Miracle-dz/UniFAR-FTUs huggingface dataset:
A genetic selection reveals functional metastable structures embedded in a toxin-encoding mRNA

Post-transcriptional regulation plays important roles to finely tune gene expression in bacteria. In particular, regulation of type I toxin-antitoxin (TA) systems is achieved through sophisticated mechanisms involving toxin mRNA folding. Here, we set up a genetic approach to decipher the molecular underpinnings behind the regulation of a type I TA in Helicobacter pylori. We used the lethality induced by chromosomal inactivation of the antitoxin to select mutations that suppress toxicity. We found that single point mutations are sufficient to allow cell survival. Mutations located either in the 5’ untranslated region or within the open reading frame of the toxin hamper its translation by stabilizing stem-loop structures that sequester the Shine-Dalgarno sequence. We propose that these short hairpins correspond to metastable structures that are transiently formed during transcription to avoid premature toxin expression. This work uncovers the co-transcriptional inhibition of translation as an additional layer of TA regulation in bacteria.
In Silico Investigation on Potential Off-target Binding and Repurposable Drugs in the Treatment of Spinal Muscular Atrophy

To effectively avoid unexpected adverse side-effect of commercial drugs, it is crucial to identify possible off-target bindings in their preclinical design processes. In this case study, identification of potential off-target bindings is focused on drugs that can potentially be repurposed to target DcpS, which is a valuable therapeutic target in the treatment of spinal muscular atrophy (SMA). By carrying out protein functional site similarity search, small molecule screening, and protein-ligand binding affinity profile analysis, possible off-target bindings in the treatment of SMA, as well as drugs that can potentially be repurposed to target DcpS are identified.
A Module System for Isolating Untrusted Software Extensions

With the recent advent of dynamically extensible software systems, in which software extensions may be dynamically loaded into the address space of a core application to augment its capabilities, there is a growing interest in protection mechanisms that can isolate untrusted software components from a host application. Existing language-based environments such as the JVM and the CLI achieves software isolation by an interposition mechanism known as stack inspection. Expressive as it is, stack inspection is known to lack declarative characterization and is brittle in the face of evolving software configurations. A run-time module system, ISOMOD, is proposed for the Java platform to facilitate software isolation. A core application may create namespaces dynamically and impose arbitrary name visibility policies to control whether a name is visible, to whom it is visible, and in what way it can be accessed. Because ISOMOD exercises name visibility control at load time, loaded code runs at full speed. Furthermore, because ISOMOD access control policies are maintained separately, they evolve independently from core application code. In addition, the ISOMOD policy language provides a declarative means for expressing a very general form of visibility constraints. Not only can the ISOMOD policy language simulate a sizable subset of permissions in the Java 2 security architecture, it does so with policies that are robust to changes in software configurations. The ISOMOD policy language is also expressive enough to completely encode a capability type system known as Discretionary Capability Confinement. In spite of its expressiveness, the ISOMOD policy language admits an efficient implementation strategy. In short, ISOMOD avoids the technical difficulties of interposition by trading off an acceptable level of expressiveness. Name visibility control in the style of ISOMOD is therefore a lightweight alternative to interposition.
Memory sharing predictor: the key to a speculative coherent DSM

Recent research advocates using general message predictors to learn and predict the coherence activity in distributed shared memory (DSM). By accurately predicting a message and timely invoking the necessary coherence actions, a DSM can hide much of the remote access latency. This paper proposes the Memory Sharing Predictors (MSPs), pattern-based predictors that significantly improve prediction accuracy and implementation cost over general message predictors. An MSP is based on the key observation that to hide the remote access latency, a predictor must accurately predict only the remote memory accesses (i.e., request messages) and not the subsequent coherence messages invoked by an access. Simulation results indicate that MSPs improve prediction accuracy over general message predictors from 81% to 93% while requiring less storage overhead. This paper also presents the first design and evaluation for a speculative coherent DSM using pattern-based predictors. We identify simple techniques and mechanisms to trigger prediction timely and perform speculation for remote read accesses. Our speculation hardware readily works with a conventional full-map write-invalidate coherence protocol without any modifications. Simulation results indicate that performing speculative read requests alone reduces execution times by 12% in our shared-memory applications.
SNP Discovery of Chicken Liver with Divergent Unsaturated Fatty Acid using Next Generation RNA Sequencing

ABSTRAKRNA sequencing memberikan peluang baru untuk mendeteksi variasi SNP (Single Nucleotide Polymorphism) pada perbedaan jaringan dengan perbedaan fenotipe. Tujuan dari penelitian ini adalah untuk mengkarakterisisasi penemuan SNP terbaru terkait perbedaan asam lemak tak jenuh pada ayam dengan menggunakan RNA sequencing. Sebanyak 6 sampel dipilih dari 62 sampel masing-masing 3 sampel tinggi dan 3 sampel rendah yang merepresentasikan perbedaan fenotip yang kontras terkait asam lemak tak jenuh dianalisis dengan menggunakan RNA Sequensing. Hasil identifikasi SNP memperlihatkan sebanyak 1208 SNP pada sampel tinggi dan rendah setelah disejajarkan dengan genom ayam Gallus gallus (GGA) v4.0. Sekitar 91% dari total SNP yang ditemukan memiliki tingkat polimorfisme yang tinggi pada 5 gen yang ditemukan terkait asam lemak yaitu gen SCD, COL6A2, CYP2J2L4, HSD17B4, dan SLC23A3. Gen SCD, HSD17B4, dan SLC23A3 memiliki jumlah titik mutasi dengan jumlah yang paling tinggi masing-masing berturut-turut 18, 13, dan 12 SNP. Tingkat level signifikan yang tinggi dan peranan dari ketiga gen tersebut yang sangat penting terkait komposisi asam lemak mengindikasikan bahwa gen SCD, HSD17B4, dan SLC23A3 merupakan tiga gen baru dan potensial untuk digunakan sebagai penanda seleksi kandungan asam lemak tak jenuh tinggi. Namun, hasil penelitian ini perlu divalidasi dan dikonfirmasi sebagai potensial kandidat gen dalam jumlah ayam yang lebih besar dan breed yang berbeda.Kata kunci: asam lemak, ayam, RNA-Seq, variasi transkriptomikABSTRACTRNA sequencing (RNA-Seq) reveals new opportunity for identification SNP discovery in different tissues with divergent phenotype. The objective of this study was to characterize SNP profile from divergent unsaturated fatty acids using RNA-Seq. Six liver samples were selected from 62 chicken which classified 3 high and 3 low unsaturated fatty acids were analyzed using RNA-Seq. The SNP identification showed 1208 SNPs in chicken samples and a large number of those corresponded to differences between high and low chicken genome assembly Gallus gallus (GGA) v4.0. Among them, about 91% of genes had multiple polymorphisms within 5 genes (SCD, COL6A2, CYP2J2L4, HSD17B4, and SLC23A3). The SCD, HSD17B4, and SLC23A3 contained the largest number of mutations with 18, 13, and 12 SNPs respectively. Combining the significant level of SNPs and gene function related with fatty acid composition allow us to suggest SCD, SLC23A3, HSD17B4 as the three novel and promising candidate genes for selecting unsaturated fatty acids. However, further validation is required to confirm the effect of these candidate genes in larger chicken populations.Keywords: chicken, fatty acids, RNA-Seq, transcriptome variants
Proof search in Hájek's basic logic

We introduce a proof system for Hájek's logic <b>BL</b> based on a relational hypersequents framework. We prove that the rules of our logical calculus, called <b>RHBL</b>, are sound and invertible with respect to any valuation of <b>BL</b> into a suitable algebra, called (ω)[0,1]. Refining the notion of reduction tree that arises naturally from <b>RHBL</b>, we obtain a decision algorithm for <b>BL</b> provability whose running time upper bound is 2<sup><i>O</i>(<i>n</i>)</sup>, where <i>n</i> is the number of connectives of the input formula. Moreover, if a formula is unprovable, we exploit the constructiveness of a polynomial time algorithm for leaves validity for providing a procedure to build countermodels in (ω)[0, 1]. Finally, since the size of the reduction tree branches is <i>O</i>(<i>n</i><sup>3</sup>), we can describe a polynomial time verification algorithm for <b>BL</b> unprovability.
Nonlinear dynamic modeling of pneumatic nailing devices

ABSTRACT This paper develops a mathematical modeling procedure for pneumatic nailing devices. The representation integrates all the operation phases composing the nailing process. The model accounts for the dynamics of the chamber pressures, the moving parts, the nonlinear interactions and impacts between the fixed and moving components, and includes the nail gun body and workpiece. All the system parameters integrated into the proposed model were established from experimental measurements. This model also integrates a nonlinear empirical formulation to predict the nail penetration resistance force. The final representation is validated through a comparison of the predicted piston motion and air pressures to experimental measurements made on a specific nail gun. The average of the percent error established at important time positions is lower than 7%.
Value Co-Creation in Supply Chains through IT Integration: The Role of Collaborative Network Structure

Firms today are increasingly dependent on external resources, and the need for collaboration across organizational boundaries continues to grow. Therefore, research has highlighted the importance of IT capabilities that integrate an organization with its network of partner organizations. However, antecedents of IT integration have been primarily derived from technology-driven contexts. In contrast, the role of organizational attributes remains largely unexplored. We therefore develop and test a theoretical model to examine collaborative network structure as an antecedent to IT integration. Based on this particular form of supply chain governance, we go on to explore how IT integration in conjunction with other capabilities leads to supply chain integration, thereby enabling the creation of value. Based on data gathered from 150 supply chain executives, the study provides evidence on the important role of organizational attributes and contributes to the stream of research that examines the role of interorganizational IT capabilities in co-creating value.
Involvement of Interleukin-10 in the Anti-Inflammatory Effect of Sanyinjiao (SP6) Acupuncture in a Mouse Model of Peritonitis

In this study, we determined the anti-inflammatory effect of manual acupuncture at the Sanyinjiao or Spleen 6 (SP6) point on carrageenan-induced peritonitis in mice and investigated mechanisms that may underlie this effect. In the first set of experiments, male Swiss mice were allocated into five groups: the control (sterile saline), dexamethasone (DEXA), invasive sham-acupuncture (non-acupoint), SP6 acupuncture and carrageenan-treated groups. Ten minutes after needle retention or 30 min after DEXA treatment, mice received an intraperitoneal injection of carrageenan (750 μg/mouse). After 4 h, total leukocyte and differential cell counts (neutrophils and mononuclear), myeloperoxidase (MPO) activity, vascular permeability and cytokine levels were evaluated. In another set of experiments, adrenalectomized (ADX) mice were used to study the involvement of the adrenal gland on the therapeutic effects of acupuncture. Mice were allocated into two groups: the ADX and sham-operated animals (Sham ADX) that were subdivided into four subgroups each: the control (sterile saline), DEXA, SP6 acupuncture and carrageenan-treated groups. The SP6 and DEXA treatments inhibited the inflammatory cell infiltration, vascular permeability and MPO activity in carrageenan-injected mice. In addition, the SP6 treatment also increased interleukin (IL)-10 levels. In contrast, when the animals were adrenalectomized, the SP6 treatment failed to reduce total leukocyte and the plasma extravasation. In conclusion, this study clearly demonstrates the anti-inflammatory effect of SP6 acupuncture in a model of carrageenan-induced peritonitis. Our results demonstrated that SP6 acupuncture depends of the adrenal glands and increased IL-10 levels to produce its anti-inflammatory action.
Exploring the feasibility of video mail for illiterate users

We present work that explores whether the asynchronous peer-to-peer communication capabilities of email can be made accessible to illiterate populations in the developing world. Building on metaphors from traditional communication systems such as postal mail, and relevant design principles established by previous research into text-free interfaces, we designed and evaluated a prototype asynchronous communication application built on standard email protocols. We considered different message formats -- text, freeform ink, audio, and video + audio -- and via iterative usage and design sessions, determined that video + audio was the most viable. Design alternatives for authentication processes were also explored. Our prototype was refined over three usability iterations, and the final version evaluated in a two-stage study with 20 illiterate users from an urban slum in Bangalore, India. Our results are mixed: On the one hand, the results show that users can understand the concept of video mail. They were able to successfully complete tasks ranging from account setup to login to viewing and creating mail, but required assistance from an online audio assistant. On the other hand, there were some surprising challenges such as a consistent difficulty understanding the notion of asynchronicity. The latter suggests that more work on the paradigm is required before the benefits of email can be brought to illiterate users.
The complete mitochondrial genome sequence of Cynoglossus roulei (Pleuronectiformes: Cynoglossidae)

Abstract The complete mitogenome of Cynoglossus roulei is 16,598 bp in length, containing 37 genes, among them, ND6 and eight tRNA genes are encoded by L-strand and other genes by H-strand, which are as same as those of typical mitogenome in fishes. The gene rearrangement related to one tRNA gene and CR were found, forming the gene order of CR-Gln-Ile-Met, which is the same as that of mitogenomes in all identified Cynoglossus speices. Phylogenetic tree based on 12 protein coding genes, tRNA and rRNA shows that C. roulei has a closer phylogenetic relationship to C. semilaevis.
SensIPro: Smart sensor analytics for Internet of things

Sensors play a vital role for realizing the vision of connected smart universe. In this paper, we present a novel sensor agnostic model SensIPro to perform robust unsupervised analysis of sensor data to support scalable analytics, a prime need for Internet of things (IoT). In the context of sensor analytics, outliers contain most delicate information. Analysis of anomaly or outlier is mostly dependent on the application domain as well as signal characteristics. Our proposed sensor analytics model SensIPro automates analysis of outliers based on inferring signal characteristics of diverse sensors from different IoT applications like healthcare, smart energy, smart transport. We apply relevant time-series algorithms using statistical analysis, information theoretic measure for sensor data analytics. We measure similarity/dissimilarity of the time series sensor data and correlate with detected outliers. Our algorithm does not require any prior knowledge of sensor data type and metadata. We present results and analysis based on real life heterogeneous sensor data sets. Obtained results further prove efficacy of the proposed mechanism.
Peste des petits ruminants virus detected in tissues from an Asiatic lion (Panthera leo persica) belongs to Asian lineage IV

In this study, peste des petits ruminants virus (PPRV) was detected in frozen pooled tissue samples from a dead Asiatic lion (Panthera leo persica). The samples were negative for canine distemper virus and positive for PPRV nucleic acids when tested with one-step RT-PCR using the appropriate virus-specific primers. Subsequent amplification, cloning, and sequencing of the partial nucleocapsid, matrix, and fusion genes confirmed the presence of PPRV nucleic acid. Comparative sequence and phylogenetic analyses of the structural genes of the isolated virus confirmed that the virus belonged to Asian lineage IV and was closely related to PPRV circulating in India.
An Investigation of the Prevalence and Causes of Malnutrition in Iran: a Review Article and Meta-analysis

Malnutrition is one of the most important health issues in developing countries, which might have adverse effects on the physical and intellectual health of children. The search process was started to find Persian and English articles published until September 2017 regarding the prevalence of malnutrition in children under the age of six in Iran using national and international databases including SID, Magiran, Irandoc, IranMedex, PubMed, Medline, Scopus, and ISI Web of Science. The data were analyzed using meta-analysis methods and the random effects model. The heterogeneity of studies was analyzed using the I2 index. The data were analyzed using R and STATA software (ver. 11.2). Twenty seven articles conducted from 2002 to 2016 were collected to be included in the meta-analysis process. The total sample size was 161,941 patients in an age range of 0-6 years. The final estimate of the prevalence of different forms of malnutrition through meta-analysis of data extracted from studies in Iran was as follows: severe underweight (1%; 95% confidence interval [CI], 1–1), moderate underweight (6%; 95% CI, 5–7), mild underweight (25%; 95% CI, 21–28), severe short stature (3%; 95% CI, 2–3), moderate short stature (8%; 95% CI, 6–9), mild short stature (21%; 95% CI, 17–24), severe slimness (1%; 95% CI, 1–1), moderate slimness (5%; 95% CI, 4–5) and mild slimness (20%; 95% CI, 17–24). Considering that the prevalence of malnutrition is relatively high in Iran, health authorities should plan to improve the nutritional status of children.
IoT: Application Protocols and Security

The Internet of Things (IoT) commands an ever-growing population of devices across the nation and abroad. The development of privacy concerns and security goals have not kept pace with the demand for new advances in IoT. We will discuss how the IoT currently functions and why the security in this field is important as the technology grows into every device we touch. This paper will also reference current security implementations and how they expect to cover this growing consumer demand for instant data on many devices at once. With IoT devices using less power and smaller processors, there is major discussion in the computing world on what methods succeed. As standard encryption methods are simply too much for small, low power devices to handle; IoT specific security methods should be highlighted.
MCAR: multi-class classification based on association rule

Summary form only given. Constructing fast, accurate classifiers for large data sets is an important task in data mining and knowledge discovery. In this research paper, a new classification method called multi-class classification based on association rules (MCAR) is presented. MCAR uses an efficient technique for discovering frequent items and employs a rule ranking method which ensures detailed rules with high confidence are part of the classifier. After experimentation with fifteen different data sets, the results indicated that the proposed method is an accurate and efficient classification technique. Furthermore, the classifiers produced are highly competitive with regards to error rate and efficiency, if compared with those generated by popular methods like decision trees, RIPPER and CBA.
Score-specific Non-maximum Suppression and Coexistence Prior for Multi-scale Face Detection

Face detection is an ultimate component to support various visual facial related tasks. However, detecting faces with extremely low resolution or high occlusion is still an open problem. In this paper, we propose a two-step general approach to refine the performance of modern face detectors according to human’s high-level context-aware ability. First, we propose Score-specific Non-Maximum Suppression (SNMS) to preserve overlapped faces. Second, we consider the coexistence prior among faces in the scene, which could raise the sensitivity of face detection in the crowd. When integrating our approach to the existing face detectors, most of them have better results on a challenging benchmark (WIDER FACE) and a newly proposed dataset (Faces in Crowd, FIC) made by us. Codes are available on https://github.com/AIoTP/SNMSandCoexistence.
Semantic memory activation in individuals at risk for developing Alzheimer disease

Objective: To determine whether whole-brain, event-related fMRI can distinguish healthy older adults with known Alzheimer disease (AD) risk factors (family history, APOE ε4) from controls using a semantic memory task involving discrimination of famous from unfamiliar names. Methods: Sixty-nine cognitively asymptomatic adults were divided into 3 groups (n = 23 each) based on AD risk: 1) no family history, no ε4 allele (control [CON]); 2) family history, no ε4 allele (FH); and 3) family history and ε4 allele (FH+ε4). Separate hemodynamic response functions were extracted for famous and unfamiliar names using deconvolution analysis (correct trials only). Results: Cognitively intact older adults with AD risk factors (FH and FH+ε4) exhibited greater activation in recognizing famous relative to unfamiliar names than a group without risk factors (CON), especially in the bilateral posterior cingulate/precuneus, bilateral temporoparietal junction, and bilateral prefrontal cortex. The increased activation was more apparent in the FH+ε4 than in the FH group. Unlike the 2 at-risk groups, the control group demonstrated greater activation for unfamiliar than familiar names, predominately in the supplementary motor area, bilateral precentral, left inferior frontal, right insula, precuneus, and angular gyrus. These results could not be attributed to differences in demographic variables, cerebral atrophy, episodic memory performance, global cognitive functioning, activities of daily living, or depression. Conclusions: Results demonstrate that a low-effort, high-accuracy semantic memory activation task is sensitive to Alzheimer disease risk factors in a dose-related manner. This increased activation in at-risk individuals may reflect a compensatory brain response to support task performance in otherwise asymptomatic older adults.