Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulating the Chemical Transport Properties of a Transmembrane Antiporter via Alternative Anion Flux

ClC-ec1 is a prototype of the ClC antiporters, proteins that stoichiometrically exchange Cl– and H+ ions in opposite directions across a membrane. It has been shown that other polyatomic anions, such as NO3– and SCN–, can also be transported by ClC-ec1, but with partially or completely uncoupled proton flux. Herein, with the help of multiscale computer simulations in which the Grotthuss mechanism of proton transport (PT) is treated explicitly, we demonstrate how the chemical nature of these anions alters the coupling mechanism and qualitatively explain the shifts in the ion stoichiometry. Multidimensional free energy profiles for PT and the coupled changes in hydration are presented for NO3– and SCN–. The calculated proton conductances agree with experiment, showing reduced or abolished proton flux. Surprisingly, the proton affinity of the anion is less influential on the PT, while its size and interactions with the protein significantly alter hydration and shift its influence on PT from facilitating to inhibiting. We find that the hydration of the cavity below the anion is relatively fast, but connecting the water network past the steric hindrance of these polyatomic anions is quite slow. Hence, the most relevant coordinate to the PT free energy barrier is the water connectivity along the PT pathway, but importantly only in the presence of the excess proton, and this coordinate is significantly affected by the nature of the bound anion. This work again demonstrates how PT is intrinsically coupled with protein cavity hydration changes as well as influenced by the protein environment. It additionally suggests ways in which ion exchange can be modulated and exchange stoichiometries altered.
Board examination stress effect on diastolic function

To this date, effects of mental stress on cardiac function have not been clearly investigated. Although hemodynamic and neuroendocrine adverse effects of daily mental stress on cardiovascular morbidity and mortality have been reported, its direct impact on diastolic function of the heart has not been previously studied. In this study, we aimed to assess the role of Board Exam anxiety on right and left ventricular diastolic function in medical residents.
Identification and monitoring of atypical PML/RARA fusion transcripts in acute promyelocytic leukemia

Once the diagnostic suspicion of acute promyelocytic leukemia (APL) has been raised, international guidelines recommend prompt initiation of tailored therapy and supportive care, while awaiting for genetic confirmation of the diagnosis, and the identification of the specific PML/RARA isoform by reverse transcriptase polymerase chain reaction (RT‐PCR). Depending on the PML break point, usually located within intron 6, exon 6, or intron 3, different PML/RARA transcript isoforms may be generated, that is, long (bcr1), variant (bcr2), and short (bcr3), respectively. We report here the characterization of three APL cases harboring atypical PML/RARA transcripts, which were not clearly detectable after standard RT‐PCR amplification. In all three cases, clinical, morphological, and immunophenotypic features were consistent with APL. Direct sequencing allowed the identification of atypical break points within the PML and RARA genes. Then, we designed a patient‐specific quantitative real‐time PCR for the atypical transcripts, which allowed for specific quantitative evaluation of minimal residual disease (MRD) during follow‐up. Despite the rarity of APL cases with an atypical PML/RARA fusion, our study indicates that an integrated laboratory approach, employing several diagnostic techniques is crucial to timely diagnose APL. This approach allows prompt initiation of specific targeted treatment and reliable MRD monitoring in atypical APL cases.
Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems

Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i.e., agents) for better scalability. However, a typical distributed system is usually implemented with a central server that collects data statistics from multiple independent machines operating on different subsets of data to build a global analytic model. This centralized communication architecture however exposes a single choke point for operational failure and places severe bottlenecks on the server's communication and computation capacities as it has to process a growing volume of communication from a crowd of learning agents. To mitigate these bottlenecks, this paper introduces a novel Collective Online Learning Gaussian Process framework for massive distributed systems that allows each agent to build its local model, which can be exchanged and combined efficiently with others via peer-to-peer communication to converge on a global model of higher quality. Finally, our empirical results consistently demonstrate the efficiency of our framework on both synthetic and real-world datasets.
Conceptual Design of a Fully Passive Transfemoral Prosthesis to Facilitate Energy-Efficient Gait

In this paper, we present the working principle and conceptual design toward the realization of a fully-passive transfemoral prosthesis that mimics the energetics of the natural human gait. The fundamental property of the conceptual design consists of realizing an energetic coupling between the knee and ankle joints of the mechanism. Simulation results show that the power flow of the working principle is comparable with that in human gait and a considerable amount of energy is delivered to the ankle joint for the push-off generation. An initial prototype in half scale is realized to validate the working principle. The construction of the prototype is explained together with the test setup that has been built for the evaluation. Finally, experimental results of the prosthesis prototype during walking on a treadmill show the validity of the working principle.
Does Prostate Median Lobe Really Matter for GreenLight HPS Laser Photovaporization of the Prostate

