Recently, the coding metasurface has gained significant attention due to its exceptional potential in controlling electromagnetic (EM) waves with the rapid development of wireless communication systems. Meanwhile, graphene shows tremendous promise for the implementation of reconfigurable antennas due to its high tunable conductivity and its unique property that makes it a very suitable material for realizing steerable coded states. In this paper, we first propose a simple structured beam reconfigurable millimeter wave (MMW) antenna using a novel graphene-based coding metasurface (GBCM). Different from the previous method, its coding state can be manipulated by altering the sheet impedance of graphene instead of bias voltage. Then, we design and simulate several most popular coding sequences, including dual-, quad-and single-beam-generated implement, 30° beam deflection, as well as a random coding sequence for radar cross-section (RCS) reduction. The theoretical and simulation results show that graphene has great potential for MMW manipulation applications, which lay a foundation for the subsequent development and fabrication of GBCM. The capability of manipulating electromagnetic (EM) waves, including the frequency , phase , magnituThe methodology of the coding beam reconfiguration needs a stable amplitude response and periodic gradient phase shifts in the range of 0–2π ,14,15,16In recent years, graphene has provided more abundant manipulation means for the regulation of EM waves by taking advantage of its tunable conductivity ,20. MeanIn this paper, for the first time, we proposed a new approach for the EM wave beam regulation with complex functions by combining the tunable sheet impedance of graphene with the coding MS, as is shown in The design of any MS starts with its basic unit cells. For beam manipulation, a unit cell needs to be able to simultaneously satisfy the ability to control the phase response and generate high reflection amplitude in a wide band ,20. TherNanomaterial graphene is a monolayer of carbon atoms arranged in a hexagonal lattice that can be modeled as an extremely thin conductive layer. The conductivity of graphene can be calculated using the Kubo formula ,30,31,32j is the imaginary unit, e = −1.6 × 10−19 C is the electron charge, −34 Js is the reduced Plank’s constant, −23 m2kg/s2K is Boltzmann’s constant, T = 300 K is the room temperature and τ is the relaxation time, At the THz band, graphene-based devices are enabled solely by the intraband transition. The graphene conductivity can be approximately expressed as:At the microwave band, based on the Kubo formula in Equation (1), the resistance of graphene impedance remains almost constant, while the reactance term tends to 0 and could be neglected. Therefore, graphene can be approximately treated as a resistive sheet without dispersion  and modeεr = 3.5) substrate, Taconic TLY-5 substrate (εr = 2.2), and the copper layer as GND. h1 = 0.075 mm height and placed on a Taconic TLY-5 substrate with h2 = 0.787 mm height. The spacing between the units should be a subwavelength to provide efficient scattering and prevent the occurrence of grating diffraction. However, it should not be too small; otherwise, the strong nearfield coupling between neighboring antennas would perturb the designed scattering amplitudes and phases , θ and ϕ are the elevation and azimuth angles of an arbitrary direction, respectively. When θ and Based on the 1-bit coding MS, the design principle of the coded MS was analyzed, as shown in Here, As previously mentioned, applying various values of sheet resistances of graphene units at different locations across the MS array results in a reconfigurable coding layout. The different distribution of coding particles leads to diverse functionalities only achieved by one planar structure. In our work, three distinct graphene coding sequences are designed to achieve multiple-beam steering. Here, by changing the sheet resistances of graphene MS units to realize beam reconfiguration. As shown in In order to further verify the beam manipulation ability of the graphene-coded MS antenna, the precise phase distribution at a 30° reflection angle was calculated in MATLAB according to Equation (8), as shown in Based on the proposed 1-bit unit cell and phase design, two 20 × 20 phased 1-bit GBCMs with beams pointing at 0° and 30° are designed with a center frequency of 55 GHz. Moreover, the simulated patterns in the xoz plane under normally incident waves at different frequencies are shown in In this work, we propose a simple structured beam steering MMW antenna using novel GBCM, and its coding state can be manipulated by altering the sheet impedance of graphene instead of bias voltage. There have previously been reported many design schemes for GBCM antennas. Paper ,25,26,27Here, for the first time, the broadband manipulation of MMW waves is acquired by introducing a multifunctional graphene-based coding metasurface (GBCM). The designed structure consists of subwavelength patterned graphene units whose operational statuses can be altered between two digital states of “0” and “1”. Its coding state can be manipulated by altering the sheet impedance of graphene instead of bias voltage. This results in a simplified design structure, providing possibilities for the subsequent fabrication of graphene MS antennas. In this work, the tunable behavior of graphene has led to various functionalities, among which we simulated five different coding sequences, implementing dual-, quad- and single-beam-generated beam deflection (30°), as well as a random coding sequence for radar cross-section (RCS) reduction.


Nontuberculous mycobacterial pulmonary disease (NTMPD) is a chronic lung infection that is challenging to treat, especially refractory NTMPD. Data are limited on the natural history, risk profile, and burden of refractory NTMPD. The objective of this study was to evaluate the burden of illness (BOI) among patients with NTMPD who later develop refractory NTMPD vs those who do not.This was a retrospective analysis using BRR data. Patients treated for NTMPD were included and stratified as refractory  or nonrefractory. Modified bronchiectasis severity index (mBSI) at baseline visit and BOI including comorbid conditions and surgeries at the treatment visit were compared by cross-sectional analysis between the two groups. Longitudinal analysis of BOI focused on exacerbations and hospitalizations in the refractory group at their pre-refractory visit and refractory visit (first visit meeting the refractory definition).A total of 1,064 patients treated for NTMPD were included: 462 (43.4%) refractory and 602 (56.6%) nonrefractory. During the baseline period, the refractory group had more severe bronchiectasis . At the time of treatment, bronchiectasis was more common in the refractory than nonrefractory (96.2% vs 89.7%) group, as were hemoptysis (26.6% vs 16.3%) and GERD (50.5% vs 39.9%). Except BMI, there were no statistically significant differences between the pre-refractory and nonrefractory treatment visits (Table 2). In the refractory group, exacerbations and hospitalizations remained high from the pre-refractory to refractory visit . Oxygen and bronchial hygiene use increased from the pre-refractory to the refractory visit  consistent with increased management of respiratory conditions when not responding to treatment (Table 2).Refractory NTMPD imposes a high disease burden on patients in the BRR. This high burden was present at pre-refractory visits and through the development of refractory status.Timothy R. Aksamit, MD, AstraZeneca: Advisor/Consultant|Baxter International, Hill-Rom: Advisor/Consultant|Insmed Incorporated: Advisor/Consultant|Johnson & Johnson: Advisor/Consultant|Redhill Biopharma: Advisor/Consultant|RespirTech: Advisor/Consultant|Spero Therapeutics: Advisor/Consultant|Zambon: Advisor/Consultant David M. Mannino, MD, AstraZeneca: Advisor/Consultant|GlaxoSmithKline: Advisor/Consultant|Up to Date: Advisor/Consultant Ping Wang, PhD, Insmed Incorporated: Salary|Insmed Incorporated: Stocks/Bonds Mariam Hassan, PhD, B. Pharm, Insmed Incorporated: Salary|Insmed Incorporated: Stocks/Bonds


There has been a lengthy debate about whether salient stimuli have the power to automatically capture attention, even when entirely task irrelevant. Theeuwes  has sugg This account claims that salient stimuli will automatically capture attention unless the attentional window is small. More precisely, if attention is broadly distributed at stimulus onset , feature information from all objects in the display is processed, allowing the most salient object to capture attention. By contrast, if attention is narrowly focused at stimulus onset , only the features at the attended location are fed forward to later processing stages and this will prevent the features of the most salient object from being processed and thereby capturing attention. This account is meant to explain a large number of previous studies that have shown that salient distractors can be ignored under conditions that promote feature-based attentional guidance by using heterogenous search displays. Although the attentional window account is an interesting and creative take on past findings, we find that it implies a model of attention that is implausible and inconsistent with previous results. In the current paper, we will describe two major shortcomings of this account.Theeuwes  outlinesThe idea that attention can be either broadly or narrowly focused is well supported by previous research e.g., , but thiguidance problem: if enough featural information is present to guide attention to the target stimulus, then there should be enough featural information for saliency to be computed and control attention (unless the salient object was being suppressed, as proposed by the signal suppression hypothesis; for a review, see Crucially, if a narrow attentional focus prevents the processing of feature information at unattended locations, then it would not be possible for featural information to guide attention. In other words, if there is enough featural information present to guide attention to items with task-relevant features, then there is certainly enough featural information present to determine that one of the distractors is a highly salient color singleton. Thus, for the attentional window account to be feasible, attention must be focused so narrowly that the first shift of attention after stimulus onset is completely random. Although it may be possible to design a search task that is so difficult that the first shift of attention is completely random, most of the tasks classified as “serial” by Theeuwes  contain was preferentially guided toward search items with target features. This suggests that featural information was available to the attentional system and was not shielded as claimed by the attentional window account. For example, Gaspelin et al. (additional singleton paradigm in which participants searched for a specific shape and were instructed to ignore color singleton distractors (In many previous studies in which Theeuwes  would arn et al. , Exp. 3 tractors . The firtractors . It doesOther approaches have also demonstrated similar evidence of attentional guidance toward the target shape in search tasks with heterogenous distractors. For example, Gaspelin et al. , Exp. 2 Even if we ignore the evidence that the attentional window was broad enough for substantial feature processing in previous experiments, the attentional window account does a poor job of accounting for the presence or absence of capture in prior studies. A key tenet of the attentional window account is that easy search tasks that encourage a “parallel” search will yield attentional capture, and difficult search tasks that encourage a “serial” search will not yield capture. A natural question is therefore whether this prediction fits the general pattern of results observed in the existing attentional capture literature.For example, a distinction between easy and difficult search tasks cannot account for the lack of capture by salient-but-irrelevant cues in the spatial-cueing paradigm e.g., . In thisThe lack of capture in the spatial-cueing paradigm is problematic for the attentional window account because these tasks typically use easy search displays that would seem to encourage a diffuse attentional window. For example, Lien et al.  measuredAn easy-difficult search dichotomy also does not explain strategic changes in performance in the additional singleton paradigm. Leber and Egeth  directlyA parallel-serial dichotomy also cannot explain the discrepant findings between bottom-up theories of capture and the signal suppression hypothesis (as suggested by exactly the same stimuli as Wang and Theeuwes (Theeuwes  now suggTheeuwes  and stilAn easy-difficult dichotomy also does not easily explain learned suppression effects, whereby individuals learn to avoid capture by salient distractors on the basis of their specific feature values . For exaIn sum, the attentional window account is unrealistic for two main reasons. First, it presumes that the attentional window is so narrowly focused at search display onset that feature processing is suppressed outside the window. However, this seems unlikely on theoretical grounds, and it is also inconsistent with clear evidence of feature-based guidance. Second, it presumes that the degree of capture should vary across studies with the difficulty of the search task. However, this does not fit the pattern observed in prior research. In our view, a more parsimonious model of attention would simply accept that salient distractors can sometimes be prevented by feature-based attentional control settings e.g., .


