Directing attention to task-relevant stimuli is crucial for successful task performance, but too much attentional selectivity implies that new and unexpected information in the environment remains undetected. A possible mechanism for optimizing this fundamental trade-off could be an error monitoring system that immediately triggers attentional adjustments following the detection of behavioral errors. However, the existence of rapid adaptive post-error adjustments has been controversially debated. While preconscious error processing reflected by an error-related negativity (Ne/ERN) in the event-related potential has been shown to occur within milliseconds after errors, more recent studies concluded that error detection even impairs attentional selectivity and that adaptive adjustments are implemented, if at all, only after errors are consciously detected. Here, we employ steady-state visual evoked potentials elicited by continuously presented stimuli to precisely track the emergence of error-induced attentional adjustments. Our results indicate that errors lead to an immediate reallocation of attention towards task-relevant stimuli, which occurs simultaneously with the Ne/ERN. Single-trial variation of this adjustment was correlated with the Ne/ERN amplitude and predicted adaptive behavioral adjustments on the post-error trial. This suggests that early error monitoring in the medial frontal cortex is directly involved in eliciting adaptive attentional adjustments. •SSVEPs were used to track the time course of attentional post-error adjustments.•Errors lead to an immediate allocation of attention to relevant stimuli.•Adjustments occur simultaneously with Ne/ERN and correlate with its amplitude.•Adjustments predict adaptive behavioral adjustments on post-error trial. This is however associated with a cost, as fully focusing on one task makes us less likely to notice information unrelated to that task. Attentional selection therefore comprises a fundamental trade-off. Too little selectivity may lead to poor task performance, but too much selectivity may lead to failure of detecting new and unexpected information. How does the brain find the right balance between too much and too little selectivity? Crucial for this ability might be an error monitoring system that continuously evaluates behavior and triggers adaptive adjustments of attention whenever necessary. As errors in tasks involving attentional selection often indicate insufficient attentional focus on task-relevant stimuli, error detection should elicit an immediate reallocation of selective attention to the relevant task, thus establishing a more optimal balance between attending task-relevant and task-irrelevant stimuli.Studies using scalp EEG methods provided evidence for an error monitoring system in the medial frontal cortex (MFC) that generates an error signal already within 50 ms after an erroneous motor response . While tMoreover, a controversy has emerged whether early attentional adjustments following errors are adaptive at all. While some studies demonstrated that post-error trials are associated with increased attention and task-related activity , others In recent years, further insights into the interplay between error monitoring in the MFC and post-error adjustments have been provided by studies investigating oscillatory brain activity. Errors are typically followed by enhanced frontocentral theta oscillations as well as decreased posterior alpha oscillations . While tIn the present study, we investigated the nature and precise time-course of attentional adjustments elicited by errors using steady-state visual evoked potentials (SSVEP). An SSVEP is the oscillatory brain response elicited by a flickering stimulus having the same frequency as the driving stimulus. SSVEP amplitude varies with the strength of attention directed to this stimulus and can be utilized to continuously measure the time-course of attentional allocation . Here, w22.1Seventeen participants  between 18 and 25 years of age (mean 22.0) with normal or corrected-to-normal vision and no history of neurological or psychiatric disorders participated in the experiment. No statistical methods were used to predetermine the sample size but our sample size was in a similar range as in previous studies using SSVEP techniques  or simil2.22).Our task was based on a global motion paradigm frequently used in SSVEP studies e.g., , which wThe task was to classify movements of the relevant stimulus  while ignoring simultaneous movements of the irrelevant stimulus . Each trial consisted of a brief interval (500 ms) of coherent motion of the relevant and irrelevant stimuli in one of the four cardinal directions . Coherence of movement was set to 75% to prevent tracking of individual dots. Relevant and irrelevant stimuli moved in independent directions. Participants had to indicate as fast as possible whether the relevant stimulus moved horizontally (left or right) or vertically (up or down) by pressing one of two response buttons with the index or middle finger of one hand. Hand and category-to-finger mapping was counterbalanced across participants. The movements of the relevant and irrelevant stimuli could be congruent  or incongruent , which should promote immediate post-error adjustments. The interval between the end of each 30 s run and the next fixation cross was 1500 ms. After 10 runs, a feedback screen was presented that provided the proportion of misses and errors. A miss was defined as a trial for which no response occurred within 1000 ms after movement onset.Prior to these experimental runs, participants worked through 24 practice runs. In contrast to the experimental runs, the feedback screen was presented after every 4 runs. During practice runs, participants received feedback on incorrect practice trials. If an incorrect response was provided, a 1500 Hz tone was immediately presented for 200 ms. If no response was provided within 1000 ms, a 600 Hz tone of the same length was presented.2.3The electroencephalogram (EEG) was recorded using a BIOSEMI Active-Two system  with 64 Ag—AgCl electrodes from channels Fp1, AF7, AF3, F1, F3, F5, F7, FT7, FC5, FC3, FC1, C1, C3, C5, TP7, CP5, CP3, CP1, P1, P3, P5, P7, P9, PO7, PO3, O1, Iz, I1, I2, Oz, POz, Pz, CPz, Fpz, Fp2, AF8, AF4, AFz, Fz, F2, F4, F6, F8, FT8, FC6, FC4, FC2, FCz, Cz, C2, C4, C6, TP8, CP6, CP4, CP2, P2, P4, P6, P8, P10, PO8, PO4, O2 as well as the left and right mastoid. The CMS (Common Mode Sense) and DRL (Driven Right Leg) electrodes were used as reference and ground electrodes. Vertical and horizontal electrooculogram (EOG) was recorded from electrodes above and below the right eye and on the outer canthi of both eyes. All electrodes were off-line re-referenced to average reference. EEG and EOG data were continuously recorded at a sampling rate of 1024 Hz, and were offline re-sampled to 512 Hz. Data were collected in DC mode and no additional hardware filters were used during data acquisition, except for the in-built anti-aliasing filter of the amplifier.2.4Each trial was assigned to a condition based on its Congruency , the Relevant Stimulus , and the post-hoc classification of Correctness . The variable Relevant Stimulus was considered only during averaging to ensure that red and blue stimuli contributed equally to all averaged data.2.4.1Trials for which no response had occurred between 200 and 1000 ms after stimulus onset (misses) were removed from this and all further analyses . Response times (RT) of correct trials and error rates were considered to investigate post-error adjustments of performance, and hence, were analyzed as a function of Correctness of the previous trial and Congruency on the current trial. This and all further analyses were restricted to trials following trials with incongruent stimuli, because only incongruent trials produced a sufficient number of errors. A frequent problem with using correctness as a predictor is that, with a non-stationary error rate, correct trials and error trials  are not sampled equally from different parts of the experiment, thus confounding correctness with effects of global performance shifts . We ther2.4.2EEG data were analyzed using EEGLAB v12.0  and custTo determine the appropriate electrode cluster for analysis, iso-contour voltage maps of the 10 Hz (blue) and 15 Hz (red) SSVEP amplitudes were calculated by means of Fourier transformation across whole epochs separately for runs in which participants had to attend to red and blue, respectively. Based on the spatial distribution of SSVEP amplitudes for each frequency  = 1.22, p = .24, dz = 0.30). To investigate whether errors led to behavioral adjustments on the subsequent trial, we subjected mean RTs of correct responses  = 6.01, p = .03, ηp2 = 0.27), and were higher following errors than following correct responses  = 15.9, p = .001, ηp2 = 0.50), but there was no significant interaction  < 0.01, p = .96, ηp2 < 0.001). Mean error rates were higher for incongruent trials than for congruent trials  = 37.5, p < .001, ηp2 = 0.70), but this congruency effect was smaller following errors than following correct responses  = 14.8, p < .001, ηp2 = 0.48). Taken together, as in a previous study (2Our task required participants to categorize brief coherent movements of dots in one color (the relevant stimulus) while ignoring concurrent congruent or incongruent movements of dots in the other color (the irrelevant stimulus). We first analyzed behavioral data to investigate whether post-error adjustments of performance are observable in this task. The mean error rate was 9.7%. Mean RTs for errors (574 ms) and correct trials (567 ms) were not significantly different  = 7.23, p < .0001, dz = 1.75), and amplitudes at 15 Hz (blue RDK) were higher when blue dots (relative to red dots) were the relevant stimuli (t(16) = 5.71, p < .0001, dz = 1.38).The time course of the attentional modulation on error and correct trials was analyzed in two stages: Following 3.3t(16) = 3.18, p < .006, dz = 0.77). To investigate whether this effect was specific to this time period, and thus reflects a relationship between Ne/ERN and post-error adjustment, we repeated this analysis for consecutive time windows within our response-locked epoch. t(16) = 0.53, p = .60, dz = 0.13). We finally included the data for relevant and irrelevant stimuli in this time range in an ANOVA with the variables Stimulus  and Amplitudes , and obtained a significant interaction between both variables  = 7.74, p = .01, ηp2 = 0.33). These results are consistent with the idea that the Ne/ERN elicits a rapid post-error adjustment of attention to the relevant stimuli but not to the irrelevant stimuli.Our results demonstrate that post-error