Miracle-dz/UniFAR-FTUs huggingface dataset:
Phage Taxonomy: We Agree To Disagree

Bacteriophage (phage) researchers are in universal agreement that phage biology has undergone a renaissance in recent years. No longer just tools of molecular biology, phage are now recognized to play critical roles in bacterial pathogenesis (3, 29) and bacterial population dynamics (5, 10). Likewise, phage therapy is enjoying renewed interest in Western medicine (24). Phage-derived proteins are currently being used as molecular machines (23), diagnostic agents (22), and therapeutic agents (15, 17, 22) and for drug discovery (14). However, the resurgence in phage popularity has only fanned the flames of an ongoing debate, namely, that of phage taxonomy. Many phage biologists feel that the current taxonomic classification scheme, devised over 3 decades ago by the International Committee on Taxonomy of Viruses (ICTV), is outdated and in need of revision in light of the postgenomic age. However, choosing one methodology on the basis of which to classify all known and yet to be discovered phage is an area where phage researchers agree to disagree. In this issue of the Journal of Bacteriology, Chibani-Chennoufi et al. provide support for a comparative genomics-based alternative classification scheme (6).
Adult Daughters’ Influence on Mothers’ Health-Related Decision Making: An Expansion of the Subjective Norms Construct

This study of mother—adult daughter communication uses qualitative methods to explore the appropriateness of including adult daughters as referents in the measurement of subjective norms (a behavioral theory construct) related to the use of mammography and other health-related tests and services. The methods were chosen to approximate as closely as possible the mother—adult daughter relationship in the context of daily life. This inductive approach contrasts with the deductive origins of the construct. A sample of nine Mexican and Filipina immigrant and U.S.-born mothers and their adult daughters was recruited. Data were collected in two phases: (a) videotaped observations of mother—daughter dyads discussing health-related topics and (b) follow-up interviews designed to obtain an emic (insider) perspective of the videotaped interaction. Results show that adult daughters influence their mothers’ ability to navigate the health care system and contribute to health-related decision making and behavior, suggesting that it may be appropriate to include adult daughters in the assessment of subjective norms.
Scheduler-Assisted Prefetching: Efficient Demand Paging for Embedded Systems

Embedded systems tend to use demand paging in order to provide more memory to applications in a cost-effective manner. However, demand paging drastically degrades the performance when the page fault rate is high. Prefetching has been known as a common remedy for page fault overhead. Although many prefetching mechanisms have been proposed, they are either effective only for specific page access patterns or too straight-forward to decrease a page fault rate to an acceptable level. We propose a scheduler-assisted prefetching mechanism which does not have such fundamental defects. As a proof of concept, our mechanism was completely implemented in Linux. We have also conducted a series of experiments to show its effectiveness. The experimental results showed a significant improvement: the number of the major page faults and the scheduling latency decreased by 30% and 51%, respectively.
MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences.

OBJECTIVE The purpose of this study was to evaluate the usefulness of diffusion-weighted echoplanar MR imaging in the examination of high-grade brain gliomas compared with that of conventional spin-echo (SE) or fast spin-echo (FSE) MR imaging. We hypothesize that diffusion-weighted MR imaging may enable us to differentiate various tumor components on the basis of differences in the diffusion of water. SUBJECTS AND METHODS Conventional SE and FSE MR images were obtained in 10 patients with high-grade brain glioma. Diffusion-weighted echoplanar MR images were obtained with a head gradient coil capable of providing diffusion-weighted imaging along the cephalocaudal axis. Using SE and FSE MR images as a baseline, we evaluated the diffusion-weighted MR images for usefulness in distinguishing tumor components on the basis of differences in diffusion. RESULTS Areas of tumor that showed significant enhancement on T1-weighted SE MR images obtained after injection of contrast material were markedly hyperintense on diffusion-weighted images and had a lower apparent diffusion coefficient (ADC) than the ADCs for nonenhancing tumor and peritumoral edema. Cystic or necrotic portions of tumor showed the most signal suppression on diffusion-weighted images and were associated with the highest ADCs. On T2-weighted FSE MR images, areas of hyperintensity observed in white matter oriented parallel to the direction of the diffusion gradient could be differentiated into two patterns on the basis of findings on diffusion-weighted images: areas that showed marked signal suppression with a higher ADC, most likely representing areas of predominantly peritumoral edema, and areas that showed a lesser degree of signal suppression with similar but slightly lower ADCs than those of edema, most likely representing areas of predominantly nonenhancing tumor. CONCLUSION Diffusion-weighted echoplanar MR imaging is a useful technique for examining high-grade cerebral gliomas. It enabled us to differentiate various components of the tumor (e.g., enhancing, nonenhancing, cystic, or necrotic) and to distinguish areas of predominantly nonenhancing tumor from areas of predominantly peritumoral edema when the abnormality was located in the white matter aligned in the direction of the diffusion-weighted gradient. Diffusion-weighted echoplanar MR imaging appears to be a powerful tool in the characterization of brain neoplasms.
Measuring Military Allocations

This study investigates the validity of different indicators of resources allocated to the military that are commonly used in conflict research. The various indicators are first described and then evaluated on four criteria for face validity. The convergent, discriminant, and predictive validity are then assessed according to the results of various statistical tests. A common hypothesis that conflict involving “overallocating” states is more likely to escalate to war is examined using all indicators under consideration. Implications and suggestions for conflict research are discussed in the conclusion.
Pattern Separation in the Dentate Gyrus and CA3 of the Hippocampus

Theoretical models have long pointed to the dentate gyrus as a possible source of neuronal pattern separation. In agreement with predictions from these models, we show that minimal changes in the shape of the environment in which rats are exploring can substantially alter correlated activity patterns among place-modulated granule cells in the dentate gyrus. When the environments are made more different, new cell populations are recruited in CA3 but not in the dentate gyrus. These results imply a dual mechanism for pattern separation in which signals from the entorhinal cortex can be decorrelated both by changes in coincidence patterns in the dentate gyrus and by recruitment of nonoverlapping cell assemblies in CA3.
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets

Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOI-optimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in which a user is ask to select her most preferred product from a set. We show that, under very general assumptions, the optimal choice query w.r. t. EVOI coincides with the optimal recommendation set, that is, a set maximizing the expected utility of the user selection. Since recommendation set optimization is a simpler, submodular problem, this can greatly reduce the complexity of both exact and approximate (greedy) computation of optimal choice queries. We also examine the case where user responses to choice queries are error-prone (using both constant and mixed multinomial logit noise models) and provide worst-case guarantees. Finally we present a local search technique for query optimization that works extremely well with large outcome spaces.
Renal cell carcinoma and malignant peritoneal mesothelioma after occupational asbestos exposure: case report.

UNLABELLED Asbestos is the main causal factor for malignant mesothelioma (MM), a relatively rare and aggressive malignancy. Some epidemiological evidence suggests a role of this agent also in the etiology of renal cell carcinoma (RCC), the most common form of kidney cancer. CASE REPORT After 7 years of asbestos exposure, a 76-year-old asbestos-cement worker came to our notice with left flank pain. Diagnostic imaging disclosed a neoplasm in the upper two thirds of the left kidney, without evidence of metastases. After surgery (nephrectomy with para-aortic lymphadenectomy), histopathology revealed clear cell RCC. One year later, the patient was hospitalized for abdominal pain. Laparoscopy showed diffuse neoplastic infiltration of the peritoneum and liver. Histological and immunohistochemical examination of the bioptic samples led to the diagnosis of biphasic MM. The subject died 2 months later. Autopsy disclosed ascites and diffuse infiltration of the abdominal wall and viscera, without evidence of RCC relapse. CONCLUSIONS This is the second reported case of association between RCC and peritoneal MM in the scientific literature. Asbestos might be involved in the causation of both malignancies.
Antibacterial activity of Ro 17-2301 and other antimicrobial agents against cefotaxime-resistant aerobic gram-negative bacilli

The in vitro activity of Ro 17-2301 was determined and compared with those of aztreonam, ceftazidime, amikacin, and piperacillin against 141 cefotaxime-resistant gram-negative bacilli. Ro 17-2301 was bactericidal, and its activity against the majority of these organisms was equal or superior to those of ceftazidime, aztreonam, and piperacillin. An inoculum effect of Ro 17-2301 and aztreonam for many Pseudomonas aeruginosa and Enterobacter spp. isolates was demonstrated.
Location of Cu2+ in CHA zeolite investigated by X-ray diffraction using the Rietveld/maximum entropy method

Rietveld/MEM analysis applied to synchrotron powder X-ray diffraction data of dehydrated CHA zeolites with catalytically active Cu2+ reveals Cu2+ in both the six- and eight-membered rings in the CHA framework, providing the first complete structural model that accounts for all Cu2+. Density functional theory calculations are used to corroborate the experimental structure and to discuss the Cu2+ coordination in terms of the Al distribution in the framework.
Specific Inhibition of p25/Cdk5 Activity by the Cdk5 Inhibitory Peptide Reduces Neurodegeneration In Vivo

