Miracle-dz/UniFAR-FTUs huggingface dataset:
A Theory of Effective Microbial Substrate Affinity Parameters in Variably Saturated Soils and an Example Application to Aerobic Soil Heterotrophic Respiration

Affinity parameters are essential for substrate kinetics‐based modeling of soil biogeochemistry. These parameters were originally defined for well‐mixed aqueous solutions to represent enzyme substrate binding. For variably saturated soils, they are often calibrated and highly uncertain. Here we develop a predictive theory of effective substrate affinity parameters to account for other processes that affect microbial substrate acquisition, so that the substrate kinetics for well‐mixed aqueous solutions can be similarly applied to variably saturated soils. The theory is based on an analytical approximation of how diffusive substrates are intercepted by soil microbial cells and integrates microbial characteristics, microsite structure, and soil physical properties. The predicted effective substrate affinity thus closely integrates the physical substrate limitation in soils with the intrinsic substrate affinity parameter. The theory predicts that, as moisture changes, the effective diffusive substrate delivery rates vary by orders of magnitude, resulting in highly variable effective affinity parameters for substrates like oxygen, methane, and nonvolatile solutes. As an example, we apply the theory with three substrate kinetics to aerobic soil incubations. Our models accurately reproduced observations of 32 soil incubations in four soil classes, demonstrating that the soil moisture versus respiration relationship varies with maximum respiration rate, soil texture, soil carbon content, and microbial biomass. This example suggests that the traditional use of a single static multiplicative function to parameterize how soil respiration depends on moisture is inappropriate. Because of its capability to integrate microbial traits and soil physical properties, our theory will help develop more robust soil biogeochemistry models.
Speech Processing Approach for Diagnosing Dementia in an Early Stage

The clinical diagnosis of Alzheimer’s disease and other dementias is very challenging, especially in the early stages. Our hypothesis is that any disease that affects particular brain regions involved in speech production and processing will also leave detectable finger prints in the speech. Computerized analysis of speech signals and computational linguistics have progressed to the point where an automatic speech analysis system is a promising approach for a low-cost non-invasive diagnostic tool for early detection of Alzheimer’s disease. We present empirical evidence that strong discrimination between subjects with a diagnosis of probable Alzheimer’s versus matched normal controls can be achieved with a combination of acoustic features from speech, linguistic features extracted from an automatically determined transcription of the speech including punctuation, and results of a mini mental state exam (MMSE). We also show that discrimination is nearly as strong even if the MMSE is not used, which implies that a fully automated system is feasible. Since commercial automatic speech recognition (ASR) tools were unable to provide transcripts for about half of our speech samples, a customized ASR system was developed.
R-Rec: A rule-based system for contextual suggestion using tag-description similarity

Contextual Suggestion deals with search techniques for complex information needs that are highly focused on context and user needs. In this paper, we propose \emph{R-Rec}, a novel rule-based technique to identify and recommend appropriate points-of-interest to a user given her past preferences. We try to embody the information that the user shares in the form of rating and tags of any previous point(s)-of-interest and use it to rank the unrated candidate suggestions. The ranking function is computed based on the similarity between a suggestion and the places that the user like and the dissimilarity between the suggestion and the places disliked by the user. Experiments carried out on TREC-Contextual Suggestion 2015 dataset reveal the efficacy of our method.
Natural and land-use drivers of primary production in a highly vulnerable region of livestock production (Sierras del Este – Uruguay)

Sierras del Este is one of the two regions in Uruguay that are most vulnerable to climate change. A relevant vulnerability factor is the variability of the natural grasslands’ productivity. The objective of this study was to analyse the role of natural and land use drivers on grassland productivity as an essential factor for increasing the adaptive capacity of livestock production and reducing their vulnerability to extreme climatic events. The period 2000–2015 was analysed using the aboveground net primary production (ANPP), rainfall patterns, soil maps and surface slopes, livestock stocking density (LSD) information, and interviews with livestock producers. The results showed a decreasing trend in ANPP between 2000 and 2009, and an increase between 2010 and 2015. These trends are associated with rainfall fluctuations: greater ANPP variability is explained by the rainfall accumulation of the 4 previous months. In addition, ANPP is affected by soil type (deeper and more clayey, higher ANPP), surface slope (steeper surface slope, lower ANPP) and LSD (higher LSD, higher ANPP). In drought periods, these relations are reversed. The main results suggested that changes in ANPP between drought and wet periods are not linearly related to the drivers analysed, and an important spatially structured pattern was detected. The evidence provides information to anticipate extreme events, allowing to define and explore strategies to reduce the impacts of drought. The reduction of vulnerability implies challenges at the individual level to increase efficiency in livestock management and at a collective level to integrate and complement favourably the various land use activities in the area. In this sense, public policy should have a leading role to promote these transformations.
Preconditioning of Arctic Stratospheric Polar Vortex Shift Events