adjustments of attention to relevant stimuli occur almost synchronously with the error response, and thus, at around the same time where early error-related brain activity is typically observed. In a further analysis, we therefore asked whether the strength of this adjustment is related to the size of the Ne/ERN. To this end, we compared Ne/ERN amplitudes between trials with high SSVEP amplitudes for relevant stimuli  and trials with low SSVEP amplitudes for relevant stimuli  in the time range where the mean adjustment reached its peak (100–200 ms after the response). Again, only incongruent trials were used in this analysis. t(16) = 1.61, p = .13, dz = 0.39). However, unlike the Ne/ERN, the Pe occurs after these adjustments, and thus, cannot reflect a causal influence of the Pe on these adjustments. These data merely show that the strength of post-error adjustment does not affect the emergence of the Pe.In addition, we analyzed the later occurring Pe as a correlate of conscious error perception . As show3.4F = 14.2, p = .002, ηp2 = 0.47), which was higher following low-adjustment trials than following high-adjustment trials  = 12.4, p = .003, ηp2 = 0.44). In fact, congruency had no significant effect at all following high-adjustment trials (t(16) = 0.78, p = .45, dz = 0.19). In the RTs  = 3.35, p = .09, ηp2 = 0.17). This analysis demonstrates that the observed rapid adjustment of attention to relevant stimuli caused the post-error reduction of interference in the error rates as depicted in Our behavioral data indicated two types of post-error adjustment: post-error slowing in RTs and a post-error reduction of interference in error rates. To investigate which of these behavioral adjustments is related to the observed adjustment of activity for relevant stimuli, we analyzed performance on trials following incongruent error trials. RTs and error rates from these post-error trials were submitted to a two-way ANOVA with the variables Congruency  and Adjustment on Previous Trial . In the error rates .This work was supported by a grant from the


Duganella sp. strain DN04, a novel species within the genus Duganella that was isolated from a maize field in North Carolina. The assembled draft genome size is 6,562,230 bp, with a total of 6,039 protein coding sequences and 3,889 functionally assigned genes, including genes putatively involved in the colonization of plants.Here, we sequenced  Duganella sp. strain DN04, a novel species within the genus Duganella that was isolated from a maize field in North Carolina. The assembled draft genome size is 6,562,230 bp, with a total of 6,039 protein coding sequences and 3,889 functionally assigned genes, including genes putatively involved in the colonization of plants.Here, we sequenced  Duganella within the family Oxalobacteraceae was identified in 1997 . Soil was added to phosphate-buffered saline, and dilutions were plated on Reasoner’s 2A (R2A) agar plates and incubated at 30°C for 1 to 2 days, followed by 20°C for 1 to 2 days. Repeated colony transfers and streaks were performed on R2A agar to acquire pure colonies. Genomic DNA was extracted from a freshly grown R2A culture using the AllPrep bacterial DNA/RNA/protein kit  following the kit protocol. A genomic library was prepared using a Nextera kit , size selected to an average fragment length of 475 bp, and sequenced using Illumina NextSeq paired-end v2 chemistry on v2.5 flow cells at 150 bp per read. A target coverage of 20× was used, and the genome was assembled using SPAdes v3.13.0  and an L50 value of 49. Assembly quality assessment using Benchmarking Universal Single-Copy Orthologs (BUSCO) (Duganella sacchari strain Sac-22 is the closest related species (98.82% identity).A total of 11,788,390 reads were obtained, with a total read length of 1,738,895,421 bp and an average read length of 148 bp. The genome length was 6,562,230 bp. In total, we found 281 contigs with an katE) and genes involved in the production of urease (ureA to ureG), biofilms (bdcA and wspC) (bioA to bioD and bioF)   , and biond bioF) , severaland ppk) . We alsoPRJNA529278, BioSample number SAMN11263562, accession number NZ_SPVG00000000, and SRA number SRX6098754.The complete genome sequence described here has been deposited in NCBI/GenBank under BioProject number


Helicobacter pylori (Hp) in the gastric tissue is essential for assessment of vaccination/therapeutic regimens.Quantitation of p < 0.001). Secondly, Hp copy numbers were quantitated in immunized mice, which demonstrated significantly lower numbeZrs, in reference to controls (p = 0.006).Here, the inhibitory effect of mouse gastric DNA (MgDNA) on amplification of Hp genomic DNA (HpDNA) was evaluated by spiking HpDNA with serial dilutions of MgDNA, which yielded concentrations of >10 ng/µl and >0.63-10 ng/µl of MgDNA, as inhibition and interference zones, respectively. Mice were then inoculated with varying doses of Hp and assessed at the inhibition-free concentration of 0.63 ng/µl. The average Hp copy numbers per microgram of gastric tissue discriminated mice having received high vs. low dose inoculums (Our method, bypassing the inhibition and/or interference imposed by MgDNA, was able to quantitate gastric tissue-colonizing Hp, segregating mice inoculated with low vs. high doses of Hp, as well as those immunized from controls. Helicobacter pylori (Hp)-related trial vaccination and/or therapeutic regimens is ultimately determined by quantitating the actual bacterial load, colonizing the gastric tissue. Several methods have been introduced for this objective, most of which have employed quantitative PCR (qPCR) to determine Hp genome copy numbers. The glmM (ureC) gene of Hp is most suitable for this purpose, as it is fully conserved and is present in one single copy[glmM, in the presence of excess human DNA. Roussel et al.[et al.[et al.[et al.[The success rate of any ngle copy. Howeverngle copy have repel et al., on the el et al.. Having l.[et al. observedl.[et al. reportedl.[et al. added thl.[et al.. Here, wglmM (ureC) gene fragment (978 bp) of Hp was cloned into pTG19T vector  following its amplification by High-Fidelity DNA polymerase , using glmM-specific primers (F: 5′- GAT GGC GTG AGG GGT AAA G-3′ and R: 5′-GATATG CCC GCT TTG CTC-3′), under the following conditions: 4 min at 94 ºC as initial denaturation, followed by 30 cycles of 60 s at 94 ºC for denaturation, 30 s at 57.4 ºC as annealing, 60 s at 72 ºC for extension, and final extension at 72 ºC for 10 min. The recombinant pTG19T-glmM vector was transformed into fresh competent E. coli TOP10F’ strain  and cultured on LB agar plates, supplemented with 100 µg/ml of ampicillin, 15 µg/ml of tetracycline, 1 mM of isopropyl B-D-1-thiogalactopyranoside (IPTG), and 40 µg/ml of X-gal. The Blue-white screening was performed to select glmM-positive clones. The cloning procedure was further confirmed by PCR and enzymatic (BamHI) digestion.The -glmM vector was linearized by XbaI enzyme, and its mean concentration was determined by OD260 measurement, in triplicates . The mass and copy number of pTG19T-glmM was calculated by the Applied Biosystems  recommended formula[-21). Since there was a single copy of the glmM gene in each plasmid template (PT) and Hp genomic DNA (HpDNA), the mass required for the preparation of serial copy number dilutions (range: 10-109 copies) was calculated. HpDNA was extracted by DNA extraction kit , and the required DNA for serial dilutions (10-108 of HpDNA copies) was prepared, as mentioned for PT, keeping in mind that the average size of HpDNA is 1.6 Mb.The circular pTG19Td formula: Mass = glmM primer (RT-F: 5’ ATG TTT GTG ATG CGT TTA 3’ and RT-R: 5’ AGC CTA TGG AAG TGA GAG 3’), and DNA templates . qPCR was conducted by Applied Biosystems StepOne™ Real-Time PCR Systems, under the following conditions: 14 min at 95 ºC as initial denaturation, followed by 50 cycles of 15 s at 95 ºC for denaturation, and 60 s at 60 ºC for annealing and extension. Melting curve analysis was assessed for the 60-95 ºC temperature range, and the cycle threshold (Ct) was calculated by StepOne v3.2 software .Serial dilutions of PT and HpDNA underwent amplification by qPCR, which was performed in total reaction volumes of 10 µl, containing 5 µl of Fast SYBR® Green Master Mix , 5 pM of each glmM gene amplification was investigated via spiking 105 copies of PT and HpDNA, with 14 serial dilutions (0-20 ng/µl) of MgDNA in qPCR reaction and co-amplified, as mentioned above. The inhibiting impact of MgDNA was investigated by Ct calculation and copy number quantitation, in the presence of various concentrations.The inhibiting role of MgDNA on 4, 3 × 105, and 3 × 106 CFU/mouse) with mouse-adapted Hp. Immunization was performed, as described previously[5 CFU of 24-hour old, liquid cultured Hp[Three groups of 4-6-week-old female C57BL/6 mice (five per group) were orally inoculated thrice in three consecutive days with different doses  of >0.99 . This analysis revealed (1) inhibition (>10 ng/µl), (2) interference (>0.63-10 ng/µl), and (3) inhibition-free (≤0.63 ng/µl) zones, which coincided for both PT and HpDNA amplification  3 × 105, and (Group C) 3 × 106 CFU. According to RUT results, every tissue sample underwent color changes with varying intensities over time, ranging from lowest (Group A) to highest (Group C). Keeping in mind the highest inhibition-free concentration of MgDNA, we quantitated Hp copy number in 0.63 ng/µl of mouse gastric DNA. Accordingly, the average Hp copy numbers per microgram of gastric tissue for each group of mice was ascertained as: (Group A) 24 ± 6, (Group B) 1042 ± 442, and (Group C) 1255 ± 145, which discriminated mice in group A from those in groups B (p = 0.0002) and C  3 × 10Quantitation of Hp in gastric tissue plays an essential role in the evaluation of any vaccination and/or therapeutic regimens. There are various molecular methods for Hp quantitation in biological samples, such as competitive PCR, noncompet al.[Roussel et al. performeet al.,19,20.1-107 and 101-109 copies of HpDNA and PT, respectively. Nevertheless, the Ct of 10 copies did not linearly follow the previous dilutions. Therefore, the quantitation of low Hp copy numbers in the stomachs of colonized mice requires further optimization.Quantitation of Hp copy number in the stomachs of treated mice is an important approach for the evaluation of vaccination/treatment efficacy. Our devIn conclusion, we have hereby developed a method for Hp copy number quantitation, which can bypass the interference and/or inhibition imposed by MgDNA and is able to quantitate the colonizing Hp copy numbers in the gastric tissue, segregating mice inoculated with low vs. high doses of Hp, as well as those immunized vs. controls.


