Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomised controlled trial of interpersonal Randomised controlled trial of interpersonal psychotherapy and cognitive^behavioural therapy psychotherapy and cognitive^behavioural therapy for depression for depression {{

Declaration of interest None. Interpersonal psychotherapy (IPT) and cognitive–behavioural therapy (CBT) are effective short-term therapies for mild to moderate depression (Beckham, 1990; Jarrett & Rush, 1994; Persons et al, 1996). Conversely, using psychotherapy for severe depression remains a contentious issue (National Health Committee, 1996; Segal et al, 2001; Ellis et al, 2002). The National Institute of Mental Health Treatment of Depression Collaborative Research Program (NIMH TDCRP) directly compared the two therapies (Elkin et al, 1985, 1989) and reported a better response to IPT than to CBT in severe depression (Elkin et al, 1995). This particular finding influenced the development of many clinical guidelines, which do not support IPT and even warn against CBT as first-line therapies for severe depression. However, authors such as Thase & Friedman (1999) reviewed the evidence for response to psychotherapy in patients with melancholic depression and advocated that a skilled therapist could work successfully with a carefully chosen patient. In the Christchurch Psychotherapy of Depression Study we compared IPT and CBT for depression. We predicted that both therapies would be equally effective in reducing depressive symptoms. We also predicted that both therapies would be less effective in severe or melancholic depression.
OP-GBEV210202 1..11

Lysozymeenzymesprovide classic examplesofmolecular adaptationandparallel evolution,however, nearly all insights todate come from chicken-type (c-type) lysozymes. Goose-type (g-type) lysozymes occur in diverse vertebrates, with multiple independent duplications reported. Most mammals possess two g-type lysozyme genes (Lyg1 and Lyg2), the result of an early duplication, although some lineages are known to have subsequently lost one copy. Here we examine g-type lysozyme evolution across >250 mammals and reveal widespread losses of either Lyg1 or Lyg2 in several divergent taxa across the mammal tree of life. At the same time, we report strong evidence of extensive losses of both gene copies in cetaceans and sirenians, with an additional putative case of parallel loss in the tarsier. To validate these findings, we inspected published short-read data and confirmed the presence of loss of function mutations. Despite these losses, comparisons of selection pressures between intact gand c-type lysozyme genes showed stronger purifying selection in the former, indicative of conserved function. Although the reasons for the evolutionary lossofg-type lysozymes in fullyaquaticmammalsarenotknown,wesuggest that this is likely toat leastpartially relate to their hairlessness. Indeed, although Lyg1 does not show tissue-specific expression, recent studies have linked Lyg2 expression to anagen hair follicle development and hair loss. Such a role for g-type lysozyme would explain why the Lyg2 gene became obsolete when these taxa lost their body hair.
Influence Factors for Customer Acceptance of Data-Driven Contracts in Insurance Ecosystems

Datafication offers several benefits to the insurance sector, but the success of data-driven insurance depends very much on customer acceptance. Thus, this study examines factors that influence customer acceptance of data-driven car and health insurance. These two types of data-driven insurance are based on fitness and driving data, both of which require access to sensor and geo-localization data. The results of an online study with 217 participants using advertisements for data-driven insurances showed that highlighting monetary incentives leads to a higher acceptance than highlighting health or safety incentives. Data-driven insurances allow for individualized tariffs, and accordingly, it is more likely that people who rate their driving skills above-average will take out a data-driven car insurance. Privacy concerns are another important influence factor. The findings demonstrate that customer acceptance of data-driven insurance can be influenced to some extent by framing decision-relevant information material.
Aberystwyth University Towards scalable fuzzy-rough feature selection

Research in the area of fuzzy-rough set theory, and its application to feature or attribute selection in particular, has enjoyed much attention in recent years. Indeed, with the growth of larger and larger data dimensionality, the number of data objects required in order to generate accurate models increases expo-nentially. Thus, for model learning, feature selection has become increasingly necessary. The use of fuzzy-rough sets as dataset pre-processors oﬀer much in the way of ﬂexibility, however the underlying complexity of the subset evaluation metric often presents a problem and can result in a great deal of potentially un-necessary computational eﬀort. This paper proposes two diﬀerent novel ways to address this problem using a neighbourhood approximation step and attribute grouping in order to alleviate the processing overhead and reduce complexity. A series of experiments are conducted on benchmark datasets which demonstrate that much computational eﬀort can be avoided, and as a result the eﬃciency of the feature selection process for fuzzy-rough sets can be improved considerably.
Nowcasting Indian GDP 1

