Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Robust Neural Networks via Sparsification

It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks. In this paper, we make the case that a systematic exploitation of sparsity is key to defending against such attacks, and that a "locally linear" model for neural networks can be used to develop a theoretical foundation for crafting attacks and defenses. We consider two defenses. The first is a sparsifying front end, which attenuates the impact of the attack by a factor of roughly $K/N$ where $N$ is the data dimension and $K$ is the sparsity level. The second is sparsification of network weights, which attenuates the worst-case growth of an attack as it flows up the network. We also devise attacks based on the locally linear model that outperform the well-known FGSM attack. We provide experimental results for the MNIST and Fashion-MNIST datasets, showing the efficacy of the proposed sparsity-based defenses.
Role of Quantitative Dynamic Contrast-Enhanced MRI in Evaluating Regional Lymph Nodes With a Short-Axis Diameter of Less Than 5 mm in Rectal Cancer.

OBJECTIVE The aim of this study was to discriminate metastatic from nonmetastatic regional lymph nodes (LNs) with short-axis diameters of less than 5 mm in rectal cancer using quantitative parameters derived from dynamic contrast-enhanced (DCE) MRI. SUBJECTS AND METHODS Sixty-five LNs from 122 patients were evaluated, including malignant LNs (n = 27) and benign LNs (n = 38). The following parameters were assessed: the forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), fractional extravascular extracellular space volume (Ve), short-axis diameter, long-axis diameter, and short- to long-axis diameter ratio. ROC curves were used to analyze statistically significant parameters. RESULTS Metastatic LNs exhibited a lower Ktrans than did nonmetastatic LNs (p < 0.001), but the other parameters were not significantly different between the two groups. The AUC of the Ktrans was 0.732, with a 95% CI of 0.610-0.854, and the diagnostic cutoff value was 0.088 min-1 (sensitivity, 60.5%; specificity, 81.5%). CONCLUSION Ktrans had moderate diagnostic performance in assessing small regional LNs in rectal cancer and appears to be a useful predictor when distinguishing malignant LNs from benign LNs only by morphology is difficult.
Seroprevalence data at a private teaching hospital in Kenya: An examination of Toxoplasma gondii, cytomegalovirus, rubella, hepatitis A, and Entamoeba histolytica

Background Relevant seroprevalence data for endemic pathogens in a given region provide insight not only into a population’s susceptibility to acute infection or risk for reactivation disease but also into the potential need for policy initiatives aimed at reducing these risks. Data from sub-Saharan Africa are sparse and since Aga Khan University Hospital Nairobi is an internationally accredited hospital equipped with a laboratory electronic medical record system, analysis of pertinent local seroprevalence data has been made possible. Methods We have analyzed serology data from laboratory electronic records at a 300 bed tertiary private teaching hospital in Kenya for the dates, 2008 to 2017 for Toxoplasma gondii, cytomegalovirus, and rubella, which were used primarily for antenatal screening. We also analyzed the data from hepatitis A and amebiasis serologies, which were used primarily for diagnostic purposes. Results For T. gondii, cytomegalovirus, and rubella, we used IgG serology to determine seroprevalence, finding rates of 32%, 86%, and 89%, respectively. There was no significant age-related difference in the 20 to 49 year old age range for any of these three pathogens. Of the Hepatitis A IgM tests that were ordered, 33% were positive with a peak positive rate of 70% in the five to nine year old age range. The seroprevalence of amebiasis was 4% and all cases of seropositivity were accompanied by compatible clinical illness (hepatic abscess). Conclusions These data provide insight into seroprevalence rates of selected pathogens that can be used to guide screening and diagnostic laboratory testing as well as private and public immunization practices.
Effects of maternal western‐style diet on amniotic fluid volume and amnion VEGF profiles in a nonhuman primate model

