We present the time invariant filters as well as the steady state filters: the classical Kalman filter and Lainiotis Filter and the Join Kalman Lainiotis Filter, which consists of the parallel usage of the two classical filters. Various implementations are proposed and compared with respect to their behavior and to their computational burden: all time invariant and steady state filters have the same behavior using both proposed models but have different computational burden. Finally, we propose a Finite Impulse Response (FIR) implementation of the Steady State Kalman, and Lainiotis filters, which does not require previous estimations but requires a well-defined set of previous measurements.We present two time invariant models for Global Systems for Mobile (GSM) position tracking, which describe the movement in  The Global Positioning System (GPS) is the most popular positioning technique in navigation providing reliable mobile location estimates in many applications –4. Thus x-axis and y-axis simultaneously or separately and (b) by using the Kalman filter and the Lainiotis filter T. The measurement vector is of dimension m = 2 and contains the measured position x-axis and y-axis: z(k) = [zx(k) zy(k)]T.The state vector is of dimension w(k) = [wx(k) wy(k)]T is Gaussian zero-mean with covariance matrix Then we take:v(k) = [vx(k) vy(k)]T is Gaussian zero-mean with covariance matrix The measurement noise Model B. The second model (model B) describes the movement in x-axis and y-axis separately. In each axis, the state vector is of dimension n = 2 and contains the position and the velocity: x(k) = [s(k) υ(k)]T. The measurement vector vector is of dimension m = 1 and contains the measured position z(k).w(k) is Gaussian zero-mean with covariance matrix Q = σq2.Then we take:v(k) is Gaussian zero-mean with covariance matrix = σr2.The measurement noise xx(k) = [sx(k) υx(k)]T for the x-axis and xy(k) = [sy(k) υy(k)]T for the y-axis. If we merge these two state vectors, we take the state vector x(k) = [sx(k) υx(k) sy(k) υy(k)]T of model A.It is obvious that we are able to describe the movement in both axes using two separate state vectors: x(k/k) and the corresponding estimation error covariance P(k/k). We also propose the Join Kalman-Lainiotis Filter which consists of the parallel (with the same measurements) usage of two filters  and combination of the results (weight 50% for each filter).In this section, we present the classical time invariant Kalman filter –9 and LaKalman Filter (KF). The following equations constitute the KF:k ≥ 0, with initial conditions x(0/0) = x0 and P(0/0) = P0.x(k/k) and the estimation error covariance P(k/k) through the prediction x(k + 1/k) and the corresponding prediction error covariance P(k + 1/k) using the Kalman filter gain K(k).The Kalman filter computes the estimation Lainiotis Filter (LF). The following equations constitute the LF:k ≥ 0, with initial conditions x(0/0) = x0 and P(0/0) = P0, whereJoin Kalman-Lainiotis Filter (JKLF). The filter consists of the parallel usage of two filters  with the same measurements and combination of the results (weight 50% for each filter):Pp of the prediction error covariance matrix, if the signal process model is asymptotically stable, or if the signal process model is not necessarily asymptotically stable, but the pair  is completely detectable and the pair  is completely stabilizable for any G1 with G1G1T = Q. Then there also exist a steady state value Pe of the estimation error covariance matrix and a steady state value K of the Kalman filter gain.For time invariant systems, it is well known  that thex(k/k) using the previous estimation and the current measurement. We also propose the Join Steady State Kalman-Lainiotis Filter, which consists of the parallel usage of two filters  with the same measurements and combination of the results (weight 50% for each filter).In this section we present the Steady State Kalman filter and Lainiotis Filter. Both algorithms compute the estimation Steady State Kalman Filter (SSKF). The following equation constitutes the SSKF:k ≥ 0, with initial condition x(0/0) = x0, whereK is computed by K = PpHT[HPpHT + R]−1, where Pp is the steady state prediction error covariance computed by solving the Riccati equation emanating from Kalman filter (REKF):olutions , 11 or iolutions , 12–15 cSteady State Lainiotis Filter (SSLF). The following equation constitutes the SSLF:k ≥ 0, with initial condition x(0/0) = x0, wherePe is the steady state estimation error covariance computed by solving the Riccati equation emanating from Lainiotis filter (RELF):he SSLF:x(k+1/k+1 (RELF):Pe=Pn+FnT for the movement in x-axis and the estimation xy(k/k) = [sy(k/k) υy(k/k)]T for the movement in y-axis, we take the state vector of model A:Px(k/k) and Py(k/k) for each movement can be merged to the estimation error covariance of model A:The use of model B, which describes the movement in In this section we compare the filters with respect to their behavior and to their computational burden.t = 1,discretization factor: Δσxq2 = σyq2 = 0.01,movement reliability: σxr2 = σyr2 = 0.1,measurements reliability: x0 = 0 and P0 = 0.initial conditions: We implemented the filters with the following parameters:the time invariant filters KF, LF and JKLF are equivalent, since they compute the same outputs (estimation and estimation error covariance), using model A or model B,the steady state filters SSKF, SSLF and JSSKLF are equivalent, since they compute the same outputs (estimation and estimation error covariance), using model A or model B,the steady state filters and the time invariant filters compute outputs very close to each other,model A is equivalent to model B, since they produce the same outputs.Concerning the behavior of the filters, we found thatThese results are depicted in Concerning the computational burden of the filters, we compared the filters with respect to their per-iteration calculation burdens, computed using the ideas in , as showKF is faster than LF,JKLF is slower than KF and LF ,SSKF is as fast as SSLF,SSKF and SSLF are faster than KF and LF,JSSKLF is slower than SSKF and SSLF,the filters using model B are faster than the same filters using model A.We observe that(i) KF is faster than LF, speedup (LF model A to KF model A) = 1.316, speedup (LF model B to KF model B) = 1.290.(ii) Model B is faster than model A, speedup (KF model A to SSKF model B) = 25.450, speedup (LF model A to SSLF model B) = 33.500.We observe thatIn this section we propose an FIR implementation of the Steady State Kalman filter and the Steady State Lainiotis Filter.k ≥ 0 with initial condition x(0/0) = x0.Recall that SSKF and SSLF have equal parameters:Then we take:A = AKF = ALF and B = BKF = BLF and M is the FIR filter order defined by ||AM|| < ε and ||AM−1|| ≥ ε, with ε a small real value.Using the ideas in , the res(1) The FIR steady state filter coefficients can be calculated off-line by solving the corresponding Riccati equation.M previous measurements. This means that we have to wait for M time moments in order to produce the results. Alternatively, we are able to use only the available measurements until time M is reached or to use SSKF until time M.(2) The FIR steady state filter does not require previous estimations but it requires a well-defined set of ε = 10−3 and we found M = 17.We implemented the FIR filter for the parameters of the example in The steady state filters and FIR steady state filters compute outputs very close to each other, as depicted in Concerning the computational burden, the FIR steady state filter possesses a constant burden while the classical steady state filter (SSKF/SSLF) possesses a constant per-iteration computational burden, as it is shown in μ time lags, where μ is the nearest integer greater than or equal to the ratio (40 + 4M)/44 = (10 + M)/11 for model A and (16 + 4M)/20 = (4 + M)/5 for model B.Thus, FIR SSKF/SSLF can be faster than SSKF/SSLF, if we take results using FIR SSKF/SSLF every (or more than) For our example, we take the results, which are appeared in x-axis and y-axis simultaneously or separately. We presented the time invariant filters as well as the steady state filters: the classical Kalman filter and Lainiotis Filter and the Join Kalman Lainiotis Filter, which consists of the parallel usage of the two classical filters. Various implementations are proposed and compared with respect to their behavior and to their computational burden. We found that all time invariant and steady state filters have the same behavior using both of the proposed models. We found that (a) Kalman filter is faster than Lainiotis Filter, (b) Join Kalman Lainiotis Filter is slower than both Kalman filter and Lainiotis Filter, (c) steady state filters are faster than time invariant filters and (d) the filters using the model, which handles the movement in x-axis and y-axis separately, are faster than the same filters using the model, which handles the movement in x-axis and y-axis simultaneously. Finally, we proposed an FIR implementation of the Steady State Kalman and Lainiotis Filters, which does not require previous estimations but it requires a well-defined set of previous measurements.In this paper we presented two time invariant models for Global Systems for Mobile (GSM) position tracking, which describe the movement in


Ego depletion theory states that humans possess a limited store of cognitive resources that, when depleted, results in deficits in self-regulation and cognitive control. In a first experiment, we used a standard ego depletion manipulation prior to performance of the Serial Interception Sequence Learning (SISL) task. Depleted participants exhibited poorer test performance than did non-depleted controls, indicating that reducing available executive resources may adversely affect implicit sequence learning, expression of sequence knowledge, or both. In a second experiment, depletion was administered either prior to or after training. Participants who reported higher levels of depletion before or after training again showed less sequence-specific knowledge on the post-training assessment. However, the results did not allow for clear separation of ego depletion effects on learning versus subsequent sequence-specific performance. These results indicate that performance on an implicitly learned sequence can be impaired by a reduction in executive resources, in spite of learning taking place outside of awareness and without conscious intent.Implicit skill learning occurs incidentally and without conscious awareness of what is learned. However, the rate and effectiveness of learning may still be affected by decreased availability of central processing resources. Dual-task experiments have generally found impairments in implicit learning, however, these studies have also shown that certain characteristics of the secondary task  can complicate the interpretation of these results. To avoid this problem, the current experiments used a novel method to impose resource constraints prior to engaging in skill learning.  