Sorting Sequence Research Articles

Ensemble Docking as a Tool for the Rational Design of Peptidomimetic Staphylococcus aureus Sortase A Inhibitors.

Sortase A (SrtA) of Staphylococcus aureus has long been shown to be a relevant molecular target for antibacterial development. Moreover, the designed SrtA inhibitors act via the antivirulence mechanism, potentially causing less evolutional pressure and reduced antimicrobial resistance. However, no marketed drugs or even drug candidates have been reported until recently, despite numerous efforts in the field. SrtA has been shown to be a tough target for rational structure-based drug design (SBDD), which hampers the regular development of small-molecule inhibitors using the available arsenal of drug discovery tools. Recently, several oligopeptides resembling the sorting sequence LPxTG (Leu-Pro-Any-Thr-Gly) of the native substrates of SrtA were reported to be active in the micromolar range. Despite the good experimental design of those works, their molecular modeling parts are still not convincing enough to be used as a basis for a rational modification of peptidic inhibitors. In this work, we propose to use the ensemble docking approach, in which the relevant SrtA conformations are extracted from the molecular dynamics simulation of the LPRDA (Leu-Pro-Arg-Asp-Ala)-SrtA complex, to effectively represent the most significant and diverse target conformations. The developed protocol is shown to describe the known experimental data well and then is applied to a series of new peptidomimetic molecules resembling the active oligopeptide structures reported previously in order to prioritize structures from this work for further synthesis and activity testing. The proposed approach is compared to existing alternatives, and further directions for its development are outlined.

A diagnostic strategy for IGBT open-circuit faults in modular multilevel converters based on improved diagnostic indices

Capacitor voltage deviations between two submodules (SMs) are always used as the diagnostic index for the insulated gate bipolar transistor (IGBT) open-circuit fault diagnosis in modular multilevel converters (MMCs). However, it is difficult to determine when MMCs operate in different conditions. To solve this issue, this article proposes a diagnostic strategy for IGBT open-circuit faults in MMCs based on improved diagnostic indices. First, two diagnostic indices, i.e., the concept of the count of periods during which the switching function is fixed (CSF) and capacitor voltage sorting sequence number (CSN) are proposed. On this basis, fault detection is implemented by judging the changing trend of the CSN in the SM whose CSF is the highest. The remaining malfunctioning SMs are located in turns by a similar process to fault detection. The proposed diagnostic indices show consistent faulty characteristics under different operating conditions, thus avoiding readjusting the threshold according to different operating conditions. In this sense, the proposed strategy can be easily used and promoted in different operating scenarios. Experimental results in a hardware-in-the-loop (HIL) platform verify the effectiveness of the proposed strategy.

Image sequence sorting algorithm for commercial tasks.

The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings. This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness. Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate. This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements.

PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling

Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics for both pin-level (slack, slew) and edge-level (net delay, cell delay), without time-consuming routing. However, it often suffers from signal decay and error accumulation due to the long timing paths in large-scale industrial circuits. To address these challenges, we propose a two-stage approach. First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist. Second, we use a novel node updating scheme for message passing on GCN, following the topological sorting sequence of the learned graph embedding and circuit graph. This scheme residually models the local time delay between two adjacent pins in the updating sequence, and extracts the lookup table information inside each cell via a new attention mechanism. To handle large-scale circuits efficiently, we introduce an order preserving partition scheme that reduces memory consumption while maintaining the topological dependencies. Experiments on 21 real world circuits achieve a new SOTA R2 of 0.93 for slack prediction, which is significantly surpasses 0.59 by previous SOTA method. Code will be available at: https://github.com/Thinklab-SJTU/EDA-AI.

Protein subcellular relocalization and function of duplicated flagellar calcium binding protein genes in honey bee trypanosomatid parasite.

The honey bee trypanosomatid parasite, Lotmaria passim, contains two genes that encode the flagellar calcium binding protein (FCaBP) through tandem duplication in its genome. FCaBPs localize in the flagellum and entire body membrane of L. passim through specific N-terminal sorting sequences. This finding suggests that this is an example of protein subcellular relocalization resulting from gene duplication, altering the intracellular localization of FCaBP. However, this phenomenon may not have occurred in Leishmania, as one or both of the duplicated genes have become pseudogenes. Multiple copies of the FCaBP gene are present in several Trypanosoma species and Leptomonas pyrrhocoris, indicating rapid evolution of this gene in trypanosomatid parasites. The N-terminal flagellar sorting sequence of L. passim FCaBP1 is in close proximity to the BBSome complex, while that of Trypanosoma brucei FCaBP does not direct GFP to the flagellum in L. passim. Deletion of the two FCaBP genes in L. passim affected growth and impaired flagellar morphogenesis and motility, but it did not impact host infection. Therefore, FCaBP represents a duplicated gene with a rapid evolutionary history that is essential for flagellar structure and function in a trypanosomatid parasite.

Upaya Peningkatan Kemampuan Motorik Halus Dengan Mengurutkan Bentuk Geometri Melalui Kegiatan Meronce (Siswa Sswi Kelompok A RA Daruttaqwa Ponggok)

This study aims to find out the application of meronce activities by sorting geometric shapes in improving fine motor skills, where the activity of sorting geometric shapes previously did not use media or only by working on student worksheets which were considered less efficient. The activity of sorting geometric shapes by tracing geometric shapes can give an interesting impression to children because they don’t feel bored with these activities and they feel happy. Researchers use the activity of tracing geometric shapes to improve fine motor skills by sequencing with the aim that children become more understanding and children don’t get bored while learning. The method used in this study is Classroom Action Research with the Kurt Lewin model. This study consisted of pre-cycle, cycle I, cycle II and cycle III. In cycle I, cycle II and cycle III consists of 4 stages, namely planning, implementation, observation and reflection. Data collection techniques in this study used observation, interviews, non-test assessments and documentation in the form of photos. The analysis technique in this study uses a qualitative description analysis. The results of this study are as follows: 1) The ability to sort geometric shapes through beading activities at RA Daruttaqwa Ponggok obtained satisfactory results. This can be seen from the results of observations of children’s activities in the first cycle of 64.70 (enough), the second cycle of 85.29 (good) and in the third cycle increased to 89.70 (very good). This is all because researchers and teachers can pay attention to every ability that is in each student, so that all students get the same teaching with various forms of assignment techniques. 2) There is an increase in the ability to sort after the implementation of the activity of tracing geometric shapes can be seen from the results of student learning completeness in that the ability to sort sequences develops very well. This can be seen from the average value showing the results of the first cycle of 50.47 (Starting to Develop), in the second cycle of 71.54 (Developing According to Expectations) and in the third cycle of 85.26 (Developing According to Expectations) and has fulfilled the establishe

The scaling of mental computation in a sorting task

Many cognitive models provide valuable insights into human behavior. Yet the algorithmic complexity of candidate models can fail to capture how human reaction times scale with increasing input complexity. In the current work, we investigate the algorithms underlying human cognitive processes. Computer science characterizes algorithms by their time and space complexity scaling with problem size. We propose to use participants’ reaction times to study how human computations scale with increasing input complexity. We tested this approach in a task where participants had to sort sequences of rectangles by their size. Our results showed that reaction times scaled close to linearly with sequence length and that participants learned and actively used latent structure whenever it was provided. This behavior was in line with a computational model that used the observed sequences to form hypotheses about the latent structures, searching through candidate hypotheses in a directed fashion. These results enrich our understanding of plausible cognitive models for efficient mental sorting and pave the way for future studies using reaction times to investigate the scaling of mental computations across psychological domains.

Investigation of the inhibitory effect and mechanism of EGCG against Streptococcus suis sortase A.

Streptococcus suis (S. suis) seriously harms people and animals, and importantly, causes great economic losses in the pig industry. Similar to most gram-positive pathogenic bacteria, sortase A (SrtA) of S. suis can mediate the anchoring of a variety of virulence factors that contain specific sorting sequences to the surface of the bacterial cell wall envelope and participate in pathogenicity. The purpose of this study is to clarify the molecular mechanism of epigallocatechin-3-gallate (EGCG) inhibiting Streptococcus suis SrtA and provide more evidence for the development of novel anti- Streptococcus suis infections drugs. Through the SrtA substrate cleavage experiment, we found that the main component of green tea, EGCG, can effectively inhibit the enzyme activity of Streptococcus suis SrtA. Further, molecular docking and molecular dynamics simulation were used to clarify the molecular mechanism of its inhibitory effect, demonstrating that EGCG mainly interacts with amino acids at 113 and 115 to exert its inhibitory function. It was previously found that EGCG can inhibit the growth of S. suis and reduce the activity of suilysin and inhibit its expression. Our research reveals a new function of EGCG in S. suis infection. Our research proves that EGCG can effectively inhibit the transpeptidase activity of SrtA. We also clarify the accompanying molecular mechanism, providing more sufficient evidence for the use of EGCG as a potential lead compound against S. suis infection.

