bluuebunny/crossref_metadata_2025_split huggingface dataset:
Safety of integrated preventive chemotherapy for neglected tropical diseases
Authors: Ciciriello Allan M. , Fairley Jessica K. , Cooke Emma , Emerson Paul M. , Hooper Pamela J. , Bolton Birgit , LaCon Genevieve , Addiss David G. 
Published: September 2022

Background
Preventive chemotherapy (PC) is a central strategy for control and elimination of neglected tropical diseases (NTDs). Increased emphasis has been given to “integration” of NTD programs within health systems and coadministration of NTD drugs offers significant programmatic benefits. Guidance from the World Health Organization (WHO) reflects current evidence for safe drug coadministration and highlights measures to prevent choking of young children during PC.

Methodology
To understand how coadministration of NTD drugs might affect PC safety, we reviewed literature on choking risk in young children and safety of coadministered NTD drugs. To understand current practices of drug coadministration, we surveyed 15 NTD program managers and implementing partners.

Principal findings
In high-income countries, choking on medication is an infrequent cause of death in young children. In low-resource settings, data are limited, but age-appropriate drug formulations are less available. During PC, fatal choking, although infrequent, occurs primarily in young children; forcing them to swallow tablets appears to be the major risk factor. The WHO currently recommends 6 drugs and 5 possible drug combinations for use in PC. Of 105 nations endemic for the 5 PC-NTDs, 72 (68.6%) are co-endemic for 2 or more diseases and could benefit from drug coadministration during PC. All 15 survey respondents reported coadministering medications during PC. Reported responses to a child refusing to take medicine included: not forcing the child to do so (60.0%), encouraging the child (46.7%), bringing the child back later (26.7%), offering powder for oral suspension (POS) for azithromycin (13.3%), and having parents or community members intervene to calm the child (6.7%).

Conclusions
Coadministration of NTD drugs during PC appears to be increasingly common. Safety of coadministered PC drugs requires attention to choking prevention, use of approved drug combinations, and increased access to age-appropriate drug formulations.
Phenotypic and genotypic detection of extended spectrum beta lactamase enzyme in Klebsiella pneumoniae
Authors: Mohammed Aso Bakr , Anwar Khanda Abdullateef 
Published: September 2022

Background
Klebsiella species are ubiquitous in nature and can be found in the natural environment and on mucosal surfaces of mammals and it is an important multidrug-resistant pathogen affecting humans and is a major source for hospital acquired infections. The aim of this study is to investigate the prevalence of ESBL enzyme among Klebsiella pneumoniae isolates by phenotypic methods from different hospital wards and detection of ESBL resistance genes such as TEM and SHV in Sulaimani city/ Kurdistan–Iraq.

Methods
Klebsiella pneumoniae isolates were collected from different clinical samples from different hospitals, the isolates were identified by standard technique. Screening of ESBLs was undertaken by using double disk diffusion and standard disk diffusion methods. Real-time PCR was used for genotypic detection of TEM and SHV genes according to the standard protocol.

Result
Out of 54 Klebsiella pneumoniae isolates; 28 were ESBL positive, The pattern of antimicrobial susceptibility testing showed that the most resistant antibiotic are AMP (100%), AMC (100%) followed by CAZ (83.33%), CTX (75.9%), CPM (74%), ATM (70.37%). Both TEM and SHV genes were detected among 28 (51.85%) ESBL positive by using Real-time PCR method.

