithm that assigns different weights to samples in the support set through multiple iterations to obtain a prototype that combines the distribution of samples. The proposed method is validated on 2 real traffic datasets. The experimental results indicate that (a) the proposed anomaly detection prototype outperforms state-of-the-art few-shot techniques with 1.3% to 4.48% accuracy and 0.18% to 4.55% recall; (b) under the premise of ensuring the accuracy and recall, the number of training samples is reduced to 5 or 10; (c) ablation experiments are designed for each module, and the results show that more accurate prototypes can be obtained by using the dynamic routing algorithm.

Speciation, leachability, and phytoaccessibility of heavy metals during thermochemical liquefaction of contaminated peanut straw.

In this study, the speciation, leachability, phytoaccessibility, and environmental risks of heavy metals (Cd, Zn, and Cu) during liquefaction of contaminated peanut straw in ethanol at different temperatures (220, 260, 300, 340, and 380 °C) were comprehensively investigated. The results showed that elevated temperatures facilitated heavy metal accumulation in the biochar. The acid-soluble/exchangeable and reducible fraction percentages of heavy metals were substantially reduced in the biochar after liquefaction as the temperature increased, and the oxidizable fraction became the dominant heavy metal fraction, accounting for 44.14-78.67%. Furthermore, although an excessively high liquefaction temperature (380 °C) increased the residual fraction percentages of Zn and Cu, it was detrimental to Cd immobilization. The acid-soluble/exchangeable Cd in the contaminated peanut straw readily migrates to the bio-oil during liquefaction, with the highest concentration of 1.60 mg/kg at 260 °C liquefaction temperature, whereas Zn and Cu are predominantly bound to the unexchangeable fraction in the bio-oil. Liquefaction inhibited heavy metal leachability and phytoaccessibility in biochar, the lowest extraction rates of Cd, Zn, and Cu were 0.71%, 1.66% and 0.95% by diethylenetriamine pentaacetic acid, respectively. However, the leaching and extraction concentrations increased when the temperature was raised to 380 °C. Additionally, heavy metal risk was reduced from medium and high risk to no and low risk. In summary, liquefaction reduces heavy metal toxicity and the risks associated with contaminated peanut straw, and a temperature range of 300-340 °C for ethanol liquefaction can be considered optimal for stabilizing heavy metals.

Establishing a disc degeneration model using computed tomography-guided percutaneous puncture technique in the rabbit.

BACKGROUND: Various animal models have been developed to investigate the complex mechanisms leading to intervertebral disc disorders and to evaluate the different therapeutic options. The needle puncture technique is commonly used to induce intervertebral degeneration in animal models. The present study aimed to establish a rabbit model of intervertebral disc degeneration using a simple, minimally invasive procedure. METHODS AND MATERIALS: The animal model was created in the rabbit using computed tomography-guided percutaneous puncture technology. An 18-gauge needle was used to induce a disc injury with a 5-mm puncture depth. Radiographic, histologic, and biochemical analyses and magnetic resonance imaging were performed to assess the consequent disc degeneration. RESULTS: Significant disc space narrowing was observed as early as 4 wk, and osteophytes were formed at 12 wk after puncture. The magnetic resonance imaging assessment demonstrated a progressive loss of T2-weighted signal intensity at the stabbed discs throughout the 12-wk period. The histologic analysis showed a progressive loss of the normal architecture from 4 wk to the end point. The biochemical assays suggested that the expression of proteoglycan decreased progressively with increasing time. CONCLUSIONS: A simple, but minimally invasive, intervertebral disc degeneration model was established successfully using computed tomography-guided percutaneous puncture technology in the rabbit. The puncture procedure can be performed with minimal damage and handling of the other structures, ensuring a uniform reproducible disc degeneration model.

Genome-wide QTL mapping for stripe rust resistance in spring wheat line PI 660122 using the Wheat 15K SNP array.

Introduction: Stripe rust is a global disease of wheat. Identification of new resistance genes is key to developing and growing resistant varieties for control of the disease. Wheat line PI 660122 has exhibited a high level of stripe rust resistance for over a decade. However, the genetics of stripe rust resistance in this line has not been studied. A set of 239 recombinant inbred lines (RILs) was developed from a cross between PI 660122 and an elite Chinese cultivar Zhengmai 9023. Methods: The RIL population was phenotyped for stripe rust response in three field environments and genotyped with the Wheat 15K single-nucleotide polymorphism (SNP) array. Results: A total of nine quantitative trait loci (QTLs) for stripe rust resistance were mapped to chromosomes 1B (one QTL), 2B (one QTL), 4B (two QTLs), 4D (two QTLs), 6A (one QTL), 6D (one QTL), and 7D (one QTL), of which seven QTLs were stable and designated as QYrPI660122.swust-4BS, QYrPI660122.swust-4BL, QYrPI660122.swust-4DS, QYrPI660122.swust-4DL, QYrZM9023.swust-6AS, QYrZM9023.swust-6DS, and QYrPI660122.swust-7DS. QYrPI660122.swust-4DS was a major all-stage resistance QTL explaining the highest percentage (10.67%-20.97%) of the total phenotypic variation and was mapped to a 12.15-cM interval flanked by SNP markers AX-110046962 and AX-111093894 on chromosome 4DS. Discussion: The QTL and their linked SNP markers in this study can be used in wheat breeding to improve resistance to stripe rust. In addition, 26 lines were selected based on stripe rust resistance and agronomic traits in the field for further selection and release of new cultivars.

Review on fate and bioavailability of heavy metals during anaerobic digestion and composting of animal manure.

Anaerobic digestion and composting are attracting increasing attention due to the increased production of animal manure. It is essential to know about the fate and bioavailability of heavy metals (HMs) for further utilisation of animal manure. This review has systematically summarised the migration of HMs and the transformation of several typical HMs (Cu, Zn, Cd, As, and Pb) during anaerobic digestion and composting. The results showed that organic matter degradation increased the HMs content in biogas residue and compost (with the exception of As in compost). HMs migrated into biogas residue during anaerobic digestion through various mechanisms. Most of HMs in biogas residue and compost exceeded relevant standards. Then, anaerobic digestion increased the bioavailable fractions proportion in Zn and Cd, decreased the F4 proportion, and raised them more than moderate environmental risks. As (III) was the main species in the digester, which extremely increased As toxicity. The increase of F3 proportion in Cu and Pb was due to sulphide formation in biogas residue. Whereas, the high humus content in compost greatly increased the F3 proportion in Cu. The F1 proportion in Zn decreased, but the plant availability of Zn in compost did not reduce significantly. Cd and As mainly converted the bioavailable fractions into stable fractions during composting, but As (V) toxicity needs to be concerned. Moreover, additives are only suitable for animal manure treated with slightly HM contaminated. Therefore, it is necessary to combine more comprehensive methods to improve the manure treatment and make product utilisation safer.