Letter from the acting Secretary of War, transmitting information relative to the claims of the State of Massachusetts for payment of the expenses of the militia ordered out by the executive authority of the state, during the late war

15th Congress, 1st session, 1817-1818, House. Doc. 81. February 20, 1817. Read, and ordered to lie upon the table.

Letter from the Secretary of War, transmitting a return of the arms and military stores furnished to the respective states, under the provisions of the law of one thousand eight hundred and twelve

At head of title: [107]. 15th Congress, 1st session, 1817-1818. House. February 20, 1818. Read, and ordered to lie upon the table.

Message from the President of the United States, transmitting a report of the Secretary of State, in obedience to a resolution of the thirteenth inst.

Full Title: 47. Message from the President of the United States, transmitting a report of the Secretary of State, in obedience to a resolution of the thirteenth inst. "requesting the President to lay before this House such documents relative to the Russian mediation as in his opinion it may not be improper to communicate." United States,13th Congress, 2d session, 1813-1814. House. Doc. no. 35. January 18, 1814. Ordered to lie on the table. One letter in French with English translation Printed by Roger C. Weightman

Message from the President of the United States, transmitting copies of a letter from the British Secretary of State for Foreign Affairs, to the Secretary of State, with the answer of the latter

January 6, 1814. Ordered to lie on the table. -------------------------------------------------------------------------------- At head of title: [22]. -------------------------------------------------------------------------------- 13th Congress, 2nd Session, House. Doc. 22. Printed by Roger C. Weightman

Returning to Play after Concussion: A Phenomenological Study

This study examines and describes athletes’ felt sense of readiness returning to play following a concussion. Analyses of the interviews yielded a description of each participant’s experiences with concussions. Descriptions of this phenomenon generated by informants provide a detailed account of the unique issues athletes face when returning to play following a concussion. Participants’ descriptions highlight that in order to play, an athlete knows that he/she ought to be emotionally and physically ready to play. However, the athletes in this study believe that there is not an actual test that can “prove” this and that they can choose to lie and/or cheat the tests to return to play while they are still symptomatic. Athletes, parents, coaches, and trainers will benefit from learning to be better educated on the severity of concussions, concussion detection, assessment and the serious health consequences that can result from playing with a concussion.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.