Mounir Ouzir

Almond oil, a rich source of macronutrients and micronutrients, is extracted for food flavorings and the cosmetics industry. In recent years, the need for high‐quality and high‐quantity production of almond oil for human consumption has been increased. The present review examines the chemical composition of almond oil, storage conditions, and clinical evidence supporting the health benefits of almond oil. From the reviewed studies, it appears that almond oil contains a significant proportion of poly and monounsaturated fatty acids, with oleic acid as the main compound, and an important amount of tocopherol and phytosterol content. Some variations in almond oil composition can be found depending on the kernel's origin and the extraction system used. Some new technologies such as ultrasonic‐assisted extraction, supercritical fluid extraction, subcritical fluid extraction, and salt‐assisted aqueous extraction have emerged as the most promising extraction techniques that allow eco‐friendly and effective recovery of almond oil. This safe oil was reported by several clinical studies to have potential roles in cardiovascular risk management, glucose homeostasis, oxidative stress reduction, neuroprotection, and many dermatologic and cosmetic applications. However, the anticarcinogenic and fertility benefits of almond oil have yet to be experimentally verified.

Cancer tumor prediction and diagnosis at an early stage has become a necessity in cancer research, as it provides an increase in the treatment success chances. Recently, DNA microarray technology became a powerful tool for cancer identification, that can analyze the expression level of a different and huge number of genes simultaneously. In microarray data, the large genes number versus a few records may affect the prediction performance. In order to handle this "curse of dimensionality” constraint of microarray dataset while improving the cancer identification performance, a dimensional reduction phase is necessary. In this paper, we proposed a framework that combines dimensional reduction methods and machine learning algorithms in order to achieve the best cancer prediction performance using different microarray datasets. In the dimensional reduction phase, a combination of feature selection and feature extraction techniques was proposed. Pearson and Ant Colony Optimization was used to select the most important genes. Principal Component Analysis and Kernel Principal Component Analysis were used to linearly and non-linearly transform the selected genes to a new reduced space. In the cancer identification phase, we proposed four algorithms C5.0, Logistic Regression, Artificial Neural Network, and Support Vector Machine. Experimental results demonstrated that the framework performs effectively and competitively compared to state-of-the-art methods. ABSTRAK: Ramalan tumor kanser dan diagnosis pada peringkat awal telah menjadi keperluan dalam kajian kanser, kerana ia membuka peluang peningkatan kejayaan dalam rawatan. Kebelakangan ini, teknologi mikrotatasusunan DNA menjadi alat berkuasa bagi mengenal pasti kanser, di mana ia mampu menganalisa level ekspresi yang pelbagai dan gen-gen yang banyak secara serentak. Dalam data mikrotatasusunan, gen-gen yang banyak ini bakal menentukan ramalan prestasi berbanding analisa melalui rekod-rekod yang sebilangan. Fasa pengurangan dimensi adalah perlu bagi mengawal kakangan “penentuan kedimensian” dataset mikrotatasusunan, sementara itu ia memantapkan lagi keberkesanan kenal pasti kanser. Kajian ini mencadangkan rangka kombinasi kaedah pengurangan dimensi dan algoritma pembelajaran mesin bagi mencapai prestasi ramalan kanser terbaik dengan menggunakan pelbagai dataset mikrotatasusunan. Dalam fasa pengurangan dimensi, kombinasi pemilihan ciri dan teknik pengekstrakan ciri telah dicadangkan, Pengoptimuman Pearson dan Koloni Semut bagi memilih gen yang paling penting, Analisis Komponen Prinsipal dan Analisis Komponen Prinsipal Kernel, bagi menukar gen terpilih yang linear dan tak linear kepada ruang baru yang dikurangkan. Dalam menentukan fasa mengenal pasti kanser, kajian ini mencadangkan empat algoritma iaitu C5.0, Regresi Logistik, Rangkaian Neural Buatan dan Mesin Vektor Sokongan. Dapatan kajian menunjukkan rangka ini adalah berkesan dan kompetitif berbanding kaedah semasa.

To examine a possible control of reproductive seasonality by melatonin, continual-release subcutaneous melatonin implants were inserted 4.5 months before the natural breeding season (October–April) into female camels (Melatonin-treated group). The animals were exposed to an artificial long photoperiod (16L:8D) for 41 days prior to implant placement to facilitate receptivity to the short-day signal that is expected with melatonin implants. The treated and control groups (untreated females) were maintained separately under outdoor natural conditions. Ovarian follicular development was monitored in both groups by transrectal ultrasonography and by plasma estradiol-17β concentrations performed weekly for 8 weeks and then for 14 weeks following implant insertion. Plasma prolactin concentrations were determined at 45 and 15 days before and 0, 14, 28, 56, and 98 days after implant insertion. Plasma melatonin concentration was determined to validate response to the artificial long photoperiod and to verify the pattern of release from the implants. Results showed that the artificial long photoperiod induced a melatonin secretion peak of significantly (P < 0.05) shorter duration (about 2.5 h). Melatonin release from the implants resulted in higher circulating plasma melatonin levels during daytime and nighttime which persisted for more than 12 weeks following implants insertion. Treatment with melatonin implants advanced the onset of follicular growth activity by 3.5 months compared to untreated animals. Plasma estradiol-17β increased gradually from the second week after the beginning of treatment to reach significantly (P < 0.01) higher concentrations (39.2 ± 6.2 to 46.4 ± 4.5 pg/ml) between the third and the fifth week post insertion of melatonin implants. Treatment with melatonin implants also induced a moderate, but significant (P < 0.05) suppressive effect on plasma prolactin concentration on the 28th day. These results demonstrate that photoperiod appears to be involved in dromedary reproductive seasonality. Melatonin implants may be a useful tool to manipulate seasonality and to improve reproductive performance in this species. Administration of subcutaneous melatonin implants during the transition period to the breeding season following an artificial signal of long photoperiod have the potential to advance the breeding season in camels by about 2.5 months.

There is consistent evidence that impulsivity is linked to tobacco consumption and to symptomatology in schizophrenia. In the current study, we propose a new integrative model of the relationship between impulsivity, psychopathological symptoms, and tobacco status in patients with paranoid schizophrenia. We investigated 33 paranoid schizophrenia patients and 37 healthy controls using a battery of psychopathological scales included the Structured Clinical Interview for DSM-IV (SCID), the five-factor model of the Positive and Negative Symptom Scale (PANSS), the Barratt Impulsiveness Scale (BIS-10), and the UPPS Impulsive Behavior scale (UPPS). Step-wise regression analysis revealed that positive factor of the PANSS and tobacco status contributed positively and significantly to the explained variance of impulsivity. In addition, non-planning impulsivity and sensation-seeking emerged as significant predictors of tobacco status, while smoking predicted non-planning impulsivity and sensation-seeking. Our results suggest that the relationship between sensation-seeking and tobacco use is reciprocal and proposes a new integrative model of the relationship between impulsivity, positive symptoms and tobacco status in patients with paranoid schizophrenia. However, the exact mechanisms for these relationships deserve further investigation.

BACKGROUND: Speech disorders such as dysphonia and dysarthria represent an early and common manifestation of Parkinson’s disease. Class prediction is an essential task in automatic speech treatment, particularly in the Parkinson’s disease case. Many