96 research outputs found

    FAKTOR-FAKTOR YANG MEMPENGARUHI PERILAKU MEROKOK SISWA KELAS VII MTS TPI SILAU DUNIA KECAMATAN SILAU KAHEAN KABUPATEN SIMALUNGUN

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    Abstract Smoking behavior is a common phenomenon in Indonesian society. Currently smoking has penetrated into the lives of school children starting from high school, junior high school and even some elementary school children have smoked. The prevalence rate of adolescent smokers in North Sumatra Province at the age of 10-19 years is 27.28% of the total population. There are many factors that influence smoking habits among students, including the influence of the parents' environment, the influence of friends, personality factors and because of advertising. This research was conducted at MTS TPI Silau Dunia, Silau Kahean District, Simalungun Regency. This type of research is a quantitative study using a cross sectional. The population determined in this study were all seventh grade students of MTS TPI Silau Dunia. The number of available samples is 58 samples taken by purposive sampling from the population. The results of the bivariate analysis of the influence of knowledge, attitudes, parental control, and peers on smoking behavior obtained p-values of 0.002, 0.001, 0.001, and 0.002, respectively. From the results of the bivariate analysis of p value < (0.05), it can be concluded that there is an influence of knowledge, attitudes, parental control, and peers on the smoking behavior of seventh grade students of MTS TPI Silau Dunia, Silau Kahean District, Simalungun Regency. The need for education from MTS TPI Silau Dunia about substances in cigarettes and the dangers of smoking to health as well as monitoring student behavior, especially students who already smoke

    Π‘ΠΈΠ½Ρ‚Π΅Π· Ρ‚Π° Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½Π° дія 8-Π°ΠΌΡ–Π½ΠΎΠ·Π°ΠΌΡ–Ρ‰Π΅Π½ΠΈΡ… 7-(2-гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-3-мСтилксантину

