Research on the Grinding Energy Density in a Jet Mill

Raw materials are used in many industrial technologies. The raw material frequently has to be prepared as an intermediate with an appropriate particle size distribution, which requires the use of grinding. In grinding processes, energy consumption is a very important profitability criterion for the applied particular size reduction technology. The paper describes the comminution process that takes place in the jet mill using a modified form of the thermodynamic theory of grinding. In this theory, new material characteristics have been added: the surface and volumetric density of grinding energy. The thermodynamic theory is a combination of the classical Kick’s theory and the modified form of Rittinger’s theory. The tested physical magnitudes are a measure of the energy consumption of the grinding process. They describe the energy that must be provided in the grinding process to overcome interactions between particles related to the volume and surface of the material. Knowledge of these magnitudes is necessary to model thermomechanical phenomena in the solid state. The paper presents the results of research on comminution in a jet mill, on the basis of which the values of the tested material magnitudes were determined. It is graphically shown how the values of the tested magnitudes depend on the grain size of the ground samples.     

From nonpeptide toward noncarbon protease inhibitors: metallacarboranes as specific and potent inhibitors of HIV protease

HIV protease (PR) represents a prime target for rational drug design, and protease inhibitors (PI) are powerful antiviral drugs. Most of the current PIs are pseudopeptide compounds with limited bioavailability and stability, and their use is compromised by high costs, side effects, and development of resistant strains. In our search for novel PI structures, we have identified a group of inorganic compounds, icosahedral metallacarboranes, as candidates for a novel class of nonpeptidic PIs. Here, we report the potent, specific, and selective competitive inhibition of HIV PR by substituted metallacarboranes. The most active compound, sodium hydrogen butylimino bis-8,8-[5-(3-oxa-pentoxy)-3-cobalt bis(1,2-dicarbollide)]di-ate, exhibited a K(i) value of 2.2 nM and a submicromolar EC(50) in antiviral tests, showed no toxicity in tissue culture, weakly inhibited human cathepsin D and pepsin, and was inactive against trypsin, papain, and amylase. The structure of the parent cobalt bis(1,2-dicarbollide) in complex with HIV PR was determined at 2.15 A resolution by protein crystallography and represents the first carborane-protein complex structure determined. It shows the following mode of PR inhibition: two molecules of the parent compound bind to the hydrophobic pockets in the flap-proximal region of the S3 and S3' subsites of PR. We suggest, therefore, that these compounds block flap closure in addition to filling the corresponding binding pockets as conventional PIs. This type of binding and inhibition, chemical and biological stability, low toxicity, and the possibility to introduce various modifications make boron clusters attractive pharmacophores for potent and specific enzyme inhibition.     

WASH and WAVE actin regulators of the Wiskott–Aldrich syndrome protein (WASP) family are controlled by analogous structurally related complexes

We recently showed that the Wiskott–Aldrich syndrome protein (WASP) family member, WASH, localizes to endosomal subdomains and regulates endocytic vesicle scission in an Arp2/3-dependent manner. Mechanisms regulating WASH activity are unknown. Here we show that WASH functions in cells within a 500 kDa core complex containing Strumpellin, FAM21, KIAA1033 (SWIP), and CCDC53. Although recombinant WASH is constitutively active toward the Arp2/3 complex, the reconstituted core assembly is inhibited, suggesting that it functions in cells to regulate actin dynamics through WASH. FAM21 interacts directly with CAPZ and inhibits its actin-capping activity. Four of the five core components show distant (approximately 15% amino acid sequence identify) but significant structural homology to components of a complex that negatively regulates the WASP family member, WAVE. Moreover, biochemical and electron microscopic analyses show that the WASH and WAVE complexes are structurally similar. Thus, these two distantly related WASP family members are controlled by analogous structurally related mechanisms. Strumpellin is mutated in the human disease hereditary spastic paraplegia, and its link to WASH suggests that misregulation of actin dynamics on endosomes may play a role in this disorder.     