A receptor by the neurosteroid allopregnanolone can be inhibited competitively by thyroid hormone , but modulation of nAChR by T3 or neurosteroids has not been investigated. Here we show that allopregnanolone inhibits the nAChR from Torpedo californica at micromolar concentrations, as do T3 and the anionic neurosteroid pregnenolone sulfate (PS). We test for the role of protein and ligand charge in mediated receptor inhibition by varying pH in a narrow range around physiological pH. We find that both T3 and PS become less potent with increasing pH, with remarkably similar trends in IC50 when T3 is neutral at pH < 7.3. After deprotonation of T3  at pH 7.3, T3 loses potency more slowly with increasing pH than PS. We interpret this result as indicating the negative charge is not required for inhibition but does increase activity. Finally, we show that both T3 and PS affect nAChR channel desensitization, which may implicate a binding site homologous to one that was recently indicated for accelerated desensitization of the GABAA receptor by PS.The nicotinic acetylcholine receptor (nAChR) is an excitatory pentameric ligand-gated ion channel (pLGIC), homologous to the inhibitory γ-aminobutyric acid (GABA) type A receptor targeted by pharmaceuticals and endogenous sedatives. Activation of the GABA The nicotinic acetylcholine receptor (nAChR) is an excitatory receptor protein localized in the central nervous system , the per3 receptors, and γ-aminobutyric acid (GABAA) receptors [de novo in brain tissue, can have hypnotic [The pLGIC superfamily also includes glycine receptors, 5-HTeceptors , as welleceptors . Neurosthypnotic , anxiolyhypnotic , anxiogehypnotic , anti-cohypnotic , and anahypnotic ), and cahypnotic , 19. Prehypnotic –23. The hypnotic , 24 or ohypnotic , 25 effehypnotic –28.A receptor [The neurosteroid 3α-hydroxy-5α-pregnan-20-one  activates the GABAreceptor , as doesreceptor –32. The receptor . In partreceptor ), illustA receptor at low concentrations [A receptor activation due to ivermectin or allopregnanolone [A receptor function [Despite structural analogy to positive allosteric modulators, T3 inhibits activity of the GABAtrations , 34–36 ananolone . The mecfunction .A receptors. In a recent crystal structure of a homopentameric receptor chimera comprised of a Gleobacter ligand-gated ion channel (GLIC) ECD and a mouse GABAAR α1 TMD, Laverty et al. [A receptors. This site is involved with the “desensitization gate” recently classified by Gielen et al. [A receptors affected receptor desensitization.Structural biology has recently provided insight into the binding mode of PS with GABAy et al.  showed tn et al. , indicatWhile lipid mixtures used for X-ray crystallography of membrane proteins have become more complex, there are still significant discrepancies with native membranes. pLGICs are extremely lipid sensitive; in particular, they may have specific interactions with anionic lipids , 41, polA receptors; for example, nonhalogenated and halogenated alkane anesthetics inhibit nAChRs but potentiate GABAA receptors , hydrocortisone, and 3α,5α,17β-3-hydroxyandrostane-17-carbonitrile (ACN) inhibit rat neuronal nAChRs [Torpedo nAChRs [ARs and nAChRs [Lipophilic modulators often have opposite effects on nAChRs and GABAview see ). Most kl nAChRs , 24, whil nAChRs , 47, ganl nAChRs , and Toro nAChRs . Severald nAChRs –52. Whild nAChRs , no effeChanges in extracellular pH also affect nAChR ion conductance, with more acidic environments yielding lower ion flux and basic environments yielding higher ion flux relative to the receptor’s acetylcholine response at a neutral pH , 55. DifTorpedo californica and used two-electrode voltage clamp (TEVC) to demonstrate functional effects of PS and T3 on nAChRs. We found that both PS and T3 inhibit nAChR activity due to acetylcholine, representing a case in which the same effect on channel function is demonstrated in both nAChRs and GABAA receptors. We also performed TEVC in differing extracellular pH environments to demonstrate the differential effects pH has on ligand charge and resultant receptor activity. Further, we show that PS and T3 modulate the effects of different pH environments on current amplitude when binding acetylcholine. We also show that T3 and PS change nAChR kinetics, increasing desensitization rate at low pHs but minimally affecting desensitization rate at high pHs.We tested the hypothesis that a population of anionic T3 transduces inhibition of nAChR at physiological pH. by extracting functional nAChRs from 2; 0.33 mM Ca(NO3)2; 0.8 mM MgSO4; 5 mM tris(hydroxymethyl)aminomethane-HCl (Tris-HCl); 2.4 mM NaHCO3); at low or high pHs, Tris-HCl was replaced with either 2-(N-morpholino)ethanesulfonic acid (MES) (T3/PS experiments at pH 6–6.7) or N-2-hydroxyethylpiperazine-N’-2-ethanesulfonic acid (HEPES) (T3 experiments at 8–9).Carbachol, acetylcholine, dimethyl sulfoxide (DMSO), asolectin, T3, NaOH, allopregnanolone, and pregnenolone sulfate were purchased from Sigma Aldrich . Isoflurane was purchased from Henry Schein Animal Health . T3 was dissolved in 0.1 M NaOH. Allopregnanolone was dissolved in 0.1% DMSO. All other ligands were dissolved directly in modified Barth’s solution . The frozen tissue (600 g) was homogenized in homogenization buffer , 5 mM ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), 10 mM iodoacetamide, 0.1 mM phenylmethylsulfonyl fluoride (PMSF), 0.02% NaN3, pH 7.5) in 30-second bursts at liquefaction setting (Oster blender) and centrifuged for 10 min at 4,080 x g. The supernatant was then filtered through 4 layers of cheesecloth and reserved. The pellets were then resuspended in 200 mL homogenization buffer and centrifugation was repeated. All of the supernatants were then pooled and centrifuged at 134,000 x g for 45 minutes. This pellet was resuspended in 32 mL 28% (w/v) sucrose buffer . In order to isolate membrane proteins, the aliquots of resuspended pellet (8 mL each) were layered on top of a discontinuous sucrose gradient  and centrifuged at 121,800 x g for 4 hours. The membrane band at the interface of the 30–35% sucrose layer was collected using a thin glass transfer pipet, diluted 1:1 with deionized water, and centrifuged at 142,400 x g for 35 minutes. The membranes in the resulting pellet were base-extracted by resuspending them to approximately 0.5 mg/mL protein in water, and the pH was carefully titrated to 11.0 with NaOH, followed by stirring for 45 minutes. The base-extracted membranes were then centrifuged at 142,400 x g for 45 minutes, resuspended in buffer A , and flash-frozen in liquid nitrogen.All steps before microinjection of resuspended receptor proteins were performed at 0 to 4 °C and were modified from the preparation method described previously . BrieflyThe protein product from extraction was diluted to 2 mg protein/mL in buffer A. Sodium cholate (10% w/v) dissolved in buffer A was added to give a final cholate concentration of 1%. The mixture was then stirred for 30 minutes, centrifuged at 142,400 x g for 30 minutes, and the supernatant was collected.Solid asolectin lipid was suspended in 4.16% cholate solution in buffer A to form a 65 mM solution. The mixture was vortexed and sonicated at 20 °C under argon gas for 45 minutes, and then stored at 0–4 °C until reconstitution.Solubilized nAChRs (1–1.65 mg/mL) were mixed with 0.3 mL of liposome/cholate mixture to yield a final volume of 1 mL, and a final concentration of 2% cholate. The mixture was dialyzed for 48 hours against 1,000 volumes buffer A, with a change of buffer every 12 hours. Before use in dialysis, buffer A was bubbled with argon gas for 15 minutes.Xenopus oocytes were purchased from Ecocyte Bioscience . The glass injectors were 1.6 to 2 mm o.d., 1.2 to 1.6 mm i.d., and were pulled using a Sutter Instrument Co. Model P-97 puller. The oocytes were injected with 46 nL asolectin-resuspended nAChRs using a digital microdispenser (Drummond Nanoject II). The injected oocytes were incubated at 19 °C in sterile Standard Barth’s solution 2; 0.8 mM MgSO4; 5 mM Tris-HCl; 2.4 mM NaHCO3) supplemented with 50 mg/L gentamicin.Ready-to-inject, defolliculated Sixteen hours after injection, the oocytes were clamped using a TEVC system. All ligands were dissolved in modified Barth’s solution at the indicated pH and were perfused into the chamber using a gravity-flow system. Thyroid hormones were initially dissolved in 0.1 M NaOH. The oocytes were impaled with two 3 M KCl-filled glass microelectrodes (1–2 MΩ each) and were clamped at -60 mV with an OC-725C Oocyte Clamp (Warner Instruments). Acetylcholine was applied to the bath surrounding the oocyte using a gravity-flow system at 10–13 mL/min. T3 or PS were co-applied with acetylcholine, also using a gravity-flow system. Each ligand perfusion lasted 60–70 seconds; after each perfusion, the bath solution was exchanged with ligand-free buffer until the current response signal had returned to baseline for at least 120 seconds before the next application of ligand(s). Data were recorded using iWorx LabScribe v1.959.The current flux signal for each administration of ligand was exported from iWorx into Matlab version 2012b. Due to significant batch-to-batch variation of ion conductance after receptor microinjection, maximal current response (peak) values for inhibition data were normalized to each oocyte's control maximal peak. For all data related to inhibitor concentration curves, the data were fit according to the conditions specified in The data were then trimmed to begin with the response as it reached 0.96 of the response peak , and end 35 seconds post-peak. The data were then fit to a two-degree exponential function  [We co-administered several neurosteroid and neurosteroid-like ligands with acetylcholine during TEVC to determine their effects on nAChR function.  ± 2 μM) .50 of 4.9 ± 3 μM PS. Triiodothyroacetic acid (triac), which lacks the amine group of T3, also inhibits nAChR, with an IC50 of 7 ± 3.1 μM triac (50: 20 ± 13 μM) (μM triac . Further± 13 μM) .Environmental pH affects molecular charge. According to the titration curve of T3 , T3’s pK50 of T3 and PS  on the IC3 and PS . A two-w50 values of T3 and PS  may be required to isolate the molecular origin of its unexpected inhibitory effects.We show here that T3 and PS both affect channel desensitization. Further, changing environmental pH levels appear to affect T3 and PS-induced nAChR desensitization. This may be a result of pH influence on channel residues at the TMD, as has been suggested by previous investigations , 55.A receptors, or with both. This work also indicates, through the inhibitory action of PS and the inhibitory, neurosteroid-like action of T3 on the nAChR, a complex structure-function relationship. The actions of both ligands relative to one another, and to the nearly identical molecules allopregnanolone and triac, can be a tool to investigate binding and inhibition of the channel.The present work adds to the mounting evidence of T3’s nongenomic effect on adult brain tissue , 77–80.


