Edge-wise analysis reveals white matter connectivity associated with focal to bilateral tonic-clonic seizures.

OBJECTIVE: Focal to bilateral tonic-clonic seizures (FBTCS) represent a challenging subtype of focal temporal lobe epilepsy (TLE) in terms of both severity and treatment response. Most studies have focused on regional brain analysis that is agnostic to the distribution of white matter (WM) pathways associated with a node. We implemented a more selective, edge-wise approach that allowed for identification of the individual connections unique to FBTCS. METHODS: T1-weighted and diffusion-weighted images were obtained from 22 patients with solely focal seizures (FS), 43 FBTCS patients, and 65 age/sex-matched healthy participants (HPs), yielding streamline (STR) connectome matrices. We used diffusion tensor-derived STRs in an edge-wise approach to determine specific structural connectivity changes associated with seizure generalization in FBTCS compared to matched FS and HPs. Graph theory metrics were computed on both node- and edge-based connectivity matrices. RESULTS: Edge-wise analyses demonstrated that all significantly abnormal cross-hemispheric connections belonged to the FBTCS group. Abnormal connections associated with FBTCS were mostly housed in the contralateral hemisphere, with graph metric values generally decreased compared to HPs. In FBTCS, the contralateral amygdala showed selective decreases in the structural connection pathways to the contralateral frontal lobe. Abnormal connections in TLE involved the amygdala, with the ipsilateral side showing increases and the contralateral decreases. All the FS findings indicated higher graph metrics for connections involving the ipsilateral amygdala. Data also showed that some FBTCS connectivity effects are moderated by aging, recent seizure frequency, and longer illness duration. SIGNIFICANCE: Data showed that not all STR pathways are equally affected by the seizure propagation of FBTCS. We demonstrated two key biases, one indicating a large role for the amygdala in the propagation of seizures, the other pointing to the prominent role of cross-hemispheric and contralateral hemisphere connections in FBTCS. We demonstrated topographic reorganization in FBTCS, pointing to the specific WM tracts involved.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning.

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42- while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3-. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl-, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43-0.70, 0.16-0.49 and 0.25-0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Macroscopic resting-state brain dynamics are best described by linear models.

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements of local field potentials via intracranial electroencephalography and of whole-brain blood-oxygen-level-dependent brain activity via functional magnetic resonance imaging. We used state-of-the-art linear and nonlinear families of models to describe spontaneous resting-state activity of 700 participants in the Human Connectome Project and 122 participants in the Restoring Active Memory project. We found that linear autoregressive models provide the best fit across both data types and three performance metrics: predictive power, computational complexity and the extent of the residual dynamics unexplained by the model. To explain this observation, we show that microscopic nonlinear dynamics can be counteracted or masked by four factors associated with macroscopic dynamics: averaging over space and over time, which are inherent to aggregated macroscopic brain activity, and observation noise and limited data samples, which stem from technological limitations. We therefore argue that easier-to-interpret linear models can faithfully describe macroscopic brain dynamics during resting-state conditions.

Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks.

The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, studies yield inconsistent findings. The extent to which the structure-function coupling reflects reliable individual differences or primarily group-common characteristics remains unclear, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the linear models. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.

Natural and anthropogenic dissolved organic matter in landfill leachate: Composition, transformation, and their coexistence characteristics.

A large number of natural and anthropogenic wastes were landfilled, and dissolved organic matter (DOM) were formed during landfill. However, the composition, transformation, and coexistence characteristics of natural and anthropogenic DOM in leachate remain unclear. Fourier transform ion cyclotron resonance mass spectrometry, size exclusion chromatography, gas chromatography coupled with mass spectrometry, and three-dimensional excitation-emission matrix spectrum were employed to clarify comprehensively the abovementioned question. The results showed that natural DOM in young leachate constituted mainly straight-chain organic acids, protein substances, and building blocks of humic substances (BB). Straight-chain organic acids vanished in old leachates, and the concentration of protein substances and BB decreased from 44% to 26% and from 47% to 12%, respectively, while CHON and CHONS were degraded to CHO and CHOS during the process. As to anthropogenic DOM, its types and relative content in leachate increased during landfill, and aromatic acids, terpenes, halogenated organics, indoles, and phenols became the main organic components in old leachate. Compared to natural DOM, anthropogenic DOM was degraded slowly and accumulated in leachate, and some of the natural DOM facilitated the dechlorination of dichlorinated organic compounds. This study demonstrates that landfill led to an increase in humic substances and halogenated organic compounds in old leachate, which was intensified with concentrated leachate recirculation.

Larger aggregate formed by self-assembly process of the mixture surfactants enhance the dissolution and oxidative removal of non-aqueous phase liquid contaminants in aquifer.

Surfactants can transfer non-aqueous phase liquid (NAPL) contaminants to the aqueous phase, and enhance the removal of the latter in groundwater. However, the extensive use of surfactants causes secondary contamination and increases the non-target consumption of oxidants. It is pressing to develop a surfactant with high phase transfer efficiency and sound compatibility with oxidants to minimize the use of surfactants for groundwater remediation. The phase transfer capability of different surfactants and their binary mixtures, their enhanced KMnO4 oxidation performance for NAPL contaminants as well as influencing factors were investigated to solve the above-mentioned question. The results showed that Tween20, SDBS and BS-12 perform best in terms of phase transfer capability among nonionic, anionic and amphoteric surfactants respectively, and only SDBS and BS-12 produce a synergistic effect among the binary mixtures. The CMC of SDBS/BS-12 was lower than its ideal CMC value, and the self-assembly process of SDBS/BS-12 also formed larger aggregates, which improved the phase transfer performance. Compared to other single surfactants, the removal efficiency of petroleum hydrocarbons in the aquifer sediments was raised by 7.4-33.8 % using the mixed surfactant. The SDBS/BS-12 mixture was compatible with KMnO4 and boosted the reaction of NAPL contaminants with KMnO4 by transferring from the NAPL phase to the aqueous phase. As a result, the NAPL toluene and phenanthrene removal efficiency increased from 37 % and 29 % to 80 % and 86 % respectively. Natural organic matters inhibited the phase transfer efficiency of the SDBS/BS-12 mixture, whereas anions and monovalent cations enhanced the phase transfer capability of the mixture. High-valent cations led to precipitation in the SDBS/BS-12, which could be eliminated by adding Na2Si2O5. The SDBS/BS-12 mixture delivered the same phase transfer efficiency with the dosage of 1.73-23.07 % of other single surfactants, and its cost was equivalent to 0.25-41.7 % of the latter, thus embracing bright application prospects.

Stereo-electroencephalography-guided three-dimensional radiofrequency thermocoagulation for mesial temporal lobe epilepsy with hippocampal sclerosis: A retrospective study with long-term follow-up.

OBJECTIVE: Stereo-electroencephalography-guided three-dimensional radiofrequency thermocoagulation (SEEG-3D RFTC) is a minimally invasive treatment for mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS). This study aimed to investigate the long-term prognosis after SEEG-3D RFTC treatment in patients with MTLE-HS. METHODS: This single-center retrospective study included 28 patients with MTLE-HS treated with SEEG-3D RFTC from January 2016 to May 2018. Postoperative curative effects were evaluated using the Engel classification, and the patients were followed up for 5 years. RESULTS: The proportions of patients categorized as Engel I between 1 and 5 years after surgery were 72.41% (12 months after surgery), 67.86% (18 months after surgery), 62.07% (24 months after surgery), 50.00% (36 months after surgery), 42.86% (48 months after surgery), and 42.86% (60 months after surgery), respectively. Regarding long-term efficacy, based on the Engel classification, SEEG-3D RFTC showed room for improvement. SIGNIFICANCE: This was the first study to evaluate the efficacy of SEEG-3D RFTC for MTLE-HS with long-term follow-up. SEEG-3D RFTC is a promising alternative for patients with MTLE-HS. PLAIN LANGUAGE SUMMARY: This study explored the potential of stereoelectroencephalography-guided three-dimensional radiofrequency thermocoagulation, a minimally invasive approach, for treating medial temporal lobe epilepsy with hippocampal sclerosis. Involving 28 patients, the research tracked the treatment's success over five years using the Engel classification. Initial results were promising, with 72.41% of patients achieving the most favorable outcome (Engel I) at one year. While there was a gradual decrease in this proportion over time, 42.86% of patients maintained this positive outcome at five years, highlighting the treatment's potential for long-term efficacy.

Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex.

Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.

Volatile and semi-volatile organic compounds in landfill leachate: Concurrence, removal and the influencing factors.

Volatile and semi-volatile organic compounds (VOCs and SVOCs) carried by landfilled wastes may enter leachate, and require appropriate treatment before discharge. However, the driving factors of the entry of VOCs and SOVCs into leachate, their removal characteristics during leachate treatment and the dominant factors remain unclear. A global survey of the VOCs and SOVCs in leachate from 103 landfill sites combined with 27 articles on leachate treatment was conducted to clarify the abovementioned question. The results showed that SVOCs such as polycyclic aromatic hydrocarbons (PAHs), phthalate acid esters (PAEs) and phenols were the most frequently detected in leachate on a global scale. However, four kinds of VOCs, i.e., toluene, ethylbenzene, xylenes and benzene, were frequently detected at high concentrations in landfill leachate as well. The concentrations of VOCs and SVOCs in leachate ranged from 1 × 10° to 1 × 108 ng/L. Solubility was a key factor driving the entry of VOCs and SOVCs into leachate, and higher solubility enables higher detectable concentrations in leachate (P<0.05). It was easiest to remove monocyclic aromatic hydrocarbons (MAHs) from leachate, followed by phenols and PAHs, and it was most difficult to remove PAEs. In terms of removing MAHs, the anoxic/oxic (A/O) process and the sequential batch reactor (SBR) process were comparable to the advanced oxidization process and far superior to the ultrafiltration and nanofiltration processes, and the removal rate increased with an increase in the Henry's constant and/or the hydrophilicity of the contaminants during the A/O and SBR processes (P<0.05). There were no significant differences among biological, advanced oxidation and reverse osmosis processes in the removal of phenolic. In terms of removing PAHs, the A/O process was comparable to the advanced oxidization process and more efficient than the other treatment processes. As to removing PAEs, the membrane bioreactor process was almost the same efficient as the advanced oxidization process and far more efficient than the other biological treatment processes. Future research should focus on the pollution of atmospheric VOCs and SVOCs near aeration units in leachate treatment plants, as well as the health risk assessment of VOCs and SVOCs in the treated leachate effluent. To the best of our knowledge, this is the first review regarding the occurrence and removal of VOCs and SVOCs from landfill leachates worldwide.

Investigation with ESI FT-ICR MS on sorbent selectivity and comprehensive molecular composition of landfill leachate dissolved organic matter.

Molecular characterization of landfill leachate dissolved organic matter (LDOM) is essential for developing effective processing techniques. However, the molecular selectivity of extraction method and ionization modes often leads to the bias of molecular characterization of LDOM. Here, seven representative sorbents were selected and electrospray ionization negative ion mode (ESI (-)) and positive ion mode (ESI (+)) Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were used to investigate the molecular composition of different LDOM samples. Obvious sorbent selectivity during extraction procedure was observed, resulting in the underestimation of molecular diversity of LDOM from 32.7% to 69.3%. Totally, 14,000-18,000 unique molecules were obtained in a single sample, indicating the unprecedented molecular diversity of LDOM. Lignins, proteins and lipids are three major molecular groups in LDOM, and N or S containing molecules occupied 83%. Although much of total organic carbon was removed during biochemical treatment process, the molecular diversity of LDOM was not reduced because a considerable of bio-recalcitrant molecules was produced. The results uncover the sorbents selectivity and ionization modes selectivity in LDOM analysis and provided a comprehensive change of LDOM molecular composition during biochemical treatment, which benefits the development of accurate methods to remove organic carbon in landfill leachate.

Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity.

Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.

Leachate leakage enhances the microbial diversity and richness but decreases Proteobacteria and weakens stable microbial ecosystem in landfill groundwater.

