Deep learning-based image deconstruction method with maintained saliency

Visual properties that primarily attract bottom-up attention are collectively referred to as saliency. In this study, to understand the neural activity involved in top-down and bottom-up visual attention, we aim to prepare pairs of natural and unnatural images with common saliency. For this purpose, we propose an image transformation method based on deep neural networks that can generate new images while maintaining the consistent feature map, in particular the saliency map. This is an ill-posed problem because the transformation from an image to its corresponding feature map could be many-to-one, and in our particular case, the various images would share the same saliency map. Although stochastic image generation has the potential to solve such ill-posed problems, the most existing methods focus on adding diversity of the overall style/touch information while maintaining the naturalness of the generated images. To this end, we developed a new image transformation method that incorporates higher-dimensional latent variables so that the generated images appear unnatural with less context information but retain a high diversity of local image structures. Although such high-dimensional latent spaces are prone to collapse, we proposed a new regularization based on Kullback-Leibler divergence to avoid collapsing the latent distribution. We also conducted human experiments using our newly prepared natural and corresponding unnatural images to measure overt eye movements and functional magnetic resonance imaging, and found that those images induced distinctive neural activities related to top-down and bottom-up attentional processing.

Confidence modulates the decodability of scene prediction during partially-observable maze exploration in humans

Prediction ability often involves some degree of uncertainty-a key determinant of confidence. Here, we sought to assess whether predictions are decodable in partially-observable environments where one's state is uncertain, and whether this information is sensitive to confidence produced by such uncertainty. We used functional magnetic resonance imaging-based, partially-observable maze navigation tasks in which subjects predicted upcoming scenes and reported their confidence regarding these predictions. Using a multi-voxel pattern analysis, we successfully decoded both scene predictions and subjective confidence from activities in the localized parietal and prefrontal regions. We also assessed confidence in their beliefs about where they were in the maze. Importantly, prediction decodability varied according to subjective scene confidence in the superior parietal lobule and state confidence estimated by the behavioral model in the inferior parietal lobule. These results demonstrate that prediction in uncertain environments depends on the prefrontal-parietal network within which prediction and confidence interact.

A multi-site, multi-disorder resting-state magnetic resonance image database

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Measurement(s)	mental or behavioural disorder • brain measurement • Demographic Data
Technology Type(s)	functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging
Factor Type(s)	age • sex • site • disorder
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329

Pain Control by Co-adaptive Learning in a Brain-Machine Interface

Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them.

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood.

Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain.

Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state.

Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

Model-based and model-free pain avoidance learning

Background: While there is good evidence that reward learning is underpinned by two distinct decision control systems – a cognitive ‘model-based’ and a habitbased ‘model-free’ system, a comparable distinction for punishment avoidance has been much less clear. Methods: We implemented a pain avoidance task that placed differential emphasis on putative model-based and model-free processing, mirroring a paradigm and modelling approach recently developed for reward-based decision-making. Subjects performed a two-step decision-making task with probabilistic pain outcomes of different quantities. The delivery of outcomes was sometimes contingent on a rule signalled at the beginning of each trial, emulating a form of outcome devaluation. Results: The behavioural data showed that subjects tended to use a mixed strategy – favouring the simpler model-free learning strategy when outcomes did not depend on the rule, and favouring a model-based when they did. Furthermore, the data were well described by a dynamic transition model between the two controllers. When compared with data from a reward-based task (albeit tested in the context of the scanner), we observed that avoidance involved a significantly greater tendency for subjects to switch between model-free and model-based systems in the face of changes in uncertainty. Conclusion: Our study suggests a dual-system model of pain avoidance, similar to but possibly more dynamically flexible than reward-based decision-making.

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

The control of tonic pain by active relief learning

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief.

