A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions

Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism and do not allow to take the uncertainty in classification into account. We present a response-time-based model-based mixture modeling approach that overcomes this limitation. The approach (a) facilitates disentangling item omissions stemming from disengagement from those going back to solution behavior, (b) considers the uncertainty in omission classification, (c) allows for omission mechanisms to vary on the item-by-examinee level, (d) supports investigating person and item characteristics associated with different types of omission behavior, and (e) gives researchers flexibility in deciding on how to handle different types of omissions. The approach exhibits good parameter recovery under realistic research conditions. We illustrate the approach on data from the Programme for the International Assessment of Adult Competencies 2012 and compare it against previous classification approaches for item omissions.

Internet-based transdiagnostic treatment for emotional disorders in Arabic- and Farsi-speaking refugees: study protocol of a randomized controlled trial

BACKGROUND

Refugee populations have an increased risk for mental disorders, such as depression, anxiety, and posttraumatic stress disorders. Comorbidity is common. At the same time, refugees face multiple barriers to accessing mental health treatment. Only a minority of them receive adequate help. The planned trial evaluates a low-threshold, transdiagnostic Internet-based treatment. The trial aims at establishing its efficacy and cost-effectiveness compared with no treatment.

METHODS

N = 131 treatment-seeking Arabic- or Farsi-speaking patients, meeting diagnostic criteria for a depressive, anxiety, and/or posttraumatic stress disorder will be randomized to either the intervention or the waitlist control group. The intervention group receives an Internet-based treatment with weekly written guidance provided by Arabic- or Farsi-speaking professionals. The treatment is based on the Common Elements Treatment Approach (CETA), is tailored to the individual patient, and takes 6-16 weeks. The control group will wait for 3 months and then receive the Internet-based treatment.

DISCUSSION

The planned trial will result in an estimate of the efficacy of a low-threshold and scalable treatment option for the most common mental disorders in refugees.

TRIAL REGISTRATION

German Registry for Clinical Trials DRKS00024154. Registered on February 1, 2021.

Disentangling Different Aspects of Change in Tests with the D-Diffusion Model

Diffusion-based item response theory models are measurement models that link parameters of the diffusion model (drift rate, boundary separation) to latent traits of test takers. Similar to standard latent trait models, they assume the invariance of the test takers' latent traits during a test. Previous research, however, suggests that traits change as test takers learn or decrease their effort. In this paper, we combine the diffusion-based item response theory model with a latent growth curve model. In the model, the latent traits of each test taker are allowed to change during the test until a stable level is reached. As different change processes are assumed for different traits, different aspects of change can be separated. We discuss different versions of the model that make different assumptions about the form (linear versus quadratic) and rate (fixed versus individual-specific) of change. In order to fit the model to data, we propose a Bayes estimator. Parameter recovery is investigated in a simulation study. The study suggests that parameter recovery is good under certain conditions. We illustrate the application of the model to data measuring visuo-spatial perspective-taking.

A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data

Careless and insufficient effort responding (C/IER) can pose a major threat to data quality and, as such, to validity of inferences drawn from questionnaire data. A rich body of methods aiming at its detection has been developed. Most of these methods can detect only specific types of C/IER patterns. However, typically different types of C/IER patterns occur within one data set and need to be accounted for. We present a model-based approach for detecting manifold manifestations of C/IER at once. This is achieved by leveraging response time (RT) information available from computer-administered questionnaires and integrating theoretical considerations on C/IER with recent psychometric modeling approaches. The approach a) takes the specifics of attentive response behavior on questionnaires into account by incorporating the distance-difficulty hypothesis, b) allows for attentiveness to vary on the screen-by-respondent level, c) allows for respondents with different trait and speed levels to differ in their attentiveness, and d) at once deals with various response patterns arising from C/IER. The approach makes use of item-level RTs. An adapted version for aggregated RTs is presented that supports screening for C/IER behavior on the respondent level. Parameter recovery is investigated in a simulation study. The approach is illustrated in an empirical example, comparing different RT measures and contrasting the proposed model-based procedure against indicator-based multiple-hurdle approaches.

