An RNA switch triggers biological functions by toggling between two conformations. RNA switches include bacterial riboswitches, where ligand binding can stabilize a bound structure. For RNAs with only one stable structure, structural prediction usually just requires a straightforward free energy minimization, but for an RNA switch, the prediction of a less stable alternative structure is often computationally costly and even problematic. The current sampling-clustering method predicts stable and alternative structures by partitioning structures sampled from the energy landscape into two clusters, but it is very time-consuming. Instead, we predict the alternative structure of an RNA switch from conditional probability calculations within the energy landscape. First, our method excludes base pairs related to the most stable structure in the energy landscape. Then, it detects stable stems (“seeds”) in the remaining landscape. Finally, it folds an alternative structure prediction around a seed. While having comparable riboswitch classification performance, the conditional-probability computations had fewer adjustable parameters, offered greater predictive flexibility, and were more than one thousand times faster than the sampling step alone in sampling-clustering predictions, the competing standard. Overall, the described approach helps traverse thermodynamically improbable energy landscapes to find biologically significant substructures and structures rapidly and effectively. In many organisms, structural rearrangements of RNA switches trigger biological functions. In bacteria, RNA switches regulate gene expression of mRNA downstream from them , 2. In eCurrent scientific strategies have produced an abundance of prokaryotic data, so most known RNA switches are in bacteria. To find putative riboswitches in prokaryotic sequences, common approaches rely on the assumption that the biologic mechanism of the riboswitch is at least partially conserved across certain bacteria. Hence, they locate conserved structural elements upstream of homologous coding regions , 12. TheBacillus subtilis , where E(S) is the free energy of S; R, the universal gas constant; and T, the folding temperature. Prediction of the alternative structure requires an efficient exploration of suboptimal  structural configurations.The Boltzmann ensemble thermodynamic model is the basis of most predictive strategies. The model conceptualizes the RNA structure of the sequence under scrutiny as a Boltzmann ensemble, estimating ensemble energies from experimentally derived nearest-neighbor free energy parameters –43. In tn of the RNA sequence increases, the number of structures in the Boltzmann ensemble explodes exponentially, so traversing the ensemble explicitly becomes infeasible  denote a base pair between sequence positions i and j. Given τ, we first build an excluded set δbs:We first computed the MFE secondary structure δ between [i·j] and [i'·j']. Base-pair distance δbb as defined in  through recursive calculations. The calculations combine various partition functions, given here in McCaskill’s notation. The restricted partition function i to j inclusive is the sum of the Boltzmann weights eE(S)/RT− over the structures Si,j where the base pair [i·j] closes Si,j into a loop structure. The full partition function Qi,j for bases from i to j inclusive is the sum of eE(S)/RT− over the corresponding structures Si,j with and without the base pair [i·j], so for example the full partition function is Z = Qn1,. The probabilities Pi,j of individual base pairs [i·j] within the ensemble of structures S can be computed as Pi,j = ∑S∋[i·j]eE(S)/RT−/Z where the sum is over structures S containing [i·j]. McCaskill’s recursive calculations follow combinatorial patterns resembling algorithms for determining the MFE structure, computing first Qi,j, and finally Pi,j from previously computed values for smaller substructures. Due to complexity of McCaskill’s algorithm, we must refer the reader to [Our methods make heavy use of the McCaskill algorithm  for calceader to  for detai·j] exists, under the constraint that none of the base pairs in Given a set of excluded base pairs Pi,j above, the McCaskill algorithm can calculate the conditional base-pair probabilities in  Software Package  calculatlgorithm .L*, a longest stem whose every base pair individually has conditional probability higher than 0.5. Let L denote any stem , where we permit L to contain bulges . Let #L count its base pairs.Given an excluded set L*. Structure S2.Finally, only constraint in energy minimization of Because the seed lies outside  folding .L* was most promising. Other seeds included the single base pair with highest We explored several criteria for seed selection. Empirically, the stem seed T = T0 other than 37°C, permitting the following generalized procedure. Predict structure L* at various temperatures T = T0, to predict the alternative structure The most recent version of UNAfold (version 4.0.0) can make energy calculations at temperatures i = 3,4…) can be made by excluding the union The procedure above could be considered the first step of an iterative process, where subsequent predictions τ = τ0, temperature T = T0, iteration i, and the type of seed. It is denoted in full by type-of-seedτ = 15, at temperature 70°C, and using a single base pair as the seed is denoted as single-base-pair-seed prediction L*. Unless otherwise stated, results used default parameters.The alternative structure prediction L*)/log10(SL), to derive Normalized Seed Length.The number of base pairs within the seed was divided by the log of the sequence length SL,  as a specific example.On the other hand, alternative structure prediction is sensitive to sequence elongation . To avoid the possibility of cherry picking sequence lengths, our study took an independent “Barsacchi dataset”  ; and prτ, τ therefore excludes progressively more base pairs from the alternative structure τ = 5. Let L*, where every base pair has a conditional probability higher than 0.5, e.g., for tenA TPP, L* contained 7 base pairs. Finally, using the stem L* as a seed, constrained energy minimization yields L*.To predict L* and τ. Our choice of τ provides some insight into our methods. Briefly, for τ = 0 the excluded set tenA TPP and its corresponding predictions. S2 with its aptamer region and anti-terminator stem. L* shown in red.Empirical results motivated our defaults for The established approach for predicting the two structural states of a riboswitch uses a Sampling-Clustering (SC) method. Every SC computation in this article followed standard protocol by sampling RNA structures at many different temperatures. tenA TPP example. S. Because our main interest is structural prediction of S2, S to S2. Structural dissimilarities of predictions to S2 are better illustrated in the ASR energy landscape than in the standard energy landscape.S2 . Within the Barsacchi Structural dataset, we compared S2, our gold standard, to three predicted alternative structures: τ = 0), and τ = 5). For the three predicted alternative structures To assess predictive accuracy of CP and SC, a subset of the Barsacchi dataset provided 12 riboswitches with known actual alternative structures l-PPV in ), along l-PPV in . Table 1LysC Lysine riboswitch (243nt), determined the speeds. Typical CP runtimes increased with increasing dissimilarity thresholds, but even a dissimilarity threshold of half the length of the sequence, τ = 122, only increased the CP runtime to 6 seconds. Our SC runtimes included the sampling step, but not the clustering step. The whole CP computation of τ = 5) took around a second, whereas the sampling step of SC alone took more than 18 mins.In computational speed on the Barsacchi Structural dataset, CP was more than three orders of magnitude faster than SC see . ComputaTo compare how well CP and SC can classify RNA sequences into riboswitch or non-riboswitch, we used the full Barsacchi Dataset (20 riboswitches vs. 20 non-riboswitches). SC classified each sequence according to the average silhouette value of the two clusters of samples; CP, according to normalized seed length see the  section.We tuned the SC computations more extensively in riboswitch classification than in other parts of our study, exploring multiple temperatures or 37°C alone to generate the samples, since performance of SC can highly depend on sampling parameters. To make our comparison to the best SC performance, we included Vienna RNA results, which had been previously used on Barsacchi Dataset. SC classification performance was better at 37°C alone , temperature T (default: 37°C), type-of-seed (default: stem seed L*), and iteration number i (default: 2). The Materials and Methods section describes the parameters in detail. Our default parameters (particularly for τ and L*) were tuned empirically by their predictions on the Barsacchi Structural dataset, but the sparsity of the data prevented methodical optimization . The following uses the xpt Guanine riboswitch [The generalized CP prediction boswitch , 162nt lS2 of xpt Guanine on their left, as a standard for comparing other structures. For example, τ, T, the type-of-seed, or the number of iterations, showing the effects of changing parameters on CP predictions of the alternative structure. τ = 0…60 produced only five distinct predictions T = 37,40,43,46,49,52,55,58,61,64,67,70 (from 37°C to an approximation of the melting temperature 69.8°C in 3°C increments) produced only four distinct predictions S2 took very few different values as the individual parameters varied , predicting an alternative biologically functional structure can be challenging. Such predictions hold the key to understanding many regulatory mechanisms, however. Although riboswitches are a very diverse subset of RNA switches, there is not enough experimentally verified data for optimizing a set of parameters of a universal RNA Switch Predictor.We used the Barsacchi dataset  as a benS2, so the CP-predicted alternative state We predicted the CP alternative structure using only the Boltzmann ensemble at 37°C. In our hands, compared to a implementation of the current SC procedure , 44, 54,To avoid cherry-picking in our Purine Riboswitch dataset, a purine riboswitch family of 133 Rfam sequences, we fixed all sequence lengths at 200nt. n and the dissimilarity threshold by τ. CP prediction has five steps (each step is followed by its time-complexity): MFE prediction (O(n4)); excluded set determination (τ2O(n)); conditional probability calculation (O(n3)); seed selection (O(n2)); and prediction of the alternative structure by constrained energy minimization (O(n4)). Thus, the total computational time tcp = τ2O(n)+O(n4). In contrast, SC prediction can be sensitive to the computed free energy of the bound structure, because folding probabilities exponentially decrease with energy. The time-complexity of exhaustive sampling is exponential in both the sequence length n and the maximum energy explored. The complexity becomes more manageable under statistical sampling, but then it depends on the number of samples (s) as well as n. A typical SC procedure such as ours consists of three steps: MFE prediction (O(n4)); sampling (sO(n2)); and clustering (s2O(n)). Thus, the total computational time tsc = O(n4)+sO(n2)+s2O(n). Thus, the time complexities of the two procedures have a term O(n4). The SC prediction requires more sampling as the sequence lengthens, burdening computer memory and increasing the computation times accordingly. CP predictions, on the other hand, require more memory as τ increases. We generally expect CP predictions to be much faster than SC even for RNA sequences of moderate length .The computed free energy of the bound structure of a riboswitch does not include ligand binding energies, and consequently it may be arbitrarily high. Regardless, CP prediction always has a polynomial time-complexity. Denote sequence length by s necessary for effective prediction remain feasible. To compare the speed of CP and SC predictions, tsc excludes the time for clustering