A NEURAL BIOMARKER FOR CHRONIC PAIN BASED ON DECODED BRAIN NETWORKS

The lack of a biomarker for chronic pain remains an important impediment to clinical and translational pain research. The problem stems from the multiple parallel but subtle abnormalties thought to represent the chronic pain state, yielding the emerging view of chronic pain as a ‘network disorder’. This suggests analysis approaches that aim to identify distributed patterns of data (multivariate, machine learning methods) might offer the best opportunity to discover biomarkers. Here, we performed a multi-center functional brain imaging study to record state functional brain networks resting in 41 patients with chronic back pain and 33 healthy control subjects. We calculated with functional covariance matrix from 160 regions of interest, and used Sparse Multinomial Logistic Regression to classify subjects as patient or control using a leave-one-out cross validation. Diagnostic accuracy was 91.9%, with sensitivity and specificity 90.2% and 93.9% respectively. We then used graph theoretic measures to characterise the pattern of network differences between the groups, and showed that the chronic pain state was associated with disrupted network ‘assortativity’. These data provide evidence to support an accurate functional biomarker of chronic pain, and open the door to the development of translatable biomarkers using similar methodologies in animals.

Decisions about decisions

A major puzzle of decision making is how the brain decides which decision system to use at any one time. In this issue of Neuron, Lee et al. (2014) provide a theoretical, behavioral, and neurobiological account of a prefrontal reliability-based arbitration system.

Prices need no preferences: social trends determine decisions in experimental markets for pain relief

OBJECTIVE

A standard view in health economics is that, although there is no market that determines the "prices" for health states, people can nonetheless associate health states with monetary values (or other scales, such as quality adjusted life year [QALYs] and disability adjusted life year [DALYs]). Such valuations can be used to shape health policy, and a major research challenge is to elicit such values from people; creating experimental "markets" for health states is a theoretically attractive way to address this. We explore the possibility that this framework may be fundamentally flawed-because there may not be any stable values to be revealed. Instead, perhaps people construct ad hoc values, influenced by contextual factors, such as the observed decisions of others.

METHOD

The participants bid to buy relief from equally painful electrical shocks to the leg and arm in an experimental health market based on an interactive second-price auction. Thirty subjects were randomly assigned to two experimental conditions where the bids by "others" were manipulated to follow increasing or decreasing price trends for one, but not the other, pain. After the auction, a preference test asked the participants to choose which pain they prefer to experience for a longer duration.

RESULTS

Players remained indifferent between the two pain-types throughout the auction. However, their bids were differentially attracted toward what others bid for each pain, with overbidding during decreasing prices and underbidding during increasing prices.

CONCLUSION

Health preferences are dissociated from market prices, which are strongly referenced to others' choices. This suggests that the price of health care in a free-market has the capacity to become critically detached from people's underlying preferences.

Immunohistochemical localization of Notch signaling molecules in ameloblastomas

We examined Notch signaling molecules, Notch1 and Jagged1, in serial large cases of typical solid/multicystic ameloblastoma. In general, Notch positive staining products were frequently detected in the cytoplasms of the cells. In the same cells, Jagged positive staining were also frequently observed, while only occasionally positive in peripheral cells, especially in cuboidal cells. The results showed that these morphogenesis regulation factors are closely related to cytological differentiation in neoplastic cells of ameloblastoma. The Notch and Jagged positive-cell ratios were frequently positive, and the ratios were nearly the same between the varied histopathological, cytological patterns. However, the less-differentiated cells were fewer in number than that of well-differentiated cells.

Choosing to make an effort: the role of striatum in signaling physical effort of a chosen action

The possibility that we will have to invest effort influences our future choice behavior. Indeed deciding whether an action is actually worth taking is a key element in the expression of human apathy or inertia. There is a well developed literature on brain activity related to the anticipation of effort, but how effort affects actual choice is less well understood. Furthermore, prior work is largely restricted to mental as opposed to physical effort or has confounded temporal with effortful costs. Here we investigated choice behavior and brain activity, using functional magnetic resonance imaging, in a study where healthy participants are required to make decisions between effortful gripping, where the factors of force (high and low) and reward (high and low) were varied, and a choice of merely holding a grip device for minimal monetary reward. Behaviorally, we show that force level influences the likelihood of choosing an effortful grip. We observed greater activity in the putamen when participants opt to grip an option with low effort compared with when they opt to grip an option with high effort. The results suggest that, over and above a nonspecific role in movement anticipation and salience, the putamen plays a crucial role in computations for choice that involves effort costs.