Conclusion
SHV gene was detected in most of the isolates of ESBL producers of Klebsiella pneumoniae.
Differences in clinical characteristics and treatment outcomes of submacular hemorrhage caused by age-related macular degeneration and retinal macroaneurysms: A multicenter survey from the Japan Clinical Retina Study (J-CREST) group
Authors: Kimura Takeshi , Araki Takashi , Yasukawa Tsutomu , Kato Aki , Kuwayama Soichiro , Kinoshita Takamasa , Okamoto Fumiki , Murakami Tomoya , Mitamura Yoshinori , Sakamoto Taiji , Terasaki Hiroto , Kusuhara Sentaro , Miki Akiko , Takamura Yoshihiro , Kondo Mineo , Matsubara Hisashi , Ueda Tetsuo , Tsujinaka Hiroki , Gomi Fumi 
Published: September 2022

Purpose
To evaluate the clinical characteristics, treatment trends, and visual prognosis of submacular hemorrhage (SMH) secondary to neovascular age-related macular degeneration (nAMD) and retinal arterial macroaneurysm (RAM).

Methods
This retrospective study enrolled 187 Japanese patients with SMH at 10 institutions from 2015 to 2018. Medical records including SMH etiology, best-corrected visual acuity (BCVA), fundus photographs, optical coherence tomography images, and selected treatments were analyzed.

Results
Major causes of SMH were typical nAMD (tnAMD) (18%), polypoidal choroidal vasculopathy (PCV) (50%) and RAM (29%). Age, male/female ratio, baseline BCVA, central retinal thickness, and involved retinal layers were significantly different between etiologies (all P<0.0001). Treatment with anti-vascular endothelial growth factor drugs with and without intravitreal gas injection was chosen for half of eyes in the tnAMD and PCV groups, whereas vitrectomy was performed in 83.7% of eyes with RAM. The final BCVA improved significantly from baseline in the PCV and RAM groups (P = 0.0009, P<0.0001) and final BCVA was significantly better in the PCV group at a level similar to the other groups (P = 0.0007, P = 0.0008). BCVA improvement from baseline was significantly greater in the RAM group compared with the tnAMD (P = 0.0152) and PCV (P = 0.017) groups. Multivariate analysis revealed better final BCVA was significantly associated with younger age (P = 0.0054), better baseline BCVA (P = 0.0021), RAM subtype (P = 0.0446), and no tnAMD (P = 0.001).

Conclusions
The characteristics of, and treatment strategy for, SMH were different between the underlying diseases. Anti-vascular endothelial growth factor treatment with or without expansile gas was mainly chosen for SMH in tnAMD and PCV, whereas vitrectomy with gas was the most common treatment for RAM, and the higher rate for vitrectomy might result in the greater BCVA improvement in the RAM group than in the other groups. Final BCVA was better in PCV, RAM, and tnAMD, in that order, because patients with PCV were younger and had better baseline BCVA.
Sex differences in the impact of resistance exercise load on muscle damage: A protocol for a randomised parallel group trial
Authors: Pearson Alice G. , Macnaughton Lindsay S. , Hind Karen 
Published: September 2022

Introduction
Resistance training can induce skeletal muscle hypertrophy and strength gains, but is also associated with acute muscle damage, characterised by muscle soreness, impaired muscle function, and structural damage to muscle cell membranes and its components. These consequences can be detrimental to future exercise performance and dampen long-term training adaptations. Previous research has considered resistance exercise intensity as a factor in exercise-induced muscle damage (EIMD), though a clear direction of the findings has not yet been established. Further, female populations are heavily underrepresented in this field of study. Therefore, we here propose a study protocol designed to examine sex differences in the muscle damage response to resistance exercise performed with low or high loads in a population of untrained, young adults.

Methods
This study will employ a randomised parallel group design. Twenty-four males and 24 females will perform an acute leg-based resistance exercise session at either 30% (low-load) or 80% (high-load) of their pre-determined one-repetition maximum (1RM). Maximal leg strength will be determined by a 1RM test 3 wk before and 72 and 168 h after the exercise bout. Additionally, muscle damage will be assessed immediately before the exercise bout and immediately, 24, 48, 72, and 168 h after the exercise bout through measures of muscle soreness, limb circumference, range of motion, and serum concentrations of creatine kinase and interleukin-6. The outcomes of this trial could inform sex-specific resistance training recommendations and help bridge the sex data gap in sport and exercise science research.
Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study
Authors: Frontera Jennifer A. , Thorpe Lorna E. , Simon Naomi M. , de Havenon Adam , Yaghi Shadi , Sabadia Sakinah B. , Yang Dixon , Lewis Ariane , Melmed Kara , Balcer Laura J. , Wisniewski Thomas , Galetta Steven L. 
Published: September 2022

Background
Post-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management.