Amyotrophic lateral sclerosis (ALS) is a multi-systemic, incurable, amyloid disease affecting the motor neurons, resulting in the death of patients. The disease is either sporadic or familial with SOD1, C9orf72, FUS, and TDP-43 constituting the majority of familial ALS. Multi-omics studies on patients and model systems like mice and yeast have helped in understanding the association of various signaling and metabolic pathways with the disease. The yeast model system has played a pivotal role in elucidating the gene amyloid interactions. We carried out an integrated transcriptomic and metabolomic analysis of the TDP-43 expressing yeast model to elucidate deregulated pathways associated with the disease. The analysis shows the deregulation of the TCA cycle, single carbon metabolism, glutathione metabolism, and fatty acid metabolism. Transcriptomic analysis of GEO datasets of TDP-43 expressing motor neurons from mice models of ALS and ALS patients shows considerable overlap with experimental results. Furthermore, a yeast model was used to validate the obtained results using metabolite addition and gene knock-out experiments. Taken together, our result shows a potential role for the TCA cycle, cellular redox pathway, NAD metabolism, and fatty acid metabolism in disease. Supplementation of reduced glutathione, nicotinate, and the keto diet might help to manage the disease. Amyotrophic lateral sclerosis (ALS) is a complex multi-systemic disease associated with the loss of motor neurons in the brain stem, spinal cord, and motor cortex . NeurodeTranscriptomic, proteomic, and metabolomic analyses have been carried out on ALS patients and transgenic model systems with SNP in SOD1, C9orf72, FUS, and TDP-43 to understand the disease . MutatioThe TDP-43 gene is located on chromosome number 18. The protein is made up of 414 amino acids . Most ofC. elegans, drosophila, zebrafish, and mice could capture many aspects of ALS pathogenesis. Omic studies like transcriptomic, proteomic, and metabolomic studies on the model systems show deregulated pathways associated with the disease  were used for pathway enrichment analysis. Pathway enrichment analysis was carried out using Enrichr [Analysis date: January 2022–March 2022 (www.maayanlab.cloud/Enrichr)]  ,40,41, aion 4.0)  was usedRT Polymerase Chain Reaction (RT-PCR): RT-PCR was performed to validate the results obtained from transcriptomics. Three genes that were expressed in the TCA cycle, signaling, and fatty acid metabolism  were identified. cDNA synthesis was performed using the Thermo Scientific cDNA synthesis kit (#AB-1453/B) as per the manufacturer instructions. PCR was performed using 384 well format Quant Studio 5 PCR machine. Primers were designed using Primer Blast, and synthesized by Barcode Biosciences . ALG9 was used as a housekeeping gene. The primer sequences used for the study are presented below : RNA sequencing results and Metabolomic results were integrated using Metabo-analyst  [ion 5.0) . A list GEO (Gene Expression Omnibus) data analysis: Gene Expression dataset pertaining to mice motor neuron expressing TDP-43 aggregates and its mutant were identified from Gene Expression Omnibus Database (GSE111775) [GSE124439) [E111775) . ApproprE111775) ,44,45,46E124439)  was alsoE124439) .Comparative analysis: Commonality analysis was carried out between the GEO-derived dataset of TDP-43-A315T, ALS Post-mortem cortex sections, and the integrated analysis pathway results of the TDP-43-Q331K Mutant. A Venn diagram was plotted using Venny . Commonality analysis was also carried out between literature-derived metabolomic datasets of human Cerebrospinal fluid (CSF) [www.bioinfogp.cnb.csic.es/tools/venny, Version 2.1, Analysis date: January 2022–March 2022).id (CSF) , Mice TDid (CSF) , and pooExperimental validation (Metabolite addition experiments and knock-out studies): Metabolite addition experiments have been carried out on various model systems [Saccharomyces cerevisiae transformed with TDP-43 and its mutants. Transformed cells were grown in URA-YNB-Dextrose medium for 12 h. The cells were then pelleted, washed, and grown in URA-YNB-Raffinose medium. After 7 h, the cells were again pelleted, washed, and grown in galactose medium. The cells were treated with different metabolites based on the transcriptomic and metabolomic results. The cells were allowed to grow in galactose medium for 8 h. After 8 h, fluorescence imaging was carried out as stated before. Samples were randomized for imaging studies. Further, fluorescence quantification was carried out using Icy software [icy.bioimageanalysis.org, version 2.1.0.0). The results were analyzed using Microsoft Excel 2019. The yeast knock-outs library from Dharmacon  was used for the study’s yeast knock-outs. Yeast knock-outs were transformed and imaged in a similar manner as described previously. Fluorescence quantification was performed using Icy software  and Microsoft Excel 2019. systems . We carrsoftware   SDS containing TBS buffer was used to assemble the bio-dot device . 150 µg of protein was heated at 100 °C for 10 min and was loaded in the wells. A vacuum was applied to enhance the binding of the protein with the membrane. After 10 min, the membrane was removed. The blot was incubated in 20 mL of TBST overnight at 4 °C with 2 µL of mouse anti-GFP antibody . On a gel rocker, the blot was washed twice with 25 mL of TBST. The cleaned blot was incubated for 2 h at room temperature with 1 µL of goat anti-mouse IgG-HRP antibody  in 25 mL of TBST. The ECL substrate  and Syngene Gbox F3 were used to create the blot. The blots were quantified using Icy software  and Microsoft Excel 2019.rotocols . The yearotocols . The bloS. cerevisiae after transformation with respective plasmids  of ≤20% after normalization with internal standards were used for further analysis. A total of 36 significant differential metabolites were obtained , and an adjusted of ≤0.05 A. Of theof ≤0.05 A. To furof ≤0.05 B. Our RTof ≤0.05 B. Havingof ≤0.05 . Our anaof ≤0.05 C. The deof ≤0.05 C. The siof ≤0.05 D. The siof ≤0.05 D. Taken Integrated analysis of transcriptomic and metabolomic datasets from the yeast model of TDP-43 aggregation was performed using Metaboanalyst, as described in the methods section. The analysis yielded 18 significantly deregulated metabolic pathways . Our anaS. cerevisiae expressing wild type or TDP-43 mutants . The metabolic profile of TDP-43 mutants (G294A and M337V) was compared to TDP-43 wild type. For G294A and M337V, a total of 57 and 21 significant differential metabolites were obtained  exhibited deregulation of metabolic pathways which are similar to those expressing Q331K, we carried out targeted metabolomic analysis of obtained A. Pathwaobtained . The metobtained B. Since Commonality analysis for overlapping pathways yielded 8 pathways as common among all the TDP-43 mutants, while 16 pathways were common between Q331K and G294A . The TDPWe further asked if our findings in the yeast model are of relevance to ALS. For this, we carried out an analysis of transcriptomic datasets of mice models of ALS and ALS patients from the GEO database. The datasets used are the TDP-43 A315T expressing transgenic mice motor neurons and cortex of ALS patients. GSEA analysis of motor neuron dataset from mice model of ALS showed deregulation of pathways like primary immunodeficiency, neuroactive ligand-receptor interaction, Parkinson’s disease, glycerolipid metabolism, spliceosome, oxidative phosphorylation, and ribosome. The detailed results are provided in the supplementary section . SimilarCommonality analysis of deregulated pathways across ALS patients, yeast and mice models of ALS was performed. Two pathways (ribosome and oxidative phosphorylation) were common to yeast, mice, and human datasets A. A totaFurther, the metabolites obtained from literature for TDP-43-A315T transgenic mice motor neurons were analysed using Metaboanalyst, and the results are provided . MetabolOur integrative analysis of transcriptomics and metabolomics shows deregulation of the TCA cycle. Similarly, the TDP-43 mice model brain and ALS patient post-mortem cortex transcriptomic data also displayed deregulation of the TCA cycle. Metabolic addition experiments using succinate and alpha-ketoglutarate show significantly elevated protein aggregates . ConsistThe transcriptomic data from our yeast studies and the mice brain data from the GEO database show deregulation of glutathione metabolism. Supplementation of reduced glutathione significantly reduced amyloidogenesis, while oxidized glutathione led to an insignificant increase in amyloidogenesis . MetabolFurther, we carried out filter retardation assay to quantify the wild type or mutant TDP-43 in the total protein, as well as in the soluble and insoluble fractions, as described in the methods section. In particular, we had used the wild type and mutant TDP-43 which are untreated or treated with butyric acid or palmitic acid. This will help to correlate the changes in total protein, and soluble and insoluble protein of TDP-43, as well as validate that the observations of fluorescence imaging experiments are reliable. Consistent with fluorescence imaging data, our analysis showed significantly higher total protein in the mutants compared to the wild type. Similarly, the amount of mutant TDP-43 in both soluble and insoluble fractions were significantly higher compared to the wild type C. We furIn this study, we used the yeast model of TDP-43 aggregation to understand the transcriptomic and metabolomic changes as a consequence of mutation in TDP-43. The relevance of deregulated pathways obtained for yeast were compared with those obtained for motor neurons from the TDP-43 mice model of ALS (GSE111775) and ALS patients (GSE124439). Further, the role of deregulated transcriptomic and metabolomic pathways on the aggregation of TDP-43 in the yeast model was studied. Furthermore, metabolic addition experiments and yeast knock-out of specific genes in deregulated pathways were used to validate their role in the disease progression. Consistent with previous reports, in the TDP-43 expressing yeast model elevated TDP-43 aggregation was observed in the mutant  compared to the wild type, which also corroborates with results of the filter retardation assay. Previous studies have also shown increased severity and aggregation in TDP-43 mutants compared to the wild type TDP-43 expressing cells .Transcriptomic analysis of yeast expressing mutant TDP-43 shows enrichment of peroxisome, ribosomes, metabolism of different amino acids, nucleotide metabolism, fatty acid metabolism, and MAPK signalling. Transcriptomic analysis of zebrafish transgenic for TDP43 (G348C) showed changes in the levels of many differentially expressed genes that were related to neuromuscular disorders, including ALS and muscular dystrophy . The genThe increased ROS in TDP-43 mutants has been reported previously . PreviouThe integrative analysis of transcriptomic and metabolomics shows that the transcriptomic changes translate into metabolic changes in the yeast model of TDP-43. Previous studies have shown increased glycolysis and metabolites belonging to the TCA cycle in ALS . To corrFurther, transcriptomic analysis of yeast and mice models of ALS shows deregulation of the glutathione pathway and oxidative stress, while transcriptomic and metabolomic analysis shows deregulation of the pentose phosphate pathway. Previous studies have also shown oxidative stress and ROS in the yeast model of TDP-43 aggregation . SupplemThe current study focuses on an integrative multi-omic analysis of the yeast model of