the samples, CP can still outperform SC in predictive accuracy  equals about 1 second; tsc, more than 18 minutes). The time for CP alternative structure prediction does increase as τ increases from its default value τ = 5, but it still remains far faster than SC prediction  equals only 6 seconds). To summarize, in our hands CP predictions were about 1000 times faster than SC predictions, with at least comparable prediction accuracy.Typically, riboswitch lengths are moderate, so the sample sizes τ effectively excludes the formation of base pairs “close to” the MFE structure. It therefore enforces topological differences between alternative and MFE structures  between structures [τ = 0 excludes all base pairs in the MFE structure, and as τ increases, the exclusion procedure progressively excludes more and more base pairs. Empirically, our default τ = 5 had a better performance than simply excluding MFE base pairs with τ = 0  improved the prediction accuracy for xpt Guanine dramatically (see τ through intricate dependencies on (e.g.) sequences or conditional probabilities did not change prediction accuracies much. Large τ values sometimes improved predictions dramatically for individual riboswitches, but unfortunately no general and systematic theme of improvement became apparent.CP generates a seed around which we fold an alternative structure. Loosely, when generating the CP seed, the dissimilarity threshold ructures ). The th = 0 see . The CP ally see . UnfortuL* made a good default seed for alternative structure prediction. We tested other seeds , but the corresponding alternative structure predictions were generally less accurate , a seed is merely a substructure that occurs frequently in the remaining structures. Empirically, the longest bulge-containing consensus stem 10 = 8); a non-riboswitch of the same length; 7bp. The use of seed length in classification takes advantage of the notion that base pairs may have evolved to stabilize an alternative structure in riboswitches [type-of-seed, for the purine riboswitches at least, biologically functional substructures often included our stem seed.A riboswitch classifier can use normalized seed length, because riboswitches tend to yield longer stem seeds than RNAs with a single structure. With our data and under the CP method, the normalized seed length of a riboswitch is 4.00bp; of a non-riboswitch, 3.50bp see the  section.switches  while thT may be particularly useful for predictions in riboswitches with thermosensory functions. Because RNA thermosensors lay beyond our purview, however, the present article relied on the default temperature 37°C for CP prediction of alternative structures . The use of other temperatures in CP prediction did not change prediction accuracy dramatically. We also considered iterating our three-step CP prediction process: (1) exclude base pairs (2) determine a stem seed; and (3) find the lowest free-energy structure containing the stem seed . Conditioning on exclusion improves the chance that an exact (McCaskill) probability calculation finds base pairs in an alternative structure. Within the limitations imposed on our ROC tests by available data, Conditional-Probability (CP) computations had classification accuracy comparable to Sampling-Clustering (SC) computations. In contrast, however, the results for computational speed were not at all tentative: CP was more than 1000 times faster than SC, its speed making it a much more promising as a predictor of alternative structures in computationally demanding settings like genomic RNA.In this article, we use τ varies the severity with which metastable substructures are excluded from predictions. Although available data is insufficient to optimize τ methodically, our default choice τ = 5 was effective enough to render CP at least comparable to SC. Moreover, our tests suggest that varying τ offers a novel and computationally efficient method of traversing the energy landscapes to find metastable structures. We also showed that another important CP parameter, the seed, could detect functional substructures in riboswitches with regulatory functions as well as being able to classify riboswitches with similar performance to the more computationally costly sampling and clustering of the RNA energy landscape.CP predictions have very few adjustable parameters. Specifically, our dissimilarity threshold parameter https://go.usa.gov/xRu79. The data used in the first dataset (Barsacchi) is available in Our CondAlt source code for predicting alternative structures is publicly available for download at S1 TableThe 20 Riboswitches and their corresponding lengths.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.


In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset.Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways.CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance.One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia. Schizophrenia is a severe psychiatric disorder demonstrating a strong genetic component with heritability and more and more GWAS analyses towards schizophrenia have been reported. To examine if these genetic schizophrenia-risk variants may affect brain structure, function or even cognitive performance, an effective strategy is to assess the genetic associations with brain-imaging phenotypes as well as behavioral measures; then, based on the identified linked factors, mediation analysis can be conducted to better delineate cognition behaviors among them.CSMD1, CNTNAP2, DCC, GABBR2 etc. The linked imaging-genetic pattern was replicated in an independent Chinese cohort and was also associated with working memory performance. Further mediation analyses suggested a “This study is the first attempt to jointly analyze brain fALFF, GM data in conjunction with PGC's reported SNPs in a large Chinese Han population (905 + 166 subjects) using a multivariate, data-driven manner. Gray matter volume reductions in thalamus, putamen and bilateral temporal gyrus in schizophrenia were associated with fALFF decrease in medial prefrontal cortex. Both these imaging impairments were associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including This study indicates that brain GM and fALFF abnormalities are significantly correlated with the schizophrenia-susceptible SNPs, and polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus may further lead to cognitive impairment in schizophrenia.Alt-text: Unlabelled Box1Schizophrenia (SZ) is a severe psychiatric disorder demonstrating a strong genetic component with heritability estimated up to 80% based on family and twin studies . A landmDISC1 and ANK3, have been revealed to be associated with reduced GM in SZ on hippocampus, prefrontal cortex and temporal regions [In this study, we focus on two imaging features, i.e., gray matter (GM) and fractional amplitude of low frequency fluctuations (fALFF), which are proven to be heritable in previous imaging-genetic analyses. Specifically, GM volume was validated to be one heritable measure as reported in ,4. Schiz regions . Moreove regions . And the regions . FALFF i regions ,16. A mu regions ; and the regions . Additio regions . Current regions , multimo regions ,24,83 an regions .COMT gene on cognitive control [In parallel, cognitive impairments are well documented in schizophrenia and there is a notion that genetic effects on cognitive abilities are mediated by brain functions. For example, prefrontal brain region has been revealed to mediate the effects of  control  . Moreov control , while t control , suggest control . All theCollectively, we aimed to use a multimodal fusion method ,30 to ex22.1t-test to measure the difference of age, duration of illness, PANSS scores and medication in controls and patients between cohort 1 and cohort 2. Chi-square test was adopted to compute the cross-cohort difference of gender as shown in The discovery dataset (cohort 1) consisted of 450 SZs and 455 HCs from Chinese Han population , which w2.2To control the discrepancy of imaging quality and performance across seven sites of corhot 1, we repeatedly scanned three healthy subjects by the scanners from all sites. Then, we evaluated the performance of imaging system by calculating signal-to-noise ratio (SNR) of the MRI system and coils in each site. Based on these SNRs, we optimized and identified the final scan parameters. Furthermore, as differences may exist in different hardware and software systems, we set the MRI sequences and parameters by following the two principles:  made the2.2.1Subjects were instructed to keep their eyes open during the scan and stare passively at a foveally presented fixation cross, as this is suggested to facilitate network delineation compared to eyes-closed conditions and helps ensure that subjects are awake. Resting state data were collected with single-shot full k-space echo-planar imaging (EPI) with ramp sampling correction using the inter commissural line (AC/PC) (anterior commissure/posterior commissure) as a reference, and details of the machine type and scanning parameters for each site in cohort 1 and cohort 2 were listed in Table S1.2.2.2The scanning parameters for each site were listed in Table S1.2.2.3We collected ethylene diamine tetraacetic acid (EDTA) anti-coagulated venous blood samples from all the participants and then extracted genomic Deoxyribonucleic acid (DNA) from their whole blood using the EZgene Blood gDNA Miniprep Kit. The whole-genome genotyping was performed on Illumina Human OmniZhongHua-8 BeadChips using the standard Illumina genotyping protocol (Illumina) spanning 900,015 SNP loci. For cohort 2, DNA was extracted from peripheral blood cell according to the standard protocol by protease K digestion, phenol-chloroform extraction and ethanol precipitation. The whole-genome genotyping was performed on Illumina human PsychArray-24 spanning 571,054 loci.Cognition assessment: Symptomatic assessment was performed using the Positive and Negative Syndrome Scale (PANSS) . Digital2.3The multimodal data used in the present study can be accessed upon request to the corresponding authors.2.4www.fil.ion.ucl.ac.uk/spm/software/spm8) using voxel based morphometry (VBM), with a unified model of image registration, bias correction, tissue classification and spatial normalization to the standard Montreal Neurological Institute (MNI) space [3, resulting in 53 × 63 × 46 voxels and smoothed with an 8 mm Gaussian model [sMRI data preprocessing. The T1-weighted sMRI data were preprocessed by Statistical Parametric Mapping 8  space . Modulatan model . A mask an model . We applhttp://brant.brainnetome.org/en/latest/) was employed to preprocess the collected fMRI data. The first 10 volumes of each functional time series were discarded for the magnetization equilibrium. Slice timing was performed with the middle slice as the reference frame. Then images were realigned using INRIalign, a motion correction algorithm that is unbiased by local signal changes, resulting in head motion parameters computed by estimating translational and rotational parameters. Each subject had a maximum displacement in a dataset that didn't exceed ±1.5 mm or  ±1.5°. Then data were spatially normalized into Montreal Neurological Institute (MNI) space [3. Denoising were further performed to regress out motion parameters, white matter, and cerebrospinal fluid. Voxel-wise fractional amplitude of low-frequency fluctuations (fALFF) were extracted to generate a map for each subject and spatially smoothed with an 8-mm isotropic Gaussian kernel. A mask was generated to include only voxels inside the brain across all subjects for each modality as our previous study [fMRI data preprocessing. Brant software package (I) space , resliceus study . We appl2.4.1p-values of SZ patients versus HCs tested against a uniform distribution showed no clear indication of population structure .Genotype data  were preprocessed using PLINK software following a series of standard quality control procedures ,36. Poor2.4.2https://www.med.unc.edu/pgc/results-and-downloads), and selected the SNPs from the GWAS