pH control for enhanced reductive bioremediation of chlorinated solvent source zones

Enhanced reductive dehalogenation is an attractive treatment technology for in situ remediation of chlorinated solvent DNAPL source areas. Reductive dehalogenation is an acid-forming process with hydrochloric acid and also organic acids from fermentation of the electron donors typically building up in the source zone during remediation. This can lead to groundwater acidification thereby inhibiting the activity of dehalogenating microorganisms. Where the soils' natural buffering capacity is likely to be exceeded, the addition of an external source of alkalinity is needed to ensure sustained dehalogenation. To assist in the design of bioremediation systems, an abiotic geochemical model was developed to provide insight into the processes influencing the groundwater acidity as dehalogenation proceeds, and to predict the amount of bicarbonate required to maintain the pH at a suitable level for dehalogenating bacteria (i.e., > 6.5). The model accounts for the amount of chlorinated solvent degraded, site water chemistry, electron donor, alternative terminal electron-accepting processes, gas release and soil mineralogy. While calcite and iron oxides were shown to be the key minerals influencing the soil's buffering capacity, for the extensive dehalogenation likely to occur in a DNAPL source zone, significant bicarbonate addition may be necessary even in soils that are naturally well buffered. Results indicated that the bicarbonate requirement strongly depends on the electron donor used and availability of competing electron acceptors (e.g., sulfate, iron (III)). Based on understanding gained from this model, a simplified model was developed for calculating a preliminary design estimate of the bicarbonate addition required to control the pH for user-specified operating conditions.     

Is Protein Folding a Thermodynamically Unfavorable,Active, Energy-Dependent Process?

The prevailing current view of protein folding is the thermodynamic hypothesis, under which the native folded conformation of a protein corresponds to the global minimum of Gibbs free energy G. We question this concept and show that the empirical evidence behind the thermodynamic hypothesis of folding is far from strong. Furthermore, physical theory-based approaches to the prediction of protein folds and their folding pathways so far have invariably failed except for some very small proteins, despite decades of intensive theory development and the enormous increase of computer power. The recent spectacular successes in protein structure prediction owe to evolutionary modeling of amino acid sequence substitutions enhanced by deep learning methods, but even these breakthroughs provide no information on the protein folding mechanisms and pathways. We discuss an alternative view of protein folding, under which the native state of most proteins does not occupy the global free energy minimum, but rather, a local minimum on a fluctuating free energy landscape. We further argue that ΔG of folding is likely to be positive for the majority of proteins, which therefore fold into their native conformations only through interactions with the energy-dependent molecular machinery of living cells, in particular, the translation system and chaperones. Accordingly, protein folding should be modeled as it occurs in vivo, that is, as a non-equilibrium, active, energy-dependent process.     

MTEP,a Selective mGluR5 Antagonist,Had a Neuroprotective Effect but Did Not Prevent the Development of Spontaneous Recurrent Seizures and Behavioral Comorbidities in the Rat Lithium–Pilocarpine Model of Epilepsy

Metabotropic glutamate receptors (mGluRs) are expressed predominantly on neurons and glial cells and are involved in the modulation of a wide range of signal transduction cascades. Therefore, different subtypes of mGluRs are considered a promising target for the treatment of various brain diseases. Previous studies have demonstrated the seizure-induced upregulation of mGluR5; however, its functional significance is still unclear. In the present study, we aimed to clarify the effect of treatment with the selective mGluR5 antagonist 3-[(2-methyl-1,3-thiazol-4-yl)ethynyl]-pyridine (MTEP) on epileptogenesis and behavioral impairments in rats using the lithium–pilocarpine model. We found that the administration of MTEP during the latent phase of the model did not improve survival, prevent the development of epilepsy, or attenuate its manifestations in rats. However, MTEP treatment completely prevented neuronal loss and partially attenuated astrogliosis in the hippocampus. An increase in excitatory amino acid transporter 2 expression, which has been detected in treated rats, may prevent excitotoxicity and be a potential mechanism of neuroprotection. We also found that MTEP administration did not prevent the behavioral comorbidities such as depressive-like behavior, motor hyperactivity, reduction of exploratory behavior, and cognitive impairments typical in the lithium–pilocarpine model. Thus, despite the distinct neuroprotective effect, the MTEP treatment was ineffective in preventing epilepsy.     

