Consequences of COVID-19 on my experiment of gene overexpression in beans

<p>My name is Salma Meléndez and I am currently a graduate in Agrogenomic Sciences. In March 2020, when COVID-19 was detected in Mexico, I was in my eighth semester of my undergraduate degree. At that time, he had an experiment of overexpression of a gene in bean roots, in order to explore its function during symbiosis with rhizobial bacteria. Unfortunately, the laboratory and the entire campus canceled their face-to-face activities in order to reduce the risk of contagion. An alternative was to take the experimental plants to my house to give the proper care, however, the situation became difficult as I did not have the space or the required conditions at home. On the other hand, other research centers with which we had collaboration agreements also canceled access, such is the case of the Optical Research Center, where we used the confocal microscope to detect subcellular location of proteins. The closure of institutions allowed me to write theoretical parts of my thesis, however, the experimental phase was definitely affected for at least six months. The experiment with the plants was almost completely lost. In the subsequent months I had the opportunity to re-enter my institution; however, under strict conditions and on staggered days, which made certain measurements that require daily continuity difficult. Currently, the laboratory is not as it used to look, full of colleagues sharing results and difficulties, exchanging advice and even certain materials. I think the pandemic has pushed us to do our work more individually and slowly. Consequently, my degree was delayed and transferred from 2020 to 2021. There are still many challenges to overcome, although activities have not been fully restored, science does not stop and we have found a way to face it, slowly but surely.</p>

A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors

The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors and, thereafter, tested on an MTS apparatus integrated with a confocal microscope and a digital microscope. The confocal microscope is focused on the notch root of the specimens, whereas the digital microscope is focused on the side of the notch. Two features, viz., the crack opening displacement (COD) and the crack length, are extracted during the tests in addition to the ultrasonic signal data. These signal data are analyzed using a machine learning framework that is built upon a symbolic time-series algorithm. This framework is interrogated for crack detection in the crack coalescence (CC) regime defined by COD of ~3 μm and detected through the confocal microscope. Additionally, the framework is probed in the crack propagation (CP) regime characterized by a crack length of ~0.2 mm and detected via the digital microscope. For the CC regime, training accuracies of 79.82% and 81.94% are achieved, whereas testing accuracies of 68.18% and 74.12% are observed for the U- and V-notched specimens, respectively. For the CP regime, overall training accuracies of 88.3% and 91.85% are observed, and accordingly, testing accuracies of 81.94% and 85.62% are obtained for the U- and V-notched specimens, respectively. The results show that a combined machine learning and pattern recognition algorithm enables robust and reliable fatigue damage detection in aerospace structural components.

Versatile, do-it-yourself, low-cost spinning disk confocal microscope

Confocal microscopy is an invaluable tool for 3D imaging of biological specimens, however, accessibility is often limited to core facilities due to the high cost of the hardware. We describe an inexpensive do-it-yourself (DIY) spinning disk confocal microscope (SDCM) module based on a commercially fabricated chromium photomask that can be added on to a laser-illuminated epifluorescence microscope. The SDCM achieves strong performance across a wide wavelength range (~400-800 nm) as demonstrated through a series of biological imaging applications that include conventional microscopy (immunofluorescence, small-molecule stains, and fluorescence in situ hybridization) and super-resolution microscopy (single-molecule localization microscopy and expansion microscopy). This low-cost and simple DIY SDCM is well-documented and should help increase accessibility to confocal microscopy for researchers.