Do terrestrial gastropods use olfactory cues to locate and select food actively?

Having been investigated for over 40 years, some aspects of the biology of terrestrial gastropod’s olfactory system have been challenging and highly contentious, while others still remain unresolved. For example, a number of terrestrial gastropod species can track the odor of food, while others have no strong preferences toward food odor; rather they find it by random encounter. Here, while assessing the most recent findings and comparing them with earlier studies, the aspects of the food selection based on olfactory cues are examined critically to highlight the speculations and controversies that have arisen. We analyzed and compared the potential role of airborne odors in the feeding behavior of several terrestrial gastropod species. The available results indicate that in the foraging of most of the terrestrial gastropod species odor cues contribute substantially to food finding and selection. The results also suggest, however, that what they will actually consume largely depends on where they live and the species of gastropod that they are. Due to the voluminous literature relevant to this object, this review is not intended to be exhaustive. Instead, I selected what I consider to be the most important or critical in studies regarding the role of the olfaction in feeding of terrestrial gastropods.     

Pitavastatin in cardiometabolic disease: therapeutic profile

Statins effectively lower low-density lipoprotein-cholesterol (LDL-C) and reduce cardiovascular risk in people with dyslipidemia and cardiometabolic diseases such as Metabolic syndrome (MetS) or type 2 diabetes (T2D). In addition to elevated levels of LDL-C, people with these conditions often have other lipid-related risk factors, such as high levels of triglycerides, low levels of high-density lipoprotein-cholesterol (HDL-C), and a preponderance of highly atherogenic, small, dense low-density lipoprotein particles. The optimal management of dyslipidemia in people with MetS or T2D should therefore address each of these risk factors in addition to LDL-C. Although statins typically have similar effects on LDL-C levels, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects, adverse event profiles and drug-drug interactions. The choice of statin should therefore depend on the characteristics and needs of the individual patient. Compared with other statins, pitavastatin has distinct pharmacological features that translate into a broad range of actions on both apolipoprotein-B-containing and apolipoprotein-A-containing lipoproteins. Studies show that pitavastatin 1 to 4 mg is well tolerated and significantly improves LDL-C and triglyceride levels to a similar or greater degree than comparable doses of atorvastatin, simvastatin or pravastatin, irrespective of diabetic status. Moreover, whereas most statins show inconsistent effects on HDL-C levels, pitavastatin-treated patients routinely experience clinically significant elevations in HDL-C that are maintained and even increased over the long term. In addition to increasing high-density lipoprotein quantity, pitavastatin appears to improve high-density lipoprotein function and to slow the progression of atherosclerotic plaques by modifying high-density lipoprotein-related inflammation and oxidation, both of which are common in patients with MetS and T2D. When choosing a statin, it is important to note that patients with MetS have an increased risk of developing T2D and that some statins can exacerbate this risk via adverse effects on glucose regulation. Unlike many statins, pitavastatin appears to have a neutral and even beneficial effect on glucose regulation, making it a useful treatment option in this high-risk group of patients. Together with pitavastatin’s beneficial effects on the cardiometabolic lipid profile and its low potential for drug-drug interactions, this suggests that pitavastatin might be a useful lipid-lowering option for people with cardiometabolic disease.     

Wastewater: A Potential Bioenergy Resource

Wastewaters are a rich source of nutrients for microorganisms. However, if left unattended the biodegradation may lead to severe environmental hazards. The wastewaters can thus be utilized for the production of various value added products including bioenergy (H₂ and CH₄). A number of studies have reported utilization of various wastewaters for energy production. Depending on the nature of the wastewater, different reactor configurations, wastewater and inoculum pretreatments, co-substrate utilizations along with other process parameters have been studied for efficient product formation. Only a few studies have reported sequential utilization of wastewaters for H₂ and CH₄ production despite its huge potential for complete waste degradation.     

Ethylene and IAA interactions in the inhibition of photoperiodic flower induction of <Emphasis Type="Italic">Pharbitis nil</Emphasis>

It has been shown that both IAA and ethylene application inhibit flower induction in the short-day plant Pharbitis nil. However application of IAA has elevated ethylene production in this plant, as well. Strong enhancement of ethylene production is also correlated with the night-break effect, which completely inhibits flowering. In order to determine what the role of IAA and ethylene is in the photoperiodic flower induction in Pharbitis nil, we measured changes in their levels during inductive and non-inductive photoperiods, and the effects of ethylene biosynthesis and action inhibitors on inhibition of flowering by IAA. Our results have shown that the inhibitory effect of IAA on Pharbitis nil flowering is not physiological but is connected with its effect on ethylene biosynthesis.     

INFOS: spectrum fitting software for NMR analysis

Software for fitting of NMR spectra in MATLAB is presented. Spectra are fitted in the frequency domain, using Fourier transformed lineshapes, which are derived using the experimental acquisition and processing parameters. This yields more accurate fits compared to common fitting methods that use Lorentzian or Gaussian functions. Furthermore, a very time-efficient algorithm for calculating and fitting spectra has been developed. The software also performs initial peak picking, followed by subsequent fitting and refinement of the peak list, by iteratively adding and removing peaks to improve the overall fit. Estimation of error on fitting parameters is performed using a Monte-Carlo approach. Many fitting options allow the software to be flexible enough for a wide array of applications, while still being straightforward to set up with minimal user input.