Hierarchical rule switching in prefrontal cortex

Most real-world decision-making problems involve consideration of numerous possible actions, and it is often impossible to evaluate all of them before settling on preferred strategy. In such situations, humans might explore actions more efficiently by searching only the most likely subspace of the whole action space. To study how the brain solves such action selection problems, we designed a Multi Feature Sorting Task in which the task rules defining an optimal action have a hierarchical structure and studied concurrent brain activity using it. The task consisted of two kinds of rule switches: a higher-order switch to search for a rule across different subspaces and a lower-order switch to change a rule within the same subspace. The results revealed that the left dorsolateral prefrontal cortex (DLPFC) was more active in the higher-order switching, and the right fronto-polar cortex (FPC) was significantly activated with the lower-order switching. We discuss a possible functional model in the prefrontal cortex where the left DLPFC encodes the hierarchical organization of behaviours and the right FPC maintains and updates multiple behavioural. This interpretation is highly consistent with the previous findings and current theories of hierarchical organization in the prefrontal functional network.

Altruistic learning

The origin of altruism remains one of the most enduring puzzles of human behaviour. Indeed, true altruism is often thought either not to exist, or to arise merely as a miscalculation of otherwise selfish behaviour. In this paper, we argue that altruism emerges directly from the way in which distinct human decision-making systems learn about rewards. Using insights provided by neurobiological accounts of human decision-making, we suggest that reinforcement learning in game-theoretic social interactions (habitisation over either individuals or games) and observational learning (either imitative of inference based) lead to altruistic behaviour. This arises not only as a result of computational efficiency in the face of processing complexity, but as a direct consequence of optimal inference in the face of uncertainty. Critically, we argue that the fact that evolutionary pressure acts not over the object of learning ('what' is learned), but over the learning systems themselves ('how' things are learned), enables the evolution of altruism despite the direct threat posed by free-riders.

Examination of in vivo gelatinolytic activity in rheumatoid arthritis synovial tissue using newly developed in situ zymography and image analyzer

OBJECTIVE

The aim of this study was to examine in vivo gelatinolytic activity of rheumatoid arthritis (RA) synovium using a newly developed in situ zymography (ISZ) method and pathological image analyzer, and to evaluate the relationship between this activity and several features on RA.

METHODS

A total of 8 samples of synovium were obtained from RA patients during surgery, and 8 samples from osteoarthritis (OA) patients were examined as controls. Furthermore, total 14 samples of syovium were obtained for comparison among radiographical classifications as Larsen grade (4 cases of grade III, 5 cases of grade IV and 5 cases of grade V). These specimens were frozen with OCT compound immediately after surgery. Frozen sections were applied to a newly developed gelatin-coated FIZ film (Fuji Film Co.Tokyo.Japan) designed for use ISZ, and incubated at 37 degrees C for 6 hours. Using an image analyzer (image processor for analytical pathology; IPAP), two variables were measured as indicators of in vivo gelatynolytic activity: optical density of gelatinolyzed area (ODG), and ratio of gelatinolyzed area (RGA). Also, we investigated the relationship between these indicators and the following variables: radiographic changes (Larsen grades), clinical data (C-reactive protein concentration), histological score of synovial tissue (modified Rooney's score), and expression of matrix metalloproteinase (MMP)-2, MMP-9, tissue inhibitor of metalloproteinase (TIMP)-1 and TIMP-2 (assessed by immunohistochemistry).