After controlling for the treatment group, first lactation cows had a significantly higher half-life (4.1 ± 2.1 h), Cmax (0.56 ± 0.2 µg/mL), and AUC0→∞ (21.6 ± h * µg/mL) relative to second lactation or greater cows, respectively. Administration of meloxicam through the subcutaneous or oral route results in appreciable, dose-dependent systemic levels.The dairy industry needs evidence-based solutions to mitigate painful procedures and conditions in dairy cattle. The objective of this study was to compare the pharmacokinetic properties of orally versus subcutaneously administered meloxicam in early-lactation dairy cattle. The study was conducted at a commercial dairy herd in southwestern Ontario, Canada. Twelve postpartum cows were enrolled in the study, receiving either subcutaneous meloxicam (MET) at 0.5 mg/kg body weight (n = 6) or oral meloxicam (MOS) at a higher dose of 1.0 mg/kg body weight (n = 6) immediately following parturition. The predicted half-life (12.5 ± 2.0 vs. 28.5 ± 2.0 h), C As the dairy industry looks to ensure its long-term sustainability, animal welfare considerations need to be addressed to maintain and improve consumer support . As an uMeloxicam is a selective cox-2 non-steroidal anti-inflammatory (NSAID) drug approved for the treatment of inflammatory and/or painful conditions in a variety of species . In cattThe pharmacokinetics of oral meloxicam in dairy cattle have been studied ,13,14; hThe objective of the current study is to evaluate the pharmacokinetic properties of orally and subcutaneously administered meloxicam in early-lactation dairy cattle. It is hypothesized that oral meloxicam will have a longer half-life, higher area under the curve, more muted maximal plasma concentration, and slower elimination rate relative to subcutaneously administered meloxicam.The present field-based study was carried out in accordance with the recommendations of the Guide to Care and Use of Farm Animals in Research, Teaching, and Testing set forth by the Canadian Council on Animal Care. The protocol was reviewed and approved by the Alberta Agriculture Institutional Animal Care and Use Committee .The study was conducted at a commercial dairy farm milking 200 cows in southwestern Ontario, Canada, between October and November 2018. Average milk production per cow was 43 kg, at 4.2% fat and 3.4% protein, at an average number of days in milk of 147.Cows were randomly allocated to receive the manufacturer label dose of either MOS  or MET (0.5 mg/kg subcutaneously). The farm staff were responsible for medication administration, blood sample collection and storage, and reporting any disease events. There was no formal sample size calculation performed prior to study commencement. Last, there was no attempt to stratify treatment allocation by lactation group to ensure even distribution of potential confounders.g in a temperature-controlled (approximately 5 °C) centrifuge. Plasma samples were then aliquoted into prelabelled  plastic vials. The vials were stored at −20 °C until submitted for analysis.Treatments were administered immediately following calving. Blood samples were collected via venipuncture of the coccygeal vein into heparinized tubes. Blood was taken at 0 (pre-medication), 2, 4, 8, 12, 24, 36, 48, and 72 h relative to treatment administration. Samples were centrifuged (within 1-h post-collection) for 20 min at 2000× Processed plasma samples received at the diagnostic laboratory  were rapidly transferred to frozen storage (−20 °C). Storage temperature was monitored by a National Institute of Standards in Technology (NIST)-certified temperature-recording device.g for 10 min to remove any particulate from the sample. Following centrifugation, 10 µL of supernatant was injected into the HPLC system. HPLC apparatus consisted of a pump system equipped with an automatic injector and UV detector (360 nm). Separation was achieved using a reverse-phase column  and a guard column . The mobile phase consisted of a mixture of acetic acid: 1% methanol (40:60) at a flow rate of 0.4 mL/min. For these conditions, meloxicam and piroxicam were eluted at a retention time of 7.8 and 5.4 min, respectively. Results for the method were linear over the calibration range of 10 to 1250 ng/mL as determined by use of a weighted linear regression model. Within-day and day-day precision were <10%. Accuracy ranged from 96% to 99%. The validated limit of quantification (LOQ) was 10 ng/mL.Plasma samples were analyzed with a validated high-performance liquid chromatography  procedure that was conducted using UV detection. An internal standard (piroxicam) was added to the untreated plasma sample. Standard concentrations used were 0.05, 0.1, 0.5, 1, 2, 3, 4, and 6 µg/mL respectively. Meloxicam and the internal standard were then extracted from plasma by solid-phase extraction (SPE). The SPE cartridges were connected to a vacuum manifold and conditioned with 1 mL methanol followed by 1 mL of water. The samples were passed through sorbents at a flow rate of less than 1 mL/min. The cartridges were then rinsed with 1 mL of 5% methanol and dried under vacuum for 2 min. Analytes were eluted with 1.5 mL of methanol. The eluent was then dried under vacuum at 40 °C for 2 h and the dried residue was reconstituted in 100 µL of mobile phase. The reconstituted sample was vortexed for 15 s then centrifuged at 14,000× Variables of interest for analysis included date and time of calving, time of meloxicam administration and blood collection, lactation number, disease information, and milk production. Variables of interest were downloaded from the commercial herd management software . All datasets were imported as comma-separated files  into themax) and time taken to reach maximum concentration (Tmax) were determined directly from the data. The area under the curve from 0 to last measurement (AUC0→last), AUC from 0 to infinity , the meloxicam plasma half-life (T1/2), and elimination rate (ke) were subsequently calculated for each cow. To estimate meloxicam concentration over time , the following Equation (1) was used:ijC is the drug concentration for the jth cow at experimental hour i, jKE is the elimination rate constant for the ith cow, and i+1jC is the new meloxicam concentration for the jth cow at time (hour) i + 1. Note, the first concentration used for this equation (C0) is Cmax, where the elimination rate constant serves to estimate the plasma elimination of meloxicam over time. The resultant concentration approximations were used to estimate time above plasma concentrations associated with clinical analgesic and anti-inflammatory efficacy.Pharmacokinetic parameters were estimated for each animal using non-compartmental analysis using PKSolver, a validated Microsoft Excel add-on . The maxCmax, Tmax, AUC0→last, AUC0→∞, T1/2, and KE. Further, descriptive analysis of cow demographics (parity and milk production) was summarized by the treatment group.Descriptive statistics were generated for the final dataset for the pharmacokinetic outcomes of interest, including max, Tmax, AUC0→∞, and T1/2. Variables explored for each model included: The treatment group (MOS vs. MET), lactation group (2 and over 3), and the non-lactating days prior to parturition group. Variables that had moderate statistical associations with the outcome of interest  were included in subsequent multivariable models.Univariable linear regression modelling was employed to study variables of interest. Statistical comparisons were made between potential predictor variables (including MOS vs. MET) and outcomes of interest. For this study, the outcomes of interest were Cp-values for partial F-tests or type III tests of fixed effects greater than 0.05 were eliminated from the model after assessing whether they were part of biologically plausible interaction terms or had a confounding effect on the outcome of interest . Continuous variables were assessed for linearity with predicted model outcomes through visually inspecting a locally weighted scatterplot smoothing (LOWESS) curve for linear relationship, as well as the significance (p < 0.05) of a quadratic term in the model. If a continuous variable did not have a linear relationship with the outcome of interest, it was subsequently categorized based on biologically relevant cut-points, or if appropriate, a quadratic term was retained in the model. Variables retained in the final model were assessed for collinearity through the examination of Pearson or Spearman rank-order correlation coefficients. When high correlation was found between variables (>0.6), the most biologically appropriate variable was chosen for inclusion in the final model. Standardized residuals were generated and visually assessed for normality and homoscedasticity for the linear mixed regression model. If the residuals were heteroskedastic or not normally distributed, appropriate transformations were performed on the outcome of interest.For plasma meloxicam pharmacokinetics, a repeated measures linear mixed model  was consThe predicted concentration-time profile of MET and MOS groups, after controlling for parity and repeated measures over time, is presented in When assessing these relationships in multivariable linear modelling, parity played a significant role in the pharmacokinetic properties of plasma meloxicam. First lactation cows had significantly higher blood levels for longer periods of time relative to mature cows in both treatment groups . First l0→∞, half-life, and Cmax relative to MET cows  of meloxicam varies between 0.2–0.7 µg/mL [50 levels for painful conditions in cattle before definitive conclusions can be drawn.Few studies have evaluated plasma meloxicam levels associated with a clinical response, with none of them having been conducted in cattle. Based on previous research in equine and canine species, the EC.7 µg/mL ,21,22,23max (1.59 ± 0.15 vs. 1.91 ± 0.39 µg/mL), and Tmax (5.33 vs. 3.20 ± 1.64 h), and AUC0→∞ (39.6 ± 7.4 vs. 24.65 ± 5.71 h * µg/mL) varied between cattle  than found in the current study [Other studies have evaluated the pharmacokinetics of parenteral (intravenous) meloxicam in immature ruminating cattle ,20. Bothnt study ,20 , a similar AUC0→∞ (115.6 ± 19 vs. 90.5 h * µg/mL), and markedly lower Cmax (1.95 ± 0.16 vs. 2.9 µg/mL), respectively [Oral administration of meloxicam tablets has been studied in ruminants. Relative to a previous study that administered meloxicam orally to ruminant calves at a dose of 0.5 mg/kg, MOS-treated cows had a longer half-life (28.5 ± 2.0 vs. 20.5 h) and Tectively . This stmax (2.92 µg/mL), Tmax (17.60 h), with similar AUC0→∞ (109.34 h * µg/mL) parameters [Another study specifically evaluating the pharmacokinetic properties of oral meloxicam (1 mg/kg) in mature, postpartum cows noted higher Crameters  relativeThe pharmacological preparations in this study were administered to healthy cows soon after parturition. While medication of healthy animals is difficult to justify, there is mounting evidence that parturition is painful in cattle ,27. The max, as well as higher AUC0→∞, Cmax, and KE parameters relative to the parenteral product. These differences were likely driven by a more prolonged absorption, as well as a higher absolute dosage, associated with oral meloxicam treatment.To the authors knowledge, this is the first study to have evaluated the pharmacokinetic properties of subcutaneously administered meloxicam in early lactation, adult dairy cattle. Overall, both drugs  attained systemic therapeutic concentrations. This study also highlights the importance of evaluating pharmacokinetic parameters in both first lactation and mature lactating cows, as the pharmacokinetics can differ markedly. The oral meloxicam product had significantly longer half-life and T