Abstract Background: Benign prostatic hyperplasia (BPH) is a common pathology in elderly patients, inducing lower urinary tract symptoms. The treatment of BPH is first a medical option, then a surgical treatment, either by endoscopy or open surgery. We here report a case of GreenLight HPS™ laser photovaporization (PV) with an impaired maintenance of median lobe postoperatively, unimportant on functional results. Case Presentation: A 68-year-old man presented with lower urinary tract symptoms in the last 2 years, treated first by medicine with good response. On digital rectal examination, the enlarged prostate was homogeneous and regular. International Prostate Symptom Score (IPSS) was 30/35, Incontinence Quality of Life (iQol) 6/6, and International Index of Erectile Function 5 14/25 with regular sexual activity. Transrectal ultrasound (TRUS) reported BPH of 62 g with a median lobe of 6 g protruding into the bladder. At uroflowmetry, maximum urinary flow rate (Qmax) was 8 mL/s for 90 cc void volume and 20 cc postvoid residual. After failure of medical treatment, we offered a surgical treatment option by laser therapy using the 180W XPS GreenLight™. At 1-month follow-up, functional outcomes were improved with a Qmax of 11 mL/s, postvoiding residual volume 0 cc, IPSS 12/35, and iQol 2/6. At 3-month follow-up, outcomes still improved, although the TRUS reported a prostate volume of 30 g with a persistent median lobe. Conclusion: Impaired maintenance of median lobe after GreenLight laser PV does not seem to affect functional results. This case report opens the way for a new therapeutic strategy for patients according to their prostate anatomy. A randomized clinical trial could be done about surgical treatment for patient BPH according to prostate volume and anatomy.
Speeding up Extreme Multi-Label Classifier by Approximate Nearest Neighbor Search

Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called “AnnexML”. At the training step, AnnexML constructs a k-nearest neighbor graph of label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method that efficiently explores the learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show that our AnnexML can significantly improve prediction accuracy, especially on data sets that have a larger label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, a state-of-the-art embeddingbased method. key words: extreme multi-label classification, k-nearest neighbor graph, approximate nearest neighbor search, learning-to-rank
The Impact of Timestamp Granularity in Optimistic Concurrency Control

Optimistic concurrency control (OCC) can exploit the strengths of parallel hardware to provide excellent performance for uncontended transactions, and is popular in high-performance in-memory databases and transactional systems. But at high contention levels, OCC is susceptible to frequent aborts, leading to wasted work and degraded performance. Contention managers, mixed optimistic/pessimistic concurrency control algorithms, and novel optimistic-inspired concurrency control algorithms, such as TicToc, aim to address this problem, but these mechanisms introduce sometimes-high overheads of their own. We show that in real-world benchmarks, traditional OCC can outperform these alternative mechanisms by simply adding fine-grained version timestamps (using different timestamps for disjoint components of each record). With fine-grained timestamps, OCC gets 1.14x TicToc's throughput in TPC-C at 128 cores (previous work reported TicToc having 1.8x higher throughput than OCC at 80 hyperthreads). Our study shows that timestamp granularity has a greater impact than previously thought on the performance of transaction processing systems, and should not be overlooked in the push for faster concurrency control schemes.
Absence of an early hippocampal encoding signal after medial temporal lesions: No consequence for the spacing effect

Immediately repeated meaningful pictures in a continuous recognition task induce a positive frontal potential at about 200–300 ms, which appears to emanate from the medial temporal lobe (MTL) centered on the hippocampus, as concluded from inverse solutions, coherence measurements, and depth electrode recordings in humans. In this study, we tested patients with unilateral MTL lesions due to stroke to verify the provenance of this signal and its association with the spacing effect (SE)—the improved learning of material encountered in spaced rather than massed presentation. We found that unilateral left or right MTL lesions abolished the early frontal MTL‐mediated signal but not the spacing effect. We conclude that the SE does not depend on MTL integrity. We suggest that the early frontal signal at 200–300 ms after immediate picture repetition may serve as a direct biomarker of MTL integrity that may be useful in the early stages of diseases like Alzheimer's.
Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely used in processing natural language tasks and achieve huge success. Traditional RNNs usually treat each token in a sentence uniformly and equally. However, this may miss the rich semantic structure information of a sentence, which is useful for understanding natural languages. Since semantic structures such as word dependence patterns are not parameterized, it is a challenge to capture and leverage structure information. In this paper, we propose an improved variant of RNN, Multi-Channel RNN (MC-RNN), to dynamically capture and leverage local semantic structure information. Concretely, MC-RNN contains multiple channels, each of which represents a local dependence pattern at a time. An attention mechanism is introduced to combine these patterns at each step, according to the semantic information. Then we parameterize structure information by adaptively selecting the most appropriate connection structures among channels. In this way, diverse local structures and dependence patterns in sentences can be well captured by MC-RNN. To verify the effectiveness of MC-RNN, we conduct extensive experiments on typical natural language processing tasks, including neural machine translation, abstractive summarization, and language modeling. Experimental results on these tasks all show significant improvements of MC-RNN over current top systems.
Intra- and inter-rater reproducibility of the 6-minute walk test and the 30-second sit-to-stand test in patients with severe and very severe COPD