This study examines the preconditioning of events in which the Arctic stratospheric polar vortex shifts toward Eurasia (EUR events), North America (NA events), and the Atlantic (ATL events) using composite analysis. An increase in blocking days over northern Europe and a decrease in blocking days over the Bering Strait favor the movement of the vortex toward Eurasia, while the opposite changes in blocking days over those regions favor the movement of the vortex toward North America. An increase in blocking days over the eastern North Atlantic and a decrease in blocking days over the Bering Strait are conducive to movement of the stratospheric polar vortex toward the Atlantic. These anomalous precursor blocking patterns are interpreted in terms of the anomalous zonal wave-1 or wave-2 planetary wave fluxes into the stratosphere that are known to influence the vortex position and strength. In addition, the polar vortex shift events are further classified into events with small and large polar vortex deformation, since the two types of events are likely to have a different impact at the surface. A significant difference in the zonal wave-2 heat flux into the lower stratosphere exists prior to the two types of events and this is linked to anomalous blocking patterns. This study further defines three types of tropospheric blocking events in which the spatial patterns of blocking frequency anomalies are similar to the blocking patterns prior to EUR, NA, and ATL events, respectively, and our reanalysis reveals that the polar vortex is indeed more likely to shift toward Eurasia, North America, and the Atlantic in the presence of the above three defined tropospheric blocking events. These shifts of the polar vortex toward Eurasia, North America, and the Atlantic lead to statistically significant negative height anomalies near the tropopause and corresponding surface cooling anomalies over these three regions.
Improved speech recognition word lattice translation by confidence measure

In conventional speech translation systems, Automatic Speech Recognition (ASR) produces a single hypothesis which is then translated by the SMT system. The translation results of SMT system are impaired by the word errors of the first best hypothesis in this approach more or less. To improve speech translation, we use a new word lattice translation approach which integrates multiple information sources from the speech recognition word lattice to discount the misrecognition. Furthermore, in order to improve speech translation and to reduce computation, we used N-bests cutoff, merging of identical word ids, and confidence measure. Experiments of Japanese-to-English speech translation showed that the proposed word lattice translation outperforms the conventional single best method.
Tyrannosaur ageing

Rate of ageing in tyrannosaurs was calculated from parameters of Weibull functions fitted to survival curves based on the estimated ages at death of fossilized remains. Although tyrannosaurs are more closely related to birds than to mammals, they apparently aged at rates similar to mammals of comparable size. Rate of growth in body mass of tyrannosaurs was similar to that of large mammals, and their rates of ageing were consistent with the estimated extrinsic mortality, which is strongly correlated with the rate of ageing across birds and mammals. Thus, tyrannosaurs appear to have had life histories resembling present-day large terrestrial mammals. Rate of ageing in warm-blooded vertebrates appears to be adjusted in response to extrinsic mortality and potential lifespan, independently of both physiological and developmental rates. However, individuals in species with the slowest rates of ageing suffer the highest proportion of ageing-related mortality, hence potentially strong selection to further postpone senescence. Thus, the longest observed lifespans in birds, tyrannosaurs and mammals might be close to the maximum possible.
Does animation in user interfaces improve decision making?

This paper reports a laboratory experiment that investigated the relative effects of images, transitions, and interactivity styles used in animated interfaces in two decision making domains. Interfaces used either realistic or abstract images, smooth or abrupt transitions, and parallel or sequential interactivity. Results suggest that decision making performance is influenced by the task domain, the user experience, the image, transition, and interactivity styles used in animated interfaces. Subjects performed better with animated interfaces based on realistic rather than abstract images. Subjects were more accurate with smooth rather than abrupt animation. Subjects were more accurate and enjoyed more the animation with parallel rather than sequential interactivity. Implications on the design of animated interfaces for decision making are provided. To account for appropriateness and interactivity, animation in HCI can be defined as: a series of varying images presented dynamically according to user actions in ways that help the user to perceive a continuous change over time and develop a more appropriate mental model of the task [7]. A task is generally considered a meaningful unit of work performance, and interactivity is usually defined by the visible or motor actions the user performs on the interface. This paper summarizes previous empirical research on animation and reports a laboratory study conducted to evaluate the decision making effectiveness of different types of images, transitions, and interactivity styles used in animated interfaces. The paper concludes with implications and recommendations concerning the design of animated interfaces for supporting decision making.
Myogenic program dysregulation is contributory to disease pathogenesis in spinal muscular atrophy