TDP-43 aggregation for the identification of deregulated pathways, and comparing it with patients and other model systems. Further, the role of these deregulated pathways in amyloid formation in the yeast model of TDP-43 aggregation is discerned using metabolic addition experiments and gene knock-outs. Metabolomic analysis of TDP43 wild type or different mutants expressed in the yeast model of TDP-43 aggregation shows deregulation of metabolic pathways. These pathways include nicotinate and nicotinamide metabolism, glutathione metabolism, and metabolism of various amino acids. Transcriptomic analysis of the yeast model of TDP-43 aggregation (Q331K) shows deregulation of the TCA cycle, oxidative phosphorylation, peroxisome, fatty acid degradation, metabolism of amino acids. The integrated transcriptomic and metabolomic analysis shows glutathione pathway, fatty acid degradation, metabolism of various amino acids. The transcriptomic and metabolomic analyses displayed considerable concordance. Further, pathways from transcriptomic datasets of the yeast model were compared with those obtained from ALS patients and mice models of ALS from the GEO database. Our analysis shows that the TCA cycle, oxidative phosphorylation, as well as protein processing and degradation are common to different datasets. Validation of the results using the yeast model showed short-chain fatty acids significantly abrogated amyloid formation, while long-chain fatty acids significantly ameliorated it. Furthermore, results of the filter retardation assay of short-chain and long-chain fatty acids treated and untreated wild type and mutant cells corroborated well with the fluorescence image data. Similarly, reduced glutathione and succinate reduced protein aggregation. Consistent with this, KO of MDH2 and KGD10 reduced protein aggregation. Taken together, our results show deregulated pathways modulate amyloid formation in the yeast model of TDP-43 aggregation. The results show a potential use for a short-chain fatty acid-containing keto diet, and antioxidants such as glutathione and nicotinate in managing and improving the living conditions of ALS patients.


It is unknown how the patterns of negative and positive attentional biases in children predict fear of COVID-19, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. The study identified profiles of negative and positive attentional biases in children and examined their association with emotional symptoms during the COVID-19 pandemic.264 children (girls: 53.8% and boys: 46.2%) of 9–10 years born in Hong Kong or mainland China from a primary school in Shenzhen, People’s Republic of China were involved in a two-wave longitudinal study. Children completed the COVID-19 Fear Scale, the Revised Child Anxiety and Depression Scale, and the Attention to Positive and Negative Information Scale to measure fear of COVID-19, anxiety and depression symptoms, and negative and positive attentional biases in classrooms. After six months, they completed the second assessment of fear of COVID-19, anxiety symptoms, and depression symptoms in classrooms. Latent profile analysis was conducted to reveal distinct profiles of attentional biases in children. A series of repeated MANOVA was performed to examine the association of profiles of attentional biases to fear of COVID-19, anxiety symptoms, and depression symptoms across 6 months.Three profiles of negative and positive attentional biases were revealed in children. Children with a “moderate positive and high negative attentional biases” profile had significantly higher fear of the COVID-19 pandemic, anxiety symptoms, and depression symptoms than children with a “high positive and moderate negative attentional biases” profile. Children with a “low positive and negative attentional biases” profile were not significantly different in fear of COVID-19, anxiety symptoms, and depression symptoms than those with the other two profiles.Patterns of negative and positive attentional biases were related to emotional symptoms during the COVID-19 pandemic. It might be important to consider children's overall patterns of negative and positive attentional biases to identify children at risk of higher emotional symptoms.The online version contains supplementary material available at 10.1186/s13034-023-00594-y. The COVID-19 pandemic might lead to an increase in the rate of emotional symptoms among children . PreviouLongitudinal studies that examine how the profiles of attentional preferences affect emotional symptoms during the COVID-19 pandemic are particularly scarce. Considering the importance of attentional bias in children’s development of internalizing symptoms, more research is needed to investigate how children’s overall patterns of attentional biases might affect the development of fear, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. These findings would add to the literature what were the overall patterns of negative and positive attentional biases in children during the COVID-19 pandemic, and how these patterns of negative and positive attentional biases were associated with emotional symptoms during the COVID-19 pandemic. Such findings might also add to our understanding of whether psychologists should consider both negative and positive attentional biases simultaneously when investigating the development of emotional symptoms in children, especially during the COVID-19 pandemic.To address these research gaps, the present study aimed to reveal profiles of attentional biases and to examine how these profiles were associated with the development of fear of COVID-19, anxiety symptoms, and depression symptoms in children during the COVID-19 pandemic.Attentional bias refers to the cognitive tendency to shift attention toward negative or positive stimuli. Based on the cognitive models, negative attentional bias is the vulnerability factor for the development of anxiety symptoms and depression symptoms in children , 33. ResAttentional training that targeted negative attentional bias has shown efficacy in modifying self-esteem as an indicator of depression symptoms in 108 participants over 17 years old with mild or minimal depression symptoms  [During the COVID-19 pandemic, children around the age of 14.23 years (N = 195) recruited from the Netherlands might experience strong fear of COVID-19 infection which was the result of communication of threatening COVID-19-related information with parents . A meta-Profiles of negative and positive attentional biases depicted overall patterns of attentional preferences to negative and positive stimuli. Although negative attentional bias might be the opposite of positive attentional bias in terms of the affective valence of preferred stimuli, people might have low negative attentional bias and low positive attentional bias simultaneously. The presence of high negative and high positive attentional biases might predict lower emotional symptoms than those with high negative and low positive attentional biases. For example, a study with an unselected community sample of 270 adults (the mean age of 22 years) from the undergraduate student population and community forum has shown that attentional bias toward threats was linked to high self-report anxiety symptoms only in individuals with a tendency to attend away from positive information . Latent The current research recruited a sample of children from a primary school. The study aimed to 1) reveal distinct profiles of negative and positive attentional biases in children during the COVID-19 pandemic. Also, the study aimed to 2) reveal the relationship of profiles of attentional biases to fear of COVID-19, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. The majority of existing studies have only investigated either negative attentional bias or positive attentional bias separately. No previous studies have used the latent profile analysis to investigate the effect of the overall patterns of attentional biases on children’s emotional symptoms during the COVID-19 pandemic. This study would be the first study that took a holistic approach to reveal profiles of attentional biases in children. Also, it would provide the first evidence from a longitudinal study for how profiles of attentional biases would affect fear of COVID-19, anxiety symptoms, and depression symptoms during the COVID-19 pandemic.Based on the previous evidence, distinct profiles of attentional biases would be revealed which might include profiles of “low negative and positive attentional bias”, “low negative and high positive attentional bias”, “high negative and low positive attentional bias” and “high negative and positive attentional bias”. Compared to the “high negative and positive attentional bias” profile, the “low negative attentional bias and high positive attentional bias” profile would predict low fear, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. While compared to the “low negative and positive attentional bias” profile, the “high negative attentional bias and low positive attentional bias” profile would be related to more fear, anxiety symptoms, and depression symptoms in children during the COVID-19 pandemic.The study initially recruited a sample of middle-class 322 children from a primary school in Shenzhen, PRC. Children had similar social economic status and education levels. They were from middle-class families and had the same grade at the same school.Convenient sampling was used to recruit participants. First, public schools where children were normal Chinese students from middle-class families were selected. This population was chosen because the middle class is rapidly growing, and takes a significantly large proportion of the population in China . Then, iboys = 124 and N girls = 138) were input into analyses.Regarding gender distribution, 53.8% (N = 171) of participants were girls, and 46.2% (N = 147) were boys; three participants did not indicate their gender. The average age of the sample was 9.54 years, ranging from 9 to 10 years (SD = 0.505 years). Exclusion criteria were (1) not able to fluently speak and understand simplified Chinese, (2) having a diagnosis of psychological disorders or developmental disorders, and (3) birthplace outside of mainland China. 50 children were excluded from the study because they failed to meet the inclusion criteria. 