Superswelling Microneedle Arrays for Dermal Interstitial Fluid (Prote)Omics

The noninvasive sampling of dermal interstitial fluid (ISF) for the monitoring of clinical biomarkers is a greatly appealing area of research. The identification of molecular biomarkers in biological fluids has been accelerated with -omics analyses but remains limited in ISF because of its time-consuming and complex extraction process. Here, the generation of microneedle (MN) patches made of superabsorbent acrylate-based hydrogels for the rapid sampling of dermal ISF is described to explore its proteome. In depth, iterative optimization allows the identification of novel acrylate-based compositions with the required chemical, mechanical, and biocompatibility properties allowing proteomic analysis of the extracted ISF for the first time after sampling with swelling MNs. The generated MN arrays show no cytotoxic effect, successfully cross the stratum corneum, and can collect up to 6 µL of dermal ISF in 10 min in vivo. Proteomics lead to the detection of 176 clinically relevant biomarkers in the collected samples validating the use of ISF as a relevant bodily fluid for disease monitoring and diagnostic. Importantly, it is discovered that extraction fingerprint is strongly dependent on the MNs chemistry, and thus specific biomarkers could be selectively extracted by tuning the composition of the patch, making the system versatile and specific.     

A method for system identification using the optimality criteria optimization approach

An algorithm similar to the optimality criteria approach used in structural optimization is presented for identifying stiffnesses of structural members by using vibration test data. A set of equivalent static inertia forces are obtained from the vibration analysis using d'Alembert's principle and are used to solve the multiple displacement constraint problem. The displacement constraint values are specified based on the measured experimental modal displacement data at critical locations. The algorithm is used to find the changes needed in the stiffnesses of the elements and the distribution of nonstructural mass of the nominal analytical model to correlate the analytical and experimental data. The algorithm alternates between the vibration analysis and static analysis to find the equivalent load vector and modify the stiffnesses. The identified stiffness properties of the structural elements can be used to control and study the dynamic response of the structure.     

A DSS for Production Planning

This paper presents a decision support system designed to aid the production planner in choosing the values of the principle parameters governing just-in-time manufacturing. The DSS uses a knowledge base composed of data from simulations of the production of the entire product line under systematically varied conditions. The planners may add data from actual production experience as it is obtained. The stochastic dominance outranking method of Martel and Zaras allows the identification of clusters of parameters that respect the preferences of the decision maker. When launching an order for a product, the decision maker may judge certain sets of parameters to be equivalent. A visual interactive simulation model will then allow their comparison according to three performance criteria.     

Comparison of voice analysis systems for perturbation measurement

Dysphonic voices are often analyzed using automated voice analysis software. However, the reliability of acoustic measures obtained from these programs remains unknown, particularly when they are applied to pathological voices. This study compared perturbation measures from CSpeech, Computerized Speech Laboratory, SoundScope, and a hand marking voice analysis system. Sustained vowels from 29 male and 21 female speakers with mild to severe dysphonia were digitized, and fundamental frequency (F0), jitter, shimmer, and harmonics- or signal-to-noise ratios were computed. Commercially available acoustical analysis programs agreed well, but not perfectly, in their measures of F0. Measures of perturbation in the various analysis packages use different algorithms, provide results in different units, and often yield values for voices that violate the assumption of quasi-periodicity. As a result, poor rank order correlations between programs using similar measures of perturbation were noted. Because measures of aperiodicity apparently cannot be reliably applied to voices that are even mildly aperiodic, we question their utility in quantifying vocal quality, especially in pathological voices.     

Pharmacological Evidence on Augmented Antiallodynia Following Systemic Co-Treatment with GlyT-1 and GlyT-2 Inhibitors in Rat Neuropathic Pain Model

The limited effect of current medications on neuropathic pain (NP) has initiated large efforts to develop effective treatments. Animal studies showed that glycine transporter (GlyT) inhibitors are promising analgesics in NP, though concerns regarding adverse effects were raised. We aimed to study NFPS and Org-25543, GlyT-1 and GlyT-2 inhibitors, respectively and their combination in rat mononeuropathic pain evoked by partial sciatic nerve ligation. Cerebrospinal fluid (CSF) glycine content was also determined by capillary electrophoresis. Subcutaneous (s.c.) 4 mg/kg NFPS or Org-25543 showed analgesia following acute administration (30–60 min). Small doses of each compound failed to produce antiallodynia up to 180 min after the acute administration. However, NFPS (1 mg/kg) produced antiallodynia after four days of treatment. Co-treatment with subanalgesic doses of NFPS (1 mg/kg) and Org-25543 (2 mg/kg) produced analgesia at 60 min and thereafter meanwhile increased significantly the CSF glycine content. This combination alleviated NP without affecting motor function. Test compounds failed to activate G-proteins in spinal cord. To the best of our knowledge for the first time we demonstrated augmented analgesia by combining GlyT-1 and 2 inhibitors. Increased CSF glycine content supports involvement of glycinergic system. Combining selective GlyT inhibitors or developing non-selective GlyT inhibitors might have therapeutic value in NP.     