Chlamydomonas reinhardtii toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, omics-driven, mode-of-action discovery.Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable  Ideally, such an exposure system features minimal test duration to facilitate rapid measurements, and is matched to an algal lifecycle to allow accurate linking of the molecular perturbations to growth inhibition to address regulatory requirements of phenotypic anchoring /median[IntControl]), for each m/z feature). This calculation was repeated for each of the three batches. The resulting density distributions of LFC values, one for each biological batch, were visually similar (p = 1.38 × 10−12).To achieve the high level of repeatability required for toxicity testing, we explored the effect of normalising each batch of exposure data to the corresponding control metabolic phenotypes. First, we calculated the log similar A. Yet up2 fold-changes were recalculated individually for the metabolite features of each of the n = 10 samples in the CB group relative to the median feature intensity of their respective control group (log2 (IntCB/median[IntControl])). This allowed PCA of these LFC values to reveal any metabolic differences between the effects of CB-treatment normalised to each batch-control, for all three batches . However, slight batch differences were still detectable along PC2 (p = 0.015). Importantly, initial batch effects in PCA  metabolic features. Three comparisons were made , and used to interrogate the rank order of m/z metabolic features obtained from the other two batches. The significance of this set enriching the leading edges (LE) of each of the two batches was calculated using the normalized enrichment score (NES) and by estimating a false discovery rate (FDR). This enrichment test was twice repeated, for SB2 and SB3. The results indicate that top 100 LFC m/z features for from each batch are strongly and significantly enriching all biological batch-metabolite set combinations  and Q2 = 0.72 (goodness of fit), which indicated good generalisability.Finally, we sought to evaluate the metabolic perturbations in the three exposure experiments using a supervised multivariate analysis, which focuses on the most consistent changes induced in the CB-treated samples. Specifically, partial least squares-discriminant analysis (PLS-DA) was conducted on  1 vs. 2 , a robusomics, so to gain insights into toxicity mechanisms on a global molecular level and for discovering mKEs [Chemical risk assessment by new approach methodologies requires test protocols with experimental designs that are tailored to applying ing mKEs ,15,36,41Chlamydomonas, Raphidocelis, Desmodesmus, Scenedesmus, Chlorella), the phenomenon of light-driven cell cycle synchronisation is a known manifestation of evolutionary adaptation to scarcity of light in natural environments that can be experimentally induced by introduction of alternating light:dark cycles during incubation [For various unicellular green algae commonly applied in growth inhibition tests , which can be precisely measured over respective time-course analyses. Light:dark cycle-induced algal cell cycle synchronisation should thus be able improve t system ,61. In nomics technologies, e.g., for applications in read-across and prioritisation. For the purpose of discovering mKEs, the commonly prescribed 72 h test duration represents an arbitrarily-selected timeframe to capture low-concentration delayed-onset mechanisms of toxicity. A warranted question in this context is why the standard test duration should not be increased to even longer durations to optimise sensitivity of the system to lower concentration effects (bioaccumulation or damage). Given the selection of inhibition of cellular division as the AO for microalgal toxicity, it can be assumed that AO-inducing key events must occur and be measurable within perturbations of preceding biological functions [Reducing the duration of a conventional algal test protocol would increase sample throughput and thus facilitate applicability for high throughput unctions ,63 and tunctions ,64. Furtunctions ,65,66,67C. reinhardtii biomass to achieve reproducible data in metabolomics and lipidomics studies has been reported at ca. 2–2.5 × 106 cells per sample [omics studies due to low cell densities, problematic physical scalability, disturbance of phase equilibria during repeat-sampling procedures, strong pH drift, or a combination thereof.Algal toxicity testing of volatile chemicals has historically proven problematic when combined with requirements for test miniaturisation and increased harvestable biomass, due to natural autotrophic requirements. Conventional agitation- and aeration-based exposure vessel systems become highly cumbersome to maintain with increasing experimental scale, and the stability of mid-concentration exposures is difficult in open flask culturing. A variety of solutions has been proposed to address the issue, including tailored complex flasks ,69,70 anr sample ,81, and r sample ,82,83,84r sample ,85. This6 harvestable cells per test vial has been substantially increased from comparable vial systems [omics technologies in parallel. In the exposure system, synchronous C. reinhardtii cultures performed well with mean growth rates at 1.14−d, well above the validity requirements of 0.92−d [Here we developed and validated a hybrid sacrificial capped-vial exposure system to address existing shortcomings. The achieved biomass of >7.5 × 10 systems ,76,77 anf 0.92−d . Low pH f 0.92−d , precludf 0.92−d ,71,86.omics studies attempting to deduce a MoA of chemicals as a dynamic processes in C. reinhardtii have employed experimental designs with relatively low numbers of sampling time-points [C. reinhardtii. By utilising small glass vials, parallelisability of test vessels and thus factorisation of experimental variables can in principle be drastically increased in individual experiments. Capacity to test hundreds of vial replicates in parallel, per individual toxicity test, has been achieved in multiple other studies in the author’s laboratory (in prep.), enabling experimental designs to accommodate for high replication, temporal resolution and multiple exposure concentrations.Past studies to hypothesise on chemical MoA in algae were frequently limited in exploring the temporal dynamics of toxicity. Many existing multi-e-points ,24,37,8750 value for the reference substance 3,5-DCP was estimated at 0.93 ±0.09 mg/L, well within the range of 0.5 to 6.1 mg/L (48 to 72 h-EC50) observed for C. reinhardtii and other green algae [50 of the highly volatile chemical chlorobenzene for C. reinhardtii was estimated at 32.52 ±3.64 mg/L (data of three repeats), again within range of published closed-test system data , and dr224 mg/L ). When a224 mg/L , the estg = Vg/(Vg + (VL·R·T/KH)) with Vg = 10% vial gas phase, VL = 90% vial liquid phase, T = 298.15 K, R = 8.205 ± 10−5 m3·atm/(K·mol), KH = 3.7 × 10−3 atm·m3/mol [Another requirement of the exposure system was to enable stable exposure concentrations of volatile toxicants over the experimental timeframe. Measurements of chlorobenzene levels at the start of the experiment were lower than nominal , however the levels did not change significantly over the 12 h 15 min incubation period. This indicates chemical loss during initial stock preparations, however high stability of concentration levels over the incubation period. According to Henry’s law constant, fraction of chlorobenzene in the gas phase is calculated as %CBm·m3/mol  suggestsRobust and reproducible determination of biomarkers and toxicological key events predictive of chemical insult from the algal metabolome is paramount for downstream utilisation of the generated data to support environmental hazard assessments. To characterise the reproducibility of data gained from our exposure approach, differences in the metabolite phenotypes between three repeated exposure studies were compared. Ranges of recorded median RSD values (20.86% to 24.99%) lay within the expected ranges for test organisms and cells, e.g., median RSD values have been reported for human cell lines between 20–22% .2 fold-change of the measured m/z feature intensities to their batch-specific biological baseline (controls), proved to mitigate existing batch differences.Small batch effects could be observed between the baseline metabolic phenotypes across the three repeats of the exposure experiment when compared in PCA. A number of possible factors might have contributed to these differences, including subtle changes in laboratory maintenance that unknowingly affected the growth and biological activity of the sensitive algal cultures, as reported previously for microalgal bioassays ,93,94,95m/z features within the other batches, adding further support to our conclusion that a consistent toxicological effect could be measured.Despite these small batch effects in PCA, the algal metabolic stress response appeared to be regulated by a consistent mechanism across studies, as indicated by PLS-DA on pooled data. Additionally, we applied set enrichment analysis to metabolomics data, an approach originally developed for analysing gene set enrichment  which laChlamydomonas reinhardtii Dangeard (1888) CC-125 wild type mt+ was purchased from the Culture Collection of Algae and Protozoa . The strain was maintained heterotrophically on agar plates (1.5% w/v agar in tris-acetate-phosphate media) on laboratory shelves at 23 °C until liquid inoculation. A modified growth medium  was developed here based on Sueoka’s high salt medium supplemented with Kropat’s trace elements [2*2H2O and MgSO4*7H2O were increased to 50 mg/L and 100 mg/L, and the concentration of total phosphates was reduced by 92.5% to 54 mg/L KH2PO4 and 108 mg/L K2HPO4 to decrease both palmelloid formation and mass spectrometric ion suppression due to phosphate accumulation in C. reinhardtii cells [3. As C. reinhardtii growth is optimal between pH 5.5 to 9 [Axenic elements ,101 withii cells ,103,104.5.5 to 9 ,106, pH 5.5 to 9 . To reta5.5 to 9 .The growth medium was sterilised by 0.22 µm-filtration to avoid degradation of MOPS, salt precipitation and shift of the carbon equilibrium caused by autoclaving . For ini6 (7 mL) were taken from synchronised samples at every time-point, and sampling volume of the non-synchronised samples were adjusted to match this cell number by prior electronic cell counting. The latter was performed using CASY TT cell counting technology  which operates via electric pulse area analysis to count suspended particle. As described below, cells were harvested and stored at −80 °C until combined metabolite extractions.The first study compared the metabolic phenotypes and mitotic activity of cell cultures grown under continuous lighting versus those grown under alternating 12 h:12 h light:dark cycles. Algae grown in either condition were seeded in vials as described below. Samples were taken at 4 h 15 min (±15 min), 8 h 15 min (±15 min) and 12 h 15 min (±15 min) post-seeding. 5.25 × 106 cells were sampled (n = 3), centrifuged at 7000× g for 3 min (23 °C), pellets washed once in 1 mL phosphate-buffered saline (PBS), and cells fixed in 70/30 ethanol/water  for 24 h at 4 °C. This last step served to permeabilise cells for propidium iodide (PI) and to extract chlorophylls which might interfere with the PI emission spectrum [4 particles per sample, PI fluorescence was measured using a Becton Dickinson FACS-Calibur , and analysed using open-source Flowing v2.5.1 (flowingsoftware.btk.fi). Absence of chlorophyll interference was confirmed with non-stained samples. Due to haploidity of C. reinhardtii vegetative cells, discrete peaks in per-particle fluorescence intensity histograms from cells sampled at a given time were equated either to the number of genome copies present within each cell particle (zoosporangia or zoospores) post nuclear division. Fluorescence areas in between discrete peaks were assigned as cells currently undergoing DNA replication and assigned as ‘S-phase’. Growth curves of both culture regimen were determined using CASY cell-counting, at 2–4 h intervals for non-synchronised cultures and at higher resolution  at the light:dark transition for synchronised cultures.Over the course of this study, further samples were taken for flow cytometric measurements of genomic material in cell particles at 4 h, 8 h, 12 h and 16 h post-seeding for non-synchronised cultures, and 3 h, 6 h, 9 h, 11 h, 13 h, 14.5 h, 16 h, 18 h post-seeding for synchronised cultures. 