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    It has been found that natural xanthines, as well as their synthetic analogs, possess the diuretic effect. Analysis of the literature proves that there is a great opportunity of applying synthetic derivatives of N-methylated xanthines as potential diuretics.Aim. To develop preparative methods of the synthesis of 8-aminosubstituted of 7-(2-hydroxy-3-p-metoxyphenoxypropyl-1)-3-methylxanthine and study their physical, chemical and biological properties. Results. The synthesis of a series of 8-aminosubstituted of 7-(2-hydroxy-3-p-metoxyphenoxypropyl-1)-3-methylxanthine was carried out. According to the results of the biological testing the compounds synthesized belong to the toxicity of class IV. 7-(2-Hydroxy-3-p-methoxyphenoxypropyl-1)-8-(furyl-2-methylamino)-3-methylxanthine xanthine shows the highest diuretic activity, and hence, requires a more in-depth study since it is twice more active than hydrochlorothiazide. It should be emphasized that all compounds synthesized exhibit a marked diuretic effect. Experimental part. 8-Bromo-7-(2-hydroxy-3-p-methoxyphenoxypropyl-1)-3-methylxanthine was obtained by heating 8-bromo-3-methylxanthine with p-methoxyphenoxymethyloxirane in butanol-1 and in the presence of N,N-dimethylbenzylamine. 8-Aminosubstitutied of 7-(2-hydroxy-3-p-metoxyphenoxypropyl-1)-3-methylxanthine was obtained by boiling of bromoalcohol with the primary and secondary amines. The structure of the compounds synthesized was unambiguously confirmed by NMR-spectroscopy. The acute toxicity of the compounds obtained was studied by Kerber method. The study of the diuretic activity of the compounds was carried out using Ye. Berkhin method. Hydrochlorothiazide was used as a reference substance. Conclusions. Simple methods for the synthesis of 8-amino-7- (2-hydroxy-3-p-methoxyphenoxypropyl-1)-3-methylxanthines have been developed. The structure of the compounds synthesized has been confirmed by the method of NMR 1H-spectroscopy. The acute toxicity and the diuretic activity of the compounds obtained have been studied.Π˜Π·Π²Π΅ΡΡ‚Π½ΠΎ, Ρ‡Ρ‚ΠΎ ΠΊΠ°ΠΊ ΠΏΡ€ΠΈΡ€ΠΎΠ΄Π½Ρ‹Π΅ ксантины, Ρ‚Π°ΠΊ ΠΈ ΠΈΡ… синтСтичСскиС Π°Π½Π°Π»ΠΎΠ³ΠΈ ΠΎΠ±Π»Π°Π΄Π°ΡŽΡ‚ диурСтичСским дСйствиСм. Анализ Π΄Π°Π½Π½Ρ‹Ρ… Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ ΡΠ²ΠΈΠ΄Π΅Ρ‚Π΅Π»ΡŒΡΡ‚Π²ΡƒΠ΅Ρ‚ ΠΎ Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ пСрспСктивС использования синтСтичСских ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½Ρ‹Ρ… N-ΠΌΠ΅Ρ‚ΠΈΠ»ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ксантинов Π² качСствС ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… диурСтичСских срСдств. ЦСль Π΄Π°Π½Π½ΠΎΠΉ Ρ€Π°Π±ΠΎΡ‚Ρ‹ – Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚ΠΈΠ²Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² синтСза 8-Π°ΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅Ρ‰Π΅Π½Π½Ρ‹Ρ… 7-(2-гидрокси-3-ΠΏ-мСтоксифСноксипропил-1)-3-мСтилксантина ΠΈ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ ΠΈΡ… Ρ„ΠΈΠ·ΠΈΠΊΠΎ-химичСских ΠΈ биологичСских свойств. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΈ ΠΈΡ… обсуТдСниС. Π‘ΠΈΠ½Ρ‚Π΅Π·ΠΈΡ€ΠΎΠ²Π°Π½ ряд 8-Π°ΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅Ρ‰Π΅Π½Π½Ρ‹Ρ… 7-(2-гидрокси-3-ΠΏ-мСтоксифСноксипропил-1)-3-мСтилксантина. По Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌ биологичСских испытаний синтСзированныС соСдинСния относятся ΠΊ IV классу токсичности. 7-(2-Гидрокси-3-ΠΏ-мСтоксифСноксипропил-1)-8-(Ρ„ΡƒΡ€ΠΈΠ»-2-ΠΌΠ΅Ρ‚ΠΈΠ»Π°ΠΌΠΈΠ½ΠΎ)-3-мСтилксантин ΠΈΠΌΠ΅Π΅Ρ‚ Π½Π°ΠΈΠ±ΠΎΠ»ΡŒΡˆΡƒΡŽ Π΄ΠΈΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ, Π° ΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ доскональноС ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅, ΠΏΠΎΡΠΊΠΎΠ»ΡŒΠΊΡƒ ΠΎΠ½ Π±ΠΎΠ»Π΅Π΅ Ρ‡Π΅ΠΌ Π² 2 Ρ€Π°Π·Π° Π°ΠΊΡ‚ΠΈΠ²Π½Π΅Π΅Β  Π³ΠΈΠ΄Ρ€ΠΎΡ…Π»ΠΎΡ€Ρ‚ΠΈΠ°Π·ΠΈΠ΄Π°. НСобходимо ΠΏΠΎΠ΄Ρ‡Π΅Ρ€ΠΊΠ½ΡƒΡ‚ΡŒ, Ρ‡Ρ‚ΠΎ всС синтСзированныС соСдинСния ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚ Π²Ρ‹Ρ€Π°ΠΆΠ΅Π½Π½ΠΎΠ΅ диурСтичСскоС дСйствиС. Π­ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Π°Ρ Ρ‡Π°ΡΡ‚ΡŒ. 8-Π‘Ρ€ΠΎΠΌ-7-(2-гидрокси-3-ΠΏ-мСтоксифСнокси­пропил-1)-3-мСтилксантин ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½ Π½Π°Π³Ρ€Π΅Π²Π°Π½ΠΈΠ΅ΠΌ 8-Π±Ρ€ΠΎΠΌ-3-мСтилксантина с ΠΏ-мСтоксифСноксимСтилоксираном Π² Π±ΡƒΡ‚Π°Π½ΠΎΠ»Π΅-1 Π² присутствии N,N-Π΄ΠΈΠΌΠ΅Ρ‚ΠΈΠ»Π±Π΅Π½Π·ΠΈΠ»Π°ΠΌΠΈΠ½Π°. 8-АминозамСщСнныС 7-(2-гидрокси-3-ΠΏ-мСтокси­фСноксипропил-1)-3-мСтилксантина синтСзированы ΠΏΡƒΡ‚Π΅ΠΌ кипячСния бромоспирта с ΠΏΠ΅Ρ€Π²ΠΈΡ‡Π½Ρ‹ΠΌΠΈ ΠΈ Π²Ρ‚ΠΎΡ€ΠΈΡ‡Π½Ρ‹ΠΌΠΈ Π°ΠΌΠΈΠ½Π°ΠΌΠΈ. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° синтСзированных соСдинСний ΠΎΠ΄Π½ΠΎΠ·Π½Π°Ρ‡Π½ΠΎ Π΄ΠΎΠΊΠ°Π·Π°Π½Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ПМР-спСктроскопии. ΠžΡΡ‚Ρ€Π°Ρ Ρ‚ΠΎΠΊΡΠΈΡ‡Π½ΠΎΡΡ‚ΡŒ синтСзированных соСдинСний ΠΈΠ·ΡƒΡ‡Π°Π»Π°ΡΡŒ ΠΏΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρƒ ΠšΠ΅Ρ€Π±Π΅Ρ€Π°. Π˜Π·ΡƒΡ‡Π΅Π½ΠΈΠ΅ диурСтичСской активности ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… соСдинСний ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΏΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρƒ Π‘Π΅Ρ€Ρ…ΠΈΠ½Π° Π•. Π‘. Π’ качСствС эталона сравнСния использовали Π³ΠΈΠ΄Ρ€ΠΎΡ…Π»ΠΎΡ€Ρ‚ΠΈΠ°Π·ΠΈΠ΄. Π’Ρ‹Π²ΠΎΠ΄Ρ‹. Π Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Ρ‹ простыС Π² Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ синтСза 8-Π°ΠΌΠΈΠ½ΠΎΠ·Π°ΠΌΠ΅Ρ‰Π΅Π½Π½Ρ‹Ρ… 7-(2-гидрокси-3-ΠΏ-мСтоксифСноксипропил-1)-3-мСтилксантина. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° синтСзированных соСдинСний Π΄ΠΎΠΊΠ°Π·Π°Π½Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ПМР-спСктроскопии. Π˜Π·ΡƒΡ‡Π΅Π½Π° острая Ρ‚ΠΎΠΊΡΠΈΡ‡Π½ΠΎΡΡ‚ΡŒ ΠΈ диурСтичСская Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… вСщСств.Π’Ρ–Π΄ΠΎΠΌΠΎ, Ρ‰ΠΎ як ΠΏΡ€ΠΈΡ€ΠΎΠ΄Π½Ρ– ксантини, Ρ‚Π°ΠΊ Ρ– Ρ—Ρ… синтСтичні Π°Π½Π°Π»ΠΎΠ³ΠΈ Π²ΠΈΡΠ²Π»ΡΡŽΡ‚ΡŒ Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½Ρƒ Π΄Ρ–ΡŽ. Аналіз Π΄Π°Π½ΠΈΡ… Π»Ρ–Ρ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€ΠΈ ΡΠ²Ρ–Π΄Ρ‡ΠΈΡ‚ΡŒ ΠΏΡ€ΠΎ Π·Π½Π°Ρ‡Π½Ρƒ пСрспСктиву використання синтСтичних ΠΏΠΎΡ…Ρ–Π΄Π½ΠΈΡ… N-ΠΌΠ΅Ρ‚ΠΈΠ»ΡŒΠΎΠ²Π°Π½ΠΈΡ… ксантинів Π² якості ΠΏΠΎΡ‚Π΅Π½Ρ†Ρ–ΠΉΠ½ΠΈΡ… Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½ΠΈΡ… засобів. ΠœΠ΅Ρ‚ΠΎΡŽ Π΄Π°Π½ΠΎΡ— Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Ρ” Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ° ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΈΡ… ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ–Π² синтСзу 8-Π°ΠΌΡ–Π½ΠΎΠ·Π°ΠΌΡ–Ρ‰Π΅Π½ΠΈΡ… 7-(2-гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-3-мСтилксантину Ρ‚Π° вивчСння Ρ—Ρ… Ρ„Ρ–Π·ΠΈΠΊΠΎ-Ρ…Ρ–ΠΌΡ–Ρ‡Π½ΠΈΡ… Ρ‚Π° Π±Ρ–ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΈΡ… властивостСй. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ Ρ‚Π° Ρ—Ρ… обговорСння. Π‘ΡƒΠ² синтСзований ряд 8-Π°ΠΌΡ–Π½ΠΎΠ·Π°ΠΌΡ–Ρ‰Π΅Π½ΠΈΡ… 7-(2-гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-3-мСтилксантину. Π—Π° Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌΠΈ Π±Ρ–ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΈΡ… Π²ΠΈΠΏΡ€ΠΎΠ±ΡƒΠ²Π°Π½ΡŒ синтСзовані сполуки Π²Ρ–Π΄Π½ΠΎΡΡΡ‚ΡŒΡΡ Π΄ΠΎ IV класу токсичності. 7-(2-Гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-8-(Ρ„ΡƒΡ€ΠΈΠ»-2-ΠΌΠ΅Ρ‚ΠΈΠ»Π°ΠΌΡ–Π½ΠΎ)-3-мСтилксантин виявляє Π½Π°ΠΉΠ²ΠΈΡ‰Ρƒ Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½Ρƒ Π°ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŒ, Π° ΠΎΡ‚ΠΆΠ΅ ΠΏΠΎΡ‚Ρ€Π΅Π±ΡƒΡ” Π±Ρ–Π»ΡŒΡˆ досконалого вивчСння, ΠΎΡΠΊΡ–Π»ΡŒΠΊΠΈ Π²Ρ–Π½ Π±Ρ–Π»ΡŒΡˆ Π½Ρ–ΠΆ Ρƒ 2 Ρ€Π°Π·ΠΈ Π°ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡˆΠΈΠΉ Π·Π° Π³Ρ–Π΄Ρ€ΠΎΡ…Π»ΠΎΡ€Ρ‚Ρ–Π°Π·ΠΈΠ΄. НСобхідно підкрСслити, Ρ‰ΠΎ всі синтСзовані сполуки Π²ΠΈΡΠ²Π»ΡΡŽΡ‚ΡŒ Π²ΠΈΡ€Π°Π·Π½Ρƒ Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½Ρƒ Π΄Ρ–ΡŽ.Π•ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Π° частина. 8-Π‘Ρ€ΠΎΠΌΠΎ-7-(2-гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-3-мСтилксантин ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π»ΠΈ нагріванням 8-Π±Ρ€ΠΎΠΌΠΎ-3-мСтилксантину Π· ΠΏ-мСтоксифСноксимСтилоксираном Ρƒ Π±ΡƒΡ‚Π°Π½ΠΎΠ»Ρ–-1 Π² присутності N,N-Π΄ΠΈΠΌΠ΅Ρ‚ΠΈΠ»Π±Π΅Π½Π·ΠΈΠ»Π°ΠΌΡ–Π½Ρƒ. 8-АмінозаміщСні 7-(2-гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-3-мСтилксантину синтСзовані ΡˆΠ»ΡΡ…ΠΎΠΌ кип’ятіння бромоспирту Π· ΠΏΠ΅Ρ€Π²ΠΈΠ½Π½ΠΈΠΌΠΈ Ρ‚Π° Π²Ρ‚ΠΎΡ€ΠΈΠ½Π½ΠΈΠΌΠΈ Π°ΠΌΡ–Π½Π°ΠΌΠΈ. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° синтСзованих сполук Π±ΡƒΠ»Π° ΠΎΠ΄Π½ΠΎΠ·Π½Π°Ρ‡Π½ΠΎ Π΄ΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ПМР-спСктроскопії. Гостра Ρ‚ΠΎΠΊΡΠΈΡ‡Π½Ρ–ΡΡ‚ΡŒ синтСзованих сполук Π±ΡƒΠ»Π° Π²ΠΈΠ²Ρ‡Π΅Π½Π° Π·Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠšΠ΅Ρ€Π±Π΅Ρ€Π°. ВивчСння Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½ΠΎΡ— Π΄Ρ–Ρ— ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΈΡ… сполук ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π·Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ Π‘Π΅Ρ€Ρ…Ρ–Π½Π° Π„. Π‘. Π’ якості Π΅Ρ‚Π°Π»ΠΎΠ½Ρƒ порівняння використовували Π³Ρ–Π΄Ρ€ΠΎΡ…Π»ΠΎΡ€Ρ‚Ρ–Π°Π·ΠΈΠ΄. Висновки. Π‘ΡƒΠ»ΠΈ Ρ€ΠΎΠ·Ρ€ΠΎΠ±Π»Π΅Π½Ρ– прості Ρƒ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ– ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ синтСзу 8-Π°ΠΌΡ–Π½ΠΎΠ·Π°ΠΌΡ–Ρ‰Π΅Π½ΠΈΡ… 7-(2-гідрокси-3-ΠΏ-мСтоксифСноксипропіл-1)-3-мСтилксантину. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° синтСзованих сполук Π±ΡƒΠ»Π° Π΄ΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ПМР-спСктроскопії. Π’ΠΈΠ²Ρ‡Π΅Π½Π° гостра Ρ‚ΠΎΠΊΡΠΈΡ‡Π½Ρ–ΡΡ‚ΡŒ Ρ‚Π° Π΄Ρ–ΡƒΡ€Π΅Ρ‚ΠΈΡ‡Π½Π° Π°ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŒ ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΈΡ… Ρ€Π΅Ρ‡ΠΎΠ²ΠΈΠ½

