Validation of a Brief Internet-Based Self-Report Measure of Maladaptive Personality and Interpersonal Schema: Confirmatory Factor Analysis.

BACKGROUND: Existing digital mental health interventions mainly focus on the symptoms of specific mental disorders, but do not focus on Maladaptive Personalities and Interpersonal Schemas (MPISs). As an initial step toward considering personalities and schemas in intervention programs, there is a need for the development of tools for measuring core personality traits and interpersonal schemas known to cause psychological discomfort among potential users of digital mental health interventions. Thus, the MPIS was developed. OBJECTIVE: The objectives of this study are to validate the MPIS by comparing 2 models of the MPIS factor structure and to understand the characteristics of the MPIS by assessing its correlations with other measures. METHODS: Data were collected from 234 participants who were using web-based community sites in South Korea, including university students, graduate students, working professionals, and homemakers. All the data were gathered through web-based surveys. Confirmatory factor analysis was used to compare a single-factor model with a 5-factor model. Reliability and correlation analyses with other scales were performed. RESULTS: The results of confirmatory factor analysis indicated that the 5-factor model (χ2550=1278.1; Tucker-Lewis index=0.80; comparative fit index=0.81; and Root Mean Square Error of Approximation=0.07) was more suitable than the single-factor model (χ2560=2341.5; Tucker-Lewis index=0.52; comparative fit index=0.54; and Root Mean Square Error of Approximation=0.11) for measuring maladaptive personality traits and interpersonal relationship patterns. The internal consistency of each factor of the MPIS was good (Cronbach α=.71-.88), and the correlations with existing measures were statistically significant. The MPIS is a validated 35-item tool for measuring 5 essential personality traits and interpersonal schemas in adults aged 18-39 years. CONCLUSIONS: This study introduced the MPIS, a concise and effective questionnaire capable of measuring maladaptive personality traits and interpersonal relationship schemas. Through analysis, the MPIS was shown to reliably assess these psychological constructs and validate them. Its web-based accessibility and reduced item count make it a valuable tool for mental health assessment. Future applications include its integration into digital mental health care services, allowing easy web-based administration and aiding in the classification of psychological therapy programs based on the obtained results. TRIAL REGISTRATION: ClinicalTrials.gov NCT05952063; https://www.clinicaltrials.gov/study/NCT05952063.

Spike Counts Based Low Complexity SNN Architecture With Binary Synapse.

In this paper, we present an energy and area efficient spike neural network (SNN) processor based on novel spike counts based methods. For the low cost SNN design, we propose hardware-friendly complexity reduction techniques for both of learning and inferencing modes of operations. First, for the unsupervised learning process, we propose a spike counts based learning method. The novel learning approach utilizes pre- and post-synaptic spike counts to reduce the bit-width of synaptic weights as well as the number of weight updates. For the energy efficient inferencing operations, we propose an accumulation based computing scheme, where the number of input spikes for each input axon is accumulated without instant membrane updates until the pre-defined number of spikes are reached. In addition, the computation skip schemes identify meaningless computations and skip them to improve energy efficiency. Based on the proposed low complexity design techniques, we design and implement the SNN processor using 65 nm CMOS process. According to the implementation results, the SNN processor achieves 87.4% of recognition accuracy in MNIST dataset using only 1-bit 230 k synaptic weights with 400 excitatory neurons. The energy consumptions are 0.26 pJ/SOP and 0.31 µJ/inference in inferencing mode, and 1.42 pJ/SOP and 2.63 µJ/learning in learning mode of operations.

Mode of membrane insertion of individual transmembrane segments in Mdl1 and Mdl2, multi-spanning mitochondrial ABC transporters.

The sorting of an individual transmembrane (TM) segment of multi-spanning membrane proteins by the TIM23 complex in the mitochondrial inner membrane is poorly understood. Using the Mgm1 fusion approach, we attempted to assess the membrane insertion of individual TM segments of Mdl1p and Mdl2p, mitochondrial ABC transporters. Although these transporters share high sequence similarity, our results show that their membrane sorting patterns differ and that specific residues in TM domains strongly influence membrane insertion or translocation. These data imply that TIM23-mediated membrane insertion highly depends on the TM domain sequence context.

Skull base osteomyelitis following a mastoidectomy.

Skull base osteomyelitis is a rare but life-threatening condition that is characterized by osteitis of the temporal bone and skull base. Although skull base osteomyelitis is recognized as a rare complication of malignant external otitis or middle ear infection, it may also occur following a mastoidectomy. We present a case of an 81-year-old woman who suffered severe otalgia with normal-looking EAC and tympanic membrane that developed six weeks following a canal wall up mastoidectomy. Otalgia was the only abnormal feature to suspect occurrence of skull base osteomyelitis.