Methods
In a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 infection, and (2) similar therapeutic interventions. Logistic regression analyses were used to evaluate the association of these symptom and therapy clusters with quantitative 12-month outcome metrics (modified Rankin Scale, Barthel Index, NIH NeuroQoL).

Results
Among 242 patients, 122 (50%) reported ≥1 PASC symptom (median 3, IQR 1–5) lasting a median of 12-months (range 1–15) post-COVID diagnosis. Cluster analysis generated three symptom groups: Cluster1 had few symptoms (most commonly headache); Cluster2 had many symptoms including high levels of anxiety and depression; and Cluster3 primarily included shortness of breath, headache and cognitive symptoms. Cluster1 received few therapeutic interventions (OR 2.6, 95% CI 1.1–5.9), Cluster2 received several interventions, including antidepressants, anti-anxiety medications and psychological therapy (OR 15.7, 95% CI 4.1–59.7) and Cluster3 primarily received physical and occupational therapy (OR 3.1, 95%CI 1.3–7.1). The most severely affected patients (Symptom Cluster 2) had higher rates of disability (worse modified Rankin scores), worse NeuroQoL measures of anxiety, depression, fatigue and sleep disorder, and a higher number of stressors (all P<0.05). 100% of those who received a treatment strategy that included psychiatric therapies reported symptom improvement, compared to 97% who received primarily physical/occupational therapy, and 83% who received few interventions (P = 0.042).

Conclusions
We identified three clinically relevant PASC symptom-based phenotypes, which received different therapeutic interventions with varying response rates. These data may be helpful in tailoring individual treatment programs.
Etiology and pattern of maxillofacial trauma
Authors: Khan Tahir Ullah , Rahat Saima , Khan Zafar Ali , Shahid Laima , Banouri Syeda Sabahat , Muhammad Nawshad 
Published: September 2022

IntroductionMaxillofacial trauma can be limited to superficial lacerations, abrasions, and facial bone fractures. The objective of this study was to determine the etiology, pattern, and predictors of soft tissue and bony injuries.Materials and methodsThis study was conducted in the department of maxillofacial surgery Lady Reading hospital Pakistan from Jan 2019 to June 2021. The nonprobability consecutive sampling technique was used for the selection of patients. All patients were assessed clinically and radiologically. The neurosensory examination was done for any altered sensation, anesthesia, or paresthesia. Motor nerve function was also assessed clinically. Data were analyzed using SPSS version 26. The etiology and pattern of maxillofacial trauma were stratified among age and genders using the chi-square test to see effect modifiers. Tests for regression analysis were also applied. P≤0.05 was considered significant.ResultsA total of 253 patients meeting inclusion criteria were included in this study. The majority of these patients were males, 223 (88.1%), while only 30 (11.9%) were females. The mean age for the group was 25.4 ± 12.6 years. RTAs were the most common causes of trauma (63.6%) followed by assault (15.0%), falls (11.5%), FAIs (5.9%), and sports (0.4%). The most vulnerable skeletal part was the mandible (22.9%) followed by Zygoma (7.1%), significantly predicted by RTAs. Soft tissue laceration analysis showed a high frequency of multiple lacerations (38%) significantly predicted by FAIs. The frequency of trigeminal nerve injury was 5.5% (14 patients) and that of the facial nerve was 1.6% (4 patients). The strongest association of nerve injury was with firearm injury (47%), followed by road traffic accidents and sports injuries.ConclusionRoad traffic accident was the most common etiological factor and mandible fracture was commonly predicted by RTA. Trigeminal nerve injuries were common, frequency of nerve injuries was highly associated with mandible fracture and was predicted by FAI.
CARMI
Authors: Zhang Jiaoyi , Gao Yihan 
Published: September 2022

Learned indexes, which use machine learning models to replace traditional index structures, have shown promising results in recent studies. However, existing learned indexes exhibit a performance gap between synthetic and real-world datasets, making them far from practical indexes.
In this paper, we identify that ignoring the importance of data partitioning during model training is the main reason for this problem. Thus, we explicitly apply data partitioning to index construction and propose a new efficient and updatable cache-aware RMI framework, called CARMI. Specifically, we introduce entropy as a metric to quantify and characterize the effectiveness of data partitioning of tree nodes in learned indexes and propose a novel cost model, laying a new theoretical foundation for future research. Then, based on our novel cost model, CARMI can automatically determine tree structures and model types under various datasets and workloads by a hybrid construction algorithm without any manual tuning. Furthermore, since memory accesses limit the performance of RMIs, a new cache-aware design is also applied in CARMI, which makes full use of the characteristics of the CPU cache to effectively reduce the number of memory accesses. Our experimental study shows that CARMI performs better than baselines, achieving an average of 2.2X/1.9X speedup compared to B+ Tree/ALEX, while using only about 0.77X memory space of B+ Tree. On the SOSD platform, CARMI outperforms all baselines, with an average speedup of 1.2X over the nearest competitor RMI, which has been carefully tuned for each dataset in advance.
Query processing on tensor computation runtimes
Authors: He Dong , Nakandala Supun C , Banda Dalitso , Sen Rathijit , Saur Karla , Park Kwanghyun , Curino Carlo , Camacho-Rodríguez Jesús , Karanasos Konstantinos , Interlandi Matteo 
Published: September 2022

The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space.
We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms SQL queries into tensor programs and executes them on TCRs. TQP is able to run the full TPC-H benchmark by implementing novel algorithms for relational operators on the tensor routines. At the same time, TQP can support various hardware while only requiring a fraction of the usual development effort. Experiments show that TQP can improve query execution time by up to 10X over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9X speedup over CPU baselines.
LlamaTune
Authors: Kanellis Konstantinos , Ding Cong , Kroth Brian , Müller Andreas , Curino Carlo , Venkataraman Shivaram 
Published: September 2022

Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. A number of recent works have leveraged ML-based approaches to guide the sampling of large parameter spaces (hundreds of tuning knobs) in search for high performance configurations. Looking at Microsoft production services operating millions of databases,
sample efficiency emerged as a crucial requirement
to use tuners on diverse workloads.

This motivates our investigation in LlamaTune, a tuner design that leverages domain knowledge to improve the sample efficiency of existing optimizers. LlamaTune employs an automated dimensionality reduction technique based on randomized projections, a biased-sampling approach to handle special values for certain knobs, and knob values bucketization, to reduce the size of the search space. LlamaTune compares favorably with the state-of-the-art optimizers across a diverse set of workloads. It identifies the best performing configurations with up to 11X fewer workload runs, and reaching up to 21% higher throughput. We also show that benefits from LlamaTune generalize across both BO-based and RL-based optimizers, as well as different DBMS versions.
While the journey to perform database tuning at cloud-scale remains long, LlamaTune goes a long way in making automatic DBMS tuning practical at scale.
Blueprint
Authors: Mishchenko Andrey , Danco Dominique , Jindal Abhilash , Blue Adrian 
Published: September 2022