Structure of the human cohesin inhibitor Wapl

Cohesin, along with positive regulators, establishes sister-chromatid cohesion by forming a ring to circle chromatin. The wings apart-like protein (Wapl) is a key negative regulator of cohesin and forms a complex with precocious dissociation of sisters protein 5 (Pds5) to promote cohesin release from chromatin. Here we report the crystal structure and functional characterization of human Wapl. Wapl contains a flexible, variable N-terminal region (Wapl-N) and a conserved C-terminal domain (Wapl-C) consisting of eight HEAT (Huntingtin, Elongation factor 3, A subunit, and target of rapamycin) repeats. Wapl-C folds into an elongated structure with two lobes. Structure-based mutagenesis maps the functional surface of Wapl-C to two distinct patches (I and II) on the N lobe and a localized patch (III) on the C lobe. Mutating critical patch I residues weaken Wapl binding to cohesin and diminish sister-chromatid resolution and cohesin release from mitotic chromosomes in human cells and Xenopus egg extracts. Surprisingly, patch III on the C lobe does not contribute to Wapl binding to cohesin or its known regulators. Although patch I mutations reduce Wapl binding to intact cohesin, they do not affect Wapl–Pds5 binding to the cohesin subcomplex of sister chromatid cohesion protein 1 (Scc1) and stromal antigen 2 (SA2) in vitro, which is instead mediated by Wapl-N. Thus, Wapl-N forms extensive interactions with Pds5 and Scc1–SA2. Wapl-C interacts with other cohesin subunits and possibly unknown effectors to trigger cohesin release from chromatin.     

Effectiveness of Intrapericardial Administration of Streptokinase in Purulent Pericarditis

BACKGROUND AND PURPOSE: Purulent pericarditis is very rare. However, among patients suffering from this disease the mortality rate is very high. The aim of this study was to evaluate the effectiveness and side effects of intrapericardial streptokinase administration in patients with confirmed purulent pericarditis. PATIENTS AND METHODS: Three patients, one 50-year-old man and two women aged 64 and 40 years, who were admitted to the intensive care unit (ICU) due to purulent pericarditis, entered the study. In all three cases a subxiphoid pericardiotomy followed by insertion of a drainage line into the pericardial space was performed. Antibiotic therapy was started immediately on admission to the hospital. Despite continued antibiotic therapy in all three patients, daily drainage from the pericardium--during several days after surgery--staggered between 50-200 ml/day. Due to considerable purulent pericardial drainage loculations and/or fibrin deposits confirmed by echocardiography, streptokinase (500,000 IU dissolved in 50 ml of normal saline) was administered into the pericardial space over 10 min, using the previously inserted drainage catheter. This regimen was repeated after 12 and 24 h. The total dose of streptokinase was 1,500,000 IU. RESULTS: The clinical effect of intrapericardial streptokinase administration was excellent. Several days after intrapericardial administration of streptokinase, drainage of purulent pericardial fluid stopped. No complications associated with intrapericardial streptokinase administration were observed. In the follow-up echocardiography (in two patients repeated 6 and 9 months after delivery of streptokinase), pericardial fluid and echocardiographic signs of pericardial constriction were not observed. CONCLUSION: Intrapericardial administration of streptokinase in purulent pericarditis is effective and safe.     

Comprehensive Knowledge-Driven AI System for Air Classification Process

Air classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging, the development of an efficient modeling system is of great practical significance. The paper introduces a novel, knowledge-based classification (FLClass) system of bulk materials. A wide range of operating parameters are considered in the study: the mean mass and the Sauter mean diameter of the fed material, classifier rotor speed, working air pressure, and test conducting time. The output variables are the Sauter mean diameter and the cut size of the classification product, as well as the performance of the process. The model was successfully validated against experimental data. The maximum relative error between the measured and predicted data is lower than 9%. The presented fuzzy-logic-based approach allows an optimization study of the process to be conducted. For the considered range of input parameters, the highest performance of the classification process is equal to almost 362 g/min. To the best of our knowledge, this paper is the first one available in open literature dealing with the fuzzy logic approach in modeling the air classification process of bulk materials.     

Inferring fitness landscapes by regression produces biased estimates of epistasis

The genotype–fitness map plays a fundamental role in shaping the dynamics of evolution. However, it is difficult to directly measure a fitness landscape in practice, because the number of possible genotypes is astronomical. One approach is to sample as many genotypes as possible, measure their fitnesses, and fit a statistical model of the landscape that includes additive and pairwise interactive effects between loci. Here, we elucidate the pitfalls of using such regressions by studying artificial but mathematically convenient fitness landscapes. We identify two sources of bias inherent in these regression procedures, each of which tends to underestimate high fitnesses and overestimate low fitnesses. We characterize these biases for random sampling of genotypes as well as samples drawn from a population under selection in the Wright–Fisher model of evolutionary dynamics. We show that common measures of epistasis, such as the number of monotonically increasing paths between ancestral and derived genotypes, the prevalence of sign epistasis, and the number of local fitness maxima, are distorted in the inferred landscape. As a result, the inferred landscape will provide systematically biased predictions for the dynamics of adaptation. We identify the same biases in a computational RNA-folding landscape as well as regulatory sequence binding data treated with the same fitting procedure. Finally, we present a method to ameliorate these biases in some cases.An organism’s fitness or expected reproductive output is determined by its genotype, environment, and, possibly, the frequencies of other genotypes in the population. In the simplified setting of a fixed environment without frequency-dependent effects, as in many experimental populations (1–5), fitnesses are described by a map from genotypes to reproductive rates called the fitness landscape.The dynamics of an adapting population fundamentally depend on characteristics of the organism’s fitness landscape (6–24). However, mapping out an organism’s fitness landscape is virtually impossible in practice because of the coarse resolution of fitness measurements and because of epistasis: the fitness contribution of one locus may depend on the states of other loci. To account for all possible forms of epistasis, a fitness landscape must assign a potentially different fitness to each genotype, and the number of genotypes increases exponentially with the number of loci.As a result of these practical difficulties, fitness landscapes have been directly measured in only very limited cases, such as for individual proteins, RNA molecules, or viruses. Even in these limited cases, genetic variation was restricted to a handful of genetic sites (25–41). Alternatively, one might try to infer properties of a fitness landscape from a time series of samples from a reproducing population. Despite considerable effort along these lines (19, 42–44), this approach is difficult, and such inferences from time series can be subject to systematic biases (45). As a result, very little is known about fitness landscapes in nature, despite their overwhelming importance in shaping the course of evolution.Technological developments now allow researchers to assay growth rates of microbes or enzymatic activities of individual proteins and RNAs for millions of variants (46–48). As a result, researchers are now beginning to sample and measure larger portions of the fitness landscapes than previously possible. Nonetheless, even in these cases, the set of sampled genotypes still represents a tiny proportion of all genotypes and also, likely, a tiny proportion of all viable genotypes.To draw conclusions from the limited number of genotypes whose fitnesses that can be assayed, researchers fit statistical models, notably by penalized regression, that approximate the fitness landscape based on the data available. This situation is perhaps best illustrated by recent studies of fitness for the HIV-1 virus based on the measured reproductive capacity of HIV-derived amplicons inserted into a resistance test vector (49, 50). These HIV genotypes were sampled from infected patients. [An alternative approach, often used for measuring activities of an individual enzyme, is to introduce mutations randomly into a WT sequence (47, 51–54).] Whereas the entire fitness landscape of HIV-1 consists of reproductive values for roughly 2^1,800 ≈ 10⁶⁰⁰ genotypes, only ≈ 70,000 genotypes were assayed in the experiment (49). Researchers therefore approximated the fitness landscape by penalized regression, based on the measured data, using an expansion in terms of main effects of loci and epistatic interactions between loci. The principle goal of estimating the underlying fitness landscape was to assess the extent and form of epistasis (49) and more generally, understand how adaptation would proceed on such a landscape (50).These studies (49, 50) and other high-throughput fitness measurement studies (46–48) produce massive amounts of data, but not nearly enough data to determine an entire fitness landscape. This difficulty presents the field with several pressing questions. Do statistical approximations based on available data faithfully reproduce the relevant aspects of the true fitness landscape and accurately predict the dynamics of adaptation? Or do biases arising from statistical fits or measurement noise influence the conclusions that we draw from such data?Here, we begin to address these fundamental questions about empirical fitness measurements and how they inform our understanding of the underlying fitness landscape and evolution on the landscape. We study the effects of approximating a fitness landscape from data in terms of main and epistatic effects of loci. We show that such approximations, which are required to draw any general conclusions from a limited sample of genotypes, are subject to two distinct sources of biases. Although these biases are known features of linear regressions, they have important consequences for the biological quantities inferred from such fitness landscapes. These biases systematically alter the form of epistasis in the inferred fitness landscape compared with the true underlying landscape. In particular, the inferred fitness landscape will typically exhibit less local ruggedness than the true landscape, and it will suggest that evolutionary trajectories are less predictable than they actually are in the true landscape.Most of our analysis is based on samples from mathematically constructed fitness landscapes. However, we argue that the types of biases that we identify apply generally and in more biologically realistic situations. Indeed, we show that the same types of biases occur in RNA-folding landscapes as well as empirically measured regulatory sequence binding landscapes.Although it may be impossible to completely remove these biases, we conclude by suggesting steps to mitigate the biases in some cases.