results with a p-value lower than 0.01. Then we identified the overlap of the three SNP datasets , resulting in 4522 SNPs. Note that we have conducted imputation only on cohort 2, but not cohort 1. The SNPs were not selected by their odds ratios with schizophrenia but only the p-value from PGC according to our previous studies [As for cohort 2, the ungenotyped SNPs were imputed using SHAPEIT v2 (r790)  and IMPU studies ,41.subj × Nvoxel) for each of the three modalities. Then three feature matrices were organized as: fALFF , GM  and SNPs (905 × 4522), which corresponded to X1, X2, X3 in The analysis flowchart is shown in 2.5X1, X2, X3 are observation matrices composed of measurements of fALFF, GM and SNP, that are the three Nsubject × Nvoxel/SNP matrices prepared above; S1, S2, S3 represent the unknown sources for each phenotype with each row representing an independent component; A1, A2, A3 are linear mixing matrices/loadings with each column representing a loading parameter associated with a specific component in S1, S2, S3; W1, W2, W3 are unmixing matrices. Based on the decomposition, the Infomax algorithm attempts to find the W matrix resulting in independence through maximizing an entropy function as defined in Eq. fy(Y) is the probability density function of Y; E is the expected value; H is the entropy function; W0 is the bias vector.The formed fALFF, GM and SNP matrices were jointly analyzed using three-way parallel independent component analysis (para-ICA) method, which estimates maximally independent component (IC) using ICA within each modality separately and maximizes the pair-wise correlations between modalities using an entropy term based on information theory . The comA1, A2, A3.Thus, three entropy terms and an additional correlation term comprise the objective function of three-way para-ICA, as shown in Eq. fi, j, k is the additional aggregation function measuring the combined connection strength of the columns i, j, k from the corresponding matrices A1, A2, A3, respectively. This objective function will then be optimized through the gradient descent method. For a full description of the methodology and mathematical details of the algorithm, we refer readers to the original publications [To enhance the inter-modality association, three-way para-ICA attempts to maximize the correlation calculated between the columns of the loadings matrices ications ,43.(3)arhttp://mialab.mrn.org/software/fit). Since the number of true sources from in-vivo data is in principal unknown, it is necessary to estimate the number of components before proceeding with the ICA factorization [The code is available for public use through the Fusion ICA Toolbox  for cohort 2  that corresponded to the extent that a given subject's data from cohort 2 could be represented by the components determined from cohort 1. This was accomplished by multiplying  with the pseudoinverse of the source of cohort 1 . The generated weights  for cohort 2 were then evaluated for inter-modality correlations to assess whether the pair-wise correlations between the identified ICs could be replicated in another independent cohort.The spatial maps of the most significantly linked SNP-fALFF-GM pattern were projected to cohort 2, yielding the projected new loadings which were used to verify the pair-wise inter-modality association as  Fig. 1)Fig. 1). p = .05). We then selected a subset of subjects in cohort 2, which consists of 49 SZ patients together with 87 HCs. The 49 patients did not show significant difference of PANSS scores compare with discovery data . Then we ran same validating procedure by projecting spatial maps of discovery cohort 1 to the subset of cohort 2.In the current study, stable antipsychotic treatment on the patients was not required at the moment of the MRI, so not all patients were on stable antipsychotic treatment at the moment of the MRI. But the PANSS total score was similar across two cohorts (2.8Z-scores| > 2) from the identified SNP component were annotated to the genes which were further applied Gene Ontology analysis by an online database WebGestalt . A hypergenometric test was used to detect an overrepresentation of the high-ranking genes among all the genes in a category. The whole genome was selected as the reference gene list. The significance values were FDR-adjusted to correct for multiple comparison.Top contributing SNPs  /PANSS scores and para-ICA-derived components. Partial Pearson correlation was applied to compute the correlations between the identified ICs with working memory scores or PANSS scores separately, in which age, gender, site and diagnosis were controlled as covariates. Medication was regressed out on the 243 patients before computing the association.2.10Once a SNP-fALFF-GM pattern was identified to be group discriminative and associated with working memory performance (e.g. DB scores), as well as exhibiting significant pair-wise inter-modality correlation, we further conducted mediation analysis to better delineate the working pathways among them as shown in enotypes , we examIn order to test the direct and indirect effects among the identified SNP, fALFF, GM and working memory scores, bootstrapping is one of the most valid and powerful methods . It is a33.1r = 0.753, p < 10−12; SNP-fALFF: r = 0.174, p = 1.32 × 10−7; SNP-GM: r = 0.173, p = 1.78 × 10−7, passing Bonferroni-correction for multiple comparison. The correlations were consistent in 10-fold stability analysis with the average correlations of 0.713 ± 0.043, 0.179 ± 0.028, 0.180 ± 0.013 for fALFF-GM, SNP-fALFF and SNP-GM respectively. The identified linked pattern was robust to component numbers, as we observed similar components and inter-modality correlations when component number was set ±3 around the original number for each modality. After controlling for the diagnosis, partial correlations among the linked components were still significant . We then included the first three PCs as additional covariates and assessed the correlation between imaging modalities and SNP modality. The correlations between the two imaging components and the SNP component were still significant: SNP-fALFF: r = 0.148, p = 8.62 × 10−6; SNP-GM: r = 0.130, p = 9.80 × 10−5 (survived Bonferroni-correction). The less minor/reference allele counts on SNPs with positive component scores induce reduction of fALFF and GM. Furthermore, we conducted three-way para-ICA analysis on SNPs yielded by two other thresholds (p < 0.005 and p < .005) and those imaging features separately. In both p < 0.005 and p < 0.005 cases, the three-way pair-wise correlations between modalities were remained, and the fALFF and GM components show highly consistent spatial patterns. For SNP component, 52.5% overlap were obtained compared with p < 0.01. Note that although the associations could not survive correction for multiple comparisons, the SNP-fALFF-SNP pattern was similar to that identified with p < 0.01, as shown in Fig. S4.One strongest pair-wise correlated SNP-fALFF-GM triplet was identified in cohort 1. The pair-wise correlations between the linked components were fALFF-GM: In addition, age and gender in general are not expected to be significantly associated with autosomal SNPs. Therefore, we ran another analysis by regressing out age, gender and site for imaging modalities and regressing only site for the SNPs. The results are presented in Fig. S5. It can be seen that the three-way para-ICA can still yield a linked SNP-fALFF-GM pattern that was highly consistent between cohort 1 and cohort 2.Since no significant correlation was observed between the identified imaging components and the medication dose measured by the chlorpromazine equivalent  based on the 243 (out of 450) medicated patients, we further conducted a sub-set three-way para-ICA analysis on the 243 medicated SZ patients and 277 demographically-matched healthy controls. In this subset, medication was treated as nuisance covariate in the analysis both for fMRI and sMRI. The results are shown in Fig. S6, similar SNP-fALFF-GM pattern was revealed as well with significant correlation between modalities and significant group difference in each modality.3.2t-test between HCs and SZs revealed significant group difference in the identified GM (p = 0.0014), fALFF (p = 0.0004) and SNP (p = 0.044) components respectively as shown in Z-values (red regions) indicated higher contribution in HC than SZ and the negative Z-values (blue regions) indicated higher contribution in SZ than HC. The spatial map of the GM component thresholded at |Z-score| > 2 was shown in Post hoc two sample 3.3r = 0.680, p < 10−12; SNP-fALFF: r = 0.220, p = 0.0045; SNP-GM: r = 0.237, p = 0.0021, passing Bonferroni correction). When controlling for the diagnosis effect, the resulted loadings in cohort 2 again yielded significant pair-wise correlations . Meanwhile, we only observed significant group difference between SZ and HC in brain imaging data, p = 8.34 × 10−4, 0.024, 0.98 for fALFF, GM and SNP respectively . In addition, when controlling for the diagnosis and medication effects, the resulted loadings in the subset of cohort 2 again yielded significant pair-wise correlations , suggesting that the identified linked fALFF-GM-SNP pattern was reliable under multiple conditions.We then projected the spatial components of cohort 1 to cohort 2 as shown in 3.4CSMD1, CNTNAP2, DCC, GABBR2 etc. 172 SNPs were selected as top SNPs with |Z| > 2. Table S4 provides a summary of the identified 172 SNPs, including their corresponding genes and Z-scores. After annotating these SNPs, 75 genes were analyzed via a WebGestalt pathway analysis. Primarily significant Gene Ontology analysis results were provided in p = 8.49 × 10−6), synapse organization (p = 3.18 × 10−5) and axon (p = 1.26 × 10−4) in our study. The full results of Gene Ontology analysis were provided in Table S5. The pathway results for different thresholds were also provided in the supplementary file (Table S6).3.5r = 0.296, p = 1.19×10−17; GM-DB: r = 0.290, p = 4.79×10−17; SNP-DB: r = 0.114, p = 0.012; fALFF-DF: r = 0.186, p = 1.18×10−9; GM-DF: r = 0.104, p = 0.0031) after controlling for group difference. The association was still significant  when further controlling for medication effects on the GM and fALFF modalities. Results indicated higher GM volume in thalamus, putamen, temporal gyrus, cerebellum, higher fALFF in prefrontal gyrus, but lower fALFF in cerebellum and more minor/reference allele counts on SNPs were associated with better working memory performance. No significant correlation was found between PANSS and the identified SNP-fALFF-GM component.3.6Table S7-A indicated that both direct  and indirect effect  of SNP component on fALFF component via GM component was significant, which suggested a mediation effect presented as “4To the best of our knowledge, this is the first study to jointly analyze fALFF, GM volume in conjunction with PGC-reported schizophrenia-susceptible SNPs in a large Chinese Han population using a multivariate, data-driven manner. One linked SNP-fALFF-GM component was identified to be both group discriminative and show significant correlation between modalities. Furthermore, post hoc mediation analyses suggested a significant “SNPOn the lined components, GM volume reduction in schizophrenia was revealed in thalamus, putamen, bilateral temporal gyrus, cuneus and cerebellum, which has been supported by multiple studies . ParticuFor the corresponding fALFF component, we observed reduced fALFF in prefrontal gyrus in SZ, which is consistent with other reported studies ,62, as wCSMD1, CNTNAP2, DCC, GABBR2 etc. A recent study investigated the specific gene expression patterns arising from the 108 schizophrenia-associated loci also revealed that the genes in these loci were intensively enriched in the thalamus, cerebellum and cortex during several developmental stages [CSMD1 plays a significant role in the etiology of schizophrenia [CNTNAP2 was reported significantly associated with schizophrenia and major depression in the Han Chinese population [DCC during development and/or throughout