Lifeboat Foundation

A group of biochemical engineers, Siavash Ahrar, Manasi Raje, Irene Lee and Elliot Hui at the University of California, Irvine, has developed a finite state machine (FSM) implemented as a pneumatic circuit using microfluidic valves to build a lab-on-a-chip. Their work is published in the journal Science Advances.

Over the past several years, biochemical and mechanical engineers have been working toward the goal of automating many of the chemical process that are currently done by hand—trained lab technicians using pipettes to determine the concentration of a chemical dissolved in a liquid, for example. Automating such tasks would not only make them less expensive, it could speed things up, potentially offering medical lab results in minutes rather than hours. To that end, engineers have been working toward building what they call a lab-on-a-chip. In this new effort, the research team has applied pneumatics to the problem.

Many chemical processes involve the movement of liquids. The researchers sought to use water pressure instead of electricity when building circuits for use on a potential lab-on-a-chip. They created a tiny sandwich comprising panes of glass as the bread and a sheet of silicone as the interior. But before making their sandwich, they etched the glass panes to allow a liquid to pass through and poked holes in the silicone sheet to connect the channels in the glass panes.

Exhaled breath VOCs showed variations in concentration associated with the different clips, even though the relative change was less distinguishable compared to the genital response (Fig. 3). For female participants, breath levels of CO₂ and isoprene during the sex clip were significantly lower compared to the anxiety and sport clips (p 0.05, Table S2). For male participants, CO₂, C₂H₄O₂ and C₆H₆O were found to have a significantly lower, higher and higher breath level, respectively, for the sex clip comparing to the other two clips (p 0.05, Table S2). The relative change of CO₂ during the sex clip was in general only 3–4% lower than that during the anxiety and sport clips but was significant for both genders (p 0.05). The minute-by-minute box plot (Fig. 2c) also shows that breath CO₂ appears to lower slightly in concentration for both genders during the sex clip. Isoprene was significantly decreased during the sex clip compared to the sport clip for both genders (males:13%, females:15%, p ≤ 0.001). For women during the sex clip, the isoprene also had a significantly lower level compared to the anxiety clip (12%, p 0.01). For the 1-min data distribution shown in Fig. 2d, participants showed not only elevated isoprene concentration during the sport and anxiety clips but also larger variations among each other reflected by the length of the box representing 25–75% data distribution. Interestingly, isoprene level peaked at the beginning of the sex clip (the second minute) for both genders.

From Fig. 3, it can be seen that several other measured VOCs, C₁₀ H₁₄ O, C₇H₈O, C₈H₁₁ NO₂ and acetaldehyde for female participants, and C₈H₇N for male participants showed large relative changes during the sex clip compared to the anxiety clip and the sport clip. However, no significant difference was identified between the sex clip and the other two clips (Table S2) for these VOCs, indicating the mean values were largely affected by outliers. In such cases, the VOCs identified as having a distinguishable change during the sex clip might be specific to an individual rather than being representative of the whole group of participants. Among the female participants, one subject (No. 39) had substantial elevation of C₁₀ H₁₄ O, C₇H₈O, C₈H₁₁ NO₂ in her breath starting in the end of the sport clip until the end of the sex clip, which caused a significant deviation in terms of the mean values. While for acetaldehyde, subject No. 20 had a much higher relative increase compared to the first neutral clip in her breath than other participants, affecting the mean values. For male participants, two persons with a strong physiological response (No. 6 and 10) had substantial elevated breath levels of C₈H₇N, C₆H₆O and C₇H₈O during the sex clip.

Among all participants, several VOCs showed change according to the genital response of certain individual participants (1 female and 2 male participants). Although the data from those participants were considered as outliers in the previous section, as there were no experimental errors identified and the breath-genital related change occurred in different clip playing order, it is unlikely that those outliers coincidentally followed the genital response pattern. Furthermore, genital response and genital temperature data was rated highly in terms of quality for those individuals. Therefore, we may use these individuals to characterize the breath marker responses for each gender in real time.

Human influences have the potential to reduce the effectivity of communication in bees, adding further stress to struggling colonies, according to new analysis.

Scientists at the University of Bristol studying honeybees, bumblebees and stingless bees found that variations in communication strategies are explained by differences in the habitats that bees inhabit and differences in the social lifestyle such colony size and nesting habits.

