255 research outputs found
Plasmid encoding matrix protein of vesicular stomatitis viruses as an antitumor agent inhibiting rat glioma growth in situ
Aim: Oncolytic effect of vesicular stomatitis virus (VSV) has been proved previously. Aim of the study is to investigate glioma inhibition effect of Matrix (M) protein of VSV in situ. Materials and Methods: A recombinant plasmid encoding VSV M protein (PM) was genetically engineered, and then transfected into cultured C6 gliomas cells in vitro. C6 transfected with Liposome-encapsulated PM (LEPM) was implanted intracranially for tumorigenicity study. In treatment experiment, rats were sequentially established intracranial gliomas with wild-typed C6 cells, and accepted LEPM injection intravenously. Possible mechanism of M protein was studied by using Hoechst staining, PI-stained flow cytometric analysis, TUNEL staining and CD31 staining. Results: M protein can induce generous gliomas lysis in vitro. None of the rats implanted with LEPM-treated cells developed any significant tumors, whereas all rats in control group developed tumors. In treatment experiment, smaller tumor volume and prolonged survival time was found in the LEPM-treated group. Histological studies revealed that possible mechanism were apoptosis and anti-angiogenesis. Conclusion: VSV-M protein can inhibit gliomas growth in vitro and in situ, which indicates such a potential novel biotherapeutic strategy for glioma treatment.Π¦Π΅Π»Ρ: ΠΈΠ·ΡΡΠΈΡΡ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΌΠ°ΡΡΠΈΠΊΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΠΈΠ½Π° (Π ΠΏΡΠΎΡΠ΅ΠΈΠ½Π°) Π²ΠΈΡΡΡΠ° Π²Π΅Π·ΠΈΠΊΡΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΡΡΠΎΠΌΠ°ΡΠΈΡΠ° (ΠΠΠ‘) ΡΠ³Π½Π΅ΡΠ°ΡΡ ΡΠΎΡΡ Π³Π»ΠΈΠΎΠΌΡ
in situ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ: ΡΠΊΠΎΠ½ΡΡΡΡΠΈΡΠΎΠ²Π°Π½Π° ΡΠ΅ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Π½ΡΠ½Π°Ρ ΠΏΠ»Π°Π·ΠΌΠΈΠ΄Π°, ΠΊΠΎΠ΄ΠΈΡΡΡΡΠ°Ρ Π ΠΏΡΠΎΡΠ΅ΠΈΠ½ ΠΠΠ‘, ΠΊΠΎΡΠΎΡΠ°Ρ Π·Π°ΡΠ΅ΠΌ Π±ΡΠ»Π°
ΡΡΠ°Π½ΡΡΠ΅ΡΠΈΡΠΎΠ²Π°Π½Π° Π² ΠΊΡΠ»ΡΡΠΈΠ²ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΊΠ»Π΅ΡΠΊΠΈ Π³Π»ΠΈΠΎΠΌΡ Π‘6 in. ΠΠ»Π΅ΡΠΊΠΈ Π³Π»ΠΈΠΎΠΌΡ Π‘6, ΡΡΠ°Π½ΡΡΠ΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΈΠ½ΠΊΠ°ΠΏΡΡΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ
Π² Π»ΠΈΠΏΠΎΡΠΎΠΌΡ Π ΠΏΡΠΎΡΠ΅ΠΈΠ½ΠΎΠΌ (ΠΠΠΠ), ΠΈΠΌΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π»ΠΈ ΠΈΠ½ΡΡΠ°ΠΊΡΠ°Π½ΠΈΠ°Π»ΡΠ½ΠΎ Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΡΠΌΠΎΡΠΎΠ³Π΅Π½Π½ΠΎΡΡΠΈ. Π ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ΅ ΠΊΡΡΡΠ°ΠΌ
Ρ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΈΠ½ΡΡΠ°ΠΊΡΠ°Π½ΠΈΠ°Π»ΡΠ½ΠΎ Π³Π»ΠΈΠΎΠΌΠΎΠΉ Π‘6 (ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΉ ΡΡΠ°ΠΌΠΌ) Π²Π½ΡΡΡΠΈΠ²Π΅Π½Π½ΠΎ Π²Π²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΠΠΠ. ΠΠΏΠΎΠΏΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅
Π ΠΏΡΠΎΡΠ΅ΠΈΠ½Π° Π½Π° ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΡΠ΅ ΠΊΠ»Π΅ΡΠΊΠΈ ΠΈΠ·ΡΡΠ°Π»ΠΈ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ»ΡΠΎΡΠ΅ΡΡΠ΅Π½ΡΠ΅Π½ΡΠ½ΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΠΊΠΎΠΏΠΈΠΈ (ΠΎΠΊΡΠ°ΡΠΈΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎ Π₯Π΅Ρ
ΡΡΡ),
