Effect of limbic structure and striatum damage caused by kainic acid on seizure reactions in animals after intracranial injury

It has been established that hippocampus, enthorhinal cortex, amygdala and substantia nigra (pars reticulata) lesions before head injury lead to a decrease of kainic acid-induced behavioral and electrographic seizure expressions. It can be concluded that after head injury the activation of limbic structures excitability due to excitation of "inputs" to these formations takes place. The obtained data indicate the significant role of nucleus caudatus in activation of posttraumatic brain excitatory mechanisms.     

The diagnosis of drug allergies. Utilizing in vitro mast cell test and IgE inhibition test

With the ever-increasing use of pharmaceuticals and the relatively high risk of developing drug allergies, particularly for patients in hospitals and for ambulatory patients with a history of drug allergy, the need to develop in vitro assays for drug allergy is great. In the early 1970's a mast cell technique was developed for diagnosis of drug allergies. A PRIST inhibition assay has also recently been developed to detect IgE antibodies to drug allergens. This test has also been referred to as the Total IgE Inhibition Test by Specific Drug Allergen, and is a variant of the in vitro RAST Test. In vitro mast cell and IgE inhibition tests are applied for identification of drug and chemical allergens and for their cautious clinical trial to prevent future drug and chemical reactions. Over the last eight years, over 1,300 patients were examined utilizing the mast cell technique. Over 100 drugs were tested, with penicillin, barbiturates, "caine" derivatives and sulfonamides most frequently employed. Of 270 patients with well-defined drug reactions, 190 (70 per cent) gave a positive response to the mast cell test. Eighty-five per cent of sera tested with Type I reactions gave a mast cell response. Of these, a group of 30 patients was studied with PRIST inhibition as well. Procedures for comparative testing of necessary drugs and/or chemicals in cases of high anaphylaxis risk of reaction in the clinical setting, hospital or office are included in the study as well as individual case reports. Mast cell assay coupled with IgE inhibition has been successfully used to diagnose drug and chemical allergic reactions. The incidence of positivity is high when the offending drug causes a Type I allergic reaction. The cases reported indicate that both the Mast Cell and the PRIST inhibition assays are useful for diagnosing and setting the clinical treatment and clinical course of the patient. The mast cell assay would be potentially employed for patient use in hospitals where the incidence of drug allergy is highest and for occupational health in the chemical industry. The greatest potential would be in outpatient care applied to patients with multiple drug allergies in the selection of safe drugs (test negative by both methods, and other clinical studies) for future drug usage.     

Using conscious reflection,group processes,and AI to facilitate development of expertise

Three experiments investigated procedures derived from research on knowledge acquisition, group processes, and artificial intelligence for facilitating the development of expertise. In each experimental session, subjects learned to control a simulated sugar production factory. Then they formulated written policies for controlling sugar production either alone as individuals or in small groups. An adaptive AI system provided feedback on policy quality. The research also investigated the use of forced reflective practice in which learners attempted to predict what their policy would do while performing the task. The AI system provided feedback about what their policy would have done in each situation and the outcome based on their policy's response or their suggested alternative response. Results indicated that group interaction and feedback from the AI system improved policy quality. However, only when all three procedures were employed, group interaction, AI feedback, and forced reflective practice, was the development of individual expertise on the task enhanced.     

Simultaneous multi-objective optimization of stainless steel clad layer on pressure vessels using genetic algorithm

Metal cladding is a process of depositing a filler material to enhance the surface properties of base material using a suitable welding process. In this work the clad specimens are produced by surfacing a layer of filler material using weld cladding process to minimize the heat loss across the walls of the pressure vessels. It is done by depositing a low thermal conductivity austenitic stainless steel grade of 316L on structural steel plates used for boiler construction using flux cored arc welding process. The experimental study is carried out as per design of experiments availed for five factors five levels central composite design using response surface methodology. The mathematical models are developed for the prediction of clad layer height, clad layer width and depth of penetration. These models are employed in formulating fitness functions for multi-objective optimization of clad layer dimensions using genetic algorithm (GA). The set of optimal solutions suggested by response surface optimizer and genetic algorithm are compared and discussed. Conformity tests are conducted to validate the prediction capability of developed models and optimum settings. Optimum clad layer dimensions have been arrived and optimized stainless steel clad specimen has been produced. The heat transfer analysis is planned to be conducted in the next phase. The findings can be used in energy efficient design of pressure vessels.     

Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods

The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.     

Facilitating a benign attentional bias reduces negative thought intrusions.

The causal role of biased attention in worry was investigated in an experiment in which high worriers were assigned either to a condition requiring attention to nonthreatening words and text while ignoring worry-related material or to a mixed-attention control condition. The former procedure led to fewer negative thought intrusions in a worry test (as rated by both participants and an assessor) than did the control condition. These findings suggest that attentional bias plays a causal role in worry and that its modification can reduce excessive worry. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Decentralised decision making in heterogeneous teams using anonymous optimisation

This paper considers the scenario where multiple autonomous agents must cooperate in making decisions to minimise a continuous and differentiable team cost function. A distributed and asynchronous optimisation algorithm is presented which allows each agent to incrementally refine their decisions while intermittently communicating with the rest of the team. A convergence analysis provides quantitative requirements on the frequency agents must communicate that is prescribed by the structure of the decision problem. In general the solution method will require every agent to communicate to and have a model of every other agent in the team. To overcome this, a specific subset of systems, called Partially Separable, is defined. These systems only require each agent to have a combined summary of the rest of the team and allows each agent to communicate locally over an acyclic communication network, greatly increasing the scalability of the system.     

Coding mammograms using the classification "probably benign finding--short interval follow-up suggested"

OBJECTIVE: Many benign breast lesions revealed by mammography show features indicating that the lesions have a high, but not complete, likelihood of being benign. The Breast Imaging Reporting and Data System (BI-RADS) allows radiologists to classify these mammograms as "probably benign finding-short interval follow-up suggested" (category 3). We explored whether certain factors are associated with the use of category 3 in a national cancer detection program. MATERIALS AND METHODS: We analyzed data from the National Breast and Cervical Cancer Early Detection Program, a comprehensive nationwide program that provides cancer screening for low-income and medically underserved women. The study population included all women at least 40 years old who had undergone mammography on or before September 30, 1996 (n = 372,760). RESULTS: Of the 372,760 mammograms, 7.7% were classified as category 3. The probability of receiving a category 3 classification decreased as patients' ages increased. Women who were symptomatic were nearly twice as likely as women who were asymptomatic to receive a category 3 classification, and women whose clinical breast examinations had abnormal findings were more than twice as likely as women with examinations having normal findings to receive a category 3 classification. The percentage of mammograms classified as category 3 by state or tribal organization ranged from 1.4% to 14.0%. CONCLUSION: Several patient variables, including patient symptomatology, were associated with the probability of having a mammogram classified as category 3. One of the most important determinants was where the patient underwent mammography, which suggests that variability exists among radiologists themselves in using this BI-RADS code for "probably benign" mammographic lesions.