The findings, published today in PNAS, reveal that anthropogenic changes, such as habitat conversion, climate change and the use of agrochemicals, are altering the world bees occupy, and it is becoming increasingly clearer that this affects communication both directly and indirectly; for example, by affecting food source availability, social interactions among nestmates and their cognitive functions.

Researchers have detected complex organic molecules in a galaxy more than 12 billion light-years away from Earth—the most distant galaxy in which these molecules are now known to exist. Thanks to the capabilities of the recently launched James Webb Space Telescope and careful analyses from the research team, a new study lends critical insight into the complex chemical interactions that occur in the first galaxies in the early universe.

University of Illinois Urbana-Champaign astronomy and physics professor Joaquin Vieira and graduate student Kedar Phadke collaborated with researchers at Texas A&M University and an international team of scientists to differentiate between infrared signals generated by some of the more massive and larger dust grains in the galaxy and those of the newly observed hydrocarbon molecules.

The study findings are published in the journal Nature.

Whether it’s baking a cake, building a house, or developing a quantum device, the quality of the end product significantly depends on its ingredients or base materials. Researchers working to improve the performance of superconducting qubits, the foundation of quantum computers, have been experimenting using different base materials in an effort to increase the coherent lifetimes of qubits.

The coherence time is a measure of how long a qubit retains quantum information, and thus a primary measure of performance. Recently, scientists discovered that using tantalum in superconducting qubits makes them perform better, but no one has been able to determine why—until now.

Scientists from the Center for Functional Nanomaterials (CFN), the National Synchrotron Light Source II (NSLS-II), the Co-design Center for Quantum Advantage (C2QA), and Princeton University investigated the fundamental reasons that these qubits perform better by decoding the chemical profile of tantalum.

In recent years, electronics engineers have been trying to develop new brain-inspired hardware that can run artificial intelligence (AI) models more efficiently. While most existing hardware is specialized in either sensing, processing or storing data, some teams have been exploring the possibility of combining these three functionalities in a single device.

Researchers at Xi’an Jiaotong University, the University of Hong Kong and Xi’an University of Science and Technology introduced a new organic transistor that can act as a sensor and processor. This transistor, introduced in a paper published in Nature Electronics, is based on a vertical traverse architecture and a crystalline-amorphous channel that can be selectively doped by ions, allowing it to switch between two reconfigurable modes.

“Conventional artificial intelligence (AI) hardware uses separate systems for data sensing, processing, and memory storage,” Prof. Wei Ma and Prof. Zhongrui Wang, two of the researchers who carried out the study, told Tech Xplore.

A University of Virginia-led study about a class of materials called associative polymers appears to challenge a long-held understanding of how the materials, which have unique self-healing and flow properties, function at the molecular level.

Liheng Cai, an assistant professor of materials science and engineering and chemical engineering at UVA, who led the study, said the new discovery has important implications for the countless ways these materials are used every day, from engineering recyclable plastics to human tissue engineering to controlling the consistency of paint so it doesn’t drip.

The discovery, which has been published in the journal Physical Review Letters, was enabled by new associative polymers developed in Cai’s lab at the UVA School of Engineering and Applied Science by his postdoctoral researcher Shifeng Nian and Ph.D. student Myoeum Kim. The breakthrough evolved from a theory Cai had co-developed before arriving at UVA in 2018.

Researchers in Canada and the United States have used deep learning to derive an antibiotic that can attack a resistant microbe, acinetobacter baumannii, which can infect wounds and cause pneumonia. According to the BBC, a paper in Nature Chemical Biology describes how the researchers used training data that measured known drugs’ action on the tough bacteria. The learning algorithm then projected the effect of 6,680 compounds with no data on their effectiveness against the germ.

In an hour and a half, the program reduced the list to 240 promising candidates. Testing in the lab found that nine of these were effective and that one, now called abaucin, was extremely potent. While doing lab tests on 240 compounds sounds like a lot of work, it is better than testing nearly 6,700.

Interestingly, the new antibiotic seems only to be effective against the target microbe, which is a plus. It isn’t available for people yet and may not be for some time — drug testing being what it is. However, this is still a great example of how machine learning can augment human brainpower, letting scientists and others focus on what’s really important.

USC Dornsife researchers employ artificial intelligence to unveil the intricate world of DNA structure and chemistry, enabling unprecedented insights into gene regulation and disease.