Morphogenesis, morphology and men: pattern formation from embryo to mind

In 1952, Alan Turing published his last work on the concept of embryonic morphogenesis, propounding a computational framework for pattern formation within the developing embryo. This concept of morphogenesis and the concept of embryo pattern formation based on chemical diffusion patterns were corroborated with the discovery of the Homeobox or Hox genes. In the following decades, Hox gene research has expanded and is now shown to underlie the variety of morphological novelties that we experience in nature, the patterning of structural aspects of different organs including the brain and also mutant animals that may in the future give rise to novel speciation. Turing had the foresight and vision and with his work created the field of computational biology and mathematical modeling in biological systems. In this paper, we will discuss the concept of Hox genes, their role in patterning the embryo, how it relates to Turing’s concept of morphogenesis and what further insights they may provide.     

Metatarsus proximus and digital divergence. Association with intermetatarsal neuromas

A retrospective radiologic study was performed to determine whether there is an increased finding of metatarsus proximus and digital divergence in patients with a confirmed diagnosis of intermetatarsal neuroma when compared with an asymptomatic group. The study included 48 patients with pathologic confirmation of neuroma and 100 asymptomatic patients. Results of the study revealed no statistical relationship between the radiologic findings of metatarsus proximus and digital divergence and the physical occurrence of neuromas. An unexpected finding was an increased intermetatarsal angle of the affected interspace in the neuroma group.     

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia.

The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is − 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient − 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29).     

Biochemistry and biomechanics of healing tendon: Part I. Effects of rigid plaster casts and functional casts

OBJECTIVE: The state psychiatric hospital is experiencing an increase in medically sick and aging patients who die of natural causes while hospitalized. This study explored the "medicalization" of the state hospital by examining the prevalence of medical illness and its relationship with psychiatric illness and age among state hospital psychiatric inpatients who died of natural causes--deaths that were not accidents, homicides, or suicides. METHODS: A total of 179 inpatients who died of natural causes at Western State Hospital in Washington State between 1989 and 1994 were studied retrospectively through case file review. Their demographic and institutional characteristics and psychiatric diagnoses were compared with those of others treated at the hospital (N=9,258). The medical diagnoses of patients who died were analyzed by age and psychiatric condition. RESULTS: The patients who died were much older than the other patients treated during the study period. Two-thirds of those who died had organic mental disorders, mostly dementia, whereas only a fifth of the other patients had these disorders. The patients who died had a mean of eight physical illnesses, with a range from none to 21. Circulatory and respiratory conditions were most prevalent, affecting half to two-thirds of patients; these conditions had high rates of comorbidity with organic mental disorders. CONCLUSIONS: The characteristics of the state hospital population and the services provided are shifting in response to mental health reform and new policies on patient self-determination. Increased emphasis on medical care added to traditional psychiatric services will require increased financial and personnel resources.