Mutations in the survival motor neuron (SMN1) gene lead to the neuromuscular disease spinal muscular atrophy (SMA). Although SMA is primarily considered as a motor neuron disease, the importance of muscle defects in its pathogenesis has not been fully examined. We use both primary cell culture and two different SMA model mice to demonstrate that reduced levels of Smn lead to a profound disruption in the expression of myogenic genes. This disruption was associated with a decrease in myofiber size and an increase in immature myofibers, suggesting that Smn is crucial for myogenic gene regulation and early muscle development. Histone deacetylase inhibitor trichostatin A treatment of SMA model mice increased myofiber size, myofiber maturity and attenuated the disruption of the myogenic program in these mice. Taken together, our work highlights the important contribution of myogenic program dysregulation to the muscle weakness observed in SMA.
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server

In this paper we describe novel techniques that make it possible to build an industrial-strength tool for automating the choice of indexes in the physical design of a SQL database. The tool takes as input a workload of SQL queries, and suggests a set of suitable indexes. We ensure that the indexes chosen are effective in reducing the cost of the workload by keeping the index selection tool and the query optimizer "in step". The number of index sets that must be evaluated to find the optimal configuration is very large. We reduce the complexity of this problem using three techniques. First, we remove a large number of spurious indexes from consideration by taking into account both query syntax and cost information. Second, we introduce optimizations that make it possible to cheaply evaluate the “goodness” of an index set. Third, we describe an iterative approach to handle the complexity arising from multicolumn indexes. The tool has been implemented on Microsoft SQL Server 7.0. We performed extensive experiments over a range of workloads, including TPC-D. The results indicate that the tool is efficient and its choices are close to optimal.
Opportunistic Routing in Multi-Radio Multi-Channel Multi-Hop Wireless Networks

Two major factors that limit the throughput in multi-hop wireless networks are the co-channel interference and unreliability of wireless transmissions. Multi-radio multi-channel technology and opportunistic routing (OR) have shown their promise to significantly improve the network capacity by combating these two limits. It raises an interesting problem on the tradeoff between multiplexing and spatial diversity when integrating these two techniques for throughput optimization. It is unknown what the capacity of the network could be when nodes have multiple radios and OR capability. In this paper, we present our study on optimizing an end-to-end throughput of the multi-radio multi-channel network when OR is available. First, we formulate the end-to-end throughput bound as a linear programming (LP) problem which jointly solves the radio-channel assignment, transmission scheduling, and forwarding candidate selection. Second, we propose an LP approach and a heuristic algorithm to find a feasible scheduling of opportunistic forwarding priorities to achieve the capacity. Simulations show that the heuristic algorithm achieves desirable performance under various number of forwarding candidates. Leveraging our analytical model, we find that 1) OR can achieve better performance than traditional routing (TR) under different radio/channel configurations, however, in particular scenario (e.g. bottleneck links exist between the sender and relays), TR is preferable; 2) OR can achieve comparable or better performance than TR by using less radio resource.
Dual‐wield NTPases: A novel protein family mined from AlphaFold DB

AlphaFold protein structure database (AlphaFold DB) archives a vast number of predicted models. We conducted systematic data mining against AlphaFold DB and discovered an uncharacterized P-loop NTPase family. The structure of the protein family was surprisingly novel, showing an atypical topology for P-loop NTPases, noticeable two-fold symmetry and two pairs of independent putative active sites. Our findings show that structural data mining is a powerful approach to identifying undiscovered protein families.
The incidence and outcome of bilirubin encephalopathy in Nigeria: a bi-centre study.

OBJECTIVE To determine the current trends in the incidence and outcome of bilirubin encephalopathy among Nigerian babies. METHODS A review of the hospital records of babies managed for bilirubin encephalopathy at the Wesley Guild Hospital (WGH), Ilesa and Olabisi Onabanjo University Teaching Hospital (OOUTH), Sagamu, both in southwest Nigeria between 2001 and 2005 was carried out. The age, sex, weight, body temperature on admission, place of delivery and outcome of hospitalization were studied. The fatal cases and the survivors were compared for risk factors for mortality. RESULTS Fifty eight (3.4%) and 57 (2.3%) babies had bilirubin encephalopathy out of 1706 and 2492 total neonatal admissions at OOUTH and WGH respectively. Of these 115 babies, 3 (2.6%), 84 (73.0%) and 28 (24.3%) were aged <3 days, 3-6 days and 7 days or more. Sixty eight (59.1%) babies were delivered in orthodox health facilities. Aside clinically suspected cases of G6PD deficiency, ABO incompatibility and septicaemia were commonly associated with bilirubin encephalopathy, Forty four (38.3%), 36 (31.3%) and 35 (30.4%) had Unconjugated bilirubin of <340 micromol/L, 341-425 micromol/L and >425 micromol/L respectively Sixty eight (59.1%) were discharged, 42 (36.5%) died while 5 (4.7%) were discharged against medical advice. Prematurity, low birth weight, severe anaemia and inability to do Exchange Blood Transfusion were significant risk factors for mortality among babies with bilirubin encephalopathy. Cerebral palsy, seizure disorders and deafness were the leading neurological sequelae (86.4%, 40.9% and 36.4% respectively) among the 22 survivors who were followed up. CONCLUSION Bilirubin encephalopathy remains a common clinical finding in Nigeria and the associated mortalities and neurological sequelae are significant.
Consumers repurchase behavior on selected consumer goods: An investigation on the moderating effects of prior product knowledge