Partial Measurement Invariance: Extending and Evaluating the Cluster Approach for Identifying Anchor Items

When measurement invariance does not hold, researchers aim for partial measurement invariance by identifying anchor items that are assumed to be measurement invariant. In this paper, we build on Bechger and Maris's approach for identification of anchor items. Instead of identifying differential item functioning (DIF)-free items, they propose to identify different sets of items that are invariant in item parameters within the same item set. We extend their approach by an additional step in order to allow for identification of homogeneously functioning item sets. We evaluate the performance of the extended cluster approach under various conditions and compare its performance to that of previous approaches, that are the equal-mean difficulty (EMD) approach and the iterative forward approach. We show that the EMD and the iterative forward approaches perform well in conditions with balanced DIF or when DIF is small. In conditions with large and unbalanced DIF, they fail to recover the true group mean differences. With appropriate threshold settings, the cluster approach identified a cluster that resulted in unbiased mean difference estimates in all conditions. Compared to previous approaches, the cluster approach allows for a variety of different assumptions as well as for depicting the uncertainty in the results that stem from the choice of the assumption. Using a real data set, we illustrate how the assumptions of the previous approaches may be incorporated in the cluster approach and how the chosen assumption impacts the results.

Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes

Complex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees' behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees' behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012.

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response

In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.

Performance of missing data approaches under nonignorable missing data conditions

Approaches for dealing with item omission include incorrect scoring, ignoring missing values, and approaches for nonignorable missing values and have only been evaluated for certain forms of nonignorability. In this paper we investigate the performance of these approaches for various conditions of nonignorability, that is, when the missing response depends on i) the item response, ii) a latent missing propensity, or iii) both. No approach results in unbiased parameter estimates of the Rasch model under all missing data mechanisms. Incorrect scoring only results in unbiased estimates under very specific data constellations of missing mechanisms i) and iii). The approach for nonignorable missing values only results in unbiased estimates under condition ii). Ignoring results in slightly more biased estimates than the approach for nonignorable missing values, while the latter also indicates the presence of nonignorablity under all simulated conditions. We illustrate the results in an empirical example on PISA data.

A Multiprocess Item Response Model for Not-Reached Items due to Time Limits and Quitting

So far, modeling approaches for not-reached items have considered one single underlying process. However, missing values at the end of a test can occur for a variety of reasons. On the one hand, examinees may not reach the end of a test due to time limits and lack of working speed. On the other hand, examinees may not attempt all items and quit responding due to, for example, fatigue or lack of motivation. We use response times retrieved from computerized testing to distinguish missing data due to lack of speed from missingness due to quitting. On the basis of this information, we present a new model that allows to disentangle and simultaneously model different missing data mechanisms underlying not-reached items. The model (a) supports a more fine-grained understanding of the processes underlying not-reached items and (b) allows to disentangle different sources describing test performance. In a simulation study, we evaluate estimation of the proposed model. In an empirical study, we show what insights can be gained regarding test-taking behavior using this model.

Using Response Times for Joint Modeling of Response and Omission Behavior

For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or already have been moving to computer-based assessment, a rich body of additional data on examinee behavior becomes easily accessible. These additional data may contain valuable information on the processes associated with nonresponse. Bringing together research on item omissions with approaches for modeling response time data, we propose a framework for simultaneously modeling response behavior and omission behavior utilizing timing information for both. As such, the proposed model allows (a) to gain a deeper understanding of response and nonresponse behavior in general and, in particular, of the processes underlying item omissions in LSAs, (b) to model the processes determining the time examinees require to generate a response or to omit an item, and (c) to account for nonignorable item omissions. Parameter recovery of the proposed model is studied within a simulation study. An illustration of the model by means of an application to real data is provided.

Psychosocial and physical long-term outcome in patients with a history of takotsubo cardiomyopathy or myocardial infarction - a multi-centered case control study