: Nowcasting has become a useful tool for making timely predictions of GDP in a data-rich environment. However, in developing economies this is more challenging due to substantial revisions in GDP data and the limited availability of predictor variables. Taking India as an important leading case, we use a dynamic factor model nowcasting method to analyse these two issues. Firstly, we propose to compare nowcasts of the ﬁrst release of GDP to those of the ﬁnal release to assess differences in their predictability. Secondly, we expand a standard set of predictors typically used for nowcasting GDP with nominal and international series, in order to proxy the variation in missing employment and service sector variables in India. We ﬁnd that the factor model improves over several benchmarks including bridge equations, but only for the ﬁnal GDP release and not for the ﬁrst release. Also, the nominal and international series improve predictions over and above real series. This suggests that future studies of nowcasting in developing economies, which have similar issues of data revisions and availability as India, should be careful in analysing ﬁrst versus ﬁnal release GDP data, and may ﬁnd that predictions are improved when including additional variables from more timely international data sources.
Routing Protocol for Low-Power and Lossy Network Performance Comparison for Objective Functions

IPv6 Routing Protocol for Low-Power and Lossy Network (RPL) is the de-facto routing standard for Wireless Sensor Network (WSN). Minimum Rank with Hysteresis Objective Function (MRHOF) and Objective Function Zero (OF0) are two Objective Functions (OFs) that drives RPL. The challenge to Application Developers is validating the Quality of Service (QoS) of RPL through its OFs, to enable them to build an appreciable confidence level. RPL Simulations was carried out on a total of one hundred (100) nodes, starting with 10 nodes and incremented by 10 until the 100th node, for a duration of twenty (20) minutes. The OFs were validated with various QoS metrics, which are Packet Delivery Ratio (PDR), Churn Rate (CR), Convergence Time (CT) and Power Consumption (PC). From the results, PDR; MRHOF had a steady value of 100% while OF0 had 78% at their minimum, MRHOF outperformed OF0. For CR, MRHOF, and OF0; 35% and 11% respectively. The network is more stable with less CR, OF0 outperformed MRHOF. For CT; MRHOF and OF0; 134224 ms and 59571 ms respectively. Delay increases with increasing CT, OF0 outperformed MRHOF. PC was measured with reference to Microcontroller Unit (MCU) power, Listen power, Transmit power and Low Power Mode (LPM) operations. From the results, MRHOF consumes more power than OF0. The validity of these results is at 98% confidence level as each simulation was performed five times. It is expected that these results will guide developers and researchers on their choice of OF in their projects.
Cell migration without a lamellipodium

The actin cytoskeleton is locally regulated for functional specializations for cell motility. Using quantitative fluorescent speckle microscopy (qFSM) of migrating epithelial cells, we previously defined two distinct F-actin networks based on their F-actin–binding proteins and distinct patterns of F-actin turnover and movement. The lamellipodium consists of a treadmilling F-actin array with rapid polymerization-dependent retrograde flow and contains high concentrations of Arp2/3 and ADF/cofilin, whereas the lamella exhibits spatially random punctae of F-actin assembly and disassembly with slow myosin-mediated retrograde flow and contains myosin II and tropomyosin (TM). In this paper, we microinjected skeletal muscle αTM into epithelial cells, and using qFSM, electron microscopy, and immunolocalization show that this inhibits functional lamellipodium formation. Cells with inhibited lamellipodia exhibit persistent leading edge protrusion and rapid cell migration. Inhibition of endogenous long TM isoforms alters protrusion persistence. Thus, cells can migrate with inhibited lamellipodia, and we suggest that TM is a major regulator of F-actin functional specialization in migrating cells.
Revealing the Effects of Reproduction and Turnover Rate on Farm Profitability through Herd Structure Dynamics

Dairy herd structure influenced by reproductive performance and herd turnover rate affects the whole farm profitability because each group of animals has a distinctive production efficiency reflected in costs and revenues. The objective of this study was to reveal the interactions among reproductive performance, turnover rate, herd structure, and farm profitability. A monthly Markov chain model was used and updated with a newly developed heifer module. A 1,000-Holstein herd producing 10,442kg/ cow per yr in the US was simulated as the baseline herd, reaching steady-state with 45% turnover rate (TR), 16% 21-d pregnancy rate (21-d PR), and 0% voluntary calf culling rate (VCC). The baseline herd was imposed 25% TR or 40% 21-d PR and VCC combinations to maximize net return or breakeven the demand of replacements. Results showed that lower TR slightly increased net return primarily due to lower replacement demand, increased cow longevity, and had minor impacts on lactating and pregnancy statuses. Higher 21-d PR improved reproductive efficiency, placed more multiparous cows in earlier stages of lactation, and generated a larger number of replacements. Under current market conditions, if calves were not voluntarily culled, it led to considerable replacement rearing, decreasing net return. The combined strategy of 25% TR, 40% 21-d PR and 44% VCC increased net return by $16/cow per mo. Additionally, this strategy decreased percentage of youngstock (44.3% to 34.4%), reduced lactating herd average DIM (187 to 169) and increased herd average longevity (49.7 to 81.6 mo). Sensitivity analysis indicated that milk price significantly affects farm profitability regardless of herd structure. The optimal VCC to maximize net return highly depends on heifer rearing cost and market springer price. VCC could be 0 when the on-farm cost of raising a springer outnumbers selling price, or 100% otherwise. However, the minimum number of heifers for herd turnover should be raised on-farm considering biosecurity and sustainability under any market conditions.For practical utilization of the model developed for this research in commercial farms, target values of the combined strategy could be imposed progressively to alleviate volatility of net return in the short- and medium-term (6 to 34 mo), but still reach the same herd performance and economic outcomes in the long-term.
Integrating Genetic and Genomic Analyses of Combined Health Data Across Ecotypes to Improve Disease Resistance in Indigenous African Chickens