ΠΏΡΠΎΡΠΎΡΠ½ΠΎΠΉ ΡΠΈΡΠΎΠΌΠ΅ΡΡΠΈΠΈ (ΠΎΠΊΡΠ°ΡΠΈΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠΏΠΈΠ΄ΠΈΡΠΌΠΎΠΌ ΠΉΠΎΠ΄ΠΈΠ΄ΠΎΠΌ), TUNEL Π²Π°ΡΠΊΡΠ»ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π»ΠΈ Π³ΠΈΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ ΠΈ Π²Π°ΡΠΊΡΠ»ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π»ΠΈ Π³ΠΈΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ ΠΈ
ΠΈΠΌΠΌΡΠ½ΠΎΠ³ΠΈΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ Π°Π½ΡΠΈ-CD31 ΠΌΠΎΠ½ΠΎΠΊΠ»ΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π°Π½ΡΠΈΡΠ΅Π». 31 ΠΌΠΎΠ½ΠΎΠΊΠ»ΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π°Π½ΡΠΈΡΠ΅Π». 31 ΠΌΠΎΠ½ΠΎΠΊΠ»ΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π°Π½ΡΠΈΡΠ΅Π». Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: Π ΠΏΡΠΎΡΠ΅ΠΈΠ½ ΠΌΠΎΠΆΠ΅Ρ ΠΈΠ½Π΄ΡΡΠΈΡΠΎΠ²Π°ΡΡ
Π»ΠΈΠ·ΠΈΡ ΠΊΠ»Π΅ΡΠΎΠΊ Π³Π»ΠΈΠΎΠΌΡ in. ΠΠΈ Ρ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΆΠΈΠ²ΠΎΡΠ½ΠΎΠ³ΠΎ Ρ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΠΊΠ»Π΅ΡΠΊΠ°ΠΌΠΈ Π³Π»ΠΈΠΎΠΌΡ, ΠΎΠ±ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΌΠΈ ΠΠΠΠ,
Π½Π΅ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π»ΠΈ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°Π·ΠΌΠ΅ΡΠ°, ΡΠΎΠ³Π΄Π° ΠΊΠ°ΠΊ Ρ Π²ΡΠ΅Ρ
ΠΊΡΡΡ ΠΈΠ· ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½ΠΎΠΉ Π³ΡΡΠΏΠΏΡ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ ΡΠ°Π·Π²ΠΈΠ²Π°Π»ΠΈΡΡ. Π Π³ΡΡΠΏΠΏΠ΅
ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
, ΠΊΠΎΡΠΎΡΡΠΌ Π²Π²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΠΠΠ, ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ Π±ΡΠ»ΠΈ ΠΌΠ΅Π½ΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ΅ΠΌΠ° ΠΈ ΠΎΡΠΌΠ΅ΡΠ°Π»ΠΈ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΆΠΈΠ·Π½ΠΈ
ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π ΠΏΡΠΎΡΠ΅ΠΈΠ½ ΠΏΡΠΎΡΠ²Π»ΡΠ΅Ρ Π°Π½ΡΠΈΠ°Π½Π³ΠΈΠΎΠ³Π΅Π½Π½ΡΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π° ΠΈ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡΡ ΠΈΠ½Π΄ΡΡΠΈΡΠΎΠ²Π°ΡΡ Π°ΠΏΠΎΠΏΡΠΎΠ·.
ΠΡΠ²ΠΎΠ΄Ρ: Π ΠΏΡΠΎΡΠ΅ΠΈΠ½ ΠΠΠ‘ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΡΠ΅Ρ ΡΠΎΡΡ Π³Π»ΠΈΠΎΠΌΡ in ΠΈ in. ΠΠ° ΡΡΠΎΠΉ ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ Π½ΠΎΠ²Π°Ρ
Π±ΠΈΠΎΡΠ΅ΡΠ°ΠΏΠ΅Π²ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡ Π΄Π»Ρ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π³Π»ΠΈΠΎΠΌΠ°ΠΌΠΈ
Bose-Einstein condensation in multilayers
The critical BEC temperature of a non interacting boson gas in a
layered structure like those of cuprate superconductors is shown to have a
minimum , at a characteristic separation between planes . It is
shown that for , increases monotonically back up to the ideal
Bose gas suggesting that a reduction in the separation between planes,
as happens when one increases the pressure in a cuprate, leads to an increase
in the critical temperature. For finite plane separation and penetrability the
specific heat as a function of temperature shows two novel crests connected by
a ridge in addition to the well-known BEC peak at associated with the
3D behavior of the gas. For completely impenetrable planes the model reduces to
many disconnected infinite slabs for which just one hump survives becoming a
peak only when the slab widths are infinite.Comment: Four pages, four figure
Dynamic Properties of Purkinje Cells Having Different Electrophysiological Parameters: A Model Study
Simple spikes and complex spikes are two distinguishing features in neurons of the cerebellar
cortex; the motor learning and memory processes are dependent on these firing patterns.
In our research, the detailed firing behaviors of Purkinje cells were investigated using a
computer compartmental neuronal model. By means of application of numerical stimuli,
the abundant dynamical properties involved in the multifarious firing patterns, such as the
Max-Min potentials of each spike and period-adding/period-doubling bifurcations, appeared.
Neuronal interspike interval (ISI) diagrams, frequency diagrams, and current-voltage diagrams
for different ions were plotted. Finally, Poincare mapping was used as a theoretical method
to strongly distinguish timing of the above firing patterns. Our simulation results indicated
that firing of Purkinje cells changes dynamically depending on different electrophysiological
parameters of these neurons, and the respective properties may play significant roles in the
formation of the mentioned characteristics of dynamical firings in the coding strategy for
information processing and learning.ΠΠ΅Π½Π΅ΡΠ°ΡΡΡ ΠΏΡΠΎΡΡΠΈΡ
ΡΠ° ΡΠΊΠ»Π°Π΄Π½ΠΈΡ
ΠΏΠΎΡΠ΅Π½ΡΡΠ°Π»ΡΠ² Π΄ΡΡ Ρ ΡΠΏΠ΅ΡΠΈΡΡΡ-
Π½ΠΎΡ Π²Π»Π°ΡΡΠΈΠ²ΡΡΡΡ Π½Π΅ΠΉΡΠΎΠ½ΡΠ² ΠΌΠΎΠ·ΠΎΡΠΊΠΎΠ²ΠΎΡ ΠΊΠΎΡΠΈ; ΠΌΠΎΡΠΎΡΠ½Π΅
Π½Π°Π²ΡΠ°Π½Π½Ρ Ρ ΠΏΡΠΎΡΠ΅ ΡΠΈ ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΠΏΠ°ΠΌβΡΡΡ Π·Π° Π»Π΅ΠΆΠ°ΡΡ
Π²ΡΠ΄ Π³Π΅Π½Π΅ΡΠ°ΡΡΡ Π΄Π°Π½ΠΈΡ
ΠΏΠ°ΡΠ΅ΡΠ½ΡΠ² ΡΠΎΠ·ΡΡΠ΄Ρ. Π Π½Π°ΡΡΠΉ ΡΠΎΠ±ΠΎΡΡ
ΠΌΠΈ ΠΏΡΠΎΠ²Π΅Π»ΠΈΠ΄Π΅ΡΠ°Π»ΡΠ½Π΅ Π΄ΠΎ ΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΏΡΠΎΡΠ΅ ΡΡΠ² Π³Π΅Π½Π΅ΡΠ°ΡΡΡ
ΡΠΌΠΏΡΠ»ΡΡΠ½ΠΎΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΊΠ»ΡΡΠΈΠ½Π°ΠΌΠΈ ΠΡΡΠΊΡΠ½βΡ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ
ΠΊΠΎΠΌΠΏΠ°ΡΡΠΌΠ΅Π½ΡΠ½ΠΎΡ (Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈ ΡΠΎΠΌΡ) ΠΌΠΎΠ΄Π΅Π»Ρ Π½Π΅ΠΉΡΠΎΠ½Π°. Π
ΡΠΌΠΎΠ²Π°Ρ
ΠΏΡΠΈΠΊΠ»Π°Π΄Π°Π½Π½Ρ ΠΎΡΠΈΡΡΠΎΠ²Π°Π½ΠΈΡ
ΡΡΠΈΠΌΡΠ»ΡΠ² Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠΎΠ²Π°Π½ΠΎΠ³ΠΎ
Π½Π΅ΠΉΡΠΎΠ½Π° ΠΏΡΠΎΡΠ²Π»ΡΠ²ΡΡ Π±Π°Π³Π°ΡΠΈΠΉ Π½Π°Π±ΡΡ Π΄ΠΈΠ½Π°ΠΌΡΡΠ½ΠΈΡ
Π²Π»Π°ΡΡΠΈΠ²ΠΎΡΡΠ΅ΠΉ, ΡΠΎ Π·ΡΠΌΠΎΠ²Π»ΡΠ²Π°Π»ΠΎ Π³Π΅Π½Π΅ΡΠ°ΡΡΡ ΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΠ½ΠΈΡ
ΡΠΎΠ·ΡΡΠ΄Π½ΠΈΡ
ΠΏΠ°ΡΠ΅ΡΠ½ΡΠ²; ΡΠ΅ Π²ΡΠ΄Π±ΠΈΠ²Π°Π»ΠΎ ΡΡ Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΈΡ
Π΄ΡΠ°Π³ΡΠ°ΠΌΠ°Ρ
ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΈΡ
/ΠΌΡΠ½ΡΠΌΠ°Π»ΡΠ½ΠΈΡ
ΠΏΠΎΡΠ΅Π½ΡΡΠ°Π»ΡΠ² Π΄Π»Ρ
ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΡΠΊΡ ΡΠ° ΠΏΠΎΡΠ²Ρ Π±ΡΡΡΡΠΊΠ°ΡΡΠΉ ΡΠ· ΡΠ΅Π½ΠΎΠΌΠ΅Π½Π°ΠΌΠΈ Π΄ΠΎΠ΄Π°Π½Π½Ρ
Π°Π±ΠΎ ΠΏΠΎΠ΄Π²ΠΎΡΠ½Π½Ρ ΠΏΠ΅ΡΡΠΎΠ΄ΡΠ². ΠΡΠ»ΠΈ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π°Π½Ρ Π΄ΡΠ°Π³ΡΠ°ΠΌΠΈ
ΠΌΡΠΆΡΠΌΠΏΡΠ»ΡΡΠ½ΠΈΡ
ΡΠ½ΡΠ΅ΡΠ²Π°Π»ΡΠ², Π·Π½Π°ΡΠ΅Π½Ρ ΡΠ°ΡΡΠΎΡΠΈ ΡΠ° Π·Π°Π»Π΅ΠΆΠ½ΠΎΡΡΠ΅ΠΉ
ΡΡΡΡΠΌβΠΏΠΎΡΠ΅Π½ΡΡΠ°Π» Π΄Π»Ρ ΡΡΠ·Π½ΠΈΡ
ΡΠΎΠ½ΡΠ². ΠΠ°ΡΠ΅ΡΡΡ, ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π°
ΠΌΠ°ΠΏ ΠΡΠ°Π½ΠΊΠ°ΡΠ΅ Π±ΡΠ»Π° Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π° ΡΠΊ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ½ΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄
Π΄Π»Ρ ΠΏΠ΅ΡΠ΅ΠΊΠΎΠ½Π»ΠΈΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΠ΅Π½ΡΡΠ°ΡΡΡ ΡΠ°ΡΠΎΠ²ΠΈΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ
Π·Π°Π·Π½Π°ΡΠ΅Π½ΠΈΡ
Π²ΠΈΡΠ΅ ΡΠΎΠ·ΡΡΠ΄Π½ΠΈΡ
ΠΏΠ°ΡΠ΅ΡΠ½ΡΠ². Π―ΠΊ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π½Π°ΡΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ, ΡΠΎΠ·ΡΡΠ΄Π½Π° Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ
ΠΊΠ»ΡΡΠΈΠ½ ΠΡΡΠΊΡΠ½βΡ Π΄ΠΈΠ½Π°ΠΌΡΡΠ½ΠΎ Π·ΠΌΡΠ½ΡΡΡΡΡΡ Π·Π°Π»Π΅ΠΆΠ½ΠΎ Π²ΡΠ΄ Π²Π°ΡΡΠ°ΡΡΡ
Π΅Π»Π΅ΠΊΡΡΠΎΡΡΠ·ΡΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ² ΡΠΈΡ
Π½Π΅ΠΉΡΠΎΠ½ΡΠ², Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½Ρ
Π²Π»Π°ΡΡΠΈΠ²ΠΎΡΡΡ ΠΌΠΎΠΆΡΡΡ Π²ΡΠ΄ΡΠ³ΡΠ°Π²Π°ΡΠΈ ΡΡΡΠΎΡΠ½Ρ ΡΠΎΠ»Ρ Ρ ΡΠΎΡΠΌΠ°ΡΡΡ
Π·Π³Π°Π΄Π°Π½ΠΈΡ
Π²ΠΈΡΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π΄ΠΈΠ½Π°ΠΌΡΡΠ½ΠΈΡ
ΡΠΎΠ·ΡΡΠ΄ΡΠ², ΡΠΎ
ΠΌΠ°ΡΡΡ Π²ΡΠ΄Π½ΠΎΡΠ΅Π½Π½Ρ Π΄ΠΎ ΡΡΡΠ°ΡΠ΅Π³ΡΡ ΠΊΠΎΠ΄ΡΠ²Π°Π½Π½Ρ Π² ΠΏΠ΅ΡΠ΅Π±ΡΠ³Ρ
ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ ΡΠ° ΠΏΡΠΎΡΠ΅ΡΡΠ² Π½Π°Π²ΡΠ°Π½Π½Ρ
Relation between flux formation and pairing in doped antiferromagnets
We demonstrate that patterns formed by the current-current correlation
function are landmarks which indicate that spin bipolarons form in doped
antiferromagnets. Holes which constitute a spin bipolaron reside at opposite
ends of a line (string) formed by the defects in the antiferromagnetic spin
background. The string is relatively highly mobile, because the motion of a
hole at its end does not raise extensively the number of defects, provided that
the hole at the other end of the line follows along the same track. Appropriate
coherent combinations of string states realize some irreducible representations
of the point group C_4v. Creep of strings favors d- and p-wave states. Some
more subtle processes decide the symmetry of pairing. The pattern of the
current correlation function, that defines the structure of flux, emerges from
motion of holes at string ends and coherence factors with which string states
appear in the wave function of the bound state. Condensation of bipolarons and
phase coherence between them puts to infinity the correlation length of the
current correlation function and establishes the flux in the system.Comment: 5 pages, 6 figure
Another Two Dark Energy Models Motivated from Karolyhazy Uncertainty Relation
The Krolyhzy uncertainty relation
indicates that there exists the minimal detectable cell over the
region in Minkowski spacetime. Due to the energy-time uncertainty
relation, the energy of the cell can not be less .
Then we get a new energy density of metric fluctuations of Minkowski spacetime
as . Motivated by the energy density, we propose two new dark
energy models. One model is characterized by the age of the universe and the
other is characterized by the conformal age of the universe. We find that in
the two models, the dark energy mimics a cosmological constant in the late
time.Comment: 10 pages, 5 figures, References are adde
DDW Order and its Role in the Phase Diagram of Extended Hubbard Models
We show in a mean-field calculation that phase diagrams remarkably similar to
those recently proposed for the cuprates arise in simple microscopic models of
interacting electrons near half-filling. The models are extended Hubbard models
with nearest neighbor interaction and correlated hopping. The underdoped region
of the phase diagram features density-wave (DDW) order. In a
certain regime of temperature and doping, DDW order coexists with
antiferromagnetic (AF) order. For larger doping, it coexists with
superconductivity (DSC). While phase diagrams of this form
are robust, they are not inevitable. For other reasonable values of the
coupling constants, drastically different phase diagrams are obtained. We
comment on implications for the cuprates.Comment: 7 pages, 3 figure
Pinned Balseiro-Falicov Model of Tunneling and Photoemission in the Cuprates
The smooth evolution of the tunneling gap of Bi_2Sr_2CaCu_2O_8 with doping
from a pseudogap state in the underdoped cuprates to a superconducting state at
optimal and overdoping, has been interpreted as evidence that the pseudogap
must be due to precursor pairing. We suggest an alternative explanation, that
the smoothness reflects a hidden SO(N) symmetry near the (pi,0) points of the
Brillouin zone (with N = 3, 4, 5, or 6). Because of this symmetry, the
pseudogap could actually be due to any of a number of nesting instabilities,
including charge or spin density waves or more exotic phases. We present a
detailed analysis of this competition for one particular model: the pinned
Balseiro-Falicov model of competing charge density wave and (s-wave)
superconductivity. We show that most of the anomalous features of both
tunneling and photoemission follow naturally from the model, including the
smooth crossover, the general shape of the pseudogap phase diagram, the
shrinking Fermi surface of the pseudogap phase, and the asymmetry of the
tunneling gap away from optimal doping. Below T_c, the sharp peak at Delta_1
and the dip seen in the tunneling and photoemission near 2Delta_1 cannot be
described in detail by this model, but we suggest a simple generalization to
account for inhomogeneity, which does provide an adequate description. We show
that it should be possible, with a combination of photoemission and tunneling,
to demonstrate the extent of pinning of the Fermi level to the Van Hove
singularity. A preliminary analysis of the data suggests pinning in the
underdoped, but not in the overdoped regime.Comment: 18 pages LaTeX, 26 ps. figure
The Narrative Frame of Daniel: A Literary Assessment
This paper presents a fuzzy multicriteria group decision making approach for evaluating and selecting information systems projects. The inherent subjectiveness and imprecision of the evaluation process is modeled by using linguistic terms characterized by triangular fuzzy numbers. A new algorithm based on the concept of the degree of dominance is developed to avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria decision making. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach
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