life confers resilience to the development of schizophrenia-like dopamine and behavioral abnormalities [GABAB receptor subunits 1 and 2 (GABBR1 and GABBR2) were reported to alter significantly in the lateral cerebellum of subjects with schizophrenia [NFASC is significantly downregulated in the superior temporal gyrus of persons with schizophrenia [On the genetic association, both imaging impairments in GM and fALFF are associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including l stages . The prel stages . CSMD1 pophrenia  and was ophrenia . CNTNAP2pulation  and highpulation ,71. The malities . Expressophrenia . The mRNophrenia . Biologiophrenia . Synapseophrenia . In the ophrenia ,78.More importantly, the identified associations were replicated in an independent Chinese Han dataset. In the validation, not only were the identified SNP-fALFF-GM spatial patterns similar between discovery and replication data, but the pair-wise association between 3 components were also replicated. In addition, we tried several strategies to control for age, medication differences across sites by running same analysis in several matched subsets. Appreciably, a similar SNP-fALFF-GM pattern could be found in all cases, suggesting that the identified imaging-genetic association impaired in SZ could be replicated in multiple conditions.SCN1A allele-dependent activation differences in superior frontal gyrus typically involved in working memory processes [CSMD1 was also proved to be associated with working memory through affecting prefrontal gyrus [PPP2R2B was suggested to affect mRNA expression of this gene in prefrontal cortex to be associated with prefrontal processing during working memory [As to the association with cognition, when assessing the correlation between the identified SNP-fALFF-GM components and working memory performance (DF and DB scores), the imaging-genetic pattern were significantly associated with DB scores. Further mediation analyses revealed the GM mediated the association between SNP and fALFF, which was consistent with our hypothesis. Many studies have supported this finding by suggesting structure can determine or predict function ,48 and srocesses . Anotheral gyrus . Furtherg memory . FinallyIn addition, the current study conducted imaging-genetic analysis in a large Chinese Han population, which would raise a question that if it is reasonable to select risk SNPs based on the PGC GWAS. The explanations could be, first, the recent Chinese Han GWAS study  has claiSeveral limitations still need to be addressed in the present work. First, though no direct correlation was found between the incomplete medication information of antipsychotic dose and the identified components, medication may still have an underlying effect on multimodal imaging measures of schizophrenia patients as medication information for part of the patients were unavailable . The sec5To the best of our knowledge, this is the first attempt to jointly analyze brain fALFF, GM in conjunction with PGC's reported SNPs in a large Chinese Han population (905 + 166 subjects), using a multivariate, data-driven approach. Results demonstrated GM reduction in thalamus, putamen, temporal gyrus, cuneus, cerebellum were correlated with fALFF alteration in prefrontal and cerebellum. Furthermore,both imaging phenotypes were significantly correlated with genetic factors enriched in neuron development, synapse organization and axon pathways. Moreover, subsequent mediation analysis supported our initial hypothesis of revealing a “Natural Science Foundation of China ; the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDBS01040100); the Science Frontier Program of the Chinese Academy of Sciences (No. QYZDJ-SSW-SMC019); National Key R&D Program of China (No. 2017YFA0105203); Primary Research & Development Plan of Jiangsu Province (No. BE2016630); the National Institute of Health  and the National Science Foundation (No. 1539067).This work was supported by the The funder of the study played no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors had full access to all the data in current study and had final responsibility for the decision to submit for publication.The authors report no biomedical financial interests or potential conflicts of interest.NL, JS, JC, DL, TJ participated in the planning and design of the study. NL conducted the data analysis. NL, JS, JC wrote the paper. NL, KX, SL, JL preprocessed the data. JS, JC, DL, VDC, JL, VMV, YC, FZ, ZY, NZ, LF participate the results interpretation. MS, DZ and TJ design the protocol of data scanning and collection. FZ, LT, YX, SL contributed to the recruitment of the subjects in the Wuxi Mental Center. LL, YY, HZ contributed to the recruitment of the subjects in the Xinxiang Site. PL, LL, HY, JY, DZ contributed to the recruitment of subjects in the Beijing site. JC, HW contributed to the recruitment of the subjects in the Wuhan Site. YC, HW contributed to the recruitment of subjects in the Xijing Site. HG, PW contributed to the recruitment of subjects in the Zhumadian Site.


We establish some new noninstantaneous impulsive inequalities using the conformable fractional calculus. The subject of fractional differential equations has evolved as an interesting and important field of research in view of numerous applications in physics, mechanics, chemistry, engineering , and so forth –3. The tVarious types of fractional derivatives were introduced: Riemann–Liouville, Caputo, Hadamard, Erdélyi–Kober, Grünwald–Letnikov, Marchaud, and Riesz, to just name a few. Commonly, all they are defined as integrals with different singular kernels, that is, they have a nonlocal structure. Due to this fact, there are many inconsistencies of the existing fractional derivatives with classical derivative. Thus they do not obey the familiar product rule, the quotient rule for two functions, and the chain rule. Also, the fractional derivatives do not have a corresponding Rolle’s theorem or a corresponding mean value theorem.conformable fractional derivative involves a limit instead of an integral; see . By  with \dobound in  is true Now, we assume that inequality  holds fohow that  is true equality  with \do formula . Therefo formula  is satisFinally, we suppose that estimate  is fulfiequality  holds fo]. Using  with \doapplying , we obtaequality  is validM and N, respectively.The following corollary can be obtained by replacing the given functions Letandbe constants. IfandthenandwhereandLet LetconstantsandIfwhereandare defined by (H2)(H3)(H4)–(H2) hold, thenforIf (H3)–(H4) hold, thenfortisfying  and (3.21)–(H2) we haveApplying Theorem 3)–(H4). This means that Next, we will show that 5) holds, then the solutionof problem (is estimated asand problem  is estimα to the first equation of problem (5) it follows thatTaking the conformable fractional integral of order  problem , we obta


Social perceptual deficits in schizophrenia are well established. Recent work suggests that the ability to extract social information from bodily cues is reduced in patients. However, little is known about the neurobiological mechanisms underlying this deficit. In the current study, 20 schizophrenia patients and 16 controls completed two tasks using point-light animations during fMRI: a basic biological motion task and an emotion in biological motion task. The basic biological motion task was used to localize activity in posterior superior temporal sulcus (pSTS), a critical region for biological motion perception. During the emotion in biological motion task, participants viewed brief videos depicting happiness, fear, anger, or neutral emotions and were asked to decide which emotion was portrayed. Activity in pSTS and amygdala was interrogated during this task. Results indicated that patients showed overall reduced activation compared to controls in pSTS and at a trend level in amygdala across emotions, despite similar task performance. Further, a functional connectivity analysis revealed that controls, but not patients, showed significant positive connectivity between pSTS and left frontal regions as well as bilateral angular gyrus during the emotion in biological motion task. These findings indicate that schizophrenia patients show aberrant neural activity and functional connectivity when extracting complex social information from simple motion stimuli, which may contribute to social perception deficits in this disorder. •Perception of social information from bodily cues is impaired in schizophrenia.•We examined neural correlates of perception of emotion from biological motion.•Activity in amygdala and posterior superior temporal sulcus was reduced in patients.•pSTS functional connectivity with frontal and parietal regions was reduced in patients.•Aberrant neural responses may contribute to social perceptual deficits in schizophrenia. The detection of motion and the ability to obtain information from motion is an important visual perception ability . ReflectThus, biological motion is an important aspect of social cue perception. From observing biological motion  we gather socially relevant information and make inferences about the intentions and motivations of others. We can also infer emotion based on particular patterns of human movement displayed by point-light animations  is critical for biological motion perception . ActivitIn schizophrenia, the ability to extract social information, including emotion, from bodily cues is reduced  and 16 healthy controls  completed the study. Patients were recruited from community outpatient treatment clinics and met diagnostic criteria for schizophrenia based on the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) . ControlAll participants were between 18 and 65 years of age, and were excluded from participation if they had: an identifiable neurological disorder, history of loss of consciousness for more than 1 h, or were not sufficiently fluent in English to consent and understand procedures. Additional exclusion criteria for patients was self-reported substance abuse in the past month or dependence in the last six months, and indication of IQ < 70 based on chart review. Additional exclusion criteria for controls was a first-degree relative with schizophrenia or another psychotic disorder, a personal history of schizophrenia or other psychotic disorder, bipolar disorder, or recurrent depression, a lifetime history of substance dependence or any substance abuse in the last 6 months, and any of the following Axis II disorders: avoidant, paranoid, schizoid, or schizotypal.2.2Psychiatric symptoms in patients were evaluated using the 24-item version of the Brief Psychiatric Rating Scale (BPRS)  and ScalAll clinical assessments were conducted by interviewers trained to reliability through the Treatment Unit of the Department of Veterans Affairs VISN 22 Mental Illness Research, Education, and Clinical Center (MIRECC) based on established procedures . The stu2.32.3.1E-Prime version 2.0 professional . Prior to the scan session, participants completed practice trials to become familiar with each task.Participants completed two tasks involving point-light animations during fMRI . The firBasic biological motion task : Stimuli consisted of 1 s animations of point-light walkers; each animation video was comprised of 12 black dots presented on a white background at central fixation. The dots were arranged and animated in a manner that corresponded to human  or random movement A. ParticEmotion in biological motion task (Emo Bio): Stimuli consisted of 20 animations of point-light walkers ; videos were comprised of 12 white dots presented on a black background at central fixation. The dots were arranged and animated in a manner that depicted happiness, fear, anger, or neutral human emotion . A 1 s f2.3.2All scanning was performed using two identical 3 T Siemens Tim Trio scanners running identical software at the UCLA Ahmanson-Lovelace Brain Mapping Center and the UCLA Staglin Center for Cognitive Neuroscience. Each scanner utilized the same quality assurance protocols daily to assess scanner performance. There