Background In patients with COPD, the 6-minute walk test (6MWT) and the 30-second sit-to-stand test (30sec-STS) are widely used as clinical outcome measures of walking capacity, lower limb muscle strength, and functional ability. Due to a documented learning effect, at least two trials are recommended for assessment. The aim of our study was to investigate the intra- and inter-rater reliability and agreement of the two tests in patients with severe and very severe COPD (FEV1 <50%). Patients and methods Fifty patients (22 females; mean [SD]: age 67 [9] years, FEV1 predicted 32 [9]%) were assessed with the 6MWT and the 30sec-STS twice by the same assessor on test-day 1 (T1) and by another assessor 7–10 days later on test-day 2 (T2). Results The 6MWT intra- and inter-rater reliability (intraclass correlation coefficient, ICC1.1) was 0.98 (lower limit 95% CI: 0.94) and 0.96 (lower limit 95% CI: 0.94), respectively, and agreement (standard error of the measurement, SEM) was 14.8 and 20.5 m, respectively. The 30sec-STS intra- and inter-rater reliability and agreement results were, respectively, ICC1.1 0.94 (lower limit 95% CI: 0.90) and 0.92 (lower limit 95% CI: 0.86), with SEM of 0.97 and 1.14 repetitions. There was no difference (95% CI: −5.3; 8.1) between the 6MWT distances on T1, while the mean walking distance improved 7.9 m (0.0 m; 15.8 m) from T1 to T2. Improvement on the same test date was less likely (OR: 3.6 [95% CI: 1.1; 11.8], Fisher’s exact test, P=0.047) in patients who walked less than 350 m in the 6MWT. We found no clinically relevant learning effect in the 30sec-STS. Conclusion In patients with severe and very severe COPD the 6MWT and the 30sec-STS showed excellent intra- and inter-rater reliability and acceptable agreement. No learning effect was documented for the tests when performed on the same day. Our data suggest that in clinical practice using different assessors is acceptable, and that a single test trial may be sufficient to assess patients with severe and very severe COPD.
Nitrogen Starvation Induces Persister Cell Formation in Escherichia coli

Bacteria often reside in environments where nutrient availability is scarce; therefore, they have evolved adaptive responses to rapidly cope with conditions of feast and famine. Understanding the mechanisms that underpin the regulation of how bacteria cope with this stress is a fundamentally important question in the wider context of understanding the biology of the bacterial cell and bacterial pathogenesis. Two major adaptive mechanisms to cope with starvation are the nitrogen regulation (ntr) stress and stringent responses. Here, I describe how these bacterial stress responses are coordinated under conditions of nitrogen starvation to promote the formation of antibiotic-tolerant persister cells by elevating levels of the secondary messenger (p)ppGpp. ABSTRACT To cope with fluctuations in their environment, bacteria have evolved multiple adaptive stress responses. One such response is the nitrogen regulation stress response, which allows bacteria, such as Escherichia coli, to cope with and overcome conditions of nitrogen limitation. This response is directed by the two-component system NtrBC, where NtrC acts as the major transcriptional regulator to activate the expression of genes to mount the response. Recently, my colleagues and I showed that NtrC directly regulates the expression of the relA gene, the major (p)ppGpp synthetase in E. coli, coupling the nitrogen regulation stress and stringent responses. As elevated levels of (p)ppGpp have been implicated in the formation of persister cells, here, I investigated whether nitrogen starvation promotes their formation and whether the NtrC-RelA regulatory cascade plays a role. The results reveal that nitrogen-starved E. coli synthesizes (p)ppGpp and forms a higher percentage of persister cells than nonstarved cells and that both NtrC and RelA are important for these processes. This study provides novel insights into how the formation of persisters can be promoted in response to a nutritional stress. IMPORTANCE Bacteria often reside in environments where nutrient availability is scarce; therefore, they have evolved adaptive responses to rapidly cope with conditions of feast and famine. Understanding the mechanisms that underpin the regulation of how bacteria cope with this stress is a fundamentally important question in the wider context of understanding the biology of the bacterial cell and bacterial pathogenesis. Two major adaptive mechanisms to cope with starvation are the nitrogen regulation (ntr) stress and stringent responses. Here, I describe how these bacterial stress responses are coordinated under conditions of nitrogen starvation to promote the formation of antibiotic-tolerant persister cells by elevating levels of the secondary messenger (p)ppGpp.
Depression Detection and Analysis