Physical long-term impacts of Takotsubo Cardiomyopathy (TTC) remain controversial and an underestimation of their severity becomes increasingly evident. Even less is known about mental long-term impacts of TTC. This study aims at a better understanding of the physical and mental long-term effects of TTC in comparison to myocardial infarctions (MI). On average 5 years after disease onset, 68 TTC patients and 68 age- and sex-matched MI patients were assessed for disease-related quality of life, depression, anxiety, chronic stress, social support, resilience, and life events prior to disease onset. Scores of TTC and MI patients were compared to each other and to normative references values. Regression analyses were used to evaluate the predictive value of the number of life events prior to disease onset for physical and mental long-term outcomes. Both groups displayed higher scores in depression and anxiety, higher levels of chronic stress, and lower scores in physical and mental quality of life in comparison to norm samples, while social support did not differ from norms. No differences between the two patient groups were observed. Within both groups, the majority of patients (TTC: 69.1%; MI: 60.3%) reported stressful life events prior to disease onset. In TTCs and MIs, the number of events had a significant impact on long-term mental health and chronic stress. Notably, both patient collectives scored higher in resilience than healthy controls. Results suggest negative long-term impacts of TTC on mental and physical wellbeing, comparable to those of MI. Besides a good somatic-medical care, psychotherapeutic support, including the development of functional coping strategies, might be warranted for TTC patients. The long-term impact of TTC should be taken as serious as that of MI.

Psychometric Modelling of Longitudinal Genetically Informative Twin Data

The often-used A(C)E model that decomposes phenotypic variance into parts due to additive genetic and environmental influences can be extended to a longitudinal model when the trait has been assessed at multiple occasions. This enables inference about the nature (e.g., genetic or environmental) of the covariance among the different measurement points. In the case that the measurement of the phenotype relies on self-report data (e.g., questionnaire data), often, aggregated scores (e.g., sum–scores) are used as a proxy for the phenotype. However, earlier research based on the univariate ACE model that concerns a single measurement occasion has shown that this can lead to an underestimation of heritability and that instead, one should prefer to model the raw item data by integrating an explicit measurement model into the analysis. This has, however, not been translated to the more complex longitudinal case. In this paper, we first present a latent state twin A(C)E model that combines the genetic twin model with an item response theory (IRT) model as well as its specification in a Bayesian framework. Two simulation studies were conducted to investigate 1) how large the bias is when sum–scores are used in the longitudinal A(C)E model and 2) if using the latent twin model can overcome the potential bias. Results of the first simulation study (e.g., AE model) demonstrated that using a sum–score approach leads to underestimated heritability estimates and biased covariance estimates. Surprisingly, the IRT approach also lead to bias, but to a much lesser degree. The amount of bias increased in the second simulation study (e.g., ACE model) under both frameworks, with the IRT approach still being the less biased approach. Since the bias was less severe under the IRT approach than under the sum–score approach and due to other advantages of latent variable modelling, we still advise researcher to adopt the IRT approach. We further illustrate differences between the traditional sum–score approach and the latent state twin A(C)E model by analyzing data of a two-wave twin study, consisting of the answers of 8,016 twins on a scale developed to measure social attitudes related to conservatism.

Using Response Times to Model Not-Reached Items due to Time Limits

Missing values at the end of a test typically are the result of test takers running out of time and can as such be understood by studying test takers' working speed. As testing moves to computer-based assessment, response times become available allowing to simulatenously model speed and ability. Integrating research on response time modeling with research on modeling missing responses, we propose using response times to model missing values due to time limits. We identify similarities between approaches used to account for not-reached items (Rose et al. in ETS Res Rep Ser 2010:i-53, 2010) and the speed-accuracy (SA) model for joint modeling of effective speed and effective ability as proposed by van der Linden (Psychometrika 72(3):287-308, 2007). In a simulation, we show (a) that the SA model can recover parameters in the presence of missing values due to time limits and (b) that the response time model, using item-level timing information rather than a count of not-reached items, results in person parameter estimates that differ from missing data IRT models applied to not-reached items. We propose using the SA model to model the missing data process and to use both, ability and speed, to describe the performance of test takers. We illustrate the application of the model in an empirical analysis.

When Nonresponse Mechanisms Change: Effects on Trends and Group Comparisons in International Large-Scale Assessments

Mechanisms causing item nonresponses in large-scale assessments are often said to be nonignorable. Parameter estimates can be biased if nonignorable missing data mechanisms are not adequately modeled. In trend analyses, it is plausible for the missing data mechanism and the percentage of missing values to change over time. In this article, we investigated (a) the extent to which the missing data mechanism and the percentage of missing values changed over time in real large-scale assessment data, (b) how different approaches for dealing with missing data performed under such conditions, and (c) the practical implications for trend estimates. These issues are highly relevant because the conclusions hold for all kinds of group mean differences in large-scale assessments. In a reanalysis of PISA (Programme for International Student Assessment) data from 35 OECD countries, we found that missing data mechanisms and numbers of missing values varied considerably across time points, countries, and domains. In a simulation study, we generated data in which we allowed the missing data mechanism and the amount of missing data to change over time. We showed that the trend estimates were biased if differences in the missing-data mechanisms were not taken into account, in our case, when omissions were scored as wrong, when omissions were ignored, or when model-based approaches assuming a constant missing data mechanism over time were used. The results suggest that the most accurate estimates can be obtained from the application of multiple group models for nonignorable missing values when the amounts of missing data and the missing data mechanisms changed over time. In an empirical example, we furthermore showed that the large decline in PISA reading literacy in Ireland in 2009 was reduced when we estimated trends using missing data treatments that accounted for changes in missing data mechanisms.

Compensation and Amplification of Attenuation Bias in Causal Effect Estimates

Covariate-adjusted treatment effects are commonly estimated in non-randomized studies. It has been shown that measurement error in covariates can bias treatment effect estimates when not appropriately accounted for. So far, these delineations primarily assumed a true data generating model that included just one single covariate. It is, however, more plausible that the true model consists of more than one covariate. We evaluate when a further covariate may reduce bias due to measurement error in another covariate and in which cases it is not recommended to include a further covariate. We analytically derive the amount of bias related to the fallible covariate's reliability and systematically disentangle bias compensation and amplification due to an additional covariate. With a fallible covariate, it is not always beneficial to include an additional covariate for adjustment, as the additional covariate can extensively increase the bias. The mechanisms for an increased bias due to an additional covariate can be complex, even in a simple setting of just two covariates. A high reliability of the fallible covariate or a high correlation between the covariates cannot in general prevent from substantial bias. We show distorting effects of a fallible covariate in an empirical example and discuss adjustment for latent covariates as a possible solution.

When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration

The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment. But is it always necessary to use latent covariates? How well do analysis of covariance (ANCOVA) or propensity score (PS) methods estimate the ATE when latent covariates are used? We first analytically delineate the conditions under which latent instead of fallible covariates are necessary to obtain the ATE. Then we empirically examine the difference between ATE estimates when adjusting for fallible or latent covariates in an applied example. We discuss the issue of fallible covariates within a stochastic theory of causal effects and analyse data of a within-study comparison with recently developed ANCOVA and PS procedures that allow for latent covariates. We show that fallible covariates do not necessarily bias ATE estimates, but point out different scenarios in which adjusting for latent covariates is required. In our empirical application, we demonstrate how latent covariates can be incorporated for ATE estimation in ANCOVA and in PS analysis.

Akkermansia muciniphila: a novel functional microbe with probiotic properties

Akkermansia muciniphila is an intestinal anaerobe which has been proposed as a new functional microbe with probiotic properties. However, the species is not included in the European Union qualified presumption of safety (QPS) list and has not yet been assessed. Moreover, products containing A. muciniphila are not on the market and are thus controlled by the Novel Foods Regulation, which requires extensive safety assessment. This review addresses the safety aspects of the use of A. muciniphila based on published information on its functions in humans and predictions based on its activity in model animals. Further, comprehensive studies related to A. muciniphila and its safety properties have gradually appeared and are summarised here. Many of the criteria required for novel food safety assessment in Europe can thus be fulfilled. However, studies focusing on the toxicological properties of A. muciniphila, including long-term and reproduction studies, have not so far been reported and are discussed in the light of the observation that most, if not all, healthy subjects are known to carry this intestinal anaerobe. As this also applies to other beneficial bacteria found in the human intestinal tract, the A. muciniphila case can be seen as a model for the comprehensive safety evaluations required by the European authorities.

