Pei-Hsin Huang

Mood disorders are an important public health issue and recent advances in genomic studies have indicated that molecules involved in neurodevelopment are causally related to mood disorders. BLM-s (BCL-2-likemolecule,small transcript isoform), a BH3-only proapoptotic BCL-2 family member, mediates apoptosis of postmitotic immature neurons during embryonic cortical development, but its role in the adult brain is unknown. To better understand the physiological role ofBlm-sgene in vivo, we generated aBlm-s-knockout (Blm-s^−/−) mouse. TheBlm-s^−/−mice breed normally and exhibit grossly normal development. However, global depletion ofBlm-sis highly associated with depression- and anxiety-related behaviors in adult mutant mice with intact learning and memory capacity. Functional magnetic resonance imaging of adultBlm-s^−/−mice reveals reduced connectivity mainly in the ventral dentate gyrus (vDG) of the hippocampus with no alteration in the dorsal DG connectivity and in total hippocampal volume. At the cellular level, BLM-s is expressed in DG granule cells (GCs), andBlm-s^−/−mice show reduced dendritic complexity and decreased spine density in mature GCs. Electrophysiology study uncovers that mature vGCs in adultBlm-s^−/−DG are intrinsically more excitable. Interestingly, certain genetic variants of the humanBlmhomologue gene (VPS50) are significantly associated with depression traits from publicly resourced UK Biobank data. Taken together, BLM-s is required for the hippocampal mood control function. Loss of BLM-s causes abnormality in the electrophysiology and morphology of GCs and a disrupted vDG neural network, which could underlieBlm-s-null-associated anxiety and depression.

RTL1/PEG11, which has been associated with anxiety disorders, is a retrotransposon-derived imprinted gene in the placenta. However, imprinting patterns and functions of RTL1 in the brain have not been well-investigated. We found Rtl1 was paternally, but not maternally, expressed in brain stem, thalamus, and hypothalamus of mice, and imprinting status of RTL1 was maintained in human brain. Paternal Rtl1 knockout (Rtl1^m+/p-) mice had higher neonatal death rates due to impaired suckling, and low body weights beginning on embryonic day 16.5. High paternal expression of Rtl1 was detected in the locus coeruleus (LC) and Rtl1^m+/p- mice showed an increased delay in time of onset for action potentials and inward currents with decreased neuronal excitability of LC neurons. Importantly, Rtl1^m+/p- mice exhibited behaviors associated with anxiety, depression, fear-related learning and memory, social dominance, and low locomotor activity. Taken together, our findings demonstrate RTL1 is imprinted in brain, mediates emotional and social behaviors, and regulates excitability in LC neurons.

Globally, liver cancer causes more than 700,000 deaths each year and is the second-leading cause of death from cancer. Hepatocellular carcinoma (HCC) is the most common type of liver cancer in adults and accounts for most deaths in cirrhosis patients. Patients with early-stage liver cancer can be treated by surgical intervention with a good prognosis; thus, early diagnosis, as confirmed by liver pathology examination, is necessary to combat HCC. Conventional manual pathology examination requires considerable time and labor, even with established expertise. It is widely accepted that intelligent classifiers may prove effective in the diagnosis process. In this study, we used a GoogLeNet (Inception-V1)-based binary classifier to classify HCC histopathology images. The classifier achieved 91.37% (±2.49) accuracy, 92.16% (±4.93) sensitivity, and 90.57% (±2.54) specificity in HCC classification. Although the classification accuracy of deep learning is reported to be positively correlated with the amount of training data, it is often uncertain how much training data are required for deep learning to achieve satisfactory performance in clinical diagnosis. Moreover, deep learning methods require annotated data to generate efficient models. However, annotated data are a relatively scarce resource and can be expensive to obtain. Hence, the relationship between classification accuracy and the number of liver histopathology images for training was investigated. An inverse power law function-based estimation model is proposed to evaluate the minimum number of annotated training images required for a desired diagnostic accuracy.

Significance: During neural development, growing axons navigate over long distances to reach their targets. A critical step in this process is the regulation of its surface receptors on the axon’s growth cone in response to environmental cues. We focus on how the UNC-5 receptor in Ca enorhabditis elegans motor axons is regulated during axon repulsion. By combining C. elegans genetics, biochemistry, and imaging, we found that MAX-1 SUMOylation and AP-3 complex have significant roles in UNC-5–mediated axon repulsion. Our findings reveal how SUMOylation and AP-3–mediated trafficking and degradation interact to help the growing axon find its final target.

Cognitive radio (CR) has been identified as an enabling technology toward meeting the high spectrum utilization efficiency demand in future internet-of-things (IoT) systems. Development of new spectrum sensing schemes better suited to CR-based IoT networks, which are typically heterogeneous with perfect network-wide synchronization difficult to achieve, is thus rather crucial. Motivated by the low-complexity feature of the energy detector (ED), this paper proposes a normalized weighted ED to cope with random arrival of the primary user (PU) signal due to imperfect transmission coordination. The proposed approach first multiplies each received sample by a weighting factor, and then normalizes the total weighted energy with respect to the raw signal energy: this is better able to discern the presence of the PU, particularly when the PU arrives very late in the sensing period. The weighting sequence is designed by minimizing the detection probability subject to a tolerable false-alarm probability constraint. In our design, the Gaussian approximation technique is used to derive an approximate formula of the detection probability, and the steepest descent algorithm is used to find a solution. Computer simulations are conducted to validate our analysis, and confirm the performance advantage of the proposed scheme.