The aberrant hyperactivation of Cyclin-dependent kinase 5 (Cdk5), by the production of its truncated activator p25, results in the formation of hyperphosphorylated tau, neuroinflammation, amyloid deposition, and neuronal death in vitro and in vivo. Mechanistically, this occurs as a result of a neurotoxic insult that invokes the intracellular elevation of calcium to activate calpain, which cleaves the Cdk5 activator p35 into p25. It has been shown previously that the p25 transgenic mouse as a model to investigate the mechanistic implications of p25 production in the brain, which recapitulates deregulated Cdk5-mediated neuropathological changes, such as hyperphosphorylated tau and neuronal death. To date, strategies to inhibit Cdk5 activity have not been successful in targeting selectively aberrant activity without affecting normal Cdk5 activity. Here we show that the selective inhibition of p25/Cdk5 hyperactivation in vivo, through overexpression of the Cdk5 inhibitory peptide (CIP), rescues against the neurodegenerative pathologies caused by p25/Cdk5 hyperactivation without affecting normal neurodevelopment afforded by normal p35/Cdk5 activity. Tau and amyloid pathologies as well as neuroinflammation are significantly reduced in the CIP–p25 tetra transgenic mice, whereas brain atrophy and subsequent cognitive decline are reversed in these mice. The findings reported here represent an important breakthrough in elucidating approaches to selectively inhibit the p25/Cdk5 hyperactivation as a potential therapeutic target to reduce neurodegeneration.
Why Deep Neural Networks for Function Approximation?

Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, we consider univariate functions on a bounded interval and require a neural network to achieve an approximation error of $\varepsilon$ uniformly over the interval. We show that shallow networks (i.e., networks whose depth does not depend on $\varepsilon$) require $\Omega(\text{poly}(1/\varepsilon))$ neurons while deep networks (i.e., networks whose depth grows with $1/\varepsilon$) require $\mathcal{O}(\text{polylog}(1/\varepsilon))$ neurons. We then extend these results to certain classes of important multivariate functions. Our results are derived for neural networks which use a combination of rectifier linear units (ReLUs) and binary step units, two of the most popular type of activation functions. Our analysis builds on a simple observation: the multiplication of two bits can be represented by a ReLU.
End-to-End Training of Deep Visuomotor Policies

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.
Retinal Expression of Wnt-Pathway Mediated Genes in Low-Density Lipoprotein Receptor-Related Protein 5 (Lrp5) Knockout Mice

Mutations in low-density lipoprotein receptor-related protein 5 (Lrp5) impair retinal angiogenesis in patients with familial exudative vitreoretinopathy (FEVR), a rare type of blinding vascular eye disease. The defective retinal vasculature phenotype in human FEVR patients is recapitulated in Lrp5 knockout (Lrp5−/−) mouse with delayed and incomplete development of retinal vessels. In this study we examined gene expression changes in the developing Lrp5−/− mouse retina to gain insight into the molecular mechanisms that underlie the pathology of FEVR in humans. Gene expression levels were assessed with an Illumina microarray on total RNA from Lrp5−/− and WT retinas isolated on postnatal day (P) 8. Regulated genes were confirmed using RT-qPCR analysis. Consistent with a role in vascular development, we identified expression changes in genes involved in cell-cell adhesion, blood vessel morphogenesis and membrane transport in Lrp5−/− retina compared to WT retina. In particular, tight junction protein claudin5 and amino acid transporter slc38a5 are both highly down-regulated in Lrp5−/− retina. Similarly, several Wnt ligands including Wnt7b show decreased expression levels. Plasmalemma vesicle associated protein (plvap), an endothelial permeability marker, in contrast, is up-regulated consistent with increased permeability in Lrp5−/− retinas. Together these data suggest that Lrp5 regulates multiple groups of genes that influence retinal angiogenesis and may contribute to the pathogenesis of FEVR.
Non-neoplastic granulosa cells within ovarian vascular channels: a rare potential diagnostic pitfall

