Effects of life career pattern on self-esteem and life satisfaction of college educated women in the middle years

This research was conducted to examine how life role patterns influence self-esteem, happiness, satisfaction with life and mental adjustment of middle-aged college-educated women. Considerable research has concluded that middle-aged women have lower self-esteem, higher rates of adjustment disturbances, and less satisfaction and happiness in their lives than either men or women in other age groups. Two hypothesized explanations for these findings were explored. One hypothesis was related to socialization sex-role theory. According to this theory, girls are encouraged to follow the traditional lifestyle of wife/homemaker/mother to the exclusion of other achievement focused activities. This exclusion results in reduced well-being for middle-aged women because society places less importance on the family roles. A second explanation is that the traditional female role prohibits women from feeling competent and thus reduces their self-esteem. Self-reports of happiness, satisfaction, levels of stress, mental health and measures of self-esteem and achievement motivation from middle-aged college-educated women were analyzed with respect to four life role patterns: Career (occupation/job, but no marriage/family), Concurrent (both occupation/job and marriage/family at the same time), Sequential (both occupation/job and marriage/family but at different times), and Homemaker (no occupation/job but marriage/family). The results indicated that: (1) the Concurrent and Sequential combination roles related to higher self-esteem at middle age than did the Career and Homemaker single roles, (2) despite lower esteem, Homemaker women at middle age were no less happy, had no greater stress, or lower levels of mental health than women who had lived their lives in the other roles, (3) middle-aged women, across all groups, were no less happy or satisfied than they reported themselves to be at earlier periods in their lives

Impact of the NAD+ salvage pathway on the motor system and bioenergetics

Nicotinamide adenine dinucleotide (NAD+) is a metabolite pivotal to numerous cellular functions, including energy metabolism, DNA repair, and protein modification. The majority of NAD+ is generated by the NAD+ salvage pathway, which is rate limited by nicotinamide phosphoribosyltransferase (NAMPT). NAMPT and the NAD+ salvage pathway are critical to most cell types, but are especially important to neuronal health and function. Additionally, the NAD+ salvage pathway is increasingly being investigated for being involved in neurodegenerative diseases. Recently, the importance of NAMPT to motor and neuromuscular junction (NMJ) activity and survival has been reported. However, how the NAD+ salvage pathway affects NMJ function and what metabolic pathway are dysregulated when the salvage pathway becomes impaired warrants further investigation. In Chapter one, I review intracellular NAD+ homeostasis, NAD+ salvage pathway enzymes, and the importance of NAD+ homeostasis in health and neurodegenerative diseases, specifically ALS. In Chapter two and Chapter three, I used inducible and conditional projection neuron-specific NAMPT knockout mouse, which previously was found to experience profound motor dysfunction and neuron loss. I investigated NMJ structure and function following NAMPT deletion and what benefits NMN administration produces. I also studied how the metabolic and transcriptional profiles in the motor cortex of these knockout mice are altered after NAMPT loss. In Chapter Four, based on the similar phenotypes between the NAMPT knockout mice and ALS mice and NAD+ reduction in ALS mouse model, I investigated how dietary NMN supplementation affects motor behavior and NMJ function in SOD1G93A ALS mice. Overall, the results from these studies provide novel insights into how the NAD+ salvage path is critical for NMJs and what metabolic stress and signaling pathways are responsible for the neuronal death after NAMPT deletionIncludes bibliographical references

First Steps Towards a Runtime Analysis of Neuroevolution

We consider a simple setting in neuroevolution where an evolutionary algorithm optimizes the weights and activation functions of a simple artificial neural network. We then define simple example functions to be learned by the network and conduct rigorous runtime analyses for networks with a single neuron and for a more advanced structure with several neurons and two layers. Our results show that the proposed algorithm is generally efficient on two example problems designed for one neuron and efficient with at least constant probability on the example problem for a two-layer network. In particular, the so-called harmonic mutation operator choosing steps of size $j$ with probability proportional to $1/j$ turns out as a good choice for the underlying search space. However, for the case of one neuron, we also identify situations with hard-to-overcome local optima. Experimental investigations of our neuroevolutionary algorithm and a state-of-the-art CMA-ES support the theoretical findings.Comment: 27 pages; full version of paper published at FOGA 2023 and available at AC