play important role in the agriculture of many African countries. The majority of chickens in sub-Saharan Africa are indigenous, raised in villages under semi-scavenging conditions. Vaccinations and biosecurity measures rarely apply, and infectious diseases remain a major cause of mortality and reduced productivity. Genomic selection for disease resistance offers a potentially sustainable solution but this requires sufﬁcient numbers of individual birds with genomic and phenotypic data, which is often a challenge to collect in the small populations of indigenous chicken ecotypes. The use of information across-ecotypes presents an attractive possibility to increase the relevant numbers and the accuracy of genomic selection. In this study, we performed a joint analysis of two distinct Ethiopian indigenous chicken ecotypes to investigate the genomic architecture of important health and productivity traits and explore the feasibility of conducting genomic selection across-ecotype. Phenotypic traits considered were antibody response to Infectious Bursal Disease (IBDV), Marek’s Disease (MDV), Fowl Cholera (PM) and Fowl Typhoid (SG), resistance to Eimeria and cestode parasitism, and productivity [body weight and body condition score (BCS)]. Combined data from the two chicken ecotypes, Horro ( n = 384) and Jarso ( n = 376), were jointly analyzed for genetic parameter estimation, genome-wide association studies (GWAS), genomic breeding value (GEBVs) calculation, genomic predictions, whole-genome sequencing (WGS), and pathways analyses. Estimates of across-ecotype heritability were signiﬁcant and moderate in magnitude (0.22–0.47) for all traits except for SG and BCS. GWAS identiﬁed several signiﬁcant genomic associations with health and productivity traits. The WGS analysis revealed putative candidate genes and mutations for IBDV ( TOLLIP, ANGPTL5, BCL9, THEMIS2 ), MDV ( GRM7 ), SG ( MAP3K21 ), Eimeria ( TOM1L1 ) cestodes ( TNFAIP1, ATG9A, NOS2 ) parasitism, which warrant further investigation. Reliability of GEBVs increased compared to within-ecotype calculations but accuracy of genomic prediction did not, probably because the genetic distance between the two ecotypes offset the beneﬁt from increased sample size. However, for some traits genomic prediction was only feasible in across-ecotype analysis. Our results generally underpin the potential of genomic selection to enhance health and productivity across-ecotypes. Future studies should establish the required minimum sample size and genetic similarity between ecotypes to ensure accurate joint genomic selection.
[Re] Rigging the Lottery: Making All Tickets Winners

For a fixed parameter count and compute budget, the proposed algorithm (RigL) claims to directly train sparse networks thatmatch or exceed the performance of existing denseto-sparse training techniques (such as pruning). RigL does so while requiring constant Floating Point Operations (FLOPs) throughout training. The technique obtains state-ofthe-art performance on a variety of tasks, including image classification and characterlevel language-modelling.
ISOLATION AND GENOMIC ANALYSIS OF PROBIOTIC ISOLATE AND ROLE OF AMYLASE ENZYME ACTIVITY IN FISH GROWTH

Probiotics are beneficial microbial population, which enhance the growth and immunity of fish under stressful environmental conditions by modulating the gut colonization of the probiotic bacterial strains. The present investigation was designed to isolate fish gut bacterial samples. Isolated strains were screened and four strains were assessed with probiotic activity towards the growth of fish in the particular habitat. The experiments were conducted by admixing the strain with water and fortified with the commercial feed for the growth of Labeo rohita . The result clearly revealed that only two strains showed better activity. These isolated probiotic microorganism’s genomic DNA was analysed using Universal 16S primers and electrophoresed to confirm the taxonomic identity. The 16S rRNA sequence of the isolates was amplified using primers using PCR. Blast analysis of 16S rRNA sequence was carried out with the sequences available in the NCBI database. The 16S rRNA sequences of the isolates were aligned with reference sequences obtained from the GenBank databases. Identical 16S rRNA sequence was recognized by Phylogenetic trees and manual comparisons. These sequences were found to be associated with enzyme amylase and peptidases. Amylase enzyme, protease and lipase enzymes are markedly increase the activity of the probiotics by improving the water quality parameters, food absorption and growth of L. rohita . The identified strain was examined for its effect on fish growth and considered for improving L. rohita fish growth.
Hybrid Mass Coordinate in WRF-ARW and Its Impact on Upper-Level Turbulence Forecasting