Aims: To describe six cases seen in consultation in which artefactual vascular involvement within the ovary by benign granulosa cells caused diagnostic confusion. Methods/Results: In five cases, the initial favoured diagnoses of the submitting pathologists were metastatic carcinoma (three cases) and immature neural elements within a teratoma (two cases). In two cases, the ovary contained a benign cystic teratoma (one with struma ovarii), in two cases endometriosis, in one case follicular cysts, and in the other no pathological lesion was present. In all cases, several small ovarian vascular channels contained cohesive groups of cells with mildly atypical nuclei and cytoplasm, which varied from scant to abundant and eosinophilic. In four cases, mitotic figures were identified. The cells were morphologically consistent with benign granulosa cells and were associated in four cases with a nearby follicle lined by similar cells. There was no evidence of a mass lesion, grossly or histologically, to suggest a granulosa cell tumour. The nature of the cells was confirmed using immunohistochemistry for α inhibin and calretinin in one case. Conclusions: This phenomenon is probably an artefact secondary to surgical trauma or sectioning within the laboratory; alternatively, it could be related to ovulation. It is important that this benign process is not misinterpreted as cancer, either primary or metastatic, which may prompt inappropriate treatment or investigations that are not needed.
Network Versus Code Metrics to Predict Defects: A Replication Study

Several defect prediction models have been proposed to identify which entities in a software system are likely to have defects before its release. This paper presents a replication of one such study conducted by Zimmermann and Nagappan on Windows Server 2003 where the authors leveraged dependency relationships between software entities captured using social network metrics to predict whether they are likely to have defects. They found that network metrics perform significantly better than source code metrics at predicting defects. In order to corroborate the generality of their findings, we replicate their study on three open source Java projects, viz., JRuby, ArgoUML, and Eclipse. Our results are in agreement with the original study by Zimmermann and Nagappan when using a similar experimental setup as them (random sampling). However, when we evaluated the metrics using setups more suited for industrial use -- forward-release and cross-project prediction -- we found network metrics to offer no vantage over code metrics. Moreover, code metrics may be preferable to network metrics considering the data is easier to collect and we used only 8 code metrics compared to approximately 58 network metrics.
Passing It Forward: Intervening and Moderating Mechanisms in the Supportive Leadership Cascading Process

Title: PASSING IT FORWARD: INTERVENING AND MODERATING MECHANISMS IN THE SUPPORTIVE LEADERSHIP CASCADING PROCESS Payal Nangia Sharma, Ph.D., 2011 Directed By: Professor Gilad Chen Department of Management and Organization Using survey data collected from enlisted soldiers and non-commissioned officers (NCOs) nested in platoons in the U.S. Army, my dissertation examines intervening and moderating mechanisms in the cascading process of supportive leadership. Typically cascading studies focus on influence processes occurring in dyadic settings, neglect to consider boundary conditions, and utilize a group mean approach to analyzing the phenomenon. In my study, however, I find support for a conditional indirect effect of cascading supportive leadership at the group level, such that supportive leadership by groups of upper-level leaders (officers) promotes social cohesion for groups of lowerlevel leaders (NCOs) under high levels of combat exposure. In turn, lower-level social cohesion is positively linked with supportive leadership by groups of leaders at lower organizational levels. In addition, analyses using within-unit standard deviations of the substantive measures (i.e., strength) indicate that combat exposure strength moderates the relationship between upper-level supportive leadership strength and lower-level social cohesion strength, such that the positive relationship is stronger when combat exposure strength is higher (i.e., when within-unit standard deviation in combat exposure is lower). PASSING IT FORWARD: INTERVENING AND MODERATING MECHANISMS IN THE SUPPORTIVE LEADERSHIP CASCADING PROCESS by Payal Nangia Sharma Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2011 Advisory Committee: Professor Gilad Chen, Chair Dr. Paul D. Bliese Professor Cheri Ostroff Professor Debra L. Shapiro Professor M. Susan Taylor © Copyright by Payal Nangia Sharma 2011
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations

The cosine-based softmax losses and their variants achieve great success in deep learning based face recognition. However, hyperparameter settings in these losses have significant influences on the optimization path as well as the final recognition performance. Manually tuning those hyperparameters heavily relies on user experience and requires many training tricks. In this paper, we investigate in depth the effects of two important hyperparameters of cosine-based softmax losses, the scale parameter and angular margin parameter, by analyzing how they modulate the predicted classification probability. Based on these analysis, we propose a novel cosine-based softmax loss, AdaCos, which is hyperparameter-free and leverages an adaptive scale parameter to automatically strengthen the training supervisions during the training process. We apply the proposed AdaCos loss to large-scale face verification and identification datasets, including LFW, MegaFace, and IJB-C 1:1 Verification. Our results show that training deep neural networks with the AdaCos loss is stable and able to achieve high face recognition accuracy. Our method outperforms state-of-the-art softmax losses on all the three datasets.