The effect of dietary soybean meal on growth, nutrient utilization, body composition and some serum biochemistry variables of two banded seabream, Diplodus vulgaris (Geoffroy Saint-Hilaire, 1817)

This study was performed to determine the optimum level of soybean meal diets for two banded seabream for growth performance, nutrient utilization, body composition and serum biochemistry. Two banded seabream were fed five experimental diets which were formulated replace fish meal by soybean meal at 0, 20, 30, 40 and 50%. Up to 40% of dietary fish meal was successfully replaced with no growth depression. Whole body composition of two banded seabream was not affected by soybean meal inclusion level. Total protein, triglyceride and total cholesterol of fish fed the SM50 diets were significantly lower compared to fish fed the soybean free diet. On the other hand, serum glucose level significantly increased as dietary soybean meal inclusion increased. Results showed that 40% fish meal can be replaced in diets for the two banded seabream by defatted soybean meal. Further studies to determine the inclusion level of soybean meal more than 40% with amino acid or enzyme supplementation are needed

The Relationship between Symptom Severity and Low Vitamin D Levels in Patients with Schizophrenia.

In recent years, the relationship between schizophrenia and environmental factors has come into prominence. This study investigated the relationship between vitamin D levels and the positive and negative symptoms of schizophrenia by comparing vitamin D levels between patients with schizophrenia and a healthy control group.The study included 80 patients diagnosed with schizophrenia and 74 age- and sex-matched controls. The Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS) were used to evaluate symptom severity. The 25-hydroxyvitamin D (25OHD) levels of all subjects both patients and healthy controls were analyzed in relation to measurements of symptom severity.There were no significant differences between the groups in terms of age, sex, or physical activity. Their mean 25OHD levels were also similar (23.46±13.98ng/mL for the patient group and 23.69±9.61ng/mL for the control group). But when patients with schizophrenia were grouped based on their vitamin D levels, the results indicated a statistically significant differences between their vitamin D levels and their total SANS, affective flattening, and total SAPS, bizarre behavior and positive formal thought disorder scores (p = 0.019, p = 0.004, p = 0.015, p = 0.009 and p = 0.019, respectively). There is a negative correlation between 25OHD levels and SANS total points (r = -0.232, p = 0.038); a negative correlation for attention points (r = -0.227, p = 0.044) and negative correlation with positive formal thoughts (r = -0.257, p = 0.021).The results of this study show a relationship between lower levels of vitamin D and the occurrence of positive and negative symptoms, along with increased severity of symptoms at lower levels of vitamin D, suggesting that treatment for schizophrenia should include assessment of patients' vitamin D levels. We recommend that patients with schizophrenia should be assessed with regard to their vitamin D levels

Estimation of Energy Management Strategy Using Neural-Network-Based Surrogate Model for Range Extended Vehicle

In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects

Distal Rektum Tümörü Nedeni ile Uzun Dönem Neoadjuvan Kemoradyoterapi Sonrası FOLFOX Tedavisi ile Ameliyatsız Takip Edilen Hastaların Erken Dönem Sonuçları

Amaç: Lokal ileri distal rektum tümörü (LİDRT) hastalarında konsolidasyon neoadjuvan kemoterapi (KNKT) sonrası klinik tam yanıt elde edilen hastalarda ameliyatsız takip (non-operative management (NOM)) stratejisi tercih edilen hastaların klinik tam yanıt, lokal nüks ve uzak metastaz açısından erken dönem sonuçlarını araştırmaktır. Gereç-Yöntem: Bu prospektif faz II kohort çalışmasında LİDRT hastalarında total mezorektal eksizyona (TME) uygun evre II veya III LİDRT hastaları, uzun dönem neoadjuvant kemoradyoterapi (nKRT) sonrası elde edilen yanıta bağlı altı kür FOLFOX (KNKT) tedavisine alındı; nKRT veya KNKT sonrası tedaviye yanıt vermeyen hastalara TME uygulandı. NOM hastaları ilk iki yılda üç ayda bir ve daha sonra altı ayda bir takip edildi. Bulgular: Eylül 2016 ve Kasım 2018 arasında, nKRT sonrası TEM ya da NOM stratejisine yönlendirilen 53 hasta belirlendi. 28 hastaya (% 52,8) nKRT sonrası TME uygulandı ve belirgin klinik yanıt elde edilen 25 hastaya (% 47,2) KNKT uygulandı. KNKT sonrası klinik tam yanıt elde edildi. 18 (%72) hastaya NOM uygulandı ve klinik tam yanıt elde edilemeyen 4 hastaya TME önerildi. Üç hastanın tedavisi devam etmektedir. Ortalama takip süresi 21,4 ay ve tümörün dentat çizgisi ile mesafesi 4.0 (0,3-6,0) cm olarak saptandı. NOM uygulanan dört (% 22,2) hastada lokal yeniden tümör büyümesi, rutin takip sürecinde tespit edildi ve kurtarma cerrahisi (TME) uygulandı. Tüm hastalarda kurtarma cerrahi sonrası pelvik kontrol sağlandı. Yeniden lokal tümör büyümelerin %75’i birinci yılda ve tümü rektum duvarında saptandı. Yeniden lokal tümör büyüme saptanan dört hastanın birinde (%5,55) ayrıca sistemik metastaz saptandı. Genel sağkalım %94,4 ve hastalıksız sağkalım NOM grubunda %77,7 olarak saptandı. Sonuç: Seçilmiş LİDRT hastalarında NOM stratejisi ile hem sfinkterin korunabildiği hem de pelvik tümör kontrolünün iyi bir şekilde sağlandığı ortaya konuldu. Fakat, en sık ilk iki yılda ve en sık da bağırsak duvarında saptadığımız lokal yeniden tümör büyüme riski nedeniyle hastaların yakın takibi kurtarma cerrahisi şansını kaçırmamaları için dikkatle yapılmalıdı

Estimation of Energy Management Strategy Using Neural-Network-Based Surrogate Model for Range Extended Vehicle

In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects

Sociodemographic and clinical characteristics of the patient and control groups.

<p>Sociodemographic and clinical characteristics of the patient and control groups.</p