RESULTS

RA synovium had significantly higher RGA and lower ODG than OA, indicating higher gelatinolytic activity in RA. Synovium from cases with Larsen grade IV or V had significantly lower ODG than cases with grade III, but there was no significant difference in RGA between grades. There was no significant correlation between gelatinolytic activity (ODG or RGA) and either CRP or modified Rooney's Histological Score. The results of ISZ indicate that the gelatinolyzed areas were mainly localized in the lining area, with a small amount scattered throughout the stroma. The results of immunohistochemistry indicate that MMP-2, MMP-9, TIMP-1 and TIMP-2 were expressed in areas of gelatinolysis.

CONCLUSIONS

The present results indicate that in vivo gelatinolytic activity of synovium is stronger in RA than in OA. They also indicate that gelatinolytic activity of RA synovial cells is stronger in cases with Larsen grade IV or V than in cases with grade III, although the gelatinolyzed area is similar. Gelatinolytic activity, as indicated by optical density and the gelatinolyzed area, differed between regions, even within the same specimen, suggesting an imbalance between production of proteinases and their inhibitors. We believe that the present zymography method can contribute to the elucidation of biological enzymatic activity of RA synovium.

Game theory of mind

This paper introduces a model of ‘theory of mind’, namely, how we represent the intentions and goals of others to optimise our mutual interactions. We draw on ideas from optimum control and game theory to provide a ‘game theory of mind’. First, we consider the representations of goals in terms of value functions that are prescribed by utility or rewards. Critically, the joint value functions and ensuing behaviour are optimised recursively, under the assumption that I represent your value function, your representation of mine, your representation of my representation of yours, and so on ad infinitum. However, if we assume that the degree of recursion is bounded, then players need to estimate the opponent's degree of recursion (i.e., sophistication) to respond optimally. This induces a problem of inferring the opponent's sophistication, given behavioural exchanges. We show it is possible to deduce whether players make inferences about each other and quantify their sophistication on the basis of choices in sequential games. This rests on comparing generative models of choices with, and without, inference. Model comparison is demonstrated using simulated and real data from a ‘stag-hunt’. Finally, we note that exactly the same sophisticated behaviour can be achieved by optimising the utility function itself (through prosocial utility), producing unsophisticated but apparently altruistic agents. This may be relevant ethologically in hierarchal game theory and coevolution.

The ability to work out what other people are thinking is essential for effective social interactions, be they cooperative or competitive. A widely used example is cooperative hunting: large prey is difficult to catch alone, but we can circumvent this by cooperating with others. However, hunting can pit private goals to catch smaller prey that can be caught alone against mutually beneficial goals that require cooperation. Understanding how we work out optimal strategies that balance cooperation and competition has remained a central puzzle in game theory. Exploiting insights from computer science and behavioural economics, we suggest a model of ‘theory of mind’ using ‘recursive sophistication’ in which my model of your goals includes a model of your model of my goals, and so on ad infinitum. By studying experimental data in which people played a computer-based group hunting game, we show that the model offers a good account of individual decisions in this context, suggesting that such a formal ‘theory of mind’ model can cast light on how people build internal representations of other people in social interactions.

Resolution of Uncertainty in Prefrontal Cortex

Making optimal decisions in the face of uncertain or incomplete information arises as a common problem in everyday behavior, but the neural processes underlying this ability remain poorly understood. A typical case is navigation, in which a subject has to search for a known goal from an unknown location. Navigating under uncertain conditions requires making decisions on the basis of the current belief about location and updating that belief based on incoming information. Here, we use functional magnetic resonance imaging during a maze navigation task to study neural activity relating to the resolution of uncertainty as subjects make sequential decisions to reach a goal. We show that distinct regions of prefrontal cortex are engaged in specific computational functions that are well described by a Bayesian model of decision making. This permits efficient goal-oriented navigation and provides new insights into decision making by humans.

