In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) - one trained on transcripts of speech produced by healthy participants and the other trained on transcripts from patients with dementia - as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance. However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (DementiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables. In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

In this paper, we analyze and implement the Dirichlet spectral-Galerkin method for approximating simply supported vibrating plate eigenvalues with variable coefficients. This is a Galerkin approximation that uses the approximation space that is the span of finitely many Dirichlet eigenfunctions for the Laplacian. Convergence and error analysis for this method is presented for two and three dimensions. Here we will assume that the domain has either a smooth or Lipschitz boundary with no reentrant corners. An important component of the error analysis is Weyl's law for the Dirichlet eigenvalues. Numerical examples for computing the simply supported vibrating plate eigenvalues for the unit disk and square are presented. In order to test the accuracy of the approximation, we compare the spectral-Galerkin method to the separation of variables for the unit disk. Whereas for the unit square we will numerically test the convergence rate for a variable coefficient problem.

Cassava a major food crop in many parts of Africa, has majorly been affected by Cassava Brown Streak Disease (CBSD). The disease affects tuberous roots and presents symptoms that include a yellow/brown, dry, corky necrosis within the starch-bearing tissues. Cassava breeders currently depend on visual inspection to score necrosis in roots based on a qualitative score which is quite subjective. In this paper we present an approach to automate root necrosis scoring using deep convolutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy achieving a mean Intersection over Union (IoU) of 0.90 on the test set. This method provides a means to use a quantitative measure for necrosis scoring on root cross-sections. This is done by segmentation and classifying the necrotized and non-necrotized pixels of cassava root cross-sections without any additional feature engineering.

Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. This paper proposes an interval type 2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as IT2 FLS LSB method. Moreover, we have developed two more methods, namely, type 1 fuzzy logic system based least significant bits (T1FLS LSB) and Euclidean distance based similarity measures for least significant bit (SM LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR, UQI, and SSIM are used. All the three steganographic methods are applied on datasets and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS LSB method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.

In this paper, some useful necessary and sufficient conditions for the unique solution of the generalized absolute value equation (GAVE) $Ax-B|x|=b$ with $A, Bin mathbb{R}^{n imes n}$ from the optimization field are first presented, which cover the fundamental theorem for the unique solution of the linear system $Ax=b$ with $Ain mathbb{R}^{n imes n}$. Not only that, some new sufficient conditions for the unique solution of the GAVE are obtained, which are weaker than the previous published works.

This paper considers a convex optimization problem with cost and constraints that evolve over time. The function to be minimized is strongly convex and possibly non-differentiable, and variables are coupled through linear constraints.In this setting, the paper proposes an online algorithm based on the alternating direction method of multipliers(ADMM), to track the optimal solution trajectory of the time-varying problem; in particular, the proposed algorithm consists of a primal proximal gradient descent step and an appropriately perturbed dual ascent step. The paper derives tracking results, asymptotic bounds, and linear convergence results. The proposed algorithm is then specialized to a multi-area power grid optimization problem, and our numerical results verify the desired properties.

Bernstein polynomials, long a staple of approximation theory and computational geometry, have also increasingly become of interest in finite element methods. Many fundamental problems in interpolation and approximation give rise to interesting linear algebra questions. When attempting to find a polynomial approximation of boundary or initial data, one encounters the Bernstein-Vandermonde matrix, which is found to be highly ill-conditioned. Previously, we used the relationship between monomial Bezout matrices and the inverse of Hankel matrices to obtain a decomposition of the inverse of the Bernstein mass matrix in terms of Hankel, Toeplitz, and diagonal matrices. In this paper, we use properties of the Bernstein-Bezout matrix to factor the inverse of the Bernstein-Vandermonde matrix into a difference of products of Hankel, Toeplitz, and diagonal matrices. We also use a nonstandard matrix norm to study the conditioning of the Bernstein-Vandermonde matrix, showing that the conditioning in this case is better than in the standard 2-norm. Additionally, we use properties of multivariate Bernstein polynomials to derive a block $LU$ decomposition of the Bernstein-Vandermonde matrix corresponding to equispaced nodes on the $d$-simplex.