Implicit learning is defined as the ability to extract statistical covariation from the environment over experience and use this to guide and improve behavior A wide range of studies have examined the effects of imposing resource constraints during implicit learning using dual task methodology e.g.,  and the Another persistent challenge of the dual-task paradigm is the possibility that the secondary task diverts perceptual attention away from the stimuli to be sequenced. Instructing participants to attend to the relevant stimulus dimensions affects whether sequence learning occurs ego depletion. Muraven, Tice, and Baumeister ego depletion might reflect a transitory effect similar to the impairments in sequence learning observed in patients with Parkinson's Disease A significant type of resource constraint for implicit learning may arise following the phenomenon of To examine the effects of ego depletion on implicit learning, we used the Serial Interception Sequence Learning (SISL) task which produces robust implicit learning similar to the SRT task but with learning effects often detectable in individual participants Mage = 20.1 years) at Northwestern University were compensated $10/hour for participation. This study was approved by Northwestern University's Institutional Review Board and participants provided written consent in accordance with IRB policy.Thirty undergraduates  when the cues overlapped target rings . ResponsCues initially scrolled down the screen at a velocity of 12.6 degrees/second, reaching the target zone .85 s after appearing on the screen. Speed was adapted based on performance, with cue velocity increasing when performance rose above 65% and dropping when performance fell below 25%. Participants were not told that cues followed a 12-item repeating sequence for 80% of the training trials; the other 20% of the trials were novel, non-repeating segments (more detail on the SISL task in n = 15) or Non-Depletion (n = 15) condition. Three participants were excluded for low SISL task compliance , resulting in 12 Depletion participants and 15 Non-Depletion participants for analysis. Participants first completed the Depletion tasks as described above, followed by a 2880-trail SISL task training session organized in six 480-trial blocks. Each training block contained 32 repetitions of the 12-item sequence and eight 12-item non-repeating sequences in a pseudo-random order. Following training, participants completed a 540-trial SISL test block to assess sequence-specific learning. The test block consisted of 15 repetitions of the trained sequence along with 15 repetitions of two novel SOC sequences in a pseudo-random order. Participants were then given a Recognition task, where they rated the trained sequence and four foils from -10 (definitely not seen) to +10 (definitely seen), with higher ratings for the trained sequence indicating some explicit knowledge.Participants were randomly assigned to a Depletion  = 28.87, p<.001, with the performance advantage for the repeating sequence compared to the foil sequences increasing from the first to last training block . The interaction between depletion condition and learning block was not significant, F = 0.79, p = .561, indicating that both conditions displayed reliable learning of the repeated sequence during the adaptive speed training.A 2×6 mixed ANOVA of depletion condition  and learning block (1–6) was used to examine learning of the trained sequence during training. Learning increased in a linear trend throughout training, F = 57.79, p<.001, with sequence blocks performed at higher accuracy than foil blocks  = 1.88, p = .183, due to the adaptive velocity that kept both groups performing at roughly 65% correct overall. However, a reliable interaction reflected a lower sequence-specific performance advantage in the Depletion group  compared to the Non-Depletion group , F = 4.33, p<.05  than participants in the Depletion condition , t(25) = 2.44, p<.05 (A 2×2 ANOVA of depletion condition (Depletion/Non-Depletion) and sequence type (trained/foils) assessed sequence-specific learning during test across groups. A main effect of sequence type reflected reliable sequence-specific knowledge in both groups, l blocks . As expe3, p<.05 . Partici4, p<.05 , suggestF = 6.31, p<.05, indicating that participants gave higher recognition ratings to the trained sequence  as compared to the foil sequences . There was no main effect of depletion condition or any interaction (Fs<1).A 2×2 ANOVA of sequence type (trained/foils) and condition (Depletion/Non-depletion) for the recognition task revealed a main effect of sequence type, Participants randomly assigned to the resource depleting condition prior to training exhibited a lower sequence-specific performance advantage and worse overall performance  than those in the less depleting condition. The smaller sequence-specific benefit at test suggests that the implicit sequence learning rate may have been slowed during training, but it is also possible that the depletion effect persisted to and affected test performance. In Experiment 2, depletion was separately administered prior to training or prior to test and a subjective assessment of the depletion manipulation effect was used to measure how consistently participants were affected by the manipulation.Mage = 19.2 years) at Northwestern University received course credit for participation. This study was approved by Northwestern University's Institutional Review Board and participants provided written consent in accordance with IRB policy.One hundred twenty-four participants  at which depletion could be administered. In the Non-Depletion task, participants were instructed to cross out every letter “e” on a page of text for 5 m and to write the sum of all e's they crossed off at the end of each line.The Depletion manipulation involved crossing out “e's” on one page of text for 5 m, following the same, more complex, regulatory-control fatiguing rules as in Experiment 1. At the end of each line, participants tallied all the silent “e's” they had not crossed off. The text was printed several shades lighter than the Non-Depletion task, making it more difficult to read.Participants provided self-reports of their own depletion levels after each encounter with the Depletion/Non-Depletion tasks. These ratings were made through a questionnaire with three 7-point scale ratings of task effort  and mental state . The sum of the three ratings was used as a self-report of general depletion level at each time point, with higher scores indicating that participants felt the task was more depleting.n = 42), Pre-test Depletion (n = 41), or Non-Depletion (n = 41). Participants with low task compliance were excluded . There were two time points at which the depletion tasks could be administered: one before training on the SISL task, and one just before the SISL test block. The Pre-training Depletion group received the depleting form of the task prior to training and the non-depleting form of the task prior to test. The Pre-test Depletion group received the Non-Depletion task pre-training but completed the Depletion task prior to test. The Non-Depletion group completed the Non-Depletion form of the task at both time points.Participants were randomly assigned to one of three conditions: Pre-training Depletion  = .16, p<.001) with the performance advantage for the repeating sequence compared to the foil sequences increasing from the first to last training block . As in Experiment 1, the interaction between depletion condition and learning block was not significant  = 0.66, p = .767), suggesting that depletion condition did not affect measures of performance during the learning phase directly.A 3×6 mixed ANOVA of depletion condition  and learning block (1–6) was used to examine learning of the trained sequence during training. Learning increased in a linear trend throughout training  = 133.94, p<.001. There was no main effect of condition , F = 1.19, p = .308, nor any interaction between condition and sequence type, F = 0.81, p = .448. However, the performance benefit for the trained sequence compared to foil sequences was highest in the Non-Depletion group , compared to both the Pre-training Depletion  and Pre-test Depletion  conditions , t(33) = .419, and lower than scores obtained following the Depletion task , ts>6.82, ps<.001, it was observed that quite a few individual participants reported depletion levels inconsistent with the experimental manipulation . Participants were therefore sorted post-hoc into depletion groups based on median split of self-reported depletion levels  at the two manipulation time points. This yielded four different overall groups  = 4.76, p<.05, a marginal effect of depletion Pre-test, F = 3.62, p = .060 and no interaction between conditions, F = 0.002, p = .966. Accordingly, participants who did not report feeling depleted at either time point (ND-ND) showed a typical performance advantage for the trained sequence , similar to that of Non-Depletion participants in Experiment 1. Participants with no self-reported depletion prior to training who subsequently reported feeling depleted before test (ND-D) showed slightly impaired performance , as did participants with the opposite depletion pattern . Finally, participants who reported feeling depleted at both time points (D-D) had the lowest overall sequence benefit , with a significantly lower sequence-specific benefit than the ND-ND group, p<.05  that groups participants by their self-reported  depletion level rather than the intended level . With the magnitude of the sequence-specific performance benefit as the measure of expression of sequence learning, this analysis revealed a main effect of depletion Pre-training, p, p<.05 . There wM = 4.38, SE = .54; foil sequences, M = −.99, SE = .40), F = 59.83, p<.001. There was no main effect of depletion condition on recognition knowledge or any interaction (Fs<1).Participants showed higher recognition for the trained sequence  should not affect the measure of implicit knowledge. Rather, the reduction in resources accomplished via the depletion manipulation affected performance of the specific, trained sequence. It appears that the ego depletion manipulation affects the neurocognitive processes either associated with extracting the hidden repeating sequential structure, or applying this knowledge later when the sequence is re-encountered.Explicit memory for the repeating sequence was not found to be affected by the depletion manipulation in either experiment arguing against the possibility raised by Jiménez & Vázquez Given that implicit learning occurs outside of awareness, the dependence on central executive resources seems somewhat counter-intuitive. A potential mechanism for this effect is through transient variation in dopaminergic functioning imposed by depletion. Disruptions in dopaminergic function have been associated with impaired sequence learning The adverse effect of depletion on skilled performance suggests that it is important to maximize central processing resources to get the greatest benefit from practice during skill learning through repetitive practice and support later performance. While the SISL task primarily measures implicit perceptual-motor skill learning, common mechanisms are hypothesized to support skill learning across a broad range of domains, including cognitive skills Data S1Depletion data.(ZIP)Click here for additional data file.