was no difference between scanners in terms of number of subjects per group or signal-to-noise ratio (Supplementary Table 1).Functional runs were acquired with T2*-weighted blood oxygen level-dependent (BOLD) gradient echo planar imaging (EPI) sequences for each activation task . Two sets of structural images were acquired from each subject for anatomical localization and registration of functional data: a high-resolution T1-weighted magnetization prepared rapid-acquisition gradient echo image (MPRAGE)  and a T2-weighted matched-bandwidth high-resolution scan with the same slice prescription as the EPI .Image preprocessing was carried out using the FMRIB Software Library . Preprocessing steps included high-pass filtering (100 s cut-off), spatial smoothing (5 mm FWHM), skull stripping , and regWe addressed potential motion artifacts in several ways. Motion estimates were inspected to ensure that relative mean displacement did not exceed 0.5 mm for any participant. In addition, all images were realigned to the middle volume using MCFLIRT and movement parameters calculated by MCFLIRT were modeled as nuisance covariates . Finally2.3.3t-tests and chi-square tests were used to assess group differences for continuous and categorical variables, respectively. For the localizer task, behavioral data were collected to ensure participants were attending to the task but these data were not analyzed as a primary outcome variable. For the Emo Bio task, separate independent samples t-tests were used to assess group differences in response time and accuracy across condition (emotion type). Analyses measured significance at the 0.05 level, two-tailed. Given our relatively small sample size, we also give weight to any findings with an effect size of medium or greater.For the demographic data, independent samples 2.3.4Analysis of fMRI data was performed separately for the two tasks. For both tasks, we used a general linear model (GLM) approach with FEAT , and a timing model based on a double-gamma hemodynamic response function. In the first-level analysis, linear contrasts of trials by condition were created for each subject. The primary contrast of interest for the Basic Biological Motion Task was “biological” > “non-biological.” For the Emotion in Biological Motion Task, the primary contrasts of interest were each condition  versus baseline.Localizer task: The primary goal of the localizer task analyses was to identify voxels responsive to biological motion within bilateral pSTS. An anatomically constrained functional region of interest (ROI) for pSTS was created using a two-step procedure . First, p < .05 was applied, with additional weight given to any findings with an effect size of medium or greater.Emo Bio task: Our primary analytic approach for the Emo Bio task was to use ROIs to focus on pSTS and amygdala. The bilateral pSTS ROI was defined as outlined above and transformed to each individual's native space. Bilateral anatomical amygdala ROIs were created in individual native space based on the HarvardOxford subcortical probability atlas at a 25% probability threshold A. First,A secondary voxel-wise whole-brain analysis was conducted using FSL. Within- and between-group averages for contrasts of interest were compared using mixed effects analysis via the FLAME  stage 1 module . VariancWe examined functional connectivity during the emotion conditions  between pSTS as a seed region and amygdala, and between pSTS and the rest of the brain. Individual subject time courses from the Emo Bio task were extracted from the preprocessed data for each subject's pSTS ROI. The time course was then entered into individual subject general linear models in FSL as a psychophysiological interaction with the combined emotion conditions. The neutral condition was entered as a separate regressor to account for variance associated with that condition. For the whole-brain voxel-wise connectivity analysis, within and between groups averages were compared using mixed effects analysis via FLAME stage 1 module using the same significance thresholds as the whole-brain GLM analysis.33.13.2Performance of the 1-back task during the Basic Bio run was examined as a validity check. There were no participants in either group who failed to complete button presses periodically throughout the task. For the Emo Bio task there was no difference in accuracy or response time, collapsing across emotion conditions, between groups .3.3Localizer task: Both patients and controls showed BOLD activation in bilateral pSTS, extending into medial temporal gyrus, supramarginal gyrus, angular gyrus and lateral occipital lobe.p = .02, ηp2 = 0.15) indicating greater activation in controls than patients across conditions. There was also a significant main effect of condition  = 86.24, p < .001, ηp2 = 0.72), driven by significantly greater activation for the emotion conditions (combined) compared to the neutral condition across groups. There was no significant group by condition interaction  = 2.25, p = .14, ηp2 = 0.06). Follow-up examination of ROI activity by emotion type revealed a significant main effect of group  = 4.80, p = .04, ηp2 = 0.12) indicating greater activation in controls than patients across emotion conditions. There was also a significant main effect of emotion  = 6.35, p = .003, ηp2 = 0.16), driven by significantly greater activation for the anger condition compared to the fear (p = .004) and happy (p = .07) conditions across groups. There was no significant group by condition interaction  = 1.78, p = .19, ηp2 = 0.05).Emo Bio task: Results of ROI analyses for the Emotion in Biological Motion Task are displayed in p = .06, ηp2 = 0.10) indicating greater activation in controls than patients across conditions. There was a significant main effect of condition  = 16.23, p < .001, ηp2 = 0.32), driven by greater activation for the emotion conditions compared to the neutral condition (p < .001) across groups. There was no significant group by condition interaction  = 0.80, p = .38, ηp2 = 0.02). Follow-up examination of ROI activity by emotion type revealed no significant main effect of group  = 2.45, p = .13, ηp2 = 0.07) or emotion  = 1.98, p = .15, ηp2 = 0.06) and no significant group by condition interaction  = 0.10, p = .90, ηp2 = 0.003).For the amygdala, there was a marginally significant main effect of group  = 3.72, Results of exploratory whole brain analyses for the three emotion conditions combined versus baseline are detailed in Supplemental Materials . Across emotion conditions, controls showed greater BOLD activity compared to patients in posterior cingulate/precuneus and bilateral fusiform gyrus. Patients did not show any areas with greater BOLD activity compared to controls.For the connectivity analyses, we initially examined functional connectivity between bilateral pSTS as a seed and amygdala. No within or between group effects survived correction for multiple comparisons. Results of seed-based pSTS whole brain functional connectivity analyses are displayed in r = 0.56, p = .01).In post hoc analyses we explored the relationship between ROI activity during the emotion conditions and measures of real world functioning  and negative symptoms  in patients. Non-parametric analyses (Spearman correlations) were chosen to account for non-Gaussian distributions of the data. After Bonferroni correction for multiple comparisons, only one observed correlation was significant. During emotion conditions, patients showed a significant positive correlation between BOLD activity in amygdala and RFS total score . Previous studies have found that patients with schizophrenia tend to over-attribute negative emotions  to neutral faces , whereas previous versions of the task included a fifth emotion condition: “sad.” This modification was necessary because we were limited to a 4-button response box in the fMRI scanner, and, as a result, needed to limit the conditions to 4 emotions. We considered it to be important to include a neutral condition, a positive emotion (happy), and two versions of negative emotions (fear and anger) that are both known to activate the amygdala robustly. That meant that the sad condition was not part of our paradigm. We believe that this modification, while not ideal, had minimal impact on the results and conclusions from this study because we were still able to assess neutral, positive, and negative emotions. Finally, the current study focused on two brain regions consistently implicated in biological motion and social cue perception. Follow-up studies could examine disturbances in more regions involved in this network, as well as the network properties of these regions using graph theoretical analyses.Social perceptual deficits in schizophrenia are well established (e.g., . The curDr. Green has been a consultant for ACADIA, DSP, Takeda, and Lundbeck, a member of the Scientific Board of Luc, and has received research funds from Forum. The rest of the authors report no biomedical financial interests or potential conflicts of interest.


Percolomonas cosmopolitus (Heterolobosea) is often observed in saline habitats worldwide, from coastal waters to saturated brines. However, only two cultures assigned to this morphospecies have been examined using molecular methods, and their 18S rRNA gene sequences are extremely different. Further the salinity tolerances of individual strains are unknown. Thus, our knowledge on the autecology and diversity in this morphospecies is deficient. Here, we report 18S rRNA gene data on seven strains similar to P. cosmopolitus from seven geographically remote locations  with sample salinities ranging from 4‰ to 280‰, and compare morphology and salinity tolerance of the nine available strains. Percolomonas cosmopolitus-like strains show few-to-no consistent morphological differences, and form six clades separated by often extremely large 18S rRNA gene divergences (up to 42.4%). Some strains grow best at salinities from 75 to 125‰ and represent halophiles. All but one of these belong to two geographically heterogeneous clusters that form a robust monophyletic group in phylogenetic trees; this likely represents an ecologically specialized subclade of halophiles. Our results suggest that P. cosmopolitus is a cluster of several cryptic species (at least), which are unlikely to be distinguished by geography. Interestingly, the 9 Percolomonas strains formed a clade in 18S rRNA gene phylogenies, unlike most previous analyses based on two sequences.The heterotrophic flagellate  The modern classification of eukaryotic microbes is usually based on molecular sequence information combined with morphological characters obtained using diverse imaging techniques, often including electron microscopy. Many taxonomists have a fundamental question whether small morphological differences among the observed organisms represent multiple species or one variable species –3. ConvePercolomonas cosmopolitus  [P. cosmopolitus has been reported in samples from an extremely wide range of saline habitats, from coastal waters to saturated brines, throughout the world [P. cosmopolitus strains are usually not inferred to be sisters in phylogenetic trees, forming instead a paraphyletic group with respect to the pseudociliate Stephanopogon, which has many more flagella and a completely different feeding system [Percolomonas cosmopolitus and similar organisms (e.g. [The species s Ruinen ) is a hes Ruinen , 26; thes Ruinen  are some (58.4%) , 28. Seche world –26, 29. g system –37. Consms (e.g. ) are intPercolomonas cosmopolitus-like organisms, two previously sequenced strains of Percolomonas cosmopolitus, and a strain recently described by Mylnikov as Percolomonas lacustris [Here, we examined six new acustris  , indicating ecological specialization. Percolomonas formed a monophyletic group in the 18S rRNA gene phylogeny.The nine Percolomonas lacustris. No specific permits were required for the described field studies. The field studies did not involve endangered or protected species. Each monoprotistan culture was established by single-cell isolation (from raw samples or crude cultures) or by serial dilution. High salinity media  was made by dilution of Medium V  with addition of Pseudomonas fluorescens bacteria as food. All cultures were incubated at 25°C and subcultured every four weeks.Seven monoprotistan strains were isolated from brackish to high salinity (4–280‰) water/sediment interface samples collected from New Zealand, Kenya, Korea, Poland, Russia, Spain, and the USA between 2008 and 2015 . Isolateter, see ) with stg for 10 min, then 900 μL of the supernatant was discarded, and the remaining volumes (i.e. 100 μL) were fixed by addition of 50 μL of 25% v/v glutaraldehyde (electron microscopy grade). The sizes of the live cells (i.e. 50 cells per culture) were measured from digital images. One-way analysis of variance (ANOVA) was used to evaluate variation among strains in their major cell dimensions, using SPSS for windows .Live flagellates mounted on glass slides were observed with phase contrast microscopy or differential interference microscopy using a Leica DM5500B microscope equipped with a DFC550 digital camera  or Carl Zeiss AxioScope A.1 microscope equipped with a AVT HORN MC-1009/S analog video camera. To observe the number and shape of the flagella, cultures (1 ml) were centrifuged at ×2,000 g for 10 min, then 900 μL of the supernatant was discarded, and the remaining volumes (i.e. 100 μL) were fixed by adding 50 μL of 25% v/v glutaraldehyde (electron microscopy grade). Fixed cells were allowed to settle (40 min) on glass coverslips coated with 1% poly-L-lysine. Cells were rinsed with sterile media, and then dehydrated with a graded ethanol series (30–100%). The glass coverslips were then critical-point dried. Fixed cells were coated with gold/platinum using an ion sputter system. Specimens were examined with a SU8220 field emission scanning electron microscope  or JSM-6510LV scanning electron microscope .Cultures (1 ml) were centrifuged at ×1,200 P. lacustris HLM-6  were extracted using a DNeasy Blood and Tissue Kit  or MasterPure Complete DNA and RNA Purification Kit , as described in the supplied protocols.Nucleic acids from the six new isolates and 5′-AACCTGGTTGATCCTGCCAGT-3′ and EukB 5′-TGATCCTTCTGCAGGTTCACCTAC-3′ [2, 0.2 μL of 5 U/μL Taq DNA polymerase , and 1–3 μL of DNA template. The cycling conditions were as follows: an initial denaturing step at 94°C for 5 min, followed by 35 cycles of 30 s at 94°C, 1 min of annealing at 55°C, and extension at 72°C for 2 min, with a final extension step for 10 min at 72°C. Amplicons were cloned into a pGEM-T Easy vector, at least five positive clones per sample were partially sequenced, and a positive clone was completely sequenced using various sequencing primers. For strain XLG1-P, the cycling conditions were slightly different: 35 cycles of 20 s at 94°C, 1 min of annealing at 55°C, and 3 min of extension at 72°C. Strain LRS was amplified using the different eukaryote primers 82F (5'-GAAACTGCGAATGGCTC-3') and 1498R (5'-CACCTACGGAAACCTTGTTA-3'). The optimized PCR condition was 2 min at 96°C , followed by 35 cycles of 30 s at 96°C, 1 min at 60°C, 2 min at 72°C, with a final extension for 10 min at 72°C. The PCR products for strains XLG1-P and LRS were directly sequenced by Sanger dideoxy sequencing without cloning.For all strains except LRS and HLM-6, the 18S rRNA gene sequences were obtained by PCR amplification using a combination of the eukaryote primers EukA CCTAC-3′ . The 20-5′-GCGCTACCTGGTTGATCCTGCC-3′ and FAD4 5′-TGATCCTTCTGCAGGTTCACCTAC-3′ [For Strain HLM-6, the primers used were PF1 CCTAC-3′ , 42. The7 generations with the default heating parameter (0.1) and sampling frequency (0.01). A burn-in of 30% was used, by which point convergence had been achieved (the average standard deviation of split frequencies for the last 75% of generations was < 0.05).The 18S rRNA gene sequences from 62 representative heterolobosean species, plus 16 other Discoba species selected as outgroups, were used for phylogenetic analysis . The daEnterobacter aerogenes at an initial density of 3.46 × 107 cells per ml (20 μL) at 7- to 14-day intervals to support the growth of the protists. All treatments were performed in duplicate. Medium V (0.96 ml) with a range of salinities (3‰–300‰) were inoculated with 20 μL of actively growing stock culture  and incubated in the dark at 25°C for at least 49 days. We confirmed the salinity range supporting growth by transferring a sample of the isolate into fresh media with the same salinity , and re-examining the culture for actively moving cells at 7- to 14- day intervals over a period of 35 days.To estimate the salinity ranges supporting growth of the six new isolates and three previously available isolates, we performed an experiment using media with 3‰ to 300‰ salinity, made from artificial seawater stock  as described earlier , 37, 46.P. lacustris (strain HLM-6) was not significantly different from other strains, except strain ATCC 50343 . Strain HLM-6 had significantly more elongated cells than ATCC 50343 , but the width was not significantly different (data not shown). Probably, morphological dimensions studied here may not be a reliable criterion for Percolomonas taxonomy.Live cells were usually ovoid-shaped or spindle-shaped with average lengths and widths ranging from 4.6 to 9.4 μm and 2.9 to 4.3 μm, respectively . The ratPercolomonas strains were closest by BLASTN search to Percolomonas cosmopolitus strain WS (AF 519443), but with a low identity of 74% to 83%. Heterolobosea formed a strong monophyletic group in phylogenetic trees of 18S rRNA gene sequences  in the original sample. The five Percolomonas strains  isolated from hypersaline habitats with >70‰ source salinity could grow best at 75‰ to 125‰ salinity  isolated from non-hypersaline habitats with < 40‰ source salinity appeared to grow best at 30‰ to 50‰ salinity , but noopolitus ), and thPercolomonas complex includes 6 distinct evolutionary lineages that are separated by genetic distances of 15.1% or more. Considering the similar morphology of P. cosmopolitus-like strains (see above), it is reasonable to regard the Percolomonas complex as a grouping of several (at least) cryptic (or sibling) species. Among protists, it has long been recognized that numerous cryptic species can exist within a single morphospecies [Percolomonas strains was observed in the two clades represented by multiple isolates, although the number of sampling sites and sequencing data are limited. Three Percolomonas strains in ‘Group A’ form a robust clade with low genetic divergences (98% to 99% identities) and are widely dispersed geographically . In addition, two Percolomonas strains in ‘Group B’ (with 98% identity) are from Africa and North America. This pattern is more consistent with widespread dispersal, as inferred for other very small protists , and, andPercPercolomonas strains (beyond the scope of this work) may reveal some fixed, morphological differences. There may also be differences in the cyst stages . Itculture; ). If so,P. cosmopolitus was detected at salinities ranging from 30‰ to saturated brines. In our study, the nine examined Percolomonas strains derived from a wide range of salinities, up to near-saturation. In general, the degree of halophily of these strains was related to the salt concentration of their original habitats. Five Percolomonas strains  were isolated from various habitats with a salinity range of 73‰ to 280‰. These all grew best at 75‰–125‰ by our qualitative estimation and most could still grow at 175‰ or even 200‰ on our experimental media. This clearly makes them halophiles according to the definition by Oren [Percolomonas strains  isolated from non-hypersaline habitats  grew optimally at 30‰ or 50‰, and failed to grow above 100‰ salinity. Interestingly, Percolmonas strain LO grew optimally at 50‰ salinity, and grew up to 100‰ salinity, although it was isolated from Lake Turkana, Kenya, with a very low salinity of 4‰. Phylogenetically, LO belongs to the putative ‘halophile clade’, and it is possible that it descended from a more halophilic ancestor (see below). Interestingly, the Lake Turkana sample was also the origin for Pharyngomonas turkanaensis, a heterolobosean amoeba that grows best at 15–30‰ salinity, but is inferred to have descended from a halophilic Pharyngomonas ancestor [Ruinen  reported by Oren , in whicancestor .sensu lato) in Heterolobosea were an example of a radiation of morphospecies that stemmed from a common halophilic ancestor. This clade included Pleurostomum flabellatum, Tulamoeba peronaphora, Tulamoeba bucina, and Aurem hypersalina, all with optimal salinities for growth of at least 150‰ [Percolomonas clade consisting of ‘Group A’ and ‘Group B’ may also represent a radiation of halophiles . All of the cultivated Percolomonas in this clade are halophiles, with one borderline case . However, this possibility of an exclusively/predominantly halophile clade could be a biased sampling artefact, and should be tested through additional isolations and study of related strains. This would also be useful to understand the nature of the closest relative of the halophile strain P5-P, which is phylogenetically isolated from others at present.The taxon Heterolobosea includes a substantial proportion of the known halophilic or halotolerant eukaryotes , 59–62, ast 150‰ , 37, 61.P. cosmopolitus were available, and these were included in many phylogenetic analyses of Heterolobosea [Stephanopogon [Percolomonas available. In the present study, with seven additional and different 18S rRNA gene sequences, we instead inferred a (moderately supported) Percolomonas clade. Future research will address the cause of this difference, and test the relationships amongst Percolomonas and Stephanopogon using other markers.For a long time only two 18S rRNA gene sequences of olobosea , 63–66. anopogon , 66. ThiPercolomonas strains studied here are morphologically very similar, in spite of the huge genetic diversity they encompass. Percolomonas strains form at least 6 genetically distinct clades in the molecular phylogenetic trees, and could be considered to represent at least as many cryptic species. The speciation of Percolomonas strains could be partially related to salinity preference, rather than spatial distribution. The clusters ‘Group A’ and ‘Group B’, which are specifically related, may collectively represent a halophilic clade.On the basis of light and scanning electron microscopic observations, all


