Human interventions in a behavioural experiment for Asian Elephants (Elephas maximus).

Experiments are widely used to investigate the behaviour and cognition of animals. While the automation of experiments to avoid potential experimenter bias is sometimes possible, not all experiments can be conducted without human presence. This is particularly true for large animals in captivity, which are often managed by professional handlers. For the safety of the animals and experimenters, a handler must be present during behavioural studies with certain species. It is not always clear to what extent cues provided by handlers affect the animals, and therefore the experimental results. In this study, we investigate handler interventions during the training process for a behavioural experiment with Asian elephants (Elephas maximus) in Nepal. We show that elephant handlers (mahouts) intervened to guide elephants in performing the learning task using vocal and behavioural cues, despite experimenters requesting minimal intervention. We found that although the frequency of mahout interventions did not decrease as the training progressed, the nature of their interventions changed. We also found more non-verbal than verbal cues across the training. Our results suggest that guidance from handlers may be common in behavioural studies, and continued consideration should be put into experimental design to reduce or account for cues that animals may receive from humans. This study also emphasises the need to take into account the presence of humans in interpreting the results of animal behavioural experiments, which not only presents challenges to behavioural research, but also represents opportunities for further study.

Influence of handler relationships and experience on health parameters, glucocorticoid responses and behaviour of semi-captive Asian elephants.

Declining wild populations combined with accumulating captive populations of e.g. livestock, pets, draught and zoo animals have resulted in some threatened species with substantial proportions of their populations in captivity. The interactions animals have with humans in captivity depend on handler familiarity and relationship quality and can affect animal health, growth and reproduction with consequences for the success of conservation programmes. However, assessments of how specific human-animal relationships affect a range of physiological and behavioural outcomes are rare. Here, we studied semi-captive Asian elephants with detailed records of elephant-handler (mahout) relationships and veterinary management, allowing assessment of multiple welfare indicators in relation to specific mahout-elephant relationship lengths and mahout experience. These included measures of physiological stress (faecal glucocorticoid metabolite [FGM], heterophil:lymphocyte ratio [H:L]), muscle damage (creatine kinase [CK]), immunological health (total white blood cell count [TWBC]) and behaviour (response to mahout verbal commands). We found no evidence that FGM or H:L related to aspects of the mahout-elephant relationship. Longer overall mahout experience (i.e. years of being a mahout) was linked to increased muscle damage and inflammation, but the lengths of specific mahout-elephant relationships were inversely associated with muscle damage in working-age elephants. Elephants responded more to familiar mahouts in behavioural tasks and faster to mahouts they had known for longer. In summary, our results found little evidence that the mahout-elephant relationship affects physiological stress in this population based on FGM and H:L, but mahout experience and relationships were linked to other physiological responses (CK, TWBC), and elephants require behavioural adjustment periods following mahout changes.

Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach.

Motor neuron diseases (MNDs) are a class of progressive neurological diseases that damage the motor neurons. An accurate diagnosis is important for the treatment of patients with MNDs because there is no standard cure for the MNDs. However, the rates of false positive and false negative diagnoses are still very high in this class of diseases. In the case of Amyotrophic Lateral Sclerosis (ALS), current estimates indicate 10% of diagnoses are false-positives, while 44% appear to be false negatives. In this study, we developed a new methodology to profile specific medical information from patient medical records for predicting the progression of motor neuron diseases. We implemented a system using Hbase and the Random forest classifier of Apache Mahout to profile medical records provided by the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) site, and we achieved 66% accuracy in the prediction of ALS progress.

Analysis and Design of the OPAC Book Intelligent Recommendation System for Libraries in Medical Colleges and Universities / 医学信息学杂志

〔Abstract〕 The paper introduces existing OPAC recommendation modules in colleges and universities.Based on characteristics of libraries in medical colleges and universities and in combination with prevailing recommendation technology, it proposes to utilize the algorithm of Mahout of Hadoop Platform to design the library intelligent recommendation system .By embedding the intelligent recommendation system into OPAC , it solves the problem of personalized recommendation and provides readers with active and intelligent personalized services .