Selective Binding of Cyclodextrins with Leflunomide and Its Pharmacologically Active Metabolite Teriflunomide

The selectivity of encapsulation of leflunomide and teriflunomide by native α-, β- and γ-cyclodextrins was investigated through ¹H NMR and molecular modeling. Thermodynamic analysis revealed the main driving forces involved in the binding. For α-cyclodextrin, the partial encapsulation was obtained while deep penetration was characterized for the other two cyclodextrins, where the remaining polar fragment of the molecule is located outside the macrocyclic cavity. The interactions via hydrogen bonding are responsible for high negative enthalpy and entropy changes accompanying the complexation of cyclodextrins with teriflunomide. These results were in agreement with the molecular modeling calculations, which provide a clearer picture of the involved interactions at the atomic level.     

Thermodynamic and Structural Properties of Sodium Lithium Niobate Solid Solutions

Thermodynamics of the Na_1−xLi _x NbO₃ system is investigated by high-temperature drop-solution calorimetry in molten 3Na₂O–4MoO₃ solvent at 973 K. Standard molar enthalpies of formation are derived. The estimated heats of transition between hypothetical and stable structures, lithium niobate and perovskite for NaNbO₃ and vice versa for LiNbO₃ are −6 kJ/mol and −10 kJ/mol, respectively. X-ray diffraction studies at room temperature showed for 0 ≤ x ≤ 0.14 there are three phases based on different ordering of the perovskite type lattice: orthorhombic with a quadrupled reduced perovskite cell at 0 ≤ x ≤ 0.02, orthorhombic with a doubled reduced perovskite cell at 0.015 ≤ x ≤ 0.14, and rhombohedral at 0.08 ≤ x ≤ 0.13. There are two two-phase (morphotropic) regions with coexistence of the two orthorhombic phases at 0.015 ≤ x ≤ 0.02 and with the second orthorhombic phase coexisting with the rhombohedral phase at 0.08 ≤ x ≤ 0.13. A reproducible anomaly in specific heat at ∼600 K, not reported previously, has been observed in pure NaNbO₃. Heat-capacity measurements confirm a phase transition at 553 K for 0.07 ≤ x ≤ 0.09. With increasing lithium concentration, a gradual disappearance of high-temperature phase transitions associated with tilting of oxygen octahedra has been observed.     

A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

This paper extends a method previously introduced by the authors for building a transparent fault classification algorithm by combining the fuzzy clustering, fuzzy logic and decision trees techniques. The baseline method transforms an opaque, fuzzy clustering-based classification model into a fuzzy logic inference model based on linguistic rules which can be represented by a decision tree formalism. The classification model thereby obtained is transparent in that it allows direct interpretation and inspection of the model. An extension in the procedure for the development of the fuzzy logic inference model is introduced to allow the treatment of more complicated cases, e.g. splitted and overlapping clusters. The corresponding computational tool developed relies on a number of parameters which can be tuned by the user to optimally compromise the level of transparency of the classification process and its efficiency. A numerical application is presented with regards to the fault classification in the Steam Generator of a Pressurized Water Reactor.     

The Signaling Pathway of TNF Receptors: Linking Animal Models of Renal Disease to Human CKD

Chronic kidney disease (CKD) has been recognized as a global public health problem. Despite the current advances in medicine, CKD-associated morbidity and mortality remain unacceptably high. Several studies have highlighted the contribution of inflammation and inflammatory mediators to the development and/or progression of CKD, such as tumor necrosis factor (TNF)-related biomarkers. The inflammation pathway driven by TNF-α, through TNF receptors 1 (TNFR1) and 2 (TNFR2), involves important mediators in the pathogenesis of CKD. Circulating levels of TNFRs were associated with changes in other biomarkers of kidney function and injury, and were described as predictors of disease progression, cardiovascular morbidity, and mortality in several cohorts of patients. Experimental studies describe the possible downstream signaling pathways induced upon TNFR activation and the resulting biological responses. This review will focus on the available data on TNFR1 and TNFR2, and illustrates their contributions to the pathophysiology of kidney diseases, their cellular and molecular roles, as well as their potential as CKD biomarkers. The emerging evidence shows that TNF receptors could act as biomarkers of renal damage and as mediators of the disease. Furthermore, it has been suggested that these biomarkers could significantly improve the discrimination of clinical CKD prognostic models.