Effect of fermentation time and acid casein concentration as nitrogen source on microbial rennet production.

We evaluated the effects of fermentation time and acid casein content on the microbial rennet obtained by solid-state fermentation using wheat bran as the carbon source. The experiments used two fermentation times (72 and 96 h), while acid casein content was 1.5, 2.0, 2.5, and 3.0 g. Rennet strength from eight enzymatic extracts was measured using pasteurized whole milk. Rennet strength of samples from 72 h of fermentation showed an increase when acid casein content increased. The rennet strength increased at 96 h of fermentation with increasing amount of casein (up to 2.5 g), and then decreased with the largest addition (3.0 g) of casein. Coagulation time for the sample with highest rennet strength was 420 s.

Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures.

Clinical evaluation of the patellar reflex is one of the most frequent diagnostic methods used by physicians and medical specialists. However, this test is usually elicited and diagnosed manually. In this work, we develop a device specifically designed to induce the patellar reflex and measure the angle and angular velocity of the leg during the course of the reflex test. We have recorded the response of 106 volunteers with the aim of finding a recognizable pattern in the responses that can allow us to classify each reflex according to the scale of the National Institute of Neurological Disorders and Stroke (NINDS). In order to elicit the patellar reflex, a hammer is attached to a specially designed pendulum, with a controlled impact force. All volunteer test subjects sit at a specific height, performing the Jendrassik maneuver during the test, and the medical staff evaluates the response in accordance with the NINDS scale. The data acquisition system is integrated by using a tapping sensor, an inertial measurement unit, a control unit, and a graphical user interface (GUI). The GUI displays the sensor behavior in real time. The sample rate is 5 kHz, and the control unit is configured for a continuous sample mode. The measured signals are processed and filtered to reduce high-frequency noise and digitally stored. After analyzing the signals, several domain-specific features are proposed to allow us to differentiate between various NINDS groups using machine learning classifiers. The results show that it is possible to automatically classify the patellar reflex into a NINDS scale using the proposed biomechanical measurements and features.