Sanitary landfill is the most prevalent and economic method for municipal solid waste disposal, and the resultant groundwater pollution has become an environmental problem due to leachate leakage. The pollution characteristics in groundwater near landfill sites have been extensively investigated, although the succession characteristics and driving mechanisms of microbial communities in leachate-contaminated groundwater and the sensitive microbial indicators for leachate leakage identification remain poorly studied. Herein, results showed that leachate leakage enhanced the microbial diversity and richness and transferred endemic bacteria from landfills into groundwater, producing an average decrease of 17.73% in the relative abundance of Proteobacteria. The key environmental factor driving the evolution of microbial communities in groundwater due to leachate pollution was organic matter, which can explain 16.13% of the changes in microbial community composition. The |ßNTI| values of the bacterial communities in all six landfills were <2, and the assembly process of microbial communities was primarily dominated using stochastic processes. Leachate pollution changed the assembly mechanism, transforming the community assembly process from an undominated process to a dispersal limitation process. Leachate pollution reduced the efficiency and stability of microbial communities in groundwater, increasing the vulnerability of the stable microbial ecosystems in groundwater. Notably, microbial indicators are more sensitive to leachate leakage and could accurately identify landfills where leachate leakage occurred and other extraneous pollutants. The phylum Proteobacteria and mcrA could act as appropriate indicators for the identification of leachate leakage. These results provide a novel insight into the monitoring, identification of groundwater pollution and the scientific guidance for appropriate remediation strategies for leachate-contaminated groundwater.

USP14 inhibition promotes recovery by protecting BBB integrity and attenuating neuroinflammation in MCAO mice.

AIM: Blood-brain barrier (BBB) dysfunction is one of the hallmarks of ischemic stroke. USP14 has been reported to play a detrimental role in ischemic brain injury. However, the role of USP14 in BBB dysfunction after ischemic stroke is unclear. METHODS: In this study, we tested the role of USP14 in disrupting BBB integrity after ischemic stroke. The USP14-specific inhibitor IU1 was injected into middle cerebral artery occlusion (MCAO) mice once a day. The Evans blue (EB) assay and IgG staining were used to assess BBB leakage 3 days after MCAO. FITC-detran test was slected to examine the BBB leakage in vitro. Behavior tests were conducted to evaluate recovery from ischemic stroke. RESULTS: Middle cerebral artery occlusion increased endothelial cell USP14 expression in the brain. Furthermore, the EB assay and IgG staining showed that USP14 inhibition through IU1 injection protected against BBB leakage after MCAO. Analysis of protein expression revealed a reduction in the inflammatory response and chemokine release after IU1 treatment. In addition, IU1 treatment was found to rescue neuronal loss resulting from ischemic stroke. Behavior tests showed a positive effect of IU1 in attenuating brain injury and improving motor function recovery. In vitro study showed that IU1 treatment could alleviate endothelial cell leakage induced by OGD in cultured bend.3 cells through modulating ZO-1 expression. CONCLUSIONS: Our results demonstrate a role for USP14 in disrupting the integrity of the BBB and promoting neuroinflammation after MCAO.

Mechanism-based target therapy in primary biliary cholangitis: opportunities before liver cirrhosis?

Primary biliary cholangitis (PBC) is an immune-mediated liver disease characterized by cholestasis, biliary injuries, liver fibrosis, and chronic non-suppurative cholangitis. The pathogenesis of PBC is multifactorial and involves immune dysregulation, abnormal bile metabolism, and progressive fibrosis, ultimately leading to cirrhosis and liver failure. Ursodeoxycholic acid (UDCA) and obeticholic acid (OCA) are currently used as first- and second-line treatments, respectively. However, many patients do not respond adequately to UDCA, and the long-term effects of these drugs are limited. Recent research has advanced our understanding the mechanisms of pathogenesis in PBC and greatly facilitated development of novel drugs to target mechanistic checkpoints. Animal studies and clinical trials of pipeline drugs have yielded promising results in slowing disease progression. Targeting immune mediated pathogenesis and anti-inflammatory therapies are focused on the early stage, while anti-cholestatic and anti-fibrotic therapies are emphasized in the late stage of disease, which is characterized by fibrosis and cirrhosis development. Nonetheless, it is worth noting that currently, there exists a dearth of therapeutic options that can effectively impede the progression of the disease to its terminal stages. Hence, there is an urgent need for further research aimed at investigating the underlying pathophysiology mechanisms with potential therapeutic effects. This review highlights our current knowledge of the underlying immunological and cellular mechanisms of pathogenesis in PBC. Further, we also address current mechanism-based target therapies for PBC and potential therapeutic strategies to improve the efficacy of existing treatments.

Volatile and semi-volatile organic compounds in landfill gas: Composition characteristics and health risks.

Gas emitted from landfills contains a large quantity of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs), some of which are carcinogenic, teratogenic, and mutagenic, thereby posing a serious threat to the health of landfill workers and nearby residents. However, the global hazards of VOCs and SVOCs in landfill gas to human health remain unclear. To quantify the global risk distributions of these pollutants, we collected the composition and concentration data of VOCs and SVOCs from 72 landfills in 20 countries from the core database of Web of Science and assessed their human health risks as well as analyzed their influencing factors. Organic compounds in landfill gas were found to primarily result from the biodegradation of natural organic waste or the emissions and volatilization of chemical products, with the concentration range of 1 × 10-1-1 × 106 µg/m3. The respiratory system, in particular, lung was the major target organ of VOCs and SVOCs, with additional adverse health impacts ranging from headache and allergies to lung cancer. Aromatic and halogenated compounds were the primary sources of health risk, while ethyl acetate and acetone from the biodegradation of natural organic waste also exceeded the acceptable levels for human health. Overall, VOCs and SVOCs affected residents within 1,000 m of landfills. Air temperature, relative humidity, air pressure, wind direction, and wind speed were the major factors that influenced the health risks of VOCs and SVOCs. Currently, landfill risk assessments of VOCs and SVOCs are primarily based on respiratory inhalation, with health risks due to other exposure routes remaining poorly elucidated. In addition, potential health risks due to the transport and transformation of landfill gas emitted into the atmosphere should be further studied.

Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

A data-driven approach for understanding the structure dependence of redox activity in humic substances.

Humic substances (HS) can facilitate electron transfer during biogeochemical processes due to their redox properties, but the structure-redox activity relationships are still difficult to describe and poorly understood. Herein, the linear (Partial Least Squares regressions; PLS) and nonlinear (artificial neural network; ANN) models were applied to monitor the structure dependence of HS redox activities in terms of electron accepting (EAC), electron donating (EDC) and overall electron transfer capacities (ETC) using its physicochemical features as input variables. The PLS model exhibited a moderate ability with R2 values of 0.60, 0.53 and 0.65 to evaluate EAC, EDC and ETC, respectively. The variable influence in the projection (VIP) scores of the PLS identified that the phenols, quinones and aromatic systems were particularly important for describing the redox activities of HS. Compared with the PLS model, the back-propagation ANN model achieved higher performance with R2 values of 0.81, 0.65 and 0.78 for monitoring the EAC, EDC and ETC, respectively. Sensitivity analysis of the ANN separately identified that the EAC highly depended on quinones, aromatics and protein-like fluorophores, while the EDC depended on phenols, aromatics and humic-like fluorophores (or stable free radicals). Additionally, carboxylic groups were the best indicator for evaluating both the EAC and EDC. Good model performances were obtained from the selected features via the PLS and sensitivity analysis, further confirming the accuracy of describing the structure-redox activity relationships with these analyses. This study provides a potential approach for identifying the structure-activity relationships of HS and an efficient machine-learning model for predicting HS redox activities.

Insights into the role of the microbial community lifestyle strategies in variations of the dissolved organic matter molecular composition along an effluent-dominated river.