Programmable Control of Nanoliter Droplet Arrays using Membrane Displacement Traps.

A unique droplet microfluidic technology enabling programmable deterministic control over complex droplet operations is presented. The platform provides software control over user-defined combinations of droplet generation, capture, ejection, sorting, splitting, and merging sequences to enable the design of flexible assays employing nanoliter-scale fluid volumes. The system integrates a computer vision system with an array of membrane displacement traps capable of performing selected unit operations with automated feedback control. Sequences of individual droplet handling steps are defined through a robust Python-based scripting language. Bidirectional flow control within the microfluidic chips is provided using an H-bridge channel topology, allowing droplets to be transported to arbitrary trap locations within the array for increased operational flexibility. By enabling automated software control over all droplet operations, the system significantly expands the potential of droplet microfluidics for diverse biological and biochemical applications by combining the functionality of robotic liquid handling with the advantages of droplet-based fluid manipulation.

Towards One Shot & Pick All: 3D-OAS, an end-to-end framework for vision guided top-down parcel bin-picking using 3D-overlapping-aware instance segmentation and GNN

Robotic Grasping and sorting is acknowledged as the most fundamental and significant manipulation task in industry. An ultimate goal of a Vision guided Robotic grasping system is to precisely and efficiently sort maximum objects with minimum inference time of vision system. So far, applicable end-to-end perception and autonomously picking of hierarchically stacked objects has not been intensively reported in previous works. Especially, in the scenario of top-down parcel bin-picking where robots are required to perceive and pick up parcels from random stacks. In this work, we focus on this challenging task by putting forward a novel end-to-end parcel bin-picking model termed 3D-OAS. Our proposal combines a 3D overlapping-aware instance segmentation and directed graph to describe the hierarchical structure of stacked objects from a top-down angle and a graph-neural-network is introduced to solve the optimal sorting orders. The experiment was conducted via a set of Vision guided Delta-Parallel robotic grasping system with a top-down RGB-D camera. Experimental Results proved the feasibility of our proposal, it could hierarchically segment stacked objects and solve sorting sequence with minimum one shot.

Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources.

In this study, genetic diversity and germplasm identification of 28 alfalfa germplasm cultivars materials were evaluated by analyzing their internal transcribed spacer 2 (ITS2), trnL-F, and psbA-trnH sequences to provide the innovative reference of alfalfa varieties genetic diversity and identify research. The results showed that the fragment average length of ITS2, trnL-F, and psbA-trnH sorting sequences were 455.7 bp, 230.3 bp, and 345.6 bp, respectively. The ITS2 sequence was too conservative to reflect the individual differences between intercultivars and intracultivars in the preliminary experiment. Furthermore, trnL-F and psbA-trnH sequence differences were relatively small between intercultivars but significant between intracultivars. Alfalfa cultivars were divided into four groups by sequence similarity clustering. Alfalfa cultivars trnL-F and psbA-trnH sequences have apparent differences, showing that chloroplast conservative sequences were independent evolution. Compared with trnL-F and psbA-trnH sequences of alfalfa cultivars, psbA-trnH sequence has abundant variation sites and can better reflect the differences between cultivars than the trnL-F sequence. Therefore, the psbA-trnH sequence can identify different alfalfa cultivars and establish the DNA sequence fingerprint.

On Modeling of Sorted Cost Consensus Negotiation Considering Efficiency and Time Based on the Stochastic Programming

In the consensus reaching process (CRP) permitting negotiation, the efficiency of negotiation is affected by the order of negotiation with decision makers (DMs), the time, and the number of moderators. In this paper, the sorted negotiation against DMs considering efficiency and time is initiated into consensus decision making, which can improve the speed and effectiveness of consensus. Based on the opinion dynamics (opinion evolution), uniform and normal distributions are used to describe the uncertainty of DMs’ opinions and negotiation time, the opinion order efficiency and cost coefficient are coined, and the cost-constrained optimal efficiency sorted negotiation model and the optimal efficiency sorted negotiation model involving multiple moderators and time constraints are respectively constructed. The optimal solution of the chance-constrained model is obtained in the context of China’s urban demolition negotiation using an improved genetic algorithm, and an optimum set of influential individuals based on opinion similarity is introduced so that assessment criteria for validating the reasonableness of the sorting sequence are determined. Sorted consensus negotiation combined with complex scenarios such as different representation formats of opinions, characteristics of DMs, other solving algorithms, Bayesian dynamics, etc. can be included in future works.

Characterization of interactions of dietary cholesterol with the murine and human gut microbiome

Consumption of dietary lipids, such as cholesterol, modulates the gut microbiome with consequences for host health through the production of microbiome-derived metabolites. Despite the implications for host metabolism, a limited number of specific interactions of the gut microbiome with diet-derived lipids have been characterized. This is partially because obtaining species-level resolution of the responsible taxa can be challenging and additional approaches are needed to identify health-relevant metabolites produced from cholesterol–microbiome interactions. Here we performed bio-orthogonal labelling sort sequence spectrometry, a click chemistry based workflow, to profile cholesterol-specific host–microbe interactions. Mice were exposed to an alkyne-functionalized variant of cholesterol and 16S ribosomal RNA gene amplicon sequencing of faecal samples identified diet-derived cholesterol-interacting microbes from the genera Bacteroides, Bifidobacterium, Enterococcus and Parabacteroides. Shotgun metagenomic analysis provided species-level resolution of diet-derived cholesterol-interacting microbes with enrichment of bile acid-like and sulfotransferase-like activities. Using untargeted metabolomics, we identify that cholesterol is converted to cholesterol sulfate in a Bacteroides-specific manner via the enzyme BT_0416. Mice monocolonized with Bacteroides thetaiotaomicron lacking Bt_0416 showed altered host cholesterol and cholesterol sulfate compared with wild-type mice, identifying a previously uncharacterized microbiome-transformation of cholesterol and a mechanism for microbiome-dependent contributions to host phenotype. Moreover, identification of a cholesterol-responsive sulfotransferase in Bacteroides suggests diet-dependent mechanisms for altering microbiome-specific cholesterol metabolism. Overall, our work identifies numerous cholesterol-interacting microbes with implications for more precise microbiome-conscious regulation of host cholesterol homeostasis.

A method for assessing the performance of sorting unit operations in a material recovery facility based on waste characterizations

AbstractThe determination of the separation efficiencies of mechanical sorting equipment is essential to improve the performance of material recovery facilities (MRFs). However, it is often a challenge to obtain these efficiencies due to the high complexity of MRFs, which often comprise several recirculation streams. In this paper, a methodology to determine the equipment separation efficiencies of a complex MRF is described and applied to an actual sorting centre located in the province of Quebec, Canada. Transfer coefficients for every unit operation and several material types, together with all material flows within the facility, have been determined for an MRF processing a stream of commingled recyclable materials. This work also provides a rare dataset of separation efficiencies for several mechanical sorting unit operations. The methodology is validated by comparing experimental data and model predictions for the recovery and purity of all main output streams. The results contribute to identify several avenues for process performance improvement, like adding a magnetic separation at the beginning of the sorting sequence or improving the separation of the 2D‐type materials collected from the second ballistic separator by changing the operation conditions or adding a quality control step. Moreover, the results also provide valuable information about material recovery and purity to help managers improve the process performance. Finally, a scenario analysis demonstrates that the performance of a second ballistic separator has an important impact on the sorting process's global efficiency and that recirculating a fraction of the rejects output stream has a negligible impact on the global performance.