Testing Students with Special Educational Needs in Large-Scale Assessments - Psychometric Properties of Test Scores and Associations with Test Taking Behavior

Assessing competencies of students with special educational needs in learning (SEN-L) poses a challenge for large-scale assessments (LSAs). For students with SEN-L, the available competence tests may fail to yield test scores of high psychometric quality, which are—at the same time—measurement invariant to test scores of general education students. We investigated whether we can identify a subgroup of students with SEN-L, for which measurement invariant competence measures of adequate psychometric quality may be obtained with tests available in LSAs. We furthermore investigated whether differences in test-taking behavior may explain dissatisfying psychometric properties and measurement non-invariance of test scores within LSAs. We relied on person fit indices and mixture distribution models to identify students with SEN-L for whom test scores with satisfactory psychometric properties and measurement invariance may be obtained. We also captured differences in test-taking behavior related to guessing and missing responses. As a result we identified a subgroup of students with SEN-L for whom competence scores of adequate psychometric quality that are measurement invariant to those of general education students were obtained. Concerning test taking behavior, there was a small number of students who unsystematically picked response options. Removing these students from the sample slightly improved item fit. Furthermore, two different patterns of missing responses were identified that explain to some extent problems in the assessments of students with SEN-L.

Taking the Missing Propensity Into Account When Estimating Competence Scores: Evaluation of Item Response Theory Models for Nonignorable Omissions

When competence tests are administered, subjects frequently omit items. These missing responses pose a threat to correctly estimating the proficiency level. Newer model-based approaches aim to take nonignorable missing data processes into account by incorporating a latent missing propensity into the measurement model. Two assumptions are typically made when using these models: (1) The missing propensity is unidimensional and (2) the missing propensity and the ability are bivariate normally distributed. These assumptions may, however, be violated in real data sets and could, thus, pose a threat to the validity of this approach. The present study focuses on modeling competencies in various domains, using data from a school sample (N = 15,396) and an adult sample (N = 7,256) from the National Educational Panel Study. Our interest was to investigate whether violations of unidimensionality and the normal distribution assumption severely affect the performance of the model-based approach in terms of differences in ability estimates. We propose a model with a competence dimension, a unidimensional missing propensity and a distributional assumption more flexible than a multivariate normal. Using this model for ability estimation results in different ability estimates compared with a model ignoring missing responses. Implications for ability estimation in large-scale assessments are discussed.

In-vivo monitoring of acute DSS-Colitis using Colonoscopy, high resolution Ultrasound and bench-top Magnetic Resonance Imaging in Mice

OBJECTIVE

The aim of this study was to establish and evaluate (colour Doppler-) high-resolution-ultrasound (hrUS) and bench-top magnetic resonance imaging (btMRI) as new methods to monitor experimental colitis.

MATERIALS AND METHODS

hrUS, btMRI and endoscopy were performed in mice without colitis (n = 15), in mice with acute colitis (n = 14) and in mice with acute colitis and simultaneous treatment with infliximab (n = 19).

RESULTS

Determination of colon wall thickness using hrUS (32 MHz) and measurement of the cross-sectional colonic areas by btMRI allowed discrimination between the treatment groups (mean a vs. b vs. c - btMRI: 922 vs. 2051 vs. 1472 pixel, hrUS: 0.26 vs. 0.45 vs. 0.31 mm). btMRI, endoscopy, hrUS and colour Doppler-hrUS correlated to histological scoring (p < 0.05), while endoscopy and btMRI correlated to post-mortem colon length (p < 0.05).

CONCLUSIONS

The innovative in vivo techniques btMRI and hrUS are safe and technically feasible. They differentiate between distinct grades of colitis in an experimental setting, and correlate with established post-mortem parameters. In addition to endoscopic procedures, these techniques provide information regarding colon wall thickness and perfusion. Depending on the availability of these techniques, their application increases the value of in vivo monitoring in experimental acute colitis in small rodents.

KEY POINTS

• Improved in vivo monitoring might balance interindividual differences in murine colitis. • In monitoring murine colitis, btMRI and hrUS are safe and technically feasible. • Very short examination times underline the usefulness especially of hrUS. • Results of btMRI and hrUS correlate with endoscopic and post-mortem findings.