During pregnancy, high fat diet (HFD) induces maternal obesity, insulin resistance, and placental inflammatory responses that compromise placental and fetal development. Whether maternal HFD would adversely affect amniotic fluid volume (AFV) has not been explored. Vascular endothelial growth factor (VEGF) is expressed in the amnion and has been proposed as a regulator of AFV. Our aim was to investigate the effects of HFD on AFV and the associated changes in VEGF and soluble VEGF receptor 1 (sFlt‐1) expression profiles in three amnion regions of a nonhuman primate model. Further, we examined the relationships between VEGF expression and HFD‐induced changes in maternal metabolic status. Japanese macaques were maintained on control or HFD and amniotic fluid index (AFI) was measured as an ultrasonic estimate of AFV. Amniotic fluid VEGF concentrations were determined by ELISA and amnion VEGF and sFlt‐1 mRNA levels by real‐time RT‐qPCR. HFD increased maternal plasma triglyceride while glucose levels were unchanged. Maternal weight gain was found in diet‐sensitive animals whereas amniotic fluid VEGF concentration was reduced in diet‐resistant animals. HFD did not alter AFI and there was no correlation between AFI and maternal weight or amniotic fluid VEGF concentrations. VEGF mRNA levels were lowest in secondary placental amnion while sFlt‐1 mRNA were lowest in the primary placental amnion. HFD did not affect amnion VEGF or sFlt‐1 mRNA expression. These findings suggest that although maternal HFD increased maternal weight in diet‐sensitive and reduced amniotic fluid VEGF concentrations in diet‐resistant phenotype, AFV as indicated by the AFI, was not significantly affected.
Efficacy and toxicities of combination maintenance therapy in the treatment of advanced colorectal cancer: a meta-analysis of randomized controlled trials.

PURPOSE We performed a systematic review and meta-analysis to investigate the efficacy and toxicities of combination maintenance therapy for the treatment of advanced colorectal cancer (CRC). METHODS Relevant trials were identified by searching electronic databases and conference meetings. Prospective randomized controlled trials (RCTs) assessing combination maintenance therapy in advanced CRC patients were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS) and grade 3-4 toxicities. RESULTS A total of 3,174 advanced CRC patients received combination maintenance treatment from 6 RCTs were included for analysis. The use of combination maintenance therapy did not significantly improved PFS (HR 0.95, 95%CI: 0.75-1.20, p =0.67) and OS (HR 1.05, 95%CI: 0.93-1.17, p =0.45) in comparison with single bevacizumab maintenance therapy for the treatment of advanced CRC, similar results were observed in sub-group analysis according to treatment regimens. In addition, combination maintenance therapy significantly improved PFS (HR 0.57, 95%CI: 0.41-0.80, p =0.001), but not for OS (HR 0.93, 95%CI: 0.76-1.14, p =0.47) in comparison with observation. Additionally, more incidences of any grade 3-4 toxicities (diarrhea, fatigue and hand-foot skin reaction) were observed in the combination maintenance therapy. CONCLUSIONS The findings of this study show that the efficacy of combination maintenance therapy is comparable to that of bevacizumab alone in terms of PFS and OS for advanced CRC patients, but at the cost of increased grade 3-4 toxicities. Thus single agent bevacizumab remains the recommended maintenance treatment for advanced CRC patients.
Inferring information flow in spike-train data sets using a trial-shuffle method

Understanding information processing in the brain requires the ability to determine the functional connectivity between the different regions of the brain. We present a method using transfer entropy to extract this flow of information between brain regions from spike-train data commonly obtained in neurological experiments. Transfer entropy is a statistical measure based in information theory that attempts to quantify the information flow from one process to another, and has been applied to find connectivity in simulated spike-train data. Due to statistical error in the estimator, inferring functional connectivity requires a method for determining significance in the transfer entropy values. We discuss the issues with numerical estimation of transfer entropy and resulting challenges in determining significance before presenting the trial-shuffle method as a viable option. The trial-shuffle method, for spike-train data that is split into multiple trials, determines significant transfer entropy values independently for each individual pair of neurons by comparing to a created baseline distribution using a rigorous statistical test. This is in contrast to either globally comparing all neuron transfer entropy values or comparing pairwise values to a single baseline value. In establishing the viability of this method by comparison to several alternative approaches in the literature, we find evidence that preserving the inter-spike-interval timing is important. We then use the trial-shuffle method to investigate information flow within a model network as we vary model parameters. This includes investigating the global flow of information within a connectivity network divided into two well-connected subnetworks, going beyond local transfer of information between pairs of neurons.
Adaptive Online Learning in Dynamic Environments