1 × 10spectrum  The cellFor each sampling time-point, vials were of sacrificial nature to maintain carbon equilibrium. Synchronised cell culturing was determined as preferable to non-synchronised cultures, as described in results, and used in all further studies.g for 3 min . The supernatant was decanted, cells were diluted in toxicant-spiked CGM to yield a final cell concentration of 7.5 × 105 cells/mL, and seeded in clear 10.5 mL glass vials with aluminium cap inlay  to establish 10% total gas phase volume within each vial ; 0, 3.5, 7, 10.5, 14, 17.5, 21, 25, 37.5, 50 mg/L ; 0, 1.5, 3, 6, 12, 18, 25, 36, 48 mg/L . Cells were exposed to 3,5-DCP at 0, 0.6, 1.2, 2.4, 4.8 and 9.6 mg/L. A 4-parameter log-logistic dose-response model and effective concentration (EC) estimation was performed using [A study conducting toxicity tests on synchronised cell cultures was performed using chlorobenzene  as a volatile substance and 3,5-dichlorophenol  as the reference substance. All tests were performed with biological quadruplicates. ed using  and drc A further exposure study was conducted to compare the metabolite phenotypes of synchronised cell cultures in pure growth medium (control) versus cells exposed to 25 mg/L CB, repeated three times, to characterise the metabolic variability and repeatability of data generated in the exposure vial system. Ten biological replicates for each of the control and CB groups were seeded in capped vials, incubated for 3 h, harvested as described below, and cell pellets stored at −80 °C until metabolite extraction. This experiment was repeated three times in identical set-up with a duration of 5d between experiments. Metabolite extraction was performed for all samples of the three repeats combined.g and −11 °C for 3 min, and the supernatant aspirated. The number of washes was first optimised in a pilot study by comparing 10 samples from each of three groups: no wash, single wash of cell pellet, or two washes. Each wash comprised of resuspension of the cell pellet in 10 mL of −20 °C 35% methanol:water, then repeating the centrifugation and supernatant aspiration (described above). Between centrifugation and aspiration steps, samples were stored in a custom-manufactured mobile freezer unit between −20 °C pre-cooled aluminum beads to minimise metabolic activity and avoid freezing of suspension and thus risk of cell lysis at lower temperatures. After aspiration, pellets were flash-frozen in liquid nitrogen, and all samples stored at −80 °C until metabolite extraction. A single pellet wash was determined as optimal . Each aliquot was dried in a SPD11V SpeedVac sample concentrator  at room temperature. Extracts were stored at −80 °C until reconstitution in injection solvent (below). The same procedure was performed to generate an extraction blank sample.Biphasic metabolite extractions were performed with a final solvent ratio of methanol:chloroform:water  of 2:2:1.8 . Cell pem/z features, the dilution of the reconstituted algal extracts was first optimised ) was found to result in the maximal number of unique m/z features post-processing, therefore this option was used for preparation of all further samples. Ten µL of each reconstituted sample was pooled into an intrastudy quality control (QC) sample. All samples  were analysed as technical quadruplicates (10 µL) on an Orbitrap Elite mass spectrometer (Thermo Scientific) with direct infusion nanoelectrospray ionisation (nESI) source  in positive ionisation mode. Individual SIM-windows of samples were stitched into single mass spectra, ≥75% replicate filtered, global peak aligned with a 3 ppm error tolerance, extraction blank filtered, QC-based locally-estimated scatterplot smoothing (LOESS) signal corrected [m/z feature, and features in QC samples with RSD values >30% omitted from the data. Missing values imputation (k-NN) was performed and the generalised log transformation applied for multivariate data analysis.Sample preparation, direct infusion mass spectrometric analyses and data processing were performed after the procedure of Southam et al.  To maximorrected , and ≥80orrected . RSD valm/z features that are high-ranking in importance for separation of phenotype in the test data. S commonly comprises elements of a biological pathway , however may contain individually defined variables. Here, S comprised m/z features with high LFC of one biological batch, which were checked for enrichment against LFC-ranked lists of m/z-values in the other biological batches. Thus, it was determined whether those m/z-values that were responsible for LFC-driven phenotype-separation in one biological batch were equally important in the other biological batches. Strength of the enrichment was calculated using NES metric , significance determined by estimating false-discovery rate corrected p-values using 1000-fold permutation of the m/z features. m/z features within S deemed most impactful towards a high enrichment metric NES are referred to as the leading edge subset  were performed using R and the R-Stats base package, PCA and PLS-DA conducted using MetaboAnalyst v4.0 . Set enrset .n = 4) was injected into 20 mL headspace vials with PTFE-lined septa  containing 9.7 mL CGM and 100 µL 0.8 M H2SO4. Samples were stored in the dark at 4 °C. One hundred µL of 10 mg/L d5-chlorobenzene  in CGM was added immediately prior to analysis . Headspace GC-MS was performed on an Agilent 5975B inert XL MSD using DB-624 column (30 m × 0.25 mm i.d. × 1.4 µm) and Gerstel Autosampler MPS2. Analytes were identified using target m/z = 112 and qualifier m/z = 77 (chlorobenzene); target m/z = 117 and qualifier m/z = 82 (d5-chlorobenzene). Sample sequence was randomised and quantification of chlorobenzene was performed relative to d5-chlorobenzene.To measure concentrations of chlorobenzene in test medium over time, algae were incubated in vials with medium containing 14 mg/L or 24 mg/L chlorobenzene  for 12 h 15 min. At 0 h and 12 h 15 min, 100 µL (C. reinhardtii (at high inoculation cell densities) for a metabolomics assay, while minimising test duration and chemical volatilisation. The latter was proven by stable exposure concentrations to chlorobenzene over the test duration. We also examined the repeatability of this C. reinhardtii exposure system, identifying batch variation when applying unsupervised multivariate analyses to the metabolomics data, yet reducing these unwanted effects by normalising each biological batch to its respective control data. Furthermore, we used set enrichment analysis as well as supervised multivariate analysis to demonstrate the consistency of the metabolic markers discovered in three repeat exposures to chlorobenzene. Secondly, we have demonstrated that the application of an omics technology to synchronised algal cell cultures grown in alternating light:dark cycles can detect characteristic metabolic phenotypes that evolve through the cell-cycle. This should benefit the interpretability of molecular data in terms of discovering early mKEs that are anchored to, and predictive of, the algal adverse phenotype of reduced growth.Firstly, we designed an alternative closed-vial chemical exposure system that allows for sufficient growth of


Results showed that (1) for diversity, α-Diversity manifested unimodal variation patterns in horizontal spaces, peaking at high latitude and low longitude. However, α-diversity was not sensitive to elevation in vertical spaces and tended to decrease with increasing elevation. With increased latitude, longitude, and elevation, β-Diversity diminished; meanwhile, the rate of species turnover decreased and the similarity of community composition enlarged. γ-Diversity demonstrated quadratic function changes that were initially incremental and then decreased with increasing longitude, elevation, and latitude from 37.5° to 40°. In general, β-diversity had positive correlation with γ-diversity and negative correlation with α-diversity, which conformed to the function of β = γ/α. (2) For biomass, changes of aboveground biomass (AB) were more obvious along latitudinal gradients, whereas variations of belowground biomass (BB) had smaller differences along longitudinal and latitudinal gradients. More biomass was allocated to BB toward the north and east, whereas root-to-shoot ratio (R/S) was more evident at greater latitude than greater longitude. With increased elevation, more biomass was also allocated to BB, and the relationship of biomass to elevation was closer in AB. In short, the relation of biomass allocation tended to belowground plant parts with different geographical scales. (3) Species diversity had the strongest positive influence on AB. The Patrick and Shannon indices had correlations of power functions with AB and R/S, respectively, indicating that an allometric model could be used to model relationships between species diversity and biomass. In conclusion, the unique geomorphological structures with a series of basins between mountain systems on the east of the Loess Plateau, meant that subalpine meadows were mostly distributed along latitudinal directions, so the spatial distribution of species diversity and biomass was more evident along latitudinal gradients, and thus the response of aboveground biomass was more sensitive to variations of spatial gradients and species diversity.Researchers frequently discuss spatial distribution patterns of species diversity and biomass together with their correlations along geographical gradients. Typical subalpine meadows occur widely on the east of the Loess Plateau, China; here, we selected nine mountains belonging to four mountain systems from north to south on the east of the plateau. We analyzed five latitudinal and longitudinal gradients together with six elevational gradients to study the spatial distribution patterns of species diversity (including  Plants serve vital roles in terrestrial ecosystems and provide humans with many ecological services in regulating climate, improving soil fertility, protecting biodiversity, and promoting productivity –4. StudiBiodiversity contributes significantly to sustaining a global ecological balance and to promoting sustainable development for humans –7. As a α-, β-, and γ-diversity [α-diversity, which mainly focuses on species number in local homogeneous habitat. At this scale, the principle factors that affect diversity are ecological niche diversity and interspecific interaction, so α-diversity is closely related to environmental energy [β-diversity, which indicates differences of species composition among different habitats and communities or differences in turnover rates of species along environmental gradients. The dominant ecological factors that control β-diversity are soil, landform, and disturbance [γ-diversity, which describes species number at regional or continental scales. The ecological processes that drive γ-diversity chiefly include hydrothermal dynamics, climate, as well as species development and evolution [α- and β-diversity constitute the overall diversity of communities or ecosystems, or habitat heterogeneity of a certain district.Measurements of species diversity are primarily conducted at three spatial scales known as iversity –22. The l energy , 21. Theturbance . The lasSimilar to species diversity, biomass is also a primary quantitive characteristic of ecosystems and reflects plants productivity; thus species diversity is a basic part of the study ecosystem function . BiomassSoil erosion is one of the most important environmental issues affecting terrestrial ecosystems. In arid and semi-arid areas, runoff and erosion can reduce soil water-holding capacity and hinder the recovery of degraded ecosystems . The Loeα, β, and γ diversities in subalpine meadows; (2) patterns of variation in biomass and biomass allocation at horizontal and elevational scales in subalpine meadows; and (3) correlations between species diversity and biomass in subalpine meadows.From a level of plant population in natural conditions, species diversity has important significance to the discussion of spatial distributions and correlations of species diversity and biomass at various levels; this illuminates the internal mechanisms of functional relationships between biodiversity and ecosystems. Given this, typical subalpine meadows were used as research objects on the east of the Loess Plateau. These ecosystems were divided into different latitudinal, longitudinal, and elevational belts, and then the following three scientific problems were addressed: (1) spatial distribution patterns of species diversity at the three scales of 2; these are mainly distributed in high-elevation belts above the timberline in the Liuleng, Lvliang, Wutai, and Zhongtiao mountain systems. Plants in subalpine meadows principally include perennial herbs that are suitable for low temperature and moderate moisture; for example, Carex, Kobresia, Asteraceae, Leguminosae, and other cyperaceous species were common [The Loess Plateau distributes the largest area of loess in the world and is characterized by rare precipitation, intense evaporation, severe soil erosion, and a low ability to resist natural hazards, so its ecological environment is harsh in nature and extremely difficult to recover when destroyed –33. The e common . The soiComparing with a vegetation-type map of the Loess Plateau  and a toAdditionally, prior to carrying out this experiment, we obtained permissions from the Luyashan National Nature Reserve (Xinzhou city) for DD, BT and DT; from the Pangquangou National Nature Reserve (Lvliang city) for ML, HY, YZ and YD; and from the Wulushan National Nature Reserve (Linfen city) for SU and SE. We successfully obtained these permissions as our field studies did not involve endangered or protected species.2 plots were installed randomly to survey plant diversity at a community scale or a total of 54 plots on all mountains. We used 1 m × 1 m quadrat frames as measurement tools, which we divided into 100 uniform grids (0.1 m × 0.1 m). In each grid, we measured plant height, abundance, coverage, and frequency of each species; the data were used to calculate species diversity indices in the plots . Meanwhile, we recorded the latitude, longitude, and elevation of each plot by a portable GPS  community dissimilarity based on species composition and (2) species replacement based on the distribution boundary [α-diversity, measurements of β-diversity could be separated into two methods: binary attribute data and quantitative data, respectively. Hence, we used two indices, the Cody and Sørenson indices, in the analysis of β-diversity based on binary attribute data; in addition, we used the Bray-Curtis index based on quantitative data. Corresponding computational formulas of these β-diversity indices are as follows:βC, βS, and βB–C are the Cody, Sørenson, and Bray-Curtis indices, respectively; a and b are the number of species in plots A and B, respectively, and c is the number of common species between plots A and B; and cIV is the sum of the relatively small important values of species common to both A and B, that is, cIV = ∑min .Species in different plots were merged in each geographical gradient belt and variations of boundary . Unlike γ-Diversity indicates species richness of all habitats within a certain geographical range and is usually used to demonstrate species number in a region or on a continent [S) in each geographic gradient belt was defined as an indicator to investigate spatial distribution patterns of subalpine meadows at the scale of γ-diversity.ontinent . Here, rα and γ diversity were recalculated basing on species number singly in each gradient belt. Then, common functions as β = γ/α, β = γ–α, and β = 1–α/γ [α, β, and γ diversity, correlation analyses were conducted in measured value of β-diversity with α and γ diversity, as well as with its simulated value.Firstly,  = 1–α/γ , 35, 36 Below- and aboveground biomass was added to calculate total biomass and the root-to-shoot ratio was also found for each unit. Therefore, aboveground biomass, belowground biomass, total biomass, and the root-to-shoot ratio were adopted as biomass indicators and were used to analyze variations of plant community biomass at different latitude, longitude, and elevation in subalpine meadows.α, β, and γ diversity plus with variations of biomass with latitude, longitude, and altitude. SPSS 16.0 software  was adopted to conduct regression analyses between α-diversity and biomass; correlation analyses among α, β, and γ diversity; together with correlation and significance analyses of α-diversity with elevation and among various mountains, respectively.Lastly, OriginPro 9.1 software  was used to draw spatial distributions of H′), Shannon (H), Pielou (E), and Patrick (R) indices in subalpine meadows. However, these α-diversity indices had relatively consistent trends that all presented variations in wave curves ranging from the northernmost DD to the southernmost SE , and their minimum appeared in ML . The maximums and minimums for the α-diversity indices all appeared in central mountains (from ML to YD) and their differences were significant (P<0.05), showing a greater fluctuation of α-diversity in the central mountains than elsewhere. iIn quantitative terms, α-diversity of the subalpine meadows was smallest in the central mountains, whereas it was greater in the northern (from DD to DT) and southern (from SU to SE) mountains with no larger differences between them. Except for E, changes were very consistent for H′, H, and R, and reached significant levels (P<0.05) between adjacent mountains from DD to YD; meanwhile, changes of E were significant (P<0.05) only between adjacent mountains from DT to HY. However, from YD to SE, the α-diversity indices had insignificant differences (P>0.05) (We observed large differences in different mountain ranges for the Simpson (nmost SE . Maximum(P>0.05) .α-diversity indices of subalpine meadows showed similar patterns of change with latitude and longitude  and relatively low longitudes (112.4° E), respectively. Maximums of R, H′, H, and E, which were 26.3, 0.92, 2.86, and 0.88, respectively, all existed near 38.7° N and 112.4° E. Trends of α-diversity indices had larger discrepancies with latitude and longitude at a latitudinal boundary of 38°. When latitude was below 38°, α-diversity indices changed more gently with latitude; with longitude, α-diversity indices experienced symmetrical changes with an axis of symmetry at 112.3°. When latitude was greater than 38°, α-diversity indices changed more dramatically with latitude; with longitude, α-diversity indices showed asymmetrical changes that was more dramatic initially and then less so. With changes in elevation, R, H′, H, and E had negative correlations with elevation , but correlations were all insignificant (P>0.05), demonstrating that α-diversity was affected insignificantly by elevation and tended to decrease with increasing elevation , Sørenson (βS), and Bray-Curtis (βB–C) indices , βC, βS, and βB–C all decreased and their reduced amplitudes were −19.45%, −15.27%, and −0.75%, respectively; along elevational gradients,βC and βB–C declined with corresponding mean amplitudes of −33.33% and −29.85%, whereas βS increased with its amplitude being 16.35%.Similarly, along longitudinal gradients, γ-diversity (S) generally exhibited a decreasing trend of a logarithmic function , but it presented a significant variation of a quadratic function with a trend of initially increasing and then decreasing at latitudinal belts from 37.5° to 40° . Along longitudinal gradients, S significantly demonstrated the variation of a quadratic function with a tendency of initially increasing and then decreasing ; its peak value appeared at longitudinal belts between112.05° and 112.5°. Along elevational gradients, S also showed a quadratic function variation with a trend of initially increasing and then decreasing , while peaking at elevational belts between 2200 m and 2300 m. Averages of S along latitudinal, longitudinal, and elevational gradients were 47.6, 48.6, and 40.3, respectively, illustrating that species distribution was slightly greater in horizontal spaces than that with changes in elevation at regional scales.The spatial distribution of subalpine meadows was highly consistent at the scale of iversity . Along lβC and βS  were all calculated basing on binary attribute data and had similar correlation with α-diversity, γ-diversity, and their simulated values  and βS (0.622) were all in the function of β = γ/α. While for βB–C , it was calculated according to quantitative data and had corresponding positive and negative correlations with γ and α diversity, which was opposite to βC and βS. Among positive correlations with its simulated value, the greatest correlation coefficient of βB–C (0.932) also existed in the function of β = γ/α. Thereby, although a larger difference appeared in β-diversity with different data types, β-diversity had positive correlation with γ-diversity and negative correlation with α-diversity, which conformed to the function of β = γ/α.d values . They haBiomass indices of subalpine meadows fluctuated in various mountains . CorrespR2 = 0.402, P>0.05) with larger values existing in the southern sites of SE and SU as well as the central site of HY. The AB was significantly larger in southern mountains (319.05 g/m2) than that in northern (112.35 g/m2) and central (126.4 g/m2) mountains (P<0.05).From the northernmost site, DD, to the southernmost, SE, AB was varied with a wave form and tended to increase  with larger values appearing in the northern sites of BT and DD as well as the central HY site. Meanwhile, BB in northern mountains (622.62 g/m2) was significantly greater than that in the central (431.39 g/m2) and southern (437.18 g/m2) mountains (P<0.05).BB changed with a wave form and tended to decrease  with larger values presenting in the central HY site, the northern BT, and the southern SE site. The TB in central mountains (557.79 g/m2) was significantly smaller than that in the northern (734.97 g/m2) and southern (756.23 g/m2) mountains (P<0.05).TB had no obvious trends (R2 = 0.6075) than longitude (R2 = 0.0677). The BB had slightly smaller tendencies with latitude (R2 = 0.0691) than longitude (R2 = 0.1384). Tendencies of TB were also slightly smaller with latitude (R2 = 0.0187) than longitude (R2 = 0.0673). That is, changes in AB were more evident along latitudinal gradients, whereas changes in BB were smaller along latitudinal and longitudinal gradients.The horizontal spatial distribution of biomass had obvious characteristics in subalpine meadows . TendencP<0.05), but the increases in BB and TB were all not significant with increasing elevation (P>0.05), illustrating that correlations were stronger between AB and elevation.Along elevational gradients, trends of biomass existed with some differences as well . AB decrR2 = 0.315, P>0.05) with larger values being in the northern BT and central YD and ML sites. The R/S in the southern mountains (1.49) was obviously smaller than that in the northern (6.19) and central (4.34) mountains (P<0.05). From DD to DT in the northern mountains, R/S had a tendency of increasing, indicating that more biomass was allocated to belowground plant parts in northern mountains.The relation of biomass allocation (R/S) changed in a wave pattern and tended to decrease  than with longitude (0.0868). That is, more biomass was allocated to belowground plant parts with developmental directions to the north and east in subalpine meadows.P<0.01), indicating that more biomass was also allocated to belowground plant parts with increasing elevation.Along elevational gradients, the R/S had significant increasing correlations with increasing elevation , whereas R/S had most significantly negative correlations with R and H (P<0.01); this means that with increasing species diversity, AB increased