8 children who failed to complete one of the self-report assessments were excluded from the research. Altogether, 58 students were excluded from the study. Consequently, data from 264 children . First, researchers assigned the informed consent and assent forms to students. After collecting the informed consent and assent forms from students, children completed the Attention to Positive and Negative Information Scale for approximately 30 min in the classrooms. Researchers answered questions regarding the scale from students in the classrooms.The present study was conducted from November 16th to November 30th, 2020, when the COVID-19 pandemic was ongoing. The prevention measures, including social distancing, were implemented during the period. Teachers in the primary school helped researchers disseminate informed consent forms and assent forms to parents and students. The signed informed consent and assent forms were collected one day before the date of data collection in the classrooms. Only children who signed the assent forms and obtained the signed informed consent forms from their parents participated in the study. On the data collection date, research assistants assigned a set of questionnaires to students during classes. The participants were informed that they could ask questions and withdraw from the study at any time without any consequence. The assessment of attentional biases was conducted concurrently with the first assessment of emotional symptoms at time one (T1) and before the second assessment of emotional symptoms at time two (T2). At time one, students completed a set of questionnaires for children to assess their negative and positive attentional biases, fear of COVID-19, anxiety symptoms, and depression symptoms for approximately 45 min in the classrooms. After approximately six months, students completed the questionnaire to assess their fear of COVID-19, anxiety symptoms, and depression symptoms at time two in the classrooms for approximately 45 min. In each assessment, a teacher and researcher were present to answer students’ questions and ensure the safety of students.The eight-item Chinese version of the Attention to Positive and Negative Information Scale (CAPNIS) was administered to children , 23. Thehttp://www.childfirst.ucla.edu/Resources.html). RCADS is a wildly used scale for childhood anxiety symptoms and depression symptoms. The 47 items of the scale can be grouped into the Anxiety Disorders and the Major Depression sub-scales, rated on a Likert scale from 0 to 3 . The scale of Anxiety Disorders has 37 items , and the scale of Major Depressive includes 10 items . An anxiety symptoms score and a depression symptoms score were calculated by summing up scores of the relevant items on the subscales of Anxiety Disorder and Major Depression, respectively. The scales showed excellent reliability with the current data .The Chinese version of the Revised Child Anxiety and Depression Scale  was usedFear of COVID-19 was measured by the COVID-19 Fear Scale (CFS). CFS was adapted from the Chinese version of the 18-item SARS Fear Scale . When adapting the CFS scale, three items were deleted from the original scale, because these items were only related to healthcare workers . Also, instead of SARS, participants were asked about their response to the COVID-19 pandemic in the COVID-19 Fear Scale.Participants were asked to rate on a 4-point Likert scale how much the statement is true in the face of the COVID-19 pandemic. 0 indicates "definitely false" and 3 indicates "definitely true." The sample item includes "Fear that I will be infected." Three items were excluded from CFS  because of their inapplicability to students. A CFS score was obtained by summing up scores from each item. The higher the CFS scores, the higher the fear of COVID-19. The scale demonstrated good internal consistency in the current study .Exploratory factor analysis was first performed to examine the structural validity of the Attention to Positive and Negative Information Scale with data from the preliminary study , the adjusted Lo–Mendell–Rubin likelihood ratio test (Adj. LMR), as well as the Bootstrapped Likelihood ratio test (BLRT), showed that a model with k number of groups was significantly better than a model with k-1 groups [Descriptive statistics were presented. Two-tailed Pearson’s partial correlation was investigated using SPSS version 22 to understand the association among psychological variables. Independent t-tests examined the gender differences in the psychological variables. Latent profile analyses were subsequently conducted to reveal profiles of negative and positive attentional biases in children with MPlus 7.4. With maximum likelihood estimation with robust standard error (MLR), heterogeneous profiles of attentional biases were revealed. The number of groups for the model was increased until the model was not significantly better than the model with one fewer group. Smaller values of the Bayesian Information Criterion (BIC) and Akaike information criteria (AIC), as well as the higher value of entropy, suggested a better model fit . The sig1 groups . Next, C1 groups , 18. The t (260) = 1.48, p = 0.14, positive attentional bias, t (260) = − 0.04, p = 0.97, and depression symptoms at T1, t (260) = 1.41, p = 0.16. Girls showed significantly higher anxiety symptoms at T1, t (260) = 3.13, p < 0.005, higher anxiety symptoms at T2, t (260) = 2.92, p < 0.005, and higher depression symptoms at T2, t (260) = 2.17, p < 0.05, than did boys. Also, girls were significantly higher in fear of COVID-19 at T1, t (257) = 2.12, p < 0.05, than boys. Girls were significantly higher in fear of COVID-19 at T2, t (256) = 2.92, p < 0.005. Refer to Table An independent t-test was performed with 262 participants to examine the difference between girls and boys in positive and negative attentional biases, fear of COVID-19, depression symptoms, and anxiety symptoms assessed at time one (T1) and time two (T2). Results revealed that girls and boys didn’t significantly differ in negative attentional bias,r = − 0.01, p = 0.82, negative attentional bias, r = 0.04, p = 0.57, anxiety symptoms at T1, r = − 0.04, p = 0.52, depression symptoms at T1, r = − 0.03, p = 0.61, anxiety symptoms at T2, r = − 0.03, p = 0.60, and depression symptoms at T2, r = − 0.02, p = 0.76.The Pearson’s partial correlation, controlling for gender revealed that age was not significantly associated with positive attentional bias, Higher anxiety symptoms at T1 and T2 were significantly related to higher depression symptoms at T1 and T2. They were also significantly associated with higher negative and lower positive attentional biases. Depression symptoms at T1 and T2 were positively and significantly associated with negative attentional bias; depression symptoms at T1 and T2 were negatively and significantly related to positive attentional bias. Higher fear of COVID-19 at T1 and T2 was associated with significantly higher anxiety symptoms and depression symptoms at T1 and T2. Also, higher fear of COVID-19 at T1 and T2 was significantly related to higher negative attentional bias. Higher fear of COVID-19 at T1 was significantly associated with lower positive attentional bias. Higher fear of COVID-19 at T2 was not significantly related to lower positive attentional bias. Higher anxiety symptoms at T1 were significantly related to higher anxiety symptoms at T2; higher depression symptoms at T1 were significantly related to higher depression symptoms at T2. Fear of COVID-19 at T1 was significantly related to higher fear of COVID-19 at T2. Negative attentional bias was not significantly associated with positive attentional bias. Refer to Table p values of VLMR, Adj. LMR and BLRT were insignificant for the four-class model, which suggested that the four-class model was not significantly better than the three-group model. Thus, the three-class model showed the best model fit.The fit indices for models with one to five groups were examined with a latent profile analysis , and was named as “low positive and negative attentional biases” profile . Class two had the highest positive attentional bias and moderate negative attentional bias , which was accordingly defined as the “high positive and moderate negative attentional biases” profile . Class three had a moderate positive attentional bias and high negative attentional bias , which was named the “moderate positive and high negative attentional biases” profile . Figure Class one had the lowest level of negative and positive attentional biases (Mean χ2 (2) = 0.23, p = 0.89, birthplace, χ2 (2) = 1.14, p = 0.57, and age groups (9 & 10 years), χ2 (2) = 1.24, p = 0.54. Refer to Table The three latent profiles were not significantly different in terms of gender, F  = 5.66, p < 0.001. The effect size was small, partial η2 = 0.042. Power for the effect of profiles on anxiety symptoms was as high as 0.86.The Bonferroni post hoc test showed that children with a “moderate positive and high negative attentional biases” profile had significantly higher anxiety symptoms  than children with a “high positive and moderate negative attentional biases” profile . Children with a “low positive and negative attentional biases” profile  were not significantly different in anxiety symptoms level from children with the other two profiles  = 6.43, p < 0.01. The effect size was small with partial η2 = 0.047. The between-subject effect had high calculated power of 0.90. Bonferroni post hoc test showed that children with a “moderate positive and high negative attentional biases” profile  had significantly higher depression symptoms than children with a “high positive and moderate negative attentional biases” profile . Children with a “low positive and negative attentional biases” profile  were not significantly different in depression symptoms from children with the other two profiles  = 6.43, p < 0.05. The effect size was small, partial η2 = 0.031. The calculated power for the effect of profiles on fear of COVID-19 was 0.72. The Bonferroni post hoc test showed that children with a “moderate positive and high negative attentional biases” profile  demonstrated significantly higher fear of COVID-19 than children with a “high positive and moderate negative attentional biases” profile . Children with a “low positive and negative attentional biases”  profile didn’t significantly differ in fear of COVID-19 from children with “moderate positive and high negative attentional biases” and “high positive and moderate negative attentional biases” , “high positive and moderate negative attentional biases” profile (38.5%), and “moderate positive and high negative attentional biases” profile (57.2%). Profiles of attentional biases showed a significant relationship with emotional symptoms over six months during the COVID-19 pandemic; the “moderate positive and high negative attentional biases” profile was related to significantly higher anxiety symptoms and depression symptoms than “high positive and moderate negative attentional biases” profile. The “low positive and negative attentional biases” profile was not significantly different from the other two profiles in emotional symptoms. Consistent with the findings, previous studies which measured negative attentional bias by behavioral tasks also found a positive association between negative attentional bias and emotional symptoms. , 16. TheMoreover, during the COVID-19 pandemic, negative attentional bias was related to higher health anxiety and higher COVID-19-related anxiety symptoms , 4. ThisThese were the first results identifying children’s patterns of attentional biases during the COVID-19 pandemic and their relationship with emotional symptoms. With latent profile analysis and the longitudinal design, the results added to the literature that it was important to consider negative and positive attentional biases simultaneously and to use profiles of attentional biases to predict the development of emotional symptoms during the COVID-19 pandemic. Evidence might provide significant implications for the cognitive model of emotional symptoms, suggesting that negative attentional bias was as important as positive attentional bias, and thus the overall patterns of negative and positive attentional bias might be more important than negative attentional bias alone.The current study revealed three profiles of positive and negative attentional biases in children during the COVID-19 pandemic. Over half of the children showed a “moderate positive and high negative attentional biases” profile (59.5%), and only a smaller number of children had a “low positive and negative attentional biases” profile (3.8%) or “high negative attentional bias and moderate positive attentional bias” profile (36.7%). Consistent with previous research, most children’s attentional bias towards negative stimuli tended to be moderate or high during the COVID-19 pandemic .


The diversity and primary productivity in the Arctic ecosystem are rapidly changing due to global warming. Microorganisms play a vital role in biogeochemical cycling. However, the diversity of planktonic microorganism communities in the Laptev Sea, one of the most important marginal seas of the Western Arctic Ocean, have not been studied sufficiently in depth. The diversity and community structure of the planktonic microorganisms in the surface water were investigated at 20 stations on the Lena River flowing into the Laptev Sea. Multivariate statistical analyses demonstrated clear spatial patterns in the α diversity and community structure for microorganisms under different salinity levels. Co-occurrence networks of microbial communities revealed that spatial variation promoted differentiation of the characteristics and stability of microbial networks in the Laptev Sea. Contrary to expectations, abundant taxa were found to not have a large influence on the stability and resilience of microbial interactions in the region. On the contrary, less-abundant taxa were found to have far greater influence. The stability and resilience of the prokaryotic and microeukaryotic networks in the Lena River estuary and the continental shelf provided valuable insights into the impact of freshwater and land inflow disturbances on microbial assemblage. Overall, these results enhance our understanding of the composition of microbial communities and provide insights into how spatial changes of abundant versus rare species alter the nature and stability of microbial networks from the Lena River estuary to the Laptev Sea. In addition, this study explored microbial interactions and their ability to resist future disturbances.IMPORTANCE The regime of the Laptev Sea depends closely on the runoff of the Lena River. Microorganisms are essential components of aquatic food webs and play a significant role in polar ecosystems. In this study, we provided a basic microbial data set as well as new insights into the microbial networks from the Lena River estuary to the Laptev Sea, while exploring their potential to resist future disturbances. A comprehensive and systematic study of the community structure and function of the planktonic microorganisms in the Laptev Sea would greatly enhance our understanding of how polar microbial communities respond to the salinity gradient under climate warming. While the earth has warmed by ~0.8°C since the late 19th century, the Arctic region has warmed by 2 to 3°C . The inpMany studies conducted in the Laptev Sea area over the past 2 decades have focused on assessing the impact of the river runoff on the Arctic Ocean ecosystem. Most previous studies have focused on the community composition, distribution, and abundance of benthic fauna , zooplanPrevious studies have shown that microbial interactions may be more important than other environmental variables in determining the structure of microbial communities , 14. FurThis study aimed to increase our understanding of the structure of prokaryotic and microeukaryotic communities, as well as the effects of terrestrial inflow on the assembly of microbial communities and interactions between microbes in the surface waters from the Lena River estuary to the Laptev Sea. Specifically, we (i) characterized the composition and distribution of the planktonic microbial community (including both prokaryotes and microeukaryotes), (ii) evaluated the importance of deterministic and stochastic processes in shaping the community structure and the response of the microbial community to elevated salinity and temperature, and (iii) explored the interactions among the microbes and identified the main factors affecting community structure and microbial interactions in the Laptev Sea.Surface water temperatures were higher in the Lena River estuary (stations LF01 to LF07) and the nearshore (stations LF08 to LF11) than in the continental shelf (LF12 to LF20 [offshore]) . The salProteobacteria. Numerous sequences affiliated with Actinobacteria and Cyanobacteria were detected in the estuary and nearshore . However, the diversity of microeukaryotes was not significantly different among the three regions (P > 0.05) (https://github.com/qian8341/SUPPLEMENTAL-MATERIAL]). The Bray-Curtis dissimilarity index revealed that β diversity was highest for the prokaryotic and microeukaryotic communities in samples from the offshore region .The Shannon-Wiener diversity of prokaryotes was significantly lower in samples from the offshore region than in those from the estuary and transitional regions (P < 0.05) . The abundant prokaryotic OTUs were negatively correlated with salinity, and a small number of prokaryotic OTUs were positively correlated with temperature. The abundant microeukaryotic OTUs were positively correlated with salinity but negatively correlated with temperature . The effect of salinity on the α diversity of prokaryotes was stronger than that of temperature  were significantly correlated with salinity, and a few of the abundant OTUs were significantly correlated with temperature (perature .12] = 0.981) separated microeukaryotic communities in the estuary and nearshore regions from those in the offshore region, and the second canonical axis (δ22 = 0.838) separated microeukaryotic communities in the nearshore from communities in the estuary and offshore regions (P < 0.005). Unlike microeukaryotes, prokaryotic communities were divided into two groups. The first canonical axis (δ22 = 0.978) separated the prokaryotic communities in the offshore region from those in the estuary as well as the nearshore. However, the second canonical axis did not separate the prokaryotic communities in the estuary from those in the nearshore (P < 0.05). However, there was no significant difference between the prokaryotic communities in the estuary and transitional region (P > 0.05).Discrimination of the community structures was plotted using canonical analysis of the principal coordinates (CAP) on the Bray-Curtis similarity matrices from square root-transformed OTU abundance data . The CAP regions . A permuearshore . A PERMAR2 values  indicated that prokaryotic community assemblages were well described by neutral-based models and that the stochastic processes made important contributions to shaping the assembly of prokaryotic communities in this region. The relative contribution of stochastic processes to the assembly of microeukaryotic communities was lower than it was for prokaryotic communities, as only 27.8, 5.5, and 29.1% of the community variance for the estuary, offshore, and entire study region, respectively, could be attributed to the stochastic processes (m) was higher for the prokaryotic community (0.213) than it was for the microeukaryotic community (0.037).The Sloan neutral community model (NCM) was used to identify the relative importance of stochastic processes in the assembly of the prokaryotic and microeukaryotic communities . The R2 rocesses . The immThe dispersal-niche continuum index (DNCI) values of prokaryotes and microeukaryotes were −28.354 and −39.394, respectively, which indicated that dispersal assembly was dominant for microbial communities. Models with both sites and species constrained showed the smallest deviation from our data, while those constraining only one of the sites showed the highest deviation . That mehttps://github.com/qian8341/SUPPLEMENTAL-MATERIAL]). The topological properties of this network are shown in Table S2. The average path length, diameter, clustering coefficient, and modularity of this network were 6.047, 170, 690, and 0.773, respectively. These topological properties indicate the existence of ecological interactions between prokaryotes and microeukaryotes from the Lena River estuary to the Laptev Sea and that ecological niches in this region are shaped by habitat partitioning/sharing.Co-occurrence networks were constructed based on the species interaction relationships to determine the co-occurrence patterns of planktonic microbial communities in surface water from the Lena River estuary to the Laptev Sea . The resThe aforementioned thresholds were applied to obtain prokaryotic and microeukaryotic networks in the region under consideration (f) to remove the top 10 or 20 nodes  with the highest betweenness centrality , the most relative abundance, and the highest degree (number of connections) was analyzed. Although the number of components and the value of f were different from those in the original networks in the case in which nodes with the highest betweenness centrality were removed, few changes were observed when nodes with the highest degree or most relative abundance were removed (Table S2). This suggests that the removal of nodes with the highest betweenness centrality can affect the stability of microbial networks more strongly than the removal of the nodes with the most abundance or the highest degrees. The strong correlations between the betweenness centrality and the degree of node normalization mean that the nodes with high betweenness centrality (gatekeepers) also tended to be of a high degree. Thus, once the gatekeepers were removed, other hub species could replace them and connect different compartments, holding the network together, which could help maintain the stability of the network and enhance its resistance to disturbance. The top 10 or 20 taxa were selected from a ranking based on abundance, degrees, and betweenness centrality, and are listed in Table S4 at the GitHub URL above. The high-betweenness-centrality nodes of the meta-network belonged to Alveolata, Opisthokonta, Stramenopiles, Archaeplastida, Alphaproteobacteria, Kiritimatiellaeota, Cyanobacteria, and Gammaproteobacteria. The high-betweenness-centrality nodes of the prokaryotic network belonged to Actinobacteria, Alphaproteobacteria, Cyanobacteria, Gammaproteobacteria, and Kiritimatiellaeota. Archaeplastida, Alveolata, and Stramenopiles were the nodes