Development of large pseudocysts adjacent to the knee joint in rheumatoid arthritis. Assessment of radiological and histopathological approaches

Abstract  This study examined the pathogenesis of large pseudocysts adjacent to knee joints in rheumatoid arthritis (RA). The radiological and histopathological features of 17 large subarticular pseudocysts in 12 knee joints of 10 patients were analyzed. Nine of the 10 patients were classified as class 2 according to Steinbrocker's functional class. Eight large pseudocysts were located at the lateral femoral condyle, seven were at the proximal part of the tibia, one was at the medial femoral condyle, and one was at the patella. The large pseudocysts were divided into two groups according to whether they did or did not connect with the joint cavity. Serial radiographs revealed that all large pseudocysts in communication with the joint cavity had enlarged gradually over the past several months. They extended from the subarticular area toward the bone marrow. Histopathological findings confirmed that holes allowing communication were located at a transitional zone between the ligament and the hyaline cartilage, and that rheumatoid granulation tissue invaded the large pseudocyst through these holes. The results of this study indicate that large pseudocysts are formed by the extension of articular inflammation. Moreover, repeated extrinsic mechanical stress due to walking and the aggressive inflammatory nature of rheumatoid arthritis play important roles in the formation of large pseudocysts.

Control of exploitation-exploration meta-parameter in reinforcement learning

In reinforcement learning (RL), the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance between exploitation and exploration. Our learning scheme is based on model-based RL, in which the Bayes inference with forgetting effect estimates the state-transition probability of the environment. The balance parameter, which corresponds to the randomness in action selection, is controlled based on variation of action results and perception of environmental change. When applied to maze tasks, our method successfully obtains good controls by adapting to environmental changes. Recently, Usher et al. [Science 283 (1999) 549] has suggested that noradrenergic neurons in the locus coeruleus may control the exploitation-exploration balance in a real brain and that the balance may correspond to the level of animal's selective attention. According to this scenario, we also discuss a possible implementation in the brain.

Effects of paclitaxel on cultured synovial cells from patients with rheumatoid arthritis

BACKGROUND

Proliferation of synovial cells is considered to play a key role in rheumatoid arthritis (RA). Using paclitaxel, a unique antineoplastic agent known to suppress collagen-induced arthritis, we conducted an in vitro study of cell kinetics on cultured synovial cells from patients with RA.

METHODS

Alterations of the cell cycle of cultured fibroblast-like synovial cells (FLSs) from patients with RA were studied using flow cytometry and laser scanning cytometry. Apoptosis and accumulation of cyclin concerning effects of paclitaxel were detected.

RESULTS

Paclitaxel induced arrest of the cell cycle at G2/M phase and apoptosis in FLSs. The late stage of apoptosis was determined by the positivity of terminal deoxynucleotidyl transferase assay. Morphological observation by combined usage of both annexin V and propidium iodide on FLSs on a slide glass showed early apoptotic changes in detail. FLSs arrested at G2/M phase showed marked accumulation of cyclin B1. The effects of paclitaxel decreased on FLSs, which diminished proliferative activity.

CONCLUSIONS

These data indicate that paclitaxel induces cell arrest at G2/M phase followed by apoptosis in human FLSs, which have high proliferative activity, and possible therapeutic effects of paclitaxel on RA.

Murine Myak, a member of a family of yeast YAK1-related genes, is highly expressed in hormonally modulated epithelia in the reproductive system and in the embryonic central nervous system

We have cloned a mouse homologue (designated Myak) of the yeast protein kinase YAK1. The 1210 aa open reading frame contains a putative protein kinase domain, nuclear localization sequences and PEST sequences. Myak appears to be a member of a growing family of YAK1-related genes that include Drosophila and human Minibrain as well as a recently identified rat gene ANPK that encode a steroid hormone receptor interacting protein. RNA blot analysis revealed that Myak is expressed at low levels ubiquitously but at high levels in reproductive tissues, including testis, epididymis, ovary, uterus, and mammary gland, as well as in brain and kidney. In situ hybridization analysis on selected tissues revealed that Myak is particularly abundant in the hormonally modulated epithelia of the epididymis, mammary gland, and uterus, in round spermatids in the testis, and in the corpora lutea in the ovary. Myak is also highly expressed in the aqueduct of the adult brain and in the brain and spinal cord of day 12.5 embryos.