and more biomass tended to be allocated to aboveground plant parts. In addition, regressive relationships of R and H conformed to power functions with AB and R/S, respectively, and their power exponents had gaps larger than 1, demonstrating that species diversity had an allometric relationship with biomass in subalpine meadows.We conducted regression analysis between pairs of  meadows . Only R α-diversity peaked at central elevations [α-diversity tended to decline gradually with increasing elevation [α-diversity had unimodal change patterns with peak values being at high latitude and low longitude in horizontal spaces in subalpine meadows of the Loess Plateau; trends were more distinct along latitudinal gradients, but α-diversity was not sensitive to elevation in vertical spaces and tended to decline with increasing elevation.Species distribution patterns are outcomes of many ecological processes, which are controlled by species evolution , variations in geography, and environmental factors ; these then create relatively large discrepancies for patterns of species diversity along geographical gradients for researchers , 35, 36.evations –43, wherlevation  or had nlevation . Our stuβ-diversity along elevational gradients, many scholars also obtained different results. These could be roughly divided into three types. First, β-diversity had no regular changes with elevation, and greater values usually appeared in community ecotones [β-diversity had monotonic decrements with increasing elevation [β-diversity rarely changed at low elevation, but sharply increased at high elevations and with increasing elevation [β-diversity was diminished, and amplitudes of variation were the greatest along latitudinal gradients, taking second place along elevational gradients, and were smallest along longitudinal gradients.From research studies on patterns of variation in ecotones . In the levation , 47. Laslevation . In our γ-Diversity presented two universal distribution patterns along elevational gradients. One was partial peak in distribution patterns with peak values of different studies appearing in different regions [γ-diversity were in accordance with unimodal change patterns and species distribution was slightly greater in horizontal spaces than with changes in elevation. regions , 49 and  regions . Our stuα-diversity showing unimodal patterns with latitude and reduced trends with increasing elevation. For species with different life-forms, β-diversity possessed similar distribution patterns along environmental gradients that decreased with increasing gradients. For γ-diversity, it mostly exhibited partial peak distribution patterns with environmental gradients. Hence, distribution patterns of species diversity were different for various vegetation forms and spatial scales. Therefore, many researchers have proposed different theories and hypotheses that can be used to decipher these differences, such as Rapoport’s law, the energy hypothesis, and the mid-domain effect [Patterns of the distribution of species diversity were closely correlated to scale and environmental factors; in addition, these factors controlled patterns of species diversity that varied tremendously at different scales . Therefon effect , 52.The horizontal spatial distribution of species diversity approximated unimodal change patterns in subalpine meadows of the Loess Plateau; these distributions were more obvious along latitudinal gradients, which were intimately related to the local natural environment and human activities. The Loess Plateau has climatically severe environments caused by the dry climate, concentrated precipitation, sparse vegetation, and serious soil erosion, as well as the influence of human disturbance such as grazing and tourism. From north to south on the east of the Loess Plateau, the elevation of mountains gradually becomes lower, but human disturbance tends to be enhanced, so that serious human disturbance has negative effects on species diversity at low latitudinal regions. However, in high latitudinal regions, the cold climate slows soil formation and plant growth, where other adverse environments exceed the limitations of tolerance of the majority of species such as intense solar radiation and large differences between day and nighttime temperatures. Therefore, the central latitudinal regions serve as transitional areas with plant species differentiation falling between the above extremes, and these area have relatively greater species diversity . This coα-diversity of subalpine meadows slightly decreased with increasing elevation, whereas β-diversity tended to decease along spatial gradients, demonstrating that species diversity was high in areas with relatively high temperature Click here for additional data file.S1 FigP<0.05). Data are from meteorological bureaus of various cities or counties: Guangling County for DD, Wutai County for BT and DT, Ningwu County for ML, Wuzhai County for HY, Yuanping City for YZ, Loufan County for YD, Yuanqu County for SU, and Yangcheng County for SE. In this figure, the upper and lower parts provide the deciphered trends of mean monthly temperature with various months on nine mountains and a significant trend of annual mean temperature on nine mountains ((TIF)Click here for additional data file.


Pembrolizumab is a novel immune checkpoint inhibitor approved for use in non-small cell lung carcinoma. There have been a few cases that have associated adverse renal outcomes with pembrolizumab. We present a case of acute kidney injury in a patient on pembrolizumab who was noted to have acute tubulointerstitial nephritis on renal biopsy. Pembrolizumab was discontinued and the patient was started on long-term corticosteroids with a taper. Her renal function improved partially with treatment. Pembrolizumab is an immune checkpoint inhibitor (CPI) approved by the United States Food and Drug Administration for use in non-small cell lung carcinoma (NSCLC) ,2,3. It 2/dose, carboplatin 550 mg, and pembrolizumab 200 mg followed by same doses of pemetrexed and pembrolizumab maintenance every 3 weeks with last dose 2 days prior to presentation. The patient had been on pembrolizumab for 6 months prior to the decline in renal function. Other past medical history included stage IA right breast cancer (estrogen receptor+ (90%), progesterone receptor+ (3–5%), and human epidermal growth factor receptor 2-negative invasive ductal carcinoma) for which she underwent a bilateral mastectomy, 6 cycles of cyclophosphamide, methotrexate, and fluorouracil, and tamoxifen for 5 years 20 years ago, hypothyroidism, and hyperlipidemia. Her home medications included levothyroxine 75 µg daily, folic acid 1 mg daily, pantoprazole 40 mg daily, rosuvastatin 5 mg nightly, dexamethasone 8 mg two doses before and after chemotherapy, olanzapine 10 mg nightly, lorazepam 0.5 mg as needed, ondansetron 8 mg as needed, prochlorperazine 10 mg as needed, and promethazine 25 mg as needed. She had smoked a pack a day for 15 years before quitting 27 years ago.The patient is a 62-year-old woman who was brought to the emergency department (ED) with 2 episodes of sudden onset substernal chest pain, each episode lasting for 30 min. Her chest pain had resolved at the time of arrival. Prior to that, she had felt nauseous which was usual for her after her chemotherapy. Chest pain was followed by right-sided, sharp diffuse abdominal pain which lasted for 10 min and resolved spontaneously. She had received her last chemotherapy infusion 2 days prior to the episode. She denied any fever, chills, cough or shortness of breath. She was diagnosed of NSCLC with bone metastases  a year ago for which she underwent radiation therapy of left hip and right upper ribs, completed palliative chemotherapy with 6 cycles of pemetrexed 500 mg/mOn examination, vitals were stable with a temperature of 36.7 °C (98.1 °F), blood pressure 139/82 mm Hg, pulse 79 beats per minute, respiratory rate 18 breaths per minute, and she was maintaining saturation on room air. Chest, cardiac, and abdominal examinations were unremarkable. Her hemoglobin was 9.1 g/dL (reference range: 12.0–16.0 g/dL), platelet count was 556,000/µL , and white count was 10,700/µL  with neutrophil count of 9800/µL (reference range: 2000–8000/µL), monocyte count of 400 (reference range: 100–1300/µL), and immature granulocyte count of 260/µL (reference range: 0–30/µL). Serial troponins and electrocardiograms were non-suggestive of an acute coronary syndrome. Her creatinine was 1.69 mg/dL (reference range: 0.6–1.3 mg/dL). It was 1.72 two days prior above her baseline of 0.6–0.9. Although d-dimer was 1.02 (reference range: <0.53 µg/mL), computed tomography (CT) pulmonary embolism protocol was not done as ultrasound lower extremity vein bilateral was negative and there was low clinical suspicion for pulmonary embolism. CT without contrast of chest/abdomen/pelvis showed decreasing right upper lobe mass and surrounding consolidation. She had bilateral enlarging metastatic lung nodules. Hepatic metastases were not identified but contrast was not used. Bone metastases were unchanged. Her kidneys and urinary tract on CT and urinalysis were unremarkable. Her acute kidney injury (AKI) was thought to be related to poor oral intake and vomiting related volume depletion. The patient was discharged home and was recommended hydration and repeat labs in a few days. Her creatinine on the day of discharge was 1.83 mg/dL. Two days post discharge, the patient called her oncologist and told that she had started feeling sick on the same day after she was discharged. She complained of fever with maximum recorded temperature was 102.7 °F along with chills and rigors. She had multiple episodes of vomiting and poor intake. She was directly admitted to the hospital. At presentation, her vitals were stable with a temperature of 37.0 °C (98.6 °F), blood pressure 140/71 mm Hg, pulse 84 beats per minute, and respiratory rate 20 breaths per minute. Chest, cardiac, and abdominal examinations were unremarkable. Her mucous membranes were moist. Her hemoglobin was 8.2 g/dL (reference range: 12.0–16.0 g/dL), platelet count was 257,000/µL , white count was 2600/µL  with neutrophil count of 1500/µL (reference range: 2000–8000/µL), monocyte count of 0/µL (reference range: 100–1300/µL), and immature granulocyte count of 50/µL (reference range: 0–30/µL). Her low cell counts were thought to be related to her chemotherapy. No eosinophilia was noted in complete blood count during hospitalization or prior to presentation. Her urinalysis was negative for infection but showed 30 mg /dL (1+) proteinuria. Her blood and urine cultures showed no growth. Her creatinine was elevated at 3.70 mg/dL and this was again thought to be related to volume depletion. The patient was started on intravenous fluids. Her renal ultrasound was unremarkable. With suspicion for pembrolizumab as a potential cause for acute kidney injury, future infusions were held. The patient was given a dose of IV methylprednisone 80 mg (1 mg/kg) and planned to be started on prednisone 80 mg daily next day. She was planned for a renal biopsy to rule out medication-related injury. Her creatinine progressively improved during her 5-day-stay and was 2.10 on discharge. We think this was with discontinuation of pembrolizumab. Her renal biopsy showed evidence of acute tubular injury, focal interstitial inflammation  with focal mild tubulitis, 14% globally sclerotic glomeruli, mild arterial thickening, and mild interstitial fibrosis . This waRenal adverse effects are rare with CPIs . The oveRenal biopsy is recommended in cases where AKI from pembrolizumab is suspected. Discontinuation of pembrolizumab and initiation of corticosteroids is the mainstay of treatment ,6,8. ProClinicians should be aware of the potential renal complications of pembrolizumab which can lead to a break in the treatment of the underlying condition. Early identification of an increase in creatinine with regular monitoring may help with prevention and early institution of effective treatment.