Biofouling on polymeric heat exchanger surfaces with E. coli and native biofilms

The biofouling affinity of different polymeric surfaces (polypropylene, polysulfone, polyethylene terephthalate, and polyether ether ketone) in comparison to stainless steel (SS) was studied for the model bacterium Escherichia coli K12 DSM 498 and native biofilms originating from Rhine water. The biofilm mass deposited on the polymer surfaces was minimized by several magnitudes compared to SS. The cell count and the accumulated biomass of E. coli on the polymer surfaces showed an opposing linear trend. The promising low biofilm formation on the polymers is attributed to the combination of inherent surface properties (roughness, surface energy and hydrophobicity) when compared to SS. The fouling characteristics of E. coli biofilms show good conformity with the more complex native biofilms investigated. The results can be utilized for the development of new polymer heat exchangers when using untreated river water as coolant or for other processes needing antifouling materials.

Neue Synthesewege zu neutralen gemischtsubstituierten Eisen-Schwefel-Clustern / Novel Synthetic Pathways to Neutral Mixed-Ligand Iron-Sulfur Clusters

The reaction of [Fe(NiSiMe3)2)2] with thiols RSH, elemental sulfur, and neutral ligands L (with sulfur donor atoms, e. g. thiourea derivatives) gives [Fe4S4(SR)2L2] clusters in high yields. The structure of [Fe4S4(2.4.6-(C3H7)3C6H2-S)2(dpdmi)2] (dpdmi: diisopropyldimethyl- imidazolthion) was determined by X-ray crystallography.

When [Fe4S4I4]2- is reacted with a large excess of PMePh2 or PMe2Ph [F6S6I2(PMePh2)4] and [Fe6S6l2(PMe2Ph)4]. respectively, are obtained in nearly quantitative yield. Basket-like structures of the [Fe6S6]2+ cores were detected by X-ray structure analysis. While [Fe4S4(SR)4]2- clusters do not react with phosphanes at ambient temperature 2:2 functional­ized species like [Fe4S4(SR)2(tmtu)2] lead to basket-type clusters [Fe6S6(SR)2(PR3)4].

Crystal Structure of (NH4)2[Mo3S(S2)6] Containing the Novel Isolated Cluster [MO3S13]2-

The novel tri-nuclear metal-sulfur cluster [Mo3S(S2)6]2- can be obtained as its ammonium salt by the reaction of a Moiv containing aqueous solutions with polysulfide. Its crystal and molecular structure has been determined by a single crystal X-ray study. The crystals are monoclinic (space group Cm, with a = 11.577(6) Å, b = 16.448(7) Å, c = 5.716(2) Å, β = 117.30(3)°, V = 967.2 Å3 , Z = 2, dexptl. = 2.54(2) g/cm3 , dcal = 2.54 g/cm3). The structure consists of isolated [Mo3S(S2)6]2- units, with three Mo atoms at the vertices of a triangle. There are bridging as well as terminal S2 2--ligands lying above and below the Mo3-plane (bond distances: Mo-Mo = 2.722 Å, Mo-S(terminal) = 2.435, Mo-S(bridging) = 2.452, Mo3-S = 2.353(4) Å and S-S = 2.04 Å (mean values)).

Effects of reduced nocturnal temperature on pig performance and energy consumption in swine nursery rooms

The objective of this investigation was to determine the effect of a reduced nocturnal temperature (RNT) regimen on performance of weaned pigs and energy consumption during the nursery phase of production. The age of weaned pigs assigned to experiments ranged from 16 to 22 d. In Exp. 1, 3 stations conducted 2 trials under a common protocol that provided data from 6 control rooms (CON; 820 pigs) and 6 RNT rooms (818 pigs). Two mirror-image nursery rooms were used at each station. Temperature in the CON room was set to 30°C for the first 7 d, then reduced by 2°C per week through the remainder of the experiment. Room temperature settings were held constant throughout the day and night. The temperature setting in the RNT room was the same as CON during the first 7 d, but beginning on the night of d 7, the room temperature setting was reduced 6°C from the daytime temperature from 1900 to 0700 h. The use of heating fuel and electricity were measured weekly in each room. Overall, ADG (0.43 kg), ADFI (0.62 kg), and G:F (0.69) were identical for CON and RNT rooms. Consumption of heating fuel [9,658 vs. 7,958 British thermal units (Btu)·pig(-1)·d(-1)] and electricity (0.138 vs. 0.125 kilowatt-hour (kWh)·pig(-1)·d(-1)] were not statistically different for CON and RNT rooms, respectively. In Exp. 2, 4 stations conducted at least 2 trials that provided data from 9 CON rooms (2,122 pigs) and 10 RNT rooms (2,176 pigs). Experimental treatments and protocols were the same as Exp. 1, except that the RNT regimen was imposed on the night of d 5 and the targeted nighttime temperature reduction was 8.3°C. Neither final pig BW (21.8 vs. 21.5 kg; SE = 0.64), ADG (0.45 vs. 0.44 kg; SE = 0.016), ADFI (0.61 vs. 0.60 kg; SE = 0.019), nor G:F (0.75 vs. 0.75; SE = 0.012) were different for pigs housed in CON or RNT rooms, respectively. Consumption of heating fuel and electricity was consistently reduced in RNT rooms for all 4 stations. Consumption of heating fuel (10,019 vs. 7,061 Btu·pig(-1)·d(-1); SE = 1,467) and electricity (0.026 vs. 0.021 kWh·pig-1·d-1; SE = 0.004) were lower (P < 0.05) in the RNT rooms compared with CON rooms. This represents a 30% reduction in heating fuel use and a 20% reduction in electrical use with no differences in pig growth performance or health. From these experiments, we conclude that imposing a RNT regimen from 1900 to 0700 h is effective in reducing energy costs in the nursery without compromising pig performance, which will reduce production costs and decrease emissions of greenhouse gases.

