Biomechanical changes at cellular level can dramatically affect living organisms in both aviation and space applications. Weightlessness induces morphological alteration of cells, which leads to tissue loss. Therefore, scientists have been studying the effect of weightlessness using cell culture based biological experiments using conventional microscopes. However, strict requirements regarding cost, weight and functionality limit the use of conventional microscopes in space environment. Lensless digital in-line holographic microscopy enables to use low-weight, low-cost and robust elements, such as a light emitting diode (LED), an aperture and an imaging sensor, instead of bulky, expensive and fragile optical elements, such as lenses, mirrors and filters. This technology offers a high field of view compared to conventional microscopes without affecting the resolution and it is also suitable for remote sensing applications with automated imaging capabilities. Here, we present a portable digital in-line holographic microscopy platform that allows to visualize cells and to analyze their viability in a microfluidic chip. The platform offers microscopic imaging with 1.55 μm spatial resolution, 21.7 mm 2 field of view and image coloring capability. This platform could potentially play an important role in space biotechnology applications by enabling low-cost, high-resolution and portable monitoring of cells.

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In this work, we generalise the categorical syllogistic logic in several dimensions to a relatively expressive logic that is sufficiently powerful to encompass a wider range of linguistic semantics. The generalisation is necessary in order to eliminate the existential ambiguity of the quantifiers and to increase expressiveness, practicality, and adaptivity of the syllogisms. The extended semantics is expressed in an extended syntax such that an algorithmic solution of the extended syllogisms can be processed. Our algorithmic approach for deduction in this logic allows for automated reasoning directly with quantified propositions, without reduction of quantifiers.-Automated deduction, automated reasoning, knowledge representation, syllogistic reasoning.

Reasoning is a core topic both for natural intelligence and for artificial intelligence. While syllogistic logics (SLs) are often studied by cognitive scientists for understanding human reasoning, description logics (DLs) are usually studied by computer scientists for performing automated reasoning. Although the studies on both of these logics are extensive, their literatures are interestingly isolated from each other. Firstly, we formally define a practical family of SLs with different levels of expressivity, including a logic which has recently been introduced for automated reasoning. Then, we reveal their theoretical properties either by defining direct algorithms for deductive reasoning or by translation rules for them into relevant DLs. These algorithms and rules prove that (i) two of our SLs (namely PolSyl and NegSyl) are tractable fragments of DLs, and (ii) other two SLs (namely ComSyl and ComSyl + ) are categorical fragments of DL ALC and DL ALCO with general TBoxes, respectively. These findings bridge the gap between (ancient) SLs and (modern) DLs. An immediate result is that it is possible to combine powerful features of both logics, for example, intuitional user interface of an SL and efficient reasoning algorithms for a DL. Finally, we propose a framework for knowledge representation in SLs and link it to sound and complete DL reasoners for automated deduction.