Incidence of Hematotoxic Effect of Snake Venom in Patients with Snake bite

Abstract: Objective: The main aim of this study was to find the incidence of hematotoxic effect of snake venom in patients with snake bite.Place and Duration: This study was carried out in a duration of 12 months from January 2018 to December 2018 in various hospitals of Lahore.Materials and Methods: 80 cases were selected which presented with the history of snake bite. Detailed history of the patient was taken and examination for symptoms like neurotoxicity and hematotoxicity was completed followed by lab investigations relating to hematotoxicity i.e. PT, APTT and complete blood profile to look for hematotoxicity. Informed consent was taken from all the patients.Results: Patients included in this study were between the ages of 22-30 years with the mean age of 26 years. Most commonly about (76%) males were the victims of the snake bite. Hematotoxic effect was seen in 57% of the cases while neurotoxicity was seen in 4.3% of the cases. Deranged levels of PT, APTT was seen in 45 (56.25%) of the snake bite victims, thrombocytopenia in 12 (15%) of the cases, bleeding from musculocutaneous site 5 (6.25%), bleeding from veni-puncture site 2 (2.5%), hematuria 15 (18.75%) and hematemesis 2 (2.5%) of the snake bite victims while neurotoxicity was seen in 4.3% of the cases having respiratory paralysis and ptosis in 100% of the cases.Conclusion: Serious health problems are associated with the toxicity caused by snake bite. Almost 2500 species of snakes are found all over the world and around 250 species are more common in Pakistan. Poisonous snakes are less common than non-poisonous but are more deadly.Keywords: Neurotoxic, hematototoxic, venom, myotoxic

Laying performance, digestibility and plasma hormones in laying hens exposed to chronic heat stress as affected by betaine, vitamin C, and/or vitamin E supplementation

Heat stress had a negative effect on laying hens' performance, thus this research was to study the influences of betaine (Bet, 1000 mg/kg betaine), vitamin C (VC, 200 mg/kg ascorbic acid), and vitamin E (VE, 150 mg/kg a-Tocopherol acetate) and their possible combinations on egg production, digestibility of nutrients, plasma hormones and reproductive organs of dual-purpose hens exposed to chronic heat stress. Two hundred and eighty eight hens and thirty-six cocks from 32 to 48 weeks of age were divided into nine treatment groups of four replicates, each containing eight hens and one cock. One group was kept under thermo-natural condition and the eight others were kept under chronic heat stress (CHS). One of these eight was used as a negative control, while the others were supplemented with VC, VE and/or betaine and their possible combinations. Body weights, laying rate, feed intake, and feed conversion ratio in hens reared under CHS rooster without any supplementation during 32 to 48 weeks of impairment (P = 0.0052) were recorded. Hens reared under heat stress and fed a diet supplemented with either Bet, VC, VE or combination of the supplements increased production traits. However, hens supplemented with VC showed the greatest production traits. Plasma glucose, estradiol-17 (E-2), progesterone (P-4), tri-iodothyronine (T-3) and thyroxine (T-4) decreased in hens reared under CHS and fed a diet with no supplementation compared to the other treatments (P = 0.001). Liver weights, spleen weights, thyroid gland weights, ovary weights, oviduct weights and oviduct lengths were lowest in hens reared under CHS and fed a diet with no supplementation (P = 0.0480). In conclusion, dual purpose hens reared under CHS and supplemented with VC at 200 mg/kg diet and Bet at 1000 mg/kg enhanced the laying performance and combated CHS

Applying Descriptive Analysis Methods in the Domain of Computer Entertainment

Pomoću raznih deskriptivnih metoda provedena je analiza u domeni računalnih igara. Korištenjem metoda eksploratorne analize poput vizualizacije i računanja osnovnih statističkih mjera poput prosjeka i medijana dobiveni su osnovni uvidi u podatkovni skup. Upotrebom strojnog učenja, preciznije grupiranja algoritmom k-srednjih vrijednosti igrači su profilirani na temelju njihovih statističkih vrijednosti odabranih pomoću provedene eksploratorne analize. Korištenjem algoritma slučajnih šuma igrači su se profilirali po njihovom primarnom oružju koristeći nekoliko statističkih vrijednosti koje su odabrane na temelju rezultata eksploratorne analize.Analysis in the domain of computer games was performed using various descriptive methods. Using exploratory analysis methods such as visualization and calculation of basic statistical measures such as averages and medians, basic insights into the data set were obtained. Using machine learning, more precise the k-means algorithm, players were profiled based on their statistical values which were ​​selected by using exploratory analysis. Using the random forests algorithm, players were profiled by their primary weapon using several statistical values ​​selected based on the results of exploratory analysis

Applying Descriptive Analysis Methods in the Domain of Computer Entertainment

Pomoću raznih deskriptivnih metoda provedena je analiza u domeni računalnih igara. Korištenjem metoda eksploratorne analize poput vizualizacije i računanja osnovnih statističkih mjera poput prosjeka i medijana dobiveni su osnovni uvidi u podatkovni skup. Upotrebom strojnog učenja, preciznije grupiranja algoritmom k-srednjih vrijednosti igrači su profilirani na temelju njihovih statističkih vrijednosti odabranih pomoću provedene eksploratorne analize. Korištenjem algoritma slučajnih šuma igrači su se profilirali po njihovom primarnom oružju koristeći nekoliko statističkih vrijednosti koje su odabrane na temelju rezultata eksploratorne analize.Analysis in the domain of computer games was performed using various descriptive methods. Using exploratory analysis methods such as visualization and calculation of basic statistical measures such as averages and medians, basic insights into the data set were obtained. Using machine learning, more precise the k-means algorithm, players were profiled based on their statistical values which were ​​selected by using exploratory analysis. Using the random forests algorithm, players were profiled by their primary weapon using several statistical values ​​selected based on the results of exploratory analysis

Leveraging Reinforced Learning Methods in the Domain of Autonomous Parking

Podržano učenje je dio strojnog učenja koji se bavi učenjem modela umjetne inteligencije kako bi bio računalni sustav bio sposoban autonomno donositi odluke ovisno o parametrima svojeg okruženja. Model s vremenom nauči maksimizirati nagradu koju dobije kada donese ispravnu odluku. Zadatak diplomskog rada je razviti model za autonomno parkiranje koristeći metode podržanog učenja. U radu će se uz pomoć metoda podržanog učenja oblikovati programska simulacija čija je svrha omogućiti računalu automatsko učenje upravljanja modela virtualnog vozila kako bi se isto moglo parkirati na simuliranom parkingu s raznim preprekama. Rad će također istražiti i primjenu dobivenih rezultata u stvarnom svijetu koristeći manji model vozila. Cilj je pružiti uvid u potencijal podržanog učenja u domeni autonomne vožnje i u razvoj inteligentnih autonomnih sustava.Reinforced learning is a subset of machine learning that specializes in training AI models in such a way that a computer system is able to make decisions autonomously depending on the parameters of its environment. Over time, the model learns to maximize the reward it gets when it makes the right decision. The goal of the thesis is to develop a model for autonomous parking using reinforced learning methods. In the thesis, with the help of reinforced learning methods, a software simulation will be created whose purpose is to enable the computer to automatically learn to control a virtual vehicle model with the goal of being able to park in a simulated parking lot with various obstacles. The thesis will also explore the application of the obtained results in the real world using a small model vehicle. The goal is to provide insight into the potential of reinforced learning in the domain of autonomous driving and into the development of intelligent autonomous systems