In this paper, we study online convex optimization in dynamic environments, and aim to bound the dynamic regret with respect to any sequence of comparators. Existing work have shown that online gradient descent enjoys an $O(\sqrt{T}(1+P_T))$ dynamic regret, where $T$ is the number of iterations and $P_T$ is the path-length of the comparator sequence. However, this result is unsatisfactory, as there exists a large gap from the $\Omega(\sqrt{T(1+P_T)})$ lower bound established in our paper. To address this limitation, we develop a novel online method, namely adaptive learning for dynamic environment (Ader), which achieves an optimal $O(\sqrt{T(1+P_T)})$ dynamic regret. The basic idea is to maintain a set of experts, each attaining an optimal dynamic regret for a specific path-length, and combines them with an expert-tracking algorithm. Furthermore, we propose an improved Ader based on the surrogate loss, and in this way the number of gradient evaluations per round is reduced from $O(\log T)$ to $1$. Finally, we extend Ader to the setting that a sequence of dynamical models is available to characterize the comparators.
The effect of passive heat stress on distress andself-control in male smokers and non-smokers

Abstract In the current study, we tested the effects of core body temperature increases (e.g. heat stress) on affect, self-reported physical discomfort, and subsequent self-control in male smokers and nonsmokers using a novel passive heat stress paradigm, within a distress tolerance framework. Twenty-eight men (14 smokers), completed both heat stress and control sessions in randomized order. Results revealed that increases in core body temperature were associated with increased anxiety, irritability, and body discomfort as well as decreased happiness, with stronger effects for smokers. Smokers and nonsmokers both evidenced less self-control during the heat session and did not differ on this measure, nor on a measure of interoceptive sensitivity. The current study indicates that heat stress is a viable method for studying distress tolerance in men, and suggests the value in examining dynamic changes in self-control as a function of distress. Implications will be discussed for distress tolerance in general and smokers in specific.
Clinical Predictors of Pseudomonas aeruginosa Bacteremia in Emergency Department

Objectives Pseudomonas aeruginosa shows higher mortality rate compared to other bacterial infections and is susceptible to a limited number of antimicrobial agents. Considering inadequate empirical treatment of Pseudomonas bacteremia has been associated with increased mortality, it is important for emergency physicians to identify infections by P. aeruginosa. Methods This was a single-center retrospective case-control study to investigate the clinical predictors of patients diagnosed as Pseudomonas bacteremia in the emergency department (ED) from June 2012 to December 2016. Patients with blood culture positive for Escherichia coli in the same period were chosen as the control group, and type of infection was matched for each patient. Results A total of 54 cases with Pseudomonas bacteremia and 108 controls with E. coli bacteremia were included. In the case group, 76% was community-acquired infection, 44% received inappropriate empirical treatment in the ED, and in-hospital mortality was 30%. Multiple logistic regression showed that respiratory tract infection was an independent risk factor for Pseudomonas bacteremia (OR 6.56, 95% CI 1.78-23.06; p = 0.004), whereas underlying diabetes mellitus (OR 0.22, 95% CI 0.07-0.61; p = 0.004) and presentation as urinary tract infection (OR 0.06, 95% CI 0.02-0.18; p < 0.001) were negative clinical predictors. Conclusions We suggest that antipseudomonal antibiotics should be considered beyond simple coverage of Gram-negative bacteria in the ED, especially if the patient is likely to have pneumonia. Having diabetes or presenting with urinary tract infection could be clinical factors unfavorable to use of antipseudomonal antibiotics.
Aberrant cystatin-C expression in blood from patients with breast cancer is a suitable marker for monitoring tumor burden