It is estimated that 20% of all cancer cases are caused by obesity. Vitamin D is thought to be one of the mechanisms underlying this association. This review aims to summarise the evidence for the mediating effect of vitamin D on the link between obesity and cancer.Three literature searches using PubMed and Embase were conducted to assess whether vitamin D plays an important role in the pathway between obesity and cancer: (1) obesity and cancer; (2) obesity and vitamin D; and (3) vitamin D and cancer. A systematic review was performed for (1) and (3), whereas a meta-analysis including random effects analyses was performed for (2).2). (3) 21 meta-analyses on circulating vitamin D levels and cancer risk were identified with different results for different types of cancer.(1) 32 meta-analyses on obesity and cancer were identified; the majority reported a positive association between obesity and risk of cancer. (2) Our meta-analysis included 12 original studies showing a pooled relative risk of 1.52 (95% CI: 1.33-1.73) for risk of vitamin D deficiency (<50 nmol/L) in obese people  contains supplementary material, which is available to authorized users. Over recent decades, the increasing prevalence of obesity has been implicated in the risk of cancer incidence and mortality –3. The lTo evaluate whether vitamin D explains how obesity affects cancer risk, one needs to assess if vitamin D is a mediator variable for the association between obesity (exposure) and cancer (outcome) , 14. In A comprehensive literature review of all published meta-analyses on the association between obesity and cancer was carried out. We used computerised search databases (PubMed search followed by an Embase search) to identify full text and abstracts focused on human subjects and published in English language within the last fifteen years. Searches were conducted both with and without MeSH terms for “obesity”, “cancer” and “meta-analysis”. This search was repeated for individual cancer types: “breast”, “colorectal”, “melanoma”, “oesophageal”, “liver”, “lung”, “ovarian”, “endometrial”, “prostate”, “pancreatic” and “kidney” cancer. Although lung cancer may not be the obvious cancer to investigate in the context of obesity , 18, somWe used computerised search databases (PubMed search followed by an Embase search) to identify full text and abstracts published within the last fifteen years, of English language and used human subjects. The searches were performed with and without MeSH terms for “vitamin D”, “25 hydroxyvitamin D”, “obesity”, and “body mass index”. We also included “grey literature” such as abstracts, letters, and articles presented at relevant conferences and meetings. All references of the selected articles were checked using hand searches.2, was the main exposure of interest. Low vitamin D levels were the outcome, defined using a cut off of <50 nmol/L, which encompasses both vitamin D insufficiency and deficiency.Initially, titles and abstracts of articles were reviewed by two researchers . If they met initial inclusion criteria both abstract and full text article were reviewed to ascertain whether all inclusion criteria were met. A detailed evaluation of methods and results was undertaken. In the case of any disagreement between the two researchers on article inclusion assessments, the full text article was reviewed by a third researcher (Mieke Van Hemelrijck - MVH). Figure All included studies were of epidemiological nature: cohort, case–control, or cross-sectional. Furthermore, all studies included measurements of vitamin D and body mass index (BMI) and assessed the association between the two. We only included those studies with a sufficient power, deemed as including more than twenty cancer cases. Obesity, defined as BMI >30 kg/mThe following details were recorded for each study: author, year of publication, country, type of study, method of vitamin D measurement, statistical tests used, number of subjects with sufficient, insufficient and deficient vitamin D status and BMI of all subjects.The association between obesity and vitamin D levels was evaluated by calculating the pooled relative risk (RR) with random effects model to allow for possible heterogeneity between studies. Potential publication bias was evaluated using Beggs Test and Eggers funnel plot. All analyses were performed with STATA version 11.0.A comprehensive literature search of all meta-analyses conducted on the association between vitamin D and cancer was performed. We used computerised search databases (PubMed search followed by an Embase search) to identify full text and abstracts focused on human subjects and published in English language within the last fifteen years. Searches were conducted both with and without MeSH terms for “vitamin D”, “cancer”, “vitamin D receptor”, “polymorphism” and “meta-analysis”. This search was repeated for specific cancer types: “breast”, “colorectal”, “melanoma”, “oesophageal”, “liver”, “lung”, “ovarian”, “endometrial”, “prostate”, “pancreatic” and “kidney” cancer. Moreover, we also searched clinicaltrials.gov for clinical trials focused on “vitamin D supplements” and “cancer” or “neoplasm” .colorectal cancer showed a positive association between BMI and colorectal cancer risk [upper gastro-intestinal cancers  were positively associated with obesity [liver cancer reported an increased risk with increasing BMI [lung cancer meta-analysis reported an inverse association with obesity  [pancreatic cancer reported a positive association with obesity [kidney cancer[prostate cancer[bladder cancer reported a positive association even when adjustment for smoking was performed [breast cancer depending on menopausal status and breast cancer subtype [ovarian cancer reported a positive association with obesity, with no difference in the histological subtypes of ovarian cancer studied [endometrial cancer[melanoma reported a positive association in men , but not in women  [Thirty-two meta-analyses were identified from our literature search on obesity and cancer Table . More spcer risk –28. When obesity –32. The  obesity , 32. Allsing BMI –36, wher73-0.85) . Meta-an obesity –40, whicey cancer, 42. Forte cancer, a protete cancer. The meterformed . Some va subtype , 47. A p subtype . The met studied . As for  studied , there wal cancer. Howeveral cancer. The met83-1.18) .Table 1The initial PubMed search produced a total of 356 (TS) and 352 (DC) papers. Further assessment of abstracts and papers based on the above-defined inclusion criteria . There was no difference between those studies looking at children and adolescents combined and those looking at an adult population .The random effects analyses showed a pooled relative risk of 1.52 (95% CI: 1.33-1.73) for the association between obesity and low vitamin D status  suggests that obesity is associated with increased risk of oesophageal adenocarcinoma, pancreatic, colorectal, postmenopausal breast, endometrial and renal cancer .There are several molecular mechanisms suggested to explain the increased risk of cancer in obese people. The most commonly postulated being the “insulin–cancer hypothesis” , suggestThe majority of the meta-analyses in our literature review included a substantial number of studies, with consistent study design. However, the meta-analysis on endometrial cancer  only incIn summary, there is consistent accumulating evidence for an association between obesity and risk of certain cancer with several suggested molecular mechanisms that can potentially explain these raised risks. However, the role of vitamin D is not addressed in detail in these studies.To our knowledge this is the largest meta-analysis to date on the association between circulating vitamin D levels and obesity. The pooled estimates suggest an inverse relationship between vitamin D and obesity.The possible relationship between vitamin D and obesity was firstly described by Rosenstreich et al. in 1971 , who sugFurthermore, certain vitamin D receptor (VDR) polymorphisms are associated with obesity , 93. UpoIn contrast, some suggest that low vitamin D itself promotes obesity. Kong and Li demonstrated that vitamin D levels may block the expression of downstream adipocyte components such as fatty acid synthase, which consequently suppresses adipogenesis . One intIn conclusion, our meta-analysis reports a modest inverse association between obesity and low vitamin D levels. The underlying biological mechanisms are unknown. The majority of studies point towards the hypothesis that, vitamin D stored in fat tissue increases local vitamin D concentrations causing activation of the VDR in adipocytes. This may lead to low energy usage and further promotion of obesity .In this literature review only those meta-analyses focusing on colorectal cancer found a consistent inverse association between circulating vitamin D levels and cancer risk –59. In cA protective effect of vitamin D in colorectal cancer was first reported by Garland and Garland . DespiteMore specifically, it is suggested that vitamin D has anti-tumour effects through its binding with the VDR. Several animal and cell culture models showed that VDR plays a key role in the anticancer effects of circulating vitamin D –11. For Interestingly, several parts of the immune system  also express the VDR, but the related effects remain to be elucidated . It has In 2008, the International Agency for Research on Cancer concluded that evidence for an association between vitamin D and cancer was inconclusive, and highlighted the need for a clinical trial with specific focus on vitamin D and colorectal cancer . The incWhen assessing the three conditions required for vitamin D to be a mediator we found only partial fulfilment . The litSeveral other difficulties occur when assessing the mediation effect of vitamin D in the context of obesity and cancer. Dichotomisation of vitamin D exposure  could lead to misclassification in exposure levels. Those with extreme high values of vitamin D may have been included in the “normal” group. Hence, bias can occur when there is misclassification of the mediator . StudiesAdditionally, the current systematic literature reviews are prone to the heterogeneity related to observational studies. For example, for the studies focused on vitamin D and obesity the included studies recruited adults residing in a particular town , from mea priori.A strength of these systematic reviews and meta-analysis is that we made all possible efforts to include all relevant publications available to date through various sources, including grey literature, and the two main online databases (PubMed and Embase). In addition, clearly defined objective criteria for exposure, outcome, and other study characteristics were specified To understand how vitamin D may play a role in the association between obesity and carcinogenesis, we assessed the strength of these three associations: 1) There was a consistent positive association between obesity and cancer with relative risks varying between 1.10 and 1.90 when addressing the existing literature; (2) Our new meta-analysis illustrated an association as strong as 1.50 between obesity and low vitamin D levels; (3) The literature for vitamin D status and cancer risk only showed consistent evidence for an inverse association with colorectal cancer. From these reviews, it seems that the significance of the mediating role of vitamin D in the biological pathways linking obesity and cancer is low. This review emphasises that further research specifically addressing the relationship between obesity, vitamin D and cancer risk in one study is needed.


