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How do I find U.S. Census data?
Visit census.gov (http://census.gov/) to browse quality information current and historical facts and figures about Americas people, places, and economy. An additional tool offered by the U.S. Census Bureau, the data.census.gov (https://data.census.gov/) is a platform designed to help users access demographic and economic data digitally. The Census Academy (https://www.census.gov/data/academy.html) has many short tutorials for searching this website. For more information, consult the Census Bureau's FAQ (https://ask.census.gov/) , or schedule an appointment with an LSU Libraries Librarian here (https://lsu.libcal.com/appointments/caple) . The census on microfilm LSU owns is limited. The only states in this collection include: Alabama, Arkansas, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, Pennsylvania, South Carolina, Tennessee, Texas, Virginia (and scattered census material for West Virginia). Information on other states may be located at the National Archives (http://www.archives.gov/research/start/index.html) in Washington D.C., the regional branches (http://www.archives.gov/locations/index.html) of the National Archives, as well as the Bluebonnet Regional Branch of the East Baton Rouge Parish Library (https://www.ebrpl.com/) . The collection of census material at LSU Libraries includes population schedules, agricultural census data, lists of manufactures, slave schedules, passenger lists for the port of New Orleans covering 1853-1899, social statistics, and scattered information concerning Defective, Dependent and Delinquent Classes. Other material that may be helpful for researching archives for genealogy information include Records of the Diocese of Louisiana and the "Floridas", New Orleans City Directories for years 1805-1945, New Orleans Christian Advocate concerning Marriage and Death Notices, Military Academy Letters, and Indian Affairs, just to name a few. If you would like to access any of these materials, contact libgovdocs@lsu.edu . Answered by: Kendall Caple

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Does soak time influence the effect of artificial light on catch efficiency in snow crab (Chionoecetes opilio) pot fishery?
In the Barents Sea commercial snow crab (Chionoecetes opilio) fishery, an increase in catch efficiency of the conical pots is important for the profitability of the industry. Light emitting diodes (LEDs) have previously been tested for increasing catch efficiency of the snow crab pots. These earlier experiments have shown varying results ranging from large increase in snow crab catches to no significant effect. These experiments have used different pot soaking times; however, the soaking time might affect the impact of LEDs on catch efficiency. In commercial snow crab fishery, the pot soak time is varying which has not been considered in earlier experiments testing the effect of LEDs. Therefore, this study examined whether pot soaking time can explain the observed differences in relative catch efficiency of snow crab pots with and without LEDs with soak times ranging from 2 to 14 days in the Barents Sea snow crab fishery. For target sizes of snow crab (≥95 mm carapace width), results indicated an increase in catch efficiency between 10 and 30% for pots with LEDs with exception of one experiment using six days soak time. However, experimental results were subjected to large uncertainties and, except from one experiment with five days soak time, the estimated increases were nonsignificant. Furthermore, the pot soak time was not found to impact the effect of white LEDs on capture efficiency.
Regulatory element in fibrin triggers tension-activated transition from catch to slip bonds
Fibrin formation and mechanical stability are essential in thrombosis and hemostasis. To reveal how mechanical load impacts fibrin, we carried out optical trap-based single-molecule forced unbinding experiments. The strength of noncovalent A:a knob-hole bond stabilizing fibrin polymers first increases with tensile force (catch bonds) and then decreases with force when the force exceeds a critical value (slip bonds). To provide the structural basis of catch–slip-bond behavior, we analyzed crystal structures and performed molecular modeling of A:a knob-hole complex. The movable flap (residues γ 295 to γ 305) containing the weak calcium-binding site γ 2 serves as a tension sensor. Flap dissociation from the B domain in the γ -nodule and translocation to knob ‘A’ triggers hole ‘a’ closure, resulting in the increase of binding affinity and prolonged bond lifetimes. The discovery of biphasic kinetics of knob-hole bond rupture is quantitatively explained by using a theory, formulated in terms of structural transitions in the binding pocket between the low-affinity (slip) and high-affinity (catch) states. We provide a general framework to understand the mechanical response of protein pairs capable of tension-induced remodeling of their association interface. Strengthening of the A:a knob-hole bonds at 30- to 40-pN forces might favor formation of nascent fibrin clots subject to hydrodynamic shear in vivo.
A new method for predicting wind-driven rain catch ratios on building facades in urban residential areas using machine learning models
The distribution of wind-driven rain on building facades significantly affects their thermal performance and durability. Accurately and efficiently predicting the wind-driven rain catch ratio on wall surfaces is crucial for building performance evaluation. This study proposes a novel computational approach to rapidly predict the wind-driven rain catch ratio on urban building facades. A predictive model was developed using extensive numerical simulations combined with machine learning algorithms. Specifically, the model replaces traditional numerical simulations by learning the influence of wind field characteristics and building geometry on raindrop catch ratios across different sizes. The research results indicate that the machine learning models can effectively substitute conventional simulation methods for wind-driven rain predictions. Notably, the Artificial Neural Network model achieved a prediction accuracy comparable to numerical simulations (RMSE: 0.009, MAE: 0.006) while being over 300 times faster. The inlet wind speed at roof height emerged as the most influential feature, and the model exhibited strong generalization performance across varying wind directions. This method is simple, efficient, and well-suited to support wind-driven rain analysis, experimental measurements, and urban energy consumption studies in residential building contexts.