The present study was performed to evaluate the efficacy of circulating cystatin-C as a tumor monitoring biomarker at different clinical time points in patients with breast cancer over a long-term follow-up period. In addition, the secretory rate of circulating cystatin-C from cancer tissue was investigated by comparing the blood and tissue expression levels of cystatin-C. Blood samples from healthy volunteers (40 males and 40 females) were obtained at yearly health examinations if laboratory and imaging abnormalities were not detected. Blood samples from 34 patients with breast cancer were obtained at 205 different time points of clinical progression. Blood levels of cystatin-C were measured using ELISA and the tissue levels were measured using immunohistochemistry. No age-associated effect was observed in male and female blood cystatin-C levels. The positivity rate was 46% in patients (38/83) and 40% in samples collected at different time points (82/205). Blood cystatin-C levels were lowest following surgery compared with patients with systemic metastasis (P<0.001). The sensitivity, specificity and accuracy rates of ELISA were 53.6, 63.6 and 53.9%, respectively. The concordance rate between blood and tissue expression was 38%. The main reason for discordance between tissue and serum expression of cytostatin-C came from low serum positivity in samples showing tissue cytostatin-C (3/11, 27%). The specificity between cytostatin-C and CA-125 was highest in tumor absence state. In conclusion, elevated blood levels of cystatin-C were observed in 40% of breast cancer cases and were tumor-volume dependent. However, the concordance rate between tissue and blood was quite low, suggesting tumor heterogeneity of cystatin-C expression or co-acting pathway activation, such as cathepsin D. As one-third of breast cancer tissues express cystatin-C without cancer antigen 15-3 elevation, cystatin-C may represent a good tumor-monitoring marker in breast cancer.
Variable population prevalence estimates of germline TP53 variants: A gnomAD‐based analysis

Reports of variable cancer penetrance in Li–Fraumeni syndrome (LFS) have raised questions regarding the prevalence of pathogenic germline TP53 variants. We previously reported higher‐than‐expected population prevalence estimates in sequencing databases composed of individuals unselected for cancer history. This study aimed to expand and further evaluate the prevalence of pathogenic and likely pathogenic germline TP53 variants in the gnomAD dataset (version r2.0.2, n = 138,632). Variants were selected and classified based on our previously published algorithm and compared with alternative estimates based on three different classification databases: ClinVar, HGMD, and the UMD_TP53 database. Conservative prevalence estimates of pathogenic and likely pathogenic TP53 variants were within the range of one carrier in 3,555–5,476 individuals. Less stringent classification increased the approximate prevalence to one carrier in every 400–865 individuals, mainly due to the inclusion of the controvertible p.N235S, p.V31I, and p.R290H variants. This study shows a higher‐than‐expected population prevalence of pathogenic and likely pathogenic germline TP53 variants even with the most conservative estimates. However, these estimates may not necessarily reflect the prevalence of the classical LFS phenotype, which is based upon family history of cancer. Comprehensive approaches are needed to better understand the interplay of germline TP53 variant classification, prevalence estimates, cancer penetrance, and LFS‐associated phenotype.
Characterization of Diffuse Gliomas With Histone H3-G34 Mutation by MRI and Dynamic 18F-FET PET

Background Recent data suggest that diffuse gliomas carrying mutations in codon 34 of the H3 histone family 3A protein represent a very rare, distinct subgroup of IDH–wild type malignant astrocytic gliomas. However, characteristics detectable by MRI and 18F-FET PET in H3-G34-mutant gliomas are unknown. Methods We report on MRI and 18F-FET PET findings in 8 patients from 4 German centers with H3-G34-mutant diffuse gliomas. MRI analyses included multifocality, contrast enhancement, necrosis, cysts, hemorrhages, calcification, and edema. 18F-FET PET characteristics were evaluated on the basis of static 18F-FET PET parameters, such as maximal tumor-to-background ratio (TBRmax) and biological tumor volume (BTV), as well as the minimal time-to-peak (TTPmin) obtained from dynamic 18F-FET PET data. Results MRI showed multifocal lesions in 2 of 8, contrast enhancement in 6 of 8, necrosis in 3 of 8, cysts in 3 of 8, hemorrhage in 1 of 8, and calcifications in 1 of 8 patients. None of the tumors showed marked peritumoral edema. However, all 8 H3-G34-mutant gliomas were characterized by a high uptake intensity on 18F-FET PET with a median TBRmax of 3.4 (range, 2.5–11.7) and a relatively diffuse uptake pattern leading to a large BTV (median, 41.9 mL; range, 7.5–115.6). Dynamic PET data revealed a short median TTPmin of 12.5 minutes. Conclusions MRI features of diffuse gliomas with H3-G34 mutation may present very heterogeneously with some cases not even fulfilling the imaging criteria of high-grade glioma. In contrast, in 18F-FET PET, these tumors show an extensive and diffuse tracer uptake resulting in large BTV with a high TBRmax and a short TTPmin, thus resembling PET characteristics of aggressive high-grade gliomas, namely, glioblastomas.
Quantifying the contribution of four resistance mechanisms to ciprofloxacin MIC in Escherichia coli: a systematic review

Background Reviews assessing the genetic basis of ciprofloxacin resistance in Escherichia coli have mostly been qualitative. However, to predict resistance phenotypes based on genotypic characteristics, it is essential to quantify the contribution of genotypic determinants to resistance. Objectives We performed a systematic review to assess the relative contribution of known genomic resistance determinants to the MIC of ciprofloxacin in E. coli. Methods PubMed and Web of Science were searched for English language studies that assessed ciprofloxacin MIC and presence or introduction of genetic determinants of ciprofloxacin resistance in E. coli. We included experimental and observational studies without time restrictions. Medians and ranges of MIC fold changes were calculated for individual resistance determinants and combinations thereof. Results We included 66 studies, describing 604 E. coli isolates that carried at least one genetic ciprofloxacin resistance determinant. Mutations in gyrA and parC, genes encoding targets of ciprofloxacin, contribute to the largest fold changes in ciprofloxacin resistance in E. coli compared with the WT. Efflux and physical blocking or enzymatic modifications confer smaller increases in ciprofloxacin MIC than mutations in gyrA and parC. However, the presence of these other resistance mechanisms in addition to target alteration mutations further increases ciprofloxacin MIC, thus resulting in ciprofloxacin MIC increases ranging from 250- to 4000-fold. Conclusions This quantitative review of genomic determinants of ciprofloxacin resistance in E. coli demonstrates the complexity of resistance phenotype prediction from genomic data and serves as a reference point for studies aiming to predict ciprofloxacin MIC from E. coli genomes.
A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors

The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and successfully used in new drug discovery. An emerging body of data regarding the interactions of small drug-like molecules with their protein targets provides the possibility of building models on “structure-property” relationships and analyzing the performance of various machine-learning techniques. In our research, we analyze several different types of descriptors in order to predict the resistance of HIV reverse transcriptase and protease to the marketed antiretroviral drugs using the Random Forest approach. First, we represented amino acid sequences as a set of short peptide fragments, which included several amino acid residues. Second, we represented nucleotide sequences as a set of fragments, which included several nucleotides. We compared these two approaches using open data from the Stanford HIV Drug Resistance Database. We have determined the factors that modulate the performance of prediction: in particular, we observed that the prediction performance was more sensitive to certain drugs than a type of the descriptor used.