ALBERT

Beschreibung: Species designations within Acanthamoeba are problematic because of pleomorphic morphology. Molecular approaches, including DNA sequencing, hinted at a resolution that has yet to be fully achieved. Alternative approaches were required. In 1996, the Byers/Fuerst lab introduced the concept of sequence types. Differences between isolates of Acanthamoeba could be quantitatively assessed by comparing sequences of the nuclear 18S rRNA gene, ultimately producing 22 sequence types, designated T1 through T22. The concept of sequence types helps our understanding of Acanthamoeba evolution. Nevertheless, substantial variation in the 18S rRNA gene differentiates many isolates within each sequence type. Because the majority of isolates with sequences in the international DNA databases have been studied for only a small segment of the gene, designated ASA.S1, genetic variation within this hypervariable region of the 18S rRNA gene has been scrutinized. In 2002, we first categorized variation in this region in a sample of T3 and T4 isolates from Hong Kong, observing ten “alleles” within type T4 and five “alleles” within T3. Subsequently, confusion occurred when different labs applied redundant numerical labels to identify different alleles. A more unified approach was required. We have tabulated alleles occurring in the sequences submitted to the international DNA databases, and determined their frequencies. Over 150 alleles have occurred more than once within 3500+ isolates of sequence type T4. Results from smaller samples of other sequence types (T3, T5, T11 and T15, and supergroup T2/6) have also been obtained. Our results provide new insights into the evolutionary history of Acanthamoeba, further illuminating the degree of genetic separation between significant taxonomic units within the genus, perhaps eventually elucidating what constitutes a species of Acanthamoeba.

Beschreibung: We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19–22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to ±7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.

Beschreibung: We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high altitude Special Sensor Microwave Imager/Sounder channels 19–22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Same channel, there is 1.2 K in average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Same channel, there is 1.3 K in average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies causing up to ± 7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.