This paper investigates the convergence of the randomized Kaczmarz algorithm for the problem of phase retrieval of complex-valued objects. While this algorithm has been studied for the real-valued case}, its generalization to the complex-valued case is nontrivial and has been left as a conjecture. This paper establishes the connection between the convergence of the algorithm and the convexity of an objective function. Based on the connection, it demonstrates that when the sensing vectors are sampled uniformly from a unit sphere and the number of sensing vectors $m$ satisfies $m>O(nlog n)$ as $n, m ightarrowinfty$, then this algorithm with a good initialization achieves linear convergence to the solution with high probability.

We show that the traveling salesman problem (TSP) and its many variants may be modeled as functional optimization problems over a graph. In this formulation, all vertices and arcs of the graph are functionals; i.e., a mapping from a space of measurable functions to the field of real numbers. Many variants of the TSP, such as those with neighborhoods, with forbidden neighborhoods, with time-windows and with profits, can all be framed under this construct. In sharp contrast to their discrete-optimization counterparts, the modeling constructs presented in this paper represent a fundamentally new domain of analysis and computation for TSPs and their variants. Beyond its apparent mathematical unification of a class of problems in graph theory, the main advantage of the new approach is that it facilitates the modeling of certain application-specific problems in their home space of measurable functions. Consequently, certain elements of economic system theory such as dynamical models and continuous-time cost/profit functionals can be directly incorporated in the new optimization problem formulation. Furthermore, subtour elimination constraints, prevalent in discrete optimization formulations, are naturally enforced through continuity requirements. The price for the new modeling framework is nonsmooth functionals. Although a number of theoretical issues remain open in the proposed mathematical framework, we demonstrate the computational viability of the new modeling constructs over a sample set of problems to illustrate the rapid production of end-to-end TSP solutions to extensively-constrained practical problems.

Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks in meteorology, such as best practices for evaluation, tuning and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of effective receptive fields, underutilized meteorological performance measures, and methods for NN interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative scientist-driven discovery process, and breaking it down into individual steps that researchers can take. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image translation.

Coordinating the operation of combined heat and power plants (CHPs) and heat pumps (HPs) at the interface between heat and power systems is essential to achieve a cost-effective and efficient operation of the overall energy system. Indeed, in the current sequential market practice, the heat market has no insight into the impacts of heat dispatch on the electricity market. While preserving this sequential practice, this paper introduces an electricity-aware heat unit commitment model. Coordination is achieved through bid validity constraints, which embed the techno-economic linkage between heat and electricity outputs and costs of CHPs and HPs. This approach constitutes a novel market mechanism for the coordination of heat and power systems, defining heat bids conditionally on electricity market prices. The resulting model is a trilevel optimization problem, which we recast as a mixed-integer linear program using a lexicographic function. We use a realistic case study based on the Danish power and heat system, and show that the proposed model yields a 4.5% reduction in total operating cost of heat and power systems compared to a traditional decoupled unit commitment model, while reducing the financial losses of each CHP and HP due to invalid bids by up-to 20.3 million euros.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

We develop a categorical compositional distributional semantics for Lambek Calculus with a Relevant Modality !L*, which has a limited edition of the contraction and permutation rules. The categorical part of the semantics is a monoidal biclosed category with a coalgebra modality, very similar to the structure of a Differential Category. We instantiate this category to finite dimensional vector spaces and linear maps via "quantisation" functors and work with three concrete interpretations of the coalgebra modality. We apply the model to construct categorical and concrete semantic interpretations for the motivating example of !L*: the derivation of a phrase with a parasitic gap. The effectiveness of the concrete interpretations are evaluated via a disambiguation task, on an extension of a sentence disambiguation dataset to parasitic gap phrase one, using BERT, Word2Vec, and FastText vectors and Relational tensors.

Different state-of-the-art methods of the detection of schizophrenia symptoms based on the estimation of text coherence have been analyzed. The analysis of a text at the level of phrases has been suggested. The method based on the graph of the consistency of phrases has been proposed to evaluate the semantic coherence and the cohesion of a text. The semantic coherence, cohesion, and other linguistic features (lexical diversity, lexical density) have been taken into account to form feature vectors for the training of a model-classifier. The training of the classifier has been performed on the set of English-language interviews. According to the retrieved results, the impact of each feature on the output of the model has been analyzed. The results obtained can indicate that the proposed method based on the graph of the consistency of phrases may be used in the different tasks of the detection of mental illness.

Welcome back! In the first part of this series, we looked at a very "low-level" way to interact with a relational database by sending it raw SQL strings and retrieving the results. We created a very simple Express application that we can use as an example and deployed it on Heroku with a Postgres database.

In this part, we're going to examine a few libraries which build on top of that foundation, adding layers of abstraction that let you read and manipulate database data in a more "JavaScript-like" way.

Handling user input with forms is the cornerstone of many common applications.

Applications use forms to enable users to log in, to update a profile, to enter sensitive information, and to perform many other data-entry tasks

The apps and devices you use are conducting surveillance with your every move Sure, you lock your home, and you probably don't share your deepest secrets with random strangers.…

This is normally the time of year when we're up to our eyeballs in concert announcements, but in these topsy-turvy times, we're instead having to write about all the concerts being canceled due to COVID-19. It's a real bummer.…

Virtually every musician starts out trying to copy their heroes.…

The Buzz Bin VAMPY VIBES…

Every spring, audiences in Moscow are typically congregating for the Kino Short Film Festival, an evening of shorts made by the University of Idaho's senior film students. Things being as they are, the Kenworthy Theater won't be open for this year's event, but the U of I will be streaming a virtual version this Friday, May 8, at 6 pm.…

If you go to check how many people are hospitalized with COVID-19 in Spokane, the Spokane County Regional Health District website will give you an answer. Right now, it lists eight people as currently hospitalized with COVID-19, and that number has been trending downward.…

If you've ever taken a stroll around downtown Spokane's west side, you may have come across Barrister Winery, tucked into a historic brick and timber building alongside the railroad tracks at 1213 Railroad Ave.…

[IMAGE-1] Visiting Spokane Valley is a little like when a child visits their grandparents' place: At first you don't understand why you're there, but after you leave with new toys, clothes and a full stomach, you can't wait to go back. And the Spokane Valley Mall has all of that.…

Don't be caught empty-handed (or empty-hearted) this Valentine's Day, coming up Friday, Feb. 14.…

While its main goal is to introduce the humble breakfast taco to more Spokane eaters, owners of the new quick food stop Taco Vado say breakfast burritos have actually been its bestselling menu item since opening about a month ago.…

Novel indansulfamide derivatives or a pharmaceutically acceptable salt thereof such as N-[(1S)-2,2,5,7-tetrafluoro-2,3-dihydro-1H-inden-1-yl]sulfamide, N-[(1S)-2,2,4,7-tetrafluoro-2,3-dihydro-1H-inden-1-yl]sulfamide, (+)-N-(2,2,4,6,7-pentafluoro-2,3-dihydro-1H-inden-1-yl)sulfamide, have an action of improving Seizure Severity Index (Score) in mice kindling model. Thus the compounds or the salt thereof are expected as a drug for treating epilepsy.

Borazine derivatives are used in the manufacture of electronic devices, in particular electroluminescent and semiconductor devices. More specifically, stable borazine derivatives include boron atoms substituted by aryl groups used in one or more layers of an electroluminescent or a semiconductor device, in particular in the emissive layer of organic light-emitting devices (OLED).

Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.

A computer-implemented method is provided for statistical data learning under privacy constraints. The method includes: receiving, by a processor, a plurality of pieces of statistical information relating to a statistical object and aggregating, by the processor, the plurality of pieces of statistical information so as to provide an estimation of the statistical object. Each piece of statistical information includes an uncertainty variable, the uncertainty variable being a value determined from a function having a predetermined mean. The number of pieces of statistical information aggregated is proportional to the reliability of the estimation of the statistical object.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

Observation-based user profiling and profile matching are provided. The network behavior of users of a computer-implemented social network are observed and used for user profiling. By observing network behavior instead of necessarily relying on user self-reported data, accurate and objective user profiles can be formed; user profiling is accomplished based on the observed network behaviors with or without the knowledge of the user being profiled. The observed network behaviors can include the customization of a visual graphic, a media preference, a communication preference, or a selection of words from a word list. The user profiles can be with respect to a domain and two or more users can be matched based on their profiles with respect to the same domain. User ratings and profile updating based on the ratings are also provided.

To provide a latent variable model estimation apparatus capable of implementing the model selection at high speed even if the number of model candidates increases exponentially as the latent state number and the kind of the observation probability increase. A variational probability calculating unit 71 calculates a variational probability by maximizing a reference value that is defined as a lower bound of an approximation amount, in which Laplace approximation of a marginalized log likelihood function is performed with respect to an estimator for a complete variable. A model estimation unit 72 estimates an optimum latent variable model by estimating the kind and a parameter of the observation probability with respect to each latent state. A convergence determination unit 73 determines whether a reference value, which is used by the variational probability calculating unit 71 to calculate the variational probability, converges.