with the highest betweenness centrality in the microeukaryotic network. These nodes have a significant effect on the structure and persistence of microorganisms, including the prokaryotic and microeukaryotic communities from the Lena River estuary to the Laptev Sea because they hold the network together. While a few of these nodes were abundant species, the abundance of most of these species was low.The response capability of network fragmentation (f) for the removal of the top 10 or 20 nodes with the highest betweenness centrality  and Gammaproteobacteria  and decreased in Actinobacteria  and Cyanobacteria  with increasing salinity levels  were also seen to increase with increasing salinity, which is in contrast to the salinity-driven patterns in the composition of prokaryote communities , which proposes that environmental filtering has a weaker effect on smaller organisms (prokaryotes) than on larger organisms (m [NCM]) of the microeukaryotic communities being lower than those of the prokaryotic communities indicated that the dispersal ability of microeukaryotes was lower than that of prokaryotes. Why the slope of the microeukaryote distance-decay pattern was steeper than that for prokaryotes could be partially explained by this  and biotic factors  as the main driver of microbial communities. However, the neutral theory argues that stochastic processes, such as birth, death, migration, speciation, and limited dispersal, shape microbial community structure . The NCM36–rganisms . Moreove by this . A complCo-occurrence network analysis can reveal significant and strong correlations between microbial taxa . In thisAlthough the stability of microbial communities can be used to draw inferences about the functioning of that ecosystem , 53, wheUnlike prokaryotes, the network interactions of microeukaryotes were more stable in the continental shelf than in the Lena River estuary. The Lena River estuary experiences constant freshwater/oceanic water input and exchange, creating a large salinity gradient from the estuary to the continental shelf. Salinity changes affect not only the biodiversity of eukaryotes and the structure of eukaryotic communities but also the network stability . The larIn this study, the prediction of bacterial functional profiles focused on key genes related to methane metabolism because our sampling sites were in geographical proximity to the recently discovered field of methane seepage, which significantly contributes to the methane emissions in the Laptev Sea . The occTo sum up, our findings provided baseline data that improve our understanding of the spatial variation and potential functions in microbial communities from the Lena River estuary to the Laptev Sea. The spatial patterns and structures in prokaryotic and microeukaryotic communities varied significantly across the transect. The results of the NCM and DNCI indicated that stochastic processes have a dominant effect on the assembly of prokaryotic communities. However, the contribution of stochastic processes to the assembly of microeukaryotic communities was weak by comparison. Changes in salinity caused by terrestrial input might affect the stability and resilience of estuarine microbial interactions from the Lena River estuary to the Laptev Sea. Compared to the dominant taxa, low-abundance groups (“gatekeepers”) had a greater contribution to the topology and stability of networks. Prokaryotes exhibit stronger and more interactive networks in the Lena River estuary than on the continental shelf. Microeukaryotic networks were more stable in the continental shelf. The results of this study demonstrate that biotic interactions play a central role in maintaining the stability of microbial networks and the integrity of the Laptev Sea microbial ecosystem.in situ.A total of 20 stations from the Lena River estuary to the continental shelf in the Laptev Sea were visited, and surface water samples (2 L each from a depth of about 1 m) were collected during September and October 2018 . The samGTGCCAGCMGCCGCGGTAA-3′) and 907R (5′-CCGTCAATTCCTTTGAGTTT-3′) (CCAGCASCYGCGGTAATTCC-3′) and TAReukREV3R  (Q < 20 or length < 200 bp) were discarded using QIIME (version 1.17) (https://github.com/qian8341/SUPPLEMENTAL-MATERIAL]).DNA was extracted by the lysozyme-SDS-phenol-chloroform method . The V4-GTTT-3′) , and theTYRA-3′)  broad miTYRA-3′) . The conon 1.17) . Tags weon 1.17) , after con 1.17) , 63. EacThe sampling map, temperature, and salinity diagrams were generated using ODV software . The PRIP < 0.01; r > 0.8 or r < −0.8) (f) was calculated as the ratio of the number of disconnected subgraphs (CL) to the overall number of nodes (N) in each network, calculated as log (CL)/log(N)  based on Spearman’s rank correlations ( < −0.8) . OTUs of)/log(N) . The ave)/log(N) . Modular)/log(N) . Networkhttps://www.omicstudio.cn) for preprocessing.Profiling of predictive microbiota was analyzed by using PICRUSt2 . The bioPRJNA786341 and PRJNA786334.The sequence data generated in the present study have been deposited in the NCBI Sequence Read Archive (SRA) database under accession no.


Science, Baughn et al. revealed that TDP-43 acts as a steric block in STMN2 pre-mRNA processing preventing inclusion of a deleterious cryptic exon, and present strategies for substituting this function to compensate for the loss of nuclear TDP-43 in a variety of neurodegenerative diseases (NDs) including amyotrophic lateral sclerosis (ALS).1In a recent study published in 3 TDP-43 is, at least in humans, essential for expression of functional full-length STMN2 protein. It binds to the pre-mRNA of STMN2 and prevents inclusion of a cryptic exon (exon 2a) located in the first intron. Nuclear loss of TDP-43 in NDs induces exon 2a inclusion that introduces an in-frame stop-codon as well as a premature polyadenylation signal resulting in a severely truncated mRNA, and loss of functional STMN2 protein  is a microtubule-associated protein specifically expressed in neurons and required for axon outgrowth and maintenance, as well as for axon regeneration after injury in vitro. Loss or reduction of this protein in mice leads to progressive sensory and motor neuropathy resembling some crucial, but not all, features of ALS.ein Fig. . This abSTMN2 mRNA. Previous data suggested that TDP-43 directly binds to a 24 bp GU-rich motif in exon 2a of the STMN2 pre-mRNA. Replacing this motif with a 19 bp stem-loop forming sequence of the bacteriophage MS2 by genome editing of human neuroblastoma cell line SH-SY5Y led to deleterious exon 2a inclusion in STMN2 mRNA. To rescue these defects, Baughn et al. expressed the MS2 coat protein (MCP) that binds to the introduced 19 bp stem-loop in STMN2 pre-mRNA with high affinity. Here, both expression of MCP alone or fused to an inactive TDP-43 variant lacking the RNA-binding domains similarly prevented exon 2a inclusion and restored correct STMN2 pre-mRNA splicing, supporting the hypothesis that TDP-43 functions as a simple steric block. This was further confirmed by introducing the human MS2-edited exon 2a including flanking regions into the first intron of the Stmn2 gene of murine Neuro2a cells. In mice, exon 2a is not conserved and Stmn2 pre-mRNA processing is not dependent on TDP-43. However, this humanization of mouse Stmn2 induced similar processing defects as reported in the human SH-SY5Y cells, and could be rescued by expression of MCP. Additionally, in SH-SY5Y cells homozygously expressing the ALS-related N352S variant of TDP-43 and displaying the described defects of STMN2 pre-mRNA processing, the authors show that not only MCP, but also the nuclease-dead CRISPR effector RfxCas13d (dCasRx) can compensate for the steric block function of TDP-43 when guided to the right position in exon 2a. Further genome editing of SH-SY5Y cells revealed that the 3’ splice acceptor site, but not the premature polyadenylation signal, of exon 2a is responsible for cryptic splicing after TDP-43 depletion. Taken together, these very elegant experiments, that were also controlled for possible effects on endogenous TDP-43 expression, leave little doubt that TDP-43 functions as a steric block for the cryptic 3’ splice acceptor site of deleterious exon 2a in STMN2 pre-mRNA processing.1Baughn et al. moved an important step forward toward a STMN2-based therapeutic approach for TDP-43 proteinopathies. First, they addressed molecular mechanisms of TDP-43-dependent cryptic exon 2a inclusion in STMN2 pre-mRNA processing -based approach to compensate for TDP-43 function in ing Fig. . 250 ASOStmn2 in ALS model mice expressing the TDP-43 variant Q331K does not induce misprocessing of Stmn2 pre-mRNA, or worsen phenotypes such as reduced grip strength. Considering that these mice do not show TDP-43 pathology, these results confirm the authors’ hypothesis that indeed nuclear loss of TDP-43 is required to induce misprocessing of Stmn2 pre-mRNA.1 However, this finding emphasizes existence of additional TDP-43-dependent mechanisms beyond nuclear loss that contribute to NDs. Nonetheless, humanization of Stmn2 in ALS model mice more closely resembling human TDP-43 neuropathology may represent an excellent approach to quantify the contribution of TDP-43 induced reduction of Stmn2 protein to the degenerative phenotype, and may simultaneously provide an ideal model to determine potential benefits of restoring Stmn2 expression using ASOs.Besides these highly promising results, Baughn et al. additionally found that homozygous humanization of In conclusion, while Baughn et al. report substantial progress in the development of a STMN2-based treatment strategy for TDP-43 proteinopathies, it remains to be determined if rescuing expression of a single downstream target of TDP-43 is sufficient to markedly delay disease progression in humans. Nevertheless, this approach is among the most promising for treating sporadic NDs linked to TDP-43, and is definitively worth being further followed-up.