Oedaleus asiaticus maintains strong N:P stoichiometric homeostasis regardless of whether grasshoppers were reared at low or high density. Grasshoppers maintained homeostasis by increasing P excretion when eating plants with higher P contents. However, while grasshoppers also maintained constant body N contents, we found no changes in N excretion in response to changing plant N content over the range measured. These results suggest that O. asiaticus maintains P homeostasis primarily by changing P absorption and excretion rates, but that other mechanisms may be more important for regulating N homeostasis. Our findings improve our understanding of consumer-driven P recycling and may help in understanding the factors affecting plant-herbivore interactions and ecosystem processes in grasslands.Nitrogen (N) and phosphorus (P) are important limiting nutrients for plant production and consumer performance in a variety of ecosystems. As a result, the N:P stoichiometry of herbivores has received increased attention in ecology. However, the mechanisms by which herbivores maintain N:P stoichiometric homeostasis are poorly understood. Here, using a field manipulation experiment we show that the grasshopper  N:P) have major effects on growth, reproductive success, and other performance characters in animals The ecological success and life history of organisms is constrained by a variety of intrinsic and extrinsic factors Body N:P ratio has been considered as a central parameter linking key aspects of a species, such as its structural investments, growth rate, and biogeography Grasshoppers are a major component in grassland food webs and are important for controlling plant biodiversity and productivity in grassland ecosystems The objective of this study was to answer the following questions: 1) Does N:P stoichiometry in grasshoppers change in response to their own density and plant N:P ratios? If not, then 2) How do grasshoppers achieve this N:P stoichiometric homeostasis? Finally, we assessed the implications of these findings in the context of the overall dynamics of grasshopper and grassland plant interactions.No specific permissions were required for all these locations, for these lands were not private and the study did not involve endangered or protected species.Leymus chinensis and Stipa grandis and accompanied by other common species such as Agropyron cristatum, Achnatherum sibiricum, Cleistogenes squarrosa, Carex korshinskyi, Koeleria cristata and Allium sp. Aboveground biomass of these seven species accounts for more than 80% of the total community primary production.The field manipulation experiment was carried out in a temperate steppe in the Xilin River Basin in northern China , which is located at the southeastern part of the Eurasian Steppe. The mean annual temperature in this area is 0.3 °C with mean monthly temperatures ranging from −21.6°C in January to 19.0 °C in July. The mean annual precipitation is 346 mm, more than 80% of which occurs during the growing season from May to September. The soil type is dark chestnut  with an average depth of ∼100 cm Oedaleus asiaticus (formerly O. decorus asiaticus), a member of the subfamily Oedipodinae (Orthoptera: Acrididae: Oedipodinae), is a dominant herbivorous insect in the study area, with a population density ranging from <10 individuals m−2 before 1990 −2 after 1990 O. asiaticus has considerable migratory capacity in its brown-colored phase, making it the dominant locust of north Asian grasslands O. asiaticus can be visually identified The grasshopper During July and August 2007 we established 72 cages within a long-term experimental plot (fenced in 2005 in order to exclude sheep grazing) of the Inner Mongolia Grassland Ecosystem Research Station, Chinese Academy of Sciences. The distance between cages was 1 m. Each cage consisted of a steel-framed and nylon-netted cube (1×1×1 m) with the lower edge sealed against the soil to prevent escape of experimental animals and intrusion of other species. A 0.5×0.5 m zippered window on the upper side of each cage allowed experimental operations such as introduction of grasshoppers, checking of grasshopper densities, and removal of predators (mostly spiders).O. asiaticus were introduced to cages at 6 treatment levels: 0 (control), 2, 4, 8, 16, 32 grasshoppers m−2. The sex ratio of each treatment was 1∶1 . There were 12 replicates for each treatment, the experiment was based on orthogonal design and had two set of the same number (6×6) of cages. After the experimental grasshoppers were introduced, we checked each cage every three days to confirm the number of living grasshoppers , repair damaged cages, and eliminate spiders and other species of grasshoppers that occurred naturally within the cages. Grasshoppers introduced at the very beginning of grazing were labeled in the pronotum with red paint in order to ensure the sampled insects were those that lived in the cages at a given density condition for 30 days.We designed a density gradient experiment to simulate grasshopper outbreaks with different intensities. On 20 July 2007, adult On 20 August 2007 (one month after the start), cages were removed. The aboveground standing biomass was harvested by clipping off all plants at the soil surface. According to the methods reliable in our previous work, plants were sorted by species, grasshoppers with red paint markers were carefully removed according to sex and then kept in small cages in lab without feeding for 24 h in order to collect their frass, which was then immediately dried. After that, grasshoppers of both sexes were dried and weighed individually. Each sample of grasshopper used for further chemical analysis was composed of male and female insects with the sex ratio of 1∶1 . All samples (grasshoppers and plants) were oven dried at 65°C for 48 h and then milled to a fine powder and screened with 0.1-mm mesh before analyzing their N and P contents.−1 , and elemental stoichiometry were molar ratios. In the mean time, all analyses are for samples pooled at the plot level.Total N of plant and insect samples was determined by using a micro-Kjeldahl procedure with sulfuric acid and digestion catalyst. Total phosphorus was analyzed colorimetrically by applying the ammonium molybdate method SPSS version 11.5 (SPSS) was used to analyze our experimental data. T-tests and one-way ANOVAs were applied to compare the N:P stoichiometric difference between different treatments. The relationships between each couple of variables were examined by calculating the Pearson Coefficient, and two-tailed tests of significances were used. Regression analysis was employed to estimate the curves fitted to each couple of experimental variables.n = 44, r = 0.569, P<0.001) and in food plants   .O. asiaticus adults maintained consistent N and P concentrations and N:P ratios, showing strong N:P homeostasis  and P  concentrations as well as N:P ratios  remained similar  at treatment-level  at quadrat (cage) level  increased with grasshopper densities  and P  concentrations both decreased significantly and to similar extents as grasshopper density increased, thus, N:P ratios of food plants  did not exhibit a strong trend with grasshopper density  level , and fure) level . In conte) level . Reflectensities . For foo density .n = 5, r = 0.944, P = 0.016)  for most of these variables with only one exception: frass P concentrations were positively and significantly related to food plant P concentrations (= 0.016) .n = 5, r = 0.896, P = 0.039), while dry weight of individual males declined ∼17% (from 0.089 to 0.074 g) and was also negatively related to density .In this study, grasshopper body size (expressed as body dry weight) declined significantly in response to increased density . As grasO. asiaticus in the present study exhibited strong N:P stoichiometric homeostasis under changing food conditions, in this case those generated by experimental manipulations of its own density  and then post-ingestively regulated P balance. This fits with prior studies on this species that demonstrated changing dietary N and protein content has powerful effects on the growth and viability of O. asiaticusWhen confined to no-choice diets, grasshoppers and other insects can post-ingestively regulate dietary N Many insects respond to high population density by decreasing body size; possible mechanisms include increased stress, competition for resources, and/or hypoxia Increasing grasshopper density has been demonstrated to exert considerable pressure on plant growth and ecosystem productivity O. asiaticus, maintains stoichiometric homeostasis in the face of changes in food abundance and elemental composition, highlighting changes in rates of absorption and excretion as an important physiological mechanism in maintaining the stability of body P content. While the ecological significance of frass N production has been reported by several authors Our experiment sheds some light on how at least one grasshopper species, Figure S12r = 0.218, F = 11.98, P = 0.001) and yielded the following equation: y = −0.029 x+2.23. In (c), a positive relationship was found between frass N:P ratio and grasshopper density  and yielded the following equation: y = 0.48 x+25.87.Effects of grasshopper density on frass N:P stoichiometry. In (b), a negative relationship was found between frass P concentration and grasshopper density (ANOVA: (DOC)Click here for additional data file.Figure S2Comparision of the effects of different grasshopper densities on N:P ratio of food plant, grasshopper body and frass. Error bars indicate ±1 SE. Different letters above different bars mean significant difference at 0.05 level, based on one-way ANOVAs.(DOC)Click here for additional data file.Figure S3r = 0.378, P = 0.530; In (b) for P analysis, r = −0.258, P = 0.675; In (c) for N:P analysis, r = −0.279, P = 0.650.Relationship of N:P stoichiometry between grasshoppers body and frass. Error bars indicate ±1 SE. In (a) for N analysis, (DOC)Click here for additional data file.


