Deep learning research has generated widespread interest leading to emergence of a large variety of technological innovations and applications. As significant proportion of deep learning research focuses on vision based applications, there exists a potential for using some of these techniques to enable low-power portable health-care diagnostic support solutions. In this paper, we propose an embedded-hardware-based implementation of microscopy diagnostic support system for PoC case study on: (a) Malaria in thick blood smears, (b) Tuberculosis in sputum samples, and (c) Intestinal parasite infection in stool samples. We use a Squeeze-Net based model to reduce the network size and computation time. We also utilize the Trained Quantization technique to further reduce memory footprint of the learned models. This enables microscopy-based detection of pathogens that classifies with laboratory expert level accuracy as a standalone embedded hardware platform. The proposed implementation is 6x more power-efficient compared to conventional CPU-based implementation and has an inference time of $\sim$ 3 ms/sample.

There exists a fascinating single-celled organism (Physarum polycephalum), informally referred to as "slime mold" or "the blob" due to its mold-like appearance and growth patterns. After analyzing how this organism intelligently expands, successfully solves mazes and is propelled toward resources throughout its lifecycle, we will then consider our current definition of what it means to be "an intelligent organism", and how this fascinating mold could reshape the requirements for being considered "truly intelligent" by human standards. We will start off by looking at what exactly this mold is and what its core components are. The slime mold has some unique traits such as having an extensive network of tubular extensions called "pseudopods" that can cover several square meters at full size, being able to double its mass every day (if resources allow), avoid potentially toxic areas and completely heal itself if it is sliced in half. Even the way this organism moves is very intriguing: "During locomotion with a speed of 1 cm/h, the size and mesh of tubes evolve depending on the position within the organism. At the frontal part of the plasmodium, small components of the tube are very densely connected and some of the small tubes gradually become thick, while most of them disappear toward the rear" one researcher says.

A common shortfall of supervised learning for medical imaging is the greedy need for human annotations, which is often expensive and time-consuming to obtain. This paper proposes a semi-supervised classification method for three kinds of apicomplexan parasites and non-infected host cells microscopic images, which uses a small number of labeled data and a large number of unlabeled data for training. There are two challenges in microscopic image recognition. The first is that salient structures of the microscopic images are more fuzzy and intricate than natural images' on a real-world scale. The second is that insignificant textures, like background staining, lightness, and contrast level, vary a lot in samples from different clinical scenarios. To address these challenges, we aim to learn a distinguishable and appearance-invariant representation by contrastive learning strategy. On one hand, macroscopic images, which share similar shape characteristics in morphology, are introduced to contrast for structure enhancement. On the other hand, different appearance transformations, including color distortion and flittering, are utilized to contrast for texture elimination. In the case where only 1% of microscopic images are labeled, the proposed method reaches an accuracy of 94.90% in a generalized testing set.

Bio-inspired computing focuses on extracting computational models for problem solving from in-depth understanding of behaviour and mechanisms of biological systems. In recent years, cellular computational models based on the structure and the processes of living cells, such as bacterial colonies [43] and viral models [23] have become an important line of research in bio-inspired computing. Physarum-computing, as an example of cellular computing model, has attracted the attention of many researchers [84]. Physarum polycephalum (Physarum for short) is an example of plasmodial slime moulds that are classified as a fungus "Myxomycetes" [21]. In recent years, research on Physarum-inspired computing has become more popular since Nakagaki et al. (2000) performed their well-known experiments showing that Physarum was able to find the shortest route through a maze [57]. Recent research has confirmed the ability of Physarum-inspired algorithms to solve a wide range of problems [103, 78]. Physarum can be modelled as a reaction-diffusion system (cytoplasmic liquid) encapsulated in an elastic growing membrane of actin-myosin cytoskeleton [2].

The Global Technical Strategy for Malaria Elimination 2016–2030 [1] recommends that countries should integrate effective surveillance as a core intervention in their malaria policies. As such, the World Health Organization (WHO) recently provided guidelines to support measurements of the most important parasitological and entomological indicators [2]. Effective entomological surveillance requires detailed quantitative understanding of key biological attributes which influence overall potential of vector populations to transmit Plasmodium to humans [3]. Such attributes may include the likelihood with which specific Anopheles populations bite humans as opposed to the other available vertebrate hosts, i.e. the human blood indices (HBI), defined as proportion of all mosquito blood meals obtained from humans [4, 5]. Other attributes include parasite infection rates, i.e. the proportion of females infected with Plasmodium [6], survivorship, i.e. whether the mosquitoes can live long enough to allow complete sporogonic development of Plasmodium inside them [7], mosquito susceptibility to insecticides commonly used to control them [8], and the location of mosquito biting, i.e. indoors or outdoors, and how it overlaps in space and time with humans [9–12].

April 12, 2018 --Visit this online directory of the nearly 200 faculty members at Hampshire College and you'll find that, listed between a professor of communications and a visiting professor of video and film, is a petri dish of yellow schmutz. The schmutz is a plasmodial slime mold, Physarum polycephalum, a glob of living cells that exhibits decidedly non-schmutzlike behavior, such as solving mazes and anticipating periodic events – so much so that in 2017 Hampshire, a private liberal arts school in Amherst, Mass., awarded it a position of "visiting non-human scholar." The abilities of non-animals to remember events, recognize patterns, and solve problems are prompting scientists and philosophers to rethink what thinking is. In the 20th century, science demolished the notion that humans are the only animals to exhibit complex thinking; in the 21st, biologists are beginning to see cognition in other biological kingdoms – not just slime molds, but also plants. This shift in thought could not only help scientists better understand cognition's workings and its origins, but it could also help in the search for intelligence beyond Earth.

The plasmodium feeds on microscopic food particles, including microbial life forms. The plasmodium placed in an environment with distributed nutrients develops a network of protoplasmic tubes spanning the nutrients' sources. Te topology of the plasmodium's protoplasmic network optimizes the plasmodium's harvesting on the scattered sources of nutrients and makes more efficient flow and transport of intracellular components [8,9,10,11]. The plasmodium is capable for approximation of shortest path [10], computation of planar proximity graphs [2] and plane tessellations [13], primitive memory [12], basic logical computing [15], and control of robot navigation[16]. The plasmodium can be considered as a general-purpose computer because the plasmodium simulates Kolmogorov-Uspenskii machine -- the storage modification machine operating on a colored set of graph nodes [1]. Preprint submitted to Elsevier Science 17 May 2014 The paper is structured as follows. In Sect. 2 we introduce the experimental gates invented in [15] and reinterpret the gates as multi-output logical gates.

Andrew Adamatzky Computing, Engineering and Mathematical Sciences, University of the West of England, Bristol, United Kingdom and Bristol Robotics Laboratory, Bristol, United Kingdom andrew.adamatzky@uwe.ac.uk Abstract Plasmodium of Physarym polycephalum is an ideal biological substrate for implementing concurrent and parallel computation, including combinatorial geometry and optimization on graphs. We report results of scoping experiments on Physarum computing in conditions of minimal friction, on the water surface. We show that plasmodium of Physarum is capable for computing a basic spanning trees and manipulating of lightweight objects. We speculate that our results pave the pathways towards design and implementation of amorphous biological robots. Key words: biological computing, amorphous robots, unconventional computation, amoeba Introduction Plasmodium, the vegetative stage of slime mould Physarum polycephalum, is a single cell, with thousands of diploid nuclei, formed when individual flagellated cells or amoebas of Physarum polycephalum swarm together and fuse.