The invention relates to Fatty Acid Fumarate Derivatives; compositions comprising an effective amount of a Fatty Acid Fumarate Derivative; and methods for treating or preventing cancer, a metabolic disorder or neurodegenerative disorder comprising the administration of an effective amount of a Fatty Acid Fumarate Derivative.

Provided are processes for the oxidative cleavage of a double bond in an unsaturated carboxylic acid. The process includes contacting the unsaturated carboxylic acid with a mild oxidizing agent and agitating the unsaturated carboxylic acid and the mild oxidizing agent for a time sufficient to cleave a double bond of the unsaturated carboxylic acid and produce a product comprising an aldehyde. The process is typically carried out in a mill, such as a ball, hammer, attrition, or jet mill.

An object of the present invention is to provide a novel trans-2-decenoic acid derivative or a pharmaceutically acceptable salt thereof and to provide a pharmaceutical agent which contains said compound as an active ingredient and has a highly safe neurotrophic factor-like activity or an alleviating action for side effect induced by administration of anti-cancer agents. The trans-2-decenoic acid derivative or a pharmaceutically acceptable salt thereof which is the compound of the present invention is specifically represented by the formula (1): (In the formula, Y is —O—, —NR— or —S—, R is hydrogen atom, alkyl group, dialkylaminoalkyl group or the like and W is a substituent such as dialkylaminoalkyl group) and has a quite high usefulness as a pharmaceutical agent such as a preventive or therapeutic agent for dementia, Alzheimer's disease, Parkinson's disease, depression, etc., a treating or repairing agent for spinal cord injury.

Novel compounds of formula (1) wherein: A is especially a linear or branched divalent alkylene radical having between 1 and 10 carbon atoms, and Y is especially a hydrogen atom.

The present invention provides: an uracil derivative represented by general formula (I) or a physiologically acceptable salt thereof (in the formula, R1 represents a hydrogen atom, a C1-10 alkyl group, a C2-6 alkene group or a 3- to 6-membered saturated or 4- to 6-membered unsaturated aliphatic ring group which may contain 1 to 2 hetero atoms independently selected from the group consisting of N, O and S; R2 represents a hydrogen atom, a halogen atom, a cyano group, —NRcRd, —N═CHN(CH3)2, or an C1-3 alkyl group; Ar1 and Ar2 independently represent a 5- to 6-membered aromatic ring group which may contain 1 to 3 hetero atoms independently selected from the group consisting of N, O and S; and L represents a 6-membered aromatic ring group which may contain 1 to 4 nitrogen atoms, a pyrazole group, a triazole group, or an imidazole group); and a therapeutic agent or prophylactic agent for various inflammatory diseases associated with elastase, comprises the compound or the like as an active ingredient.

[Problem] To provide a GPR40 activating agent having, as an active ingredient, a novel compound having a GPR40 agonist action, a salt of the compound, a solvate of the salt or the compound, or the like, particularly, an insulin secretagogues and a prophylactic and/or therapeutic agent against diabetes, obesity, or other diseases.[Means of Solving the Problem]A compound of Formula (1): (where n is 0 to 2; p is 0 to 4; h is 0 to 3; j is 0 to 3; k is 0 to 2; a ring B is an aryl group or a heteroaryl group; X is O, S, or —NR7—; J1 is —CR11aR11b— or —NR11c—; J2 is —CR12aR12b— or —NR12c—; and R1 to R12c are specific groups),a salt of the compound, or a solvate of the salt or the compound.

The present application describes modulators of MCP-1 or CCR-2 of formula or stereoisomers or prodrugs or pharmaceutically acceptable salts thereof, wherein m, n, W, X, R1 and R6, are defined herein. In addition, methods of treating and preventing inflammatory diseases such as asthma and allergic diseases, as well as autoimmune pathologies such as rheumatoid arthritis and transplant rejection using modulators of formula (I) are disclosed.

The invention relates compounds of general formula (I) and pharmaceutically acceptable salts and solvates thereof wherein R1 is halogen, vinylene-aryl, substituted aryl, heteroaryl or a benzo[1,3]dioxolil group,W is a group of formula —NH—SO2—R2 or heteroaryl group or NHR3 group where R3 is hydrogen or heteroaryl; and n is 1, 2, 3 or 4. Furthermore, the present invention is directed to pharmaceutical composition containing at least one compound of general formula (I) and/or pharmaceutically acceptable salts or solvates thereof and for the use of them for the preparation of pharmaceutical compositions for the prophylaxis and/or the treatment of protein kinase related, especially CDK9-related diseases e.g. cell proliferative disease, infectious disease, pain, cardiovascular disease and inflammation.

The invention relates to oxazolopyrimidine compounds of formula I, where A, R1 and R2 are defined as stated in the claims. The compounds of formula I are suitable, for example, for wound healing.

The present invention relates to compounds of formula (I) or pharmaceutically acceptable salts thereof, wherein Q, T, V, W, X, Y, Z, ring A, R1, R2, R3, R4, R5, R5a, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17 and m are defined herein. There novel pyrrolopyrimidine and purine derivatives are useful in the treatment of abnormal cell growth, such as cancer, in mammals. Additional embodiments relate to pharmaceutical compositions containing the compounds and to methods of using the compounds and compositions in the treatment of abnormal cell growth in mammals.

The invention relates to quinazoline derivatives of formula (I), wherein m is an integer from 1 to 3; R1 represents halogeno or C1-3alkyl; X1 represents —O—; R2 is selected from one of the following three groups: 1) C1-5alkylR3, wherein R3 is piperidinyl-4-yl which may bear one or two substituents selected from hydroxy, halogeno, C1-4alkyl, C1-4hydroxyalkyl and C1-4alkoxy; 2) C2-5alkenylR3, wherein R3 is as defined herein; 3) C2-5alkynylR3, wherein R3 is as defined herein; and wherein any alkyl, alkenyl or alkynyl group may bear one or more substituents selected from hydroxy, halogeno and amino; and salts thereof; processes for their preparation; pharmaceutical compositions containing a compound of formula (I) or a pharmaceutically acceptable salt thereof as an active ingredient.

The present invention provides a compound useful as an agent for the prevention or treatment of a sex hormone-dependent disease or the like. That is, the present invention provides a fused heterocyclic derivative represented by the following general formula (I), a pharmaceutical composition containing the same, a medicinal use thereof and the like. In the formula (I), ring A represents 5-membered cyclic unsaturated hydrocarbon or 5-membered heteroaryl; RA represents halogen, alkyl, alkenyl, alkynyl, carboxy, alkoxy, carbamoyl, alkylcarbamoyl or the like ; ring B represents aryl or heteroaryl; RB represents halogen, alkyl, carboxy, alkoxy, carbamoyl, alkylcarbamoyl or the like; E1 and E2 represent an oxygen atom or the like; U represents a single bond or alkylene; X represents a group represented by Y, —SO2—Y, —O—(alkylene)—Y, —O—Z in which Y represents Z, amino or the like; Z represents cycloalkyl, heterocycloalkyl, aryl, heteroaryl or the like; or the like.

The present invention is related to methods for the preparation of pharmaceutically acceptable salts of (+)-7-[4-(4-fluorophenyl)-6-isopropyl-2-(methanesulfonyl-methyl-amino)-pyrimidin-5-yl]-(3R,5S,6E)-dihydroxy-hept-6-enoic acid, intermediates thereof and methods for producing said intermediates.

A rubber composition for manufacturing tyres is based on one or more diene elastomers, one or more reinforcing fillers, and a vulcanization system. The vulcanization system includes one or more 1,2,4-triazine compounds chosen from compounds of formula I and compounds of formula II: Certain specific 1,2,4-triazine derivatives are described.

Beta-lactamase inhibiting compounds of the formula ##STR1## or a pharmaceutically acceptable acid addition or carboxylate salt thereof; where n is zero, 1 or 2; X3 is H or Br, R1 is H, the residue of certain carboxy-protecting groups or the residue of an ester group readily hydrolyzable in vivo; one of R12 and R13 is H and the other is vinyl, certain aryl, alkylthio, alkylsulfonyl or certain heterocyclyl, aminomethyl, thiocarboxyamido or amidino groups; one of R2 and R3 is H and the other is as disclosed for the other of R12 and R13, or is Cl or CH2 OH, and R18 is H or certain acyl groups; intermediates useful in their production, methods for their preparation and use, and pharmaceutical compositions containing them.

1,4-Dihydropyridine-3-carboxylate derivatives are produced having vasodilating and hypotensive action.