Modern management of cases with premature rupture of membranes

Peer Reviewe

Opening Remarks

Peer Reviewe

Glycosylated hemoglobin (HbA1), glucose tolerance and neonatal outcome in gestational diabetic and non-diabetic mothers

Peer Reviewe

Patho-physiology, clinical relevance of continuous measurement of pH and/or CO2 in the fetus

Peer Reviewe

Avoiding moving obstacles

To successfully move our hand to a target, we must consider how to get there without hitting surrounding objects. In a dynamic environment this involves being able to respond quickly when our relationship with surrounding objects changes. People adjust their hand movements with a latency of about 120 ms when the visually perceived position of their hand or of the target suddenly changes. It is not known whether people can react as quickly when the position of an obstacle changes. Here we show that quick responses of the hand to changes in obstacle position are possible, but that these responses are direct reactions to the motion in the surrounding. True adjustments to the changed position of the obstacle appeared at much longer latencies (about 200 ms). This is even so when the possible change is predictable. Apparently, our brain uses certain information exceptionally quickly for guiding our movements, at the expense of not always responding adequately. For reaching a target that changes position, one must at some time move in the same direction as the target did. For avoiding obstacles that change position, moving in the same direction as the obstacle is not always an adequate response, not only because it may be easier to avoid the obstacle by moving the other way, but also because one wants to hit the target after passing the obstacle. Perhaps subjects nevertheless quickly respond in the direction of motion because this helps avoid collisions when pressed for time. © 2008 Springer-Verlag

Phytoplankton of the middle Caspian Sea: analysis of changes in the structure of the community over the past decades

Aim.  Analysis  of  changes  in  quantitative  and  structural  indicators  of phytoplankton in the western and central part of the middle Caspian Sea over the past decades, including according to remote sensing data.Material  and  Methods.  The  data  was  obtained  in  2004–2008  and 2019–2022 at different seasons of the year at 40 stations in the central and western part of the middle Caspian Sea. Phytoplankton samples were taken  from  4–6 layers.  A  total  of  300 samples  of phytoplankton  were analyzed. Determination of species and counting of the number of cells was carried out under the “Ergaval” light microscope. WoRMS guided matters of nomenclature.Results. The spring phytoplankton is dominated by the species traditional for the Caspian Sea – Cyclotella caspia diatoms and Prorocetrum micans dinoflagellates. The maximum abundance of C. caspia (5.0 x 104 cell/l) was recorded at depths of 35–40 m. In summer, the maximum phytoplankton biomass (2.2 g/m3) was noted in the seasonal thermocline and was formed due to small flagellates and dinoflagellates. Phytoplankton biomass during winter blooms reached 4.5–5.0 g/m3 and was determined by the development of diatoms (up to 96–99%). Winter blooms were formed by the diatom species traditional for the sea, as well as by the Pseudo‐nitschia seriata and Cerataulina pelagica species.Conclusion. It is shown that in the middle Caspian Sea, the winter and autumn seasons are characterized by a highly productive status. In January–February, periodic blooms of diatoms are observed, as confirmed by satellite data and in situ observations. In summer, phytoplankton biomass is determined by the mass development of dinoflagellates in the seasonal thermocline layer, which has not been recorded by remote methods. In the autumn phytoplankton the main role is played by the diatom component, represented mainly by alien species

Grasping Kinematics from the Perspective of the Individual Digits: A Modelling Study

Grasping is a prototype of human motor coordination. Nevertheless, it is not known what determines the typical movement patterns of grasping. One way to approach this issue is by building models. We developed a model based on the movements of the individual digits. In our model the following objectives were taken into account for each digit: move smoothly to the preselected goal position on the object without hitting other surfaces, arrive at about the same time as the other digit and never move too far from the other digit. These objectives were implemented by regarding the tips of the digits as point masses with a spring between them, each attracted to its goal position and repelled from objects' surfaces. Their movements were damped. Using a single set of parameters, our model can reproduce a wider variety of experimental findings than any previous model of grasping. Apart from reproducing known effects (even the angles under which digits approach trapezoidal objects' surfaces, which no other model can explain), our model predicted that the increase in maximum grip aperture with object size should be greater for blocks than for cylinders. A survey of the literature shows that this is indeed how humans behave. The model can also adequately predict how single digit pointing movements are made. This supports the idea that grasping kinematics follow from the movements of the individual digits