Urbanization has dramatically changed the quality and quantity of dissolved organic matter (DOM) fluxes in rivers, thereby affecting the diversity and lifestyle strategies of microbial communities. However, relationships between DOM molecular composition and microbial lifestyle strategies in effluent-dominated rivers are poorly understood. Herein, we investigated the variations in DOM structure and composition of an effluent-dominated river and further revealed how these changes alter the abundance and lifestyle strategies of microbial communities. Results demonstrated that macromolecular (MW > 35 kDa) humic-like substances constituted the major components of effluent-dominated riverine DOM. Also, due to the degradation of humic-like substances, the accumulation of protein-like substances was observed from upstream to downstream areas, corresponding to an apparent decrease in overall aromaticity. The abundance of bacterial, Actinobacteria, and eukaryotic was higher in the upstream and midstream areas but relatively lower in the downstream area. The response of bacterial and Actinobacteria communities to the changes in DOM composition was more prominent as compared to that of eukaryotic. Based on multivariate statistical analysis, the decrease in aromatic components (MW > 35 kDa) was mainly attributed to the degradation of Proteobacteria and Actinobacteria (K-strategists), resulting in a decrease in their relative abundance along the river course. Proteins and polysaccharides (15 kDa < MW < 35 kDa, MW < 6 kDa) were more easily utilized by Firmicutes and Bacteroidetes (r-strategists), leading to an increase in their relative abundance. With the decrease of macromolecular humic-like substances and the increase of protein-like substances, river microbial communities shifted from K-strategists to r-strategists. This work unveils the evolution of DOM in an effluent-dominated river and the influence of the degradation of macromolecular humic-like substances on r/K-strategists.

Ceramsite made from drinking water treatment residue for water treatment: A critical review in association with typical ceramsite making.

The use of ceramsite to construct filtration systems (e.g., biofilters) is a common method for water treatment. To promote such applications, the development of low-cost, high-performance, and environmentally friendly ceramsites has received increasing attention from scientists, and a critical step in the development is the preparation of raw materials. As an inevitable and non-hazardous by-product during potable water production, drinking water treatment residue (DWTR) is typically recycled to make water treatment ceramsite to promote recycling in filtration systems. This study aims to bridge the knowledge gap regarding DWTR in making ceramsites for water treatment. The results suggest that the fabrication methods for DWTR-based ceramsite can be generally classified into sintering and non-sintering procedures. For the sintering method, owing to the heterogeneous properties (especially aluminum, iron, and calcium), DWTR has been applied as various sub-ingredients for raw materials preparations. In contrast, for the non-sintering method, DWTR is commonly applied as the main ingredient, and natural curing, physical crosslinking, and thermal treatment methods have been typically adopted to make ceramsite. However, DWTR-based ceramsites tend to have a high adsorption capability and favorable microbial effects to control different kinds of pollution (e.g., phosphorus, nitrogen, and organic matter). Future work is typically recommended to thoroughly evaluate the performance of DWTR-based ceramsite-constructed filtration systems to control water pollution concerning the making procedures, the potential to control pollution, the stability, and the safety of raw DWTR-based ceramsite, providing systematic information to design more proper planning for beneficial recycling.

Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy.

Cognitive impairment is a common comorbidity of epilepsy and adversely impacts people with both frontal lobe (FLE) and temporal lobe (TLE) epilepsy. While its neural substrates have been investigated extensively in TLE, functional imaging studies in FLE are scarce. In this study, we profiled the neural processes underlying cognitive impairment in FLE and directly compared FLE and TLE to establish commonalities and differences. We investigated 172 adult participants (56 with FLE, 64 with TLE and 52 controls) using neuropsychological tests and four functional MRI tasks probing expressive language (verbal fluency, verb generation) and working memory (verbal and visuo-spatial). Patient groups were comparable in disease duration and anti-seizure medication load. We devised a multiscale approach to map brain activation and deactivation during cognition and track reorganization in FLE and TLE. Voxel-based analyses were complemented with profiling of task effects across established motifs of functional brain organization: (i) canonical resting-state functional systems; and (ii) the principal functional connectivity gradient, which encodes a continuous transition of regional connectivity profiles, anchoring lower-level sensory and transmodal brain areas at the opposite ends of a spectrum. We show that cognitive impairment in FLE is associated with reduced activation across attentional and executive systems, as well as reduced deactivation of the default mode system, indicative of a large-scale disorganization of task-related recruitment. The imaging signatures of dysfunction in FLE are broadly similar to those in TLE, but some patterns are syndrome-specific: altered default-mode deactivation is more prominent in FLE, while impaired recruitment of posterior language areas during a task with semantic demands is more marked in TLE. Functional abnormalities in FLE and TLE appear overall modulated by disease load. On balance, our study elucidates neural processes underlying language and working memory impairment in FLE, identifies shared and syndrome-specific alterations in the two most common focal epilepsies and sheds light on system behaviour that may be amenable to future remediation strategies.