MulTFBS: A Spatial-Temporal Network with Multichannels for Predicting Transcription Factor Binding Sites.

Revealing the mechanisms that influence transcription factor binding specificity is the key to understanding gene regulation. In previous studies, DNA double helix structure and one-hot embedding have been used successfully to design computational methods for predicting transcription factor binding sites (TFBSs). However, DNA sequence as a kind of biological language, the method of word embedding representation in natural language processing, has not been considered properly in TFBS prediction models. In our work, we integrate different types of features of DNA sequence to design a multichanneled deep learning framework, namely MulTFBS, in which independent one-hot encoding, word embedding encoding, which can incorporate contextual information and extract the global features of the sequences, and double helix three-dimensional structural features have been trained in different channels. To extract sequence high-level information effectively, in our deep learning framework, we select the spatial-temporal network by combining convolutional neural networks and bidirectional long short-term memory networks with attention mechanism. Compared with six state-of-the-art methods on 66 universal protein-binding microarray data sets of different transcription factors, MulTFBS performs best on all data sets in the regression tasks, with the average R2 of 0.698 and the average PCC of 0.833, which are 5.4% and 3.2% higher, respectively, than the suboptimal method CRPTS. In addition, we evaluate the classification performance of MulTFBS for distinguishing bound or unbound regions on TF ChIP-seq data. The results show that our framework also performs well in the TFBS classification tasks.

Comparing the impact of different adsorbed layers on the local glass transition of polymer matrices.

Chain adsorption to nanofiller interfaces creating bound layers has become central to understanding property changes in polymer nanocomposites. We determine the impact different kinds of adsorbed layers can have on the local glass transition temperature Tg of polymer matrices in a model film system using a localized fluorescence method. This work compares the adsorption and desorption of adsorbed layers grown in solution with the solution washing characteristics of adsorbed layers formed in the melt, leveraging knowledge about polymer adsorption in solution to infer the structure of adsorbed layers formed in the melt. In the limit of zero concentration after a long time in solution, we find that both kinds of adsorbed layers reach the same limiting adsorbed amount h∞(c → 0) ≈ 1 nm, appearing to evolve to the same thermodynamic equilibrium state of a near monolayer of surface coverage. We propose that melt annealing leads to a coarsening of polymer segment-surface contacts, increasing the length of trains and shrinking loops and tails, slowing the subsequent kinetics of these adsorbed chains in solution. Considering how the pyrene-labeled chains intermix with the adsorbed layer enables us to discriminate between the impact of tails, loops, and trains as threading of loops takes longer. We find that large fluffy loops, tails, and trains have little to no impact on the local Tg. A large 30 K increase in local Tg is observed for 30-min solvent washed well-annealed films at long intermixing times that we attribute to the threading of small tight loops.

Near-infrared mechanoluminescence sensor: A new method for on-site infrastructure detection.

Near-infrared mechanoluminescence is a phenomenon that produces high penetrating near-infrared light under external stimulation. Near-infrared light coincides with the biological window, lower optical loss, and the fact that the mechanoluminescence material is a medium that converts mechanical energy into light energy. The near-infrared mechanoluminescence material has potential application prospects in the fields of biological imaging, medical diagnosis, and monitoring of building materials. In this article, we report on a perovskite-type Sr3Sn2O7:Nd3+ near-infrared mechanoluminescence material, and its peaks locate in the first near-infrared window (800-1000 nm) and the second near-infrared window (1080, 1350 nm), respectively. Under the condition of pre-sintering with Li2CO3 as flux, the best sintering conditions are obtained, and the luminescence of material is in perfect agreement with the applied mechanical stress. In addition, a near-infrared mechanoluminescence sensor is proposed to solve the problem of building damage and timely maintenance.

Exploring the prebiotic potential of hydrolyzed fucoidan fermented in vitro with human fecal inocula: Impact on microbiota and metabolome.

Fucoidan is widely applied in food and pharmaceutical industry for the promising bioactivities. Low-molecular weight hydrolyzed fucoidan has gained attention for its beneficial health effects. Here, the modulation on microbiome and metabolome features of fucoidan and its acidolyzed derivatives (HMAF, 1.5-20 kDa; LMAF, <1.5 kDa) were investigated through human fecal cultures. Fucose is the main monosaccharide component in fucoidan and LMAF, while HMAF contains abundant glucuronic acid. LMAF fermentation resulted in the highest production of short-chain fatty acids, with acetate and propionate reaching maximum levels of 13.46 mmol/L and 11.57 mmol/L, respectively. Conversely, HMAF exhibited a maximum butyrate production of 9.28 mmol/L. Both fucoidan and acidolyzed derivatives decreased the abundance of Escherichia-Shigella and Klebsiella in human fecal cultures. Fucoidan and HMAF prefer to improve the abundance of Bacteroides. However, LMAF showed positive influence on Bifidobacterium, Lactobacillus, and Megamonas. Untargeted metabolome indicated that fucoidan and its derivatives mainly altered the metabolic level of lipids, indole, and their derivatives, with fucoidan and HMAF promoting higher level of indole-3-propionic acid and indole-3-carboxaldehyde compared to LMAF. Considering the chemical structural differences, this study suggested that hydrolyzed fucoidan can provide potential therapeutic applications for targeted regulation of microbial communities.

Microstrain-Stimulated Elastico-Mechanoluminescence with Dual-Mode Stress Sensing.

Elastico-mechanoluminescence technology has shown significant application prospects in stress sensing, artificial skin, remote interaction, and other research areas. Its progress mainly lies in realizing stress visualization and 2D or even 3D stress-sensing effects using a passive sensing mode. However, the widespread promotion of mechanoluminescence (ML) technology is hindered by issues such as high stress or strain thresholds and a single sensing mode based on luminous intensity. In this study, a highly efficient green-emitting ML with dual-mode stress-sensing characteristics driven by microscale strain is developed using LiTaO3:Tb3+. In addition to single-mode sensing based on the luminous intensity, the self-defined parameter (Q) is also introduced as a dual-mode factor for sensing the stress velocity. Impressively, the fabricated LiTaO3:Tb3+ film is capable of generating discernible ML signals even when supplied with strains as low as 500 µst. This is the current minimum strain value that can drive green-emitting ML. This study offers an ideal photonic platform for exploring the potential applications of rare-earth-doped elastico-ML materials in remote interaction devices, high-precision stress sensors, and single-molecule biological imaging.

Weight Loss Using an mHealth App Among Individuals With Obesity in Different Economic Regions of China: Cohort Study.

Background: With the increasing prevalence of obesity, weight loss has become a critical issue in China. Self-managed weight loss through a mobile health (mHealth) app may be a prospective method. However, its practicability in different economic regions of China is unknown. Objective: This study aims to evaluate the effectiveness of self-managed weight loss through an mHealth app among individuals with obesity in different economic regions of China and to demonstrate the feasibility of online self-management for weight loss. Methods: A total of 165,635 Chinese adults who signed up for the mHealth app were included to analyze the body composition characteristics of individuals from different economic regions by χ2 analyses. Furthermore, 2 types of participants with obesity using mHealth monitoring, including 74,611 participants with a BMI ≥24.0 kg/m2 and 22,903 participants with a normal BMI but an excessive percentage of body fat (PBF), were followed for 6 months to explore the weight loss and fat loss effects in different economic regions of China and to find independent predictors associated with weight loss success by 2-tailed Student t test and multivariable logistic regression analysis. Results: There were 32,129 users from low-income regions and 133,506 users from high-income regions. The proportion of users with obesity in low-income regions was higher than in high-income regions, both based on BMI (15,378/32,129, 47.9% vs 59,233/133,506, 44.4%; P<.001) and PBF classification (19,146/32,129, 59.6% vs 72,033/133,506, 54%; P<.001). Follow-up analyses showed that the weight loss effect among participants with overweight or obesity in low-income regions was greater than in high-income regions (mean -4.93, SD 6.41 vs mean -4.71, SD 6.14 kg; P<.001), while there was no significant difference in fat loss (mean -2.06%, SD 3.14% vs mean -2.04%, SD 3.19%; P=.54). In the population with normal-weight obesity, the weight loss (mean -2.42, SD 4.07 vs mean -2.23, SD 4.21 kg; P=.004) and fat loss effects (mean -1.43%, SD 2.73% vs mean -1.27%, SD 2.63%; P<.001) were stronger in high-income regions than in low-income regions. In addition, multivariable logistic regression analyses showed that age, baseline PBF, skeletal muscle rate, and measurement frequency were related to weight loss, whereas gender and baseline body metabolic rate only showed a correlation with weight loss in the population in high-income regions. Conclusions: This study found a high proportion of mHealth app users with obesity in low-income regions. Individuals with overweight and obesity in different economic regions of China experienced significant weight loss and fat loss using an mHealth app. Moreover, individuals in high-income regions paid more attention to body fat and had better fat reduction effects. Therefore, promoting self-monitoring of weight and PBF through an mHealth app could be an important intervention that could be implemented across all regions of China.

Endothelial TET2 regulates the white adipose browning and metabolism via fatty acid oxidation in obesity.

Obesity is a complex metabolic disorder, manifesting as excessive accumulation of body fat. Ten-Eleven Translocation-2 (TET2) has garnered significant attention in the context of obesity due to its crucial role in epigenetic regulation and metabolic homeostasis. In this study, we aimed to investigate the effect of endothelial TET2 on obesity and explore the potential mechanism. We generated endothelial cell-specific TET2 deficiency mice and investigated endothelial TET2 using transcriptomic and epigenomic analyses. We determined the downregulation of endothelial TET2 in white adipose tissues. Furthermore, we identified that endothelial TET2 loss aggravated high-fat diet-induced obesity by inhibiting vascularization and thus suppressing white adipose tissue browning. Mechanistically, endothelial TET2 modulates obesity by engaging in endothelial fatty acid oxidation and angiocrine-mediated secretion of bone morphogenetic protein 4 (BMP4), in which nuclear factor-erythroid 2-related factor 2 (NRF2) serves as a key mediator. Our study reveals that endothelial TET2 regulates white adipose tissue browning by interacting with NRF2 to facilitate fatty acid oxidation and lipolysis in adipocytes.

Drug screening on digital microfluidics for cancer precision medicine.

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm2 chip in a device measuring 23 × 16 × 3.5 cm3. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.