Maternal risk factors in cleft lip and palate: case control study

Three hundred and six mothers who gave birth to babies with cleft lip, or palate, or both, were matched with 306 who gave birth to healthy babies in the same area during the same time period. Significantly more babies in the cleft group had a family history of clefts (48/306 compared with 7/306, P<0.0001). In the cases studied, combined cleft lip and palate was significantly more common among boys (82/157 compared with 57/149, P=0.02) and cleft palate alone among girls (48/149 compared with 22/157, P=0.0002). Significantly more mothers reported some sort of illness during early pregnancy (101/306 compared with 74/306, P=0.02). There were no differences between the groups as far as dietary preferences were concerned but during early pregnancy the mothers who gave birth to babies with defects tended to drink less alcohol (<1 unit/week) (236 compared with 199, P=0.001) and less coffee (<1 cup/week) (159/306 compared with 131, P=0.03). However, in each case similar proportions gave up once the pregnancy was confirmed. Large multicentre studies are required to confirm or refute these findings.

Polymerase chain reaction and an outer membrane protein gene probe for the detection of Porphyromonas gingivalis

A sensitivity assay for Porphyromonas gingivalis based upon the polymerase chain reaction (PCR) was developed. A 426-bp sequence, including a DraI-HincII DNA fragment (278 bp) encoding the 40-kDa outer membrane protein of the P. gingivalis gene was amplified. PCR products were obtained from chromosomal DNAs of the P. gingivalis strains tested but not from those of other oral microorganisms. The lower limit of template DNA detection was 10 pg with 30 cycles and 100 fg with 40 cycles of PCR by agarose gel electrophoresis. The PCR products were hybridized with DraI-HincII DNA fragment internal to the PCR primers regions used. The lower limit of hybridization detection was 10 pg and 10 fg of template DNA with 30 and 40 cycles of PCR, respectively. These results demonstrated the simplicity, rapidity and specificity of the procedure, as well as the use of the DraI-HincII DNA fragment in the identification of P. gingivalis.

Regioselective synthesis and characterization of naphthylethylcarbamoyl-beta-cyclodextrins

Regioselective reactions of 1-(1-naphthyl)ethyl isocyanate (NEIC) with beta-cyclodextrin (beta-CD) were studied with and without NaH activation of beta-CD in N,N-dimethylformamide (DMF) and pyridine. All six possible monosubstituted CD products were separated and characterized by proton NMR. Primary substitution product predominates when the reaction was carried out under reflux condition in pyridine without NaH activation. The C-2 substitution product predominates when the reaction was carried out in DMF. Conversion of 2-O-(1-(1-naphthyl)ethylcarbamoyl)-beta-CD to 6-O-(1-(1-naphthyl)ethylcarbamoyl)-beta-CD was observed when NaH was used to activate hydroxyl groups of CD.

Effects of transforming growth factor-beta on collagen gene expression and collagen synthesis level in mineralizing cultures of osteoblast-like cell line, MC3T3-E1

1. High levels of type I collagen mRNA and [3H]proline incorporation into collagenase digestable protein by MC3T3-E1 cells were detected during the first 7 days of culture, after which they declined. 2. Type I collagen gene expression was stimulated by TGF-beta in the early culture stage when the collagen gene expression was fully functioning. 3. However, these stimulatory effects disappeared at the differentiation stages. Although collagen gene expression was stimulated by TGF-beta (2.0 ng/ml) in early culture, collagen synthesis in medium was not. 4. This study shows that collagen synthesis and collagen gene expression were affected by the state of differentiation in MC3T3-E1 cells and that the rate of stimulation by TGF-beta in collagen gene expression decreased over time in culture.

