FS5 sun exposure survivability analysis

During the Acquisition and Safe Hold (ASH) mode, FORMOAT-5 (FS5) satellite attitude is not fully controlled. Direct sun exposure on the Remote Sensing Instrument (RSI) satellite telescope sensor may occur. The sun exposure effect on RSI sensor performance is investigated to evaluate the instrument’s survivability in orbit. Both satellite spin speed and sun exposure duration are considered as the key parameters in this study. A simple radiometry technique is used to calculate the total sun radiance exposure to examine the RSI sensor integrity. Total sun irradiance on the sensor is computed by considering the spectral variation effect through the RSI’s five-band filter. Experiments that directly expose the sensor to the sun on the ground were performed with no obvious performance degradation found. Based on both the analysis and experiment results, it is concluded that the FS5 RSI sensor can survive direct sun exposure during the ASH mode

AMPK- mediated formation of stress granules is required for dietary restriction- induced longevity in Caenorhabditis elegans

Stress granules (SGs) are nonmembranous organelles that are dynamically assembled and disassembled in response to various stressors. Under stressed conditions, polyadenylated mRNAs and translation factors are sequestrated in SGs to promote global repression of protein synthesis. It has been previously demonstrated that SG formation enhances cell survival and stress resistance. However, the physiological role of SGs in organismal aging and longevity regulation remains unclear. In this study, we used TIAR- 1::GFP and GTBP- 1::GFP as markers to monitor the formation of SGs in Caenorhabditis elegans. We found that, in addition to acute heat stress, SG formation could also be triggered by dietary changes, such as starvation and dietary restriction (DR). We found that HSF- 1 is required for the SG formation in response to acute heat shock and starvation but not DR, whereas the AMPK- eEF2K signaling is required for starvation and DR- induced SG formation but not heat shock. Moreover, our data suggest that this AMPK- eEF2K pathway- mediated SG formation is required for lifespan extension by DR, but dispensable for the longevity by reduced insulin/IGF- 1 signaling. Collectively, our findings unveil a novel role of SG formation in DR- induced longevity.In addition to heat stress, starvation and dietary restriction (DR) can activate stress granule (SG) formation in Caenorhabditis elegans. HSF- 1 and AMPK are two key regulators for the SG formations. HSF- 1 is required for the SG formation in response to acute heat shock and starvation but not DR, whereas the AMPK- eEF2K pathway is required for starvation and DR- induced SG formation but not heat shock. Furthermore, AMPK- mediated SG formation contributes to DR- induced longevity.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/1/acel13157-sup-0008-Figurelegends.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/2/acel13157-sup-0001-FigS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/3/acel13157-sup-0006-TableS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/4/acel13157-sup-0007-TableS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/5/acel13157-sup-0005-FigS5.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/6/acel13157-sup-0003-FigS3.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/7/acel13157.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/8/acel13157-sup-0002-FigS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/9/acel13157-sup-0004-FigS4.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155936/10/acel13157_am.pd

Improving protein secondary structure prediction based on short subsequences with local structure similarity

<p>Abstract</p> <p>Background</p> <p>When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.</p> <p>Results</p> <p>In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an <it>n-</it>gram pattern of amino acids that reflects the sequence variation in a protein’s evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.</p> <p>On a large non-redundant dataset of 8,297 protein chains (<it>DsspNr-25</it>), the average <it>Q</it>₃ of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (<it>EVA Set_1</it> and <it>EVA_Set2</it>), the average <it>Q</it>₃ of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.</p> <p>Conclusions</p> <p>Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at <url>http://bio-cluster.iis.sinica.edu.tw/SymPred/</url>.</p

Osteomyelitis of Multiple Lumbar Vertebrae Associated with Infected Aortic Aneurysm: A Case Report