Lysosomal dysfunction causes neurodegeneration in mucolipidosis II 'knock-in' mice

Mucolipidosis II is a neurometabolic lysosomal trafficking disorder of infancy caused by loss of mannose 6-phosphate targeting signals on lysosomal proteins, leading to lysosomal dysfunction and accumulation of non-degraded material. However, the identity of storage material and mechanisms of neurodegeneration in mucolipidosis II are unknown. We have generated 'knock-in' mice with a common mucolipidosis II patient mutation that show growth retardation, progressive brain atrophy, skeletal abnormalities, elevated lysosomal enzyme activities in serum, lysosomal storage in fibroblasts and brain and premature death, closely mimicking the mucolipidosis II disease in humans. The examination of affected mouse brains at different ages by immunohistochemistry, ultrastructural analysis, immunoblotting and mass spectrometric analyses of glycans and anionic lipids revealed that the expression and proteolytic processing of distinct lysosomal proteins such as α-l-fucosidase, β-hexosaminidase, α-mannosidase or Niemann-Pick C2 protein are more significantly impacted by the loss of mannose 6-phosphate residues than enzymes reaching lysosomes independently of this targeting mechanism. As a consequence, fucosylated N-glycans, GM2 and GM3 gangliosides, cholesterol and bis(monoacylglycero)phosphate accumulate progressively in the brain of mucolipidosis II mice. Prominent astrogliosis and the accumulation of organelles and storage material in focally swollen axons were observed in the cerebellum and were accompanied by a loss of Purkinje cells. Moreover, an increased neuronal level of the microtubule-associated protein 1 light chain 3 and the formation of p62-positive neuronal aggregates indicate an impairment of constitutive autophagy in the mucolipidosis II brain. Our findings demonstrate the essential role of mannose 6-phosphate for selected lysosomal proteins to maintain the capability for degradation of sequestered components in lysosomes and autophagolysosomes and prevent neurodegeneration. These lysosomal proteins might be a potential target for a valid therapeutic approach for mucolipidosis II disease.

Immune response of broiler chickens fed different levels of arginine and vitamin E to a coccidiosis vaccine and Eimeria challenge