[Clinical effect and pharmacokinetics of intermediate dose Ara-C therapy in a patient with acute non-lymphocytic leukemia with two CNS recurrences]

A case of two repeated CNS recurrences of acute non-lymphocytic leukemia (M2) was treated with intermediate dose Ara-C therapy and achieved 2 complete remissions. The clinical effect and pharmacokinetics of intermediate dose Ara-C therapy in this patient were discussed. A 55-year-old male with acute non-lymphocytic leukemia (M2) achieved complete remission by combination chemotherapy of Behenoyl-ara-C, Daunorubicin, 6-Mercaptopurine and Prednisolone in July, 1985. He subsequently received consolidation and intensification therapy with periodical intrathecal injection of Methotrexate (MTX), but 13 months later he developed his first CNS recurrence which was resistant to the intrathecal administration of Ara-C and MTX. As he also relapsed systemically, Ara-C was administered in intermediate dose (1 g/m2 every 12 hrs for 5 days) and he achieved complete remission both in the CNS and systemic manifestations. Six months later he was diagnosed as having a second CNS recurrence and another systemic relapse. Intermediate dose Ara-C was administered again, and he achieved complete remission in the CNS and partial remission in systemic manifestations. Pharmacokinetic study revealed high peaks of Ara-C concentration in plasma (6.2 microM immediately after the end of the infusion) and high degree of its penetration into the CNS (5.6 microM at 3 hr after the end of the infusion) suggesting the effective and perhaps a uniform level of Ara-C is achieved throughout the CNS by this therapy. In 3 other patients without CNS involvement 0.88 +/- 0.44 microM of Ara-C, which is enough concentrations for its cytostatic effect, was detected at 3 hr after the end of infusion, suggesting the efficacy of the therapy for CNS prophylaxis. In this case the relapse occurred after repeated administration of antileukemic drugs, including Behenoyl-ara-C, an analog of Ara-C, and was resistant to the intrathecal administration of Ara-C. These findings suggest that intermediate dose Ara-C therapy was effective to overcome a resistance to antileukemic drugs, including Ara-C, and also, in some cases, more effective than intrathecal injection of antileukemic drugs for the treatment of CNS leukemia.

Anti-CNS antibodies in childhood neurologic diseases

To study the incidence of circulating anti-CNS antibodies in childhood neurologic diseases, a population study was undertaken. Serum samples were obtained from a total of 348 children and stored at -80 degrees C until being studied. The samples were collected when routine blood tests were being performed. In all cases informed consent was obtained. This study was approved by hospital ethics review committees. One hundred and ninety-nine of the samples were from children with no known neurologic illnesses and served as the control group. One hundred and twenty-one of the samples were from children with epilepsy and the remaining 28 from a number of different neurologic conditions. The serum samples were screened against normal, adult, autopsy-derived cerebellar and frontal cortex tissue sections and Western blots. Serum immunoreactivity was revealed using HRP-conjugated anti-human IgG. Significant findings included: (1) patients with epilepsy had an increased incidence of anti-CNS reactivity as revealed on frontal cortex immunoblots (p less than 0.05) but not on cerebellar immunoblots; (2) there was an increase in the incidence of immunoblot reactivity with age in the controls and the neurology cases; (3) there was an increased incidence of immunoblot reactivity in those cases with a presumed inflammatory central or peripheral neurologic disease; (4) in six additional cases with opsoclonus-myoclonus there was cerebellar-specific immunoreactivity with identified antigenic molecular weights of 27 and 35, and 62 kDaltons; (5) in 31 additional cases of systemic lupus erythematosus there was significant immunoblot reactivity (p less than 0.001) when compared to a subset of age-matched controls. There was no difference in immunoreactivity between males and females. There was no significant increase in immunoreactivity in those children with cognitive disturbances including developmental delay and mental retardation.