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Do Attractants Bias the Results of Malaise Trap Research?

Martinez et al. | Jan 22, 2020

Do Attractants Bias the Results of Malaise Trap Research?

Malaise traps are commonly used to collect flying insects for a variety of research. In this study, researchers hypothesized the attractants used in these traps may create bias in insect studies that could lead to misinterpreted data. To test this hypothesis two different kinds of attractant were used in malaise traps, and insect diversity was assessed. Attractants were found to alter the dispersion of insects caught in traps. These findings can inform future malaise traps studies on insect diversity.

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Effects of Coolant Temperature on the Characteristics of Soil Cooling Curve

Wang et al. | Jan 16, 2020

Effects of Coolant Temperature on the Characteristics of Soil Cooling Curve

In this article, the authors investigate whether coolant temperature affects soil cooling curves of soil with otherwise identical properties. The coolant temperature is representative of environmental temperature, and the authors hypothesized that differences in this temperature would not affect the freezing temperature of soil. Their findings validated their hypothesis providing helpful information relevant to understanding how frost heaves happen and how to predict their occurrence more accurately.

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Phytoplankton Plastid Proteomics: Cracking Open Diatoms to Understand Plastid Biochemistry Under Iron Limitation

Nunn et al. | Feb 10, 2017

Phytoplankton Plastid Proteomics: Cracking Open Diatoms to Understand Plastid Biochemistry Under Iron Limitation

In many areas of the world’s oceans, diatoms such as Thalassiosira pseudonana are limited in growth by the availability of iron (Fe), which is an essential nutrient for diatoms. The authors of this study examined if Fe-limitation makes a significant difference in the proteins expressed within the chloroplast, the power source for diatoms, utilizing a new plastid isolation technique specific to diatoms and completing 14 mass spectrometry experiments.

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The Effect of Font Type on a School’s Ink Cost

Mirchandani et al. | May 10, 2013

The Effect of Font Type on a School’s Ink Cost

Your choice of font can impact more than style. Here the authors demonstrate that font choice can affect the amount of ink a given print-out requires. The authors estimate that a switch to Garamond font, size 12, by all teachers in his school district would save almost $21,000 annually.

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Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin

Talwar et al. | Jan 23, 2024

Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin
Image credit: Flavio

Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.

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Analyzing breath sounds by using deep learning in diagnosing bronchial blockages with artificial lung

Bae et al. | Jan 22, 2024

Analyzing breath sounds by using deep learning in diagnosing bronchial blockages with artificial lung

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

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Predicting baseball pitcher efficacy using physical pitch characteristics

Oberoi et al. | Jan 11, 2024

Predicting baseball pitcher efficacy using physical pitch characteristics
Image credit: Antoine Schibler

Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

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