Vd = 49.7 ± 1.9 pL). Moreover, we monitored the water evaporation trends on the sensing surface and observed total evaporation time per droplet of 39.0 ± 1.8 s against a theoretically determined value of about 37.14 s. By using monodispersed particles in water, i.e., magnetic polystyrene particles (MPS) and polymethyl methacrylate (PMMA), and adsorbing them on a dynamic cantilever sensor, the mass and number of these particles were measured and determined comparatively using resonant frequency response measurements and SEM particle count analysis, respectively. As a result, we observed and reported monolayer particles assembled on the sensor with the lowest MPS particles count of about 19 ± 2.Cantilever-based sensors have attracted considerable attention in the recent past due to their enormous and endless potential and possibilities coupled with their dynamic and unprecedented sensitivity in sensing applications. In this paper, we present a technique that involves depositing and vaporizing (at ambient conditions) a particle-laden water droplet onto a defined sensing area on in-house fabricated and commercial-based silicon microcantilever sensors. This process entailed the optimization of dispensing pressure and time to generate and realize a small water droplet volume ( With the recent advancement in micro- and nanofabrication technologies, cantilever-based sensors have increasingly become versatile tools for various sensing applications due to their fascinating intrinsic flexibility and robustness. They have widely been embraced and applied in many scientific fields ranging from physical and chemical sensing, material characterization, magnetometry, explosive detection, to biological disease diagnosis ,2,3,4. Ff) and energy loss (measured as dissipation change).In the measurement of particle concentration, for instance, some various measurement technologies such as dynamic light scattering, nanoparticle tracking analysis, and resonant mass sensing can be deployed. These technologies provide particle concentration measurement based on particle fluorescence, particle size analysis and ensemble particle zeta potential measurement, and particle mass measurements, respectively. For the latter, differential stress at the surface to the beam of the cantilever causes bending or deflection of the cantilever. Most equipment for determining small masses, e.g., mass spectrometers  and suspQ ~ 180) due to severe damping and often yields randomized adsorption of particles on the whole cantilever surface. Furthermore, uniformity in particle adsorption or distribution over the sensing region is never guaranteed. In our study, we explored and presented an alternative droplet-based approach that can be utilized to precisely deposit particles on a specified sensing area on a cantilever for particle concentration or mass sensing applications. We successfully demonstrated localized deposition (utilizing liquid dispensing) of monodispersed magnetic polystyrene (MPS) particles onto a piezoresistive silicon cantilever sensors. Furthermore, we discussed some uncertainty parameters that influenced our measurements.In providing a low-cost measurement approach, dipping a cantilever sensor into a magnetic particle solution has also recently been devised and implemented . Howevertev) and trends thereof for water droplets of known volume (Vd). The particles used herein included magnetic polystyrene (MPS) from Micromod GmbH, and polymethylmethacrylate (PMMA) from Sigma-Aldrich Inc. It is worth noting that the two test particles have comparable  diameters and densities, but contrasting magnetic properties, with MPS and PMMA being magnetic and non-magnetic, respectively. However, similar to silicon, PMMA has magnetic susceptibility of less than 1 and is thus diamagnetic in nature. The essence of using these two particle types was to compare the results and assess the performance of our measurement approach.Through an optimized liquid-dispensing mechanism, we sub-sampled and loaded small water droplets with and without particles onto in-house fabricated cantilever sensors. Additionally, as a self-validating process, particle-laden droplets were similarly deposited on surface-treated silicon substrates and upon solvent evaporation, particle count estimations were performed using scanning electron microscopy (SEM). We also monitored and determined the droplet evaporation time (Magnetic polymer particles (such as MPS) are usually synthesized by embedding magnetic nanoparticles  into a non-magnetic spherical (polymer) matrix . Magneti−3, from micromod Partikeltechnologie GmbH . They are basically monodispersed particles consisting of magnetite (Fe3O4) around an organic matrix of a styrene-maleic acid (SMA) copolymer, which are finally encapsulated with a polymer (polystyrene) layer [In this study, we used magnetic polystyrene (MPS) beads of nominal diameter ~2 μm and density 1.1 gcme) layer  Hence, Mm or the number Np of particles was varied for every particle solution dispensed on the target substrate or sensing surfaces.Originally, monodispersed MPS particle solution was obtained in an aqueous suspension of 50 mg/mL concentration and thoroughly mixed (by sonication) prior to use or dilution. The solution was otherwise diluted with deionized water to tune the concentration levels, i.e., into approximately 20, 10, 1 and 0.1 mg/mL. This was the main variable, besides the droplet size, through which the mass Δ2) used in this study were cut from an n-type Si (100) wafer . These wafers had resistivity and thickness of 1–10 Ωcm and 275 ± 15 µm, respectively. After cleaving, the substrates were individually cleaned by: (a) boiling (5 min) in an oxidant solution containing a mixture of sulfuric acid  and hydrogen peroxide  in a volume ratio of 1:1; (b) immersing in a water bath (for 5 min); (c) rinsing with deionized water; and (d) blow drying with nitrogen gas. Such a cleaning process was necessary in removing organic contaminants  and ionic or metal contaminants from the silicon surface, prior to use or surface treatment.The silicon bulk substrates  for 2 min at RF power of 220 W, while others were treated with PSS/PDDA/PSS [θ was measured for each set of substrates: as-cleaned and after O2 plasma treatment.Upon cleaning, some pre-selected bulk substrates were then dipped in HF solution (6–7%) for about 5 s (to etch the native oxide from silicon surface). Immediately thereafter, some set of samples were treated with OPDDA/PSS . This waMeasurement of contact angle was carried out by the sessile-drop method using a dispensing system integrated with an imaging system which comprises of a light source, a collimating mask, an adjustable stage, and a digital microscope camera . The latter was helpful in visualizing and capturing the water droplet on each silicon substrate. Careful attention was taken to generate a homogeneous background behind the drop. For this reason, we positioned a diffuser between the lamp and the water droplet. The height of the camera was also adjusted so that the actual drop and its reflection on the silicon surface could clearly be observed. Consequently, the intersection point at the solid–liquid–air interfaces would precisely be determined. The internal nominal diameter of the dispensing tip  was ~0.10 mm, and a low pressure (~0.06 bar) was applied to dispense a droplet at room temperature (21.8 ± 0.5 °C).θ can be measured, in which case θ is the angle formed between the drop tangent and the solid surface at the tri-phase contact point of a simple spherical droplet, as depicted in θ from sessile drop images based on the Young–Laplacian equation (Equation (1)) for spherical approximation for half-angle method, in which rc and h denote the contact radius and the height of the drop above the solid surface, respectively.In determining the contact angle, the profile of the drop image was extracted by an image-fitting in ImageJ  using thθ depends on the surface tensions for solid–liquid (γsl), solid–air (γsv), and liquid–air (γlv) interfaces. For small drops (of liquid density ρ), surface tension dominates over gravity, g ≈ 9.81 ms−2. Consequently, a drop tends to form a spherical-cap shape to minimize total free surface energy thereby resulting into a capillary length, or capillary constant Lc, given as:Furthermore, the angle ρ represents the difference in density of the involved phases . For water  at room temperature and standard pressure conditions, Lc ≈ 1.35 × 10−5 m−2. The LBADSA method further considers the pressure gradient across a surface to the drop curvature, and the contact angle is usually approximated based on a perturbation solution of the contour of an axisymmetric sessile drop and assumes gravity as the only external force, and follows Equations (2) and (3). Equation (3) relates the principal radii of curvature (R1 and R2) at any point on the drop, the height h of the drop, the capillary constant Lc and the radius of curvature b at the apex.The parameter Δθ = 47.3° ± 2.7°, whereas contact angle after O2 plasma treatment (2 min) was about 9°. Obviously, our surface treatment with O2 plasma yielded enhanced hydrophilicity compared to the 24.5–46.5° (for 1–20 min O2-RIE plasma treatment duration) reported by Alam et al. [2 plasma treatment.In our study, as-cleaned samples yielded m et al. . It is wn-type (100) silicon  and conventional bulk micromachining technologies as described by Wasisto et al. [2 pieces and cleaning them accordingly; cf. 6/O2), contact holes formation, and metallization of electrical contacts (Cr/Au = 300/3000 Å) coupled with a lift-off process. The general cantilever fabrication process utilizing bulk micromachining is schematically illustrated in Fabrication process of our cantilever-based sensors was exclusively done in-house, using o et al. . The pro2) on top of the pre-cleaned silicon substrate at 1100 °C using thermal oxidation process. Hereafter, an intermediary substrate-cleaning process was performed by sonication in acetone and water rinses followed by nitrogen blow-dry. The silica layer (~100 nm) provided an excellent protection during dopant diffusion processes, in which case heating furnace temperature was set and maintained at 1100 °C (n+- and p-diffusions) and at 1200 °C (p+-diffusion).Fabrication process basically began with the deposition of an isolation layer  on a mask to the surface of the silicon substrates. A preliminary surface preparation process involving bubbling of hexamethyldisilazane (HMDS) within an enclosed chamber was executed (at ~115 °C for ~5 min) to enhance hydrophobicity of the substrate. Hereafter, pattern transfer ensued. Firstly, the prepared substrate was held onto a vacuum chunk and a photoresist (AZ 5214) was dispensed onto the silicon surface and spin-coated . The sample was then partially evaporated in a soft baking process (110 °C for ~50 s) to improve adhesion and uniformity, and optimize light absorbance characteristics of the photoresist. To transfer the mask image to the resist-coated wafer, both the mask and substrate were mounted onto respective stages/holders on the mask aligner equipment  followed by alignment and UV exposure . To dissolve the soluble areas of the exposed photoresist and realize visible patterns on the wafer, the sample was immersed and gently agitated in a developer chemical solution (AZ 726) for ~60 s and thoroughly rinsed with deionized water. The resulting pattern after resist development would then be inspected (under a light microscope) followed by a hard-baking process (at 100 °C for ~30 min). Subsequently, the hardened-patterned sample was immersed in a buffered hydrofluoric acid for about 10 min to remove the entire SiO6/O2) from the backside to create a membrane. Lastly, the patterning process (by lithography) was undertaken to form the desired free-ends  of our Water droplet with/without MPS particles was generated from a needle tip (of specified size) by applying specified dispensing pressure over a defined pulse duration. By keeping the size of the needle tip constant, the drop size could reliably be tuned mainly by varying the magnitude of the dispensing pressure and/or time. Through X-ray computer tomography (xCT) measurements (undertaken at PTB), we precisely calibrated and determined the internal and external diameters of our dispensing stainless needle tip see  to be 0.X,Y), i.e., by specifying a coordinate along the axisymmetric and the vibrating axis of the cantilever. The needle tip would then be mounted into a barrel (containing a thoroughly mixed solution of MPS particles) and aligned directly on top of deposit target point. During the alignment process, a 3D micro-positioning system (with a resolution of 10 μm) was used to carefully move the needle tip to the target point; and with an aid of a camera, the height from the sensor and the tip would carefully be adjusted (Z-coordinate). To further enhance the accuracy of positioning the tip on the sensing surface and minimize sensor breakages, a camera was also incorporated in the alignment process. The tip was aligned and adjusted very closely above the sensor target deposit point, in orders of few microns. Finally, the droplet was generated  and deposited on the sensor by applying defined dispensing parameters . Typically, minimum dispensing pressure (~60 mbar) was applied and different pulse durations were used. This was desirable in generating a small droplet as much as possible.Firstly, each cantilever mass sensor was mounted on top of a piezo actuator within the sampler and carefully aligned in the horizontal plane. At this point, the particles collection target point would be defined (dc) and detect the voltage output from the