Heavy Metal Removal from Wastewaters by Biosorption: Mechanisms and Modeling

Many industrial activities result in heavy metal dispersion in the environment worldwide. Heavy metals are persistent contaminants, which get into contact with living organisms and humans creating serious environmental disorders. Metals are commonly removed from wastewaters by means of physical-chemical processes, but often microbes are also enrolled to control metal fate. When microorganisms are used as biosorbents for metal entrapment, a process called “biosorption” occurs. Biosorption efficiency is significantly influenced by many parameters such as environmental factors, the sorbing material and the metal species to be removed, and highly depends on whether microbial cultures are alive or dead. Moreover, the presence of biofilm agglomerates is of major importance for metal uptake onto extracellular polymeric substances. In this chapter, the effect of the above mentioned variables on biosorption performance was reviewed. Among the environmental factors, pH rules metal mobility and speciation. Temperature has a lower influence with an optimal value ranging between 20 and 35 °C. The co-presence of more metals usually decreases the biosorption efficiency of each single metal. Biosorption efficiency can be enhanced by using living microorganisms due to the interaction with active functional groups and the occurrence of transport phenomena into the cells. The existing mathematical modeling approaches used for heavy metal biosorption were overviewed. Several isotherms, obtained in batch conditions, are available for modeling biosorption equilibria and kinetics. In continuous systems, most of the models are used to predict the breakthrough curves. However, the modeling of complex continuous-flow reactors requires further research efforts for better incorporating the effect of the operating parameters and hydrodynamics

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (>59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30\u201350 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of 6435 or a UHDRS motor score of 645 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P <.001). Overall motor and cognitive performance (P <.001) were worse, however only disease motor progression was slower (coefficient, 120.58; SE 0.16; P <.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P <.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P <.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients