Miracle-dz/UniFAR-FTUs huggingface dataset:
Fungi Unearthed: Transcripts Encoding Lignocellulolytic and Chitinolytic Enzymes in Forest Soil

Background Fungi are the main organisms responsible for the degradation of biopolymers such as lignin, cellulose, hemicellulose, and chitin in forest ecosystems. Soil surveys largely target fungal diversity, paying less attention to fungal activity. Methodology/Principal Findings Here we have focused on the organic horizon of a hardwood forest dominated by sugar maple that spreads widely across Eastern North America. The sampling site included three plots receiving normal atmospheric nitrogen deposition and three that received an extra 3 g nitrogen m−2 y−1 in form of sodium nitrate pellets since 1994, which led to increased accumulation of organic matter in the soil. Our aim was to assess, in samples taken from all six plots, transcript-level expression of fungal genes encoding lignocellulolytic and chitinolytic enzymes. For this we collected RNA from the forest soil, reverse-transcribed it, and amplified cDNAs of interest, using both published primer pairs as well as 23 newly developed ones. We thus detected transcript-level expression of 234 genes putatively encoding 26 different groups of fungal enzymes, notably major ligninolytic and diverse aromatic-oxidizing enzymes, various cellulose- and hemicellulose-degrading glycoside hydrolases and carbohydrate esterases, enzymes involved in chitin breakdown, N-acetylglucosamine metabolism, and cell wall degradation. Among the genes identified, 125 are homologous to known ascomycete genes and 105 to basidiomycete genes. Transcripts corresponding to all 26 enzyme groups were detected in both control and nitrogen-supplemented plots. Conclusions/Significance Many of these enzyme groups are known to be important in soil turnover processes, but the contribution of some is probably underestimated. Our data highlight the importance of ascomycetes, as well as basidiomycetes, in important biogeochemical cycles. In the nitrogen-supplemented plots, we have detected no transcript-level gap likely to explain the observed increased carbon storage, which is more likely due to community changes and perhaps transcriptional and/or post-transcriptional down-regulation of relevant genes.
Perspectives on the application of nanotechnology in photodynamic therapy for the treatment of melanoma

Malignant melanoma is the most aggressive form of skin cancer and has been traditionally considered difficult to treat. The worldwide incidence of melanoma has been increasing faster than any other type of cancer. Early detection, surgery, and adjuvant therapy enable improved outcomes; nonetheless, the prognosis of metastatic melanoma remains poor. Several therapies have been investigated for the treatment of melanoma; however, current treatment options for patients with metastatic disease are limited and non-curative in the majority of cases. Photodynamic therapy (PDT) has been proposed as a promising minimally invasive therapeutic procedure that employs three essential elements to induce cell death: a photosensitizer, light of a specific wavelength, and molecular oxygen. However, classical PDT has shown some drawbacks that limit its clinical application. In view of this, the use of nanotechnology has been considered since it provides many tools that can be applied to PDT to circumvent these limitations and bring new perspectives for the application of this therapy for different types of diseases. On that ground, this review focuses on the potential use of developing nanotechnologies able to bring significant benefits for anticancer PDT, aiming to reach higher efficacy and safety for patients with malignant melanoma.
Node Failure Localization via Network Tomography

We investigate the problem of localizing node failures in a communication network from end-to-end path measurements, under the assumption that a path behaves normally if and only if it does not contain any failed nodes. To uniquely localize node failures, the measurement paths must show different symptoms under different failure events, i.e., for any two distinct sets of failed nodes, there must be a measurement path traversing one and only one of them. This condition is, however, impractical to test for large networks. Our first contribution is a characterization of this condition in terms of easily verifiable conditions on the network topology with given monitor placements under three families of probing mechanisms, which differ in whether measurement paths are (i) arbitrarily controllable, (ii) controllable but cycle-free, or (iii) uncontrollable (i.e., determined by the default routing protocol). Our second contribution is a characterization of the maximum identifiability of node failures, measured by the maximum number of simultaneous failures that can always be uniquely localized. Specifically, we bound the maximal identifiability from both the upper and the lower bounds which differ by at most one, and show that these bounds can be evaluated in polynomial time. Finally, we quantify the impact of the probing mechanism on the capability of node failure localization under different probing mechanisms on both random and real network topologies. We observe that despite a higher implementation cost, probing along controllable paths can significantly improve a network's capability to localize simultaneous node failures.
ILO Core Conventions : Who Rati ̄ es and When 3 . The Decision to Ratify 4 . Empirical Framework and Results 5

The rati ̄cation of ILO Labor Standards Conventions is a key explanatory variable in the empirical literature linking labor standards to economic performance. The assumption is that rati ̄cation gives information on labor standards implemented in a country. This paper investigates the determinants of rati ̄cation directly and, indirectly, the determinants of labor standards. We ̄nd considerable variation across di®erent Conventions, and across developing and developed countries. But there are some systematic and interesting patterns. While economic variables such as real per capita income do not explain rati ̄cation, legal systems do. Most interestingly, for some Conventions, even after controlling for basic economic characteristics and domestic legal institutions, we ̄nd that peer e®ects are in play { the probability of adopting an international standard depends on how many other countries in a peer group have already adopted that standard. JEL Classi ̄cation: F16, J58
In practice: UML software architecture and design description

The Unified Modeling Language has attracted many organizations and practitioners. UML is now the de facto modeling language for software development. Several features account for its popularity: it's a standardized notation, rich in expressivity; UML 2.0 provides 13 diagram types that enable modeling several different views and abstraction levels. Furthermore, UML supports domain-specific extensions using stereotypes and tagged values. Finally, several case tools integrate UML modeling with other tasks such as generating code and reverse-engineering models from code. Our study focused on UML use and model quality in actual projects rather than on its adequacy as a notation or language.
Staam: Fitting a 2D+3D AAM to Stereo Images

This paper proposes a stereo active appearance model fitting algorithm (STAAM), that fits a 2D+3D active appearance model to stereo images acquired from calibrated perspective cameras. The STAAM uses geometrical relationship between cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. Also, the STAAM uses a modified simultaneous update fitting method that reduces the fitting computation greatly. Experimental results show that (2) the STAAM shows a better fitting stability than the existing multi-view AAM, (2) the modified simultaneous update algorithm accelerates the AAM fitting speed.
Current Status of Veterinary Vaccines

SUMMARY The major goals of veterinary vaccines are to improve the health and welfare of companion animals, increase production of livestock in a cost-effective manner, and prevent animal-to-human transmission from both domestic animals and wildlife. These diverse aims have led to different approaches to the development of veterinary vaccines from crude but effective whole-pathogen preparations to molecularly defined subunit vaccines, genetically engineered organisms or chimeras, vectored antigen formulations, and naked DNA injections. The final successful outcome of vaccine research and development is the generation of a product that will be available in the marketplace or that will be used in the field to achieve desired outcomes. As detailed in this review, successful veterinary vaccines have been produced against viral, bacterial, protozoal, and multicellular pathogens, which in many ways have led the field in the application and adaptation of novel technologies. These veterinary vaccines have had, and continue to have, a major impact not only on animal health and production but also on human health through increasing safe food supplies and preventing animal-to-human transmission of infectious diseases. The continued interaction between animals and human researchers and health professionals will be of major importance for adapting new technologies, providing animal models of disease, and confronting new and emerging infectious diseases.
Non-parametric Estimation of Operational Risk and Expected Shortfall

This paper proposes improvements to advanced measurement approach (AMA) to estimating operational risks, and applies the improved methods to US business losses categorised into five business lines and three event types operational losses. The AMA involves, among others, modelling a loss severity distribution and estimating the Expected Loss and the 99.9% operational value-at-risk (OpVaR). These measures form a basis for calculating the levels of regulatory and economic capitals required to cover risks arising from operational losses. In this paper, Expected Loss and OpVaR are estimated consistently and efficiently by nonparametric methods, which use the large (tail) losses as primary inputs. In addition, the 95% intervals for the underlying true OpVaR are estimated by the weighted empirical likelihood method. As an alternate measure to OpVaR, the Expected Shortfall - a coherent risk- is also estimated. The empirical findings show that the interval estimates are asymmetric, with very large upper bounds, highlighting the extent of uncertainties associated with the 99.9% OpVaR point estimates. The Expected Shortfalls are invariably greater than the corresponding OpVaRs. The heavier the loss severity distribution the greater the difference between OpVaR and Expected Shortfall, from which we infer that the latter would provide the right level of capital to cover risks than would the former, particularly during
Research on Intelligent Control Strategy used in CTCS-3 Train Control System

With the description of Chinese Train Control System Level 3 (CTCS-3) system structure and work principle, the author proposes an Intelligent Control Strategy (ICS) to improve CTCS-3 safety. Through the integration with two different train-wayside communication methods that based on GSM for Railways (GSM-R) and track-circuit, CTCS-3 realizes the cross line and degraded operation in high-speed railway. CTCS-3 with independent control unit of C3 and C2 that have been used to control 300∼350km/h train and C2 200∼250km/h train respectively still carries the whole function and equipment of CTCS-2. Through the exchange of information between C2 and C3 control unit, the ICS produces more reliable and precise core train control data after the intelligent data fusion of movement authority (MA), track data and temporary speed restriction (TSR), etc. After intelligence fusion, the probability of common cause errors has reduced and the scope of security directive role has been limited. The integrated date allows us to calculate permitted speed and generate supervising profile that ensures the safety of automatic train protection (ATP).
The role of atmospheric ions in aerosol nucleation – a review

Atmospheric aerosols affect climate and yet the reason for many observed events of new aerosol formation is not understood. One of the theories put forward to explain these events is that the presence of ions can enhance the formation of aerosols. The theory is called Ion Induced Nucleation and in this paper the state of observations, theory and experiments within the field will be reviewed. While evidence for Ion Induced Nucleation is accumulating the exact mechanism is still not known and more research is required to understand and quantify the effect.
Tag-based social image retrieval: An empirical evaluation

Tags associated with social images are valuable information source for superior image search and retrieval experiences. Although various heuristics are valuable to boost tag-based search for images, there is a lack of general framework to study the impact of these heuristics. Specifically, the task of ranking images matching a given tag query based on their associated tags in descending order of relevance has not been well studied. In this article, we take the first step to propose a generic, flexible, and extensible framework for this task and exploit it for a systematic and comprehensive empirical evaluation of various methods for ranking images. To this end, we identified five orthogonal dimensions to quantify the matching score between a tagged image and a tag query. These five dimensions are: (i) tag relatedness to measure the degree of effectiveness of a tag describing the tagged image; (ii) tag discrimination to quantify the degree of discrimination of a tag with respect to the entire tagged image collection; (iii) tag length normalization analogous to document length normalization in web search; (iv) tag-query matching model for the matching score computation between an image tag and a query tag; and (v) query model for tag query rewriting. For each dimension, we identify a few implementations and evaluate their impact on NUS-WIDE dataset, the largest human-annotated dataset consisting of more than 269K tagged images from Flickr . We evaluated 81 single-tag queries and 443 multi-tag queries over 288 search methods and systematically compare their performances using standard metrics including Precision at top-K, Mean Average Precision ( MAP ), Recall, and Normalized Discounted Cumulative Gain (NDCG). (This work was done during Ge Bai's intership at NTU.) © 2011 Wiley Periodicals, Inc.
Architecture driven circuit partitioning

We propose an architecture driven partitioning algorithm for netlists with multi terminal nets. Our target architecture is a multi FPGA emulation system with folded Clos network for board routing. Our goal is to minimize the number of FPGA chips used and maximize the routability. To that end, we introduce a new cost function: the average number of pseudo terminals per net in a multi way cut. Experiment result shows that our algorithm is very effective in terms of the number of chips used and the routability as compared to other methods.
Window and Acknowledgement Strategy in TCP

This RFC describes implementation strategies to deal with two mechanisms in TCP, the window and the acknowledgement. It also presents a particular set of algorithms which have received testing in the field, and which appear to work properly with each other. With more experience, these algorithms may become part of the formal specification, until such time their use is recommended.
Objective priors for the bivariate normal model

Summary Objective Bayesian inference for the multivariate normal distribution is illustrated, using dieren t types of formal objective priors (Jereys, invariant, reference and matching), dieren t modes of inference (Bayesian and frequentist), and dieren t criteria involved in selecting optimal objective priors (ease of computation, frequentist performance, marginalization paradoxes, and decision-theoretic evaluation). In the course of the investigation of the bivariate normal model in Berger and Sun (2006), a variety of surprising results were found, including the availability of objective priors that yield exact frequentist inferences for many functions of the bivariate normal parameters, such as the correlation coecien t. Certain of these results are generalized to the multivariate normal situation. The prior that most frequently yields exact frequentist inference is the rightHaar prior, which unfortunately is not unique. Two natural proposals are studied for dealing with this non-uniqueness: rst, mixing over the right-Haar priors; second, choosing the ‘empirical Bayes’ right-Haar prior, that which maximizes the marginal likelihood of the data. Quite surprisingly, we show that neither of these possibilities yields a good solution. This is disturbing and sobering. It is yet another indication that improper priors do not behave as do proper priors, and that it can be dangerous to apply ‘understandings’ from the world of proper priors to the world of improper priors.
Statistical Tests of Real-Money versus Play-Money Prediction Markets

Prediction markets are mechanisms that aggregate information such that an estimate of the probability of some future event is produced. It has been established that both real‐money and play‐money prediction markets are reasonably accurate. An SPRT‐like test is used to determine whether there are statistically significant differences in accuracy between the two markets. The results establish that real‐money markets are significantly more accurate for non‐sports events. We also examine the effect of volume and whether differences between forecasts are market specific.
Kaehler structures on _

Let K be a compact semi-simple Lie group. We classify K-invariant Kaehler structures on the space Kc/(P,P), where Kc is the complexification of K, P is a parabolic subgroup of Kc, and (P,P) the commutator subgroup. For each Kaehler structure, we study its moment map and associated pre-quantum line bundle for geometric quantization. Some holomorphic sections of the line bundle form a unitary K-representation space, and we study the multiplicity of its irreducible subrepresentations.
Linear Detection via Belief Propagation

In this paper, the paradigm of linear detection is reformulated as a Gaussian belief propagation (GaBP) schem e, without resorting to direct matrix inversion. The derived i terative framework allows for a distributive message-passing imple mentation of this important class of sub-optimal tractable estimators. The properties of GaBP-based linear detection are addresse d, while its faster convergence, in comparison with conventio nal iterative solution methods, is demonstrated experimental ly.
Emotional sentence identification in a story

In this paper, we investigate the methods of fusing different classifiers to identify emotional sentences in text. The Extreme Learning Machine (ELM) and Support Vector Machines (SVM) are two classifiers used to predict a sentence neutral or emotional. We use the UniGram, subjective words, and special punctuations, etc. as features. A method of calculating emotion value of a word is presented, and the values are employed to compose the features of an emotional sentence. To further enhance the system performance, we divide the features into three subsets, and train different models of the two classifiers on each feature set. The six models are then combined through a weighted summation fusion method and FoCal fusion method. We evaluate the system performance on a corpus of children's tales, and the experimental results demonstrate that the fusion of models can improve system performance.
Archimedes: an approach to architecture-independent modeling for high-level simulation

A means of describing computer models on a high level and a means of generating the necessary data about the model for simulation are presented. The method is exemplified by using it to describe a RISC processor model. The debugging environment and interfaces for the simulator are presented. The use of the simulator to evaluate the performance of the HITAC M-880 multiprocessor system is reported. <<ETX>>
miR-150 Deficiency Protects against FAS-Induced Acute Liver Injury in Mice through Regulation of AKT

Although miR-150 is implicated in the regulation of immune cell differentiation and activation, it remains unknown whether miR-150 is involved in liver biology and disease. This study was performed to explore the potential role of miR-150 in LPS/D-GalN and Fas-induced liver injuries by using wild type and miR-150 knockout (KO) mice. Whereas knockout of miR-150 did not significantly alter LPS/D-GalN-induced animal death and liver injury, it protected against Fas-induced liver injury and mortality. The Jo2-induced increase in serum transaminases, apoptotic hepatocytes, PARP cleavage, as well as caspase-3/7, caspase-8, and caspase-9 activities were significantly attenuated in miR-150 KO mice. The liver tissues from Jo2-treated miR-150 KO mice expressed higher levels of Akt1, Akt2, total Akt, as well as p-Akt(Ser473) compared to the wild type livers. Pretreatment with the Akt inhibitor V reversed Jo2-induced liver injury in miR-150 KO mice. The primary hepatocytes isolated from miR-150 KO mice also showed protection against Fas-induced apoptosis in vitro (characterized by less prominent PARP cleavage, less nuclear fragmentation and less caspase activation) in comparison to hepatocytes from wild type mice. Luciferase reporter assays in hepatocytes transfected with the Akt1 or Akt2 3’-UTR reporter constructs (with or without mutation of miR-150 binding site) established Akt1 and Akt2 as direct targets of miR-150. Tail vein injection of lentiviral particles containing pre-miR-150 enhanced Jo2-induced liver injury in miR-150 KO mice. These findings demonstrate that miR-150 deficiency prevents Fas-induced hepatocyte apoptosis and liver injury through regulation of the Akt pathway.
Fine-grained disclosure control for app ecosystems

The modern computing landscape contains an increasing number of app ecosystems, where users store personal data on platforms such as Facebook or smartphones. APIs enable third-party applications (apps) to utilize that data. A key concern associated with app ecosystems is the confidentiality of user data. In this paper, we develop a new model of disclosure in app ecosystems. In contrast with previous solutions, our model is data-derived and semantically meaningful. Information disclosure is modeled in terms of a set of distinguished security views. Each query is labeled with the precise set of security views that is needed to answer it, and these labels drive policy decisions. We explain how our disclosure model can be used in practice and provide algorithms for labeling conjunctive queries for the case of single-atom security views. We show that our approach is useful by demonstrating the scalability of our algorithms and by applying it to the real-world disclosure control system used by Facebook.
An integer programming placement approach to FPGA clock power reduction

Clock signals are responsible for a significant portion of dynamic power in FPGAs owing to their high toggle frequency and capacitance. Clock signals are distributed to loads through a programmable routing tree network, designed to provide low delay and low skew. The placement step of the FPGA CAD flow plays a key role in influencing clock power, as clock tree branches are connected based solely on the placement of the clock loads. In this paper, we present a placement-based approach to clock power reduction based on an integer linear programming (ILP) formulation. Our technique is intended to be used as an optimization post-pass executed after traditional placement, and it offers fine-grained control of the amount by which clock power is optimized versus other placement criteria. Results show that the proposed technique reduces clock network capacitance by over 50% with minimal deleterious impact on post-routed wirelength and circuit speed.