A 73-year-old male patient presented with a pulsating abdominal mass and intractable low back pain for several days. Magnetic resonance imaging revealed an infected abdominal aortic aneurysm invading the second, third, and fourth lumbar vertebrae. He underwent radical debridement of the infected aneurysm with reconstruction using vascular bypass, partial corpectomy of the L2 to L4 vertebrae, anterior reconstruction with autogenous fibular shaft, and posterior instrumentation with posterolateral fusion. Culture of the necrotic tissues grew oxacillin-resistant Staphylococcus aureus. He received intravenous vancomycin infusion for 4 weeks and oral ciprofloxacin for 6 months postoperatively. After a 15-month follow-up, no apparent signs of further infection were noted. C-reactive protein and erythrocyte sedimentation rate returned to normal during follow-up. No neurologic symptoms other than mild low back soreness were noted. The stability of the lumbar spine was maintained using long segment reconstruction with autogenous fibula shaft and posterior instrumentation along with posterolateral fusion. Infected aortic aneurysm with vertebral osteomyelitis is a rare clinical entity. Prompt diagnosis and adequate treatment are essential

Protein subcellular localization prediction of eukaryotes using a knowledge-based approach

<p>Abstract</p> <p>Background</p> <p>The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.</p> <p>Results</p> <p>In this study, we propose a knowledge based method, called KnowPred_site, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred_site's performance. The experiment results show that KnowPred_siteachieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPred_siteis 91.7%. For multi-localized proteins, the overall accuracy of KnowPred_siteis 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPred_site.</p> <p>Conclusion</p> <p>KnowPred_sitedemonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPred_siteis a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPred_siteis transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPred_siteprediction server is available at <url>http://bio-cluster.iis.sinica.edu.tw/kbloc/</url>.</p

p-Cu2O-shell/n-TiO2-nanowire-core heterostucture photodiodes

This study reports the deposition of cuprous oxide [Cu2O] onto titanium dioxide [TiO2] nanowires [NWs] prepared on TiO2/glass templates. The average length and average diameter of these thermally oxidized and evaporated TiO2 NWs are 0.1 to 0.4 μm and 30 to 100 nm, respectively. The deposited Cu2O fills gaps between the TiO2 NWs with good step coverage to form nanoshells surrounding the TiO2 cores. The p-Cu2O/n-TiO2 NW heterostructure exhibits a rectifying behavior with a sharp turn-on at approximately 0.9 V. Furthermore, the fabricated p-Cu2O-shell/n-TiO2-nanowire-core photodiodes exhibit reasonably large photocurrent-to-dark-current contrast ratios and fast responses

A PERIOD3 variant causes a circadian phenotype and is associated with a seasonal mood trait.

In humans, the connection between sleep and mood has long been recognized, although direct molecular evidence is lacking. We identified two rare variants in the circadian clock gene PERIOD3 (PER3-P415A/H417R) in humans with familial advanced sleep phase accompanied by higher Beck Depression Inventory and seasonality scores. hPER3-P415A/H417R transgenic mice showed an altered circadian period under constant light and exhibited phase shifts of the sleep-wake cycle in a short light period (photoperiod) paradigm. Molecular characterization revealed that the rare variants destabilized PER3 and failed to stabilize PERIOD1/2 proteins, which play critical roles in circadian timing. Although hPER3-P415A/H417R-Tg mice showed a mild depression-like phenotype, Per3 knockout mice demonstrated consistent depression-like behavior, particularly when studied under a short photoperiod, supporting a possible role for PER3 in mood regulation. These findings suggest that PER3 may be a nexus for sleep and mood regulation while fine-tuning these processes to adapt to seasonal changes

Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To learn fine-grained visual understanding, we decouple spatial-temporal modeling and propose a hybrid pipeline, Decoupled Spatial-Temporal Encoders, integrating an image- and a video-language encoder. The former encodes spatial semantics from larger but sparsely sampled frames independently of time, while the latter models temporal dynamics at lower spatial but higher temporal resolution. To help the video-language model learn temporal relations for video QA, we propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Extensive experiments demonstrate that our model outperforms previous work pre-trained on orders of magnitude larger datasets.Comment: BMVC 2022. Code is available at https://github.com/shinying/des