Monash University

About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.

Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond.

Abstract: A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.

InceptionTime: Finding AlexNet for time series classification

Abstract: This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE cannot be applied to many real-world datasets because of its high training time complexity in $$O(N^2\cdot T^4)$$ for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 8 days to learn from a small dataset with $$N=1500$$ time series of short length $$T=46$$ . Meanwhile deep learning has received enormous attention because of its high accuracy and scalability. Recent approaches to deep learning for TSC have been scalable, but less accurate than HIVE-COTE. We introduce InceptionTime—an ensemble of deep Convolutional Neural Network models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime is on par with HIVE-COTE in terms of accuracy while being much more scalable: not only can it learn from 1500 time series in one hour but it can also learn from 8M time series in 13 h, a quantity of data that is fully out of reach of HIVE-COTE.

Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales

Abstract: linear relations with measurements of solar irradiance (r 2 = 0.95, relative bias: 8%), gross primary productivity (r 2 = 0.86, relative bias: 5%) and evapotranspiration (r 2 = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 � 26 PgC yr � 1 and 500 � 104 mm yr � 1 (equivalent to 63,000 � 13,100 km 3 yr � 1 ), respectively. BESS-derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine-learning, data-driven products, but the process-oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process-based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8-day interval over multiple years.

Changes in fat-free mass during significant weight loss: a systematic review

Abstract: To identify the proportion of weight lost as fat-free mass (FFM) by various weight loss interventions. Medline and Embase were systematically searched for reliable measurements of FFM before and after weight loss of >10 kg and eligible data were pooled. In a fixed effect model of % FFM loss/weight loss (%FFML), linear regression analysis was used to determine the influence of degree of caloric restriction, exercise, magnitude of weight loss, initial body mass index (BMI) and type of surgery. Data were included from 26 cohorts treated with dietary and behavioral interventions and 29 cohorts of bariatric surgery patients. The degree of caloric restriction was positively associated with %FFML (r2=0.31, P=0.006) and in three randomized controlled trials exercise was shown to decrease %FFML. Compared with laparoscopic adjustable gastric banding (LAGB), biliopancreatic diversion (BPD) and roux en Y gastric bypass (RYGB) caused greater loge (natural log) %FFML (r2=0.453, P<0.001). Differences in loge %FFML between surgical procedures were independent of initial BMI and magnitude of weight loss. The degree of caloric restriction, exercise and rate of weight loss influence the proportion of weight lost as FFM after non-surgical interventions. For surgical interventions, BPD and RYGB result in greater %FFML than LAGB.