Contourite systems around the northern exit from the Vema Channel

Several drifts of different types and sizes are identified near the northern exit of the Vema Channel, within the projects Neogene-Quaternary contourites of the Central and South Atlantic and Lateral sedimentation in the deep ocean (on the examples from the Central and South-Western Atlantic). Herein, we discuss new results of a multidisciplinary study of sixteen sediment cores and high-resolution sub-bottom (seismoacoustic) profiles, to address the impact of Lower Circumpolar Deep Water (LCDW) and Antarctic Bottom Water (AABW) passing through the Vema gateway on contourite accumulation and erosion. To the west of the northern exit from the Vema Channel, an anticyclonic gyre of LCDW is instrumental in the development of contourite drifts and sediment waves. The contourite origin of generally silty terrigenous sediments in the Santa Catarina Plateau – São Paulo Plateau area is ascertained by both morpho-seismic and sedimentary characteristics. Sedimentary features include: a lack of primary sedimentary structures and pervasive bioturbation; sharp erosional contacts, local hiatuses and stiff mud horizons; some sandy/silty layers and indistinct bedding; mostly fine grain-size, very poor sorting and distinctive bi-gradational sequences; a high degree of correlation between the content of sortable silt (SS) in the total < 63 μm size fraction and SS mean sizes in all eight cores studied. Biostratigraphy, oxygen isotope records and ten new accelerator mass-spectrometry (AMS) 14C dates reveal the mid to late Quaternary age of this mainly terrigenous contourite depositional system (CDS).By contrast, the dominantly calcareous Ioffe Drift, overlying the Florianopolis Fracture Zone (FFZ) ridge to the northeast of the Vema Channel, is far from any source of terrigenous material, and accumulated in an area of low biological productivity. The overall asymmetric geometry, mainly lenticular, upward-convex seismic units separated by erosional unconformities, reflection truncation, small-scale moats dissecting the drift surface collectively indicate its contourite origin. The interpretation of the drift as the CDS is supported by sediment characteristics including common hiatuses corresponding in some cases to erosional contacts, pervasive bioturbation, generally poor sediment sorting, and more or less well-developed bi-gradational sequences. The erosion and deposition in the drift area are mostly controlled by the main LCDW branch following northeastward along the FFZ. Extensive erosion by bottom currents has created numerous hiatuses and markedly reduced the thickness of drift's sediments. The stratigraphic record from the Ioffe Drift sediment cores reveals an Upper Pliocene – Quaternary succession. The results of detailed analyses of six cores from the Ioffe Drift area, compared with two cores and the upper part of DSDP Site 516 from the Rio Grande Rise, notably the use of a SS analogue, provide new information on calcareous biogenic contourites deposited in the pelagic realm of the South Atlantic.

Mixed modeling approach for mechanical sorting processes based on physical properties of municipal solid waste

Material recovery facilities (MFRs) play an important role in today’s waste management systems to maximize recycling efficiency for several waste materials. These facilities face multiple challenges, often due to a poor understanding of the mechanisms occurring within the sorting equipment. Improving modeling techniques of these unit operations appear to be a promising opportunity to mitigates these challenges. Mechanical sorting efficiency of municipal solid waste is often predicted from simple transfer coefficients, which are obtained for a specific set of operating conditions of the sorting equipment and sorting sequence configuration. When these transfer coefficients are used in situations that are different to those in which they were obtained, poor predictions can be expected. To overcome these limitations, a new predictive tool, based on the integration of mechanistic models and transfer coefficients, is presented. Mechanistic models are developed only for the most influential unit operations in a MRF, in order to predict their sorting efficiency based on the physical phenomena occurring. Integration of these models with the use of transfer coefficients for the other unit operations allows the entire predictive tool to remain as simple as possible while providing high prediction accuracy and flexibility. The use of the tool is validated with a real case study of a material recovery facility. Results indicate a good prediction of the mass flows of the facility. Moreover, a new modeling technique is proposed for the representation of a ballistic separator based on the shape factor of the waste items.

A new 1.375-approximation algorithm for sorting by transpositions

Backgroundsorting by transpositions (SBT) is a classical problem in genome rearrangements. In 2012, SBT was proven to be mathcal {NP}-hard and the best approximation algorithm with a 1.375 ratio was proposed in 2006 by Elias and Hartman (EH algorithm). Their algorithm employs simplification, a technique used to transform an input permutation pi into a simple permutation{hat{pi }}, presumably easier to handle with. The permutation {hat{pi }} is obtained by inserting new symbols into pi in a way that the lower bound of the transposition distance of pi is kept on {hat{pi }}. The simplification is guaranteed to keep the lower bound, not the transposition distance. A sequence of operations sorting {hat{pi }} can be mimicked to sort pi.Results and conclusionsFirst, using an algebraic approach, we propose a new upper bound for the transposition distance, which holds for all S_n. Next, motivated by a problem identified in the EH algorithm, which causes it, in scenarios involving how the input permutation is simplified, to require one extra transposition above the 1.375-approximation ratio, we propose a new approximation algorithm to solve SBT ensuring the 1.375-approximation ratio for all S_n. We implemented our algorithm and EH’s. Regarding the implementation of the EH algorithm, two other issues were identified and needed to be fixed. We tested both algorithms against all permutations of size n, 2le n le 12. The results show that the EH algorithm exceeds the approximation ratio of 1.375 for permutations with a size greater than 7. The percentage of computed distances that are equal to transposition distance, computed by the implemented algorithms are also compared with others available in the literature. Finally, we investigate the performance of both implementations on longer permutations of maximum length 500. From the experiments, we conclude that maximum and the average distances computed by our algorithm are a little better than the ones computed by the EH algorithm and the running times of both algorithms are similar, despite the time complexity of our algorithm being higher.