In this regard, two graphene sensors with different ZnO loadings of 5 wt.% and 20 wt.% were prepared, and their responses towards NO2 at room temperature and different conditions were compared. The experimental results demonstrate that the graphene loaded with 5 wt.% ZnO NPs (G95/5) shows better performance at detecting low concentrations of the target gas than the one loaded with 20 wt.% ZnO NPs (G80/20). Moreover, measurements under dry and humid conditions of the G95/5 sensor revealed that the material is very sensitive to ambient moisture, showing an almost eight-fold increase in NO2 sensitivity when the background changes from dry to 70% relative humidity. Regarding sensor selectivity, it presents a significant selectivity towards NO2 compared to other gas compounds.This paper investigates the effect of decorating graphene with zinc oxide (ZnO) nanoparticles (NPs) for the detection of NO According to the World Health Organization (WHO), annually, 7 million premature deaths occur worldwide  caused ben death . Furtheren death . To limi2 [2 at room temperature, other alternatives to MOX sensors have been investigated, such as chemiresistors. Among these alternatives, it is worth mentioning graphene [2, in order to boost the graphene-based sensor performance. In particular, it was proven that the addition of MOX to graphene has resulted in substantial enhancement of its properties and sensing abilities, with ZnO as the leading MOX for this type of addition. Furthermore, ZnO is a semiconductor with a band gap of 3.37 eV and exciton binding energy of 60 meV, which makes it very useful for making stable devices. It is also considered thermally and chemically stable, which makes it also perfect for the production of devices for a long period of use [2 detection by making graphene decorated by ZnO nanoparticle gas sensors [Many types of gas sensors emerged intending to continuously monitor trace level concentrations of the previously mentioned gases, such as gas chromatographic systems , as well2 ,15. Howe2 . In this2 . For detgraphene , MXenes graphene , transitgraphene ,21,22 orgraphene . Graphengraphene . But notd of use . But mosd of use . Recent d of use . Additio sensors .In this work, we have synthesized graphene/ZnO nanocomposites using a novel procedure that uses moderate temperatures and pressures jointly with non-toxic solvents, allowing the mass production of the nanocomposites. These nanocomposites have been employed to manufacture chemoresistive gas sensors, which have been characterized. Their performance is then compared with the one of previously reported sensors making use of similar materials.G80/20 and G95/5 nanomaterials, with a weight ratio of 80:20 and 95:5, were synthesized by the insertion of zinc oxide nanoparticles in the pristine graphene nanoplatelets, using a novel nanotechnological process based on patented procedures (Patent number ES2678419A1). Briefly, pristine graphene nanoplatelets were dispersed in malonic acid (molar rario 1:6), in which the starting ZnO had been previously dissolved. After homogenization, ZnO nanoparticles were precipitated between graphene nanoplatelets with a basic solution (NaOH 2.5 M) under controlled parameters, such as vigorous agitation, temperature (50 °C) and nanomaterial proportion according to the weight ratio. Bare graphene, as well as pure ZnO, were also prepared to have reference samples.2O3) substrates via the spray coating method while heating the substrate to 70 °C. 2, CO, H2, H2S, ethanol and NH3.The different nanomaterials were deposited on commercial alumina . Samples were outgassed at 423 K for 12 h under vacuum (6 mTorr) to eliminate chemisorbed volatiles before the adsorption isotherm was measured. Surface areas were calculated using the Brunauer-Emmett-Teller theory (BET) method.Morphological characterization of the nanocomposite was conducted through a Transmission Electron Microscopy (TEM)  operated at 100 kV, and the images were taken on the powder samples without any previous treatment.The two obtained sensors were characterized using different techniques, such as Raman via a Raman spectrometer , with a laser wavelength of 514 nm to check the crystallinity of the materials, and a Field Emission Scanning Electron Microscope (FESEM) using a Carl Zeiss AG-Ultra 55  to study the surface morphology and to check the distribution of the NPs on the graphene layer and Photoluminiscece (PL) measurements to analyze the defects in the sensing layer. These later measurements were performed at room temperature using a chopped Kimmon IK Series He-Cd laser (325 nm and 40 mW). Fluorescence was dispersed with an Oriel Corner Stone 1/8 74,000 monochromator, detected using a Hamamatsu H8259-02 with a socket assembly E717-500 photomultiplier, and amplified through a Stanford Research Systems SR830 DSP. A filter in 360 nm was used to stray light. All spectra were corrected for the response function of the setups. FESEM was also used to determine the deposited nanocomposite layer thickness using a cross-sectional view.3. A continuous stream of dry air  and diluted gases were passed through the testing chamber with a total flow of 100 mL/min via a set of Bronkhorst mass-flow controllers. The target gases from calibrated bottles  were further diluted by means of the mass flow controllers set. The resistance changes were continuously acquired using an Agilent HP 34972A multimeter. The humidity effect on the sensing performance was assessed by humidifying the gas stream through a controller evaporator mixer (CEM). The sensing responses were calculated using this formula R (%) = ((Rg − Ra)/Ra) × 100, where Rg and Ra correspond to the resistance level after and before gas exposure, respectively.Gas sensing measurements were conducted by placing the different sensors in an airtight Teflon chamber with a volume of 35 cm−1 for the D-band, 1576 cm−1 for the G-Band, and the 2D band at 2696 cm−1. Despite the presence of the ZnO in the graphene, the peak corresponding to the NPs is absent in the spectrum due to its low concentration of 5%. The same graphene peaks were observed also in −1, 1579 cm−1, and 2704 cm−1, although in this spectrum a new peak is present at 415 cm−1, which can be attributed to the ZnO nanoparticles since its specific peak usually appears at around 430 cm−1. These typical peak positions were widely reported for graphene samples, but peak intensities vary depending on the crystallinity of the samples [2 bonded carbon, and by performing a polarization study of the band under uniaxial strain, it is possible to determine the orientation of the graphene on the substrate [−1, revealing that a thin graphene layer was achieved [ samples . Specifiubstrate . Additioubstrate ; specifiachieved . In addiachieved .The obtained FESEM images of the layers present on the surface of the G95/5 and G80/20 sensors using a back-scattered electron detector (BSE) shows a black layer which is the graphene layer, big grey chunks corresponding to the alumina substrate surface, and the zinc oxide nanoparticles can be detected by the bright spots. Some ZnO nanoparticles can be seen in EDX analysis with a quantitative elemental analysis can be found in the 2/g was obtained by means of BET analysis. In For the G95/5 sample, a surface area of 392.734 mThe quantity and type of defects in ZnO can be estimated based on PL measurements . Figure 2 gas according to the European Environment Agency is 106 ppb (200 ug/m3), gas measurement tests of different dilutions of this target gas were conducted in the range of 50 to 500 ppb at room temperature. The ZnO sensor operated at room temperature exhibited a high resistance (in the range of 10 MΩ), leading to very noisy behavior. In this case, no noticeable response was obtained in the concentration range studied. −1 in this concentration range. 2 in dry conditions. As expected, the figure shows a p-type behavior of the sensor, with the resistance decreasing when put in contact with NO2, which is an oxidizing gas. As can be seen in 2 from the gas sensitive surface, thus fully regaining its baseline.Since the hourly limit of exposure to NO2, it was the subject of further measurements closer to real conditions (in other words under humidity), to check the effect of ambient moisture on sensor performance. Two different relative humidity percentages of 20 and 70% were applied, while doing the gas tests towards different concentrations of NO2 of 50, 150, 250, 350 and 500 ppb at room temperature. As can be seen in Since the G95/5 sensor was able to efficiently detect very low concentrations of NOOne can see that increasing the moisture level, the sensitivity of the senor is increased.The limit of detection (LOD) was calculated for the three cases using the equation:2 under dry conditions. However, the LOD increased to 47 ppb when the relative humidity was increased to 20% and to 36.9 for 70% humidity.Here, Sy represents the standard deviation of y-residuals obtained from the calibration curves, while b corresponds to the slope. The sensor exhibited a LOD of 21.9 ppb for NO2 concentration.These results imply that in a practical situation this sensor should be operated jointly with a humidity sensor, in order to enable using the correct calibration curve for determining NO2 of 5.1% seems to be better than the works presented in the references [2 tested are way higher: 10 times higher for [To have an idea of the position of this work compared to the literature, the sensing performance of the G95/5 sensor was compared with previously reported sensors and gathered in ferences ,37 with ferences . Meanwhiferences ,39 have gher for  and 200 2 for detecting other target gases. In this regard, 500 ppb of NO2, 200 ppm of H2, 20 ppm of H2S and ethanol 25 ppm of CO and 50 ppm of NH3 were detected at room temperature. As can be seen in 2, although the concentration of those gases is more than 40 times higher. The highest response is obtained for hydrogen, but is less than half the one obtained for NO2, due to the concentration being 200 times higher. This proves the high selectivity of this sensor to nitrogen dioxide gas (NO2).The selectivity of the sensors was evaluated by repeating the same experimental conditions when detecting NOFinally, to check the long-term stability of the sensors, measurements shown in 2, which is a strong oxidant gas (electron-withdrawing gas), the interaction between the graphene layer and the gas results in a decrease in the sensor resistance. Conversely, when detecting reducing gases, such as NH3 (electron donor gas), the sensor resistance increases [Bare graphene is a mild p-type