Aplysia juliana to examine whether or not bursting patterns arealtered in response to temperature change. Here we found that the inter-burstinterval, burst duration, and number of spike during burst decreased as temperatureincreased. On the other hand, the numbers of bursts per minute and numbers of spikesper minute increased and then decreased, but interspike interval during burst firstlydecreased and then increased. We also tested the reproducibility oftemperature-dependent changes in bursting patterns and AP parameters. Finally weperformed computational simulations of these phenomena by using a modified Plantmodel composed of equations with temperature-dependent scaling factors tomathematically clarify the temperature-dependent changes of bursting patterns inburst-firing neurons. Taken together, we found that the modified Plant model couldtrace the ionic mechanism underlying the temperature-dependent change in burstingpattern from experiments with bursting pacemaker neurons in the abdominal ganglia ofAplysia juliana.Many electrophysiological properties of neuron including firing rates and rhythmicaloscillation change in response to a temperature variation, but the mechanismunderlying these correlations remains unverified. In this study, we analyzed variousaction potential (AP) parameters of bursting pacemaker neurons in the abdominalganglion of  Aplysiajuliana, crabs, crayfish, frogs, lobsters, locusts, snails, and squids ]. Es]. EsAplystudied [-12]] an] anAplysstudied [-19]]. H]. HAplysstudied ,21]]. T]. TAplys]. T]. TAChanges in temperature can produce numerous effects on the neural tissue of mostorganisms. Indeed it has been reported that hyperthermic temperature may inducedepolarization and spontaneous firing of pyramidal neurons leading to enhancedexcitability of hippocampus ]. O]. O23]].A. juliana) were collected locally in Seogwipo, South Korea and allexperimental procedures were approved by the Jeju National University Animal Care andUse Committee. These animals were dissected to observe the temperature-dependency ofbursting patterns generated by pacemaker neurons R15 in the abdominal ganglia ofA. juliana as described in previously published paper ,,] develop,] de] de29]] To mathematically clarify the temperature-dependent changes of bursting patterns inburst-firing neurons, it was suggested to perform computational simulations of thesephenomena by using a proper model composed of equations with temperature-dependentscaling factors.j were multiplied by temperature-like scaling factor for ionickinetics 0 = 23°C), we could get these figures drawn in redsolid lines and these were shown on these panels. In the results of simulations withthese modified models, it could be found that burst duration and number of spikeduring burst increased as temperature increased. But these results were notconsistent with our experimental results. So, although the model presented byCanavier et al. ,[31,[31K(Ca)iables) . This mNa, fast potassiumcurrent IK, inward calcium current ICa, calcium-activatedpotassium current IK(Ca), and leakage current, IL) and 6 staticvariables . There are two fast and slow processes inthis model. The fast process had three components: the activation and inactivationvariables for Na+ channels “m” and “h”,respectively, and the activation of K+ channels n. The slow process hadtwo components: a slow conductance for Ca2+-current “X”, andintracellular free calcium concentration “Ca”. Not only Plant model had asimple structure, but also theoretical analyses of Plant’s model were performedalready [[Now, it would be necessary to look into another simple model to investigate themechanisms of temperature-dependent changes of bursting patterns which share a fewsimilarities with mammalian cold receptors. Plant model consisted of 5 currents and 6static variables: 5 currents  as a key bursting current in R15 and focusedon the inward current with slow activating and inactivating components. Plant modelwas based on the works of Gorman and Thomas [[K(Ca) was linearly dependent onincreasing concentration of intracellular calcium ions (Ca2+) injectedinto the cytoplasm and expected to be activated during a burst. Chay [[Aplysiabursting neurons with involving the properties of Ca2+-activatedK+ channel: intracellular Ca2+ concentration increasesgradually during activity to levels where it activates the IK(Ca) [[K(Ca) did not play a role in burst terminationin bursting neurons of Aplysia, Adams and Levitan [[K(Ca) in the repolarization ofaction potential. But they asked the question: why a long-lasting IK(Ca)did not be activated by action potential? The plausible answer was the amount ofcalcium ions that entered into the cell during the activity of action potential wasinsufficient to generate it. Even if Canavier et al. [[K(Ca), it was suggested as alimitations of this model that more experimental data of IK(Ca) wererequired for further investigation. Bertram [[K(Ca) conductance depended only on theconstant calcium concentration of the soma. Besides this, it has been known that thecalcium activated potassium channels are located in R15 soma [[A. juliana to measure the membrane potentials in ourexperiments. Thus, we cannot underestimate the impact of IK(Ca), but wedid not here want to claim the quantitative application of Plant model, rather, weused it fr understanding the mechanism underlying the temperature dependence ofbusting patterns by computer simulations: it might be challenging to work later witha Rinzel and Lee’s model based on the hypothesis that it is necessary for burstgeneration to consider an inward current which slowly inactivates as Ca2+accumulates during the burst to the exclusion of IK(Ca) [[Aplysia juliana might be derived from temperature-likescaling factors ρ(T) and ϕ(T) for the maximum conductances and the timeconstants, respectively; together with reference temperatureT0 = 23°C, Q10 of 3.0 for the activationvariable, and Q10 of 1.3 for temperature dependences of maximumconductances.However, it is not easy to justify the reason of our choice of Plant model, becausesome papers excluded I Thomas , who det. Chay  constru IK(Ca) . Althou IK(Ca)  concludLevitan  did not et al.  developBertram  revised15 soma  and ele IK(Ca) . By makAplysia juliana and to unravel the mechanism oftemperature dependence in bursting patterns.Taken together, it was suggested that a modified Plant model could be used tosimulate the temperature-dependent bursting activity of bursting pacemaker neurons inthe abdominal ganglia of AP, action potential amplitudeAAP, ave, averaged action potential amplitudeAAP, max, maximum action potential amplitudeACa, slow change in intracellular free calcium concentrationFrequency, spontaneous firing frequency (=1/ISI)r1 + ΔtAP,1/2 + Δtf2)ISI, interspike interval , calcium-activated potassium currentIL, leak currentINa, fast sodium currentI0, reference temperature (=23°C)TV, membrane potentialCa, reversal potential for calcium currentVK, reversal potential for potassium currentVL, reversal potential for leak currentVNa, reversal potential for sodium currentVnp, membrane potential at the negative peakVpp, membrane potential at the positive peakVr1, first half of the rising phase of APΔtr2, last half of the rising phase of APΔtf1, first half of the falling phase of APΔtf2, last half of the falling phase of APΔtAP, 1/2, AP half-width duration(=Δtr2 + Δtf1)Δt1, inverse tangent of the ratio of half AAP toΔtr2θ2, inverse tangent of the ratio of half AAP toΔtf1θh, voltage-dependent inactivation variable for sodium channel;h∞ = steady state value of inactivation hm, voltage-dependent activation variable for sodium channel;m∞ = steady state value of activation mn, activation variable for potassium channel;n∞ = steady state value of activation nm, constant value involved in the defining equation form∞μh, constant value involved in the defining equation forh∞μn, vn, constant values involved in the defining equation forn∞μα, β, γ, δ, constant values involved in the defining equation forsteady state values of activation (inactivation) variables and relaxationconstants1/λ, maximal relaxation time constant of h and nh, relaxation constant of hτχ, relaxation constant of χτn, relaxation constant of nτ∞ = steady state value of activation χχ, slowly activating conductance for calcium current;χ−1, estimation for the time-constant of the Ca-equationρρ(T), temperature-dependent scaling factor; ϕ(T), temperature-dependent scaling factor; SI, slow inward Ca2+-currentINS, nonspecific cation currentIR, anomalous rectifier currentINaCa, Na+-Ca2+ exchanger currentINaK, Na+-K+ pump currentICaP, Ca2+ pump currentIK1, inhibitory potassium currentINSR, calcium current which initiate the burstID, cation-nonspecific currentIR, potassium current which is activated only near the potassiumequilibrium potentialINap, slow depolarizing noninactivating Na+-currentIThe authors declare that they have no competing interests.N.G.H. carried out electrophysiological experiments, performed computer simulation ofexperimental data, and drafted the manuscript. K.H.H. and K.B.H. participated incomputer programming for simulation of experimental data. K.L. drafted and revised themanuscript. All authors read and approved the final manuscript.


Recent innovations in neuroimaging technology have provided opportunities for researchers to investigate connectivity in the human brain by examining the anatomical circuitry as well as functional relationships between brain regions. Existing statistical approaches for connectivity generally examine resting-state or task-related functional connectivity (FC) between brain regions or separately examine structural linkages. As a means to determine brain networks, we present a unified Bayesian framework for analyzing FC utilizing the knowledge of associated structural connections, which extends an approach by Patel et al.  that con There has been a recent emergence of complex functional brain network analyses. A typical neuroimaging network analysis involves defining brain regions (nodes) representing the whole brain, quantifying a measure of association between all pairs of brain regions to produce a connectivity matrix, thresholding these associations to obtain a more sparse connectivity matrix, and then calculating summary statistics that reflect properties of the network. Network summaries include metrics that reflect local or global communication ability , centrality metrics , and community structure .A critical step in network analyses is that of quantifying associations, or functional connectivity (FC), between every pair of nodes because it fundamentally impacts the integrity, reliability, and interpretability of the resulting networks and their associated properties  contribute to FC amongst homolog brain regions  on a 3 Tesla Siemens scanner. The functional data were collected during an auditory spatial-cueing task, which has been described in previous publications  were collected using a single-shot, gradient-echo echoplanar pulse sequence . A total of T* = 483 fMRI scans were collected for each of the N = 32 subjects, 161 for each of three runs, after eliminating the first scan of each run to account for equilibrium effects. Two DTI scans with b = 800 s/mm2 and 30 diffusion gradients were acquired using a twice-refocused spin echo sequence to reduce the effects of eddy currents and artifacts associated with head movement and to allow increased time for diffusion sensitizing gradients. Additional DTI scanning parameters include the following: 72 interleaved slices, TE = 84 ms, TR = 9 s, flip angle = 90, slice thickness = 2.0 mm, FOV = 256 × 256 mm, matrix size = 128 × 128, voxel resolution = 2 × 2 × 2 mm3  = 2.53 s, 7° flip angle, number of excitations (NEX) = 1, slice thickness = 1 mm, FOV (field of view) = 256 mm, resolution = 256 × 256] and T2 2 + [((θ3 + θ4) + (θ2 + θ4))/2]2; Fleiss' κ-statistic  + P(Ab = 1) is close to 1, which ensures that the chance-agreement probability does not exceed 0.5. In addition, other statisticians (Strijbos et al., We propose a functional coherence measurement that builds on Cohen's κ-statistic, which evaluates the levels of agreement adjusted for chance. Here, the chance agreement is defined as (θc Scott, , in whic Fleiss, , which i Fleiss, . The maj Fleiss,  argue thThe study of functional networks in the human brain is important to understand basic cognition, mental and neurological disorders, and response to treatments for these disorders. Moreover, the structural circuitry underlying functional connections may offer additional insights. We develop a Bayesian model that combines both functional and structural information to help characterize FC networks. Leveraging SC to quantify FC, our model yields more accurate and more informative results than considering solely functional data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases  upon amorphous cellulose. The model can predict evolutions of the chain lengths of insoluble cellulose polymers and production of soluble sugars during hydrolysis. Simultaneously, a modelling framework for uncertainty analysis is built based on a quasi-Monte-Carlo method and global sensitivity analysis, which can systematically identify key parameters, help refine the model and improve its identifiability. The model, initially comprising 27 parameters, is found to be over-parameterized with structural and practical identification problems under usual operating conditions (low enzyme loadings). The parameter estimation problem is therefore mathematically ill posed. The framework allows us, on the one hand, to identify a subset of 13 crucial parameters, of which more accurate confidence intervals are estimated using a given experimental dataset, and, on the other hand, to overcome the identification problems. The model’s predictive capability is checked against an independent set of experimental data. Finally, the optimal composition of cellulases cocktail is obtained by model-based optimisation both for enzymatic hydrolysis and for the process of simultaneous saccharification and fermentation. Caccessible binding sites on cellulose unoccupied by enzymes (mmol sites/L)cov(ϴ)covariance matrix of estimated parameters1,…,p and in all the parameters, respectivelyvariances in model outputs associated with simultaneous changes in the parameters ϴdiagdiagonal elements of a matrixN)DPEloadenzyme loading (g/L)El, fconcentration of free enzyme in liquid phase (g/L)El, tconcentration of total enzymein liquid phase (g/L)Es, fconcentration of free enzyme in solid phase (g/L)Es,tconcentration of total enzyme in solid phase (g/L)substrate-enzyme complex (g enzyme/L)Fafraction of accessible β-glucosidic bondsG1, G2, G3concentration of glucose, cellobiose, cellotriose (mmol/L), respectivelyGiconcentration of cellulose polymer with polymerization degree i(mmol/L)inhibition constant of glucose, cellobiose, cellotriose (g/L), respectivelyJ(ϴ)sum of squares of residuals between simulations and measurementsadsorption equilibrium constant (L/mmol sites)equilibrium constant of enzymatic hydrolysis (mmol β-glucosidic bonds/L)KE“apparent” reaction constant (103 mmol/(g enzyme × h))k*E“intrinsic” reaction constant (103 mmol/(g enzyme × h))kfadsorptionrate constant (L/(mmol sites × h))kr−1)desorptionrate constant (hEMenzyme molecular weight (g/mmol)m, n, pnumber of model variables, data points, parameters, respectivelyrelative values between first-order and total effect sensitivity indicesi, ϴj)R(ϴi and ϴjcorrelation coefficient between the estimated parameters ϴGiri (mmol/(L × h))production rate of Gfirst-order, total effect sensitivity indices with respect to parameter i, respectivelyWiweighting matrixGN to soluble sugars (% C/C)conversion of cellulose measured variables, estimated variable values, respectivelytαt-testsignificance level for number of cellobiose lattice occupied by one molecule of enzyme (mmol sites/mmol enzyme)lb, ϴub, ϴ0ϴ, ϴparameter vector, lower and upper bounds, initial guesses, respectivelyvconfidence intervals of parameters at αt significance levelreferϴreference values for corresponding parameters, cited from literatureEλfirst order deactivation constant of enzyme (1/h)Eδbinding capacity of substrate (mmol sites/mmol β-glucosidic bonds)errorvariance of jth meansurement1Enzymatic hydrolysis of cellulosic materials to produce reducing sugars has long been pursued for its potential for providing abundant food and energy resources. It is a multi-step process that takes place in a heterogeneous reaction system Mechanistic understanding of the overall hydrolysis system is certainly interesting for designing rational approaches for enzymatic hydrolysis and subsequent fermentation processes. However, the complexity of the system, which arises from the concerted action of several enzymes on a solid substrate/mixture in a heterogeneous system, makes experimental kinetic studies very difficult. Accordingly, although many models of enzymatic hydrolysis have been developed over the past decades, most of them are empirical correlations and data-driven and as a result are only applicable to specific cases/conditions Efforts to propose mechanistic models have been made to enhance understanding of the enzymatic hydrolysis of cellulose Following the above advances, this work develops a mechanistic depolymerisation (scission) model of enzymatic hydrolysis, taking into account the enzyme adsorption/desorption processes in this heterogeneous system. Furthermore, a systematic sensitivity analysis-based method is proposed for parametric studies, model reduction and verification with published experimental data. Finally, the model’s predictive capability is checked against an independent set of data and model-based optimisation studies are presented.22.1(1)There are several studies about how to modify and improve the various physical properties of cellulose as a substrate, such as particle size, fibre structure, accessibility, crystallinity index, and amorphicity index (2)The enzymatic hydrolysis takes place in a well-stirred tank reactor. As a result, there is no mass transfer limitation during enzymatic hydrolysis.(3)The binding probability of enzyme to one polymer molecule is proportional to the molecule’s polymerization degree. In other words, the enzyme has equal accessibility to every β-glucosidic bond of the polymer (4)The quasi-steady state approximation holds for any intermediate complex, i.e.,(5)The loading of cellulases  is low. More specifically, the loading is no more than 15 mg/g-glucan and 750 mg/L. Accordingly, interaction/crowding effects between different kinds of enzymes are negligible during adsorption/desorption. The adsorption/desorption of each kind of enzyme is described separately and considered reversible.(6)DP ≥ 4) are depolymerized by endoglucanases and exoglucanases, while soluble shorter polymers (namely cellotriose and cellobiose) are exclusively cleaved by β-glucosidases. This is a reasonable assumption if one compares the enzyme specific activities between insoluble and soluble substrates. It has been found experimentally that both endoglucanases and exoglucanases have relatively low catalytic activities upon soluble sugars compared with those upon solid cellulose Cellulosic polymers with chain lengths over four are deemed to exist in solid phase and cannot dissolve into liquid. As shown by (7)The enzymes are inhibited by soluble sugars  non-compModel development is based on the following assumptions:2.2C is the accessible binding sites onto the β-glucosidic bonds of cellulose uncovered by enzymes, El,tE and Es,tE are the total concentrations of one type of enzyme in liquid phase and adsorbed onto solid phase respectively, Efk and Erk are adsorption and desorption rate constants, respectively.The reaction of the enzyme-substrate complex is initiated upon physical contact of endoglucanases and exoglucanases with the surface of an insoluble substrate. Equilibrium of enzyme adsorption/desorption is represented by the Langmuir isotherm model. The time required for equilibrium to be reached is relatively short compared to the hydrolysis time EEload is the loading of any type of enzyme, Eσ is the binding capacity of β-glucosidic bonds, iG represents the cellulose with polymerization degree i  iG), 2αE is the number of cellobiose lattice occupied by one molecule of enzyme, and EM is the enzyme molecular weight.The material balances of enzymes and accessible binding sites on cellulose are represented by Eq. C from Eq. By eliminating the term El,tE using Eq. Furthermore, by eliminating the term By solving the above quadratic equation, we obtain the real root, taking into account Es,tE should be non-negative (≥0), not bigger than total enzyme loading EEload or total binding capacity of substrate The random hydrolysis action by endoglucanases in solid phase is described by Michaelis–Menten kinetics Using the enzyme mass balance Eq. , we can iG.From Eqs. iG equals the contributions of kG  minus the loss of iG, where iG is cleaved by endoglucanases with a probability of The overall production rate of Additionally, the formation rate of soluble sugars dissolving into the liquid phase is obtained by2.3.2Processive hydrolysis by exoglucanases is represented bySimilar to Eq. Since exoglucanases act on cellulose by means of chain-end scission, the total substrate quantity in the solid phase is represented by The formation rate of and the production rates of cellotriose and cellobiose are obtained by Eq. 2.3.3Processive hydrolysis of soluble sugars  by β-glucosidase is described byLike exoglucanases, β-glucosidases cleave off glucose from cellotriose and cellobiose in the way of processive hydrolysis. Similar to the analyses in Section The production/consumption rates of cellotriose, cellobiose and glucose are calculated by Eq. 2.3.4It is often observed that the effectiveness of cellulases is drastically reduced during hydrolysis. Until now, this phenomenon is not well understood and remains an open research question that merits further studies such as t time areλE represent the first order deactivation constants.Additionally, thanks to first order kinetics of enzyme deactivation 2.4The overall formation rates of each species are obtained as follows33.1CP  had an average activity of 31.2 filter paper units (FPU)/mL and was diluted to 1 and 3 FPU by adding buffer solutions. Another set of data for phosphoric acid swollen cellulose hydrolysis was used to check the model’s predictive ability, where mixtures of different mole percentage of Humicola insolens endoglucanase and enxoglucanase (CBH II), and Penicillium brasilianum β-glucosidase were used to hydrolyze phosphoric acid swollen cellulose One set of data for non-crystalline cellulose hydrolysis is used for parameter estimation The values of parameters are iteratively adjusted to obtain the most accurate agreement between predictions and measurements, i.e., by minimizing the weighted sum of squares of residuals:In order to tackle some drawbacks in multimodal optimisation problems such as dependency on initial guesses, a hybrid solution strategy is employed, combining the genetic algorithm with the Nelder–Mead simplex search method 2jσ, the inverse co-variance matrix  is Assuming that the error terms for each experiment are additive, independently/uncorrelated and identically distributed normally with zero mean and variance When maximum likelihood estimation is used, the covariance matrix of θ is obtained by linear approximation t significance level areThen, the confidence intervals of θ at αFurthermore, the approximate correlation matrix of θ can be obtained, the ijth element of which is given byThe diagonal elements of the matrix are all unity and the off-diagonal elements are in the interval .3.2D are the variation in model outputs associated with changes in each parameter θ1,…,p and in all parameters simultaneously, respectively.Global sensitivity analysis (GSA), specifically the Sobol’ GSA method, is used to further evaluate the identifiability of parameters, i.e., sensitivities of model outputs (y) to variations of model parameters θ The first-order indices,Additionally, a matrix of relative values calculated by Eq. 3.3As an optimisation criterion for cellulose hydrolysis, the percent conversion to soluble sugars in terms of % C/C is defined asGeobacillus thermoglucosidasius strain TM242 which can produce ethanol with an inoculum of 0.15 g DCW/L. Through systematic metabolic network reduction Process performance is compared with that of a simultaneous saccharification and fermentation (SSF) process By calculating percent conversion 44.1Uncertainty analysis assesses the confidence in modelling