Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are ...3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under light–dark cycles and under total darkness and obtained eight time-ordered gene coexpression networks (TO-GCNs), which can be used to predict upstream regulators of any genes in the GCNs. One of the eight TO-GCNs is light-independent and likely includes all genes involved in the development of Kranz anatomy, which is a structure crucial for the high efficiency of photosynthesis in C₄ plants. Using this TO-GCN, we predicted and experimentally validated a regulatory cascade upstream of SHORTROOT1, a key Kranz anatomy regulator. Moreover, we applied the method to compare transcriptomes from maize and rice leaf segments and identified regulators of maize C₄ enzyme genes and RUBISCO SMALL SUBUNIT2. Our study provides not only a powerful method but also novel insights into the regulatory networks underlying Kranz anatomy development and C₄ photosynthesis.
This article presents a novel static random access memory computing-in-memory (SRAM-CIM) structure designed for high-precision multiply-and-accumulate (MAC) operations with high energy efficiency ...(EF), high readout accuracy, and short compute latency. The proposed device employs 1) a time-domain incremental-accumulation (TDIA) scheme to enable high-accumulation MAC operations while maintaining a large signal margin across MAC values (MACVs), 2) a dynamic differential-reference (D2REF) scheme based on software-hardware co-design to reduce read energy consumption, and 3) a low-dMACV-aware recursive time-to-digital converter (LMAR-TDC) for implementation with the D2REF scheme to further suppress readout energy consumption. A 28 nm 1 Mb SRAM-CIM macro fabricated using foundry-provided compact 6T-SRAM cells achieved EF of 39.31 TOPS/W and compute latency of 6.6 ns for 8b-MAC operations with 64 accumulations per cycle and near-full output precision (22b).
Introduction
Bone loss is a major health concern for astronauts during long-term spaceflight and for patients during prolonged bed rest or paralysis. It is essential to develop therapeutic strategies ...to combat the bone loss occurring in people afflicted with disuse atrophy on earth as well as in astronauts in space, especially during prolonged missions. Although several drugs have been demonstrated for treating postmenopausal osteoporosis or bone-related diseases, their effects on microgravity-induced bone loss are still unclear.
Materials and methods
Here, we employed the hindlimb-unloading (HLU) tail suspension model and compared the preventive efficiencies of five agents including alendronate (ALN), raloxifene (Rox), teriparatide (TPTD), anti-murine RANKL monoclonal antibody (anti-RANKL) and proteasome inhibitor bortezomib (Bzb) on mechanical unloading-induced bone loss. Bone mineral density (BMD) was measured by quantitative computed tomography. The osteoblastic and osteoclastic activity were measured by serum ELISA, histology analysis, and histomorphometric analysis.
Results
Compared to the control, ALN and anti-RANKL antibody could restore bone mass close to sham levels by inhibiting bone resorption. Bzb could increase the whole bone mass and strength by inhibiting bone resorption and promoting bone formation simultaneously. Meanwhile, Rox did not affect bone loss caused by HLU. TPTD stimulated cortical bone formation but the total bone mass was not increased significantly.
Conclusions
We demonstrated for the first time that anti-RANKL antibody and Bzb had a positive effect on preventing mechanical unloading-induced bone loss. This finding puts forward the potential use of anti-RANKL and Bzb on bone loss therapies or prophylaxis of astronauts in spaceflight.
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision ...medicine and drug discovery. In this study, we performed comparative studies between deep neural networks (DNN) and other ligand-based virtual screening (LBVS) methods to demonstrate that DNN and random forest (RF) were superior in hit prediction efficiency. By using DNN, several triple-negative breast cancer (TNBC) inhibitors were identified as potent hits from a screening of an in-house database of 165,000 compounds. In broadening the application of this method, we harnessed the predictive properties of trained model in the discovery of G protein-coupled receptor (GPCR) agonist, by which computational structure-based design of molecules could be greatly hindered by lack of structural information. Notably, a potent (~ 500 nM) mu-opioid receptor (MOR) agonist was identified as a hit from a small-size training set of 63 compounds. Our results show that DNN could be an efficient module in hit prediction and provide experimental evidence that machine learning could identify potent hits in silico from a limited training set.
ABCG2 is a member of the ATP binding cassette (ABC) transporters, which can pump a wide variety of endogenous and exogenous compounds out of cells. Widely expressed in stem cells, ABCG2 is also found ...to confer the side population phenotype and is recognized as a universal marker of stem cells. Although the precise physiological role of ABCG2 in stem cells is still unclear, existing data strongly suggest that ABCG2 plays an important role in promoting stem cell proliferation and the maintenance of the stem cell phenotype. In addition, ABCG2 is also found to be expressed in a number of cancer cells and appears to be a marker of cancer stem cells. Moreover, ABCG2 expression in tumors may contribute to their formation and progression. Thus, ABCG2 has potential applications in stem cell and tumor therapy.