nanomaterial, and when gas molecules get adsorbed on its large surface area, the local carrier concentration quickly changes, inducing resistance changes. In the case of NOncreases .Considering the use of p-type graphene loaded with n-type ZnO nanoparticles, it is expected that a p-n junction will form on the contact surface. Specifically, electrons from the ZnO conduction band probably flows to the graphene, resulting in a depletion layer at the interface of the two materials. As a result, ZnO@Graphene films with a ratio of 20/80 exhibit a higher resistance baseline than their 5/95 counterparts. This is because the ZnO nanoparticles transfer electrons to the graphene, reducing the concentration of majority carriers in p-type graphene. Consequently, the conductivity of the sensing film is reduced.When ZnO@Graphene is exposed to air, oxygen molecules capture electrons from the valence band and become adsorbed onto the sensor surface (1).2 molecules interact with the sensor surface, two distinct reactions may occur. Firstly, NO2 can adsorb onto the hybrid nanocomposite (2). Secondly, NO2 may interact with adsorbed oxygen species, which can lead to changes in the resistivity of the sensitive film (3). In both cases, NO2 adsorption captures free electrons from the sensitive film, leading to a significant increase in the conductivity [When NOuctivity .2 molecules [However, the p-n junction also enhances the sensor’s sensitivity by expanding the depletion region when the film interacts with NOolecules . Figure It is interesting to note that G95/5 demonstrates a higher sensing performance than G80/20. In other words, a higher content of ZnO does not necessarily translate into better sensitivity, despite the higher flow of electrons from ZnO to graphene. This is probably because a lower amount of nanoparticles brings graphene sheets closer and better interconnected. As a result, the charge transfer is more efficient compared to higher amounts of nanoparticles, which may obstruct the conduction paths along the graphene sheets .2, causing an increase in sensitivity towards the target gas [Finally, it is worth noting that ambient moisture usually has an enhancing effect on the sensitivity of graphene-based sensors . Considerget gas .2 sensing at room temperature. The synthesis method has been already scaled up for the mass production of gas sensitive nanomaterials (from few g up to kg). ZnO(5%)-loaded graphene sensors clearly outperform pure graphene or pure ZnO sensors when operated at room temperature. The loading of the graphene with two different percentages of ZnO has shown that the lower the loading, the better the sensing capabilities of the sensor, since the G80/20 was not capable of detecting NO2 at concentrations lower than 250 ppb. Meanwhile, G95/5 could detect as low as 50 ppb of the target gas. Furthermore, the G95/5 sensor showed very good responses, stability, and reproducibility in the range of 50-250-500 ppb of NO2 The sensing performance becomes significantly enhanced under a humid atmosphere to reach almost five-fold, the response under a dry atmosphere, which makes it a very promising material for NO2 detection for industrial use in farms or factories.This work reports the fabrication of a graphene-based sensor, decorated with ZnO nanoparticles using a novel and patented nanotechnological method for enhanced NO


The implementation of an energy storage system (ESS) as a container-type package is common due to its ease of installation, management, and safety. The control of the operating environment of an ESS mainly considers the temperature rise due to the heat generated through the battery operation. However, the relative humidity of the container often increases by over 75% in many cases because of the operation of the air conditioner which pursues temperature-first control. Humidity is a major factor which can cause safety issues such as fires owing to insulation breakdown caused by condensation. However, the importance of humidity control in ESS is underestimated compared to temperature control. In this study, temperature and humidity monitoring and management issues were addressed for a container-type ESS by building sensor-based monitoring and control systems. Furthermore, a rule-based air conditioner control algorithm was proposed for temperature and humidity management. A case study was conducted to compare the conventional and proposed control algorithms and verify the feasibility of the proposed algorithm. The results showed that the proposed algorithm reduced the average humidity by 11.4% compared to the value achieved with the existing temperature control method while also maintaining the temperature. An energy storage system (ESS) is a system that has the flexibility to store power and use it when required. An ESS can be one of the solutions to mitigate the intermittency effect of variable renewable energy (VRE), such as photovoltaic and wind power ,2,3. An Apart from indoor temperature, indoor humidity is highly correlated with battery safety problems. The Pacific Northwest National Laboratory (PNNL) and International Business Machines Corporation (IBM) consider humidity as an ESS operating environment factor and recommend an appropriate range ,13. In a2 concentration and humidity ratio data, while Tanyer et al. , and P is the ESS output [W]. Equation (2) represents the heat Equation (1) represents the amount of heat When the lumped model is applied to the ESS, the temperature change in the ESS can be expressed using Equation (3).By substituting Equations (1) and (2) into Equation (3), Equation (4) can be obtained as:In Equation (4), s Rth=τC , is usedP ≠ 0, which implies that the ESS is operating. When P = 0, the ESS does not operate, and Equation (5) can be expressed as Equation (7), where Equation (5) can be expressed as Equation (6) when The predicted battery temperature T.The absolute humidity The target absolute humidity As shown in The battery temperature increases as per the operation of the ESS, which affects the battery life. The battery temperature can be predicted using the ESS charging–discharging schedule and environmental temperature.[P1] Measure [P2] Calculate the [C1] Check whether the safety range is maintained by comparing the battery temperature prediction value with the battery temperature upper limit value, [C2] Compare [C3] If [P3] Turn off the air conditioner when additional air conditioning is not required.[C4] When the air conditioner is not being operated, check whether the predicted temperature exceeds the temperature range compared to the upper limit of the temperature maintenance range, [P4] Set the air conditioner set temperature [P5] Set The steps of the rule-based air conditioner control algorithm are as follows:η was 94%, which was set as the average operating efficiency of the ESS calculated for one year. The input parameter setting values for each case are listed in To verify the feasibility of the proposed air conditioner-based indoor temperature and humidity control algorithm, the testing was performed in an ESS container environment. For the ESS used in this study, an operating environment within a temperature range of 18~28 °C and relative humidity of 35~75% is recommended. The case study consisted of two cases depending on the application of the proposed air conditioner control algorithm. Case 1 consisted of an ESS operating environment that considered only conventional temperature control. The experiment was conducted by setting the air conditioner set temperature Each case was operated for one similar consecutive day to verify the effectiveness of the proposed algorithm. While the indoor temperature remained constant during the period of operation, the indoor humidity was divided into approximate ranges of 60–70% and 52–63% during the operation period. This could be caused by the air conditioner operation algorithm related to the indoor and outdoor temperatures and humidity.The cost of maintaining the environment inside the container was mostly used to run the air conditioner. In this study, a sensor-based control system was developed to manage the indoor temperature and humidity of a container-type ESS. The data from the sensors were stored in a database system built to collect and monitor the environmental data of the ESS container. A rule-based air conditioner control algorithm was proposed based on the ESS operation data to solve the problem of the humidity exceeding an appropriate range. The proposed algorithm enhances the operating environment of an ESS container by controlling the operation of commercial air conditioners using thermal and humidity models. Based on the proposed algorithm, the indoor temperature of the ESS container was maintained at 21 °C on average, whereas the relative humidity was suppressed to a value of less than 75%. The average indoor humidity was maintained at 59.4%, which was 11.4% lower than the value achieved with the existing temperature control method. In addition, the standard deviation of the humidity was reduced from 10 to 5.6%. These results indicate that the battery management environment improved, which would consequently slow battery life reduction. In addition, the proposed method can reduce energy consumption by 10.7% compared to the current daily energy consumption without any additional equipment in the case study, resulting in economical operation.The proposed algorithm controls the set temperature of the air conditioner to manage the indoor temperature and humidity of the ESS; therefore, the ESS container operation environment is improved. However, this requires frequent air-conditioner operation to prevent the reverse flow of moisture from the indoor evaporator. If the air conditioner fan control can be combined with the indoor temperature control of the ESS, better results can be obtained. It is assumed that the air conditioner has sufficient capacity to maintain room temperature throughout the year. If the energy management system considers energy consumption for managing the room environment, the battery operation schedule can be changed. In addition, although this study estimated the battery temperature based on the lump-sum capacitance model, advanced battery models can be used to accurately estimate battery temperature.This study sought appropriate technology to economically control temperature and humidity by making the most of the existing facilities. Future research should take into account the combination of sealing, control, and energy consumption in indoor environment management and explore how the optimal operation schedule of batteries can be achieved.