One-day-old broiler chicks (n = 300) were orally vaccinated (Coccivac-B) and divided into 6 groups to evaluate Arg at 3 levels of supplementation, 0, 0.3, or 0.6% [normal level (NARG), medium level (MARG), or high level (HARG), respectively], and 2 levels of vitamin E (VE), 40 or 80 IU/kg of feed (VE40 or VE80, respectively), in a factorial experiment. Birds were reared in floor pens with fresh pine shavings and provided a corn-soybean-based diet and water ad libitum. At d 14, all chickens were orally challenged with a mixture of Eimeria field isolates (Eimeria acervulina, Eimeria maxima, and Eimeria tenella). In vitro heterophil and monocyte oxidative burst (HOB and MOB, respectively) was measured at d 21 from cells isolated from peripheral blood. Antibody levels (IgG, IgM, and IgA isotypes, ELISA) and NO were measured at d 14 and 28. The HOB was lower in birds fed the VE40 diets but was increased with the MARG and HARG treatments, whereas birds fed the VE80 diet had a higher HOB irrespective of Arg level. Birds fed the VE80 diet had high levels of MOB, which was not further improved by Arg, whereas birds fed the VE40-MARG diet had the highest MOB response. Plasma NO was not affected by diet at d 14, but at d 28, plasma NO was higher in birds fed the VE80-MARG or the VE40-NARG diet and lower in birds fed the VE80-NARG or the VE40-MARG diet. Birds fed the VE40-HARG or VE80-MARG diet had the highest IgG levels at d 14, but at d 28, birds fed the VE80-MARG diet had the highest IgG levels. The IgM concentration was lower in birds fed NARG levels irrespective of VE levels at d 14, but at d 28, IgM levels were higher in birds fed the VE40-HARG or the VE80-MARG feed. The IgA concentration was not consistently affected at d 14 or 28. These results suggest that Arg and VE fed at levels higher than those recommended by the NRC may play complementary roles on the innate and humoral immune response against an Eimeria challenge, potentially improving vaccine efficacy and response to field infections.

Use of Encapsulated Stem Cells to Overcome the Bottleneck of Cell Availability for Cell Therapy Approaches

Nowadays cell-based therapy is rarely in clinical practice because of the limited availability of appropriate cells. To apply cells therapeutically, they must not cause any immune response wherefore mainly autologous cells have been used up to now. The amount of vital cells in patients is limited, and under certain circumstances in highly degenerated tissues no vital cells are left. Moreover, the extraction of these cells is connected with additional surgery; also the expansion in vitro is difficult. Other approaches avoid these problems by using allo-or even xenogenic cells. These cells are more stable concerning their therapeutic behavior and can be produced in stock. To prevent an immune response caused by these cells, cell encapsulation (e.g. with alginate) can be performed. Certain studies showed that encapsulated allo- and xenogenic cells achieve promising results in treatment of several diseases. For such cell therapy approaches, stem cells, particularly mesenchymal stem cells, are an interesting cell source. This review deals on the one hand with the use of encapsulated cells, especially stem cells, in cell therapy and on the other hand with bioreactor systems for the expansion and differentiation of mesenchymal stem cells in reproducible and sufficient amounts for potential clinical use.

Modeling Common Traits and Method Effects in Multitrait-Multimethod Analysis

Method effects often occur when constructs are measured by different methods. In traditional multitrait-multimethod (MTMM) models method effects are regarded as residuals, which implies a mean method effect of zero and no correlation between trait and method effects. Furthermore, in some recent MTMM models, traits are modeled to be specific to a certain method. However, often we are not interested in a method-specific trait but in a trait that is common to all methods. Here we present the Method Effect model with common trait factors, which allows modeling "common" trait factors and method factors that represent method "effects" rather than residuals. The common trait factors are defined as the mean of the true-score variables of all variables measuring the same trait and the method factors are defined as differences between true-score variables and means of true-score variables. Because the model allows estimating mean method effects, correlations between method factors, and correlations between trait and method factors, new research questions may be investigated. The application of the model is demonstrated by 2 examples studying the effect of negative, as compared with positive, item wording for the measurement of mood states.

Production process for stem cell based therapeutic implants: expansion of the production cell line and cultivation of encapsulated cells

Cell based therapy promises the treatment of many diseases like diabetes mellitus, Parkinson disease or stroke. Microencapsulation of the cells protects them against host-vs-graft reactions and thus enables the usage of allogenic cell lines for the manufacturing of cell therapeutic implants. The production process of such implants consists mainly of the three steps expansion of the cells, encapsulation of the cells, and cultivation of the encapsulated cells in order to increase their vitality and thus quality. This chapter deals with the development of fixed-bed bioreactor-based cultivation procedures used in the first and third step of production. The bioreactor system for the expansion of the stem cell line (hMSC-TERT) is based on non-porous glass spheres, which support cell growth and harvesting with high yield and vitality. The cultivation process for the spherical cell based implants leads to an increase of vitality and additionally enables the application of a medium-based differentiation protocol.