Blueprint is a declarative domain-specific language for document extraction. Users describe document layout using spatial, textual, semantic, and numerical fuzzy constraints, and the language runtime extracts the field-value mappings that best satisfy the constraints in a given document.
We used Blueprint to develop several document extraction solutions in a commercial setting. This approach to the extraction problem proved powerful. Concise Blueprint programs were able to generate good accuracy on a broad set of use cases. However, a major goal of our work was to build a system that non-experts, and in particular non-engineers, could use effectively, and we found that writing declarative fuzzy constraint-based extraction programs was not intuitive for many users: a large up-front learning investment was required to be effective, and debugging was often challenging.
To address these issues, we developed a no-code IDE for Blueprint, called Studio, as well as program synthesis functionality for automatically generating Blueprint programs from training data, which could be created by labeling document samples in our IDE. Overall, the IDE significantly improved the Blueprint development experience and the results users were able to achieve.
In this paper, we discuss the design, implementation, and deployment of Blueprint and Studio. We compare our system with a state-of-the-art deep-learning based extraction tool and show that our system can achieve comparable accuracy results, with comparable development time, for appropriately-chosen use cases, while providing better interpretability and debuggability.
WebMILE
Authors: He Yuntian , Zhang Yue , Gurukar Saket , Parthasarathy Srinivasan 
Published: September 2022

In recent years, we have seen the success of network representation learning (NRL) methods in diverse domains ranging from computational chemistry to drug discovery and from social network analysis to bioinformatics algorithms. However, each such NRL method is typically prototyped in a programming environment familiar to the developer. Moreover, such methods rarely scale out to large-scale networks or graphs. Such restrictions are problematic to domain scientists or end-users who want to scale a particular NRL method-of-interest on large graphs from their specific domain.
In this work, we present a novel system, WebMILE to democratize this process. WebMILE can scale an unsupervised network embedding method written in the user's preferred programming language on large graphs. It provides an easy-to-use Graphical User Interface (GUI) for the end-user. The user provides the necessary input (embedding method file, graph, required packages information) through a simple GUI, and WebMILE executes the input network embedding method on the given input graph. WebMILE leverages a pioneering multi-level method, MILE (alternatively DistMILE if the user has access to a cluster), that can scale a network embedding method on large graphs. The language agnosticity is achieved through a simple Docker interface. In this demonstration, we will showcase how a domain scientist or end-user can utilize WebMILE to rapidly prototype and learn node embeddings of a large graph in a flexible and efficient manner - ensuring the twin goals of high productivity and high performance.
IsoBugView
Authors: Ripberger Drew , Gan Yifan , Ren Xueyuan , Blanas Spyros , Wang Yang 
Published: September 2022

Database applications frequently use weaker isolation levels, such as Read Committed, for better performance, which may lead to bugs that do not happen under Serializable. Although a number of works have proposed methods to identify such isolation-related bugs, the difficulty of analyzing reported bugs is often underestimated, since these bugs often involve multiple complicated transactions interleaved in a specific order and they often require users' feedback to improve the accuracy of bug analysis.
This paper presents IsoBugView, a tool to visualize isolation bugs and incorporate users' feedback: to address the challenge that a complicated bug may include much information and thus is hard to present, IsoBugView displays a high-level overview of the bug first and displays further information of individual pieces if the developer needs further investigation. To incorporate users' feedback, IsoBugView embeds hook functions into the backend analysis tool to preprocess a dependency graph and postprocess a found cycle and further allows a user to apply predefined hook functions in its graphic user interface. Our experience shows that IsoBugView has greatly improved our productivity of analyzing isolation bugs.
From BERT to GPT-3 codex
Authors: Trummer Immanuel 
Published: September 2022

Large language models have recently advanced the state of the art on many natural language processing benchmarks. The newest generation of models can be applied to a variety of tasks with little to no specialized training. This technology creates various opportunities for applications in the context of data management.
The tutorial will introduce participants to basic background on language models, discuss different methods to use language models, and give an overview and short demonstration of available libraries and APIs. Models for generating natural language will be considered as well as models, such as GPT-3 Codex, which complete program code or generate code from natural language instructions. Finally, the tutorial will discuss recent research in the database community that exploits language models in the context of traditional database systems or proposes novel system architectures that are based on them.
The tutorial is targeted at database researchers. No prior background on language models is required. The goal of the tutorial is to introduce database researchers to the latest generation of language models, and to their use cases in the domain of data management.