Landfill gases at an abandoned open dump: a case study at Udapalatha/Gampola site in the Central Province of Sri Lanka

There are very limited studies on landfill gas on uncontrolled open dumps in developing countries. In this study, landfill gas samples at 1 m depth from an abandoned open dump (AOD) in the Central Province of Sri Lanka (N 7º 09', E 80º 35') were collected and the typical landfill gas composition such as O2, N2, CH4, CO2, H2, H2S, and N2O were measured. Buried waste samples at 1 m depth were also taken from the site and organic carbon and nitrogen contents in the residue (< 2 mm) were measured. The samples were taken from some marked plots inside the dump with waste ages of around 0.5 and 7 years (AOD0.5 and AOD7) and outside intact (AODint). Measured CH4 concentration for AOD0.5 and AOD7 ranged in 19–58 % and 0–12 %, respectively, suggesting that the dumped waste at 1 m depth was in the process to be the ‘stabilization phase’ at least 7 years after dumping. This is likely to be a much shorter time period to reach the phase after dumping than those in mid-latitude regions (typically in several decades). The carbon contents in the waste residue in AOD0.5 and AOD7 were 151±67 and 29±7 mg g-1, respectively, implying that high waste decomposition and leaching of organic compounds might have been enhanced due to high temperature and precipitation at the site. A further study for the landfill gas and waste quality in the deeper layer is required to judge whether whole of the dumpsite had reached the stabilization phase rapidly

Spatial variation in landfill gas composition under different precipitation condition and waste age in Sri Lanka

A study was conducted to assess the effect of precipitation and age of waste on the stabilization of the dumped waste. Landfill gas samples at 1-m depth were collected from 13 waste landfill sites in Sri Lanka with different annual precipitation ranging from 1,000 to 4,000 mm and waste age ranging from 1 to 120 months. Typical landfill gases O2, N2, CH4 and CO2 were measured quantitatively by a gas chromatograph. Buried waste samples at 1-m depth were also taken from all locations to determine organic carbon contents in the residue (< 2 mm). With the age of wastes, the measured O2 and N2 concentration (ranged in 1 - 20% and 2 - 80% respectively) in collected landfill gas samples were increased and the CH4 and CO2 concentration (ranged in 0-60% and 1-68%, respectively) decreased, implying the buried wastes are getting stabilized within 120 months after dumped (typically in several decades in mid-latitude regions). However, the correlations between measured gas concentrations and the annual precipitations at the sampling site show no definite results. Organic carbon contents in the waste residues (ranged in 24-236 mg g-1) were not fully related to the waste age and the precipitation amount, while significant time-dependent decreases of the organic carbon contents can be observed in some investigated landfill sites. Further studies are needed with continuous monitoring of rainfall with gas emission

SecA, a remarkable nanomachine

Biological cells harbor a variety of molecular machines that carry out mechanical work at the nanoscale. One of these nanomachines is the bacterial motor protein SecA which translocates secretory proteins through the protein-conducting membrane channel SecYEG. SecA converts chemically stored energy in the form of ATP into a mechanical force to drive polypeptide transport through SecYEG and across the cytoplasmic membrane. In order to accommodate a translocating polypeptide chain and to release transmembrane segments of membrane proteins into the lipid bilayer, SecYEG needs to open its central channel and the lateral gate. Recent crystal structures provide a detailed insight into the rearrangements required for channel opening. Here, we review our current understanding of the mode of operation of the SecA motor protein in concert with the dynamic SecYEG channel. We conclude with a new model for SecA-mediated protein translocation that unifies previous conflicting data

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery