gebhart/patrepeval huggingface dataset:
Methane Nonoxidative Aromatization over Ru−Mo/HZSM-5 in a Membrane Catalytic Reactor (2002)

Low-temperature oxygen-free methane aromatization was carried out over Ru−Mo/HZSM-5 in a catalytic membrane reactor. The 0.5% Ru−3% Mo/HZSM-5 catalyst, prepared by incipient wetness coimpregnation, was highly selective toward benzene production. Methane aromatization was evaluated under two sets of conditions: (i) without hydrogen permeation in a fixed-bed conventional catalytic reactor (CR) and (ii) with hydrogen permeation in a catalytic membrane reactor (CMR). In CR mode, the catalyst exhibited remarkable stability with no significant deactivation for 24 h on stream. Switching to CMR mode gave rise to a significant increase in conversion, which reached levels well beyond the thermodynamic conversion. The continuous withdrawal of coproduced H2 promoted the formation of carbonaceous species with a low H/C ratio and led to a decrease in benzene production. At a methane space velocity of 270 mL (STP)·h-1·g-1 and a temperature of 873 K, the CR mode yielded a maximum conversion of methane to benzene equal to 3.8%, i.e., 73% of the thermodynamic equilibrium conversion (5.2%). Under similar conditions, the maximum conversion to benzene attained in CMR mode was 9%. Alternating CR and CMR sequences under moderate feed flow rates proved to be a viable strategy for maintaining high catalyst activity toward benzene production for more than 100 h on stream. The CR step, through an increased hydrogen concentration, helped regenerate the active sites by hydrogenating the carbonaceous species, while the CMR step contributed in the overshoot of benzene conversion due to the equilibrium shift brought about by hydrogen withdrawal. A two-active-site model was proposed to rationalize the experimental observations. Further tests dealing with hydrogen addition to the methane feed flow clearly demonstrated the beneficial influence of hydrogen on the catalyst activity.
Unraveling the mechanism of action of thiazolidinediones (2000)

Thiazolidinediones (TZDs) are a new class of antidiabetic agents and include three compounds that have come to clinical use — troglitazone (Rezulin®), rosiglitazone (Avandia®), and pioglitazone (Actos®) — as well as several others that have been limited to pre-clinical study. TZDs were initially discovered by screening compounds for a hypoglycemic action in the ob/ob mouse (1), and subsequently they were shown to improve insulin action in a variety of obese and diabetic animal models with insulin resistance (2). In these model systems, TZDs reduce plasma glucose and insulin levels and improve some of the abnormalities of lipid metabolism. Consistent with animal studies, clinical studies have shown that treatment of type 2 diabetic patients with TZDs can lower serum glucose and insulin levels, increase peripheral glucose uptake, and decrease triglyceride levels (3). In euglycemic clamp studies, this is associated with an increase in insulin sensitivity in peripheral tissues (mainly represented by muscle in clamp studies), with relatively little effect on hepatic glucose output (4).
TLR11 Activation of Dendritic Cells by a Protozoan Profilin-Like Protein (2005)

Mammalian Toll-like receptors (TLRs) play an important role in the innate recognition of pathogens by dendritic cells (DCs). Although TLRs are clearly involved in the detection of bacteria and viruses, relatively little is known about their function in the innate response to eukaryotic microorganisms. Here we identify a profilin-like molecule from the protozoan parasite Toxoplasma gondii that generates a potent interleukin-12 (IL-12) response in murine DCs that is dependent on myeloid differentiation factor 88. T. gondii profilin activates DCs through TLR11 and is the first chemically defined ligand for this TLR. Moreover, TLR11 is required in vivo for parasite-induced IL-12 production and optimal resistance to infection, thereby establishing a role for the receptor in host recognition of protozoan pathogens.
Preparation and characterisation of pheroid vesicles (2006)

Pheroid is a patented system comprising of a unique submicron emulsion type formulation.Pheroid vesicles consist mainly of plant and essential fatty acids and can entrap, transport and deliver pharmacologically active compounds and other useful molecules.The aim of this study was to show that a modulation of components and parameters is necessary to obtain the optimum formula to be used in pharmaceutical preparations.Non-optimal or non-predictable stability properties of emulsions can be limiting for the applications of emulsions (Bjerregaard et a/., 2001:23).Careful consideration was given to the apparatus used during the processing along with the ratios of the various components added to the formulation and the storage conditions of the Pheroid vesicles.A preliminary study was performed to optimize the most accurate processing parameters during emulsification.The effect of emulsification rate and time, the temperature of the aqueous phase, the number of days the water phase were gassed, the concentration of the surfactant, cremophorB RH 40, used and the concentration of Vitamin F Ethyl Ester CLR added to the oil phase of the o/w emulsion has been studied.Quantification of the mean particle size, zeta potential, turbidity, pH and current values were used to characterize the emulsions.The samples were characterised after 1, 2, 3, 7, 14, 21 and 28 days of storage.The emulsions were also characterised with confocal laser scanning microscopy (CLSM) to measure the number and size and size distribution of the vesicles.After determination of the processing variables influencing the emulsion stability an accelerated stability test was conducted on a final formula.In the present study, accelerated stability testing employing elevated temperatures and relative humidity were used with good accuracy to predict long-term stability of an olw emulsion kept at both 5 and 25 O C with 60 % relative humidity and 40 O C with 75 % relative humidity.The results of the stability tests were presented in histograms of the physical properties 24 hours, 1 month, 2 months and 3 months after preparation of the emulsion.It was concluded that Pheroid vesicles demonstrate much potential as a drug delivery system.The high stability of this formula allows its use in a wide variety of applications in the pharmaceutical industry.
Machine Intelligence in Decoding of Forward Error Correction Codes. (2017)

A deep learning algorithm for improving the performance of the Sum-ProductAlgorithm (SPA) based decoders is investigated. The proposed Neural NetworkDecoders (NND) [22] generalizes the SPA by assigning weights to the edges ofthe Tanner graph. We elucidate the peculiar design, training, and working of theNND. We analyze the edge weight’s distribution of the trained NND and providea deeper insight into its working. The training process of NND learns the edgeweights in such a way that the effects of artifacts in the Tanner graph (such ascycles or trapping sets) are mitigated, leading to a significant improvement inperformance over the SPA.We conduct an extensive analysis of the training hyper-parameters affectingthe performance of the NND, and present hypotheses for determining theirappropriate choices for different families and sizes of codes. Experimental resultsare used to verify the hypotheses and rationale presented. Furthermore,we propose a new loss-function that improves performance over the standardcross-entropy loss. We also investigate the limitations of the NND in termsof complexity and performance. Although the SPA based design of the NNDenables faster training and reduced complexity, the design constraints restrictthe neural network to reach its maximum potential. Our experiments show thatthe NND is unable to reach Maximum Likelihood (ML) performance thresholdfor any plausible set of hyper-parameters. However for short length (n 128)High Density Parity Check (HDPC) codes such as Polar or BCH codes, theperformance improvement over the SPA is significant.
Integration of database systems and Smalltalk (2002)

The authors present an object-oriented data model, that allows uniform specification of database requests and application programs. The user interface is based on Smalltalk, and the object-oriented data model is represented in terms of classes and messages. Techniques are discussed for implementing such a model on top of an underlying relational database system. Those parts of application programs that cannot be translated into a relational language are handled by a Smalltalk processor. The semantics of database requests is defined in terms of a meta-model and meta-messages, using an object-oriented approach. Hence we derive rules for translation of database requests into SQL queries over a binary relational view.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
High-Individuality Voice Conversion Based On Concatenative Speech Synthesis (2007)

Concatenative speech synthesis is a method that can make speech sound which has naturalness and high-individuality of a speaker by introducing a large speech corpus. Based on this method, in this paper, we propose a voice conversion method whose conversion speech has high-individuality and naturalness. The authors also have two subjective evaluation experiments for evaluating individuality and sound quality of conversion speech. From the results, following three facts have be confirmed: (a) the proposal method can convert the individuality of speakers well, (b) employing the framework of unit selection (especially join cost) of concatenative speech synthesis into conventional voice conversion improves the sound quality of conversion speech, and (c) the proposal method is robust against the difference of genders between a source speaker and a target speaker.
Lipopolysaccharide Induced Monocyte Thromboplastin Synthesis and Coagulation Responses in Patients Undergoing Coronary Bypass Surgery after Preoperative Supplementation with n-3 Fatty Acids (1993)

Twenty patients with coronary heart disease (CHD) and elevated serum lipids were randomized into 2 groups of 10 to receive encapsulated preparations of either a concentrated ethylester form of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) or corn oil in doses of 6 g per day, given double blindly for approximately two months prior to coronary bypass surgery. Lipopolysaccharide (LPS) induced monocyte thromboplastin synthesis was studied during the preoperative period and one week following surgery. The ability of n-3 fatty acids to modify tissue factor pathway inhibitor (TFPI) and tissue plasminogen activator inhibitor (PAI-1) was also evaluated along with fibrinogen and thrombin-antithrombin III (TAT) complexes. No significant changes were noted preoperatively. Monocyte reactivity, PAI-1, fibrinogen and TAT increased significantly after surgery. These changes were not modified by preoperative loading with n-3 fatty acids.
Dumping Syndrome in Children (1988)