A fast compound algorithm for mining generators,closed itemsets,and computing links between equivalence classes

In pattern mining and association rule mining, there is a variety of algorithms for mining frequent closed itemsets (FCIs) and frequent generators (FGs), whereas a smaller part further involves the precedence relation between FCIs. The interplay of these three constructs and their joint computation have been studied within the formal concept analysis (FCA) field yet none of the proposed algorithms is scalable. In frequent pattern mining, at least one suite of efficient algorithms has been designed that exploits basically the same ideas and follows the same overall computational schema. Based on an in-depth analysis of the aforementioned interplay that is rooted in a fundamental duality from hypergraph theory, we propose a new schema that should enable for a more parsimonious computation. We exemplify the new schema in the design of Snow-Touch, a concrete FCI/FG/precedence miner that reuses an existing algorithm, Charm, for mining FCIs, and completes it with two original methods for mining FGs and precedence, respectively. The performance of Snow-Touch and of its closest competitor, Charm-L, were experimentally compared using a large variety of datasets. The outcome of the experimental study suggests that our method outperforms Charm-L on dense data while on sparse one the trend is reversed. Furthermore, we demonstrate the usefulness of our method and the new schema through an application to the analysis of a genome dataset. The initial results reported here confirm the capacity of the method to focus on significant associations.     

Addition of ferulic acid,ethyl ferulate,and feruloylated monoacyl- and diacylglycerols to salad oils and frying oils

To determine antioxidative effects of ferulic acid and esterified ferulic acids, these compounds were added to soybean oils (SBO), which were evaluated for oxidative stability and frying stability. Additives included feruloylated MAG and DAG (FMG/FDG), ferulic acid, ethyl ferulate, and TBHQ. After frying tests with potato chips, oils were analyzed for retention of additives and polar compounds. Chips were evaluated for hexanal and rancid odor. After 15 h frying, 71% of FMG/FDG was retained, whereas 55% of ethyl ferulate was retained. TBHQ and ferulic acid levels were 6% and <1%, respectively. Frying oils with ethyl ferulate or TBHQ produced significantly less polar compounds than SBO with no additives. Chips fried in SBO with TBHQ or ferulic acid had significantly lower amounts of hexanal and significantly less rancid odor after 8 d at 60°C than other samples. Oils were also aged at 60°C, and stability was analyzed by PV, hexanal, and rancid odor. Oils with TBHQ or FMG/FDG had significantly less peroxides and hexanal, and a lower rancid odor intensity than the control. FMG/FDG inhibited deterioration at 60°C, whereas ethyl ferulate inhibited the formation of polar compounds in frying oil. Ferulic acid acted as an antioxidant in aged fried food. TBHQ inhibited oil degradation at both temperatures. Presented at the 94th AOCS Meeting & Expo, Kansas City, MO, May 4–7, 2003.     

Generating Rare Association Rules Using the Minimal Rare Itemsets Family

Rare association rules correspond to rare, or infrequent, itemsets, as opposed to frequent ones that are targeted by conventional pattern miners. Rare rules reflect regularities of local, rather than global, scope that can nevertheless provide valuable insights to an expert, especially in areas such as genetics and medical diagnosis where some specific deviations/illnesses occur only in a small number of cases. The work presented here is motivated by the long-standing open question of efficiently mining strong rare rules, i.e., rules with high confidence and low support. We also propose an efficient solution for finding the set of minimal rare itemsets. This set serves as a basis for generating rare association rules.     

Approximation Bounds for Minimum Information Loss Microaggregation

The NP-hard microaggregation problem seeks a partition of data points into groups of minimum specified size k, so as to minimize the sum of the squared euclidean distances of every point to its group's centroid. One recent heuristic provides an O(k³) guarantee for this objective function and an O(k²) guarantee for a version of the problem that seeks to minimize the sum of the distances of the points to its group's centroid. This paper establishes approximation bounds for another microaggregation heuristic, providing better approximation guarantees of O(k²) for the squared distance measure and O(k) for the distance measure.