    A Modular Network Architecture Resolving Memory Interference through Inhibition

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    International audienceIn real learning paradigms like pavlovian conditioning, several modes of learning are associated, including generalization from cues and integration of specific cases in context. Associative memories have been shown to be interesting neuronal models to learn quickly specific cases but they are hardly used in realistic applications because of their limited storage capacities resulting in interferences when too many examples are considered. Inspired by biological considerations, we propose a modular model of associative memory including mechanisms to manipulate properly multimodal inputs and to detect and manage interferences. This paper reports experiments that demonstrate the good behavior of the model in a wide series of simulations and discusses its impact both in machine learning and in biological modeling

    Development of an autonomous IoT-based drone for campus security

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    In recent years, drone technology has gained popularity across the world because of its numerous applications, particularly in security and surveillance. This technology can be further revolutionized with the deployment of Industrial Revolution 4.0 Technology. This paper discusses the development of an IoT-based autonomous drone for more comprehensive campus security and surveillance system. The drone is featured with the capability of conducting a fully autonomous aerial surveillance, being the first responder in emergencies, streaming video while flying, avoiding obstacles, following a target and communicating with the current IoT based UTM’s security patrolling system for data transfer and drone control. This has been accomplished by using the open source ArduPilot software, Pixhawk flight controller along with Dronekit python library installed on a Raspberry Pi 4. The findings show that the actual performance of the designed drone is fairly similar to the simulation results. The drone has successfully performed autonomous navigation to incident location with 1 to 2 meter accuracy as well as follow-me mode. The cellular technology utilized for drone communication also is more robust and provides promising solution to overcome short operation range and interference

    Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

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    The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain

    Causes of death among infants and children in the Child Health and Mortality Prevention Surveillance (CHAMPS) network

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    Importance The number of deaths of children younger than 5 years has been steadily decreasing worldwide, from more than 17 million annual deaths in the 1970s to an estimated 5.3 million in 2019 (with 2.8 million deaths occurring in those aged 1-59 months [53% of all deaths in children aged <5 years]). More detailed characterization of childhood deaths could inform interventions to improve child survival. Objective To describe causes of postneonatal child deaths across 7 mortality surveillance sentinel sites in Africa and Asia. Design, Setting, and Participants The Child Health and Mortality Prevention Surveillance (CHAMPS) Network conducts childhood mortality surveillance in sub-Saharan Africa and South Asia using innovative postmortem minimally invasive tissue sampling (MITS). In this cross-sectional study, MITS was conducted in deceased children aged 1 to 59 months at 7 sites in sub-Saharan Africa and South Asia from December 3, 2016, to December 3, 2020. Data analysis was conducted between October and November 2021. Main Outcomes and Measures The expert panel attributed underlying, intermediate, and immediate conditions in the chain of events leading to death, based on histopathologic analysis, microbiological diagnostics, clinical data, and verbal autopsies. Results In this study, MITS was performed in 632 deceased children (mean [SD] age at death, 1.3 [0.3] years; 342 [54.1%] male). The 6 most common underlying causes of death were malnutrition (104 [16.5%]), HIV (75 [11.9%]), malaria (71 [11.2%]), congenital birth defects (64 [10.1%]), lower respiratory tract infections (LRTIs; 53 [8.4%]), and diarrheal diseases (46 [7.2%]). When considering immediate causes only, sepsis (191 [36.7%]) and LRTI (129 [24.8%]) were the 2 dominant causes. An infection was present in the causal chain in 549 of 632 deaths (86.9%); pathogens most frequently contributing to infectious deaths included Klebsiella pneumoniae (155 of 549 infectious deaths [28.2%]; 127 [81.9%] considered nosocomial), Plasmodium falciparum (122 of 549 [22.2%]), and Streptococcus pneumoniae (109 of 549 [19.9%]). Other organisms, such as cytomegalovirus (57 [10.4%]) and Acinetobacter baumannii (39 [7.1%]; 35 of 39 [89.7%] considered nosocomial), also played important roles. For the top underlying causes of death, the median number of conditions in the chain of events leading to death was 3 for malnutrition, 3 for HIV, 1 for malaria, 3 for congenital birth defects, and 1 for LRTI. Expert panels considered 494 of 632 deaths (78.2%) preventable and 26 of 632 deaths (4.1%) preventable under certain conditions. Conclusions and Relevance In this cross-sectional study investigating causes of child mortality in the CHAMPS Network, results indicate that, in these high-mortality settings, infectious diseases continue to cause most deaths in infants and children, often in conjunction with malnutrition. These results also highlight opportunities for action to prevent deaths and reveal common interaction of various causes in the path toward death

    Noninvasive Prenatal Diagnosis of Fetal Trisomy 18 and Trisomy 13 by Maternal Plasma DNA Sequencing

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    Massively parallel sequencing of DNA molecules in the plasma of pregnant women has been shown to allow accurate and noninvasive prenatal detection of fetal trisomy 21. However, whether the sequencing approach is as accurate for the noninvasive prenatal diagnosis of trisomy 13 and 18 is unclear due to the lack of data from a large sample set. We studied 392 pregnancies, among which 25 involved a trisomy 13 fetus and 37 involved a trisomy 18 fetus, by massively parallel sequencing. By using our previously reported standard z-score approach, we demonstrated that this approach could identify 36.0% and 73.0% of trisomy 13 and 18 at specificities of 92.4% and 97.2%, respectively. We aimed to improve the detection of trisomy 13 and 18 by using a non-repeat-masked reference human genome instead of a repeat-masked one to increase the number of aligned sequence reads for each sample. We then applied a bioinformatics approach to correct GC content bias in the sequencing data. With these measures, we detected all (25 out of 25) trisomy 13 fetuses at a specificity of 98.9% (261 out of 264 non-trisomy 13 cases), and 91.9% (34 out of 37) of the trisomy 18 fetuses at 98.0% specificity (247 out of 252 non-trisomy 18 cases). These data indicate that with appropriate bioinformatics analysis, noninvasive prenatal diagnosis of trisomy 13 and trisomy 18 by maternal plasma DNA sequencing is achievable

    Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network

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    BACKGROUND: The current burden of >5 million deaths yearly is the focus of the Sustainable Development Goal (SDG) to end preventable deaths of newborns and children under 5 years old by 2030. To accelerate progression toward this goal, data are needed that accurately quantify the leading causes of death, so that interventions can target the common causes. By adding postmortem pathology and microbiology studies to other available data, the Child Health and Mortality Prevention Surveillance (CHAMPS) network provides comprehensive evaluations of conditions leading to death, in contrast to standard methods that rely on data from medical records and verbal autopsy and report only a single underlying condition. We analyzed CHAMPS data to characterize the value of considering multiple causes of death. METHODS AND FINDINGS: We examined deaths identified from December 2016 through November 2020 from 7 CHAMPS sites (in Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa), including 741 neonatal, 278 infant, and 241 child <5 years deaths for which results from Determination of Cause of Death (DeCoDe) panels were complete. DeCoDe panelists included all conditions in the causal chain according to the ICD-10 guidelines and assessed if prevention or effective management of the condition would have prevented the death. We analyzed the distribution of all conditions listed as causal, including underlying, antecedent, and immediate causes of death. Among 1,232 deaths with an underlying condition determined, we found a range of 0 to 6 (mean 1.5, IQR 0 to 2) additional conditions in the causal chain leading to death. While pathology provides very helpful clues, we cannot always be certain that conditions identified led to death or occurred in an agonal stage of death. For neonates, preterm birth complications (most commonly respiratory distress syndrome) were the most common underlying condition (n = 282, 38%); among those with preterm birth complications, 256 (91%) had additional conditions in causal chains, including 184 (65%) with a different preterm birth complication, 128 (45%) with neonatal sepsis, 69 (24%) with lower respiratory infection (LRI), 60 (21%) with meningitis, and 25 (9%) with perinatal asphyxia/hypoxia. Of the 278 infant deaths, 212 (79%) had β‰₯1 additional cause of death (CoD) beyond the underlying cause. The 2 most common underlying conditions in infants were malnutrition and congenital birth defects; LRI and sepsis were the most common additional conditions in causal chains, each accounting for approximately half of deaths with either underlying condition. Of the 241 child deaths, 178 (75%) had β‰₯1 additional condition. Among 46 child deaths with malnutrition as the underlying condition, all had β‰₯1 other condition in the causal chain, most commonly sepsis, followed by LRI, malaria, and diarrheal disease. Including all positions in the causal chain for neonatal deaths resulted in 19-fold and 11-fold increases in attributable roles for meningitis and LRI, respectively. For infant deaths, the proportion caused by meningitis and sepsis increased by 16-fold and 11-fold, respectively; for child deaths, sepsis and LRI are increased 12-fold and 10-fold, respectively. While comprehensive CoD determinations were done for a substantial number of deaths, there is potential for bias regarding which deaths in surveillance areas underwent minimally invasive tissue sampling (MITS), potentially reducing representativeness of findings. CONCLUSIONS: Including conditions that appear anywhere in the causal chain, rather than considering underlying condition alone, markedly changed the proportion of deaths attributed to various diagnoses, especially LRI, sepsis, and meningitis. While CHAMPS methods cannot determine when 2 conditions cause death independently or may be synergistic, our findings suggest that considering the chain of events leading to death can better guide research and prevention priorities aimed at reducing child deaths
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