350 million people suffer from one of the most common men- tal disorders, depression. Depression can be of varied inten-sities. A prolonged phase of depression can lead to serious mental health issues. It greatly affects the productivity of a person and at worst may lead to self harm and suicide. A few of the symptoms are anxiety, feeling of loneliness, considering oneself not worthy enough, mood swings, eating disor- ders among a variety of others. Each person shows a different set of symptoms. During a phase of Depression, a person does not feel comfortable to talk to others about his/her problems to anyone. The paper focuses on an application which will take inputs as speech or text, identify the best response or rec-ommendation. This system determines if the person suffers from depression and the root problem, or cause of depression is aimed to be identiﬁed. As the system gets trained by inputs given by the user, it starts customizing recommendations and responses that address the root cause.
Emerging cell cycle inhibitors for treating metastatic castration-resistant prostate cancer

ABSTRACT Introduction: Disease progression despite androgen suppression defines lethal castration-resistant prostate cancer (CRPC). Most of these cancers remain androgen receptor (AR)-signaling dependent. Therapy for metastatic CRPC includes abiraterone acetate, enzalutamide, docetaxel, cabazitaxel, sipuleucel-T, and radium-223. However, survival remains modest for men with progressive disease despite AR-targeted therapy and docetaxel, and therefore novel treatments are needed. Areas covered: Recent evidence of genomic heterogeneity and sensitivity to PARP inhibitors supports investigation of targeted agents in CRPC. Cell cycle inhibitors are therefore logical molecules to investigate. Review of the current literature identified cell cycle inhibitors under study in early phase clinical trials targeting the G1 (palbociclib, ribociclib, AZD-5363, ipatasertib), S (M-6620, prexasertib), G2 (adavosertib), and M (alisertib) phases of the cell cycle. Expert opinion: Strategies combining cell cycle inhibitors with active agents in CRPC are most likely to have clinical impact with CDK4/6 and Wee1 inhibitors appearing most promising. Identification of predictive biomarkers may be essential and currently trials are testing circulating cell-free DNA as an approach. Incremental toxicities such as neutropenia are important in this population. Results from most current clinical trials of cell cycle inhibitors in CRPC are still pending but it is anticipated they will provide important insights into the heterogeneous biology of CRPC.
Frame-Based Knowledge Representation Using Large Specialized Corpora

This paper explores the benefits of building large specialized corpora automatically for creating semantic frames on special fields of knowledge, using term co-occurrences. We develop and evaluate a method to automatically compile large specialized corpora. Our evaluation serves two purposes: 1) verify if our automatic corpus compilation method returns large specialized corpora of the same quality as a small corpus built with human-selected documents; 2) see how the occurrence frequency of predicative terms in a specialized corpus influences the extraction of frame element realizations, using distributional co-occurrences. This will allow us to estimate the optimal size of a specialized corpus for capturing linguistic and knowledge patterns in the form of part-of-speech patterns, in accordance to frame semantics’ framework criteria. As our goal is to automate the creation of semantic frames using distributional analysis, building large quality specialized corpora is crucial since it will provide the groundwork for all future steps.
Strategic Interaction between Operators in the Context of Spectrum Sharing for 5G Networks

5G networks will make network sharing agreements between mobile operators technically possible. However, depending on the agreed and implemented quality-of-service isolation, the provision of services may lead to unsustainable business cases. In this paper, the economic feasibility of such arrangements is analyzed for the case of two operators. Concretely, while one network operator owns the spectrum, one virtual operator does not, and each one provides service to its subscriber base. Two sharing alternatives, namely, pooling and priority sharing, are studied regarding the profits that each operator gets. We conclude that the network operator is worse off under any circumstances under a pooling agreement, while a lump sum payment may leave the network operator better off under a priority sharing agreement.
REM1.3's phospho-status defines its plasma membrane nanodomain organization and activity in restricting PVX cell-to-cell movement

Plants respond to pathogens through dynamic regulation of plasma membrane-bound signaling pathways. To date, how the plant plasma membrane is involved in responses to viruses is mostly unknown. Here, we show that plant cells sense the Potato virus X (PVX) COAT PROTEIN and TRIPLE GENE BLOCK 1 proteins and subsequently trigger the activation of a membrane-bound calcium-dependent kinase. We show that the Arabidopsis thaliana CALCIUM-DEPENDENT PROTEIN KINASE 3-interacts with group 1 REMORINs in vivo, phosphorylates the intrinsically disordered N-terminal domain of the Group 1 REMORIN REM1.3, and restricts PVX cell-to-cell movement. REM1.3's phospho-status defines its plasma membrane nanodomain organization and is crucial for REM1.3-dependent restriction of PVX cell-to-cell movement by regulation of callose deposition at plasmodesmata. This study unveils plasma membrane nanodomain-associated molecular events underlying the plant immune response to viruses.