Cotangent Similarity Measure of Consistent Neutrosophic Sets and Application to Multiple Attribute Decision-Making Problems in Neutrosophic Multi-Valued Setting

A neutrosophic multi-valued set (NMVS) is a crucial representation for true, false, and indeterminate multi-valued information. Then, a consistent single-valued neutrosophic set (CSVNS) can effectively reflect the mean and consistency degree of true, false, and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS. However, there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature. This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting. The cosine similarity measures show the cosine of the angle between two vectors projected into a multidimensional space, rather than their distance. The cotangent similarity measures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent. Then, a decision-making approach is presented in view of the established cotangent similarity measures in the case of NMVSs. Finally, the developed decision-making approach is applied to selection problems of potential cars. The proposed approach has obtained two different results, which have the same sort sequence as the compared literature. The decision results prove its validity and effectiveness. Meantime, it also provides a new manner for neutrosophic multi-valued decision-making issues.

MicroRNA sequence codes for small extracellular vesicle release and cellular retention.

Exosomesand othersmall extracellular vesicles (sEVs) provide a unique mode of cell-to-cell communication in which microRNAs (miRNAs) produced and released from one cell are taken up by cells at a distance where they can enact changes in gene expression1-3. However, the mechanism by which miRNAs are sorted into exosomes/sEVs or retained in cells remains largely unknown. Here we demonstrate that miRNAs possess sorting sequences that determine their secretion in sEVs (EXOmotifs) or cellular retention (CELLmotifs) and that different cell types, including white and brown adipocytes, endothelium, liver and muscle, make preferential use of specific sorting sequences, thus defining the sEV miRNA profile of that cell type. Insertion or deletion of these CELLmotifs or EXOmotifs in a miRNA increases or decreases retention in the cell of production or secretion into exosomes/sEVs. Two RNA-binding proteins, Alyref and Fus, are involved in the export of miRNAs carrying one of the strongest EXOmotifs, CGGGAG. Increased miRNA delivery mediated by EXOmotifs leads to enhanced inhibition of target genes in distant cells. Thus, this miRNA code not only provides important insights that link circulating exosomal miRNAs to tissues of origin, but also provides an approach for improved targeting in RNA-mediated therapies.

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Reinforcement learning (RL) has been applied to prioritizing test cases in Continuous Integration (CI) testing, where the reward plays a crucial role. It has been demonstrated that historical information-based reward function can improve the effectiveness of the test case prioritization (TCP). However, the inherent character of frequent iterations in CI can produce a considerable accumulation of historical information, which may decrease TCP efficiency and result in slow feedback. In this paper, the partial historical information is considered in the reward computation, where sliding window techniques are adopted to capture the possible efficient information. Firstly, the fixed-size sliding window is introduced to set a fixed length of recent historical information for each CI test. Then dynamic sliding window techniques are proposed, where the window size is continuously adaptive to each CI testing. Two methods are proposed, the test suite-based dynamic sliding window and the individual test case-based dynamic sliding window. The empirical studies are conducted on fourteen industrial-level programs, and the results reveal that under limited time, the sliding window-based reward function can effectively improve the TCP effect, where the NAPFD (Normalized Average Percentage of Faults Detected) and Recall of the dynamic sliding windows are better than that of the fixed-size sliding window. In particular, the individual test case-based dynamic sliding window approach can rank 74.18% failed test cases in the top 50% of the sorting sequence, with 1.35% improvement of NAPFD and 6.66 positions increased in TTF (Test to Fail).