Dumping syndrome is rarely seen in children. We studied ten children with symptoms following various types of gastric surgery. All had abnormal blood glucose and breath hydrogen responses after oral glucose challenge, indicating reduction of both glucose tolerance and glucose absorption, as compared to controls. Glucose tolerance was best expressed as the difference between peak and subsequent lowest blood glucose level, the upper limit of normal (mean + 3 SD) being 5.9 mmol/l. In eight children with persisting symptoms we tested the effects of glucomannan, a highly hygroscopic tasteless fibre, on glucose tolerance and glucose absorption. Glucomannan significantly improved glucose tolerance, but did have no overall effect on glucose absorption. Four children tested glucomannan added to the main meals. Breath hydrogen excretion, as established with the daytime breath hydrogen profile, decreased significantly in two, but complaints increased in three children. Glucomannan does not appear to be suitable for the treatment of dumping syndrome in children as the side effects seem to overrule the beneficial effects.
Integrated motor drives: state of the art and future trends (2016)

With increased need for high power density, high efficiency and high temperature capabilities in aerospace and automotive applications, integrated motor drives (IMD) offers a potential solution. However, close physical integration of the converter and the machine may also lead to an increase in components temperature. This requires careful mechanical, structural and thermal analysis; and design of the IMD system. This study reviews existing IMD technologies and their thermal effects on the IMD system. The effects of the power electronics position on the IMD system and its respective thermal management concepts are also investigated. The challenges faced in designing and manufacturing of an IMD along with the mechanical and structural impacts of close physical integration is also discussed and potential solutions are provided. Potential converter topologies for an IMD like the matrix converter, two‐level bridge, three‐level neutral point clamped and multiphase full bridge converters are also reviewed. Wide band gap devices like silicon carbide and gallium nitride and their packaging in power modules for IMDs are also discussed. Power modules components and packaging technologies are also presented.
Utilization of a tailormade condition monitoring device for third party motors (2018)

Our society moves towards digitalization and the industry is not an exception. Siemenshas developed a wireless condition monitoring device called Simotics Connect in order tohelp them to move forward in the world of digitalization. The Simotics Connect has threeinbuilt sensors. One for temperature, one for vibrations and one for magnetic flux density,a product that is new in the market. This master thesis has investigated its usability forthird party motors, which has not been done.Four areas were investigated, the status in the current market, creating a motorgeometry estimation based on nameplate data, presenting a temperature model to calculatea motor’s cross section temperature and, finally, proposed a stator current model using themagnetic field measurement.Market research has shown that a space for the Simotics Connect to thrive in mostdefinitely exists.The motor geometry estimation, that is based on preliminary electromagnetic sizing,creates a digital twin for the motor that has sufficient accuracy as a tool when calculatinge.g. temperature calculations but lacks accuracy for more advanced and sensitivecalculations e.g for magnetic flux density measurement usability.The temperature model that is presented shows great accuracy when calculating thecross section temperature in the stator but the accuracy decreases for the cross sectiontemperature in the rotor.A stator current model is proposed using a proportional relationship between themagnetic flux density and stator current. The results indicates a linear relationship, thoughusing the digital twin to calculate the proportional constant were concluded to not beaccurate enough.
An aluminum hydroxide:CpG adjuvant enhances protection elicited by a SARS-CoV-2 receptor binding domain vaccine in aged mice (2022)

Global deployment of vaccines that can provide protection across several age groups is still urgently needed to end the COVID-19 pandemic, especially in low- and middle-income countries. Although vaccines against SARS-CoV-2 based on mRNA and adenoviral vector technologies have been rapidly developed, additional practical and scalable SARS-CoV-2 vaccines are required to meet global demand. Protein subunit vaccines formulated with appropriate adjuvants represent an approach to address this urgent need. The receptor binding domain (RBD) is a key target of SARS-CoV-2 neutralizing antibodies but is poorly immunogenic. We therefore compared pattern recognition receptor (PRR) agonists alone or formulated with aluminum hydroxide (AH) and benchmarked them against AS01B and AS03-like emulsion-based adjuvants for their potential to enhance RBD immunogenicity in young and aged mice. We found that an AH and CpG adjuvant formulation (AH:CpG) produced an 80-fold increase in anti-RBD neutralizing antibody titers in both age groups relative to AH alone and protected aged mice from the SARS-CoV-2 challenge. The AH:CpG-adjuvanted RBD vaccine elicited neutralizing antibodies against both wild-type SARS-CoV-2 and the B.1.351 (beta) variant at serum concentrations comparable to those induced by the licensed Pfizer-BioNTech BNT162b2 mRNA vaccine. AH:CpG induced similar cytokine and chemokine gene enrichment patterns in the draining lymph nodes of both young adult and aged mice and enhanced cytokine and chemokine production in human mononuclear cells of younger and older adults. These data support further development of AH:CpG-adjuvanted RBD as an affordable vaccine that may be effective across multiple age groups.
Liquid crystalline semifluorinated ionic dendrimers (2007)

Poly(propyleneimine) [PPI‐(NH2) n ] and poly(amidoamine) [PAMAM‐(NH2) n ] dendrimers with n = 4, 8, 16, 32 and 64, have been functionalized with a semifluorinated carboxylic acid [CF3(CF2)7CH2CH2COOH] on the surface. The thermal and liquid crystal properties of the resulting ionic dendrimers have been studied by polarized optical microscopy (POM), differential scanning calorimetry (DSC) and X‐ray diffraction (XRD). All dendrimers show a smectic A (SmA) mesophase, except that derived from the fifth generation of PPI, which displays a columnar mesophase. The mesomorphic behaviour of the dendrimers is attributed to the formation of ionic bonds and to the microsegregation of the dendritic branches and the fluorinated peripheral chains.
SmartRoad: A New Approach to Law Enforcement in Dense Traffic Environments (2015)

Road transport law enforcement is facing new challenges against the background of increasingly congested traffic conditions. An ideal law enforcement system must enable authorities to detect, identify and act against illegal road users with minimum disruption of normal traffic flows. This paper presents a new concept for in-traffic law enforcement that overcomes many of the limitations of existing approaches. The SmartRoad system combines the best abilities of imaging, automated identification, radio communications and artificial intelligence technologies to identify not only vehicles with anomalous identities but also anomalous behavior displayed by legal vehicles. The proposed system utilizes the combined abilities of traffic cameras and RFID to firstly detect vehicles and subsequently verify their true identity. Such information is passed downstream to enable the removal of such vehicles from the road with minimal disruption to other road users. As more historical statistics representing normal and abnormal road usage are collected the system will learn from such data in order to improve its ability to reduce both false positives and negatives. Successful deployment of the SmartRoad concept should therefore result in a self-regulating traffic system that discourages unacceptable road use and that adapts itself to new forms of road behavior.
An Omni-Directional Wall-Climbing Microrobot with Magnetic Wheels Directly Integrated with Electromagnetic Micromotors (2012)

This paper presents an omni-directional wall-climbing microrobot with magnetic wheels. The integral design with an actuator and adhesive is realized by integrating stators and rotors of an MEMS-based electromagnetic micromotor with a magnetic wheel. The omni-directional wall-climbing mechanism is designed by a set of steering gears and three standard magnetic wheels. The required torque and magnetic force for microrobot movement are derived by its static analysis. The size of the magnetic wheel is optimized, with consideration of its own design constraints, by ANSOFT and Pro/Engineer simulation so as to reduce unnecessary torque consumption under the same designed load. Related experiments demonstrate that the microrobot (diameter: 26mm; height: 16.4; mass: 7.2g; load capacity: 3g) we have developed has a good wall-climbing ability and flexible mobility, and it can perform visual detection in a ferromagnetic environment.
Estimation of Position Specific Energy as a Feature of Protein Residues from Sequence Alone for Structural Classification (2016)

A set of features computed from the primary amino acid sequence of proteins, is crucial in the process of inducing a machine learning model that is capable of accurately predicting three-dimensional protein structures. Solutions for existing protein structure prediction problems are in need of features that can capture the complexity of molecular level interactions. With a view to this, we propose a novel approach to estimate position specific estimated energy (PSEE) of a residue using contact energy and predicted relative solvent accessibility (RSA). Furthermore, we demonstrate PSEE can be reasonably estimated based on sequence information alone. PSEE is useful in identifying the structured as well as unstructured or, intrinsically disordered region of a protein by computing favorable and unfavorable energy respectively, characterized by appropriate threshold. The most intriguing finding, verified empirically, is the indication that the PSEE feature can effectively classify disorder versus ordered residues and can segregate different secondary structure type residues by computing the constituent energies. PSEE values for each amino acid strongly correlate with the hydrophobicity value of the corresponding amino acid. Further, PSEE can be used to detect the existence of critical binding regions that essentially undergo disorder-to-order transitions to perform crucial biological functions. Towards an application of disorder prediction using the PSEE feature, we have rigorously tested and found that a support vector machine model informed by a set of features including PSEE consistently outperforms a model with an identical set of features with PSEE removed. In addition, the new disorder predictor, DisPredict2, shows competitive performance in predicting protein disorder when compared with six existing disordered protein predictors.