In this paper, a framework which integrates several dimensions (attributes importance, interpersonal influence and demographic variables) affecting consumer decision making and repurchase intention as well as the possible relationship among all variables is developed. The results revealed that quality, brand name, product information and informational influence had significant direct relationship on repurchase intention for high involvement products. While for low involvement products, price and brand name significantly predict consumers' repurchase intention. The influence of significant others did not significantly affect consumers' repurchase intention regardless of whether the products are low or high involvement products. Finally, the effect of prior product knowledge in moderating the relationship among all sets of variables did not significantly apply to all types of products regardless of whether these products are categorized as low or high involvement products. In conclusion, the research model will provide an important input to the marketing decision-making process and management decision such as marketers, product managers and/or brand managers to streamline their marketing plan and strategies. In addition, the findings of this research also contribute to the body of knowledge on consumer behavior and marketing management.
Comparison of Chemical Evolution Models for the Galactic Disk

The {\it best} chemical evolution models for the galactic disk computed by different groups with different assumptions are compared with each other and with the observational constraints. Differences and similarities between the models are discussed, emphasizing the importance of testing not only the solar neighbourhood but the whole disk. The history of the metal abundance gradients is found to be very sensitive to the different model assumptions on the star formation and infall rates, and can therefore provide important clues on the disk evolutionary scenario. All the examined models indicate that the deuterium depletion from its primordial abundance to the present one has been at most a factor of 3 and that the stellar nucleosynthesis of some elements (e.g. $^3$He, $^{13}$C and, possibly, $^{18}$O) requires some revisions.
Genome Organization, Phylogenies, Expression Patterns, and Three-Dimensional Protein Models of Two Acetylcholinesterase Genes from the Red Flour Beetle

Since the report of a paralogous acetylcholinesterase (AChE, EC3.1.1.7) gene in the greenbug (Schizaphis graminum) in 2002, two different AChE genes (Ace1 and Ace2) have been identified in each of at least 27 insect species. However, the gene models of Ace1 and Ace2, and their molecular properties have not yet been comprehensively analyzed in any insect species. In this study, we sequenced the full-length cDNAs, computationally predicted the corresponding three-dimensional protein models, and profiled developmental stage and tissue-specific expression patterns of two Ace genes from the red flour beetle (Tribolium castaneum; TcAce1 and TcAce2), a globally distributed major pest of stored grain products and an emerging model organism. TcAce1 and TcAce2 encode 648 and 604 amino acid residues, respectively, and have conserved motifs including a choline-binding site, a catalytic triad, and an acyl pocket. Phylogenetic analysis show that both TcAce genes are grouped into two insect Ace clusters and TcAce1 is completely diverged from TcAce2, suggesting that these two genes evolve from their corresponding Ace gene lineages in insect species. In addition, TcAce1 is located on chromosome 5, whereas TcAce2 is located on chromosome 2. Reverse transcription polymerase chain reaction (PCR) and quantitative real-time PCR analyses indicate that both genes are virtually transcribed in all the developmental stages and predominately expressed in the insect brain. Our computational analyses suggest that the TcAce1 protein is a robust acetylcholine (ACh) hydrolase and has susceptibility to sulfhydryl agents whereas the TcAce2 protein is not a catalytically efficient ACh hydrolase.
Development of an Automatic Emotional Music Accompaniment System by Fuzzy Logic and Adaptive Partition Evolutionary Genetic Algorithm

Music is everywhere in the world, and its applications in commerce are extremely versatile. Generally speaking, in order to create some music for background music, it is necessary to engage sound recordists and instrumental performers. However, the process is very time-consuming and costly. In this paper, a real-time emotion-based music accompaniment system is proposed to solve this issue. For different emotions, a fuzzy logic controller is designed to adjust the tempo of the music, and an adaptive partition evolutionary genetic algorithm is developed to create corresponding melodies. The chord progressions are generated via music theory, and the instrumentation is disposed by the conception of the probability. What is noteworthy is that all the processes can be output by Virtual Studio Technology in real time so that users can listen directly to the composing results from any emotions. From the experimental results, the proposed adaptive partition evolutionary genetic algorithm performs better than other optimal algorithms in such topics.