Although a terrain-following vertical coordinate is well suited for the application of surface boundary conditions,itiswellknownthattheinﬂuencesoftheterrainonthecoordinatesurfacescancontributetoincreasenumericalerrors,particularlyoversteeptopography.Toreducetheseerrors,ahybridsigma–pressurecoordinate is formulated in the Weather Research and Forecasting (WRF) Model, and its effects are illustrated for both an idealized test case and a real-data forecast for upper-level turbulence. The idealized test case conﬁrms that with the basic sigma coordinate, signiﬁcant upper-level disturbances can be produced due to numerical errors that arise as the advection of strong horizontal ﬂow is computed along coordinate surfaces that are perturbed by smaller-scale terrain inﬂuences. With the hybrid coordinate, this artiﬁcial noise is largely eliminated as the mid- and upper-level coordinate surfaces correspond much more closely to constant pressure surfaces. Inreal-data simulations for upper-level turbulence forecasting, the WRF Model using the basic sigma coordinate tends to overpredict the strength of upper-air turbulence over mountainous regions because of numerical errors arising as a strong upper-level jet is advected along irregular coordinate surfaces. With the hybrid coordinate, these errors are reduced, resulting in an improvedforecast of upper-level turbulence. Analysis of kinetic energy spectra for these simulations conﬁrms that artiﬁcial amplitudes in the smaller scales at upper levels that arise with the basic sigma coordinate are effectively removed when the hybrid coordinate is used.
Dynamic ISAR Imaging and Autofocusing of Maneuvering Targets Based on Sequential GP-SOONE Method and Eigenvalue Decomposition

A long coherent processing interval (CPI) is needed for achieving a high-resolution inverse synthetic aperture radar (ISAR) image. However, for a maneuvering target, the time-varying Doppler shifts cause a blurring effect on the ISAR image. Sparse representation-based algorithms can obtain a high-resolution image in a short CPI, while the Doppler shifts remain constant. Recently, a sequential order one negative exponential (SOONE) function has been introduced to measure the sparsity, and a gradient projection (GP) method has been used to solve the SOONE function and recover the sparse signal. In this paper, a 2D sequential GP-SOONE method for sparse recovery and dynamic ISAR imaging is proposed, which has a lower computational complexity than that of the 2D-GP-SOONE algorithm. Moreover, the performance of the proposed approach is the same as the 2D-GP-SOONE and better than the 2D smoothed L0 algorithm. Another problem of dynamic ISAR imaging is sequentially autofocusing the image. Hence, a fast parametric method based on eigenvalue decomposition and minimum entropy for dynamic ISAR autofocusing is proposed which has a faster convergence than the conventional methods. The proposed method has also comparable performance with the conventional ones. Several simulations and real data are used to show the superiority of the proposed methods.
Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University Dynamic joint sentiment-topic mode Dynamic joint sentiment-topic mode

Social media data are produced continuously by a large and uncontrolled number of users. The dynamic na- ture of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-speciﬁc word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-speciﬁc word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and short-timescale distributions are taken into consideration. We derive efﬁcient online inference procedures to se- quentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.
Product placement effectiveness in music videos: Measuring neurophysiological signals, fixations and self-report data.

Product placement is a marketing technique that, by inserting products into a narrative structure, constitutes a likely effective tool to increase the visibility and notoriety of a brand. For years, the opportunities for product placement in music videos were limited. Recently there has been a growth of interest for this tool/advertising modality since the digital community allowed the possibility to move videos from television to the Internet. The scope of this study is to investigate the effectiveness of product placement in music videos. An electroencephalographic (EEG) index called mental effort (ME) has been analyzed, in addition to the emotional index (EI), calculated by the combination of Galvanic Skin Response (GSR) and Heart Rate (HR) signals. Self-report responses have also been collected through an online questionnaire and interviews, since one experimental question was to investigate whether viewing a video containing a commercial product could influence the declared recall of the product inserted in it and the spontaneous recall of the video itself. Furthermore, fixations related to the product inserted in videos have been obtained by the eye tracking technique (ET). Higher values of the ME (p=0,016) and EI (p = 0,033) have been found for videos with product placement in comparison to videos without it. In addition, results show that the number of fixations affects the recall of the showed products (p<0,001). These findings highlight that using product placement in famous singers’ music videos is an effective technique for prompting product recall and how it helps to focus the visual attention on them.