results (including parameter estimates and model outputs/predictions) by quantification of the propagation of various sources of errors in the model input and design. The errors may originate from two sources: one is the quality and amount of data used to develop the model ; another is the model structure, which may not be a perfect representation of the real system. As shown in Eqs. CP are assumed to be 12% w/w endoglucanases, 80% w/w exoglucanases, 4% w/w β-glucosidases, and other enzymes One set of data for non-crystalline cellulose hydrolysis was used for assessing model fits and parameter estimation k*E, KdisE, σE, KadE, IG1E, IG2E′, IG3E′λE, 2αE , aF and DP of cellulose. For detailed definitions of the parameters, please refer to the nomenclature section. Initially, after GSA analysis was performed, eight non-significant parameters were set at their nominal values: reported values of aF and DP were used, i.e., 0.12 and 152, respectively λE values were fixed at 0.0122 h−1E were assumed to be the values of 43, 65, 110 for endoglucanases, exoglucanases, and β-glucosidases, respectively The model includes 27 parameters in total, i.e., 0 are initial guesses for the parameters. Preliminary parameter values estimated using this methodology and their 95% confidence intervals are summarized in As shown in σE, implied by Moreover, the covariance matrix obtained by Eq. 4.2lb ≤ θ ≤ θub, as shown in σex and σbg are restricted to be non-negative.As shown by the θ in CP cellulases loadings of 0.5, 1.5, 3, 6 PFU/g-glucan. The model prediction uncertainty is represented using mean, 10th, and 90th percentile of the distribution of each model output at each time instant in At first, the QMC simulations obtained by simulating 1000 samples using the model result in 1000 time-series of dynamic profiles in aF, DP, λE and 2αEover time in the given but reasonable spaces as noted in adKE, G2Ien, G3Ien, and G3Iex. This can be explained as follows. Firstly, only low cellulases loadings (≤750 mg/L), i.e.,adKE can be set leaving just σE to be estimated. Secondly, thanks to the format of Michaelis–Menten kinetics and the fact that the preliminary estimates of IG2en, IG3en, and IG3ex  and with 19 parameters (using the means) (results not shown). By comparison, the 4.3According to the results of the prediction uncertainty analysis, the model can be reduced to 13 crucial parameters with the other parameters eliminated or fixed at the values from literature under usual operating conditions. Note that the model should be carefully extrapolated to hydrolysis processes with high loadings of enzymes (>15 mg/g-glucan or >750 mg/L). Fortunately, high loadings are usually not used in industry applications due to high cost.Besides direct validation with the data of Peri et al. in silico simulations are performed under 1000 different make-ups of the three enzymes. Percent conversion This detailed mechanistic model provides a tool for optimising the cellulose hydrolysis process. An obvious target is to determine the optimal composition of cellulases cocktail including the three core constituents/enzymes. The optimised cocktail can, in theory, reduce the loading of enzymes without sacrificing the hydrolysis yield or increase the sugar yield in shorter incubation time. The optimisation procedure is as follows. Firstly, A reasonable enzyme loading would lie at a value between 2.5 and 7.5 mg/g-glucan as an activity-cost trade off . From the simulation results, in batch enzymatic hydrolysis, only around 40% of 50 g/L cellulose can be converted into soluble sugars by the optimal cellulases cocktail with 7.5 mg/g-glucan loading in 96 h. By comparison, about 90% of the cellulose is hydrolysed with the same cellulases cocktail in 96 h thanks to reduced inhibitions of products, which are simultaneously fermented into ethanol. According to subplots (c) and (d) of As mentioned in the introduction, experimental validation using empirical models and pure mechanistic analysis of cellulose enzymatic hydrolysis  has typically been performed separately 5In this work we developed a detailed mechanistic model of depolymerisation (chain-end scission) of amorphous cellulose by three core cellulolytic enzymes in a heterogeneous catalytic system. Separate steps for enzyme adsorption, formation of a catalytically active complex with cellulosic chains/ends, and enzyme desorption have been included. Through parametric analysis and experimental validation, we show that the resulting model with the reduced parameter set  is capable of predicting the evolution of distribution of insoluble cellulose chain lengths (from four to DP) as well as the production of soluble sugars during synergistic enzymatic hydrolysis. Finally, model-based optimisation for hydrolysis and SSF conclude that the optimal composition of cellulases cocktail is approximately en:ex:bg ≅ 60–85:10–30:3–5(w/w/w) both for enzymatic hydrolysis and for more advantageous SSF process.


Malus x domestica genome, as well as to examine their distribution and impact.Copy number variation (CNV) is a common feature of eukaryotic genomes, and a growing body of evidence suggests that genes affected by CNV are enriched in processes that are associated with environmental responses. Here we use next generation sequence (NGS) data to detect copy-number variable regions (CNVRs) within the M. x domestica analyzed using the read-depth method, as implemented in the CNVrd2 software. To improve the reliability of our results, we developed a quality control and analysis procedure that involved checking for organelle DNA, not repeat masking, and the determination of CNVR identity using a permutation testing procedure.CNVRs were detected using NGS data derived from 30 accessions of P < 1e-13, Fisher’s Exact test). Putative CNVRs overlapped 845 gene models and were enriched for resistance (R) gene models . Of note was a cluster of resistance gene models on chromosome 2 near a region containing multiple major gene loci conferring resistance to apple scab.Overall, we identified 876 CNVRs, which spanned 3.5 % of the apple genome. To verify that detected CNVRs were not artifacts, we analyzed the B- allele-frequencies (BAF) within a single nucleotide polymorphism (SNP) array dataset derived from a screening of 185 individual apple accessions and found the CNVRs were enriched for SNPs having aberrant BAFs  contains supplementary material, which is available to authorized users. The availability of genome sequence data from individuals within a species now enables the investigation of a range of inherited genetic variations at a high resolution. Traditionally, genomic analysis of DNA variants has focused on the identification of single nucleotide polymorphisms (SNPs), and small insertions and deletions (INDELS). However in recent years, other forms of genomic variation have also begun to receive attention. One such form is copy number variation (CNV), defined as a deletion, duplication or insertion of DNA sequence fragments longer than 50 base pairs in length .plasma coagulation 12 (FXII) gene exposes a deficiency in the remaining copy [ED1 gene causes anhidrotic ectodermal dysplasia [KIT gene [Studies of CNV in eukaryotic organisms, such as dog , barley ing copy . CNV canysplasia ; and in KIT gene , 8. CNV KIT gene .Vrn-A1 and Ppd-B1 have been shown to influence flowering time [Rht-D1b locus is associated with dwarfism [Rhg1 gene is responsible for nematode resistance [Bot1 locus [CNV is a common feature of plant genomes, and recent work has highlighted its functional relevance. In barley, genes affected by CNV were enriched for potential functions in disease resistance , a findiing time , and a Cdwarfism . In soybsistance , while it1 locus . Based oMalus x domestica Borkh.) first appeared in the Near East around 4,000 years ago. In 2010, a high-quality draft genome of ‘Golden Delicious’ was released [Malus sylvestris and M. sieversii have shaped the genomes of modern domesticated cultivars [The domesticated apple , were the first high-throughput approaches used to identify CNVs at the genome-wide scale . More reM. x domestica) accessions was developed using Illumina GAII technology, with one lane per individual as described in [Malus x domestica ‘Braeburn’, ‘CrimsonCrisp’, ‘Delicious’, , ‘Duchess of Oldenburg’, F2268292-2, ‘Geneva’, ‘Golden Delicious’, ‘Granny Smith’, ‘Haralson’, ‘Honeycrisp’, ‘Idared’, ‘James Grieve’, ‘Jonathan’, ‘McIntosh’, ‘Ralls Janet’, ‘Red Dougherty’, ‘Rome Beauty’, ‘Splendour’, ‘Big Red’, ‘Papa Noel’, ‘Merton Russet’, ‘Wilmont Russet’, ‘David’ o.p., ‘Malling 9’, and four advanced selections. The raw sequencing data for many of the accessions is publicly available, and for the other accessions data can be made available on request  files used for CNV analysis were filtered based on mapping quality (reads with MAPQ < 20 were discarded), and processed using CNVrd2  (v1.4.0)To identify CNVRs, a trimmed standard deviation  was calculated to compare the segmentation scores in each sample for each window. This trimmed standard deviation was used to remove outliers from the calculations. Permutation testing  was used to determine CNVR significance. This involved shuffling the segmented regions in each sample, recalculating trimmed standard deviations, and calculating an empirical false discovery rate (FDR) . The FDRA SNP dataset was used for validation of CNVRs which contained information on the scoring of 10,685 SNP polymorphic markers from the apple Illumina Infinium 20 k SNP array  screenedP-values were adjusted using the FDR-controlling approach of Benjamimi and Hochberg [The consensus gene models were obtained from the Genome Database for Rosaceae . A gene Hochberg . A separHochberg . The repHochberg  against Hochberg  databaseBefore quality control (QC), the average sequencing coverage for each variety varied between 3.51× and 13.60×. After QC, which included removing reads overlapping the organelle and repeated regions, the average sequencing coverage for each variety was reduced to between 0.95× and 4.96× , meaning that for every unit increase in average coverage; the model estimated that the average number of segmented regions increased by 82.58. Although integer copy number assignment for an individual sample can be performed using the read-depth method, given the low-coverage sequencing data used in our study and the incomplete apple genome assembly, we chose instead to focus on CNVRs that displayed significant variation in segmentation scores and not to attempt integer copy number assignment. These significantly variable CNVRs were determined by summarizing each window using a trimmed standard deviation (removing the minimum and maximum), followed by a permutation testing procedure to calculate the threshold used to identify potential CNVRs: any window with a trimmed standard deviation above this threshold was considered to lie within a CNVR. A threshold of 0.25 gave an acceptable FDR of 11 %, and was used in downstream analyses  between coverage and the number of regions detected, which is an indication that CNVs are more likely to be detected in samples of higher coverage  [Gypsy elements  and a significant enrichment for Copia elements   were observed for hAT, Cassandra, and LINE elements, and no overall enrichment  was detected for repeated elements. The genome-wide GC content of CNVRs was nominally  higher (average 37.9 %) than that of the pseudohaplotype assembly (37.8 %). In contrast, the genome-wide average for genic GC content was nominally  lower than that of CNVRs (average 37.6 %)   were tesy P = 0.0, T-test P < 0.0001, Fisher’s Exact test). Interestingly, 98.5 % of the SNPs represented on the apple Illumina Infinium 20 k SNP array were located in non-CNV regions, indicating a significant depletion of SNPs in CNVRs  and suggests that the SNP array design was biased away from CNVRs.Although low density SNP microarrays do not enable copy-number detection directly, because of their limited probe density, they do present a method of independently validating CNVRs detected by read-depth analysis. We accomplished this by extracting the BAFs from an apple Illumina Infinium 20-k SNP array dataset that contained the genotyping information for 185 accessions. After the removal of SNPs that overlapped the windows which were previously removed from the analysis because they contained Ns, repeats, or organelle DNA, 10,685 SNPs remained. BAF thresholds of 0.05–0.35 and 0.65–0.95 were considered aberrant , with the proportion of the genome assembly spanned by CNVRs (3.5 %) being greater than the proportion containing gene models. Over half (470) the CNVRs did not contain a gene model, with the remaining 406 regions (46.3 %) containing at least one gene model. Functional enrichment analysis of CNVRs revealed 20 GO terms overrepresented after FDR p-value adjustment  . Ten of these enriched GO terms were not attributable to R gene models, such as “ion transport”, “chloride transport”, and “voltage-gated chloride channel activity”.Of the consensus gene models from the Genome Database for Rosaceae, 29,015 were located in genomic regions that were analyzed. A total of 845 (2.9 %) of these gene models exhibited a minimum of 70 % of their base pairs overlapping putative CNVRs , the presence of paralogs made it difficult to design primers for the SNPs linked to the respective loci, which amplified alleles only at the specific region of interest, without co-amplifying the paralogous sequences. Paralogs located in tandem would be considered a segmental duplication, which are hotspots for CNVs [Pl2, a major locus conferring resistance to powdery mildew (Posdosphaera leucotricha), failed to amplify when resistance was not observed. This would be expected if resistance were conferred by the insertion of a segment of DNA, which contains the SNPs that failed to amplify, and is absent from susceptible individuals [Although CNV had not been investigated previously in the apple genome, there has been some evidence that CNV might play an important biological role here. A study investigating QTLs associated with bud phenology in apple identified a candidate tive CNV . More retive CNV . Firstlyfor CNVs . Secondlividuals .Malus x domestica Whole Genome v1.0p) [Because of apple’s high heterozygosity, its genome was not assembled as a single reference sequence but rather into four haplotypes, or individual sequences. The primary pseudohaplotype assembly (e v1.0p) , which we v1.0p) . Repeatee v1.0p) , create e v1.0p) , 55. HowLow-coverage NGS re-sequencing data were used to identify the CNVRs that exhibited significant variation in copy-number within a set of apple accessions with genetic links to international breeding programs. The CNVRs identified were found to vary greatly in copy-number among the accessions, and represent candidate gene loci that may be involved in control of variability in traits, such as pest and disease resistance. The number of samples used (30 apple accessions) is higher than employed in some recent read-depth studies in domesticated organisms [e.g., the number of samples was five and 16 in CNV studies of cows  and pigsThe read-depth analysis identified 876 CNVRs in the apple genome, ranging in length between 3 kb and 99 kb, with an average of 18 kb. It should be noted that because the CNVRs separated by windows that were removed were not merged, the number of detected CNVRs is likely to be an overestimate, as a number of CNVRs interleaved with removed regions may actually comprise a single large CNVR. Our results suggest that 3.5 % (14.4 Mb) of the analyzed assembly lies within a CNVR. This percentage is lower than found in previous CNV studies in plants; for example, the CNV percentages found in maize  and barlIn total, 845 gene models overlapped the CNVRs detected. Initial functional enrichment analysis suggests that these gene models are involved in ion transport, signal transduction, and the defense response. This result is concordant with other studies in a wide range of species, such as humans , and soyRvi4/Vh4 and Rvi15/Vr2 loci were originally mapped to apple linkage group 2 [Pl2, a major gene conferring resistance to powdery mildew. Pl2 was originally mapped using expressed sequence tags-based markers [Pl2 [Numerous studies have focused on identifying genomic regions associated with horticultural traits in apple, using genetic linkage mapping and QTL analysis [see Troggio et al. for a review . Three r group 2 , 61 and  group 2 , close t markers  and the ers [Pl2  enabled ers [Pl2 . Future We identified 876 CNVRs with an average size of 16.4 kb, comprising 3.5 % of the apple genome. These results represent the first catalogue and investigation of a previously unexplored form of genetic variation in a tree species. The CNVRs identified in this study are enriched in resistance (R) gene models and overlap with major gene loci of agricultural significance. Further investigation of apple CNV using higher coverage NGS data will enable integer-level copy-number assignment and break-point identification. This will facilitate the discovery of the causative CNV and improve the design of molecular markers that segregate with the trait. Ultimately, we believe that the focused investigation of CNV in the apple genome will lead to the genetic improvement of apple cultivars and a deeper understanding of the role CNV plays within the apple genome and other long-lived tree species.


Leptomonas moramango. This virus belongs to a newly discovered group of bunyavirus-like elements termed Leishbunyaviruses (LBV), the first discovered from protists related to arboviruses infecting humans.We report here the sequences for all three segments of a novel RNA virus (LepmorLBV1) from the insect trypanosomatid parasite  Bunyaviridae comprise >350 species, often arthropod-borne and including serious human, animal, and plant pathogens . Within each pair, one was more abundant, as judged by read depth coverage ; we arbitrarily grouped the more abundant segments as “a” and refer to the viruses as LepmorLBV1a and 1b. The G+C compositions of LepmorLBV1s were similar to each other and to that of the meta-transcriptomic assembly (38 to 41%). We were able to detect 9-nt “panhandle” repeats (Comparisons between the LepmorLBV1a/b segment open reading frames (ORFs) showed significant nucleotide and amino acid identity . L segments displayed a single ORF encoding 1,979 or 1,982 amino acids (aa), bearing motifs found typically in bunyavirus RNA-dependent RNA polymerases (RdRp) , 7. M sePhlebovirus or other genera. Additional affinities to Bunyaviridae include a negative single-stranded RNA genome, terminal repeated panhandle sequences important for replication and transcription, and three segments typically encoding the viral RdRp, envelope glycoproteins, and the nucleocapsid .Phylogenetic analysis of the RdRp domain showed that LepmorLBV1s grouped together with bunyaviruses, relative to other viral outgroups, but as a clearly distinct clade well separated from eocapsid , 5, 7. TL. moramango may bear cytoplasmic virus-like particles (articles  that couarticles .KX280012 to KX280017.The sequences of all segments of LepmorLBV1a and 1b were deposited in GenBank under the accession numbers


K) were 2.60 × 104, 4.59 × 104, and 7.01 × 104 M−1 at 288, 300, and 310 K, respectively. Based on fluorescence resonance energy transfer, the distance between the HSA and patulin was determined to be 2.847 nm. The ΔG0, ΔH0, and ΔS0 values across various temperatures indicated that hydrophobic interaction was the predominant binding force. The UV-Vis and CD results confirmed that the secondary structure of HSA was altered in the presence of patulin. The AFM results revealed that the individual HSA molecule dimensions were larger after interaction with patulin. In addition, molecular modeling showed that the patulin-HSA complex was stabilized by hydrophobic and hydrogen bond forces. The study results suggested that a weak intermolecular interaction occurred between patulin and HSA. Overall, the results are potentially useful for elucidating the toxigenicity of patulin when it is combined with the biomolecular function effect, transmembrane transport, toxicological, testing and other experiments.The interaction of patulin with human serum albumin (HSA) was studied in vitro under normal physiological conditions. The study was performed using fluorescence, ultraviolet-visible spectroscopy (UV-Vis), circular dichroism (CD), atomic force microscopy (AFM), and molecular modeling techniques. The quenching mechanism was investigated using the association constants, the number of binding sites, and basic thermodynamic parameters. A dynamic quenching mechanism occurred between HSA and patulin, and the binding constants ( Penicillium, Aspergillus genera, and Byssochlamys species  is the quencher concentration. KSV is the Stern-Volmer quenching constant. The KSV values at different temperatures are listed in KSV values increased with increasing temperature, which suggests that a dynamic quenching mechanism occurred between HSA and patulin.To further confirm the quenching mechanism, the fluorescence quenching data were analyzed with the Stern-Volmer equation:τ0〉 = 〈τ〉) [χ2 = 1.007) (with τ1 = 3.06 ns [α1 (fraction) = 0.64], τ2 = 7.80 ns [α2 (fraction) = 0.36], and 〈τ〉 = 5.37 ns). These results are in agreement with those obtained for Trp-214 by Helms et al.  is the total quencher concentration. The values of KA and n for patulin-HSA are also listed in KA values increased with temperature, which indicates that the patulin-HSA quenching mechanism was dynamic. This result agrees with the results described in The apparent binding constant (H0), and entropy change (ΔS0) of reaction are important for confirming what binding mode is active. Therefore, the temperature dependence of the binding constant was studied. ΔH0 and ΔS0 were obtained using the van't Hoff equations ]H0), anned from  and R isH0, ΔS0, and ΔG0 values are also listed in H0 and ΔS0 values of patulin-HSA were 8.06 kJ mol−1 and 106.0 J mol−1 K−1, respectively. According to the results of Timasheff and Subramanian regarding various protein interactions , which further conformed the experimental results and demonstrated that the spontaneous binding between patulin and HSA occurred.As shown in In this study, the interactions of patulin with HSA were investigated under simulated physiological conditions using different spectroscopic methods: AFM and molecular modeling. Steady-state and time-resolved fluorescence spectra suggest that a dynamic quenching mechanism occurred in patulin-HSA complexes. The binding parameters for the reaction were determined using the Stern-Volmer equation. The thermodynamic parameters obtained at different temperatures and the molecular modeling results indicate that hydrophobic interaction was the dominant binding force and also suggest the formation of hydrogen bonds between patulin and HSA. This finding is expected to elucidate the toxigenicity of patulin when it is combined with the biomolecular function effect, transmembrane transport, toxicological testing, and other experiments.