bridge, a lock-in amplifier  was connected to the cantilever contact pads (as shown in pp) was similarly supplied to the actuator by the MFLI instrument. The connections to and from the MFLI were accomplished through coaxial cables and SubMiniature version A (SMA) connectors. The required MFLI parameters  and the signal read-out from sensor  were managed using a computer via a control software.In detecting cantilever bending due to the mass of the droplet or adsorbed particles, different sensing or read-out methods can be deployed, such as piezoelectric, electrodynamic, tunneling, hard contact, optical reflection, interferometric, capacitive, and piezoresistive techniques . The latf0) and after (fr) load deposition were measured. This was necessary to precisely determine the resonant frequency shift (Δf = fr − f0) due to the loaded mass Δm . Consequently, the particle and/or droplet mass was determined at a point x from the fixed end of the cantilever beam of length L. With the measured resonant frequency shift Δf, the mass of the cantilever m0 and the mode-shape function U(xΔm), the drop or adsorbate mass Δm can be calculated according to [λ represents a modal constant, which is about 1.8751 for the first mode. To begin a new measurement cycle, the adsorbed particles were removed/cleaned from the cantilever surface and f0 was measured again to average and compare with the initially obtained value.Initially, and for each particle adsorption cycle, the resonant frequencies of the cantilever before (rding to :(4)Δm = Whenever particles or analytes are collected on a silicon cantilever for mass detection or otherwise, they often adhere on the sensing surface. For instance, airborne particles such as cigarette smoke sticking on the surface of silicon cantilever can easily be removed by immersing and gentle agitating the cantilever in a glass containing acetone (for a few minutes) . HoweverIn this work, therefore, regeneration of particle-adsorbed cantilever sensors a was mait). By multiplying droplet mass with the water density (ρw = 1 gcm−3), the drop volume was obtained. Obviously, a small droplet size Vd = 49.7 ± 1.9 pL was measured for t = 10 ms for a dispensing pressure p = 60 mbar. Similarly, Vd = 98.1 ± 1.0 pL and 0.2 nL corresponded to t = 0.1 s for p = 0.06 bar and 0.2 bar dispensing air pressures, respectively.By varying dispensing time and output pressure, various droplet sizes would be obtained. It was clearly observed  that, foQ before and after depositing the water droplet on the cantilever were, respectively, 1800 ± 250 and 153 ± 50. The latter shows a significant reduction in Q, which obviously depicts severe damping of the resonator when the water rests on the sensing surface. By assuming a spherical-shaped droplet of Vd ≈ 50 pL, a diameter ~45.6 μm was computed, which is much smaller than the width of the cantilever sensor (w ≈ 170 μm).Consequently, we used low output air pressure (60 mbar) and the shortest pulse duration (10 ms) to generate and realize all the droplets particularly aimed at sampling the particles from the solution. The quality factor tev, and the minimum particle adsorption or waiting time prior to particle-mass measurement. Therefore, in determining tev, the loss of droplet mass was initially monitored over time under ambient conditions through an offline resonant frequency measurement system (using a MFLI instrument). It should be noted (f0 ~ 215.415 kHz shifted to fr ~ 213.968 kHz), which corresponds to the added mass by the droplet. The total time taken for the cantilever to completely lose water from the surface and restore fr to almost f0 (except for the shift induced by the mass of the attached particles) is equivalent to the drop evaporation time, tev = 39.0 ± 1.8 s.Water microdroplets on a cantilever sensor are bound to evaporate at ambient conditions. In our study, the loss of water droplet mass from the sensing surface was monitored by measuring the shift in resonant frequencies over time (as delineated in be noted a that upf0 rapidly shifts to a minimum whereupon the sensor momentarily stabilizes and recovers from this droplet-deposit impact. The cantilever then loses the water (~50 pL) into the ambient atmosphere within a span of about 39 s. Rapid evaporation and instantaneously loss of the water towards the last evaporation period was however observed. Subsequently, this culminates to a rapid shift of frequency  and an abrupt rise in Q . The corresponding trends and changes in resonant frequency and quality factor were evidently found to significantly change with the evaporation time , having a volume Vc given by [rc and θ is explicitly given in Equation (1) and can equivalently be related with Rs as: t can be computed as follows [Vc,i denotes the initial droplet volume (at time t = 0 s) and the term 2/3Kf(θ)t represents the decrement in volume with time. The function θ) = f(θ0), where θ0 is the contact angle prior to evaporation  = 0.263676, β = 0.1514, RS = 0.070 mm and Vc,i = 49.7 pL). The parameter K ≈ 12.37467091DΔc/(ρLβ1/3), in which the symbols D, Δc and ρL denote the diffusivity (in cm2s−1) of the vapor molecules in the gas , the difference cs − c∞ between the saturation concentration of water vapor at the sphere surface cs (in gcm−3) and the ambient vapor concentration at infinite distance c∞ (in gcm−3) to the droplet, and the liquid density (in gcm−3), respectively. Assuming complete loss of water from the sensing surface, Equation (6) thus reduces to:To theoretically evaluate the approximate evaporation time, we consider a spherical droplet cap of height given by :(5)Vc=βπ follows :(6)Vc2/3ρl = ρw ≈ 1 gcm−3, D ≈ 0.282 cm2s−1 and cs = pvMw/(RT) ~1.7081 × 10−4 gcm−3 given a molecular mass wM = 18.01528 gmol−1, the gas constant R = 8.314 J(mol·K) −1 and the vapor pressure pv in air of 22.2 mmHg (≅ 29.6 mbar) at a temperature T ≈ 24 °C [2s−1. Based on Equation (7), Vc,i = 49.7 pL and f(θ0) = 0.263676, we can therefore estimate the total time taken for the water droplet to completely vanish from the surface of a cantilever sensor to be about 37.14 s, which agrees very well with the experimental droplet evaporation time (tev ≈ 39 s).For a water droplet,  ≈ 24 °C  cm2s−1. f0  to fr1 (cantilever + water + particles) as the droplet lands on the cantilever. Secondly, after a time lapse of tev the water is expected to evaporate and completely vanish from the sensing surface thereby shifting the resonant frequency fr1 to fr2 . The mass of the adsorbed particles can therefore most conveniently be computed (using Equation (4)) from the frequency shift of fr1 to fr2, i.e., Δ= ff r2 − fr1. Similarly, the mass of the particle carrier fluid  can at the same time be determined from the frequency changes of fr1 (wet mass) to fr2 (dry mass), which nearly corresponds to the previously discussed evaporation trends . These MPS beads were positioned at a point x ≈ 618.32 ± 44.20 µm  ≈ −2.80) from the fixed end of the cantilever. It should be noted that, for x ≈ L = 1000 µm, the value of U(xΔm) ≈ −6.08. The measurements shown Q = 2080 ± 430, for both the bare and loaded-state of the cantilever.A typical water droplet containing MPS particles is presented in https://www.cismst.de/en/loesungen/mikrotastspitzen/). This silicon-based sensor was first cleaned, wire bonded  by −15.42 ± 0.04 Hz. This yielded a calculated particles mass of about 0.13 µg ± 0.3 ng, which is equivalent to about 36,473 ± 84 particles. This number was computed from the ratio of the adsorbate mass to single particle mass . From frequency response measurements . The observed resonant frequency shift in Q ≈ 1700) while in f ≈ −11.8 Hz (with Q ≈ 285), corresponding to approximately 5.73 ng  and 4.07 ng , respectively. The number of calculated PMMA particles (based on point-mass condition) from other measurements ranged from about 56 to 6169. The assumed particle density and measured diameter were 1.18 gcm−3 and 1.90 ± 0.05 µm, respectively. A comparison of the number of particles estimates (both for MPS and PMMA) based on resonant frequency responses and particle counts (from SEM) is presented in the next section.In Particles dispersed in the liquid (droplet) are transported by the flow and deposited or assembled on a solid surface as the solvent evaporates. Depending on solid-surface properties and particle concentration, particles may form monolayers, multilayers, or both. In the latter case, for instance, a monolayer segment might be formed at the center while a progressive accumulation of particles leads to the formation of cluster-ring-shaped patterns after drying is complete, which often results in multilayers ,36. In tx) were estimated in ImageJ [Initially, MPS particle count estimate (from SEM micrographs) for single droplets deposited on Si bulk substrates was found to be 104,470 ± 420. However, by tuning the particle concentration and droplet size, a small particle assembly was realized on the cantilever surface  plays a critical role. In our study, these parameters (rH and T) were adequately monitored in every measurement cycle and found to be relatively stable. It is however worth noting that some little water from ambient atmosphere would possibly be adsorbed on the sensing surface courtesy of the hydrophilicity of our sensing surface and magnetic particles. In the current study, because of stable rH environment, we considered its contribution to the calculated mass to be negligible.In sensing and determining the mass due to frequency shift induced by adsorbed particles or otherwise, knowledge of the measurement environment ). Depositing particles away from the intended axisymmetric or eccentric target points would therefore clearly influence such output. In our work, such observations were evidently shown e.g., b and werm0 = 2 −5 ng and a sensitivity of about 7.8 fg/Hz meant for the detection of airborne nanoparticles [https://www.sclsensortech.com/self-sensing-cantilevers-tipless/) with m0 ~ 0.5 µg  into our measurement setup. These sensors offer the possibility of sensing micro particles with increased sensitivity. Calibration of these cantilever mass sensors would also favorably be considered to give minimal bias between the resonant measurement and SEM-particle count estimations.Moreover, to enhance mass sensitivity towards detectability of single liquid-borne particles, scaling down of our micro-sized sensors is further intended. In our recent works, efforts towards this course have successfully culminated to a self-readable resonant cantilever of a mass of articles ,39. NoneVd = 49.7 ± 1.9 pL which perfectly fitted on the sensing surface under consideration. Furthermore, water droplet evaporation trends were also investigated to ascertain minimum waiting time prior to particle-mass measurements. In general, a total evaporation time tev ≈ 39 s was determined and found to compare well with the theoretical estimation (37.14 s). Particle adsorption on hydrophilic Si bulk substrates and piezoresistive microcantilevers is also presented, followed by a determination of their mass and number based on both resonant frequency shift measurements and SEM image analysis for particle counting. Monolayer particles assembly on the sensor with a lowest particle count of about 19 and 47 for MPS and PMMA particles were, respectively, realized with a high-quality factor Q ≈ 1900, which nearly corresponds to Q of a bare cantilever.In this work, we sampled and adsorbed liquid dispersed particles on our in-house fabricated silicon-based piezoresistive cantilever sensors and further successfully tested the same approach on CiS commercial cantilever sensors. The mass sensitivities of our sensors ranged from about 27 Hz/ng to 14.73 Hz/ng for rectangular and triangular free-ended cantilever, respectively. With these cantilever sensors, we determined the masses of attached water droplets and particles through resonant frequency responses and compared the findings thereof with SEM particle count analysis. In our measurement process, droplet dispensing parameters were optimized to realize a small water droplet volume