The throat is an ecological assemblage involved human cells and microbiota, and the colonizing bacteria are important factors in balancing this environment. However, this bacterial community profile ...has thus been poorly investigated. The purpose of this study was to investigate the microbial biology of the larynx and to analyze the throat biodiversity in laryngeal carcinoma patients compared to a control population in a case-control study. Barcoded pyrosequencing analysis of the 16S rRNA gene was used. We collected tissue samples from 29 patients with laryngeal carcinoma and 31 control patients with vocal cord polyps. The findings of high-quality sequence datasets revealed 218 genera from 13 phyla in the laryngeal mucosa. The predominant communities of phyla in the larynx were Firmicutes (54%), Fusobacteria (17%), Bacteroidetes (15%), Proteobacteria (11%), and Actinobacteria (3%). The leading genera were Streptococcus (36%), Fusobacterium (15%), Prevotella (12%), Neisseria (6%), and Gemella (4%). The throat bacterial compositions were highly different between laryngeal carcinoma subjects and control population (p = 0.006). The abundance of the 26 genera was significantly different between the laryngeal cancer and control groups by metastats analysis (p<0.05). Fifteen genera may be associated with laryngeal carcinoma by partial least squares discriminant analysis (p<0.001). In summary, this study revealed the microbiota profiles in laryngeal mucosa from tissue specimens. The compositions of bacteria community in throat were different between laryngeal cancer patients and controls, and probably were related with this carcinoma. The disruption of this bio-ecological niche might be a risk factor for laryngeal carcinoma.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Significance Maize is a major crop and a model plant for studying C4 leaf development. However, its regulatory network of leaf development is poorly understood. We used transcriptomes of developing ...leaves to study gene-expression dynamics and coexpression to reveal functional transition during maize leaf development. More significantly, we developed methods to predict transcription factor-binding sites (TFBSs) and their cognate transcription factors (TFs) or to use the known Arabidopsis TF–TFBS pairs to predict the maize TF–TFBS pairs. In total, we predicted 1,340 novel TFBSs and 253 new TF–TFBS pairs in maize. Twelve predicted TF–TFBS interactions were validated by functional tests, suggesting that our methods perform well. Our study has significantly expanded our knowledge of the regulatory network of maize leaf development.
Maize is a major crop and a model plant for studying C4 photosynthesis and leaf development. However, a genomewide regulatory network of leaf development is not yet available. This knowledge is useful for developing C3 crops to perform C4 photosynthesis for enhanced yields. Here, using 22 transcriptomes of developing maize leaves from dry seeds to 192 h post imbibition, we studied gene up- and down-regulation and functional transition during leaf development and inferred sets of strongly coexpressed genes. More significantly, we developed a method to predict transcription factor binding sites (TFBSs) and their cognate transcription factors (TFs) using genomic sequence and transcriptomic data. The method requires not only evolutionary conservation of candidate TFBSs and sets of strongly coexpressed genes but also that the genes in a gene set share the same Gene Ontology term so that they are involved in the same biological function. In addition, we developed another method to predict maize TF–TFBS pairs using known TF–TFBS pairs in Arabidopsis or rice. From these efforts, we predicted 1,340 novel TFBSs and 253 new TF–TFBS pairs in the maize genome, far exceeding the 30 TF–TFBS pairs currently known in maize. In most cases studied by both methods, the two methods gave similar predictions. In vitro tests of 12 predicted TF–TFBS interactions showed that our methods perform well. Our study has significantly expanded our knowledge on the regulatory network involved in maize leaf development.
Children with developmental coordination disorder (DCD) have been commonly observed and drawn an increasing amount of attention over the past decades. The aim of the present study is to evaluate the ...origin, current hotspots, and research trends on children with DCD using a bibliometric tool. After searching with "children" and "developmental coordination disorder" as the "topic" and "title" words, respectively, 635 original articles with 12,559 references were obtained from the electronic databases, Web of Science Core Collection (WoSCC). CiteSpace V.5.7.R2 was used to perform the analysis. The number of publications in this field was increasing over the past two decades. John Cairney from the Department of Family Medicine, McMaster University, Canada, was found to be the most productive researcher. Meanwhile, McMaster University and Canada were the most productive research institution and country, respectively. Reference and journal co-citation analyses revealed the top landmark articles and clusters in this field.
was the most strength burst keyword. Moreover,
, and
will be the active research hotspots in future. These findings provide the trends and frontiers in the field of children with DCD, and valuable information for clinicians and scientists to identify new perspectives with potential collaborators and cooperative countries.
In this brief, a tunable Wilkinson power divider (WPD) with port match and isolation is proposed based on a reconfigurable compact structure. The reconfigurable unit is composed of one varactor ...cascaded by a transmission line (TL) with specific characteristic impedance and electrical length. Each unit can be alternately connected with two cascaded varactors to constitute a T-type structure, where the operating frequency can be selected by providing proper bias voltages for the three varactors. By adjusting the characteristic impedance and electrical length of the transmission line in each unit, the continuous tuning range can be easily extended to exceed 120% tunability. The reconfigurable units can also be flexibly configured to work in designated discrete tuning range as well. An experimental reconfigurable WPD with wide continuous tuning range of 0.96-1.73 GHz (57.3% tunability) is designed and fabricated. The measured results show that more than -20dB return losses and isolations can be realized in the entire tuning range.
Computing-in-memory (CIM) chips have demonstrated the potential high energy efficiency for low-power neural network (NN) processors. Even with energy-efficient CIM macros, the existing system-level ...CIM chips still lack deep exploration on sparsity and large models, which prevents a higher system energy efficiency. This work presents a CIM NN processor with more sufficient support of sparsity and higher utilization rate. Three key innovations are proposed. First, a set-associate blockwise sparsity strategy is designed, which simultaneously saves execution time, power, and storage space. Second, a ping-pong weight update mechanism is proposed for a higher utilization rate, enabling simultaneous execution of CIM and write operations. Third, an efficient CIM macro is implemented with adaptive analog-digital converter (ADC) precision for better sparsity utilization and performance-accuracy trade-off. The 65-nm fabricated chip shows 9.5-TOPS/W system energy efficiency at 4-bit precision, with 6.25<inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> actual improvement compared with a state-of-the-art CIM chip. Besides, this work supports high CIM execution accuracy on the ImageNet dataset.
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